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base: rust:quickwit-0.5
Branches Tags
rust:main rust:stuhood.upstream-erased-sort-computer rust:trinity.pointard/ff-query rust:bucket_id_agg rust:paradedb/fix-agg-validation rust:flatheadmill-geo rust:paradedb-paradedb/fix-overflow-issue rust:low_card_optimization rust:paul.masurel/clippy rust:fix_size_hint rust:qw-airmail-20250916 rust:unit-test-aggregation-sum-other rust:clippy-and-cleanups rust:trinity.pointard/opti-sstable-sorted-ords-to-term rust:release_0.24 rust:dependabot/cargo/rand-0.9.1 rust:dependabot/cargo/lru-0.14.0 rust:dependabot/cargo/fs4-0.13.1 rust:tagged-user-input-ast rust:1686a/parse-agg-res rust:dependabot/cargo/rand_distr-0.5.1 rust:issue/2411-adding-panic-handler-to-rayon-merge-thread-pool rust:dependabot/cargo/nom-8 rust:upgrade_deps rust:test_parser rust:dependabot/github_actions/codecov/codecov-action-5 rust:binggan-0.12.0 rust:agg_format rust:path_collector rust:test_order_bug rust:test_columnar_multi rust:columnar_optionalmask_in_multivalues rust:prefix-phrase-query-optim rust:tantivy-with-oneshot rust:update_example rust:store-reader-enumerate rust:document-deserialize-seed rust:check_doc_mem rust:experimental-do-not-merge rust:expdonotmerge rust:cherry-pick-vec-instead-of-btreemap rust:validate_length rust:raphael_bench_avx_filter rust:raphael_optim_memset2 rust:query-field-name-avoid-alloc rust:check_bug rust:missing_test rust:list_fields rust:block-cache-control rust:trinity--multilayer-sstable rust:tracing-tantivy2 rust:tracing-tantivy rust:index_writer_err rust:default_fast_field_tokenizer rust:fmassot/add-preserve-original-ascii-folding rust:numeric_with_str_col rust:straightforward-simplify rust:fmassot/fake-test-branch rust:fmassot/dynamic-tokenizer-poc-follow-up rust:fmassot/add-box-token-filter-and-refactor rust:dynamic-tokenizer-proof-of-concept rust:fmassot/add-box-tokenizer-and-token-filter rust:tokenizer-api-0.1.1 rust:addconversion rust:removed-extra-enum-alternative-for-datetime-precision rust:issue/1981-reproduce-bm25-bug rust:tokenizer_docs rust:merge-proptests rust:columnar-todo rust:quickwit-0.5 rust:remove-byteorder rust:remove_dyn rust:pr/alexcole/1855-inject-bm25-statistics rust:evance-expose-build-query-from-user-input-ast rust:missing-specialized-method-arcdyncolumn rust:fast-u64-range-query rust:0.19.2 rust:columnar-cli2 rust:trinity--pub-term-as-slice rust:typed-column3 rust:columnar-merge3 rust:columnar-merge-exploring-fn rust:use_columnar rust:typed-column2 rust:term_agg_bucket_limit rust:range rust:typed-column rust:columnar-main rust:forced-types rust:u128-columnar rust:use_stats rust:truncation_comment rust:0.19.1 rust:trinity/yoke-documents rust:column-handle rust:columnar-cli rust:columnar rust:revert-1711-sparse_dense_index rust:commit-changes rust:sparse_dense_index_headroom rust:optional_column rust:no-column-for-multivalued rust:fmassot--bench-hdfs-with-array rust:bugfix-position-broken rust:float rust:simplify rust:debug_time rust:sparse_codecs rust:fix_estimate rust:column-reader rust:column-reader-b rust:columnreader-simple rust:col-trait-refact rust:ip_fastfield rust:column-trait-2 rust:refact-dyn-column rust:column-trait rust:refact-codec-trait rust:pr/PSeitz/1485-1 rust:fastfieldcodec rust:termmap_per_field rust:fastfield-codecs-tests rust:rayon-indexing-writers rust:issue/1251 rust:index-bench rust:fix_open_bytes_index rust:warming rust:revert-1222-issue/1195 rust:add-random-bench-for-fast-field rust:wasm-friendly-exploratory rust:revert-1159-patch-1 rust:debug-position-panic-0.15.3 rust:fix-issue-1111 rust:remove_rand rust:unused-deps rust:hfb/phrase_match_slop rust:0.15.3 rust:expose-min-max-on-multi-fast-field-reader rust:revert-1094-expose-min-max-on-multi-fast-field-reader rust:hotfix-1088 rust:owned-bytes-instead-of-rawdoc rust:rihardsk-date-ranges rust:issue/997 rust:0.13.3 rust:fail-fast-store-checkpoint rust:debugging-store rust:issue/reproduce-897 rust:issue/969-reproduce rust:troublescooter-static rust:intermediate-mergeable-troublescooter rust:barrotsteindev-filter-collector-tpredicatevalue rust:issue/938b rust:issue/938 rust:barrotsteindev-filter-collector rust:issue/922 rust:issue/896 rust:quickfix-explain rust:slog rust:limit-random-seeks rust:0.13.1 rust:issue/866b rust:bugfix-unittest-macos rust:githubactions rust:fieldnorm_readers rust:blockwand rust:nodeffeatfails rust:removedaliveiterator rust:robyoung-introduce-offset-to-top-docs rust:specialization-of-foreach-for-union-scorer rust:elshize-bmw rust:cutt rust:nrt rust:storewriter-out rust:segment_writer_in_ram2 rust:polling rust:audunhalland-query-boost rust:poll-meta rust:segment_writer_in_ram rust:bundle rust:petr-tik-n662_footer_is_incompatible_impl rust:race-condition-in-tests rust:hotfix-0.10.3-issue681 rust:remove-tru rust:kkoziara-pre-tokenized-text rust:issue/681 rust:issue/680 rust:refactoring/field rust:updating/uuid rust:fix-bench rust:hotfix-656 rust:readd-macos-ci rust:incremental-search rust:crate-division rust:streamer-with-state rust:criterion rust:python-binding-changes rust:bump-version rust:gh-pages rust:issue/554 rust:perf/experimental-union-for_each rust:issue/526b rust:softcommits rust:0.9.1-hotfix rust:revert-521-fix-non-english-stemmers rust:issue/500-hotfix rust:hotfix/0.8.2 rust:issue/483 rust:bug/merge-deted rust:issue/407 rust:issue/457 rust:segment_fruits rust:doc rust:dds/lenient rust:issue/webasm rust:issue/396 rust:broken_collector_with_result rust:dss/automaton-weight-suggestion rust:wasm rust:common-crawl rust:tantivy-imhotep
rust:0.25.0 rust:0.22.1 rust:0.24.2 rust:0.24.1 rust:tantivy-query-grammar-v0.24.0 rust:tantivy-columnar-v0.5.0 rust:tantivy-bitpacker-v0.8.0 rust:ownedbytes-v0.9.0 rust:tantivy-stacker-v0.5.0 rust:tantivy-sstable-v0.5.0 rust:tantivy-common-v0.9.0 rust:tantivy-tokenizer-api-v0.5.0 rust:0.24 rust:qw-airmail-20250224-hotfix-merge-panic rust:qw-airmail-20250219-hotfix-merge-panic rust:qw-airmail-202406 rust:qw-airmail-20240611 rust:qw-airmail-20240606 rust:quickwit-rev rust:0.22.0 rust:tantivy-stacker-v0.3.0 rust:ownedbytes-v0.7.0 rust:tantivy-tokenizer-api-v0.3.0 rust:tantivy-columnar-v0.3.0 rust:tantivy-common-v0.7.0 rust:tantivy-query-grammar-v0.22.0 rust:tantivy-sstable-v0.3.0 rust:tantivy-bitpacker-v0.6.0 rust:0.21.1 rust:0.21 rust:0.20.2 rust:0.20.1 rust:0.20 rust:quickwit-0.5-rev rust:0.19.2 rust:0.19.1 rust:0.19 rust:0.18.1 rust:0.18 rust:0.17 rust:0.16.1 rust:0.15.3 rust:0.15.2 rust:0.15.1 rust:0.15 rust:0.14 rust:0.13.3 rust:0.13.2 rust:0.13.1 rust:0.13 rust:0.12 rust:0.11.3 rust:0.11.1 rust:0.11 rust:0.10.3 rust:0.1 rust:0.10.2 rust:0.10.1 rust:0.10.0 rust:0.9.1 rust:0.9 rust:0.8.2 rust:0.8.2b rust:0.8.1 rust:0.8.0 rust:0.7.2 rust:0.7.1 rust:0.7.0 rust:0.6.1 rust:0.6.0 rust:0.5.2 rust:0.5.1 rust:0.5.0 rust:0.4.3 rust:0.4.2 rust:0.4.0 rust:0.3.1 rust:0.3.0 rust:0.2.0
...
compare: rust:1686a/parse-agg-res
Branches Tags
rust:stuhood.upstream-erased-sort-computer rust:main rust:trinity.pointard/ff-query rust:bucket_id_agg rust:paradedb/fix-agg-validation rust:flatheadmill-geo rust:paradedb-paradedb/fix-overflow-issue rust:low_card_optimization rust:paul.masurel/clippy rust:fix_size_hint rust:qw-airmail-20250916 rust:unit-test-aggregation-sum-other rust:clippy-and-cleanups rust:trinity.pointard/opti-sstable-sorted-ords-to-term rust:release_0.24 rust:dependabot/cargo/rand-0.9.1 rust:dependabot/cargo/lru-0.14.0 rust:dependabot/cargo/fs4-0.13.1 rust:tagged-user-input-ast rust:1686a/parse-agg-res rust:dependabot/cargo/rand_distr-0.5.1 rust:issue/2411-adding-panic-handler-to-rayon-merge-thread-pool rust:dependabot/cargo/nom-8 rust:upgrade_deps rust:test_parser rust:dependabot/github_actions/codecov/codecov-action-5 rust:binggan-0.12.0 rust:agg_format rust:path_collector rust:test_order_bug rust:test_columnar_multi rust:columnar_optionalmask_in_multivalues rust:prefix-phrase-query-optim rust:tantivy-with-oneshot rust:update_example rust:store-reader-enumerate rust:document-deserialize-seed rust:check_doc_mem rust:experimental-do-not-merge rust:expdonotmerge rust:cherry-pick-vec-instead-of-btreemap rust:validate_length rust:raphael_bench_avx_filter rust:raphael_optim_memset2 rust:query-field-name-avoid-alloc rust:check_bug rust:missing_test rust:list_fields rust:block-cache-control rust:trinity--multilayer-sstable rust:tracing-tantivy2 rust:tracing-tantivy rust:index_writer_err rust:default_fast_field_tokenizer rust:fmassot/add-preserve-original-ascii-folding rust:numeric_with_str_col rust:straightforward-simplify rust:fmassot/fake-test-branch rust:fmassot/dynamic-tokenizer-poc-follow-up rust:fmassot/add-box-token-filter-and-refactor rust:dynamic-tokenizer-proof-of-concept rust:fmassot/add-box-tokenizer-and-token-filter rust:tokenizer-api-0.1.1 rust:addconversion rust:removed-extra-enum-alternative-for-datetime-precision rust:issue/1981-reproduce-bm25-bug rust:tokenizer_docs rust:merge-proptests rust:columnar-todo rust:quickwit-0.5 rust:remove-byteorder rust:remove_dyn rust:pr/alexcole/1855-inject-bm25-statistics rust:evance-expose-build-query-from-user-input-ast rust:missing-specialized-method-arcdyncolumn rust:fast-u64-range-query rust:0.19.2 rust:columnar-cli2 rust:trinity--pub-term-as-slice rust:typed-column3 rust:columnar-merge3 rust:columnar-merge-exploring-fn rust:use_columnar rust:typed-column2 rust:term_agg_bucket_limit rust:range rust:typed-column rust:columnar-main rust:forced-types rust:u128-columnar rust:use_stats rust:truncation_comment rust:0.19.1 rust:trinity/yoke-documents rust:column-handle rust:columnar-cli rust:columnar rust:revert-1711-sparse_dense_index rust:commit-changes rust:sparse_dense_index_headroom rust:optional_column rust:no-column-for-multivalued rust:fmassot--bench-hdfs-with-array rust:bugfix-position-broken rust:float rust:simplify rust:debug_time rust:sparse_codecs rust:fix_estimate rust:column-reader rust:column-reader-b rust:columnreader-simple rust:col-trait-refact rust:ip_fastfield rust:column-trait-2 rust:refact-dyn-column rust:column-trait rust:refact-codec-trait rust:pr/PSeitz/1485-1 rust:fastfieldcodec rust:termmap_per_field rust:fastfield-codecs-tests rust:rayon-indexing-writers rust:issue/1251 rust:index-bench rust:fix_open_bytes_index rust:warming rust:revert-1222-issue/1195 rust:add-random-bench-for-fast-field rust:wasm-friendly-exploratory rust:revert-1159-patch-1 rust:debug-position-panic-0.15.3 rust:fix-issue-1111 rust:remove_rand rust:unused-deps rust:hfb/phrase_match_slop rust:0.15.3 rust:expose-min-max-on-multi-fast-field-reader rust:revert-1094-expose-min-max-on-multi-fast-field-reader rust:hotfix-1088 rust:owned-bytes-instead-of-rawdoc rust:rihardsk-date-ranges rust:issue/997 rust:0.13.3 rust:fail-fast-store-checkpoint rust:debugging-store rust:issue/reproduce-897 rust:issue/969-reproduce rust:troublescooter-static rust:intermediate-mergeable-troublescooter rust:barrotsteindev-filter-collector-tpredicatevalue rust:issue/938b rust:issue/938 rust:barrotsteindev-filter-collector rust:issue/922 rust:issue/896 rust:quickfix-explain rust:slog rust:limit-random-seeks rust:0.13.1 rust:issue/866b rust:bugfix-unittest-macos rust:githubactions rust:fieldnorm_readers rust:blockwand rust:nodeffeatfails rust:removedaliveiterator rust:robyoung-introduce-offset-to-top-docs rust:specialization-of-foreach-for-union-scorer rust:elshize-bmw rust:cutt rust:nrt rust:storewriter-out rust:segment_writer_in_ram2 rust:polling rust:audunhalland-query-boost rust:poll-meta rust:segment_writer_in_ram rust:bundle rust:petr-tik-n662_footer_is_incompatible_impl rust:race-condition-in-tests rust:hotfix-0.10.3-issue681 rust:remove-tru rust:kkoziara-pre-tokenized-text rust:issue/681 rust:issue/680 rust:refactoring/field rust:updating/uuid rust:fix-bench rust:hotfix-656 rust:readd-macos-ci rust:incremental-search rust:crate-division rust:streamer-with-state rust:criterion rust:python-binding-changes rust:bump-version rust:gh-pages rust:issue/554 rust:perf/experimental-union-for_each rust:issue/526b rust:softcommits rust:0.9.1-hotfix rust:revert-521-fix-non-english-stemmers rust:issue/500-hotfix rust:hotfix/0.8.2 rust:issue/483 rust:bug/merge-deted rust:issue/407 rust:issue/457 rust:segment_fruits rust:doc rust:dds/lenient rust:issue/webasm rust:issue/396 rust:broken_collector_with_result rust:dss/automaton-weight-suggestion rust:wasm rust:common-crawl rust:tantivy-imhotep
rust:0.25.0 rust:0.22.1 rust:0.24.2 rust:0.24.1 rust:tantivy-query-grammar-v0.24.0 rust:tantivy-columnar-v0.5.0 rust:tantivy-bitpacker-v0.8.0 rust:ownedbytes-v0.9.0 rust:tantivy-stacker-v0.5.0 rust:tantivy-sstable-v0.5.0 rust:tantivy-common-v0.9.0 rust:tantivy-tokenizer-api-v0.5.0 rust:0.24 rust:qw-airmail-20250224-hotfix-merge-panic rust:qw-airmail-20250219-hotfix-merge-panic rust:qw-airmail-202406 rust:qw-airmail-20240611 rust:qw-airmail-20240606 rust:quickwit-rev rust:0.22.0 rust:tantivy-stacker-v0.3.0 rust:ownedbytes-v0.7.0 rust:tantivy-tokenizer-api-v0.3.0 rust:tantivy-columnar-v0.3.0 rust:tantivy-common-v0.7.0 rust:tantivy-query-grammar-v0.22.0 rust:tantivy-sstable-v0.3.0 rust:tantivy-bitpacker-v0.6.0 rust:0.21.1 rust:0.21 rust:0.20.2 rust:0.20.1 rust:0.20 rust:quickwit-0.5-rev rust:0.19.2 rust:0.19.1 rust:0.19 rust:0.18.1 rust:0.18 rust:0.17 rust:0.16.1 rust:0.15.3 rust:0.15.2 rust:0.15.1 rust:0.15 rust:0.14 rust:0.13.3 rust:0.13.2 rust:0.13.1 rust:0.13 rust:0.12 rust:0.11.3 rust:0.11.1 rust:0.11 rust:0.10.3 rust:0.1 rust:0.10.2 rust:0.10.1 rust:0.10.0 rust:0.9.1 rust:0.9 rust:0.8.2 rust:0.8.2b rust:0.8.1 rust:0.8.0 rust:0.7.2 rust:0.7.1 rust:0.7.0 rust:0.6.1 rust:0.6.0 rust:0.5.2 rust:0.5.1 rust:0.5.0 rust:0.4.3 rust:0.4.2 rust:0.4.0 rust:0.3.1 rust:0.3.0 rust:0.2.0

451 Commits
quickwit-0 ... 1686a/pars

Author SHA1 Message Date
trinity Pointard
c73d4a7166 reorder parsing of aggregations results 2025-02-26 18:06:09 +01:00
SteveLauC
c48c649436 refactor: use std AtomicU64 and remove wrapper (#2585) 2025-02-24 03:56:15 +01:00
Paul Masurel
58c0739953 Merge pull request #2581 from quickwit-oss/merge_dict_column_repro 
use usize in bitpacker
 2025-02-21 10:53:07 +09:00
Pascal Seitz
e7daf69de9 use usize in bitpacker 
use usize in bitpacker to enable larger columns in the columnar store

Godbolt comparison with u32 vs u64 for get access: https://godbolt.org/z/cjf7nenYP

Add a mini-tool to inspect columnar files created by tantivy. (very basic functionality which can be extended later)
 2025-02-20 15:39:10 +01:00
trinity-1686a
f060e86bc6 Merge pull request #2578 from quickwit-oss/1686a/buildable-histo-agg 
make DateHistogramAggregationReq buildable
 2025-02-18 15:30:54 +01:00
trinity Pointard
0368162ef0 make DateHistogramAggregationReq buildable 2025-02-18 11:45:24 +01:00
trinity-1686a
e843c71015 Merge pull request #2568 from quickwit-oss/trinity/wildcard-query-parser 
allow term starting with wildcard in query parser
 2025-02-12 16:47:25 +01:00
trinity Pointard
5cea16ef9f improve handling of spcial char after exist query 2025-01-22 16:04:31 +01:00
dependabot[bot]
4aa8cd2470 Update downcast-rs requirement from 1.2.1 to 2.0.1 (#2566) 
Updates the requirements on [downcast-rs](https://github.com/marcianx/downcast-rs) to permit the latest version.
- [Changelog](https://github.com/marcianx/downcast-rs/blob/master/CHANGELOG.md)
- [Commits](https://github.com/marcianx/downcast-rs/compare/v1.2.1...v2.0.1)

---
updated-dependencies:
- dependency-name: downcast-rs
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2025-01-22 10:32:24 +01:00
trinity Pointard
4d4ee1b0ac allow term starting with wildcard in query parser 2025-01-15 10:27:48 +01:00
dependabot[bot]
43c89b4360 Update itertools requirement from 0.13.0 to 0.14.0 (#2563) 
Updates the requirements on [itertools](https://github.com/rust-itertools/itertools) to permit the latest version.
- [Changelog](https://github.com/rust-itertools/itertools/blob/master/CHANGELOG.md)
- [Commits](https://github.com/rust-itertools/itertools/compare/v0.13.0...v0.14.0)

---
updated-dependencies:
- dependency-name: itertools
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2025-01-08 17:11:46 +01:00
trinity-1686a
d281ca3e65 Merge pull request #2559 from quickwit-oss/trinity/sstable-partial-automaton 
allow warming partially an sstable for an automaton
 2025-01-08 16:35:35 +01:00
trinity Pointard
be17daf658 split iterator 2025-01-08 16:24:34 +01:00
trinity Pointard
6ca84a61fa make termdict always clone 2025-01-08 16:19:54 +01:00
trinity Pointard
037d12c9c9 fix deadlocking on automaton warmup 2025-01-06 11:58:58 +01:00
Remi Dettai
71cf19870b Exist queries match subpath fields (#2558) 
* Exist queries match subpath fields

* Make subpath check optional

* Add async subpath listing
 2025-01-06 10:17:39 +01:00
trinity Pointard
175a529c41 use executor for cpu-heavy sstable decompression for automaton 2025-01-03 19:14:07 +01:00
trinity Pointard
fe0c7c5408 change rangebound style 2025-01-02 11:56:05 +01:00
Harrison Burt
148594f0f9 Improve IndexWriter customisation via builder (#2562) 
* Improve `IndexWriter` customisation via builder

* Remove change noise from PR

* Correct documentation

* Resolve comments and add test
 2025-01-02 09:43:22 +01:00
dependabot[bot]
8edb439440 Update rustc-hash requirement from 1.1.0 to 2.1.0 (#2551) 
---
updated-dependencies:
- dependency-name: rustc-hash
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2024-12-26 10:25:05 +01:00
trinity Pointard
dfff5f3bcb rename merge_holes_under => merge_holes_under_bytes 2024-12-23 16:17:44 +01:00
trinity-1686a
ebf4d84553 add comment about cpu-intensive operation in async context 2024-12-20 12:23:49 +01:00
trinity-1686a
42efc7f7c8 clippy 2024-12-20 11:00:11 +01:00
trinity-1686a
192395c311 attempt at simplifying can_block_match_automaton 2024-12-20 10:25:38 +01:00
trinity-1686a
a1447cc9c2 remove breaking change in sstable public api 2024-12-19 17:30:05 +01:00
trinity-1686a
c39d91f827 Merge pull request #2547 from quickwit-oss/trinity/count-str 
add support for counting non integer in aggregation
 2024-12-17 15:27:30 +01:00
trinity Pointard
32b6e9711b add tests 2024-12-13 16:06:24 +01:00
trinity-1686a
24c5dc2398 allow warming up automaton 2024-12-10 13:32:12 +01:00
trinity-1686a
9e2ddec4b3 merge adjacent block when building delta for automaton 2024-12-10 13:32:12 +01:00
trinity-1686a
1f6a8e74bb support iterating over partially loaded sstable 2024-12-10 13:32:12 +01:00
trinity-1686a
7e901f523b get iter for blocks of sstable matching automaton 2024-12-10 13:32:12 +01:00
trinity-1686a
3c30a41c14 add helper to figure if block can match automaton 2024-12-10 13:32:12 +01:00
dependabot[bot]
0f99d4f420 Update measure_time requirement from 0.8.2 to 0.9.0 (#2557) 
---
updated-dependencies:
- dependency-name: measure_time
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2024-12-09 21:39:01 +01:00
Pierre Barre
6e02c5cb25 Make NUM_MERGE_THREADS configurable (#2535) 
* Make `NUM_MERGE_THREADS` configurable

* Remove unused import

* Reword comment src/index/index.rs

Co-authored-by: PSeitz <PSeitz@users.noreply.github.com>

---------

Co-authored-by: PSeitz <PSeitz@users.noreply.github.com>
 2024-12-09 16:53:11 +08:00
PSeitz
876a579e5d queryparser: add field respecification test (#2550) 2024-12-02 14:17:12 +01:00
PSeitz
4c52499622 clippy (#2549) 2024-11-29 16:08:21 +08:00
trinity-1686a
0bac391291 add support for counting non integer in aggregation 2024-11-28 19:52:47 +01:00
PSeitz
52d4e81e70 update CHANGELOG (#2546) 2024-11-27 20:49:35 +08:00
dependabot[bot]
c71ea7b2ef Update thiserror requirement from 1.0.30 to 2.0.1 (#2542) 
Updates the requirements on [thiserror](https://github.com/dtolnay/thiserror) to permit the latest version.
- [Release notes](https://github.com/dtolnay/thiserror/releases)
- [Commits](https://github.com/dtolnay/thiserror/compare/1.0.30...2.0.1)

---
updated-dependencies:
- dependency-name: thiserror
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2024-11-09 08:08:34 +08:00
Paul Masurel
c35a782747 Updating rustc-hash and clippy fixes (#2532) 
* Updating rustc-hash and clippy fixes

* fix terms_aggregation_min_doc_count_special_case

---------

Co-authored-by: Pascal Seitz <pascal.seitz@gmail.com>
 2024-11-01 13:46:26 +08:00
dependabot[bot]
c66af2c0a9 Update binggan requirement from 0.12.0 to 0.14.0 (#2530) 
* Update binggan requirement from 0.12.0 to 0.14.0

---
updated-dependencies:
- dependency-name: binggan
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

* fix build

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Pascal Seitz <pascal.seitz@gmail.com>
 2024-10-24 09:41:35 +08:00
Joan Antoni RE
f9ac055847 Fix some links in architecture docs (#2528) 2024-10-23 21:06:54 +09:00
PSeitz
21d057059e clippy (#2527) 
* clippy

* clippy

* clippy

* clippy

* convert allow to expect and remove unused

* cargo fmt

* cleanup

* export sample

* clippy
 2024-10-22 09:26:54 +08:00
PSeitz
dca508b4ca remove read_postings_no_deletes (#2526) 
closes #2525
 2024-10-22 09:52:43 +09:00
PSeitz
aebae9965d add RegexPhraseQuery (#2516) 
* add RegexPhraseQuery

RegexPhraseQuery supports phrase queries with regex. It supports regex
and wildcards. E.g. a query with wildcards:
"b* b* wolf" matches "big bad wolf"
Slop is supported as well:
"b* wolf"~2 matches "big bad wolf"

Regex queries may match a lot of terms where we still need to
keep track which term hit to load the positions.
The phrase query algorithm groups terms by their frequency
together in the union to prefilter groups early.

This PR comes with some new datastructures:

SimpleUnion - A union docset for a list of docsets. It doesn't do any
caching and is therefore well suited for datasets with lots of skipping.
(phrase search, but intersections in general)

LoadedPostings - Like SegmentPostings, but all docs and positions are loaded in
memory. SegmentPostings uses 1840 bytes per instance with its caches,
which is equivalent to 460 docids.
LoadedPostings is used for terms which have less than 100 docs.
LoadedPostings is only used to reduce memory consumption.

BitSetPostingUnion - Creates a `Posting` that uses the bitset for docid
hits and the docsets for positions. The BitSet is the precalculated
union of the docsets
In the RegexPhraseQuery there is a size limit of 512 docsets per PreAggregatedUnion,
before creating a new one.

Renamed Union to BufferedUnionScorer
Added proptests to test different union types.

* cleanup

* use Box instead of Vec

* use RefCell instead of term_freq(&mut)

* remove wildcard mode

* move RefCell to outer

* clippy
 2024-10-21 18:29:17 +08:00
Marvin
e7e3e3f44c make casing in docs more consistent (#2524) 
* make casing in docs more consistent

* more

* lowercase tantivy
 2024-10-21 17:59:41 +09:00
PSeitz
2f2db16ec1 store DateTime as nanoseconds in doc store (#2486) 
* store DateTime as nanoseconds in doc store

The doc store DateTime was truncated to microseconds previously. This
removes this truncation, while still keeping backwards compatibility.

This is done by adding the trait `ConfigurableBinarySerializable`, which
works like `BinarySerializable`, but with a config that allows de/serialize
as different date time precision currently.

bump version format to 7.
add compat test to check the date time truncation.

* remove configurable binary serialize, add enum for doc store version

* test doc store version ord
 2024-10-18 10:50:20 +08:00
Paul Masurel
d152e29687 Fixed citation (#2523) 2024-10-17 10:19:50 +09:00
Paul Masurel
285bcc25c9 Added citation.cff (#2522) 2024-10-17 09:43:35 +09:00
PSeitz
7b65ad922d use binggan for stacker bench (#2492) 
* use binggan for stacker bench

```
alice (num terms: 174693)
hashmap                    Memory: 1.3 MB     Avg: 367.19 MiB/s (-1.34%)    Median: 368.10 MiB/s (-1.34%)    [378.75 MiB/s .. 352.81 MiB/s]
hasmap with postings       Memory: 2.4 MB     Avg: 237.29 MiB/s (-2.19%)    Median: 240.22 MiB/s (-1.61%)    [248.26 MiB/s .. 210.66 MiB/s]
fxhashmap ref postings     Memory: 2.9 MB     Avg: 171.94 MiB/s (-3.22%)    Median: 174.13 MiB/s (-2.69%)    [185.94 MiB/s .. 152.43 MiB/s]
fxhasmap owned postings    Memory: 3.5 MB     Avg: 96.993 MiB/s (-4.20%)    Median: 97.410 MiB/s (-4.48%)    [102.78 MiB/s .. 82.745 MiB/s]
numbers unique 100k
hashmap                 Memory: 5.2 MB     Avg: 334.17 MiB/s (-3.06%)    Median: 352.61 MiB/s (+0.77%)    [362.60 MiB/s .. 213.03 MiB/s]
hasmap with postings    Memory: 6.3 MB     Avg: 316.96 MiB/s (-0.02%)    Median: 325.16 MiB/s (-0.04%)    [338.36 MiB/s .. 218.60 MiB/s]
zipfs numbers 100k
hashmap                 Memory: 1.3 MB     Avg: 1.2342 GiB/s (+2.87%)    Median: 1.2677 GiB/s (+4.66%)    [1.3130 GiB/s .. 915.93 MiB/s]
hasmap with postings    Memory: 2.4 MB     Avg: 485.16 MiB/s (+2.68%)    Median: 494.70 MiB/s (+4.42%)    [505.31 MiB/s .. 413.14 MiB/s]
numbers unique 1mio
hashmap                 Memory: 35.7 MB     Avg: 169.68 MiB/s (-1.08%)    Median: 166.80 MiB/s (-3.87%)    [201.33 MiB/s .. 154.26 MiB/s]
hasmap with postings    Memory: 39.8 MB     Avg: 149.49 MiB/s (-3.07%)    Median: 150.85 MiB/s (-1.45%)    [160.76 MiB/s .. 130.94 MiB/s]
zipfs numbers 1mio
hashmap                 Memory: 1.3 MB     Avg: 1.2185 GiB/s (-2.33%)     Median: 1.2291 GiB/s (-2.33%)     [1.2905 GiB/s .. 1.0742 GiB/s]
hasmap with postings    Memory: 5.5 MB     Avg: 358.43 MiB/s (-11.63%)    Median: 356.95 MiB/s (-12.85%)    [444.94 MiB/s .. 302.46 MiB/s]
numbers unique 2mio
hashmap                 Memory: 70.3 MB     Avg: 163.65 MiB/s (+8.37%)    Median: 162.83 MiB/s (+8.80%)    [190.20 MiB/s .. 144.70 MiB/s]
hasmap with postings    Memory: 78.6 MB     Avg: 148.00 MiB/s (+7.75%)    Median: 151.53 MiB/s (+9.11%)    [166.92 MiB/s .. 120.09 MiB/s]
zipfs numbers 2mio
hashmap                 Memory: 1.3 MB     Avg: 1.2535 GiB/s (+2.59%)    Median: 1.2654 GiB/s (+0.36%)    [1.2938 GiB/s .. 1.0592 GiB/s]
hasmap with postings    Memory: 9.7 MB     Avg: 377.96 MiB/s (-4.94%)    Median: 381.82 MiB/s (-3.67%)    [426.14 MiB/s .. 335.66 MiB/s]
numbers unique 5mio
hashmap                 Memory: 277.9 MB     Avg: 121.30 MiB/s (+2.00%)    Median: 121.99 MiB/s (+2.99%)    [132.51 MiB/s .. 110.32 MiB/s]
hasmap with postings    Memory: 295.7 MB     Avg: 114.23 MiB/s (+2.13%)    Median: 115.26 MiB/s (+2.94%)    [124.08 MiB/s .. 103.38 MiB/s]
zipfs numbers 5mio
hashmap                 Memory: 1.3 MB      Avg: 1.2326 GiB/s (+0.63%)    Median: 1.2400 GiB/s (+0.71%)    [1.2755 GiB/s .. 1.0923 GiB/s]
hasmap with postings    Memory: 25.4 MB     Avg: 360.49 MiB/s (+1.07%)    Median: 363.44 MiB/s (+1.27%)    [404.88 MiB/s .. 300.38 MiB/s]
```

* rename bench

* update binggan

* rename to HASHMAP_CAPACITY
 2024-10-16 11:41:33 +08:00
dependabot[bot]
99be20cedd Update binggan requirement from 0.10.0 to 0.12.0 (#2519) 
* Update binggan requirement from 0.10.0 to 0.12.0

---
updated-dependencies:
- dependency-name: binggan
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

* fix build

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Pascal Seitz <pascal.seitz@gmail.com>
 2024-10-16 11:36:04 +08:00
Bruce Mitchener
5f026901b8 Update MSRV to 1.75 (#2515) 
This is required by the `fs4` dependency. There are other
things that need something later than 1.66.

Both quickwit and the Python binding already require something
newer.
 2024-10-16 10:32:16 +08:00
baishen
6dfa2df06f fix OwnedBytes debug panic (#2512) 2024-10-16 10:31:40 +08:00
Bruce Mitchener
c17e513377 Reduce typo count. (#2510) 2024-10-10 09:55:37 +08:00
PSeitz
2f5a269c70 update packages (#2500) 
fixes some warnings
 2024-09-25 17:46:18 +08:00
PSeitz
50532260e3 update changelog (#2496) 2024-09-25 10:28:53 +08:00
Tri
8bd6eb06e6 feat: make SegmentMeta.with_max_doc public (#2499) 
* chore: add container

* feat: make max doc editable externally

* chore: expose another method

* chore: remove comments

* remove unused devcontainer

* chore: manually match nightly format

* chore: change weird formating

* revert format change

* fix: format with nightly
 2024-09-23 12:39:36 +08:00
PSeitz
55b0b52457 Fix AggregationLimits (#2495) 
* change AggregationLimits behavior

This fixes an issue encountered with the current behaviour of
AggregationLimits.
Previously we had AggregationLimits and RessourceLimitGuard, which both
track the memory, but only RessourceLimitGuard released memory when
dropped, while AggregationLimits did not.

This PR changes AggregationLimits to be a guard itself and removes the
RessourceLimitGuard.

* rename AggregationLimits to AggregationLimitsGuard
 2024-09-17 14:25:47 +08:00
dependabot[bot]
56fc56c5b9 Update binggan requirement from 0.8.0 to 0.10.0 (#2493) 
* Update binggan requirement from 0.8.0 to 0.10.0

---
updated-dependencies:
- dependency-name: binggan
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

* update PR

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Pascal Seitz <pascal.seitz@gmail.com>
 2024-09-10 14:26:06 +08:00
trinity-1686a
85395d942a fix clippy lints from 1.80-1.81 (#2488) 
* fix some clippy lints

* fix clippy::doc_lazy_continuation

* fix some lints for 1.82
 2024-09-05 14:33:05 +02:00
PSeitz
a206c3ccd3 add compat tests (#2485) 2024-09-04 18:26:57 +08:00
Chaya
dc5d31c116 grammar and misspellings (#2483) 
* grammar

* grammar

* misspelling
 2024-09-04 12:45:31 +08:00
gezihuzi
95a4ddea3e Fix: Improve collapse_overlapped_ranges function (#2474) 
* Fix: Improve collapse_overlapped_ranges function

- Refactor into separate sort_and_deduplicate_ranges and merge_overlapping_ranges functions
- Enhance sorting to consider both start and end of ranges
- Optimize merging logic to handle adjacent ranges
- Add comprehensive examples in function documentation
- Ensure proper handling of duplicate and unsorted input ranges
- Improve overall efficiency and readability of range collapsing algorithm

* move debug_assert

---------

Co-authored-by: PSeitz <PSeitz@users.noreply.github.com>
 2024-09-04 12:39:13 +08:00
trinity-1686a
ab5125d3dc remove unused trait bounds and outdated doc comment (#2478) 2024-09-03 16:31:51 +02:00
trinity-1686a
9f81d59ecd make find_field_with_default return json fields without path (#2476) 
* make find_field_with_default return json fields without path

* add tests for find_field_with_default
 2024-08-19 15:25:29 +02:00
PSeitz
c71ec8086d add FastFieldRangeQuery, rename (#2477) 
* add FastFieldRangeQuery, rename

* remove Query impl
 2024-08-19 09:02:00 +02:00
PSeitz
27be6aed91 lift clauses in LogicalAst (#2449) 
(a OR b) OR (c OR d) can be simplified to (a OR b OR c OR d)
(a AND b) AND (c AND d) can be simplified to (a AND b AND c AND d)

This directly affects how queries are executed

remove unused SumWithCoordsCombiner
the number of fields is unused and private
 2024-08-14 19:21:26 +02:00
PSeitz
3d1c4b313a support ff range queries on json fields (#2456) 
* support ff range queries on json fields

* fix term date truncation

* use inverted index range query for phrase prefix queries

* rename to InvertedIndexRangeQuery

* fix column filter, add mixed column test
 2024-08-02 00:06:50 +08:00
PSeitz
0d4e319965 add Key::I64 and Key::U64 variants in aggregation (#2468) 
* add Key::I64 and Key::U64 variants in aggregation

Currently all `Key` numerical values are returned as f64. This causes problems in some
cases with the precision and the way f64 is serialized.

This PR adds `Key::I64` and `Key::U64` variants and uses them in the term
aggregation.

* add clarification comment
 2024-07-31 20:29:32 +08:00
PSeitz
75dc3eb298 extend custom order deserialization (#2451) 
allow arrays
improve validation
closes https://github.com/quickwit-oss/tantivy/issues/2435
 2024-07-30 18:36:08 +08:00
PSeitz
3f6d225086 fix potential endless loop in merge (#2457) 
avoid single segments lists without deletes as merge candidates, as they will be moved
to a merge operation and filtered for merging in the next
consider_merge_options call. In rare cases this may end up in a endless
merge loop where only single segments where nothing is to be done are
merged.
 2024-07-30 16:37:20 +08:00
PSeitz
d8843c608c make FastFieldRangeWeight::new pub (#2460) 2024-07-29 10:39:27 +08:00
PSeitz
7ebcc15b17 add support for str fast field range query (#2453) 
* add support for str fast field range query

Add support for range queries on fast fields, by converting term bounds to
term ordinals bounds.

closes https://github.com/quickwit-oss/tantivy/issues/2023

* extend tests, rename

* update comment

* update comment
 2024-07-17 09:31:42 +08:00
PSeitz
1b4076691f refactor fast field query (#2452) 
As preparation of #2023 and #1709

* Use Term to pass parameters
* merge u64 and ip fast field range query

Side note: I did not rename range_query_u64_fastfield, because then git can't track the changes.
 2024-07-15 18:08:05 +08:00
Robert Caulk
eab660873a doc: fix typo in readme (#2450) 2024-07-09 15:12:22 +08:00
PSeitz
232f37126e fix coverage (#2448) 2024-07-05 12:04:18 +08:00
PSeitz
13e9885dfd faster term aggregation fetch terms (#2447) 
big impact for term aggregations with large `size` parameter (e.g. 1000)
add top 1000 term agg bench

full
terms_few                                      Memory: 27.3 KB (+79.09%)    Avg: 3.8058ms (+2.40%)      Median: 3.7192ms (+3.47%)       [3.6224ms .. 4.3721ms]
terms_many                                     Memory: 6.9 MB               Avg: 12.6102ms (-4.70%)     Median: 12.1389ms (-6.58%)      [10.2847ms .. 15.4857ms]
terms_many_top_1000                            Memory: 6.9 MB               Avg: 15.8216ms (-83.19%)    Median: 15.4899ms (-83.46%)     [13.4250ms .. 20.6897ms]
terms_many_order_by_term                       Memory: 6.9 MB               Avg: 14.7820ms (-3.95%)     Median: 14.2236ms (-4.28%)      [12.6669ms .. 21.0968ms]
terms_many_with_top_hits                       Memory: 58.2 MB              Avg: 551.6218ms (+7.18%)    Median: 549.8826ms (+11.01%)    [496.7371ms .. 592.1299ms]
terms_many_with_avg_sub_agg                    Memory: 27.8 MB              Avg: 197.7029ms (+2.66%)    Median: 190.1564ms (+0.64%)     [167.9226ms .. 245.6651ms]
terms_many_json_mixed_type_with_avg_sub_agg    Memory: 42.0 MB (+0.00%)     Avg: 242.0121ms (+0.92%)    Median: 237.7084ms (-2.85%)     [201.9959ms .. 302.2136ms]
terms_few_with_cardinality_agg                 Memory: 10.6 MB              Avg: 122.6036ms (+1.21%)    Median: 119.0033ms (+2.60%)     [109.2859ms .. 161.5858ms]
range_agg_with_term_agg_few                    Memory: 45.4 KB (+39.75%)    Avg: 24.5454ms (+2.14%)     Median: 24.2861ms (+2.44%)      [23.5109ms .. 27.8406ms]
range_agg_with_term_agg_many                   Memory: 6.9 MB               Avg: 56.8049ms (+3.01%)     Median: 50.9706ms (+1.52%)      [41.4517ms .. 90.3934ms]
dense
terms_few                                      Memory: 28.8 KB (+81.74%)    Avg: 8.9092ms (-2.24%)      Median: 8.7143ms (-1.31%)      [8.6148ms .. 10.3868ms]
terms_many                                     Memory: 6.9 MB (-0.00%)      Avg: 17.9604ms (-10.18%)    Median: 17.1552ms (-11.93%)    [14.8979ms .. 26.2779ms]
terms_many_top_1000                            Memory: 6.9 MB               Avg: 21.4963ms (-78.90%)    Median: 21.2924ms (-78.98%)    [18.2033ms .. 28.0087ms]
terms_many_order_by_term                       Memory: 6.9 MB               Avg: 20.4167ms (-9.13%)     Median: 19.5596ms (-11.37%)    [17.5153ms .. 29.5987ms]
terms_many_with_top_hits                       Memory: 58.2 MB              Avg: 518.4474ms (-6.41%)    Median: 514.9180ms (-9.44%)    [471.5550ms .. 579.0220ms]
terms_many_with_avg_sub_agg                    Memory: 27.8 MB              Avg: 263.6702ms (-2.78%)    Median: 260.8775ms (-2.55%)    [239.5754ms .. 304.6669ms]
terms_many_json_mixed_type_with_avg_sub_agg    Memory: 42.0 MB              Avg: 299.9791ms (-2.01%)    Median: 302.2180ms (-3.08%)    [239.2080ms .. 346.3649ms]
terms_few_with_cardinality_agg                 Memory: 10.6 MB              Avg: 136.3303ms (-3.12%)    Median: 132.3831ms (-2.88%)    [123.7564ms .. 164.7914ms]
range_agg_with_term_agg_few                    Memory: 47.1 KB (+37.81%)    Avg: 35.4538ms (+0.66%)     Median: 34.8754ms (-0.56%)     [34.2287ms .. 40.0884ms]
range_agg_with_term_agg_many                   Memory: 6.9 MB               Avg: 72.2269ms (-4.38%)     Median: 66.1174ms (-4.98%)     [55.5125ms .. 124.1622ms]
sparse
terms_few                                      Memory: 27.3 KB (+69.68%)    Avg: 19.6053ms (-1.15%)     Median: 19.4543ms (-0.38%)     [19.3056ms .. 24.0547ms]
terms_many                                     Memory: 1.8 MB               Avg: 21.2886ms (-6.28%)     Median: 21.1287ms (-6.65%)     [20.6640ms .. 24.6144ms]
terms_many_top_1000                            Memory: 2.6 MB               Avg: 23.4869ms (-85.53%)    Median: 23.3393ms (-85.61%)    [22.7789ms .. 25.0896ms]
terms_many_order_by_term                       Memory: 1.8 MB               Avg: 21.7437ms (-7.78%)     Median: 21.6272ms (-7.66%)     [21.0409ms .. 23.6517ms]
terms_many_with_top_hits                       Memory: 13.1 MB              Avg: 43.7926ms (-2.76%)     Median: 44.3602ms (+0.01%)     [37.8039ms .. 51.0451ms]
terms_many_with_avg_sub_agg                    Memory: 7.5 MB               Avg: 34.6307ms (+3.72%)     Median: 33.4522ms (+1.16%)     [32.4418ms .. 41.4196ms]
terms_many_json_mixed_type_with_avg_sub_agg    Memory: 7.4 MB               Avg: 46.4318ms (+1.16%)     Median: 46.4050ms (+2.03%)     [44.5986ms .. 48.5142ms]
terms_few_with_cardinality_agg                 Memory: 680.0 KB (-0.04%)    Avg: 35.4410ms (+2.05%)     Median: 35.1384ms (+1.19%)     [34.4402ms .. 39.1082ms]
range_agg_with_term_agg_few                    Memory: 45.7 KB (+39.44%)    Avg: 22.7760ms (+0.44%)     Median: 22.5152ms (-0.35%)     [22.3078ms .. 26.1567ms]
range_agg_with_term_agg_many                   Memory: 1.8 MB               Avg: 25.7696ms (-4.45%)     Median: 25.4009ms (-5.61%)     [24.7874ms .. 29.6434ms]
multivalue
terms_few                                      Memory: 244.4 KB            Avg: 15.1253ms (-2.85%)     Median: 15.0988ms (-0.54%)     [14.8790ms .. 15.8193ms]
terms_many                                     Memory: 6.9 MB (-0.00%)     Avg: 26.3019ms (-6.24%)     Median: 26.3662ms (-4.94%)     [21.3553ms .. 31.0564ms]
terms_many_top_1000                            Memory: 6.9 MB              Avg: 29.5212ms (-72.90%)    Median: 29.4257ms (-72.84%)    [24.2645ms .. 35.1607ms]
terms_many_order_by_term                       Memory: 6.9 MB              Avg: 28.6076ms (-4.93%)     Median: 28.1059ms (-6.64%)     [24.0845ms .. 34.1493ms]
terms_many_with_top_hits                       Memory: 58.3 MB             Avg: 570.1548ms (+1.52%)    Median: 572.7759ms (+0.53%)    [525.9567ms .. 617.0862ms]
terms_many_with_avg_sub_agg                    Memory: 27.8 MB             Avg: 305.5207ms (+0.24%)    Median: 296.0101ms (-0.22%)    [277.8579ms .. 373.5914ms]
terms_many_json_mixed_type_with_avg_sub_agg    Memory: 42.0 MB (-0.00%)    Avg: 324.7342ms (-2.51%)    Median: 319.0025ms (-2.58%)    [298.7122ms .. 368.6144ms]
terms_few_with_cardinality_agg                 Memory: 10.8 MB             Avg: 151.6126ms (-2.54%)    Median: 149.0616ms (-0.32%)    [136.5592ms .. 181.8942ms]
range_agg_with_term_agg_few                    Memory: 248.2 KB            Avg: 49.5225ms (+3.11%)     Median: 48.3994ms (+3.18%)     [46.4134ms .. 60.5989ms]
range_agg_with_term_agg_many                   Memory: 6.9 MB              Avg: 85.9824ms (-3.66%)     Median: 78.4266ms (-3.85%)     [64.1231ms .. 128.5279ms]
 2024-07-03 12:42:59 +08:00
PSeitz
56d79cb203 fix cardinality aggregation performance (#2446) 
* fix cardinality aggregation performance

fix cardinality performance by fetching multiple terms at once. This
avoids decompressing the same block and keeps the buffer state between
terms.

add cardinality aggregation benchmark

bump rust version to 1.66

Performance comparison to before (AllQuery)
```
full
cardinality_agg                   Memory: 3.5 MB (-0.00%)    Avg: 21.2256ms (-97.78%)    Median: 21.0042ms (-97.82%)    [20.4717ms .. 23.6206ms]
terms_few_with_cardinality_agg    Memory: 10.6 MB            Avg: 81.9293ms (-97.37%)    Median: 81.5526ms (-97.38%)    [79.7564ms .. 88.0374ms]
dense
cardinality_agg                   Memory: 3.6 MB (-0.00%)    Avg: 25.9372ms (-97.24%)    Median: 25.7744ms (-97.25%)    [24.7241ms .. 27.8793ms]
terms_few_with_cardinality_agg    Memory: 10.6 MB            Avg: 93.9897ms (-96.91%)    Median: 92.7821ms (-96.94%)    [90.3312ms .. 117.4076ms]
sparse
cardinality_agg                   Memory: 895.4 KB (-0.00%)    Avg: 22.5113ms (-95.01%)    Median: 22.5629ms (-94.99%)    [22.1628ms .. 22.9436ms]
terms_few_with_cardinality_agg    Memory: 680.2 KB             Avg: 26.4250ms (-94.85%)    Median: 26.4135ms (-94.86%)    [26.3210ms .. 26.6774ms]
```

* clippy

* assert for sorted ordinals
 2024-07-02 15:29:00 +08:00
Paul Masurel
0f4c2e27cf Fixes bug that causes out-of-order sstable key. (#2445) 
The previous way to address the problem was to replace \u{0000}
with 0 in different places.

This logic had several flaws:
Done on the serializer side (like it was for the columnar), there was
a collision problem.

If a document in the segment contained a json field with a \0 and
antoher doc contained the same json field but `0` then we were sending
the same field path twice to the serializer.

Another option would have been to normalizes all values on the writer
side.

This PR simplifies the logic and simply ignore json path containing a
\0, both in the columnar and the inverted index.

Closes #2442
 2024-07-01 15:40:07 +08:00
落叶乌龟
f9ae295507 feat(query): Make BooleanQuery supports minimum_number_should_match (#2405) 
* feat(query): Make `BooleanQuery` supports `minimum_number_should_match`. see issue #2398

In this commit, a novel scorer named DisjunctionScorer is introduced, which performs the union of inverted chains with the minimal required elements. BTW, it's implemented via a min-heap. Necessary modifications on `BooleanQuery` and `BooleanWeight` are performed as well.

* fixup! fix test

* fixup!: refactor code.

1. More meaningful names.
2. Add Cache for `Disjunction`'s scorers, and fix bug.
3. Optimize `BooleanWeight::complex_scorer`

Thanks
 Paul Masurel <paul@quickwit.io>

* squash!: come up with better variable naming.

* squash!: fix naming issues.

* squash!: fix typo.

* squash!: Remove CombinationMethod::FullIntersection
 2024-07-01 15:39:41 +08:00
Raphael Coeffic
d9db5302d9 feat: cardinality aggregation (#2337) 
* WiP: cardinality aggregation

* Collect unique entries first, then insert into HyperLogLog

* Handle `missing`

* Hybrid approach

* Review changes

- insert `missing` value at most once
- `term_id` -> `term_ord`
- iterate directly over entries without collecting first

* Use salted hasher to include column type

* fix: formatting

* More review fixes

* Add cardinality to test_aggregation_flushing

* Formatting
 2024-07-01 07:49:42 +08:00
Paul Masurel
e453848134 Recycling buffer in PrefixPhraseScorer (#2443) 2024-06-24 17:11:53 +09:00
PSeitz
59084143ef use optional index in multivalued index (#2439) 
* use optional index in multivalued index

For mostly empty multivalued indices there was a large overhead during
creation when iterating all docids. This is alleviated by placing an
optional index in the multivalued index to mark documents that have values.

There's some performance overhead when accessing values in a multivalued
index. The accessing cost is now optional index + multivalue index. The
sparse codec performs relatively bad with the binary_search when accessing
data. This is reflected in the benchmarks below.

This changes the format of columnar to v2, but code is added to handle the v1
formats.

```
     Running benches/bench_access.rs (/home/pascal/Development/tantivy/optional_multivalues/target/release/deps/bench_access-ea323c028db88db4)
multi sparse 1/13
access_values_for_doc        Avg: 42.8946ms (+241.80%)    Median: 42.8869ms (+244.10%)    [42.7484ms .. 43.1074ms]
access_first_vals            Avg: 42.8022ms (+421.93%)    Median: 42.7553ms (+439.84%)    [42.6794ms .. 43.7404ms]
multi 2x
access_values_for_doc        Avg: 31.1244ms (+24.17%)    Median: 30.8339ms (+23.46%)    [30.7192ms .. 33.6059ms]
access_first_vals            Avg: 24.3070ms (+70.92%)    Median: 24.0966ms (+70.18%)    [23.9328ms .. 26.4851ms]
sparse 1/13
access_values_for_doc        Avg: 42.2490ms (+0.61%)    Median: 42.2346ms (+2.28%)    [41.8988ms .. 43.7821ms]
access_first_vals            Avg: 43.6272ms (+0.23%)    Median: 43.6197ms (+1.78%)    [43.4920ms .. 43.9009ms]
dense 1/12
access_values_for_doc        Avg: 8.6184ms (+23.18%)    Median: 8.6126ms (+23.78%)    [8.5843ms .. 8.7527ms]
access_first_vals            Avg: 6.8112ms (+4.47%)     Median: 6.8002ms (+4.55%)     [6.7887ms .. 6.8991ms]
full
access_values_for_doc        Avg: 9.4073ms (-5.09%)    Median: 9.4023ms (-2.23%)    [9.3694ms .. 9.4568ms]
access_first_vals            Avg: 4.9531ms (+6.24%)    Median: 4.9502ms (+7.85%)    [4.9423ms .. 4.9718ms]
```

```
     Running benches/bench_merge.rs (/home/pascal/Development/tantivy/optional_multivalues/target/release/deps/bench_merge-475697dfceb3639f)
merge_multi 2x_and_multi 2x                          Avg: 20.2280ms (+34.33%)    Median: 20.1829ms (+35.33%)    [19.9933ms .. 20.8806ms]
merge_multi sparse 1/13_and_multi sparse 1/13        Avg: 0.8961ms (-78.04%)     Median: 0.8943ms (-77.61%)     [0.8899ms .. 0.9272ms]
merge_dense 1/12_and_dense 1/12                      Avg: 0.6619ms (-1.26%)      Median: 0.6616ms (+2.20%)      [0.6473ms .. 0.6837ms]
merge_sparse 1/13_and_sparse 1/13                    Avg: 0.5508ms (-0.85%)      Median: 0.5508ms (+2.80%)      [0.5420ms .. 0.5634ms]
merge_sparse 1/13_and_dense 1/12                     Avg: 0.6046ms (-4.64%)      Median: 0.6038ms (+2.80%)      [0.5939ms .. 0.6296ms]
merge_multi sparse 1/13_and_dense 1/12               Avg: 0.9111ms (-83.48%)     Median: 0.9063ms (-83.50%)     [0.9047ms .. 0.9663ms]
merge_multi sparse 1/13_and_sparse 1/13              Avg: 0.8451ms (-89.49%)     Median: 0.8428ms (-89.43%)     [0.8411ms .. 0.8563ms]
merge_multi 2x_and_dense 1/12                        Avg: 10.6624ms (-4.82%)     Median: 10.6568ms (-4.49%)     [10.5738ms .. 10.8353ms]
merge_multi 2x_and_sparse 1/13                       Avg: 10.6336ms (-22.95%)    Median: 10.5925ms (-22.33%)    [10.5149ms .. 11.5657ms]
```

* Update columnar/src/columnar/format_version.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

* Update columnar/src/column_index/mod.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2024-06-19 14:54:12 +08:00
PSeitz
511b027350 update columnar bench (#2438) 
* update columnar bench

* fix compile
 2024-06-14 10:42:35 +08:00
Philippe Noël
322f47eb47 Add ParadeDB to Companies List (#1) (#2437) 
* Add ParadeDB logo
 2024-06-14 09:12:58 +09:00
PSeitz
72f61ff89c remove index sorting (#2434) 
closes https://github.com/quickwit-oss/tantivy/issues/2352
 2024-06-13 15:51:53 +08:00
PSeitz
a141c3ec59 add columnar format compatibiliy tests (#2433) 
* add columnar format compatibiliy tests

* always try to write current format
 2024-06-13 15:04:52 +08:00
PSeitz
e90e7a25ae add access benchmark for columnar (#2432) 2024-06-12 14:29:15 +08:00
PSeitz
c3b92a5412 fix compiler warning, cleanup (#2393) 
fix compiler warning for missing feature flag
remove unused variables
cleanup unused methods
 2024-06-11 16:03:50 +08:00
PSeitz
2f55511064 extend indexwriter proptests (#2342) 
* index random values in proptest

* add proptest with multiple docs
 2024-06-11 16:02:57 +08:00
trinity-1686a
08b9fc0b31 fix de-escaping too much in query parser (#2427) 
* fix de-escaping too much in query parser
 2024-06-10 11:19:01 +02:00
PSeitz
714f363d43 add bench & test for columnar merging (#2428) 
* add merge columnar proptest

* add columnar merge benchmark
 2024-06-10 16:26:16 +08:00
PSeitz
93ff7365b0 reduce top hits aggregation memory consumption (#2426) 
move request structure out of top hits aggregation collector and use from the
passed structure instead

full
terms_many_with_top_hits    Memory: 58.2 MB (-43.64%)    Avg: 425.9680ms (-21.38%)    Median: 415.1097ms (-23.56%)    [395.5303ms .. 484.6325ms]
dense
terms_many_with_top_hits    Memory: 58.2 MB (-43.64%)    Avg: 440.0817ms (-19.68%)    Median: 432.2286ms (-21.10%)    [403.5632ms .. 497.7541ms]
sparse
terms_many_with_top_hits    Memory: 13.1 MB (-49.31%)    Avg: 33.3568ms (-32.19%)    Median: 33.0834ms (-31.86%)    [32.5126ms .. 35.7397ms]
multivalue
terms_many_with_top_hits    Memory: 58.2 MB (-43.64%)    Avg: 414.2340ms (-25.44%)    Median: 413.4144ms (-25.64%)    [403.9919ms .. 430.3170ms]
 2024-06-06 22:32:58 +08:00
Adam Reichold
8151925068 Panicking in spawned Rayon tasks will abort the process by default. (#2409) 2024-06-04 17:04:30 +09:00
dependabot[bot]
b960e40bc8 Update sketches-ddsketch requirement from 0.2.1 to 0.3.0 (#2423) 
Updates the requirements on [sketches-ddsketch](https://github.com/mheffner/rust-sketches-ddsketch) to permit the latest version.
- [Release notes](https://github.com/mheffner/rust-sketches-ddsketch/releases)
- [Commits](https://github.com/mheffner/rust-sketches-ddsketch/compare/v0.2.1...v0.3.0)

---
updated-dependencies:
- dependency-name: sketches-ddsketch
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2024-06-04 15:50:23 +08:00
giovannicuccu
1095c9b073 Issue 1787 extended stats (#2247) 
* first version of extended stats along with its tests

* using IntermediateExtendStats instead of IntermediateStats with all tests passing

* Created struct for request and response

* first test with extended_stats

* kahan summation and tests with approximate equality

* version ready for merge

* removed approx dependency

* refactor for using ExtendedStats only when needed

* interim version

* refined version with code formatted

* refactored a struct

* cosmetic refactor

* fix after merge

* fix format

* added extended_stat bench

* merge and new benchmark for extended stats

* split stat segment collectors

* wrapped intermediate extended stat with a box to limit memory usage

* Revert "wrapped intermediate extended stat with a box to limit memory usage"

This reverts commit 5b4aa9f393.

* some code reformat, commented kahan summation

* refactor after review

* refactor after code review

* fix after incorrectly restoring kahan summation

* modifications for code review + bug fix in merge_fruit

* refactor assert_nearly_equals macro

* update after code review

---------

Co-authored-by: Giovanni Cuccu <gcuccu@imolainformatica.it>
 2024-06-04 14:25:17 +08:00
PSeitz
c0686515a9 update one_shot (#2420) 2024-05-31 11:07:35 +08:00
trinity-1686a
455156f51c improve query parser (#2416) 
* support escape sequence in more place

and fix bug with singlequoted strings

* add query parser test for range query on default field
 2024-05-30 17:29:27 +02:00
Meng Zhang
4143d31865 chore: fix build as the rev is gone (#2417) 2024-05-29 09:49:16 +08:00
Hamir Mahal
0c634adbe1 style: simplify strings with string interpolation (#2412) 
* style: simplify strings with string interpolation

* fix: formatting
 2024-05-27 09:16:47 +02:00
PSeitz
2e3641c2ae return CompactDocValue instead of trait (#2410) 
The CompactDocValue is easier to handle than the trait in some cases like comparison
and conversion
 2024-05-27 07:33:50 +02:00
Paul Masurel
b806122c81 Fixing flaky test (#2407) 2024-05-22 10:10:55 +09:00
PSeitz
e1679f3fb9 compact doc (#2402) 
* compact doc

* add any value type

* pass references when building CompactDoc

* remove OwnedValue from API

* clippy

* clippy

* fail on large documents

* fmt

* cleanup

* cleanup

* implement Value for different types

fix serde_json date Value implementation

* fmt

* cleanup

* fmt

* cleanup

* store positions instead of pos+len

* remove nodes array

* remove mediumvec

* cleanup

* infallible serialize into vec

* remove positions indirection

* remove 24MB limitation in document

use u32 for Addr
Remove the 3 byte addressing limitation and use VInt instead

* cleanup

* extend test

* cleanup, add comments

* rename, remove pub
 2024-05-21 10:16:08 +02:00
dependabot[bot]
5a80420b10 --- (#2406) 
updated-dependencies:
- dependency-name: binggan
  dependency-type: direct:production
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 2024-05-21 04:36:32 +02:00
dependabot[bot]
aa26ff5029 Update binggan requirement from 0.6.2 to 0.7.0 (#2401) 
---
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  dependency-type: direct:production
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 2024-05-17 02:53:25 +02:00
dependabot[bot]
e197b59258 Update itertools requirement from 0.12.0 to 0.13.0 (#2400) 
Updates the requirements on [itertools](https://github.com/rust-itertools/itertools) to permit the latest version.
- [Changelog](https://github.com/rust-itertools/itertools/blob/master/CHANGELOG.md)
- [Commits](https://github.com/rust-itertools/itertools/compare/v0.12.0...v0.13.0)

---
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  dependency-type: direct:production
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 2024-05-17 02:53:02 +02:00
PSeitz
5b7cca13e5 lower contention on AggregationLimits (#2394) 
PR https://github.com/quickwit-oss/quickwit/pull/4962 fixes an issue
where the AggregationLimits are not passed correctly. Since the
AggregationLimits are shared properly we run into contention issues.

This PR includes some straightforward improvement to reduce contention,
by only calling if the memory changed and avoiding the second read.

We probably need some sharding with multiple counters or local caching before updating the
global after some threshold.
 2024-05-15 12:25:40 +02:00
dependabot[bot]
a79590477e Update binggan requirement from 0.5.2 to 0.6.2 (#2399) 
---
updated-dependencies:
- dependency-name: binggan
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2024-05-15 05:40:37 +02:00
Paul Masurel
6181c1eb5e Small changes in the Executor API. (#2391) 
Warning, this change is mildly not backward compatible
so I bumped tantivy's version.
 2024-05-10 17:19:12 +09:00
Adam Reichold
1ee5f90761 Give allocation control to the caller instead of force a clone (#2389) 
Achieved by moving the boxes out of the temporary reference wrappers which are
cloneable themselves, i.e. if required the caller can clone them already or
consume them to reuse existing allocations.
 2024-05-09 16:01:13 +09:00
PSeitz
71f3b4e4e3 fix ReferenceValue API flaw (#2372) 
* fix ReferenceValue API flaw

Remove `Facet` and `TokenizedString` values from the `ReferenceValue` API,
as this requires the trait value to have them stored somewhere.

Since `TokenizedString` is quite niche, I just copy it into a Box,
instead of designing a reference API around it.

* fix comment link
 2024-05-09 06:14:42 +02:00
trinity-1686a
8cd7ddc535 run block decompression from executor (#2386) 
* run block decompression from executor

* add a wrapper with is_closed to oneshot channel

* add cancelation test to Executor::spawn_blocking
 2024-05-08 12:22:44 +02:00
Paul Masurel
2b76335a95 Removed usage of num_cpus (#2387) 
* Removed usage of num_cpus
* handling error
 2024-05-08 13:32:52 +09:00
PSeitz
c6b213d8f0 use bingang for agg benchmark (#2378) 
* use bingang for agg benchmark

use bingang for agg benchmark, which includes memory consumption

Output:
```
full
histogram                     Memory: 15.8 KB              Avg: 10.9322ms  (+5.44%)    Median: 10.8790ms  (+9.28%)     Min: 10.7470ms    Max: 11.3263ms
histogram_hard_bounds         Memory: 15.5 KB              Avg: 5.1939ms  (+6.61%)     Median: 5.1722ms  (+10.98%)     Min: 5.0432ms     Max: 5.3910ms
histogram_with_avg_sub_agg    Memory: 48.7 KB              Avg: 23.8165ms  (+4.57%)    Median: 23.7264ms  (+10.06%)    Min: 23.4995ms    Max: 24.8107ms
dense
histogram                     Memory: 17.3 KB              Avg: 15.6810ms  (-8.54%)    Median: 15.6174ms  (-8.89%)    Min: 15.4953ms    Max: 16.0702ms
histogram_hard_bounds         Memory: 15.4 KB              Avg: 10.0720ms  (-7.33%)    Median: 10.0572ms  (-7.06%)    Min: 9.8500ms     Max: 10.4819ms
histogram_with_avg_sub_agg    Memory: 50.1 KB              Avg: 33.0993ms  (-7.04%)    Median: 32.9499ms  (-6.86%)    Min: 32.8284ms    Max: 34.0529ms
sparse
histogram                     Memory: 16.3 KB              Avg: 19.2325ms  (-0.44%)    Median: 19.1211ms  (-1.26%)    Min: 19.0348ms    Max: 19.7902ms
histogram_hard_bounds         Memory: 16.1 KB              Avg: 18.5179ms  (-0.61%)    Median: 18.4552ms  (-0.90%)    Min: 18.3799ms    Max: 19.0535ms
histogram_with_avg_sub_agg    Memory: 34.7 KB              Avg: 21.2589ms  (-0.69%)    Median: 21.1867ms  (-1.05%)    Min: 21.0342ms    Max: 21.9900ms
```

* add more bench with term as sub agg
 2024-05-07 11:29:49 +02:00
PSeitz
eea70030bf cleanup top level exports (#2382) 
remove some top level exports
 2024-05-07 09:59:41 +02:00
PSeitz
92b5526310 allow more JSON values, fix i64 special case (#2383) 
This changes three things:
- Reuse positions_per_path hashmap instead of allocating one per
  indexed JSON value
- Try to cast u64 values to i64 to streamline with search behaviour
- Allow top level json values to be of any type, instead of limiting it
  to JSON objects. Remove special JSON object handling method.

TODO: We probably should also try to check f64 to i64 and u64 when
indexing, as values may get converted to f64 by the JSON parser
 2024-05-01 12:08:12 +02:00
PSeitz
99a59ad37e remove zero byte check (#2379) 
remove zero byte checks in columnar. zero bytes are converted during serialization now.
unify code paths
extend test for expected column names
 2024-04-26 06:03:28 +02:00
trinity-1686a
6a66a71cbb modify fastfield range query heuristic (#2375) 2024-04-25 10:06:11 +02:00
PSeitz
ff40764204 make convert_to_fast_value_and_append_to_json_term pub (#2370) 
* make convert_to_fast_value_and_append_to_json_term pub

* clippy
 2024-04-23 04:05:41 +02:00
PSeitz
047da20b5b add json path constructor to term (#2367) 2024-04-22 12:23:35 +02:00
PSeitz
1417eaf3a7 fix coverage (#2368) 2024-04-22 12:23:15 +02:00
PSeitz
4f8493d2de improve document docs (#2359) 2024-04-22 12:05:16 +02:00
Paul Masurel
8861366137 Owned value relying on Vec instead of BTreeMap (#2364) 
* Owned value relying on Vec instead of BTreeMap

* fmt

* fix build

* fix serialization

---------

Co-authored-by: Pascal Seitz <pascal.seitz@gmail.com>
 2024-04-22 09:38:05 +02:00
PSeitz
0e9fced336 remove JsonTermWriter (#2238) 
* remove JsonTermWriter

remove JsonTermWriter
remove path truncation logic, add assertion

* fix json_path_writer add sep logic
 2024-04-18 16:28:05 +02:00
PSeitz
b257b960b3 validate sort by field type (#2336) 
* validate sort by field type

* Update src/index/index.rs

Co-authored-by: Adam Reichold <adamreichold@users.noreply.github.com>

---------

Co-authored-by: Adam Reichold <adamreichold@users.noreply.github.com>
 2024-04-16 04:42:24 +02:00
Adam Reichold
4708171a32 Fix some of the things current Clippy complains about (#2363) 2024-04-16 04:27:06 +02:00
Adam Reichold
b493743f8d Fix trait bound of StoreReader::iter (#2360) 
* Fix trait bound of StoreReader::iter

Similar to `StoreReader::get`, `StoreReader::iter` should only require
`DocumentDeserialize` and not `Document`.

* Mark the iterator returned by SegmentReader::doc_ids_alive as Send so it can be used in impls of Stream/AsyncIterator.
 2024-04-15 15:50:02 +02:00
trinity-1686a
d2955a3fd2 extend field grouping (#2333) 
* extend field grouping
 2024-04-15 10:36:32 +02:00
PSeitz
17d5869ad6 update CHANGELOG, use github API in cliff (#2354) 
* update CHANGELOG, use github API in cliff

* reset version to 0.21.1, before release

* chore: Release

* remove unreleased from CHANGELOG
 2024-04-15 10:07:20 +02:00
PSeitz
dfa3aed32d check unsupported parameters top_hits (#2351) 
* check unsupported parameters top_hits

* move to function
 2024-04-10 08:20:52 +02:00
PSeitz
398817ce7b add index sorting deprecation warning (#2353) 
* add index sorting deprecation warning

* remove deprecated IntOptions and DatePrecision
 2024-04-10 08:09:09 +02:00
PSeitz
74940e9345 clippy (#2349) 
* fix clippy

* fix clippy

* fix duplicate imports
 2024-04-09 07:54:44 +02:00
PSeitz
1e9fc51535 update ahash (#2344) 2024-04-09 06:35:39 +02:00
PSeitz
92c32979d2 fix postcard compatibility for top_hits, add postcard test (#2346) 
* fix postcard compatibility for top_hits, add postcard test

* fix top_hits naming, delay data fetch

closes #2347

* fix import
 2024-04-09 06:17:25 +02:00
PSeitz
b644d78a32 fix null byte handling in JSON paths (#2345) 
* fix null byte handling in JSON paths

closes https://github.com/quickwit-oss/tantivy/issues/2193
closes https://github.com/quickwit-oss/tantivy/issues/2340

* avoid repeated term truncation

* fix test

* Apply suggestions from code review

Co-authored-by: Paul Masurel <paul@quickwit.io>

* add comment

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2024-04-05 09:53:35 +02:00
PSeitz
4e79e11007 add collect_block to BoxableSegmentCollector (#2331) 2024-03-21 09:10:25 +01:00
PSeitz
67ebba3c3c expose collect_block buffer size (#2326) 
* expose buffer of collect_block

* flip shard_size segment_size
 2024-03-15 08:02:08 +01:00
PSeitz
7ce950f141 add method to fetch block of first vals in columnar (#2330) 
* add method to fetch block of first vals in columnar

add method to fetch block of first vals in columnar (this is way faster
than single calls for full columns)
add benchmark
fix import warnings

```
test bench_get_block_first_on_full_column                  ... bench:          56 ns/iter (+/- 26)
test bench_get_block_first_on_full_column_single_calls     ... bench:         311 ns/iter (+/- 6)
test bench_get_block_first_on_multi_column                 ... bench:         378 ns/iter (+/- 15)
test bench_get_block_first_on_multi_column_single_calls    ... bench:         546 ns/iter (+/- 13)
test bench_get_block_first_on_optional_column              ... bench:         291 ns/iter (+/- 6)
test bench_get_block_first_on_optional_column_single_calls ... bench:         362 ns/iter (+/- 8)
```

* use remainder
 2024-03-15 08:01:47 +01:00
dependabot[bot]
0cffe5fb09 Update base64 requirement from 0.21.0 to 0.22.0 (#2324) 
Updates the requirements on [base64](https://github.com/marshallpierce/rust-base64) to permit the latest version.
- [Changelog](https://github.com/marshallpierce/rust-base64/blob/master/RELEASE-NOTES.md)
- [Commits](https://github.com/marshallpierce/rust-base64/compare/v0.21.0...v0.22.0)

---
updated-dependencies:
- dependency-name: base64
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2024-03-15 15:50:34 +09:00
PSeitz
b0e65560a1 handle ip adresses in term aggregation (#2319) 
* handle ip adresses in term aggregation

Stores IpAdresses during the segment term aggregation via u64 representation
and convert to u128(IpV6Adress) via downcast when converting to intermediate results.

Enable Downcasting on `ColumnValues`
Expose u64 variant for u128 encoded data via `open_u64_lenient` method.
Remove lifetime in VecColumn, to avoid 'static lifetime requirement coming
from downcast trait.

* rename method
 2024-03-14 09:41:18 +01:00
PSeitz
ec37295b2f add fast path for full columns in fetch_block (#2328) 
Spotted in `range_date_histogram` query in quickwit benchmark:
5% of time copying docs around, which is not needed in the full index case

remove Column to ColumnIndex deref
 2024-03-14 04:07:11 +01:00
trinity-1686a
f6b0cc1aab allow some mixing of occur and bool in strict query parser (#2323) 
* allow some mixing of occur and bool in strict query parser

* allow all mixing of binary and occur in strict parser
 2024-03-07 15:17:48 +01:00
PSeitz
7e41d31c6e agg: support to deserialize f64 from string (#2311) 
* agg: support to deserialize f64 from string

* remove visit_string

* disallow NaN
 2024-03-05 05:49:41 +01:00
Adam Reichold
40aa4abfe5 Make FacetCounts defaultable and cloneable. (#2322) 2024-03-05 04:11:11 +01:00
dependabot[bot]
2650317622 Update fs4 requirement from 0.7.0 to 0.8.0 (#2321) 
Updates the requirements on [fs4](https://github.com/al8n/fs4-rs) to permit the latest version.
- [Release notes](https://github.com/al8n/fs4-rs/releases)
- [Commits](https://github.com/al8n/fs4-rs/commits)

---
updated-dependencies:
- dependency-name: fs4
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2024-02-27 03:38:04 +01:00
Paul Masurel
6739357314 Removing split_size and adding split_size and shard_size as segmnet_size (#2320) 
aliases.
 2024-02-26 11:35:22 +01:00
PSeitz
d57622d54b support bool type in term aggregation (#2318) 
* support bool type in term aggregation

* add Bool to Intermediate Key
 2024-02-20 03:22:22 +01:00
PSeitz
f745dbc054 fix Clone for TopNComputer, add top_hits bench (#2315) 
* fix Clone for TopNComputer, add top_hits bench

add top_hits agg bench

test aggregation::agg_bench::bench::bench_aggregation_terms_many_with_sub_agg                                            ... bench: 123,475,175 ns/iter (+/- 30,608,889)
test aggregation::agg_bench::bench::bench_aggregation_terms_many_with_sub_agg_multi                                      ... bench: 194,170,414 ns/iter (+/- 36,495,516)
test aggregation::agg_bench::bench::bench_aggregation_terms_many_with_sub_agg_opt                                        ... bench: 179,742,809 ns/iter (+/- 29,976,507)
test aggregation::agg_bench::bench::bench_aggregation_terms_many_with_sub_agg_sparse                                     ... bench:  27,592,534 ns/iter (+/- 2,672,370)
test aggregation::agg_bench::bench::bench_aggregation_terms_many_with_top_hits_agg                                       ... bench: 552,851,227 ns/iter (+/- 71,975,886)
test aggregation::agg_bench::bench::bench_aggregation_terms_many_with_top_hits_agg_multi                                 ... bench: 558,616,384 ns/iter (+/- 100,890,124)
test aggregation::agg_bench::bench::bench_aggregation_terms_many_with_top_hits_agg_opt                                   ... bench: 554,031,368 ns/iter (+/- 165,452,650)
test aggregation::agg_bench::bench::bench_aggregation_terms_many_with_top_hits_agg_sparse                                ... bench:  46,435,919 ns/iter (+/- 13,681,935)

* add comment
 2024-02-20 03:22:00 +01:00
PSeitz
79b041f81f clippy (#2314) 2024-02-13 05:56:31 +01:00
PSeitz
0e16ed9ef7 Fix serde for TopNComputer (#2313) 
* Fix serde for TopNComputer

The top hits aggregation changed the TopNComputer to be serializable,
but capacity needs to be carried over, as it contains logic which is
checked against when pushing elements (capacity == 0 is not allowed).

* use serde from deser

* remove pub, clippy
 2024-02-07 12:52:06 +01:00
mochi
88a3275dbb add shared search executor (#2312) 2024-02-05 09:33:00 +01:00
PSeitz
1223a87eb2 add fuzz test for hashmap (#2310) 2024-01-31 10:30:21 +01:00
PSeitz
48630ceec9 move into new index module (#2259) 
move core modules to index module
 2024-01-31 10:30:04 +01:00
Adam Reichold
72002e8a89 Make test builds Clippy clean. (#2277) 2024-01-31 02:47:06 +01:00
trinity-1686a
3c9297dd64 report if posting list was actually loaded when warming it up (#2309) 2024-01-29 15:23:16 +01:00
Tushar
0e04ec3136 feat(aggregators/metric): Add a top_hits aggregator (#2198) 
* feat(aggregators/metric): Implement a top_hits aggregator

* fix: Expose get_fields

* fix: Serializer for top_hits request

Also removes extraneous the extraneous third-party
serialization helper.

* chore: Avert panick on parsing invalid top_hits query

* refactor: Allow multiple field names from aggregations

* perf: Replace binary heap with TopNComputer

* fix: Avoid comparator inversion by ComparableDoc

* fix: Rank missing field values lower than present values

* refactor: Make KeyOrder a struct

* feat: Rough attempt at docvalue_fields

* feat: Complete stab at docvalue_fields

- Rename "SearchResult*" => "Retrieval*"
- Revert Vec => HashMap for aggregation accessors.
- Split accessors for core aggregation and field retrieval.
- Resolve globbed field names in docvalue_fields retrieval.
- Handle strings/bytes and other column types with DynamicColumn

* test(unit): Add tests for top_hits aggregator

* fix: docfield_value field globbing

* test(unit): Include dynamic fields

* fix: Value -> OwnedValue

* fix: Use OwnedValue's native Null variant

* chore: Improve readability of test asserts

* chore: Remove DocAddress from top_hits result

* docs: Update aggregator doc

* revert: accidental doc test

* chore: enable time macros only for tests

* chore: Apply suggestions from review

* chore: Apply suggestions from review

* fix: Retrieve all values for fields

* test(unit): Update for multi-value retrieval

* chore: Assert term existence

* feat: Include all columns for a column name

Since a (name, type) constitutes a unique column.

* fix: Resolve json fields

Introduces a translation step to bridge the difference between
ColumnarReaders null `\0` separated json field keys to the common
`.` separated used by SegmentReader. Although, this should probably
be the default behavior for ColumnarReader's public API perhaps.

* chore: Address review on mutability

* chore: s/segment_id/segment_ordinal instances of SegmentOrdinal

* chore: Revert erroneous grammar change
 2024-01-26 16:46:41 +01:00
Paul Masurel
9b7f3a55cf Bumped census version 2024-01-26 19:32:02 +09:00
PSeitz
1dacdb6c85 add histogram agg test on empty index (#2306) 2024-01-23 16:27:34 +01:00
François Massot
30483310ca Minor improvement of README.md (#2305) 
* Update README.md

* Remove useless paragraph

* Wording.
 2024-01-19 17:46:48 +09:00
Tushar
e1d18b5114 chore: Expose TopDocs::order_by_u64_field again (#2282) 2024-01-18 05:58:24 +01:00
trinity-1686a
108f30ba23 allow newline where we allow space in query parser (#2302) 
fix regression from the new parser
 2024-01-17 14:38:35 +01:00
PSeitz
5943ee46bd Truncate keys to u16::MAX in term hashmap (#2299) 
Truncate keys to u16::MAX, instead e.g. storing 0 bytes for keys with length u16::MAX + 1

The term hashmap has a hidden API contract to only accept terms with lenght up u16::MAX.
 2024-01-11 10:19:12 +01:00
PSeitz
f95a76293f add memory arena test (#2298) 
* add memory arena test

* add assert

* Update stacker/src/memory_arena.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2024-01-11 07:18:48 +01:00
Paul Masurel
014328e378 Fix bug that can cause get_docids_for_value_range to panic. (#2295) 
* Fix bug that can cause `get_docids_for_value_range` to panic.

When `selected_docid_range.end == num_rows`, we would get a panic
as we try to access a non-existing blockmeta.

This PR accepts calls to rank with any value.
For any value above num_rows we simply return non_null_rows.

Fixes #2293

* add tests, merge variables

---------

Co-authored-by: Pascal Seitz <pascal.seitz@gmail.com>
 2024-01-09 14:52:20 +01:00
Adam Reichold
53f2fe1fbe Forward regex parser errors to enable understandin their reason. (#2288) 2023-12-22 11:01:10 +01:00
PSeitz
9c75942aaf fix merge panic for JSON fields (#2284) 
Root cause was the positions buffer had residue positions from the
previous term, when the terms were alternating between having and not
having positions in JSON (terms have positions, but not numerics).

Fixes #2283
 2023-12-21 11:05:34 +01:00
PSeitz
bff7c58497 improve indexing benchmark (#2275) 2023-12-11 09:04:42 +01:00
trinity-1686a
9ebc5ed053 use fst for sstable index (#2268) 
* read path for new fst based index

* implement BlockAddrStoreWriter

* extract slop/derivation computation

* use better linear approximator and allow negative correction to approximator

* document format and reorder some fields

* optimize single block sstable size

* plug backward compat
 2023-12-04 15:13:15 +01:00
PSeitz
0b56c88e69 Revert "Preparing for 0.21.2 release." (#2258) 
* Revert "Preparing for 0.21.2 release. (#2256)"

This reverts commit 9caab45136.

* bump version to 0.21.1

* set version to 0.22.0-dev
 2023-12-01 13:46:12 +01:00
PSeitz
24841f0b2a update bitpacker dep (#2269) 2023-12-01 13:45:52 +01:00
PSeitz
1a9fc10be9 add fields_metadata to SegmentReader, add columnar docs (#2222) 
* add fields_metadata to SegmentReader, add columnar docs

* use schema to resolve field, add test

* normalize paths

* merge for FieldsMetadata, add fields_metadata on Index

* Update src/core/segment_reader.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

* merge code paths

* add Hash

* move function oustide

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-11-22 12:29:53 +01:00
PSeitz
07573a7f19 update fst (#2267) 
update fst to 0.5 (deduplicates regex-syntax in the dep tree)
deps cleanup
 2023-11-21 16:06:57 +01:00
BlackHoleFox
daad2dc151 Take string references instead of owned values building Facet paths (#2265) 2023-11-20 09:40:44 +01:00
PSeitz
054f49dc31 support escaped dot, add agg test (#2250) 
add agg test for nested JSON
allow escaping of dot
 2023-11-20 03:00:57 +01:00
PSeitz
47009ed2d3 remove unused deps (#2264) 
found with cargo machete
remove pprof (doesn't work)
 2023-11-20 02:59:59 +01:00
PSeitz
0aae31d7d7 reduce number of allocations (#2257) 
* reduce number of allocations

Explanation makes up around 50% of all allocations (numbers not perf).
It's created during serialization but not called.

- Make Explanation optional in BM25
- Avoid allocations when using Explanation

* use Cow
 2023-11-16 13:47:36 +01:00
Paul Masurel
9caab45136 Preparing for 0.21.2 release. (#2256) 2023-11-15 10:43:36 +09:00
Chris Tam
6d9a7b7eb0 Derive Debug for SchemaBuilder (#2254) 2023-11-15 01:03:44 +01:00
dependabot[bot]
7a2c5804b1 Update itertools requirement from 0.11.0 to 0.12.0 (#2255) 
Updates the requirements on [itertools](https://github.com/rust-itertools/itertools) to permit the latest version.
- [Changelog](https://github.com/rust-itertools/itertools/blob/master/CHANGELOG.md)
- [Commits](https://github.com/rust-itertools/itertools/compare/v0.11.0...v0.12.0)

---
updated-dependencies:
- dependency-name: itertools
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-11-15 01:03:08 +01:00
François Massot
5319977171 Merge pull request #2253 from quickwit-oss/issue/2251-bug-merge-json-object-with-number 
Fix bug occuring when merging JSON object indexed with positions.
 2023-11-14 17:28:29 +01:00
trinity-1686a
828632e8c4 rustfmt 2023-11-14 15:05:16 +01:00
Paul Masurel
6b59ec6fd5 Fix bug occuring when merging JSON object indexed with positions. 
In JSON Object field the presence of term frequencies depend on the
field.
Typically, a string with postiions indexed will have positions
while numbers won't.

The presence or absence of term freqs for a given term is unfortunately
encoded in a very passive way.

It is given by the presence of extra information in the skip info, or
the lack of term freqs after decoding vint blocks.

Before, after writing a segment, we would encode the segment correctly
(without any term freq for number in json object field).
However during merge, we would get the default term freq=1 value.
(this is default in the absence of encoded term freqs)

The merger would then proceed and attempt to decode 1 position when
there are in fact none.

This PR requires to explictly tell the posting serialize whether
term frequencies should be serialized for each new term.

Closes #2251
 2023-11-14 22:41:48 +09:00
PSeitz
b60d862150 docid deltas while indexing (#2249) 
* docid deltas while indexing

storing deltas is especially helpful for repetitive data like logs.
In those cases, recording a doc on a term costed 4 bytes instead of 1
byte now.

HDFS Indexing 1.1GB Total memory consumption:
Before:  760 MB
Now:     590 MB

* use scan for delta decoding
 2023-11-13 05:14:27 +01:00
PSeitz
4837c7811a add missing inlines (#2245) 2023-11-10 08:00:42 +01:00
PSeitz
5a2397d57e add sstable ord_to_term benchmark (#2242) 2023-11-10 07:27:48 +01:00
PSeitz
927b4432c9 Perf: use term hashmap in fastfield (#2243) 
* add shared arena hashmap

* bench fastfield indexing

* use shared arena hashmap in columnar

lower minimum resize in hashtable

* clippy

* add comments
 2023-11-09 13:44:02 +01:00
trinity-1686a
7a0064db1f bump index version (#2237) 
* bump index version

and add constant for lowest supported version

* use range instead of handcoded bounds
 2023-11-06 19:02:37 +01:00
PSeitz
2e7327205d fix coverage run (#2232) 
coverage run uses the compare_hash_only feature which is not compativle
with the test_hashmap_size test
 2023-11-06 11:18:38 +00:00
Paul Masurel
7bc5bf78e2 Fixing functional tests. (#2239) 2023-11-05 18:18:39 +09:00
giovannicuccu
ef603c8c7e rename ReloadPolicy onCommit to onCommitWithDelay (#2235) 
* rename ReloadPolicy onCommit to onCommitWithDelay

* fix format issues

---------

Co-authored-by: Giovanni Cuccu <gcuccu@imolainformatica.it>
 2023-11-03 12:22:10 +01:00
PSeitz
28dd6b6546 collect json paths in indexing (#2231) 
* collect json paths in indexing

* remove unsafe iter_mut_keys
 2023-11-01 11:25:17 +01:00
trinity-1686a
1dda2bb537 handle * inside term in query parser (#2228) 2023-10-27 08:57:02 +02:00
PSeitz
bf6544cf28 fix mmap::Advice reexport (#2230) 2023-10-27 14:09:25 +09:00
PSeitz
ccecf946f7 tantivy 0.21.1 (#2227) 2023-10-27 05:01:44 +02:00
PSeitz
19a859d6fd term hashmap remove copy in is_empty, unused unordered_id (#2229) 2023-10-27 05:01:32 +02:00
PSeitz
83af14caa4 Fix range query (#2226) 
Fix range query end check in advance
Rename vars to reduce ambiguity
add tests

Fixes #2225
 2023-10-25 09:17:31 +02:00
PSeitz
4feeb2323d fix clippy (#2223) 2023-10-24 10:05:22 +02:00
PSeitz
07bf66a197 json path writer (#2224) 
* refactor logic to JsonPathWriter

* use in encode_column_name

* add inlines

* move unsafe block
 2023-10-24 09:45:50 +02:00
trinity-1686a
0d4589219b encode some part of posting list as -1 instead of direct values (#2185) 
* add support for delta-1 encoding posting list

* encode term frequency minus one

* don't emit tf for json integer terms

* make skipreader not pub(crate) mutable
 2023-10-20 16:58:26 +02:00
PSeitz
c2b0469180 improve docs, rework exports (#2220) 
* rework exports

move snippet and advice
make indexer pub, remove indexer reexports

* add deprecation warning

* add architecture overview
 2023-10-18 09:22:24 +02:00
PSeitz
7e1980b218 run coverage only after merge (#2212) 
* run coverage only after merge

coverage is a quite slow step in CI. It can be run only after merging

* Apply suggestions from code review

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-10-18 07:19:36 +02:00
PSeitz
ecb9a89a9f add compat mode for JSON (#2219) 2023-10-17 10:00:55 +02:00
PSeitz
5e06e504e6 split into ReferenceValueLeaf (#2217) 2023-10-16 16:31:30 +02:00
PSeitz
182f58cea6 remove Document: DocumentDeserialize dependency (#2211) 
* remove Document: DocumentDeserialize dependency

The dependency requires users to implement an API they may not use.

* remove unnecessary Document bounds
 2023-10-13 07:59:54 +02:00
dependabot[bot]
337ffadefd Update lru requirement from 0.11.0 to 0.12.0 (#2208) 
Updates the requirements on [lru](https://github.com/jeromefroe/lru-rs) to permit the latest version.
- [Changelog](https://github.com/jeromefroe/lru-rs/blob/master/CHANGELOG.md)
- [Commits](https://github.com/jeromefroe/lru-rs/compare/0.11.0...0.12.0)

---
updated-dependencies:
- dependency-name: lru
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-10-12 12:09:56 +02:00
dependabot[bot]
22aa4daf19 Update zstd requirement from 0.12 to 0.13 (#2214) 
Updates the requirements on [zstd](https://github.com/gyscos/zstd-rs) to permit the latest version.
- [Release notes](https://github.com/gyscos/zstd-rs/releases)
- [Commits](https://github.com/gyscos/zstd-rs/compare/v0.12.0...v0.13.0)

---
updated-dependencies:
- dependency-name: zstd
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-10-12 04:24:44 +02:00
PSeitz
493f9b2f2a Read list of JSON fields encoded in dictionary (#2184) 
* Read list of JSON fields encoded in dictionary

add method to get list of fields on InvertedIndexReader

* add field type
 2023-10-09 12:06:22 +02:00
PSeitz
e246e5765d replace ReferenceValue with Self in Value (#2210) 2023-10-06 08:22:15 +02:00
PSeitz
6097235eff fix numeric order, refactor Document (#2209) 
fix numeric order to prefer i64
rename and move Document stuff
 2023-10-05 16:39:56 +02:00
PSeitz
b700c42246 add AsRef, expose object and array iter on Value (#2207) 
add AsRef
expose object and array iter
add to_json on Document
 2023-10-05 03:55:35 +02:00
PSeitz
5b1bf1a993 replace Field with field name (#2196) 2023-10-04 06:21:40 +02:00
PSeitz
041d4fced7 move to_named_doc to Document trait (#2205) 2023-10-04 06:03:07 +02:00
dependabot[bot]
166fc15239 Update memmap2 requirement from 0.7.1 to 0.9.0 (#2204) 
Updates the requirements on [memmap2](https://github.com/RazrFalcon/memmap2-rs) to permit the latest version.
- [Changelog](https://github.com/RazrFalcon/memmap2-rs/blob/master/CHANGELOG.md)
- [Commits](https://github.com/RazrFalcon/memmap2-rs/compare/v0.7.1...v0.9.0)

---
updated-dependencies:
- dependency-name: memmap2
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-10-04 05:00:46 +02:00
PSeitz
514a6e7fef fix bench compile, fix Document reexport (#2203) 2023-10-03 17:28:36 +02:00
dependabot[bot]
82d9127191 Update fs4 requirement from 0.6.3 to 0.7.0 (#2199) 
Updates the requirements on [fs4](https://github.com/al8n/fs4-rs) to permit the latest version.
- [Release notes](https://github.com/al8n/fs4-rs/releases)
- [Commits](https://github.com/al8n/fs4-rs/commits/0.7.0)

---
updated-dependencies:
- dependency-name: fs4
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-10-03 04:43:09 +02:00
PSeitz
03a1f40767 rename DocValue to Value (#2197) 
rename DocValue to Value to avoid confusion with lucene DocValues
rename Value to OwnedValue
 2023-10-02 17:03:00 +02:00
Harrison Burt
1c7c6fd591 POC: Tantivy documents as a trait (#2071) 
* fix windows build (#1)

* Fix windows build

* Add doc traits

* Add field value iter

* Add value and serialization

* Adjust order

* Fix bug

* Correct type

* Fix generic bugs

* Reformat code

* Add generic to index writer which I forgot about

* Fix missing generics on single segment writer

* Add missing type export

* Add default methods for convenience

* Cleanup

* Fix more-like-this query to use standard types

* Update API and fix tests

* Add doc traits

* Add field value iter

* Add value and serialization

* Adjust order

* Fix bug

* Correct type

* Rebase main and fix conflicts

* Reformat code

* Merge upstream

* Fix missing generics on single segment writer

* Add missing type export

* Add default methods for convenience

* Cleanup

* Fix more-like-this query to use standard types

* Update API and fix tests

* Add tokenizer improvements from previous commits

* Add tokenizer improvements from previous commits

* Reformat

* Fix unit tests

* Fix unit tests

* Use enum in changes

* Stage changes

* Add new deserializer logic

* Add serializer integration

* Add document deserializer

* Implement new (de)serialization api for existing types

* Fix bugs and type errors

* Add helper implementations

* Fix errors

* Reformat code

* Add unit tests and some code organisation for serialization

* Add unit tests to deserializer

* Add some small docs

* Add support for deserializing serde values

* Reformat

* Fix typo

* Fix typo

* Change repr of facet

* Remove unused trait methods

* Add child value type

* Resolve comments

* Fix build

* Fix more build errors

* Fix more build errors

* Fix the tests I missed

* Fix examples

* fix numerical order, serialize PreTok Str

* fix coverage

* rename Document to TantivyDocument, rename DocumentAccess to Document

add Binary prefix to binary de/serialization

* fix coverage

---------

Co-authored-by: Pascal Seitz <pascal.seitz@gmail.com>
 2023-10-02 10:01:16 +02:00
PSeitz
b525f653c0 replace BinaryHeap for TopN (#2186) 
* replace BinaryHeap for TopN

replace BinaryHeap for TopN with variant that selects the median with QuickSort,
which runs in O(n) time.

add merge_fruits fast path

* call truncate unconditionally, extend test

* remove special early exit

* add TODO, fmt

* truncate top n instead median, return vec

* simplify code
 2023-09-27 09:25:30 +02:00
ethever.eth
90586bc1e2 chore: remove unused Seek impl for Writers (#2187) (#2189) 
Co-authored-by: famouscat <onismaa@gmail.com>
 2023-09-26 17:03:28 +09:00
PSeitz
832f1633de handle exclusive out of bounds ranges on fastfield range queries (#2174) 
closes https://github.com/quickwit-oss/quickwit/issues/3790
 2023-09-26 08:00:40 +02:00
PSeitz
38db53c465 make column_index pub (#2181) 2023-09-22 08:06:45 +02:00
PSeitz
34920d31f5 Fix DateHistogram bucket gap (#2183) 
* Fix DateHistogram bucket gap

Fixes a computation issue of the number of buckets needed in the
DateHistogram.

This is due to a missing normalization from request values (ms) to fast field
values (ns), when converting an intermediate result to the final result.
This results in a wrong computation by a factor 1_000_000.
The Histogram normalizes values to nanoseconds, to make the user input like
extended_bounds (ms precision) and the values from the fast field (ns precision for date type) compatible.
This normalization happens only for date type fields, as other field types don't have precision settings.
The normalization does not happen due a missing `column_type`, which is not
correctly passed after merging an empty aggregation (which does not have a `column_type` set), with a regular aggregation.

Another related issue is an empty aggregation, which will not have
`column_type` set, will not convert the result to human readable format.

This PR fixes the issue by:
- Limit the allowed field types of DateHistogram to DateType
- Instead of passing the column_type, which is only available on the segment level, we flag the aggregation as `is_date_agg`.
- Fix the merge logic

Add a flag to to normalization only once. This is not an issue
currently, but it could become easily one.

closes https://github.com/quickwit-oss/quickwit/issues/3837

* use older nightly for time crate (breaks build)
 2023-09-21 10:41:35 +02:00
trinity-1686a
0241a05b90 add support for exists query syntax in query parser (#2170) 
* add support for exists query syntax in query parser

* rustfmt

* make Exists require a field
 2023-09-19 11:10:39 +02:00
PSeitz
e125f3b041 fix test (#2178) 2023-09-19 08:21:50 +02:00
PSeitz
c520ac46fc add support for date in term agg (#2172) 
support DateTime in TermsAggregation
Format dates with Rfc3339
 2023-09-14 09:22:18 +02:00
PSeitz
2d7390341c increase min memory to 15MB for indexing (#2176) 
With tantivy 0.20 the minimum memory consumption per SegmentWriter increased to
12MB. 7MB are for the different fast field collectors types (they could be
lazily created). Increase the minimum memory from 3MB to 15MB.

Change memory variable naming from arena to budget.

closes #2156
 2023-09-13 07:38:34 +02:00
dependabot[bot]
03fcdce016 Bump actions/checkout from 3 to 4 (#2171) 
Bumps [actions/checkout](https://github.com/actions/checkout) from 3 to 4.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v3...v4)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-09-11 10:47:33 +02:00
Ping Xia
e4e416ac42 extend FuzzyTermQuery to support json field (#2173) 
* extend fuzzy search for json field

* comments

* comments

* fmt fix

* comments
 2023-09-11 05:59:40 +02:00
Igor Motov
19325132b7 Fast-field based implementation of ExistsQuery (#2160) 
Adds an implementation of ExistsQuery that takes advantage of fast fields.

Fixes #2159
 2023-09-07 11:51:49 +09:00
Paul Masurel
389d36f760 Added comments 2023-09-04 11:06:56 +09:00
PSeitz
49448b31c6 chore: Release (#2168) 
* chore: Release

* update CHANGELOG
 2023-09-01 13:58:58 +02:00
PSeitz
ebede0bed7 update CHANGELOG (#2167) 2023-08-31 10:01:44 +02:00
PSeitz
b1d8b072db add missing aggregation part 2 (#2149) 
* add missing aggregation part 2

Add missing support for:
- Mixed types columns
- Key of type string on numerical fields

The special aggregation is slower than the integrated one in TermsAggregation and therefore not
chosen by default, although it can cover all use cases.

* simplify, add num_docs to empty
 2023-08-31 07:55:33 +02:00
ethever.eth
ee6a7c2bbb fix a small typo (#2165) 
Co-authored-by: famouscat <onismaa@gmail.com>
 2023-08-30 20:14:26 +02:00
PSeitz
c4e2708901 fix clippy, fmt (#2162) 2023-08-30 08:04:26 +02:00
PSeitz
5c8cfa50eb add missing parameter for percentiles (#2157) 2023-08-29 13:04:24 +02:00
PSeitz
73cb71762f add missing parameter for stats,min,max,count,sum,avg (#2151) 
* add missing parameter for stats,min,max,count,sum,avg

add missing parameter for stats,min,max,count,sum,avg
closes #1913
partially #1789

* Apply suggestions from code review

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-08-28 08:59:51 +02:00
Harrison Burt
267dfe58d7 Fix testing on windows (#2155) 
* Fix missing trait imports

* Fix building tests on windows

* Revert other PR change
 2023-08-27 09:20:44 +09:00
Harrison Burt
131c10d318 Fix missing trait imports (#2154) 2023-08-27 09:20:26 +09:00
Chris Tam
e6cacc40a9 Remove outdated fast field documentation (#2145) 2023-08-24 07:49:49 +02:00
PSeitz
48d4847b38 Improve aggregation error message (#2150) 
* Improve aggregation error message

Improve aggregation error message by wrapping the deserialization with a
custom struct. This deserialization variant is slower, since we need to
keep the deserialized data around twice with this approach.
For now the valid variants list is manually updated. This could be
replaced with a proc macro.
closes #2143

* Simpler implementation

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-08-23 20:52:15 +02:00
PSeitz
59460c767f delayed column opening during merge (#2132) 
* lazy columnar merge

This is the first part of addressing #3633
Instead of loading all Column into memory for the merge, only the current column_name
group is loaded. This can be done since the sstable streams the columns lexicographically.

* refactor

* add rustdoc

* replace iterator with BTreeMap
 2023-08-21 08:55:35 +02:00
Paul Masurel
756156beaf Fix doc 2023-08-17 17:47:45 +09:00
PSeitz
480763db0d track memory arena memory usage (#2148) 2023-08-16 18:19:42 +02:00
PSeitz
62ece86f24 track ff dictionary indexing memory consumption (#2147) 2023-08-16 14:00:08 +02:00
Caleb Hattingh
52d9e6f298 Fix doc typos in count aggregation metric (#2127) 2023-08-15 08:50:23 +02:00
Caleb Hattingh
47b315ff18 doc: escape the backslash (#2144) 2023-08-14 19:10:07 +02:00
PSeitz
ed1deee902 fix sort index by date (#2124) 
closes #2112
 2023-08-14 17:36:52 +02:00
PSeitz
2e109018b7 add missing parameter to term agg (#2103) 
* add missing parameter to term agg

* move missing handling to block accessor

* add multivalue test, fix multivalue case, add comments

* add documentation, deactivate special case

* cargo fmt

* resolve merge conflict
 2023-08-14 14:22:18 +02:00
Adam Reichold
22c35b1e00 Fix explanation of boost queries seeking beyond query result. (#2142) 
* Make current nightly Clippy happy.

* Fix explanation of boost queries seeking beyond query result.
 2023-08-14 11:59:11 +09:00
trinity-1686a
b92082b748 implement lenient parser (#2129) 
* move query parser to nom

* add suupport for term grouping

* initial work on infallible parser

* fmt

* add tests and fix minor parsing bugs

* address review comments

* add support for lenient queries in tantivy

* make lenient parser report errors

* allow mixing occur and bool in query
 2023-08-08 15:41:29 +02:00
PSeitz
c2be6603a2 alternative mixed field aggregation collection (#2135) 
* alternative mixed field aggregation collection

instead of having multiple accessor in one AggregationWithAccessor split it into
multiple independent AggregationWithAccessor

* Update src/aggregation/agg_req_with_accessor.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-07-27 12:25:31 +02:00
Adam Reichold
c805f08ca7 Fix a few more upcoming Clippy lints (#2133) 2023-07-24 17:07:57 +09:00
Adam Reichold
ccc0335158 Minor improvements to OwnedBytes (#2134) 
This makes it obvious where the `StableDerefTrait` is invoked and avoids
`transmute` when only a lifetime needs to be extended. Furthermore, it makes use
of `slice::split_at` where that seemed appropriate.
 2023-07-24 17:06:33 +09:00
Adam Reichold
42acd334f4 Fixes the new deny-by-default incorrect_partial_ord_impl_on_ord_type Clippy lint (#2131) 2023-07-21 11:36:17 +09:00
Adam Reichold
820f126075 Remove support for Brotli and Snappy compression (#2123) 
LZ4 provides fast and simple compression whereas Zstd is exceptionally flexible
so that the additional support for Brotli and Snappy does not really add
any distinct functionality on top of those two algorithms.

Removing them reduces our maintenance burden and reduces the number of choices
users have to make when setting up their project based on Tantivy.
 2023-07-14 16:54:59 +09:00
Adam Reichold
7e6c4a1856 Include only built-in compression algorithms as enum variants (#2121) 
* Include only built-in compression algorithms as enum variants

This enables compile-time errors when a compression algorithm is requested which
is not actually enabled for the current Cargo project. The cost is that indexes
using other compression algorithms cannot even be loaded (even though they
are not fully accessible in any case).

As a drive-by, this also fixes `--no-default-features` on `cfg(unix)`.

* Provide more instructive error messages for unsupported, but not unknown compression variants.
 2023-07-14 11:02:49 +09:00
Adam Reichold
5fafe4b1ab Add missing query_terms impl for TermSetQuery. (#2120) 2023-07-13 14:54:29 +02:00
PSeitz
1e7cd48cfa remove allocations in split compound words (#2080) 
* remove allocations in split compound words

* clear reused data
 2023-07-13 09:43:02 +09:00
dependabot[bot]
7f51d85bbd Update lru requirement from 0.10.0 to 0.11.0 (#2117) 
Updates the requirements on [lru](https://github.com/jeromefroe/lru-rs) to permit the latest version.
- [Changelog](https://github.com/jeromefroe/lru-rs/blob/master/CHANGELOG.md)
- [Commits](https://github.com/jeromefroe/lru-rs/compare/0.10.0...0.11.0)

---
updated-dependencies:
- dependency-name: lru
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-07-13 09:42:21 +09:00
PSeitz
ad76e32398 Update CHANGELOG.md (#2091) 
* Update CHANGELOG.md

* Update CHANGELOG.md
 2023-07-11 13:58:49 +08:00
dependabot[bot]
7575f9bf1c Update itertools requirement from 0.10.3 to 0.11.0 (#2098) 
Updates the requirements on [itertools](https://github.com/rust-itertools/itertools) to permit the latest version.
- [Changelog](https://github.com/rust-itertools/itertools/blob/master/CHANGELOG.md)
- [Commits](https://github.com/rust-itertools/itertools/compare/v0.10.5...v0.11.0)

---
updated-dependencies:
- dependency-name: itertools
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-07-07 11:14:46 +02:00
Naveen Aiathurai
67bdf3f5f6 fixes order_by_u64_field and order_by_fast_field should allow sorting in ascending order #1676 (#2111) 
* feat: order_by_fast_field allows sorting using parameter order

* chore: change the corresponding values to original one

* chore: fix formatting issues

* fix: first_or_default_col should also sort by order

* chore: empty doc to testcase and docstest fixes

* chore: fix failure tests

* core: add empty document without fastfield

* chore: fix fmt

* chore: change variable name
 2023-07-06 05:10:10 +02:00
François Massot
3c300666ad Merge pull request #2110 from quickwit-oss/fulmicoton/dynamic-follow-up 
Add dynamic filters to text analyzer builder.
 2023-07-03 21:49:24 +02:00
François Massot
b91d3f6be4 Clean comment on 'TextAnalyzerBuilder::filter_dynamic' method. 2023-07-03 18:45:59 +02:00
François Massot
a8e76513bb Remove useless clone. 2023-07-03 22:05:11 +09:00
François Massot
0a23201338 Fix stackoverflow and add docs. 2023-07-03 22:05:11 +09:00
François Massot
81330aaf89 WIP 2023-07-03 22:05:10 +09:00
Paul Masurel
98a3b01992 Removing the BoxedTokenizer 2023-07-03 22:05:10 +09:00
Paul Masurel
d341520938 Dynamic follow up 2023-07-03 22:05:10 +09:00
François Massot
5c9af73e41 Followup fulmicoton poc. 2023-07-03 22:05:10 +09:00
Paul Masurel
ad4c940fa3 proof of concept for dynamic tokenizer. 2023-07-03 22:05:10 +09:00
Paul Masurel
910b0b0c61 Cargo fmt 2023-07-03 22:03:31 +09:00
PSeitz
3fef052bf1 fix flaky test (#2107) 
closes #2099
 2023-06-29 14:30:56 +08:00
PSeitz
040554f2f9 Update to lz4_flex 0.11 (#2106) 2023-06-29 14:16:00 +08:00
PSeitz
17186ca9c9 improve docs (#2105) 2023-06-27 13:37:14 +08:00
François Massot
212d59c9ab Merge pull request #2102 from quickwit-oss/fmassot/ngram-new-should-return-error 
Ngram tokenizer now returns an error with invalid arguments.
 2023-06-27 05:36:09 +02:00
dependabot[bot]
1a1f252a3f Update memmap2 requirement from 0.6.0 to 0.7.1 (#2104) 
Updates the requirements on [memmap2](https://github.com/RazrFalcon/memmap2-rs) to permit the latest version.
- [Changelog](https://github.com/RazrFalcon/memmap2-rs/blob/master/CHANGELOG.md)
- [Commits](https://github.com/RazrFalcon/memmap2-rs/compare/v0.6.0...v0.7.1)

---
updated-dependencies:
- dependency-name: memmap2
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-06-27 05:15:43 +02:00
François Massot
d73706dede Ngram tokenizer now returns an error with invalid arguments. 2023-06-25 20:13:24 +02:00
PSeitz
44850e1036 move fail dep to dev only (#2094) 
wasm compilation fails with dep only
 2023-06-22 06:59:11 +02:00
Adam Reichold
3b0cbf8102 Cosmetic updates to the warmer example. (#2095) 
Just some cosmetic tweaks to make the example easier on the eyes as a colleague
was staring at this for quite some time this week.
 2023-06-22 11:25:01 +09:00
Adam Reichold
4aa131c3db Make TextAnalyzerBuilder publically accessible (#2097) 
This way, client code can name the type to e.g. store it inside structs without
resorting to generics and it means that its documentation is part of the crate
documentation generated by `cargo doc`.
 2023-06-22 11:24:21 +09:00
Naveen Aiathurai
59962097d0 fix: #2078 return error when tokenizer not found while indexing (#2093) 
* fix: #2078 return error when tokenizer not found while indexing

* chore: formatting issues

* chore: fix review comments
 2023-06-16 04:33:55 +02:00
Adam Reichold
ebc78127f3 Add BytesFilterCollector to support filtering based on a bytes fast field (#2075) 
* Do some Clippy- and Cargo-related boy-scouting.

* Add BytesFilterCollector to support filtering based on a bytes fast field

This is basically a copy of the existing FilterCollector but modified and
specialised to work on a bytes fast field.

* Changed semantics of filter collectors to consider multi-valued fields
 2023-06-13 14:19:58 +09:00
PSeitz
8199aa7de7 bump version to 0.20.2 (#2089) 2023-06-12 18:56:54 +08:00
PSeitz
657f0cd3bd add missing Bytes validation to term_agg (#2077) 
returns empty for now instead of failing like before
 2023-06-12 16:38:07 +08:00
Adam Reichold
3a82ef2560 Fix is_child_of function not considering the root facet. (#2086) 2023-06-12 08:35:18 +02:00
PSeitz
3546e7fc63 small agg limit docs improvement (#2073) 
small docs improvement as follow up on bug https://github.com/quickwit-oss/quickwit/issues/3503
 2023-06-12 10:55:24 +09:00
PSeitz
862f367f9e release without Alice in Wonderland, bump version to 0.20.1 (#2087) 
* Release without Alice in Wonderland

* bump version to 0.20.1
 2023-06-12 10:54:03 +09:00
PSeitz
14137d91c4 Update CHANGELOG.md (#2081) 2023-06-12 10:53:40 +09:00
François Massot
924fc70cb5 Merge pull request #2088 from quickwit-oss/fmassot/align-type-priorities-for-json-numbers 
Align numerical type priority order on the search side.
 2023-06-11 22:04:54 +02:00
François Massot
07023948aa Add test that indexes and searches a JSON field. 2023-06-11 21:47:52 +02:00
François Massot
0cb53207ec Fix tests. 2023-06-11 12:13:35 +02:00
François Massot
17c783b4db Align numerical type priority order on the search side. 2023-06-11 11:49:27 +02:00
Harrison Burt
7220df8a09 Fix building on windows with mmap (#2070) 
* Fix windows build

* Make pub

* Update docs

* Re arrange

* Fix compilation error on unix

* Fix unix borrows

* Revert "Fix unix borrows"

This reverts commit c1d94fd12b.

* Fix unix borrows and revert original change

* Fix warning

* Cleaner code.

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-06-10 18:32:39 +02:00
PSeitz
e3eacb4388 release tantivy (#2083) 
* prerelease

* chore: Release
 2023-06-09 10:47:46 +02:00
PSeitz
fdecb79273 tokenizer-api: reduce Tokenizer overhead (#2062) 
* tokenizer-api: reduce Tokenizer overhead

Previously a new `Token` for each text encountered was created, which
contains `String::with_capacity(200)`
In the new API the token_stream gets mutable access to the tokenizer,
this allows state to be shared (in this PR Token is shared).
Ideally the allocation for the BoxTokenStream would also be removed, but
this may require some lifetime tricks.

* simplify api

* move lowercase and ascii folding buffer to global

* empty Token text as default
 2023-06-08 18:37:58 +08:00
PSeitz
27f202083c Improve Termmap Indexing Performance +~30% (#2058) 
* update benchmark

* Improve Termmap Indexing Performance +~30%

This contains many small changes to improve Termmap performance.
Most notably:
* Specialized byte compare and equality versions, instead of glibc calls.
* ExpUnrolledLinkedList to not contain inline items.

Allow compare hash only via a feature flag compare_hash_only:
64bits should be enough with a good hash function to compare strings by
their hashes instead of comparing the strings. Disabled by default

CreateHashMap/alice/174693
                        time:   [642.23 µs 643.80 µs 645.24 µs]
                        thrpt:  [258.20 MiB/s 258.78 MiB/s 259.41 MiB/s]
                 change:
                        time:   [-14.429% -13.303% -12.348%] (p = 0.00 < 0.05)
                        thrpt:  [+14.088% +15.344% +16.862%]
                        Performance has improved.
CreateHashMap/alice_expull/174693
                        time:   [877.03 µs 880.44 µs 884.67 µs]
                        thrpt:  [188.32 MiB/s 189.22 MiB/s 189.96 MiB/s]
                 change:
                        time:   [-26.460% -26.274% -26.091%] (p = 0.00 < 0.05)
                        thrpt:  [+35.301% +35.637% +35.981%]
                        Performance has improved.
CreateHashMap/numbers_zipf/8000000
                        time:   [9.1198 ms 9.1573 ms 9.1961 ms]
                        thrpt:  [829.64 MiB/s 833.15 MiB/s 836.57 MiB/s]
                 change:
                        time:   [-35.229% -34.828% -34.384%] (p = 0.00 < 0.05)
                        thrpt:  [+52.403% +53.440% +54.390%]
                        Performance has improved.

* clippy

* add bench for ids

* inline(always) to inline whole block with bounds checks

* cleanup
 2023-06-08 11:13:52 +02:00
PSeitz
ccb09aaa83 allow histogram bounds to be passed as Rfc3339 (#2076) 2023-06-08 09:07:08 +02:00
Valerii
4b7c485a08 feat: add stop words for Hungarian language (#2069) 2023-06-02 07:26:03 +02:00
PSeitz
3942fc6d2b update CHANGELOG (#2068) 2023-06-02 05:00:12 +02:00
Adam Reichold
b325d569ad Expose phrase-prefix queries via the built-in query parser (#2044) 
* Expose phrase-prefix queries via the built-in query parser

This proposes the less-than-imaginative syntax `field:"phrase ter"*` to
perform a phrase prefix query against `field` using `phrase` and `ter` as the
terms. The aim of this is to make this type of query more discoverable and
simplify manual testing.

I did consider exposing the `max_expansions` parameter similar to how slop is
handled, but I think that this is rather something that should be configured via
the querser parser (similar to `set_field_boost` and `set_field_fuzzy`) as
choosing it requires rather intimiate knowledge of the backing index.

* Prevent construction of zero or one term phrase-prefix queries via the query parser.

* Add example using phrase-prefix search via surface API to improve feature discoverability.
 2023-06-01 13:03:16 +02:00
Paul Masurel
7ee78bda52 Readding s in datetime precision variant names (#2065) 
There is no clear win and it change some serialization in quickwit.
 2023-06-01 06:39:46 +02:00
Paul Masurel
184a9daa8a Cancels concurrently running actions for the same PR. (#2067) 2023-06-01 12:57:38 +09:00
Paul Masurel
47e01b345b Simplified linear probing code (#2066) 2023-06-01 04:58:42 +02:00
PSeitz
3af456972e Fix min doc_count empty merge bug (#2057) 
This fixes an issue when min_doc==0 loads terms from the dictionary from
one segment and merges the same term with a subaggregation from another
segment.
Previously the empty structure was not correctly initialized to contain
the subaggregation so the merge was incorrect.
 2023-05-29 14:20:50 +08:00
PSeitz
e56addc63e enable tokenizer on json fields (#2053) 
* enable tokenizer on json fields

enable tokenizer on json fields for type text

* Avoid making the tokenizer within the TextAnalyzer pub(crate)

* Moving BoxableTokenizer to tantivy.

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-05-24 10:47:39 +02:00
dependabot[bot]
4be6f83b0a Update criterion requirement from 0.4 to 0.5 (#2056) 
Updates the requirements on [criterion](https://github.com/bheisler/criterion.rs) to permit the latest version.
- [Changelog](https://github.com/bheisler/criterion.rs/blob/master/CHANGELOG.md)
- [Commits](https://github.com/bheisler/criterion.rs/compare/0.4.0...0.5.0)

---
updated-dependencies:
- dependency-name: criterion
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-05-24 15:59:51 +09:00
Adrien Guillo
a789ad9aee Rename DatePrecision to DateTimePrecision (#2051) 2023-05-23 17:09:11 +02:00
Sergei Lavrentev
8cf26da4b2 Add possibility to set up highlighten prefix and postfix for snippet (#1422) 
* add possibility to change highlight prefix and postfix

* add comment to Snippet::new

* add test for highlighten elements

* add default highlight prefix and postfix constants

* fix spelling

* fix tests

* fix spelling

* do fixes after code review

* reduce test_snippet_generator_custom_highlighted_elements code

* fix fmt

* change names to more convenient

---------

Co-authored-by: Sergei Lavrentev <23312691+lavrxxx@users.noreply.github.com>
 2023-05-23 15:09:24 +02:00
trinity-1686a
a3f001360f add support for warming up range of terms (#2042) 
* add support for warming up range of terms

* simplify handling of limit
 2023-05-22 14:29:35 +02:00
trinity-1686a
6564e0c467 fix phrase prefix query (#2043) 
* fix phrase prefix query

it would fail spectacularly when no doc in the segment would match the phrase part of the query

* clippy
 2023-05-22 12:36:20 +02:00
Paul Masurel
d7e97331e5 Minor refactoring find field (#2055) 
* Minor refactoring

Moving find_field_with_default to Schema.

* Clippy comments
 2023-05-22 15:00:48 +09:00
Paul Masurel
4417be165d Minor refactoring (#2054) 
Moving find_field_with_default to Schema.
 2023-05-22 14:56:38 +09:00
PSeitz
6239697a02 switch to ms in histogram for date type (#2045) 
* switch to ms in histogram for date type

switch to ms in histogram, by adding a normalization step that converts
to nanoseconds precision when creating the collector.

closes #2028
related to #2026

* add missing unit long variants

* use single thread to avoid handling test case

* fix docs

* revert CI

* cleanup

* improve docs

* Update src/aggregation/bucket/histogram/histogram.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-05-19 08:15:44 +02:00
Paul Masurel
62709b8094 Change in the query grammar. (#2050) 
* Change in the query grammar.

Quotation mark can now be used for phrase queries.
The delimiter is part of the `UserInputLeaf`.
That information is meant to be used in Quickwit to solve #3364.

This PR also adds support for quotation marks escaping in phrase
queries.

* Apply suggestions from code review
 2023-05-19 12:07:10 +09:00
PSeitz
04562c0318 add fastfield tokenizer to IndexBuilder (#2046) 2023-05-18 04:33:42 +02:00
PSeitz
2dfe37940d handle multiple types in term aggregation (#2041) 2023-05-15 11:57:38 +02:00
Denis Bazhenov
e248a4959f Enforcing "NOT" and "-" queries consistency in UserInputAst (#1609) 
* Enforcing "NOT" and "-" queries consistency in UserInputAst

* Mutable implementation if rewrite_ast_clause()
 2023-05-13 00:27:48 +09:00
PSeitz
00c5df610c update termmap benchmark (#2040) 2023-05-12 07:35:06 +02:00
Adam Reichold
fedd9559e7 Expose create a query from a user input AST. (#2039) 2023-05-11 21:53:18 +09:00
Paul Masurel
fe3ecf9567 Added support for madvise (#2036) 
Added support for madvise
 2023-05-11 05:39:17 +02:00
PSeitz
ba3a885a3b handle multiple agg results (#2035) 
handle multiple intermediate aggregation results with the same name.
 2023-05-10 15:00:38 +02:00
PSeitz
d1988be8e9 fix and extend benchmark (#2030) 
* add benchmark, add missing inlines

* fix stacker bench

* add wiki benchmark

* move line split out of bench
 2023-05-10 13:01:56 +02:00
PSeitz
0eafbaab8e fix slop (#2031) 
Fix slop by carrying slop so far for multiterms.
Define slop contract in the API
 2023-05-10 11:45:14 +02:00
PSeitz
d3357a8426 fix ArenaHashMap default (#2034) 
an empty ArenaHashMap is invalid and causes a panic when combined with `get`
 2023-05-10 11:39:47 +02:00
Yuri Astrakhan
74275b76a6 Inline format arguments where makes sense (#2038) 
Applied this command to the code, making it a bit shorter and slightly
more readable.

```
cargo +nightly clippy --all-features --benches --tests --workspace --fix -- -A clippy::all -W clippy::uninlined_format_args
cargo +nightly fmt --all
```
 2023-05-10 18:03:59 +09:00
dependabot[bot]
f479840a1b Update memmap2 requirement from 0.5.3 to 0.6.0 (#2033) 
Updates the requirements on [memmap2](https://github.com/RazrFalcon/memmap2-rs) to permit the latest version.
- [Changelog](https://github.com/RazrFalcon/memmap2-rs/blob/master/CHANGELOG.md)
- [Commits](https://github.com/RazrFalcon/memmap2-rs/compare/v0.5.3...v0.6.0)

---
updated-dependencies:
- dependency-name: memmap2
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-05-10 03:50:14 +02:00
PSeitz
4ee1b5cda0 add seperate tokenizer manager for fast fields (#2019) 
* add seperate tokenizer manager for fast fields

* rename
 2023-05-08 11:22:31 +02:00
PSeitz
45ff0e3c5c clear memory consumption in AggregationLimits (#2022) 
* clear memory consumption in AggregationLimits

clear memory consumption in AggregationLimits at the end of segment collection

* switch to ResourceLimitGuard

* unduplicate code

* merge methods

* Apply suggestions from code review

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-05-08 10:15:09 +02:00
PSeitz
4c58b0086d allow slop in both directions (#2020) 
* allow slop in both directions

allow slop in both directions
so "big wolf"~3 can also match "wolf big"

This also fixes #1934, when the docsets were reordered by size and didn't
match the terms.

* remove count

* add test for repeating tokens, unduplicate tests
 2023-05-07 12:05:21 +09:00
Tomoko Uchida
85df322ceb fix typo in the architecture doc (#2009) 2023-05-07 12:04:07 +09:00
François Massot
38c863830f Merge pull request #2027 from quickwit-oss/fmassot/fix-date-histogram 
Fix date histogram bounds and field name.
 2023-05-05 13:03:25 +02:00
François Massot
992f755298 Fix clippy. 2023-05-05 10:51:29 +02:00
François Massot
c8df843f96 Fix date histogram bounds and field name. 2023-05-05 00:52:55 +02:00
Paul Masurel
f28ddb711e Exposing u64-based FastFieldRangeWeight (#2024) 2023-05-03 18:32:00 +09:00
tottoto
73452284ae Remove unused crates from dependencies (#2018) 
* Remove unused crates from dependencies

* Revert rand to columnar

* Revert criterion to stacker
 2023-05-02 12:34:20 +02:00
PSeitz
ba309e18a1 switch to nanosecond precision (#2016) 2023-05-01 03:32:20 +02:00
PSeitz
cbf2bdc75b change bucket count type (#2013) 
* change bucket count type

closes #2012

* Update src/aggregation/agg_limits.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

* Update src/directory/managed_directory.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

* fix test

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-04-27 15:47:31 +08:00
PSeitz
1f06997d04 fix single collector special case (#2014) 2023-04-27 09:30:19 +02:00
PSeitz
c599bf3b6c chore!:drop JSON support on intermediate agg result (#1992) 
* chore!:drop JSON support on intermediate agg result

add support for other formats by removing skip_serialize and untagged
JSON support is broken anyway due it's lack on f64::INF etc. handling

* Update src/aggregation/intermediate_agg_result.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

* move from impl

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-04-26 13:05:16 +02:00
PSeitz
80df1d9835 Handle error for exists on MMapDirectory (#1988) 
`exists` will return false in case of other io errors, like permission denied
 2023-04-25 09:20:33 +02:00
PSeitz
2e369db936 switch to Aggregation without serde_untagged (#2003) 
* refactor result handling

* remove Internal stuff

* merge different accessors

* switch to Aggregation without serde_untagged

* fix doctests
 2023-04-25 08:54:51 +02:00
PSeitz
7b31100208 refactor vint (#2010) 
- improve performance of vint
vint serialization shows up in performance profiles during indexing.
It would also make sense to limit the value space to u29 and operate on 4 bytes only.
- remove unused code
- add missing inlines
- fix regex test
 2023-04-25 08:49:36 +02:00
trinity-1686a
9c93bfeb51 optimise warmup code path (#2007) 
* optimise warmup code path

* better function naming
 2023-04-21 11:23:09 +02:00
PSeitz
74f9eafefc refactor Term (#2006) 
* refactor Term

add ValueBytes for serialized term values
add missing debug for ip
skip unnecessary json path validation
remove code duplication
add DATE_TIME_PRECISION_INDEXED constant
add missing Term clarification
remove weird value_bytes_mut() API

* fix naming
 2023-04-20 15:31:43 +02:00
RT_Enzyme
ff3d3313c4 fix BooleanQuery document (#1999) 
* fix BooleanQuery document

* Update src/query/boolean_query/boolean_query.rs

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-04-20 11:37:20 +02:00
Paul Masurel
fbda511a1a Making more things public for quickwit. (#2005) 2023-04-20 11:37:45 +09:00
Adam Reichold
c1defdda05 Bump aho-corasick dependency to version 1.0 and adjust to API changes (#2002) 
* Drop additional Arc-layer as the automaton itself is now cheap-to-clone.
* Drop state ID type parameter as it is not exposed by the library any more.
 2023-04-18 07:34:30 +02:00
PSeitz
e522163a1c use json in agg tests (#1998) 
* switch to JSON in tests, add flat aggregation types

* use method

* clippy

* remove commented file
 2023-04-17 14:08:48 +02:00
PSeitz
e83abbfe4a perf: faster term hash map (#1940) 
* add term hashmap benchmark

* refactor arena hashmap

add inlines
remove occupied array and use table_entry.is_empty instead (saves 4 bytes per entry)
reduce saturation threshold from 1/3 to 1/2 to reduce memory
use u32 for UnorderedId (we have the 4billion limit anyways on the Columnar stuff)
fix naming LinearProbing
remove byteorder dependency

memory consumption went down from 2Gb to 1.8GB on indexing wikipedia dataset in tantivy

* Update stacker/src/arena_hashmap.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-04-17 09:07:33 +02:00
trinity-1686a
780e26331d sstable compression (#1946) 
* compress sstable with zstd

* add some details to sstable readme

* compress only block which benefit from it

* multiple changes to sstable

make compression optional
use OwnedBytes instead of impl Read in sstable, required for next point
use zstd bulk api, which is much faster on small records

* cleanup and use bulk api for compression

* use dedicated byte for compression

* switch block len and compression flag

* change default zstd level in sstable
 2023-04-14 16:25:50 +02:00
trinity-1686a
0286ecea09 re-export a few sstable functions on dicitonary (#1996) 
* re-export a few sstable functions on dicitonary

* Update documentation

Co-authored-by: François Massot <francois.massot@gmail.com>

---------

Co-authored-by: François Massot <francois.massot@gmail.com>
 2023-04-14 11:13:48 +02:00
PSeitz
b0ef9a6252 use crates.io dependency (#1990) 2023-04-14 09:35:20 +08:00
François Massot
36138c493b Merge pull request #1994 from quickwit-oss/fmassot/expose-simple-token-stream 
Expose `SimpleTokenStream` to use it in quickwit for the multilanguage tokenizer
 2023-04-13 18:55:02 +02:00
François Massot
64bce340b2 Expose to use it in quickwit. 2023-04-13 18:28:53 +02:00
trinity-1686a
205e8a0a92 encode dictionary type in fst footer (#1968) 
* encode additional footer for dictionary kind in fst
 2023-04-12 09:43:01 +02:00
Paul Masurel
4b01cc4c49 Made BooleanWeight and BoostWeight public (#1991) 2023-04-12 10:26:30 +09:00
PSeitz
0ed13eeea8 add sparse to agg benchmark (#1986) 
* add sparse to agg benchmark

* Update src/aggregation/agg_bench.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-04-11 08:13:32 +02:00
Tony-X
91a38058fe Fix typo in READEME.md (#1989) 2023-04-11 12:07:20 +09:00
PSeitz
41af70799d add percentiles aggregations (#1984) 
* add percentiles aggregations

add percentiles aggregation
fix disabled agg benchmark

* Update src/aggregation/metric/percentiles.rs

Co-authored-by: Paul Masurel <paul@quickwit.io>

* Apply suggestions from code review

Co-authored-by: Paul Masurel <paul@quickwit.io>

* fix import

* fix import

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-04-07 07:18:28 +02:00
Paul Masurel
f853bf204b Align the numerical type priority order with columnar. (#1978) 
Closes #1956
 2023-04-07 10:07:54 +09:00
Tony-X
11ae48d3bc Update benchmarks section in READEME.md to link to the bench repo (#1985) 
* Update benchmarks section in READEME.md to link to the bench repo

* Apply suggestions from code review

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-04-07 10:07:06 +09:00
Paul Masurel
5eb12173d6 Proptest merge columnar (#1976) 
* Added proptest on columnar merge with a shuffle

Made column serialization more explicit.
Bugfix when a bytes column is missing, and with a shuffle.
Improved the cardinality detection logic / column detection.

* Code review

* CR comments

* Following CR
 2023-04-04 11:28:42 +09:00
PSeitz
5c4ea6a708 tokenizer option on text fastfield (#1945) 
* tokenizer option on text fastfield

allow to set tokenizer option on text fastfield (fixes #1901)
handle PreTokenized strings in fast field

* change visibility

* remove custom de/serialization
 2023-03-31 10:03:38 +02:00
PSeitz
4cf93dab7d fix build (#1973) 2023-03-31 13:54:03 +09:00
PSeitz
5c380b76e7 Better mixed types support in aggs and fix serialization issue (#1971) 
* Better mixed types support in aggs and fix serialization issue

- Improve support for mixed types in JSON field aggregations (pick the right field, #1913)
- Resolve the issue with JSON serialization for numeric keys (fixes #1967)
- Add JSON round-trip test for term buckets
- Remove `u64_lenient`, as this is a footgun without the type
- move aggregation benchmarks

* remove shadowing
 2023-03-31 05:52:11 +02:00
PSeitz
571735c5f7 Fix index sort by on optional/multicolumn (#1972) 
Fix index sort by on optional/multicolumn
add optional columns to proptest
extend proptests for sort
add columnar sort tests
 2023-03-31 04:24:11 +02:00
zhouhui
8e92f960d3 Fix comment: change max_merge_size to max_docs_before_merge. (#1970) 2023-03-28 22:49:00 +09:00
Paul Masurel
057211c3d8 Fixing build on arm (#1966) 2023-03-27 22:42:57 +09:00
Paul Masurel
059fc767ea Added ::MIN ::MAX DateTime. (#1965) 2023-03-27 15:32:53 +09:00
Paul Masurel
694a056255 Faster range (#1954) 
* Faster range queries

This PR does several changes
- ip compact space now uses u32
- the bitunpacker now gets a get_batch function
- we push down range filtering, removing GCD / shift in the bitpacking
  codec.
- we rely on AVX2 routine to do the filtering.

* Apply suggestions from code review

* Apply suggestions from code review

* CR comments
 2023-03-27 14:56:32 +09:00
Paul Masurel
2955e34452 Added proptests for building/merging columnar. (#1963) 2023-03-27 14:56:02 +09:00
Paul Masurel
821208480b Adding Debug/Display impl. Refining the ColumnIndex::get_cardinality 2023-03-26 14:40:37 +09:00
Paul Masurel
a2e3c2ed5b Renaming Column::idx -> Column::index (#1961) 
There was some variable name ghosting happening.
 2023-03-26 13:58:50 +09:00
PSeitz
835f228bfa fix cardinality when merging empty columns (#1960) 
fixes #1958
 2023-03-25 15:58:15 +09:00
Paul Masurel
2b6a4da640 Exposing empty column builder. (#1959) 2023-03-24 16:34:41 +09:00
PSeitz
d6a95381ee add memory check for term agg (#1957) 2023-03-24 06:47:45 +01:00
PSeitz
da2804644f fetch blocks of vals in aggregation for all cardinality (#1950) 
* fetch blocks of vals in aggregation for all cardinality

* move caching in common accessor
 2023-03-23 08:41:11 +01:00
PSeitz
5504cfd012 remove IterColumn (#1955) 
fixes #1658
 2023-03-23 06:43:17 +01:00
trinity-1686a
482b4155e8 fix bug with new sstable index format (#1953) 2023-03-22 10:22:36 +01:00
Till Wegmüller
1a35f6573d Switch fs2 to fs4 as it is now unmaintained and does not support illumos (#1944) 
Signed-off-by: Till Wegmueller <toasterson@gmail.com>
 2023-03-22 13:48:49 +09:00
trinity-1686a
e5e50603a8 new sstable format (#1943) 
* document a new sstable format

* add support for changing target block size

* use new format for sstable index

* handle sstable version errror

* use very small blocks for proptests

* add a footer structure
 2023-03-21 15:03:52 +01:00
PSeitz
8f7f1d6be4 add Display for ByteCount (#1949) 
* add Display for ByteCount

* export missing AggregationLimits
 2023-03-21 08:02:35 +01:00
PSeitz
6a7a1106d6 work in batches of docs (#1937) 
* work in batches of docs

* add fill_buffer test
 2023-03-21 06:57:44 +01:00
PSeitz
9e2faecf5b add memory limit for aggregations (#1942) 
* add memory limit for aggregations

introduce AggregationLimits to set memory consumption limit and bucket limits
memory limit is checked during aggregation, bucket limit is checked before returning the aggregation request.

* Apply suggestions from code review

Co-authored-by: Paul Masurel <paul@quickwit.io>

* add ByteCount with human readable format

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-03-16 06:21:07 +01:00
PSeitz
b6703f1b3c fix validation in date histogram (#1936) 
fix validation in date histogram for parameters interval and date_interval
 2023-03-15 06:10:43 +01:00
PSeitz
2fb3740cb0 handle missing column for aggs (#1920) 
* handle missing column for aggs

add empty column fallback for missing column in aggs.
Fix sort for term agg on sub-agg with missing value (null is smallest)

* add error when field is not fast
 2023-03-15 06:09:59 +01:00
PSeitz
8459efa32c split term collection count and sub_agg (#1921) 
use unrolled ColumnValues::get_vals
 2023-03-13 04:37:41 +01:00
PSeitz
61cfd8dc57 fix clippy (#1927) 2023-03-13 03:12:02 +01:00
trinity-1686a
064518156f refactor tokenization pipeline to use GATs (#1924) 
* refactor tokenization pipeline to use GATs

* fix doctests

* fix clippy lints

* remove commented code
 2023-03-09 09:39:37 +01:00
PSeitz
a42a96f470 fix panic in dict column merge (#1930) 
* fix panic in dict column merge

* Bugfix and added unit test

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-03-08 22:04:37 +09:00
trinity-1686a
fcf5a25d93 use DeltaReader directly to implement Dictionnary::ord_to_term (#1928) 2023-03-08 11:15:56 +09:00
dependabot[bot]
c0a5b28fd3 Update lru requirement from 0.9.0 to 0.10.0 (#1932) 
Updates the requirements on [lru](https://github.com/jeromefroe/lru-rs) to permit the latest version.
- [Release notes](https://github.com/jeromefroe/lru-rs/releases)
- [Changelog](https://github.com/jeromefroe/lru-rs/blob/master/CHANGELOG.md)
- [Commits](https://github.com/jeromefroe/lru-rs/compare/0.9.0...0.10.0)

---
updated-dependencies:
- dependency-name: lru
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2023-03-07 15:09:02 +09:00
trinity-1686a
a4f7ca8309 use DeltaReader directly to implement Dictionnary::term_ord (#1925) 
* use DeltaReader directly to implement Dictionnary::term_ord

* add some additional test case for Dictionary::term_ord
 2023-03-06 09:45:22 +01:00
Paul Masurel
364e321415 Clippy fix (#1926) 2023-03-06 10:37:17 +09:00
Paul Masurel
ed5a3b3172 Bumped murmurhash version 2023-03-03 21:24:32 +09:00
PSeitz
ca20bfa776 add date_histogram (#1900) 
* add date_histogram

* add return result
 2023-03-02 05:17:35 +01:00
PSeitz
faa706d804 add coerce option for text and numbers types (#1904) 
* add coerce option for text and numbers types

allow to coerce the field type when indexing if the type does not match

* Apply suggestions from code review

Co-authored-by: Paul Masurel <paul@quickwit.io>

* add tests,add COERCE flag, include bool in coercion

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-03-01 11:36:59 +01:00
PSeitz
850a0d7ae2 add agg benchmark for optional and multi value (#1916) 
closes #1870
 2023-03-01 17:01:52 +09:00
Paul Masurel
7fae4d98d7 Adapting for quickwit2 (#1912) 
* Adapting tantivy to make it possible to be plugged to quickwit.

* Apply suggestions from code review

Co-authored-by: PSeitz <PSeitz@users.noreply.github.com>

* Added unit test

---------

Co-authored-by: PSeitz <PSeitz@users.noreply.github.com>
 2023-03-01 16:27:46 +09:00
PSeitz
bc36458334 move buffer in front of dynamic dispatch (#1915) 
dynamic dispatch seems to be really expensive, move the buffer in front of the dynamic dispatch, to reduce the number of calls into the dynamic dispatched collector.
 2023-02-28 13:07:50 +08:00
trinity-1686a
8a71e00da3 allow limiting the number of matched term in range query (#1899) 2023-02-27 10:44:08 +01:00
PSeitz
e510f699c8 feat: add support for u64,i64,f64 fields in term aggregation (#1883) 
* feat: add support for u64,i64,f64 fields in term aggregation

* hash enum values

* fix build

* Apply suggestions from code review

Co-authored-by: Paul Masurel <paul@quickwit.io>

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
 2023-02-27 15:04:41 +08:00
Paul Masurel
d25fc155b2 Making some of the column/termdict operations async-friendly (#1902) 2023-02-27 15:34:47 +09:00
Paul Masurel
8ea97e7d6b Minor refactoring preparing for getting columnar integrated in quickwit. (#1911) 2023-02-27 14:23:30 +09:00
Paul Masurel
0a726a0897 Added Empty ColumnIndex (#1910) 2023-02-27 13:59:22 +09:00
Paul Masurel
66ff53b0f4 Various minor code cleanup (#1909) 2023-02-27 13:48:34 +09:00
Paul Masurel
d002698008 Re-export of query grammar. (#1908) 2023-02-27 12:26:34 +09:00
Paul Masurel
c838aa808b Removedc the extra nesting in unit test file (#1907) 2023-02-27 12:17:52 +09:00
Paul Masurel
06850719dc Renaming .values(DocId) to .values_for_doc(DocId) (#1906) 2023-02-27 12:15:13 +09:00
PSeitz
5f23bb7e65 switch to sparse collection for histogram (#1898) 
* switch to sparse collection for histogram

Replaces histogram vec collection with a hashmap. This approach works much better for sparse data and enables use cases like drill downs (filter + small interval).
It is slower for dense cases (1.3x-2x slower). This can be alleviated with a specialized hashmap in the future.
closes #1704
closes #1370

* refactor, clippy

* fix bucket_pos overflow issue
 2023-02-23 07:02:58 +01:00
trinity-1686a
533ad99cd5 add PhrasePrefixQuery (#1842) 
* add PhrasePrefixQuery
 2023-02-22 11:18:33 +01:00
PSeitz
c7278b3258 remove schema in aggs (#1888) 
* switch to ColumnType, move tests

* remove Schema dependency in agg
 2023-02-22 04:50:28 +01:00
Paul Masurel
6b403e3281 Re-export of columnar 2023-02-22 11:23:54 +09:00
Paul Masurel
789cc8703e Adding unit test testing docfreq after merge (#1895) 2023-02-22 11:05:34 +09:00
Paul Masurel
e5098d9fe8 Moving test around reenabling tests that were disabled. (#1894) 2023-02-22 10:31:52 +09:00
Paul Masurel
f537334e4f Adding a write schema to columnar's merge operations. (#1884) 
* Adding a write schema to columnar's merge operations.

* Added unit test checking min/max when columns are empty.

* CR comment

* Rename to value_type_to_column_type
 2023-02-21 18:25:16 +09:00
Paul Masurel
e2aa5af075 Clippy warnings fixes (#1885) 2023-02-20 19:04:13 +09:00
Paul Masurel
02bebf4ff5 Cargo fmt 2023-02-20 09:40:04 +09:00
Paul Masurel
0274c982d5 Refactoring. (#1881) 
`ColumnValues` wrongly located in column_values/column.rs due to
historical reason moves to column_values/mod.rs

u128 stuff gets its own directory like u64 stuff.
 2023-02-17 21:57:14 +09:00
PSeitz
74bf60b4f7 implement SegmentAggregationCollector on bucket aggs (#1878) 2023-02-17 12:53:29 +01:00
PSeitz
bf1449b22d update examples for literate docs (#1880) 2023-02-17 11:48:22 +01:00
PSeitz
111f25a8f7 clippy (#1879) 
* fix clippy

* fix clippy

* fmt
 2023-02-17 11:34:21 +01:00
PSeitz
019db10e8e refactor aggregations (#1875) 
* add specialized version for full cardinality

Pre Columnar
test aggregation::tests::bench::bench_aggregation_average_u64                                                            ... bench:   6,681,850 ns/iter (+/- 1,217,385)
test aggregation::tests::bench::bench_aggregation_average_u64_and_f64                                                    ... bench:  10,576,327 ns/iter (+/- 494,380)

Current
test aggregation::tests::bench::bench_aggregation_average_u64                                                            ... bench:  11,562,084 ns/iter (+/- 3,678,682)
test aggregation::tests::bench::bench_aggregation_average_u64_and_f64                                                    ... bench:  18,925,790 ns/iter (+/- 17,616,771)

Post Change
test aggregation::tests::bench::bench_aggregation_average_u64                                                            ... bench:   9,123,811 ns/iter (+/- 399,720)
test aggregation::tests::bench::bench_aggregation_average_u64_and_f64                                                    ... bench:  13,111,825 ns/iter (+/- 273,547)

* refactor aggregation collection

* add buffering collector
 2023-02-16 13:15:16 +01:00
Paul Masurel
7423f99719 Issue/columnar for json (#1876) 
Adding support for JSON fast field.
 2023-02-16 20:38:32 +09:00
Alex Cole
f2f38c43ce Make BM25 scoring more flexible (#1855) 
* Introduce Bm25StatisticsProvider to inject statistics

* fix formatting I accidentally changed
 2023-02-16 19:14:12 +09:00
PSeitz
71f43ace1d fix dynamic dispatch regression for range queries (#1871) 2023-02-14 16:56:40 +01:00
PSeitz
347614c841 test error for avg agg on ip field (#1873) 
closes #1835
 2023-02-14 23:22:56 +08:00
Paul Masurel
097fd6138d Fix clippy comments (#1872) 2023-02-14 23:12:45 +09:00
PSeitz
01e5a22759 switch to new ff api (#1868) 2023-02-14 15:57:32 +08:00
Antoine Gauthier
b60b7d2afe fix(CI) enable coverage on doctest (#1839) 
* fix(CI) enable coverage on doctest
⚠️ Marked as [unstable](https://github.com/taiki-e/cargo-llvm-cov/issues/2)
refs #1761

* remove obsolete CI directory
 2023-02-14 16:42:44 +09:00
Yukun Guo
dfe4e95fde Make index compatible with virtual drives on Windows (#1843) 
* Make index compatible with virtual drives on Windows

* Get rid of normpath
 2023-02-14 16:41:48 +09:00
Paul Masurel
60cc2644d6 Fixing test_fail_on_flush_segment_but_one_worker_remains (#1869) 
The new fast field code, based on columnar, had a larger minimum memory
footprint, causing the first docuemnt to trigger a flush of the asegment
in this unit test.

This PR prevents the allocation of a large capacity for the different hashmap tables
using in the columnar writer.

Closes #1859
 2023-02-14 16:09:42 +09:00
Paul Masurel
10bccac61b Bugfix in parse_into_milliseconds (#1867) 2023-02-14 15:06:40 +09:00
PSeitz
1cfb9ce59a improve range query performance (#1864) 
fix RowId vs DocId naming
fixes #1863
 2023-02-14 13:25:39 +09:00
trinity-1686a
539ff08a79 move DateTime to tantivy_common (#1861) 
* move DateTime to tantivy_common

* resolve imports of columnar::DateTime as import of common::DateTime
 2023-02-11 17:03:06 +01:00
PSeitz
dab93df94e fix benchmarks (#1862) 2023-02-11 15:44:47 +09:00
trinity-1686a
3120147a76 re-enable examples (#1860) 2023-02-10 14:51:37 +01:00
PSeitz
cbcafae04c fix: doc store for files larger 4GB (#1856) 
Fixes an issue in the skip list deserialization, which deserialized the byte start offset incorrectly as u32.
`get_doc` will fail for any docs that live in a block with start offset larger than u32::MAX (~4GB).
Causes index corruption, if a segment with a doc store larger 4GB is merged.

tantivy version 0.19 is affected
 2023-02-10 14:29:43 +01:00
PSeitz
36c6138e7f fix: auto downgrade index record option, instead of vint error (#1857) 
Prev: thread 'main' panicked at 'called `Result::unwrap()` on an `Err` value: IoError(Custom { kind: InvalidData, error: "Reach end of buffer while reading VInt" })', src/main.rs:46:14
Now: Automatic downgrade to next available level
 2023-02-10 13:45:23 +01:00
PSeitz
7a9befd18d fix sort order test for term aggregation (#1858) 
fix sort order test for term aggregation
fix invalid request test
 2023-02-10 10:26:58 +01:00
Paul Masurel
62c811df2b Added a columnar cli 2023-02-09 19:02:16 +01:00
PSeitz
03345f0aa2 fmt code, update lz4_flex (#1838) 
formatting on nightly changed
 2023-02-10 01:42:32 +09:00
Paul Masurel
b7bfa20e38 Fixed test performance. 2023-02-09 17:39:55 +01:00
Paul Masurel
db8583db75 Fixing unit test 2023-02-09 16:53:05 +01:00
trinity-1686a
1390834ae8 make Term::as_slice public (#1846) 2023-02-09 15:37:07 +01:00
trinity-1686a
3ac973bea4 fix invalid endianness in documentation (#1845) 
* fix doc about term endianness

* rustfmt
 2023-02-09 15:36:38 +01:00
Paul Masurel
405e2cf4d9 Merge with main 2023-02-09 14:28:57 +01:00
Paul Masurel
b63c6c27bc adding change from main 2023-02-09 14:18:46 +01:00
Paul Masurel
bd5eea9852 Integrated columnar work. 2023-02-09 13:14:31 +01:00
490 changed files with 54007 additions and 29894 deletions
Expand all files Collapse all files

15
.github/workflows/coverage.yml vendored
View File

@@ -2,21 +2,24 @@ name: Coverage
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
branches: [main]
# Ensures that we cancel running jobs for the same PR / same workflow.
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
coverage:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v4
- name: Install Rust
run: rustup toolchain install nightly --profile minimal --component llvm-tools-preview
run: rustup toolchain install nightly-2024-07-01 --profile minimal --component llvm-tools-preview
- uses: Swatinem/rust-cache@v2
- uses: taiki-e/install-action@cargo-llvm-cov
- name: Generate code coverage
run: cargo +nightly llvm-cov --all-features --workspace --lcov --output-path lcov.info
run: cargo +nightly-2024-07-01 llvm-cov --all-features --workspace --doctests --lcov --output-path lcov.info
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v3
continue-on-error: true

7
.github/workflows/long_running.yml vendored
View File

@@ -8,13 +8,18 @@ env:
CARGO_TERM_COLOR: always
NUM_FUNCTIONAL_TEST_ITERATIONS: 20000
# Ensures that we cancel running jobs for the same PR / same workflow.
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v4
- name: Install stable
uses: actions-rs/toolchain@v1
with:

18
.github/workflows/test.yml vendored
View File

@@ -9,13 +9,18 @@ on:
env:
CARGO_TERM_COLOR: always
# Ensures that we cancel running jobs for the same PR / same workflow.
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
check:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v4
- name: Install nightly
uses: actions-rs/toolchain@v1
@@ -34,6 +39,13 @@ jobs:
- name: Check Formatting
run: cargo +nightly fmt --all -- --check
- name: Check Stable Compilation
run: cargo build --all-features
- name: Check Bench Compilation
run: cargo +nightly bench --no-run --profile=dev --all-features
- uses: actions-rs/clippy-check@v1
with:
@@ -48,14 +60,14 @@ jobs:
strategy:
matrix:
features: [
{ label: "all", flags: "mmap,stopwords,brotli-compression,lz4-compression,snappy-compression,zstd-compression,failpoints" },
{ label: "all", flags: "mmap,stopwords,lz4-compression,zstd-compression,failpoints" },
{ label: "quickwit", flags: "mmap,quickwit,failpoints" }
]
name: test-${{ matrix.features.label}}
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v4
- name: Install stable
uses: actions-rs/toolchain@v1

2
.gitignore vendored
View File

@@ -13,3 +13,5 @@ benchmark
.idea
trace.dat
cargo-timing*
control
variable

6
ARCHITECTURE.md
View File

@@ -46,7 +46,7 @@ The file of a segment has the format
```segment-id . ext```
The extension signals which data structure (or [`SegmentComponent`](src/core/segment_component.rs)) is stored in the file.
The extension signals which data structure (or [`SegmentComponent`](src/index/segment_component.rs)) is stored in the file.
A small `meta.json` file is in charge of keeping track of the list of segments, as well as the schema.
@@ -102,7 +102,7 @@ but users can extend tantivy with their own implementation.
Tantivy's document follows a very strict schema, decided before building any index.
The schema defines all of the fields that the indexes [`Document`](src/schema/document.rs) may and should contain, their types (`text`, `i64`, `u64`, `Date`, ...) as well as how it should be indexed / represented in tantivy.
The schema defines all of the fields that the indexes [`Document`](src/schema/document/mod.rs) may and should contain, their types (`text`, `i64`, `u64`, `Date`, ...) as well as how it should be indexed / represented in tantivy.
Depending on the type of the field, you can decide to
@@ -254,7 +254,7 @@ The token positions of all of the terms are then stored in a separate file with
The [TermInfo](src/postings/term_info.rs) gives an offset (expressed in position this time) in this file. As we iterate through the docset,
we advance the position reader by the number of term frequencies of the current document.
## [fieldnorms/](src/fieldnorms): Here is my doc, how many tokens in this field?
## [fieldnorm/](src/fieldnorm): Here is my doc, how many tokens in this field?
The [BM25](https://en.wikipedia.org/wiki/Okapi_BM25) formula also requires to know the number of tokens stored in a specific field for a given document. We store this information on one byte per document in the fieldnorm.
The fieldnorm is therefore compressed. Values up to 40 are encoded unchanged.

264
CHANGELOG.md
View File

@@ -1,3 +1,263 @@
Tantivy 0.23 - Unreleased
================================
Tantivy 0.23 will be backwards compatible with indices created with v0.22 and v0.21. The new minimum rust version will be 1.75.
#### Bugfixes
- fix potential endless loop in merge [#2457](https://github.com/quickwit-oss/tantivy/pull/2457)(@PSeitz)
- fix bug that causes out-of-order sstable key. [#2445](https://github.com/quickwit-oss/tantivy/pull/2445)(@fulmicoton)
- fix ReferenceValue API flaw [#2372](https://github.com/quickwit-oss/tantivy/pull/2372)(@PSeitz)
- fix `OwnedBytes` debug panic [#2512](https://github.com/quickwit-oss/tantivy/pull/2512)(@b41sh)
#### Breaking API Changes
- remove index sorting [#2434](https://github.com/quickwit-oss/tantivy/pull/2434)(@PSeitz)
#### Features/Improvements
- **Aggregation**
- Support for cardinality aggregation [#2337](https://github.com/quickwit-oss/tantivy/pull/2337) [#2446](https://github.com/quickwit-oss/tantivy/pull/2446) (@raphaelcoeffic @PSeitz)
- Support for extended stats aggregation [#2247](https://github.com/quickwit-oss/tantivy/pull/2247)(@giovannicuccu)
- Add Key::I64 and Key::U64 variants in aggregation to avoid f64 precision issues [#2468](https://github.com/quickwit-oss/tantivy/pull/2468)(@PSeitz)
- Faster term aggregation fetch terms [#2447](https://github.com/quickwit-oss/tantivy/pull/2447)(@PSeitz)
- Improve custom order deserialization [#2451](https://github.com/quickwit-oss/tantivy/pull/2451)(@PSeitz)
- Change AggregationLimits behavior [#2495](https://github.com/quickwit-oss/tantivy/pull/2495)(@PSeitz)
- lower contention on AggregationLimits [#2394](https://github.com/quickwit-oss/tantivy/pull/2394)(@PSeitz)
- fix postcard compatibility for top_hits, add postcard test [#2346](https://github.com/quickwit-oss/tantivy/pull/2346)(@PSeitz)
- reduce top hits memory consumption [#2426](https://github.com/quickwit-oss/tantivy/pull/2426)(@PSeitz)
- check unsupported parameters top_hits [#2351](https://github.com/quickwit-oss/tantivy/pull/2351)(@PSeitz)
- Change AggregationLimits to AggregationLimitsGuard [#2495](https://github.com/quickwit-oss/tantivy/pull/2495)(@PSeitz)
- **Range Queries**
- Support fast field range queries on json fields [#2456](https://github.com/quickwit-oss/tantivy/pull/2456)(@PSeitz)
- Add support for str fast field range query [#2460](https://github.com/quickwit-oss/tantivy/pull/2460) [#2452](https://github.com/quickwit-oss/tantivy/pull/2452) [#2453](https://github.com/quickwit-oss/tantivy/pull/2453)(@PSeitz)
- modify fastfield range query heuristic [#2375](https://github.com/quickwit-oss/tantivy/pull/2375)(@trinity-1686a)
- add FastFieldRangeQuery for explicit range queries on fast field (for `RangeQuery` it is autodetected) [#2477](https://github.com/quickwit-oss/tantivy/pull/2477)(@PSeitz)
- add format backwards-compatibility tests [#2485](https://github.com/quickwit-oss/tantivy/pull/2485)(@PSeitz)
- add columnar format compatibility tests [#2433](https://github.com/quickwit-oss/tantivy/pull/2433)(@PSeitz)
- Improved snippet ranges algorithm [#2474](https://github.com/quickwit-oss/tantivy/pull/2474)(@gezihuzi)
- make find_field_with_default return json fields without path [#2476](https://github.com/quickwit-oss/tantivy/pull/2476)(@trinity-1686a)
- feat(query): Make `BooleanQuery` support `minimum_number_should_match` [#2405](https://github.com/quickwit-oss/tantivy/pull/2405)(@LebranceBW)
- **RegexPhraseQuery**
`RegexPhraseQuery` supports phrase queries with regex. E.g. query "b.* b.* wolf" matches "big bad wolf". Slop is supported as well: "b.* wolf"~2 matches "big bad wolf" [#2516](https://github.com/quickwit-oss/tantivy/pull/2516)(@PSeitz)
- **Optional Index in Multivalue Columnar Index**
For mostly empty multivalued indices there was a large overhead during creation when iterating all docids (merge case).
This is alleviated by placing an optional index in the multivalued index to mark documents that have values.
This will slightly increase space and access time. [#2439](https://github.com/quickwit-oss/tantivy/pull/2439)(@PSeitz)
- **Store DateTime as nanoseconds in doc store** DateTime in the doc store was truncated to microseconds previously. This removes this truncation, while still keeping backwards compatibility. [#2486](https://github.com/quickwit-oss/tantivy/pull/2486)(@PSeitz)
- **Performace/Memory**
- lift clauses in LogicalAst for optimized ast during execution [#2449](https://github.com/quickwit-oss/tantivy/pull/2449)(@PSeitz)
- Use Vec instead of BTreeMap to back OwnedValue object [#2364](https://github.com/quickwit-oss/tantivy/pull/2364)(@fulmicoton)
- Replace TantivyDocument with CompactDoc. CompactDoc is much smaller and provides similar performance. [#2402](https://github.com/quickwit-oss/tantivy/pull/2402)(@PSeitz)
- Recycling buffer in PrefixPhraseScorer [#2443](https://github.com/quickwit-oss/tantivy/pull/2443)(@fulmicoton)
- **Json Type**
- JSON supports now all values on the root level. Previously an object was required. This enables support for flat mixed types. allow more JSON values, fix i64 special case [#2383](https://github.com/quickwit-oss/tantivy/pull/2383)(@PSeitz)
- add json path constructor to term [#2367](https://github.com/quickwit-oss/tantivy/pull/2367)(@PSeitz)
- **QueryParser**
- fix de-escaping too much in query parser [#2427](https://github.com/quickwit-oss/tantivy/pull/2427)(@trinity-1686a)
- improve query parser [#2416](https://github.com/quickwit-oss/tantivy/pull/2416)(@trinity-1686a)
- Support field grouping `title:(return AND "pink panther")` [#2333](https://github.com/quickwit-oss/tantivy/pull/2333)(@trinity-1686a)
- add access benchmark for columnar [#2432](https://github.com/quickwit-oss/tantivy/pull/2432)(@PSeitz)
- extend indexwriter proptests [#2342](https://github.com/quickwit-oss/tantivy/pull/2342)(@PSeitz)
- add bench & test for columnar merging [#2428](https://github.com/quickwit-oss/tantivy/pull/2428)(@PSeitz)
- Change in Executor API [#2391](https://github.com/quickwit-oss/tantivy/pull/2391)(@fulmicoton)
- Removed usage of num_cpus [#2387](https://github.com/quickwit-oss/tantivy/pull/2387)(@fulmicoton)
- use bingang for agg and stacker benchmark [#2378](https://github.com/quickwit-oss/tantivy/pull/2378)[#2492](https://github.com/quickwit-oss/tantivy/pull/2492)(@PSeitz)
- cleanup top level exports [#2382](https://github.com/quickwit-oss/tantivy/pull/2382)(@PSeitz)
- make convert_to_fast_value_and_append_to_json_term pub [#2370](https://github.com/quickwit-oss/tantivy/pull/2370)(@PSeitz)
- remove JsonTermWriter [#2238](https://github.com/quickwit-oss/tantivy/pull/2238)(@PSeitz)
- validate sort by field type [#2336](https://github.com/quickwit-oss/tantivy/pull/2336)(@PSeitz)
- Fix trait bound of StoreReader::iter [#2360](https://github.com/quickwit-oss/tantivy/pull/2360)(@adamreichold)
- remove read_postings_no_deletes [#2526](https://github.com/quickwit-oss/tantivy/pull/2526)(@PSeitz)
Tantivy 0.22
================================
Tantivy 0.22 will be able to read indices created with Tantivy 0.21.
#### Bugfixes
- Fix null byte handling in JSON paths (null bytes in json keys caused panic during indexing) [#2345](https://github.com/quickwit-oss/tantivy/pull/2345)(@PSeitz)
- Fix bug that can cause `get_docids_for_value_range` to panic. [#2295](https://github.com/quickwit-oss/tantivy/pull/2295)(@fulmicoton)
- Avoid 1 document indices by increase min memory to 15MB for indexing [#2176](https://github.com/quickwit-oss/tantivy/pull/2176)(@PSeitz)
- Fix merge panic for JSON fields [#2284](https://github.com/quickwit-oss/tantivy/pull/2284)(@PSeitz)
- Fix bug occurring when merging JSON object indexed with positions. [#2253](https://github.com/quickwit-oss/tantivy/pull/2253)(@fulmicoton)
- Fix empty DateHistogram gap bug [#2183](https://github.com/quickwit-oss/tantivy/pull/2183)(@PSeitz)
- Fix range query end check (fields with less than 1 value per doc are affected) [#2226](https://github.com/quickwit-oss/tantivy/pull/2226)(@PSeitz)
- Handle exclusive out of bounds ranges on fastfield range queries [#2174](https://github.com/quickwit-oss/tantivy/pull/2174)(@PSeitz)
#### Breaking API Changes
- rename ReloadPolicy onCommit to onCommitWithDelay [#2235](https://github.com/quickwit-oss/tantivy/pull/2235)(@giovannicuccu)
- Move exports from the root into modules [#2220](https://github.com/quickwit-oss/tantivy/pull/2220)(@PSeitz)
- Accept field name instead of `Field` in FilterCollector [#2196](https://github.com/quickwit-oss/tantivy/pull/2196)(@PSeitz)
- remove deprecated IntOptions and DateTime [#2353](https://github.com/quickwit-oss/tantivy/pull/2353)(@PSeitz)
#### Features/Improvements
- Tantivy documents as a trait: Index data directly without converting to tantivy types first [#2071](https://github.com/quickwit-oss/tantivy/pull/2071)(@ChillFish8)
- encode some part of posting list as -1 instead of direct values (smaller inverted indices) [#2185](https://github.com/quickwit-oss/tantivy/pull/2185)(@trinity-1686a)
- **Aggregation**
- Support to deserialize f64 from string [#2311](https://github.com/quickwit-oss/tantivy/pull/2311)(@PSeitz)
- Add a top_hits aggregator [#2198](https://github.com/quickwit-oss/tantivy/pull/2198)(@ditsuke)
- Support bool type in term aggregation [#2318](https://github.com/quickwit-oss/tantivy/pull/2318)(@PSeitz)
- Support ip addresses in term aggregation [#2319](https://github.com/quickwit-oss/tantivy/pull/2319)(@PSeitz)
- Support date type in term aggregation [#2172](https://github.com/quickwit-oss/tantivy/pull/2172)(@PSeitz)
- Support escaped dot when addressing field [#2250](https://github.com/quickwit-oss/tantivy/pull/2250)(@PSeitz)
- Add ExistsQuery to check documents that have a value [#2160](https://github.com/quickwit-oss/tantivy/pull/2160)(@imotov)
- Expose TopDocs::order_by_u64_field again [#2282](https://github.com/quickwit-oss/tantivy/pull/2282)(@ditsuke)
- **Memory/Performance**
- Faster TopN: replace BinaryHeap with TopNComputer [#2186](https://github.com/quickwit-oss/tantivy/pull/2186)(@PSeitz)
- reduce number of allocations during indexing [#2257](https://github.com/quickwit-oss/tantivy/pull/2257)(@PSeitz)
- Less Memory while indexing: docid deltas while indexing [#2249](https://github.com/quickwit-oss/tantivy/pull/2249)(@PSeitz)
- Faster indexing: use term hashmap in fastfield [#2243](https://github.com/quickwit-oss/tantivy/pull/2243)(@PSeitz)
- term hashmap remove copy in is_empty, unused unordered_id [#2229](https://github.com/quickwit-oss/tantivy/pull/2229)(@PSeitz)
- add method to fetch block of first values in columnar [#2330](https://github.com/quickwit-oss/tantivy/pull/2330)(@PSeitz)
- Faster aggregations: add fast path for full columns in fetch_block [#2328](https://github.com/quickwit-oss/tantivy/pull/2328)(@PSeitz)
- Faster sstable loading: use fst for sstable index [#2268](https://github.com/quickwit-oss/tantivy/pull/2268)(@trinity-1686a)
- **QueryParser**
- allow newline where we allow space in query parser [#2302](https://github.com/quickwit-oss/tantivy/pull/2302)(@trinity-1686a)
- allow some mixing of occur and bool in strict query parser [#2323](https://github.com/quickwit-oss/tantivy/pull/2323)(@trinity-1686a)
- handle * inside term in lenient query parser [#2228](https://github.com/quickwit-oss/tantivy/pull/2228)(@trinity-1686a)
- add support for exists query syntax in query parser [#2170](https://github.com/quickwit-oss/tantivy/pull/2170)(@trinity-1686a)
- Add shared search executor [#2312](https://github.com/quickwit-oss/tantivy/pull/2312)(@MochiXu)
- Truncate keys to u16::MAX in term hashmap [#2299](https://github.com/quickwit-oss/tantivy/pull/2299)(@PSeitz)
- report if a term matched when warming up posting list [#2309](https://github.com/quickwit-oss/tantivy/pull/2309)(@trinity-1686a)
- Support json fields in FuzzyTermQuery [#2173](https://github.com/quickwit-oss/tantivy/pull/2173)(@PingXia-at)
- Read list of fields encoded in term dictionary for JSON fields [#2184](https://github.com/quickwit-oss/tantivy/pull/2184)(@PSeitz)
- add collect_block to BoxableSegmentCollector [#2331](https://github.com/quickwit-oss/tantivy/pull/2331)(@PSeitz)
- expose collect_block buffer size [#2326](https://github.com/quickwit-oss/tantivy/pull/2326)(@PSeitz)
- Forward regex parser errors [#2288](https://github.com/quickwit-oss/tantivy/pull/2288)(@adamreichold)
- Make FacetCounts defaultable and cloneable. [#2322](https://github.com/quickwit-oss/tantivy/pull/2322)(@adamreichold)
- Derive Debug for SchemaBuilder [#2254](https://github.com/quickwit-oss/tantivy/pull/2254)(@GodTamIt)
- add missing inlines to tantivy options [#2245](https://github.com/quickwit-oss/tantivy/pull/2245)(@PSeitz)
Tantivy 0.21.1
================================
#### Bugfixes
- Range queries on fast fields with less values on that field than documents had an invalid end condition, leading to missing results. [#2226](https://github.com/quickwit-oss/tantivy/issues/2226)(@appaquet @PSeitz)
- Increase the minimum memory budget from 3MB to 15MB to avoid single doc segments (API fix). [#2176](https://github.com/quickwit-oss/tantivy/issues/2176)(@PSeitz)
Tantivy 0.21
================================
#### Bugfixes
- Fix track fast field memory consumption, which led to higher memory consumption than the budget allowed during indexing [#2148](https://github.com/quickwit-oss/tantivy/issues/2148)[#2147](https://github.com/quickwit-oss/tantivy/issues/2147)(@PSeitz)
- Fix a regression from 0.20 where sort index by date wasn't working anymore [#2124](https://github.com/quickwit-oss/tantivy/issues/2124)(@PSeitz)
- Fix getting the root facet on the `FacetCollector`. [#2086](https://github.com/quickwit-oss/tantivy/issues/2086)(@adamreichold)
- Align numerical type priority order of columnar and query. [#2088](https://github.com/quickwit-oss/tantivy/issues/2088)(@fmassot)
#### Breaking Changes
- Remove support for Brotli and Snappy compression [#2123](https://github.com/quickwit-oss/tantivy/issues/2123)(@adamreichold)
#### Features/Improvements
- Implement lenient query parser [#2129](https://github.com/quickwit-oss/tantivy/pull/2129)(@trinity-1686a)
- order_by_u64_field and order_by_fast_field allow sorting in ascending and descending order [#2111](https://github.com/quickwit-oss/tantivy/issues/2111)(@naveenann)
- Allow dynamic filters in text analyzer builder [#2110](https://github.com/quickwit-oss/tantivy/issues/2110)(@fulmicoton @fmassot)
- **Aggregation**
- Add missing parameter for term aggregation [#2149](https://github.com/quickwit-oss/tantivy/issues/2149)[#2103](https://github.com/quickwit-oss/tantivy/issues/2103)(@PSeitz)
- Add missing parameter for percentiles [#2157](https://github.com/quickwit-oss/tantivy/issues/2157)(@PSeitz)
- Add missing parameter for stats,min,max,count,sum,avg [#2151](https://github.com/quickwit-oss/tantivy/issues/2151)(@PSeitz)
- Improve aggregation deserialization error message [#2150](https://github.com/quickwit-oss/tantivy/issues/2150)(@PSeitz)
- Add validation for type Bytes to term_agg [#2077](https://github.com/quickwit-oss/tantivy/issues/2077)(@PSeitz)
- Alternative mixed field collection [#2135](https://github.com/quickwit-oss/tantivy/issues/2135)(@PSeitz)
- Add missing query_terms impl for TermSetQuery. [#2120](https://github.com/quickwit-oss/tantivy/issues/2120)(@adamreichold)
- Minor improvements to OwnedBytes [#2134](https://github.com/quickwit-oss/tantivy/issues/2134)(@adamreichold)
- Remove allocations in split compound words [#2080](https://github.com/quickwit-oss/tantivy/issues/2080)(@PSeitz)
- Ngram tokenizer now returns an error with invalid arguments [#2102](https://github.com/quickwit-oss/tantivy/issues/2102)(@fmassot)
- Make TextAnalyzerBuilder public [#2097](https://github.com/quickwit-oss/tantivy/issues/2097)(@adamreichold)
- Return an error when tokenizer is not found while indexing [#2093](https://github.com/quickwit-oss/tantivy/issues/2093)(@naveenann)
- Delayed column opening during merge [#2132](https://github.com/quickwit-oss/tantivy/issues/2132)(@PSeitz)
Tantivy 0.20.2
================================
- Align numerical type priority order on the search side. [#2088](https://github.com/quickwit-oss/tantivy/issues/2088) (@fmassot)
- Fix is_child_of function not considering the root facet. [#2086](https://github.com/quickwit-oss/tantivy/issues/2086) (@adamreichhold)
Tantivy 0.20.1
================================
- Fix building on windows with mmap [#2070](https://github.com/quickwit-oss/tantivy/issues/2070) (@ChillFish8)
Tantivy 0.20
================================
#### Bugfixes
- Fix phrase queries with slop (slop supports now transpositions, algorithm that carries slop so far for num terms > 2) [#2031](https://github.com/quickwit-oss/tantivy/issues/2031)[#2020](https://github.com/quickwit-oss/tantivy/issues/2020)(@PSeitz)
- Handle error for exists on MMapDirectory [#1988](https://github.com/quickwit-oss/tantivy/issues/1988) (@PSeitz)
- Aggregation
- Fix min doc_count empty merge bug [#2057](https://github.com/quickwit-oss/tantivy/issues/2057) (@PSeitz)
- Fix: Sort order for term aggregations (sort order on key was inverted) [#1858](https://github.com/quickwit-oss/tantivy/issues/1858) (@PSeitz)
#### Features/Improvements
- Add PhrasePrefixQuery [#1842](https://github.com/quickwit-oss/tantivy/issues/1842) (@trinity-1686a)
- Add `coerce` option for text and numbers types (convert the value instead of returning an error during indexing) [#1904](https://github.com/quickwit-oss/tantivy/issues/1904) (@PSeitz)
- Add regex tokenizer [#1759](https://github.com/quickwit-oss/tantivy/issues/1759)(@mkleen)
- Move tokenizer API to separate crate. Having a separate crate with a stable API will allow us to use tokenizers with different tantivy versions. [#1767](https://github.com/quickwit-oss/tantivy/issues/1767) (@PSeitz)
- **Columnar crate**: New fast field handling (@fulmicoton @PSeitz) [#1806](https://github.com/quickwit-oss/tantivy/issues/1806)[#1809](https://github.com/quickwit-oss/tantivy/issues/1809)
- Support for fast fields with optional values. Previously tantivy supported only single-valued and multi-value fast fields. The encoding of optional fast fields is now very compact.
- Fast field Support for JSON (schemaless fast fields). Support multiple types on the same column. [#1876](https://github.com/quickwit-oss/tantivy/issues/1876) (@fulmicoton)
- Unified access for fast fields over different cardinalities.
- Unified storage for typed and untyped fields.
- Move fastfield codecs into columnar. [#1782](https://github.com/quickwit-oss/tantivy/issues/1782) (@fulmicoton)
- Sparse dense index for optional values [#1716](https://github.com/quickwit-oss/tantivy/issues/1716) (@PSeitz)
- Switch to nanosecond precision in DateTime fastfield [#2016](https://github.com/quickwit-oss/tantivy/issues/2016) (@PSeitz)
- **Aggregation**
- Add `date_histogram` aggregation (only `fixed_interval` for now) [#1900](https://github.com/quickwit-oss/tantivy/issues/1900) (@PSeitz)
- Add `percentiles` aggregations [#1984](https://github.com/quickwit-oss/tantivy/issues/1984) (@PSeitz)
- [**breaking**] Drop JSON support on intermediate agg result (we use postcard as format in `quickwit` to send intermediate results) [#1992](https://github.com/quickwit-oss/tantivy/issues/1992) (@PSeitz)
- Set memory limit in bytes for aggregations after which they abort (Previously there was only the bucket limit) [#1942](https://github.com/quickwit-oss/tantivy/issues/1942)[#1957](https://github.com/quickwit-oss/tantivy/issues/1957)(@PSeitz)
- Add support for u64,i64,f64 fields in term aggregation [#1883](https://github.com/quickwit-oss/tantivy/issues/1883) (@PSeitz)
- Allow histogram bounds to be passed as Rfc3339 [#2076](https://github.com/quickwit-oss/tantivy/issues/2076) (@PSeitz)
- Add count, min, max, and sum aggregations [#1794](https://github.com/quickwit-oss/tantivy/issues/1794) (@guilload)
- Switch to Aggregation without serde_untagged => better deserialization errors. [#2003](https://github.com/quickwit-oss/tantivy/issues/2003) (@PSeitz)
- Switch to ms in histogram for date type (ES compatibility) [#2045](https://github.com/quickwit-oss/tantivy/issues/2045) (@PSeitz)
- Reduce term aggregation memory consumption [#2013](https://github.com/quickwit-oss/tantivy/issues/2013) (@PSeitz)
- Reduce agg memory consumption: Replace generic aggregation collector (which has a high memory requirement per instance) in aggregation tree with optimized versions behind a trait.
- Split term collection count and sub_agg (Faster term agg with less memory consumption for cases without sub-aggs) [#1921](https://github.com/quickwit-oss/tantivy/issues/1921) (@PSeitz)
- Schemaless aggregations: In combination with stacker tantivy supports now schemaless aggregations via the JSON type.
- Add aggregation support for JSON type [#1888](https://github.com/quickwit-oss/tantivy/issues/1888) (@PSeitz)
- Mixed types support on JSON fields in aggs [#1971](https://github.com/quickwit-oss/tantivy/issues/1971) (@PSeitz)
- Perf: Fetch blocks of vals in aggregation for all cardinality [#1950](https://github.com/quickwit-oss/tantivy/issues/1950) (@PSeitz)
- Allow histogram bounds to be passed as Rfc3339 [#2076](https://github.com/quickwit-oss/tantivy/issues/2076) (@PSeitz)
- `Searcher` with disabled scoring via `EnableScoring::Disabled` [#1780](https://github.com/quickwit-oss/tantivy/issues/1780) (@shikhar)
- Enable tokenizer on json fields [#2053](https://github.com/quickwit-oss/tantivy/issues/2053) (@PSeitz)
- Enforcing "NOT" and "-" queries consistency in UserInputAst [#1609](https://github.com/quickwit-oss/tantivy/issues/1609) (@bazhenov)
- Faster indexing
- Refactor tokenization pipeline to use GATs [#1924](https://github.com/quickwit-oss/tantivy/issues/1924) (@trinity-1686a)
- Faster term hash map [#2058](https://github.com/quickwit-oss/tantivy/issues/2058)[#1940](https://github.com/quickwit-oss/tantivy/issues/1940) (@PSeitz)
- tokenizer-api: reduce Tokenizer allocation overhead [#2062](https://github.com/quickwit-oss/tantivy/issues/2062) (@PSeitz)
- Refactor vint [#2010](https://github.com/quickwit-oss/tantivy/issues/2010) (@PSeitz)
- Faster search
- Work in batches of docs on the SegmentCollector (Only for cases without score for now) [#1937](https://github.com/quickwit-oss/tantivy/issues/1937) (@PSeitz)
- Faster fast field range queries using SIMD [#1954](https://github.com/quickwit-oss/tantivy/issues/1954) (@fulmicoton)
- Improve fast field range query performance [#1864](https://github.com/quickwit-oss/tantivy/issues/1864) (@PSeitz)
- Make BM25 scoring more flexible [#1855](https://github.com/quickwit-oss/tantivy/issues/1855) (@alexcole)
- Switch fs2 to fs4 as it is now unmaintained and does not support illumos [#1944](https://github.com/quickwit-oss/tantivy/issues/1944) (@Toasterson)
- Made BooleanWeight and BoostWeight public [#1991](https://github.com/quickwit-oss/tantivy/issues/1991) (@fulmicoton)
- Make index compatible with virtual drives on Windows [#1843](https://github.com/quickwit-oss/tantivy/issues/1843) (@gyk)
- Add stop words for Hungarian language [#2069](https://github.com/quickwit-oss/tantivy/issues/2069) (@tnxbutno)
- Auto downgrade index record option, instead of vint error [#1857](https://github.com/quickwit-oss/tantivy/issues/1857) (@PSeitz)
- Enable range query on fast field for u64 compatible types [#1762](https://github.com/quickwit-oss/tantivy/issues/1762) (@PSeitz) [#1876]
- sstable
- Isolating sstable and stacker in independent crates. [#1718](https://github.com/quickwit-oss/tantivy/issues/1718) (@fulmicoton)
- New sstable format [#1943](https://github.com/quickwit-oss/tantivy/issues/1943)[#1953](https://github.com/quickwit-oss/tantivy/issues/1953) (@trinity-1686a)
- Use DeltaReader directly to implement Dictionary::ord_to_term [#1928](https://github.com/quickwit-oss/tantivy/issues/1928) (@trinity-1686a)
- Use DeltaReader directly to implement Dictionary::term_ord [#1925](https://github.com/quickwit-oss/tantivy/issues/1925) (@trinity-1686a)
- Add separate tokenizer manager for fast fields [#2019](https://github.com/quickwit-oss/tantivy/issues/2019) (@PSeitz)
- Make construction of LevenshteinAutomatonBuilder for FuzzyTermQuery instances lazy. [#1756](https://github.com/quickwit-oss/tantivy/issues/1756) (@adamreichold)
- Added support for madvise when opening an mmapped Index [#2036](https://github.com/quickwit-oss/tantivy/issues/2036) (@fulmicoton)
- Rename `DatePrecision` to `DateTimePrecision` [#2051](https://github.com/quickwit-oss/tantivy/issues/2051) (@guilload)
- Query Parser
- Quotation mark can now be used for phrase queries. [#2050](https://github.com/quickwit-oss/tantivy/issues/2050) (@fulmicoton)
- PhrasePrefixQuery is supported in the query parser via: `field:"phrase ter"*` [#2044](https://github.com/quickwit-oss/tantivy/issues/2044) (@adamreichold)
- Docs
- Update examples for literate docs [#1880](https://github.com/quickwit-oss/tantivy/issues/1880) (@PSeitz)
- Add ip field example [#1775](https://github.com/quickwit-oss/tantivy/issues/1775) (@PSeitz)
- Fix doc store cache documentation [#1821](https://github.com/quickwit-oss/tantivy/issues/1821) (@PSeitz)
- Fix BooleanQuery document [#1999](https://github.com/quickwit-oss/tantivy/issues/1999) (@RT_Enzyme)
- Update comments in the faceted search example [#1737](https://github.com/quickwit-oss/tantivy/issues/1737) (@DawChihLiou)
Tantivy 0.19
================================
#### Bugfixes
@@ -24,7 +284,7 @@ Tantivy 0.19
- Add support for phrase slop in query language [#1393](https://github.com/quickwit-oss/tantivy/pull/1393) (@saroh)
- Aggregation
- Add aggregation support for date type [#1693](https://github.com/quickwit-oss/tantivy/pull/1693)(@PSeitz)
- Add support for keyed parameter in range and histgram aggregations [#1424](https://github.com/quickwit-oss/tantivy/pull/1424) (@k-yomo)
- Add support for keyed parameter in range and histogram aggregations [#1424](https://github.com/quickwit-oss/tantivy/pull/1424) (@k-yomo)
- Add aggregation bucket limit [#1363](https://github.com/quickwit-oss/tantivy/pull/1363) (@PSeitz)
- Faster indexing
- [#1610](https://github.com/quickwit-oss/tantivy/pull/1610) (@PSeitz)
@@ -467,7 +727,7 @@ Tantivy 0.4.0
- Raise the limit of number of fields (previously 256 fields) (@fulmicoton)
- Removed u32 fields. They are replaced by u64 and i64 fields (#65) (@fulmicoton)
- Optimized skip in SegmentPostings (#130) (@lnicola)
- Replacing rustc_serialize by serde. Kudos to @KodrAus and @lnicola
- Replacing rustc_serialize by serde. Kudos to benchmark@KodrAus and @lnicola
- Using error-chain (@KodrAus)
- QueryParser: (@fulmicoton)
- Explicit error returned when searched for a term that is not indexed

10
CITATION.cff Normal file
View File

@@ -0,0 +1,10 @@
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- alias: Quickwit Inc.
website: "https://quickwit.io"
title: "tantivy"
version: 0.22.0
doi: 10.5281/zenodo.13942948
date-released: 2024-10-17
url: "https://github.com/quickwit-oss/tantivy"

126
Cargo.toml
View File

@@ -1,6 +1,6 @@
[package]
name = "tantivy"
version = "0.19.0"
version = "0.23.0"
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
license = "MIT"
categories = ["database-implementations", "data-structures"]
@@ -11,73 +11,87 @@ repository = "https://github.com/quickwit-oss/tantivy"
readme = "README.md"
keywords = ["search", "information", "retrieval"]
edition = "2021"
rust-version = "1.62"
rust-version = "1.75"
exclude = ["benches/*.json", "benches/*.txt"]
[dependencies]
oneshot = "0.1.5"
base64 = "0.21.0"
oneshot = "0.1.7"
base64 = "0.22.0"
byteorder = "1.4.3"
crc32fast = "1.3.2"
once_cell = "1.10.0"
regex = { version = "1.5.5", default-features = false, features = ["std", "unicode"] }
aho-corasick = "0.7"
tantivy-fst = "0.4.0"
memmap2 = { version = "0.5.3", optional = true }
lz4_flex = { version = "0.9.2", default-features = false, features = ["checked-decode"], optional = true }
brotli = { version = "3.3.4", optional = true }
zstd = { version = "0.12", optional = true, default-features = false }
snap = { version = "1.0.5", optional = true }
tempfile = { version = "3.3.0", optional = true }
regex = { version = "1.5.5", default-features = false, features = [
"std",
"unicode",
] }
aho-corasick = "1.0"
tantivy-fst = "0.5"
memmap2 = { version = "0.9.0", optional = true }
lz4_flex = { version = "0.11", default-features = false, optional = true }
zstd = { version = "0.13", optional = true, default-features = false }
tempfile = { version = "3.12.0", optional = true }
log = "0.4.16"
serde = { version = "1.0.136", features = ["derive"] }
serde_json = "1.0.79"
num_cpus = "1.13.1"
fs2 = { version = "0.4.3", optional = true }
fs4 = { version = "0.8.0", optional = true }
levenshtein_automata = "0.2.1"
uuid = { version = "1.0.0", features = ["v4", "serde"] }
crossbeam-channel = "0.5.4"
rust-stemmers = "1.2.0"
downcast-rs = "1.2.0"
bitpacking = { version = "0.8.4", default-features = false, features = ["bitpacker4x"] }
census = "0.4.0"
rustc-hash = "1.1.0"
thiserror = "1.0.30"
downcast-rs = "2.0.1"
bitpacking = { version = "0.9.2", default-features = false, features = [
"bitpacker4x",
] }
census = "0.4.2"
rustc-hash = "2.0.0"
thiserror = "2.0.1"
htmlescape = "0.3.1"
fail = "0.5.0"
murmurhash32 = "0.2.0"
time = { version = "0.3.10", features = ["serde-well-known"] }
fail = { version = "0.5.0", optional = true }
time = { version = "0.3.35", features = ["serde-well-known"] }
smallvec = "1.8.0"
rayon = "1.5.2"
lru = "0.9.0"
lru = "0.12.0"
fastdivide = "0.4.0"
itertools = "0.10.3"
measure_time = "0.8.2"
async-trait = "0.1.53"
itertools = "0.14.0"
measure_time = "0.9.0"
arc-swap = "1.5.0"
bon = "3.3.1"
columnar = { version="0.1", path="./columnar", package ="tantivy-columnar" }
sstable = { version="0.1", path="./sstable", package ="tantivy-sstable", optional = true }
stacker = { version="0.1", path="./stacker", package ="tantivy-stacker" }
tantivy-query-grammar = { version= "0.19.0", path="./query-grammar" }
tantivy-bitpacker = { version= "0.3", path="./bitpacker" }
common = { version= "0.5", path = "./common/", package = "tantivy-common" }
fastfield_codecs = { version= "0.3", path="./fastfield_codecs", default-features = false }
tokenizer-api = { version="0.1", path="./tokenizer-api", package="tantivy-tokenizer-api" }
columnar = { version = "0.3", path = "./columnar", package = "tantivy-columnar" }
sstable = { version = "0.3", path = "./sstable", package = "tantivy-sstable", optional = true }
stacker = { version = "0.3", path = "./stacker", package = "tantivy-stacker" }
query-grammar = { version = "0.22.0", path = "./query-grammar", package = "tantivy-query-grammar" }
tantivy-bitpacker = { version = "0.6", path = "./bitpacker" }
common = { version = "0.7", path = "./common/", package = "tantivy-common" }
tokenizer-api = { version = "0.3", path = "./tokenizer-api", package = "tantivy-tokenizer-api" }
sketches-ddsketch = { version = "0.3.0", features = ["use_serde"] }
hyperloglogplus = { version = "0.4.1", features = ["const-loop"] }
futures-util = { version = "0.3.28", optional = true }
futures-channel = { version = "0.3.28", optional = true }
fnv = "1.0.7"
[target.'cfg(windows)'.dependencies]
winapi = "0.3.9"
[dev-dependencies]
binggan = "0.14.0"
rand = "0.8.5"
maplit = "1.0.2"
matches = "0.1.9"
pretty_assertions = "1.2.1"
proptest = "1.0.0"
criterion = "0.4"
test-log = "0.2.10"
env_logger = "0.10.0"
pprof = { version = "0.11.0", features = ["flamegraph", "criterion"] }
futures = "0.3.21"
paste = "1.0.11"
more-asserts = "0.3.1"
rand_distr = "0.4.3"
time = { version = "0.3.10", features = ["serde-well-known", "macros"] }
postcard = { version = "1.0.4", features = [
"use-std",
], default-features = false }
[target.'cfg(not(windows))'.dev-dependencies]
criterion = { version = "0.5", default-features = false }
[dev-dependencies.fail]
version = "0.5.0"
@@ -88,27 +102,44 @@ opt-level = 3
debug = false
debug-assertions = false
[profile.bench]
opt-level = 3
debug = true
debug-assertions = false
[profile.test]
debug-assertions = true
overflow-checks = true
[features]
default = ["mmap", "stopwords", "lz4-compression"]
mmap = ["fs2", "tempfile", "memmap2"]
mmap = ["fs4", "tempfile", "memmap2"]
stopwords = []
brotli-compression = ["brotli"]
lz4-compression = ["lz4_flex"]
snappy-compression = ["snap"]
zstd-compression = ["zstd"]
failpoints = ["fail/failpoints"]
unstable = [] # useful for benches.
failpoints = ["fail", "fail/failpoints"]
unstable = [] # useful for benches.
quickwit = ["sstable"]
quickwit = ["sstable", "futures-util", "futures-channel"]
# Compares only the hash of a string when indexing data.
# Increases indexing speed, but may lead to extremely rare missing terms, when there's a hash collision.
# Uses 64bit ahash.
compare_hash_only = ["stacker/compare_hash_only"]
[workspace]
members = ["query-grammar", "bitpacker", "common", "fastfield_codecs", "ownedbytes", "stacker", "sstable", "tokenizer-api", "columnar"]
members = [
"query-grammar",
"bitpacker",
"common",
"ownedbytes",
"stacker",
"sstable",
"tokenizer-api",
"columnar",
]
# Following the "fail" crate best practises, we isolate
# tests that define specific behavior in fail check points
@@ -120,7 +151,7 @@ members = ["query-grammar", "bitpacker", "common", "fastfield_codecs", "ownedbyt
[[test]]
name = "failpoints"
path = "tests/failpoints/mod.rs"
required-features = ["fail/failpoints"]
required-features = ["failpoints"]
[[bench]]
name = "analyzer"
@@ -130,3 +161,6 @@ harness = false
name = "index-bench"
harness = false
[[bench]]
name = "agg_bench"
harness = false

2
Makefile
View File

@@ -1,5 +1,5 @@
test:
echo "Run test only... No examples."
@echo "Run test only... No examples."
cargo test --tests --lib
fmt:

42
README.md
View File

@@ -5,28 +5,29 @@
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Crates.io](https://img.shields.io/crates/v/tantivy.svg)](https://crates.io/crates/tantivy)
![Tantivy](https://tantivy-search.github.io/logo/tantivy-logo.png)
<img src="https://tantivy-search.github.io/logo/tantivy-logo.png" alt="Tantivy, the fastest full-text search engine library written in Rust" height="250">
**Tantivy** is a **full-text search engine library** written in Rust.
## Fast full-text search engine library written in Rust
It is closer to [Apache Lucene](https://lucene.apache.org/) than to [Elasticsearch](https://www.elastic.co/products/elasticsearch) or [Apache Solr](https://lucene.apache.org/solr/) in the sense it is not
an off-the-shelf search engine server, but rather a crate that can be used
to build such a search engine.
**If you are looking for an alternative to Elasticsearch or Apache Solr, check out [Quickwit](https://github.com/quickwit-oss/quickwit), our distributed search engine built on top of Tantivy.**
Tantivy is closer to [Apache Lucene](https://lucene.apache.org/) than to [Elasticsearch](https://www.elastic.co/products/elasticsearch) or [Apache Solr](https://lucene.apache.org/solr/) in the sense it is not
an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.
Tantivy is, in fact, strongly inspired by Lucene's design.
If you are looking for an alternative to Elasticsearch or Apache Solr, check out [Quickwit](https://github.com/quickwit-oss/quickwit), our search engine built on top of Tantivy.
## Benchmark
# Benchmark
The following [benchmark](https://tantivy-search.github.io/bench/) breakdowns
The following [benchmark](https://tantivy-search.github.io/bench/) breaks down the
performance for different types of queries/collections.
Your mileage WILL vary depending on the nature of queries and their load.
<img src="doc/assets/images/searchbenchmark.png">
# Features
Details about the benchmark can be found at this [repository](https://github.com/quickwit-oss/search-benchmark-game).
## Features
- Full-text search
- Configurable tokenizer (stemming available for 17 Latin languages) with third party support for Chinese ([tantivy-jieba](https://crates.io/crates/tantivy-jieba) and [cang-jie](https://crates.io/crates/cang-jie)), Japanese ([lindera](https://github.com/lindera-morphology/lindera-tantivy), [Vaporetto](https://crates.io/crates/vaporetto_tantivy), and [tantivy-tokenizer-tiny-segmenter](https://crates.io/crates/tantivy-tokenizer-tiny-segmenter)) and Korean ([lindera](https://github.com/lindera-morphology/lindera-tantivy) + [lindera-ko-dic-builder](https://github.com/lindera-morphology/lindera-ko-dic-builder))
@@ -42,7 +43,7 @@ Your mileage WILL vary depending on the nature of queries and their load.
- Single valued and multivalued u64, i64, and f64 fast fields (equivalent of doc values in Lucene)
- `&[u8]` fast fields
- Text, i64, u64, f64, dates, ip, bool, and hierarchical facet fields
- Compressed document store (LZ4, Zstd, None, Brotli, Snap)
- Compressed document store (LZ4, Zstd, None)
- Range queries
- Faceted search
- Configurable indexing (optional term frequency and position indexing)
@@ -52,11 +53,11 @@ Your mileage WILL vary depending on the nature of queries and their load.
- Searcher Warmer API
- Cheesy logo with a horse
## Non-features
### Non-features
Distributed search is out of the scope of Tantivy, but if you are looking for this feature, check out [Quickwit](https://github.com/quickwit-oss/quickwit/).
# Getting started
## Getting started
Tantivy works on stable Rust and supports Linux, macOS, and Windows.
@@ -66,7 +67,7 @@ index documents, and search via the CLI or a small server with a REST API.
It walks you through getting a Wikipedia search engine up and running in a few minutes.
- [Reference doc for the last released version](https://docs.rs/tantivy/)
# How can I support this project?
## How can I support this project?
There are many ways to support this project.
@@ -77,16 +78,16 @@ There are many ways to support this project.
- Contribute code (you can join [our Discord server](https://discord.gg/MT27AG5EVE))
- Talk about Tantivy around you
# Contributing code
## Contributing code
We use the GitHub Pull Request workflow: reference a GitHub ticket and/or include a comprehensive commit message when opening a PR.
Feel free to update CHANGELOG.md with your contribution.
## Tokenizer
### Tokenizer
When implementing a tokenizer for tantivy depend on the `tantivy-tokenizer-api` crate.
## Clone and build locally
### Clone and build locally
Tantivy compiles on stable Rust.
To check out and run tests, you can simply run:
@@ -97,10 +98,11 @@ cd tantivy
cargo test
```
# Companies Using Tantivy
## Companies Using Tantivy
<p align="left">
<img align="center" src="doc/assets/images/etsy.png" alt="Etsy" height="25" width="auto" />&nbsp;
<img align="center" src="doc/assets/images/etsy.png" alt="Etsy" height="25" width="auto" /> &nbsp;
<img align="center" src="doc/assets/images/paradedb.png" alt="ParadeDB" height="25" width="auto" /> &nbsp;
<img align="center" src="doc/assets/images/Nuclia.png#gh-light-mode-only" alt="Nuclia" height="25" width="auto" /> &nbsp;
<img align="center" src="doc/assets/images/humanfirst.png#gh-light-mode-only" alt="Humanfirst.ai" height="30" width="auto" />
<img align="center" src="doc/assets/images/element.io.svg#gh-light-mode-only" alt="Element.io" height="25" width="auto" />
@@ -109,7 +111,7 @@ cargo test
<img align="center" src="doc/assets/images/element-dark-theme.png#gh-dark-mode-only" alt="Element.io" height="25" width="auto" />
</p>
# FAQ
## FAQ
### Can I use Tantivy in other languages?

21
RELEASE.md Normal file
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@@ -0,0 +1,21 @@
# Release a new Tantivy Version
## Steps
1. Identify new packages in workspace since last release
2. Identify changed packages in workspace since last release
3. Bump version in `Cargo.toml` and their dependents for all changed packages
4. Update version of root `Cargo.toml`
5. Publish version starting with leaf nodes
6. Set git tag with new version
In conjucation with `cargo-release` Steps 1-4 (I'm not sure if the change detection works):
Set new packages to version 0.0.0
Replace prev-tag-name
```bash
cargo release --workspace --no-publish -v --prev-tag-name 0.19 --push-remote origin minor --no-tag --execute
```
no-tag or it will create tags for all the subpackages

18
TODO.txt Normal file
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@@ -0,0 +1,18 @@
Make schema_builder API fluent.
fix doc serialization and prevent compression problems
u64 , etc. should return Result<Option> now that we support optional missing a column is really not an error
remove fastfield codecs
ditch the first_or_default trick. if it is still useful, improve its implementation.
rename FastFieldReaders::open to load
remove fast field reader
find a way to unify the two DateTime.
readd type check in the filter wrapper
add unit test on columnar list columns.
make sure sort works

23
appveyor.yml
View File

@@ -1,23 +0,0 @@
# Appveyor configuration template for Rust using rustup for Rust installation
# https://github.com/starkat99/appveyor-rust
os: Visual Studio 2015
environment:
matrix:
- channel: stable
target: x86_64-pc-windows-msvc
install:
- appveyor DownloadFile https://win.rustup.rs/ -FileName rustup-init.exe
- rustup-init -yv --default-toolchain %channel% --default-host %target%
- set PATH=%PATH%;%USERPROFILE%\.cargo\bin
- if defined msys_bits set PATH=%PATH%;C:\msys64\mingw%msys_bits%\bin
- rustc -vV
- cargo -vV
build: false
test_script:
- REM SET RUST_LOG=tantivy,test & cargo test --all --verbose --no-default-features --features lz4-compression --features mmap
- REM SET RUST_LOG=tantivy,test & cargo test test_store --verbose --no-default-features --features lz4-compression --features snappy-compression --features brotli-compression --features mmap
- REM SET RUST_BACKTRACE=1 & cargo build --examples

462
benches/agg_bench.rs Normal file
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@@ -0,0 +1,462 @@
use binggan::plugins::PeakMemAllocPlugin;
use binggan::{black_box, InputGroup, PeakMemAlloc, INSTRUMENTED_SYSTEM};
use rand::prelude::SliceRandom;
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use rand_distr::Distribution;
use serde_json::json;
use tantivy::aggregation::agg_req::Aggregations;
use tantivy::aggregation::AggregationCollector;
use tantivy::query::{AllQuery, TermQuery};
use tantivy::schema::{IndexRecordOption, Schema, TextFieldIndexing, FAST, STRING};
use tantivy::{doc, Index, Term};
#[global_allocator]
pub static GLOBAL: &PeakMemAlloc<std::alloc::System> = &INSTRUMENTED_SYSTEM;
/// Mini macro to register a function via its name
/// runner.register("average_u64", move |index| average_u64(index));
macro_rules! register {
($runner:expr, $func:ident) => {
$runner.register(stringify!($func), move |index| {
$func(index);
})
};
}
fn main() {
let inputs = vec![
("full", get_test_index_bench(Cardinality::Full).unwrap()),
(
"dense",
get_test_index_bench(Cardinality::OptionalDense).unwrap(),
),
(
"sparse",
get_test_index_bench(Cardinality::OptionalSparse).unwrap(),
),
(
"multivalue",
get_test_index_bench(Cardinality::Multivalued).unwrap(),
),
];
bench_agg(InputGroup::new_with_inputs(inputs));
}
fn bench_agg(mut group: InputGroup<Index>) {
group.add_plugin(PeakMemAllocPlugin::new(GLOBAL));
register!(group, average_u64);
register!(group, average_f64);
register!(group, average_f64_u64);
register!(group, stats_f64);
register!(group, extendedstats_f64);
register!(group, percentiles_f64);
register!(group, terms_few);
register!(group, terms_many);
register!(group, terms_many_top_1000);
register!(group, terms_many_order_by_term);
register!(group, terms_many_with_top_hits);
register!(group, terms_many_with_avg_sub_agg);
register!(group, terms_many_json_mixed_type_with_avg_sub_agg);
register!(group, cardinality_agg);
register!(group, terms_few_with_cardinality_agg);
register!(group, range_agg);
register!(group, range_agg_with_avg_sub_agg);
register!(group, range_agg_with_term_agg_few);
register!(group, range_agg_with_term_agg_many);
register!(group, histogram);
register!(group, histogram_hard_bounds);
register!(group, histogram_with_avg_sub_agg);
register!(group, avg_and_range_with_avg_sub_agg);
group.run();
}
fn exec_term_with_agg(index: &Index, agg_req: serde_json::Value) {
let agg_req: Aggregations = serde_json::from_value(agg_req).unwrap();
let reader = index.reader().unwrap();
let text_field = reader.searcher().schema().get_field("text").unwrap();
let term_query = TermQuery::new(
Term::from_field_text(text_field, "cool"),
IndexRecordOption::Basic,
);
let collector = get_collector(agg_req);
let searcher = reader.searcher();
black_box(searcher.search(&term_query, &collector).unwrap());
}
fn average_u64(index: &Index) {
let agg_req = json!({
"average": { "avg": { "field": "score", } }
});
exec_term_with_agg(index, agg_req)
}
fn average_f64(index: &Index) {
let agg_req = json!({
"average": { "avg": { "field": "score_f64", } }
});
exec_term_with_agg(index, agg_req)
}
fn average_f64_u64(index: &Index) {
let agg_req = json!({
"average_f64": { "avg": { "field": "score_f64" } },
"average": { "avg": { "field": "score" } },
});
exec_term_with_agg(index, agg_req)
}
fn stats_f64(index: &Index) {
let agg_req = json!({
"average_f64": { "stats": { "field": "score_f64", } }
});
exec_term_with_agg(index, agg_req)
}
fn extendedstats_f64(index: &Index) {
let agg_req = json!({
"extendedstats_f64": { "extended_stats": { "field": "score_f64", } }
});
exec_term_with_agg(index, agg_req)
}
fn percentiles_f64(index: &Index) {
let agg_req = json!({
"mypercentiles": {
"percentiles": {
"field": "score_f64",
"percents": [ 95, 99, 99.9 ]
}
}
});
execute_agg(index, agg_req);
}
fn cardinality_agg(index: &Index) {
let agg_req = json!({
"cardinality": {
"cardinality": {
"field": "text_many_terms"
},
}
});
execute_agg(index, agg_req);
}
fn terms_few_with_cardinality_agg(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "text_few_terms" },
"aggs": {
"cardinality": {
"cardinality": {
"field": "text_many_terms"
},
}
}
},
});
execute_agg(index, agg_req);
}
fn terms_few(index: &Index) {
let agg_req = json!({
"my_texts": { "terms": { "field": "text_few_terms" } },
});
execute_agg(index, agg_req);
}
fn terms_many(index: &Index) {
let agg_req = json!({
"my_texts": { "terms": { "field": "text_many_terms" } },
});
execute_agg(index, agg_req);
}
fn terms_many_top_1000(index: &Index) {
let agg_req = json!({
"my_texts": { "terms": { "field": "text_many_terms", "size": 1000 } },
});
execute_agg(index, agg_req);
}
fn terms_many_order_by_term(index: &Index) {
let agg_req = json!({
"my_texts": { "terms": { "field": "text_many_terms", "order": { "_key": "desc" } } },
});
execute_agg(index, agg_req);
}
fn terms_many_with_top_hits(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "text_many_terms" },
"aggs": {
"top_hits": { "top_hits":
{
"sort": [
{ "score": "desc" }
],
"size": 2,
"doc_value_fields": ["score_f64"]
}
}
}
},
});
execute_agg(index, agg_req);
}
fn terms_many_with_avg_sub_agg(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "text_many_terms" },
"aggs": {
"average_f64": { "avg": { "field": "score_f64" } }
}
},
});
execute_agg(index, agg_req);
}
fn terms_many_json_mixed_type_with_avg_sub_agg(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "json.mixed_type" },
"aggs": {
"average_f64": { "avg": { "field": "score_f64" } }
}
},
});
execute_agg(index, agg_req);
}
fn execute_agg(index: &Index, agg_req: serde_json::Value) {
let agg_req: Aggregations = serde_json::from_value(agg_req).unwrap();
let collector = get_collector(agg_req);
let reader = index.reader().unwrap();
let searcher = reader.searcher();
black_box(searcher.search(&AllQuery, &collector).unwrap());
}
fn range_agg(index: &Index) {
let agg_req = json!({
"range_f64": { "range": { "field": "score_f64", "ranges": [
{ "from": 3, "to": 7000 },
{ "from": 7000, "to": 20000 },
{ "from": 20000, "to": 30000 },
{ "from": 30000, "to": 40000 },
{ "from": 40000, "to": 50000 },
{ "from": 50000, "to": 60000 }
] } },
});
execute_agg(index, agg_req);
}
fn range_agg_with_avg_sub_agg(index: &Index) {
let agg_req = json!({
"rangef64": {
"range": {
"field": "score_f64",
"ranges": [
{ "from": 3, "to": 7000 },
{ "from": 7000, "to": 20000 },
{ "from": 20000, "to": 30000 },
{ "from": 30000, "to": 40000 },
{ "from": 40000, "to": 50000 },
{ "from": 50000, "to": 60000 }
]
},
"aggs": {
"average_f64": { "avg": { "field": "score_f64" } }
}
},
});
execute_agg(index, agg_req);
}
fn range_agg_with_term_agg_few(index: &Index) {
let agg_req = json!({
"rangef64": {
"range": {
"field": "score_f64",
"ranges": [
{ "from": 3, "to": 7000 },
{ "from": 7000, "to": 20000 },
{ "from": 20000, "to": 30000 },
{ "from": 30000, "to": 40000 },
{ "from": 40000, "to": 50000 },
{ "from": 50000, "to": 60000 }
]
},
"aggs": {
"my_texts": { "terms": { "field": "text_few_terms" } },
}
},
});
execute_agg(index, agg_req);
}
fn range_agg_with_term_agg_many(index: &Index) {
let agg_req = json!({
"rangef64": {
"range": {
"field": "score_f64",
"ranges": [
{ "from": 3, "to": 7000 },
{ "from": 7000, "to": 20000 },
{ "from": 20000, "to": 30000 },
{ "from": 30000, "to": 40000 },
{ "from": 40000, "to": 50000 },
{ "from": 50000, "to": 60000 }
]
},
"aggs": {
"my_texts": { "terms": { "field": "text_many_terms" } },
}
},
});
execute_agg(index, agg_req);
}
fn histogram(index: &Index) {
let agg_req = json!({
"rangef64": {
"histogram": {
"field": "score_f64",
"interval": 100 // 1000 buckets
},
}
});
execute_agg(index, agg_req);
}
fn histogram_hard_bounds(index: &Index) {
let agg_req = json!({
"rangef64": { "histogram": { "field": "score_f64", "interval": 100, "hard_bounds": { "min": 1000, "max": 300000 } } },
});
execute_agg(index, agg_req);
}
fn histogram_with_avg_sub_agg(index: &Index) {
let agg_req = json!({
"rangef64": {
"histogram": { "field": "score_f64", "interval": 100 },
"aggs": {
"average_f64": { "avg": { "field": "score_f64" } }
}
}
});
execute_agg(index, agg_req);
}
fn avg_and_range_with_avg_sub_agg(index: &Index) {
let agg_req = json!({
"rangef64": {
"range": {
"field": "score_f64",
"ranges": [
{ "from": 3, "to": 7000 },
{ "from": 7000, "to": 20000 },
{ "from": 20000, "to": 60000 }
]
},
"aggs": {
"average_in_range": { "avg": { "field": "score" } }
}
},
"average": { "avg": { "field": "score" } }
});
execute_agg(index, agg_req);
}
#[derive(Clone, Copy, Hash, Default, Debug, PartialEq, Eq, PartialOrd, Ord)]
enum Cardinality {
/// All documents contain exactly one value.
/// `Full` is the default for auto-detecting the Cardinality, since it is the most strict.
#[default]
Full = 0,
/// All documents contain at most one value.
OptionalDense = 1,
/// All documents may contain any number of values.
Multivalued = 2,
/// 1 / 20 documents has a value
OptionalSparse = 3,
}
fn get_collector(agg_req: Aggregations) -> AggregationCollector {
AggregationCollector::from_aggs(agg_req, Default::default())
}
fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
let mut schema_builder = Schema::builder();
let text_fieldtype = tantivy::schema::TextOptions::default()
.set_indexing_options(
TextFieldIndexing::default().set_index_option(IndexRecordOption::WithFreqs),
)
.set_stored();
let text_field = schema_builder.add_text_field("text", text_fieldtype);
let json_field = schema_builder.add_json_field("json", FAST);
let text_field_many_terms = schema_builder.add_text_field("text_many_terms", STRING | FAST);
let text_field_few_terms = schema_builder.add_text_field("text_few_terms", STRING | FAST);
let score_fieldtype = tantivy::schema::NumericOptions::default().set_fast();
let score_field = schema_builder.add_u64_field("score", score_fieldtype.clone());
let score_field_f64 = schema_builder.add_f64_field("score_f64", score_fieldtype.clone());
let score_field_i64 = schema_builder.add_i64_field("score_i64", score_fieldtype);
let index = Index::create_from_tempdir(schema_builder.build())?;
let few_terms_data = ["INFO", "ERROR", "WARN", "DEBUG"];
let lg_norm = rand_distr::LogNormal::new(2.996f64, 0.979f64).unwrap();
let many_terms_data = (0..150_000)
.map(|num| format!("author{num}"))
.collect::<Vec<_>>();
{
let mut rng = StdRng::from_seed([1u8; 32]);
let mut index_writer = index.writer_with_num_threads(1, 200_000_000)?;
// To make the different test cases comparable we just change one doc to force the
// cardinality
if cardinality == Cardinality::OptionalDense {
index_writer.add_document(doc!())?;
}
if cardinality == Cardinality::Multivalued {
index_writer.add_document(doc!(
json_field => json!({"mixed_type": 10.0}),
json_field => json!({"mixed_type": 10.0}),
text_field => "cool",
text_field => "cool",
text_field_many_terms => "cool",
text_field_many_terms => "cool",
text_field_few_terms => "cool",
text_field_few_terms => "cool",
score_field => 1u64,
score_field => 1u64,
score_field_f64 => lg_norm.sample(&mut rng),
score_field_f64 => lg_norm.sample(&mut rng),
score_field_i64 => 1i64,
score_field_i64 => 1i64,
))?;
}
let mut doc_with_value = 1_000_000;
if cardinality == Cardinality::OptionalSparse {
doc_with_value /= 20;
}
let _val_max = 1_000_000.0;
for _ in 0..doc_with_value {
let val: f64 = rng.gen_range(0.0..1_000_000.0);
let json = if rng.gen_bool(0.1) {
// 10% are numeric values
json!({ "mixed_type": val })
} else {
json!({"mixed_type": many_terms_data.choose(&mut rng).unwrap().to_string()})
};
index_writer.add_document(doc!(
text_field => "cool",
json_field => json,
text_field_many_terms => many_terms_data.choose(&mut rng).unwrap().to_string(),
text_field_few_terms => few_terms_data.choose(&mut rng).unwrap().to_string(),
score_field => val as u64,
score_field_f64 => lg_norm.sample(&mut rng),
score_field_i64 => val as i64,
))?;
if cardinality == Cardinality::OptionalSparse {
for _ in 0..20 {
index_writer.add_document(doc!(text_field => "cool"))?;
}
}
}
// writing the segment
index_writer.commit()?;
}
Ok(index)
}

27
benches/analyzer.rs
View File

@@ -1,11 +1,13 @@
use criterion::{criterion_group, criterion_main, Criterion};
use tantivy::tokenizer::TokenizerManager;
use tantivy::tokenizer::{
LowerCaser, RemoveLongFilter, SimpleTokenizer, TextAnalyzer, TokenizerManager,
};
const ALICE_TXT: &str = include_str!("alice.txt");
pub fn criterion_benchmark(c: &mut Criterion) {
let tokenizer_manager = TokenizerManager::default();
let tokenizer = tokenizer_manager.get("default").unwrap();
let mut tokenizer = tokenizer_manager.get("default").unwrap();
c.bench_function("default-tokenize-alice", |b| {
b.iter(|| {
let mut word_count = 0;
@@ -16,7 +18,26 @@ pub fn criterion_benchmark(c: &mut Criterion) {
assert_eq!(word_count, 30_731);
})
});
let mut dynamic_analyzer = TextAnalyzer::builder(SimpleTokenizer::default())
.dynamic()
.filter_dynamic(RemoveLongFilter::limit(40))
.filter_dynamic(LowerCaser)
.build();
c.bench_function("dynamic-tokenize-alice", |b| {
b.iter(|| {
let mut word_count = 0;
let mut token_stream = dynamic_analyzer.token_stream(ALICE_TXT);
while token_stream.advance() {
word_count += 1;
}
assert_eq!(word_count, 30_731);
})
});
}
criterion_group!(benches, criterion_benchmark);
criterion_group! {
name = benches;
config = Criterion::default().sample_size(200);
targets = criterion_benchmark
}
criterion_main!(benches);

1000
benches/gh.json Normal file
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267
benches/index-bench.rs
View File

@@ -1,10 +1,99 @@
use criterion::{criterion_group, criterion_main, Criterion};
use pprof::criterion::{Output, PProfProfiler};
use tantivy::schema::{INDEXED, STORED, STRING, TEXT};
use tantivy::Index;
use criterion::{criterion_group, criterion_main, BatchSize, Bencher, Criterion, Throughput};
use tantivy::schema::{TantivyDocument, FAST, INDEXED, STORED, STRING, TEXT};
use tantivy::{tokenizer, Index, IndexWriter};
const HDFS_LOGS: &str = include_str!("hdfs.json");
const NUM_REPEATS: usize = 2;
const GH_LOGS: &str = include_str!("gh.json");
const WIKI: &str = include_str!("wiki.json");
fn benchmark(
b: &mut Bencher,
input: &str,
schema: tantivy::schema::Schema,
commit: bool,
parse_json: bool,
is_dynamic: bool,
) {
if is_dynamic {
benchmark_dynamic_json(b, input, schema, commit, parse_json)
} else {
_benchmark(b, input, schema, commit, parse_json, |schema, doc_json| {
TantivyDocument::parse_json(schema, doc_json).unwrap()
})
}
}
fn get_index(schema: tantivy::schema::Schema) -> Index {
let mut index = Index::create_in_ram(schema.clone());
let ff_tokenizer_manager = tokenizer::TokenizerManager::default();
ff_tokenizer_manager.register(
"raw",
tokenizer::TextAnalyzer::builder(tokenizer::RawTokenizer::default())
.filter(tokenizer::RemoveLongFilter::limit(255))
.build(),
);
index.set_fast_field_tokenizers(ff_tokenizer_manager.clone());
index
}
fn _benchmark(
b: &mut Bencher,
input: &str,
schema: tantivy::schema::Schema,
commit: bool,
include_json_parsing: bool,
create_doc: impl Fn(&tantivy::schema::Schema, &str) -> TantivyDocument,
) {
if include_json_parsing {
let lines: Vec<&str> = input.trim().split('\n').collect();
b.iter(|| {
let index = get_index(schema.clone());
let mut index_writer: IndexWriter =
index.writer_with_num_threads(1, 100_000_000).unwrap();
for doc_json in &lines {
let doc = create_doc(&schema, doc_json);
index_writer.add_document(doc).unwrap();
}
if commit {
index_writer.commit().unwrap();
}
})
} else {
let docs: Vec<_> = input
.trim()
.split('\n')
.map(|doc_json| create_doc(&schema, doc_json))
.collect();
b.iter_batched(
|| docs.clone(),
|docs| {
let index = get_index(schema.clone());
let mut index_writer: IndexWriter =
index.writer_with_num_threads(1, 100_000_000).unwrap();
for doc in docs {
index_writer.add_document(doc).unwrap();
}
if commit {
index_writer.commit().unwrap();
}
},
BatchSize::SmallInput,
)
}
}
fn benchmark_dynamic_json(
b: &mut Bencher,
input: &str,
schema: tantivy::schema::Schema,
commit: bool,
parse_json: bool,
) {
let json_field = schema.get_field("json").unwrap();
_benchmark(b, input, schema, commit, parse_json, |_schema, doc_json| {
let json_val: serde_json::Value = serde_json::from_str(doc_json).unwrap();
tantivy::doc!(json_field=>json_val)
})
}
pub fn hdfs_index_benchmark(c: &mut Criterion) {
let schema = {
@@ -14,7 +103,14 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
schema_builder.add_text_field("severity", STRING);
schema_builder.build()
};
let schema_with_store = {
let schema_only_fast = {
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
schema_builder.add_u64_field("timestamp", FAST);
schema_builder.add_text_field("body", FAST);
schema_builder.add_text_field("severity", FAST);
schema_builder.build()
};
let _schema_with_store = {
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
schema_builder.add_u64_field("timestamp", INDEXED | STORED);
schema_builder.add_text_field("body", TEXT | STORED);
@@ -23,99 +119,100 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
};
let dynamic_schema = {
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
schema_builder.add_json_field("json", TEXT);
schema_builder.add_json_field("json", TEXT | FAST);
schema_builder.build()
};
let mut group = c.benchmark_group("index-hdfs");
group.throughput(Throughput::Bytes(HDFS_LOGS.len() as u64));
group.sample_size(20);
group.bench_function("index-hdfs-no-commit", |b| {
b.iter(|| {
let index = Index::create_in_ram(schema.clone());
let index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
let benches = [
("only-indexed-".to_string(), schema, false),
//("stored-".to_string(), _schema_with_store, false),
("only-fast-".to_string(), schema_only_fast, false),
("dynamic-".to_string(), dynamic_schema, true),
];
for (prefix, schema, is_dynamic) in benches {
for commit in [false, true] {
let suffix = if commit { "with-commit" } else { "no-commit" };
{
let parse_json = false;
// for parse_json in [false, true] {
let suffix = if parse_json {
format!("{suffix}-with-json-parsing")
} else {
suffix.to_string()
};
let bench_name = format!("{prefix}{suffix}");
group.bench_function(bench_name, |b| {
benchmark(b, HDFS_LOGS, schema.clone(), commit, parse_json, is_dynamic)
});
}
})
}
}
}
pub fn gh_index_benchmark(c: &mut Criterion) {
let dynamic_schema = {
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
schema_builder.add_json_field("json", TEXT | FAST);
schema_builder.build()
};
let dynamic_schema_fast = {
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
schema_builder.add_json_field("json", FAST);
schema_builder.build()
};
let mut group = c.benchmark_group("index-gh");
group.throughput(Throughput::Bytes(GH_LOGS.len() as u64));
group.bench_function("index-gh-no-commit", |b| {
benchmark_dynamic_json(b, GH_LOGS, dynamic_schema.clone(), false, false)
});
group.bench_function("index-hdfs-with-commit", |b| {
b.iter(|| {
let index = Index::create_in_ram(schema.clone());
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
}
index_writer.commit().unwrap();
})
group.bench_function("index-gh-fast", |b| {
benchmark_dynamic_json(b, GH_LOGS, dynamic_schema_fast.clone(), false, false)
});
group.bench_function("index-hdfs-no-commit-with-docstore", |b| {
b.iter(|| {
let index = Index::create_in_ram(schema_with_store.clone());
let index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
}
})
group.bench_function("index-gh-fast-with-commit", |b| {
benchmark_dynamic_json(b, GH_LOGS, dynamic_schema_fast.clone(), true, false)
});
group.bench_function("index-hdfs-with-commit-with-docstore", |b| {
b.iter(|| {
let index = Index::create_in_ram(schema_with_store.clone());
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
}
index_writer.commit().unwrap();
})
}
pub fn wiki_index_benchmark(c: &mut Criterion) {
let dynamic_schema = {
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
schema_builder.add_json_field("json", TEXT | FAST);
schema_builder.build()
};
let mut group = c.benchmark_group("index-wiki");
group.throughput(Throughput::Bytes(WIKI.len() as u64));
group.bench_function("index-wiki-no-commit", |b| {
benchmark_dynamic_json(b, WIKI, dynamic_schema.clone(), false, false)
});
group.bench_function("index-hdfs-no-commit-json-without-docstore", |b| {
b.iter(|| {
let index = Index::create_in_ram(dynamic_schema.clone());
let json_field = dynamic_schema.get_field("json").unwrap();
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
let json_val: serde_json::Map<String, serde_json::Value> =
serde_json::from_str(doc_json).unwrap();
let doc = tantivy::doc!(json_field=>json_val);
index_writer.add_document(doc).unwrap();
}
}
index_writer.commit().unwrap();
})
});
group.bench_function("index-hdfs-with-commit-json-without-docstore", |b| {
b.iter(|| {
let index = Index::create_in_ram(dynamic_schema.clone());
let json_field = dynamic_schema.get_field("json").unwrap();
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
let json_val: serde_json::Map<String, serde_json::Value> =
serde_json::from_str(doc_json).unwrap();
let doc = tantivy::doc!(json_field=>json_val);
index_writer.add_document(doc).unwrap();
}
}
index_writer.commit().unwrap();
})
group.bench_function("index-wiki-with-commit", |b| {
benchmark_dynamic_json(b, WIKI, dynamic_schema.clone(), true, false)
});
}
criterion_group! {
name = benches;
config = Criterion::default().with_profiler(PProfProfiler::new(100, Output::Flamegraph(None)));
config = Criterion::default();
targets = hdfs_index_benchmark
}
criterion_main!(benches);
criterion_group! {
name = gh_benches;
config = Criterion::default();
targets = gh_index_benchmark
}
criterion_group! {
name = wiki_benches;
config = Criterion::default();
targets = wiki_index_benchmark
}
criterion_main!(benches, gh_benches, wiki_benches);

1000
benches/wiki.json Normal file
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3
bitpacker/Cargo.toml
View File

@@ -1,6 +1,6 @@
[package]
name = "tantivy-bitpacker"
version = "0.3.0"
version = "0.6.0"
edition = "2021"
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
license = "MIT"
@@ -15,6 +15,7 @@ homepage = "https://github.com/quickwit-oss/tantivy"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]
bitpacking = { version = "0.9.2", default-features = false, features = ["bitpacker1x"] }
[dev-dependencies]
rand = "0.8"

190
bitpacker/src/bitpacker.rs
View File

@@ -1,10 +1,13 @@
use std::convert::TryInto;
use std::io;
use std::ops::{Range, RangeInclusive};
use bitpacking::{BitPacker as ExternalBitPackerTrait, BitPacker1x};
pub struct BitPacker {
mini_buffer: u64,
mini_buffer_written: usize,
}
impl Default for BitPacker {
fn default() -> Self {
BitPacker::new()
@@ -19,7 +22,7 @@ impl BitPacker {
}
#[inline]
pub fn write<TWrite: io::Write>(
pub fn write<TWrite: io::Write + ?Sized>(
&mut self,
val: u64,
num_bits: u8,
@@ -43,7 +46,7 @@ impl BitPacker {
Ok(())
}
pub fn flush<TWrite: io::Write>(&mut self, output: &mut TWrite) -> io::Result<()> {
pub fn flush<TWrite: io::Write + ?Sized>(&mut self, output: &mut TWrite) -> io::Result<()> {
if self.mini_buffer_written > 0 {
let num_bytes = (self.mini_buffer_written + 7) / 8;
let bytes = self.mini_buffer.to_le_bytes();
@@ -54,7 +57,7 @@ impl BitPacker {
Ok(())
}
pub fn close<TWrite: io::Write>(&mut self, output: &mut TWrite) -> io::Result<()> {
pub fn close<TWrite: io::Write + ?Sized>(&mut self, output: &mut TWrite) -> io::Result<()> {
self.flush(output)?;
Ok(())
}
@@ -91,14 +94,14 @@ impl BitUnpacker {
#[inline]
pub fn get(&self, idx: u32, data: &[u8]) -> u64 {
let addr_in_bits = idx * self.num_bits;
let addr = (addr_in_bits >> 3) as usize;
let addr_in_bits = idx as usize * self.num_bits as usize;
let addr = addr_in_bits >> 3;
if addr + 8 > data.len() {
if self.num_bits == 0 {
return 0;
}
let bit_shift = addr_in_bits & 7;
return self.get_slow_path(addr, bit_shift, data);
return self.get_slow_path(addr, bit_shift as u32, data);
}
let bit_shift = addr_in_bits & 7;
let bytes: [u8; 8] = (&data[addr..addr + 8]).try_into().unwrap();
@@ -118,6 +121,125 @@ impl BitUnpacker {
let val_shifted = val_unshifted_unmasked >> bit_shift;
val_shifted & self.mask
}
// Decodes the range of bitpacked `u32` values with idx
// in [start_idx, start_idx + output.len()).
//
// #Panics
//
// This methods panics if `num_bits` is > 32.
fn get_batch_u32s(&self, start_idx: u32, data: &[u8], output: &mut [u32]) {
assert!(
self.bit_width() <= 32,
"Bitwidth must be <= 32 to use this method."
);
let end_idx = start_idx + output.len() as u32;
let end_bit_read = end_idx * self.num_bits;
let end_byte_read = (end_bit_read + 7) / 8;
assert!(
end_byte_read as usize <= data.len(),
"Requested index is out of bounds."
);
// Simple slow implementation of get_batch_u32s, to deal with our ramps.
let get_batch_ramp = |start_idx: u32, output: &mut [u32]| {
for (out, idx) in output.iter_mut().zip(start_idx..) {
*out = self.get(idx, data) as u32;
}
};
// We use an unrolled routine to decode 32 values at once.
// We therefore decompose our range of values to decode into three ranges:
// - Entrance ramp: [start_idx, fast_track_start) (up to 31 values)
// - Highway: [fast_track_start, fast_track_end) (a length multiple of 32s)
// - Exit ramp: [fast_track_end, start_idx + output.len()) (up to 31 values)
// We want the start of the fast track to start align with bytes.
// A sufficient condition is to start with an idx that is a multiple of 8,
// so highway start is the closest multiple of 8 that is >= start_idx.
let entrance_ramp_len = 8 - (start_idx % 8) % 8;
let highway_start: u32 = start_idx + entrance_ramp_len;
if highway_start + BitPacker1x::BLOCK_LEN as u32 > end_idx {
// We don't have enough values to have even a single block of highway.
// Let's just supply the values the simple way.
get_batch_ramp(start_idx, output);
return;
}
let num_blocks: u32 = (end_idx - highway_start) / BitPacker1x::BLOCK_LEN as u32;
// Entrance ramp
get_batch_ramp(start_idx, &mut output[..entrance_ramp_len as usize]);
// Highway
let mut offset = (highway_start * self.num_bits) as usize / 8;
let mut output_cursor = (highway_start - start_idx) as usize;
for _ in 0..num_blocks {
offset += BitPacker1x.decompress(
&data[offset..],
&mut output[output_cursor..],
self.num_bits as u8,
);
output_cursor += 32;
}
// Exit ramp
let highway_end = highway_start + num_blocks * BitPacker1x::BLOCK_LEN as u32;
get_batch_ramp(highway_end, &mut output[output_cursor..]);
}
pub fn get_ids_for_value_range(
&self,
range: RangeInclusive<u64>,
id_range: Range<u32>,
data: &[u8],
positions: &mut Vec<u32>,
) {
if self.bit_width() > 32 {
self.get_ids_for_value_range_slow(range, id_range, data, positions)
} else {
if *range.start() > u32::MAX as u64 {
positions.clear();
return;
}
let range_u32 = (*range.start() as u32)..=(*range.end()).min(u32::MAX as u64) as u32;
self.get_ids_for_value_range_fast(range_u32, id_range, data, positions)
}
}
fn get_ids_for_value_range_slow(
&self,
range: RangeInclusive<u64>,
id_range: Range<u32>,
data: &[u8],
positions: &mut Vec<u32>,
) {
positions.clear();
for i in id_range {
// If we cared we could make this branchless, but the slow implementation should rarely
// kick in.
let val = self.get(i, data);
if range.contains(&val) {
positions.push(i);
}
}
}
fn get_ids_for_value_range_fast(
&self,
value_range: RangeInclusive<u32>,
id_range: Range<u32>,
data: &[u8],
positions: &mut Vec<u32>,
) {
positions.resize(id_range.len(), 0u32);
self.get_batch_u32s(id_range.start, data, positions);
crate::filter_vec::filter_vec_in_place(value_range, id_range.start, positions)
}
}
#[cfg(test)]
@@ -200,4 +322,58 @@ mod test {
test_bitpacker_aux(num_bits, &vals);
}
}
#[test]
#[should_panic]
fn test_get_batch_panics_over_32_bits() {
let bitunpacker = BitUnpacker::new(33);
let mut output: [u32; 1] = [0u32];
bitunpacker.get_batch_u32s(0, &[0, 0, 0, 0, 0, 0, 0, 0], &mut output[..]);
}
#[test]
fn test_get_batch_limit() {
let bitunpacker = BitUnpacker::new(1);
let mut output: [u32; 3] = [0u32, 0u32, 0u32];
bitunpacker.get_batch_u32s(8 * 4 - 3, &[0u8, 0u8, 0u8, 0u8], &mut output[..]);
}
#[test]
#[should_panic]
fn test_get_batch_panics_when_off_scope() {
let bitunpacker = BitUnpacker::new(1);
let mut output: [u32; 3] = [0u32, 0u32, 0u32];
// We are missing exactly one bit.
bitunpacker.get_batch_u32s(8 * 4 - 2, &[0u8, 0u8, 0u8, 0u8], &mut output[..]);
}
proptest::proptest! {
#[test]
fn test_get_batch_u32s_proptest(num_bits in 0u8..=32u8) {
let mask =
if num_bits == 32u8 {
u32::MAX
} else {
(1u32 << num_bits) - 1
};
let mut buffer: Vec<u8> = Vec::new();
let mut bitpacker = BitPacker::new();
for val in 0..100 {
bitpacker.write(val & mask as u64, num_bits, &mut buffer).unwrap();
}
bitpacker.flush(&mut buffer).unwrap();
let bitunpacker = BitUnpacker::new(num_bits);
let mut output: Vec<u32> = Vec::new();
for len in [0, 1, 2, 32, 33, 34, 64] {
for start_idx in 0u32..32u32 {
output.resize(len, 0);
bitunpacker.get_batch_u32s(start_idx, &buffer, &mut output);
for (i, output_byte) in output.iter().enumerate() {
let expected = (start_idx + i as u32) & mask;
assert_eq!(*output_byte, expected);
}
}
}
}
}
}

4
bitpacker/src/blocked_bitpacker.rs
View File

@@ -64,10 +64,8 @@ fn mem_usage<T>(items: &Vec<T>) -> usize {
impl BlockedBitpacker {
pub fn new() -> Self {
let mut compressed_blocks = vec![];
compressed_blocks.resize(8, 0);
Self {
compressed_blocks,
compressed_blocks: vec![0; 8],
buffer: vec![],
offset_and_bits: vec![],
}

365
bitpacker/src/filter_vec/avx2.rs Normal file
View File

@@ -0,0 +1,365 @@
//! SIMD filtering of a vector as described in the following blog post.
//! <https://quickwit.io/blog/filtering%20a%20vector%20with%20simd%20instructions%20avx-2%20and%20avx-512>
use std::arch::x86_64::{
__m256i as DataType, _mm256_add_epi32 as op_add, _mm256_cmpgt_epi32 as op_greater,
_mm256_lddqu_si256 as load_unaligned, _mm256_or_si256 as op_or, _mm256_set1_epi32 as set1,
_mm256_storeu_si256 as store_unaligned, _mm256_xor_si256 as op_xor, *,
};
use std::ops::RangeInclusive;
const NUM_LANES: usize = 8;
const HIGHEST_BIT: u32 = 1 << 31;
#[inline]
fn u32_to_i32(val: u32) -> i32 {
(val ^ HIGHEST_BIT) as i32
}
#[inline]
unsafe fn u32_to_i32_avx2(vals_u32x8s: DataType) -> DataType {
const HIGHEST_BIT_MASK: DataType = from_u32x8([HIGHEST_BIT; NUM_LANES]);
op_xor(vals_u32x8s, HIGHEST_BIT_MASK)
}
pub fn filter_vec_in_place(range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
// We use a monotonic mapping from u32 to i32 to make the comparison possible in AVX2.
let range_i32: RangeInclusive<i32> = u32_to_i32(*range.start())..=u32_to_i32(*range.end());
let num_words = output.len() / NUM_LANES;
let mut output_len = unsafe {
filter_vec_avx2_aux(
output.as_ptr() as *const __m256i,
range_i32,
output.as_mut_ptr(),
offset,
num_words,
)
};
let reminder_start = num_words * NUM_LANES;
for i in reminder_start..output.len() {
let val = output[i];
output[output_len] = offset + i as u32;
output_len += if range.contains(&val) { 1 } else { 0 };
}
output.truncate(output_len);
}
#[target_feature(enable = "avx2")]
unsafe fn filter_vec_avx2_aux(
mut input: *const __m256i,
range: RangeInclusive<i32>,
output: *mut u32,
offset: u32,
num_words: usize,
) -> usize {
let mut output_tail = output;
let range_simd = set1(*range.start())..=set1(*range.end());
let mut ids = from_u32x8([
offset,
offset + 1,
offset + 2,
offset + 3,
offset + 4,
offset + 5,
offset + 6,
offset + 7,
]);
const SHIFT: __m256i = from_u32x8([NUM_LANES as u32; NUM_LANES]);
for _ in 0..num_words {
let word = load_unaligned(input);
let word = u32_to_i32_avx2(word);
let keeper_bitset = compute_filter_bitset(word, range_simd.clone());
let added_len = keeper_bitset.count_ones();
let filtered_doc_ids = compact(ids, keeper_bitset);
store_unaligned(output_tail as *mut __m256i, filtered_doc_ids);
output_tail = output_tail.offset(added_len as isize);
ids = op_add(ids, SHIFT);
input = input.offset(1);
}
output_tail.offset_from(output) as usize
}
#[inline]
#[target_feature(enable = "avx2")]
unsafe fn compact(data: DataType, mask: u8) -> DataType {
let vperm_mask = MASK_TO_PERMUTATION[mask as usize];
_mm256_permutevar8x32_epi32(data, vperm_mask)
}
#[inline]
#[target_feature(enable = "avx2")]
unsafe fn compute_filter_bitset(val: __m256i, range: std::ops::RangeInclusive<__m256i>) -> u8 {
let too_low = op_greater(*range.start(), val);
let too_high = op_greater(val, *range.end());
let inside = op_or(too_low, too_high);
255 - std::arch::x86_64::_mm256_movemask_ps(std::mem::transmute::<DataType, __m256>(inside))
as u8
}
union U8x32 {
vector: DataType,
vals: [u32; NUM_LANES],
}
const fn from_u32x8(vals: [u32; NUM_LANES]) -> DataType {
unsafe { U8x32 { vals }.vector }
}
const MASK_TO_PERMUTATION: [DataType; 256] = [
from_u32x8([0, 0, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 0, 0, 0, 0, 0, 0, 0]),
from_u32x8([1, 0, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 0, 0, 0, 0, 0, 0]),
from_u32x8([2, 0, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 0, 0, 0, 0, 0, 0]),
from_u32x8([1, 2, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 0, 0, 0, 0, 0]),
from_u32x8([3, 0, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 0, 0, 0, 0, 0, 0]),
from_u32x8([1, 3, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 0, 0, 0, 0, 0]),
from_u32x8([2, 3, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 0, 0, 0, 0, 0]),
from_u32x8([1, 2, 3, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 0, 0, 0, 0]),
from_u32x8([4, 0, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 4, 0, 0, 0, 0, 0, 0]),
from_u32x8([1, 4, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 4, 0, 0, 0, 0, 0]),
from_u32x8([2, 4, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 4, 0, 0, 0, 0, 0]),
from_u32x8([1, 2, 4, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 4, 0, 0, 0, 0]),
from_u32x8([3, 4, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 4, 0, 0, 0, 0, 0]),
from_u32x8([1, 3, 4, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 4, 0, 0, 0, 0]),
from_u32x8([2, 3, 4, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 4, 0, 0, 0, 0]),
from_u32x8([1, 2, 3, 4, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 4, 0, 0, 0]),
from_u32x8([5, 0, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 5, 0, 0, 0, 0, 0, 0]),
from_u32x8([1, 5, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 5, 0, 0, 0, 0, 0]),
from_u32x8([2, 5, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 5, 0, 0, 0, 0, 0]),
from_u32x8([1, 2, 5, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 5, 0, 0, 0, 0]),
from_u32x8([3, 5, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 5, 0, 0, 0, 0, 0]),
from_u32x8([1, 3, 5, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 5, 0, 0, 0, 0]),
from_u32x8([2, 3, 5, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 5, 0, 0, 0, 0]),
from_u32x8([1, 2, 3, 5, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 5, 0, 0, 0]),
from_u32x8([4, 5, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 4, 5, 0, 0, 0, 0, 0]),
from_u32x8([1, 4, 5, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 4, 5, 0, 0, 0, 0]),
from_u32x8([2, 4, 5, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 4, 5, 0, 0, 0, 0]),
from_u32x8([1, 2, 4, 5, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 4, 5, 0, 0, 0]),
from_u32x8([3, 4, 5, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 4, 5, 0, 0, 0, 0]),
from_u32x8([1, 3, 4, 5, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 4, 5, 0, 0, 0]),
from_u32x8([2, 3, 4, 5, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 4, 5, 0, 0, 0]),
from_u32x8([1, 2, 3, 4, 5, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 4, 5, 0, 0]),
from_u32x8([6, 0, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 6, 0, 0, 0, 0, 0, 0]),
from_u32x8([1, 6, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 6, 0, 0, 0, 0, 0]),
from_u32x8([2, 6, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 6, 0, 0, 0, 0, 0]),
from_u32x8([1, 2, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 6, 0, 0, 0, 0]),
from_u32x8([3, 6, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 6, 0, 0, 0, 0, 0]),
from_u32x8([1, 3, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 6, 0, 0, 0, 0]),
from_u32x8([2, 3, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 6, 0, 0, 0, 0]),
from_u32x8([1, 2, 3, 6, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 6, 0, 0, 0]),
from_u32x8([4, 6, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 4, 6, 0, 0, 0, 0, 0]),
from_u32x8([1, 4, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 4, 6, 0, 0, 0, 0]),
from_u32x8([2, 4, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 4, 6, 0, 0, 0, 0]),
from_u32x8([1, 2, 4, 6, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 4, 6, 0, 0, 0]),
from_u32x8([3, 4, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 4, 6, 0, 0, 0, 0]),
from_u32x8([1, 3, 4, 6, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 4, 6, 0, 0, 0]),
from_u32x8([2, 3, 4, 6, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 4, 6, 0, 0, 0]),
from_u32x8([1, 2, 3, 4, 6, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 4, 6, 0, 0]),
from_u32x8([5, 6, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 5, 6, 0, 0, 0, 0, 0]),
from_u32x8([1, 5, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 5, 6, 0, 0, 0, 0]),
from_u32x8([2, 5, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 5, 6, 0, 0, 0, 0]),
from_u32x8([1, 2, 5, 6, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 5, 6, 0, 0, 0]),
from_u32x8([3, 5, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 5, 6, 0, 0, 0, 0]),
from_u32x8([1, 3, 5, 6, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 5, 6, 0, 0, 0]),
from_u32x8([2, 3, 5, 6, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 5, 6, 0, 0, 0]),
from_u32x8([1, 2, 3, 5, 6, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 5, 6, 0, 0]),
from_u32x8([4, 5, 6, 0, 0, 0, 0, 0]),
from_u32x8([0, 4, 5, 6, 0, 0, 0, 0]),
from_u32x8([1, 4, 5, 6, 0, 0, 0, 0]),
from_u32x8([0, 1, 4, 5, 6, 0, 0, 0]),
from_u32x8([2, 4, 5, 6, 0, 0, 0, 0]),
from_u32x8([0, 2, 4, 5, 6, 0, 0, 0]),
from_u32x8([1, 2, 4, 5, 6, 0, 0, 0]),
from_u32x8([0, 1, 2, 4, 5, 6, 0, 0]),
from_u32x8([3, 4, 5, 6, 0, 0, 0, 0]),
from_u32x8([0, 3, 4, 5, 6, 0, 0, 0]),
from_u32x8([1, 3, 4, 5, 6, 0, 0, 0]),
from_u32x8([0, 1, 3, 4, 5, 6, 0, 0]),
from_u32x8([2, 3, 4, 5, 6, 0, 0, 0]),
from_u32x8([0, 2, 3, 4, 5, 6, 0, 0]),
from_u32x8([1, 2, 3, 4, 5, 6, 0, 0]),
from_u32x8([0, 1, 2, 3, 4, 5, 6, 0]),
from_u32x8([7, 0, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 7, 0, 0, 0, 0, 0, 0]),
from_u32x8([1, 7, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 7, 0, 0, 0, 0, 0]),
from_u32x8([2, 7, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 7, 0, 0, 0, 0, 0]),
from_u32x8([1, 2, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 7, 0, 0, 0, 0]),
from_u32x8([3, 7, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 7, 0, 0, 0, 0, 0]),
from_u32x8([1, 3, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 7, 0, 0, 0, 0]),
from_u32x8([2, 3, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 7, 0, 0, 0, 0]),
from_u32x8([1, 2, 3, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 7, 0, 0, 0]),
from_u32x8([4, 7, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 4, 7, 0, 0, 0, 0, 0]),
from_u32x8([1, 4, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 4, 7, 0, 0, 0, 0]),
from_u32x8([2, 4, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 4, 7, 0, 0, 0, 0]),
from_u32x8([1, 2, 4, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 4, 7, 0, 0, 0]),
from_u32x8([3, 4, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 4, 7, 0, 0, 0, 0]),
from_u32x8([1, 3, 4, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 4, 7, 0, 0, 0]),
from_u32x8([2, 3, 4, 7, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 4, 7, 0, 0, 0]),
from_u32x8([1, 2, 3, 4, 7, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 4, 7, 0, 0]),
from_u32x8([5, 7, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 5, 7, 0, 0, 0, 0, 0]),
from_u32x8([1, 5, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 5, 7, 0, 0, 0, 0]),
from_u32x8([2, 5, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 5, 7, 0, 0, 0, 0]),
from_u32x8([1, 2, 5, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 5, 7, 0, 0, 0]),
from_u32x8([3, 5, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 5, 7, 0, 0, 0, 0]),
from_u32x8([1, 3, 5, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 5, 7, 0, 0, 0]),
from_u32x8([2, 3, 5, 7, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 5, 7, 0, 0, 0]),
from_u32x8([1, 2, 3, 5, 7, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 5, 7, 0, 0]),
from_u32x8([4, 5, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 4, 5, 7, 0, 0, 0, 0]),
from_u32x8([1, 4, 5, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 4, 5, 7, 0, 0, 0]),
from_u32x8([2, 4, 5, 7, 0, 0, 0, 0]),
from_u32x8([0, 2, 4, 5, 7, 0, 0, 0]),
from_u32x8([1, 2, 4, 5, 7, 0, 0, 0]),
from_u32x8([0, 1, 2, 4, 5, 7, 0, 0]),
from_u32x8([3, 4, 5, 7, 0, 0, 0, 0]),
from_u32x8([0, 3, 4, 5, 7, 0, 0, 0]),
from_u32x8([1, 3, 4, 5, 7, 0, 0, 0]),
from_u32x8([0, 1, 3, 4, 5, 7, 0, 0]),
from_u32x8([2, 3, 4, 5, 7, 0, 0, 0]),
from_u32x8([0, 2, 3, 4, 5, 7, 0, 0]),
from_u32x8([1, 2, 3, 4, 5, 7, 0, 0]),
from_u32x8([0, 1, 2, 3, 4, 5, 7, 0]),
from_u32x8([6, 7, 0, 0, 0, 0, 0, 0]),
from_u32x8([0, 6, 7, 0, 0, 0, 0, 0]),
from_u32x8([1, 6, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 1, 6, 7, 0, 0, 0, 0]),
from_u32x8([2, 6, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 2, 6, 7, 0, 0, 0, 0]),
from_u32x8([1, 2, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 2, 6, 7, 0, 0, 0]),
from_u32x8([3, 6, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 3, 6, 7, 0, 0, 0, 0]),
from_u32x8([1, 3, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 3, 6, 7, 0, 0, 0]),
from_u32x8([2, 3, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 2, 3, 6, 7, 0, 0, 0]),
from_u32x8([1, 2, 3, 6, 7, 0, 0, 0]),
from_u32x8([0, 1, 2, 3, 6, 7, 0, 0]),
from_u32x8([4, 6, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 4, 6, 7, 0, 0, 0, 0]),
from_u32x8([1, 4, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 4, 6, 7, 0, 0, 0]),
from_u32x8([2, 4, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 2, 4, 6, 7, 0, 0, 0]),
from_u32x8([1, 2, 4, 6, 7, 0, 0, 0]),
from_u32x8([0, 1, 2, 4, 6, 7, 0, 0]),
from_u32x8([3, 4, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 3, 4, 6, 7, 0, 0, 0]),
from_u32x8([1, 3, 4, 6, 7, 0, 0, 0]),
from_u32x8([0, 1, 3, 4, 6, 7, 0, 0]),
from_u32x8([2, 3, 4, 6, 7, 0, 0, 0]),
from_u32x8([0, 2, 3, 4, 6, 7, 0, 0]),
from_u32x8([1, 2, 3, 4, 6, 7, 0, 0]),
from_u32x8([0, 1, 2, 3, 4, 6, 7, 0]),
from_u32x8([5, 6, 7, 0, 0, 0, 0, 0]),
from_u32x8([0, 5, 6, 7, 0, 0, 0, 0]),
from_u32x8([1, 5, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 1, 5, 6, 7, 0, 0, 0]),
from_u32x8([2, 5, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 2, 5, 6, 7, 0, 0, 0]),
from_u32x8([1, 2, 5, 6, 7, 0, 0, 0]),
from_u32x8([0, 1, 2, 5, 6, 7, 0, 0]),
from_u32x8([3, 5, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 3, 5, 6, 7, 0, 0, 0]),
from_u32x8([1, 3, 5, 6, 7, 0, 0, 0]),
from_u32x8([0, 1, 3, 5, 6, 7, 0, 0]),
from_u32x8([2, 3, 5, 6, 7, 0, 0, 0]),
from_u32x8([0, 2, 3, 5, 6, 7, 0, 0]),
from_u32x8([1, 2, 3, 5, 6, 7, 0, 0]),
from_u32x8([0, 1, 2, 3, 5, 6, 7, 0]),
from_u32x8([4, 5, 6, 7, 0, 0, 0, 0]),
from_u32x8([0, 4, 5, 6, 7, 0, 0, 0]),
from_u32x8([1, 4, 5, 6, 7, 0, 0, 0]),
from_u32x8([0, 1, 4, 5, 6, 7, 0, 0]),
from_u32x8([2, 4, 5, 6, 7, 0, 0, 0]),
from_u32x8([0, 2, 4, 5, 6, 7, 0, 0]),
from_u32x8([1, 2, 4, 5, 6, 7, 0, 0]),
from_u32x8([0, 1, 2, 4, 5, 6, 7, 0]),
from_u32x8([3, 4, 5, 6, 7, 0, 0, 0]),
from_u32x8([0, 3, 4, 5, 6, 7, 0, 0]),
from_u32x8([1, 3, 4, 5, 6, 7, 0, 0]),
from_u32x8([0, 1, 3, 4, 5, 6, 7, 0]),
from_u32x8([2, 3, 4, 5, 6, 7, 0, 0]),
from_u32x8([0, 2, 3, 4, 5, 6, 7, 0]),
from_u32x8([1, 2, 3, 4, 5, 6, 7, 0]),
from_u32x8([0, 1, 2, 3, 4, 5, 6, 7]),
];

165
bitpacker/src/filter_vec/mod.rs Normal file
View File

@@ -0,0 +1,165 @@
use std::ops::RangeInclusive;
#[cfg(target_arch = "x86_64")]
mod avx2;
mod scalar;
#[derive(Clone, Copy, Eq, PartialEq, Debug)]
#[repr(u8)]
enum FilterImplPerInstructionSet {
#[cfg(target_arch = "x86_64")]
AVX2 = 0u8,
Scalar = 1u8,
}
impl FilterImplPerInstructionSet {
#[inline]
pub fn is_available(&self) -> bool {
match *self {
#[cfg(target_arch = "x86_64")]
FilterImplPerInstructionSet::AVX2 => is_x86_feature_detected!("avx2"),
FilterImplPerInstructionSet::Scalar => true,
}
}
}
// List of available implementation in preferred order.
#[cfg(target_arch = "x86_64")]
const IMPLS: [FilterImplPerInstructionSet; 2] = [
FilterImplPerInstructionSet::AVX2,
FilterImplPerInstructionSet::Scalar,
];
#[cfg(not(target_arch = "x86_64"))]
const IMPLS: [FilterImplPerInstructionSet; 1] = [FilterImplPerInstructionSet::Scalar];
impl FilterImplPerInstructionSet {
#[inline]
#[allow(unused_variables)] // on non-x86_64, code is unused.
fn from(code: u8) -> FilterImplPerInstructionSet {
#[cfg(target_arch = "x86_64")]
if code == FilterImplPerInstructionSet::AVX2 as u8 {
return FilterImplPerInstructionSet::AVX2;
}
FilterImplPerInstructionSet::Scalar
}
#[inline]
fn filter_vec_in_place(self, range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
match self {
#[cfg(target_arch = "x86_64")]
FilterImplPerInstructionSet::AVX2 => avx2::filter_vec_in_place(range, offset, output),
FilterImplPerInstructionSet::Scalar => {
scalar::filter_vec_in_place(range, offset, output)
}
}
}
}
#[inline]
fn get_best_available_instruction_set() -> FilterImplPerInstructionSet {
use std::sync::atomic::{AtomicU8, Ordering};
static INSTRUCTION_SET_BYTE: AtomicU8 = AtomicU8::new(u8::MAX);
let instruction_set_byte: u8 = INSTRUCTION_SET_BYTE.load(Ordering::Relaxed);
if instruction_set_byte == u8::MAX {
// Let's initialize the instruction set and cache it.
let instruction_set = IMPLS
.into_iter()
.find(FilterImplPerInstructionSet::is_available)
.unwrap();
INSTRUCTION_SET_BYTE.store(instruction_set as u8, Ordering::Relaxed);
return instruction_set;
}
FilterImplPerInstructionSet::from(instruction_set_byte)
}
pub fn filter_vec_in_place(range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
get_best_available_instruction_set().filter_vec_in_place(range, offset, output)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_get_best_available_instruction_set() {
// This does not test much unfortunately.
// We just make sure the function returns without crashing and returns the same result.
let instruction_set = get_best_available_instruction_set();
assert_eq!(get_best_available_instruction_set(), instruction_set);
}
#[cfg(target_arch = "x86_64")]
#[test]
fn test_instruction_set_to_code_from_code() {
for instruction_set in [
FilterImplPerInstructionSet::AVX2,
FilterImplPerInstructionSet::Scalar,
] {
let code = instruction_set as u8;
assert_eq!(instruction_set, FilterImplPerInstructionSet::from(code));
}
}
fn test_filter_impl_empty_aux(filter_impl: FilterImplPerInstructionSet) {
let mut output = vec![];
filter_impl.filter_vec_in_place(0..=u32::MAX, 0, &mut output);
assert_eq!(&output, &[]);
}
fn test_filter_impl_simple_aux(filter_impl: FilterImplPerInstructionSet) {
let mut output = vec![3, 2, 1, 5, 11, 2, 5, 10, 2];
filter_impl.filter_vec_in_place(3..=10, 0, &mut output);
assert_eq!(&output, &[0, 3, 6, 7]);
}
fn test_filter_impl_simple_aux_shifted(filter_impl: FilterImplPerInstructionSet) {
let mut output = vec![3, 2, 1, 5, 11, 2, 5, 10, 2];
filter_impl.filter_vec_in_place(3..=10, 10, &mut output);
assert_eq!(&output, &[10, 13, 16, 17]);
}
fn test_filter_impl_simple_outside_i32_range(filter_impl: FilterImplPerInstructionSet) {
let mut output = vec![u32::MAX, i32::MAX as u32 + 1, 0, 1, 3, 1, 1, 1, 1];
filter_impl.filter_vec_in_place(1..=i32::MAX as u32 + 1u32, 0, &mut output);
assert_eq!(&output, &[1, 3, 4, 5, 6, 7, 8]);
}
fn test_filter_impl_test_suite(filter_impl: FilterImplPerInstructionSet) {
test_filter_impl_empty_aux(filter_impl);
test_filter_impl_simple_aux(filter_impl);
test_filter_impl_simple_aux_shifted(filter_impl);
test_filter_impl_simple_outside_i32_range(filter_impl);
}
#[test]
#[cfg(target_arch = "x86_64")]
fn test_filter_implementation_avx2() {
if FilterImplPerInstructionSet::AVX2.is_available() {
test_filter_impl_test_suite(FilterImplPerInstructionSet::AVX2);
}
}
#[test]
fn test_filter_implementation_scalar() {
test_filter_impl_test_suite(FilterImplPerInstructionSet::Scalar);
}
#[cfg(target_arch = "x86_64")]
proptest::proptest! {
#[test]
fn test_filter_compare_scalar_and_avx2_impl_proptest(
start in proptest::prelude::any::<u32>(),
end in proptest::prelude::any::<u32>(),
offset in 0u32..2u32,
mut vals in proptest::collection::vec(0..u32::MAX, 0..30)) {
if FilterImplPerInstructionSet::AVX2.is_available() {
let mut vals_clone = vals.clone();
FilterImplPerInstructionSet::AVX2.filter_vec_in_place(start..=end, offset, &mut vals);
FilterImplPerInstructionSet::Scalar.filter_vec_in_place(start..=end, offset, &mut vals_clone);
assert_eq!(&vals, &vals_clone);
}
}
}
}

13
bitpacker/src/filter_vec/scalar.rs Normal file
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@@ -0,0 +1,13 @@
use std::ops::RangeInclusive;
pub fn filter_vec_in_place(range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
// We restrict the accepted boundary, because unsigned integers & SIMD don't
// play well.
let mut output_cursor = 0;
for i in 0..output.len() {
let val = output[i];
output[output_cursor] = offset + i as u32;
output_cursor += if range.contains(&val) { 1 } else { 0 };
}
output.truncate(output_cursor);
}

1
bitpacker/src/lib.rs
View File

@@ -1,5 +1,6 @@
mod bitpacker;
mod blocked_bitpacker;
mod filter_vec;
use std::cmp::Ordering;

23
ci/before_deploy.ps1
View File

@@ -1,23 +0,0 @@
# This script takes care of packaging the build artifacts that will go in the
# release zipfile
$SRC_DIR = $PWD.Path
$STAGE = [System.Guid]::NewGuid().ToString()
Set-Location $ENV:Temp
New-Item -Type Directory -Name $STAGE
Set-Location $STAGE
$ZIP = "$SRC_DIR\$($Env:CRATE_NAME)-$($Env:APPVEYOR_REPO_TAG_NAME)-$($Env:TARGET).zip"
# TODO Update this to package the right artifacts
Copy-Item "$SRC_DIR\target\$($Env:TARGET)\release\hello.exe" '.\'
7z a "$ZIP" *
Push-AppveyorArtifact "$ZIP"
Remove-Item *.* -Force
Set-Location ..
Remove-Item $STAGE
Set-Location $SRC_DIR

33
ci/before_deploy.sh
View File

@@ -1,33 +0,0 @@
# This script takes care of building your crate and packaging it for release
set -ex
main() {
local src=$(pwd) \
stage=
case $TRAVIS_OS_NAME in
linux)
stage=$(mktemp -d)
;;
osx)
stage=$(mktemp -d -t tmp)
;;
esac
test -f Cargo.lock || cargo generate-lockfile
# TODO Update this to build the artifacts that matter to you
cross rustc --bin hello --target $TARGET --release -- -C lto
# TODO Update this to package the right artifacts
cp target/$TARGET/release/hello $stage/
cd $stage
tar czf $src/$CRATE_NAME-$TRAVIS_TAG-$TARGET.tar.gz *
cd $src
rm -rf $stage
}
main

47
ci/install.sh
View File

@@ -1,47 +0,0 @@
set -ex
main() {
local target=
if [ $TRAVIS_OS_NAME = linux ]; then
target=x86_64-unknown-linux-musl
sort=sort
else
target=x86_64-apple-darwin
sort=gsort # for `sort --sort-version`, from brew's coreutils.
fi
# Builds for iOS are done on OSX, but require the specific target to be
# installed.
case $TARGET in
aarch64-apple-ios)
rustup target install aarch64-apple-ios
;;
armv7-apple-ios)
rustup target install armv7-apple-ios
;;
armv7s-apple-ios)
rustup target install armv7s-apple-ios
;;
i386-apple-ios)
rustup target install i386-apple-ios
;;
x86_64-apple-ios)
rustup target install x86_64-apple-ios
;;
esac
# This fetches latest stable release
local tag=$(git ls-remote --tags --refs --exit-code https://github.com/japaric/cross \
| cut -d/ -f3 \
| grep -E '^v[0.1.0-9.]+$' \
| $sort --version-sort \
| tail -n1)
curl -LSfs https://japaric.github.io/trust/install.sh | \
sh -s -- \
--force \
--git japaric/cross \
--tag $tag \
--target $target
}
main

30
ci/script.sh
View File

@@ -1,30 +0,0 @@
#!/usr/bin/env bash
# This script takes care of testing your crate
set -ex
main() {
if [ ! -z $CODECOV ]; then
echo "Codecov"
cargo build --verbose && cargo coverage --verbose --all && bash <(curl -s https://codecov.io/bash) -s target/kcov
else
echo "Build"
cross build --target $TARGET
if [ ! -z $DISABLE_TESTS ]; then
return
fi
echo "Test"
cross test --target $TARGET --no-default-features --features mmap
cross test --target $TARGET --no-default-features --features mmap query-grammar
fi
for example in $(ls examples/*.rs)
do
cargo run --example $(basename $example .rs)
done
}
# we don't run the "test phase" when doing deploys
if [ -z $TRAVIS_TAG ]; then
main
fi

93
cliff.toml Normal file
View File

@@ -0,0 +1,93 @@
# configuration file for git-cliff{ pattern = "foo", replace = "bar"}
# see https://github.com/orhun/git-cliff#configuration-file
[remote.github]
owner = "quickwit-oss"
repo = "tantivy"
[changelog]
# changelog header
header = """
"""
# template for the changelog body
# https://tera.netlify.app/docs/#introduction
body = """
## What's Changed
{%- if version %} in {{ version }}{%- endif -%}
{% for commit in commits %}
{% if commit.github.pr_title -%}
{%- set commit_message = commit.github.pr_title -%}
{%- else -%}
{%- set commit_message = commit.message -%}
{%- endif -%}
- {{ commit_message | split(pat="\n") | first | trim }}\
{% if commit.github.pr_number %} \
[#{{ commit.github.pr_number }}]({{ self::remote_url() }}/pull/{{ commit.github.pr_number }}){% if commit.github.username %}(@{{ commit.github.username }}){%- endif -%} \
{%- endif %}
{%- endfor -%}
{% if github.contributors | filter(attribute="is_first_time", value=true) | length != 0 %}
{% raw %}\n{% endraw -%}
## New Contributors
{%- endif %}\
{% for contributor in github.contributors | filter(attribute="is_first_time", value=true) %}
* @{{ contributor.username }} made their first contribution
{%- if contributor.pr_number %} in \
[#{{ contributor.pr_number }}]({{ self::remote_url() }}/pull/{{ contributor.pr_number }}) \
{%- endif %}
{%- endfor -%}
{% if version %}
{% if previous.version %}
**Full Changelog**: {{ self::remote_url() }}/compare/{{ previous.version }}...{{ version }}
{% endif %}
{% else -%}
{% raw %}\n{% endraw %}
{% endif %}
{%- macro remote_url() -%}
https://github.com/{{ remote.github.owner }}/{{ remote.github.repo }}
{%- endmacro -%}
"""
# remove the leading and trailing whitespace from the template
trim = true
# changelog footer
footer = """
"""
postprocessors = [
]
[git]
# parse the commits based on https://www.conventionalcommits.org
# This is required or commit.message contains the whole commit message and not just the title
conventional_commits = false
# filter out the commits that are not conventional
filter_unconventional = true
# process each line of a commit as an individual commit
split_commits = false
# regex for preprocessing the commit messages
commit_preprocessors = [
{ pattern = '\((\w+\s)?#([0-9]+)\)', replace = ""},
]
#link_parsers = [
#{ pattern = "#(\\d+)", href = "https://github.com/quickwit-oss/tantivy/pulls/$1"},
#]
# regex for parsing and grouping commits
# protect breaking changes from being skipped due to matching a skipping commit_parser
protect_breaking_commits = false
# filter out the commits that are not matched by commit parsers
filter_commits = false
# glob pattern for matching git tags
tag_pattern = "v[0-9]*"
# regex for skipping tags
skip_tags = "v0.1.0-beta.1"
# regex for ignoring tags
ignore_tags = ""
# sort the tags topologically
topo_order = false
# sort the commits inside sections by oldest/newest order
sort_commits = "newest"
# limit the number of commits included in the changelog.
# limit_commits = 42

37
columnar/Cargo.toml
View File

@@ -1,27 +1,38 @@
[package]
name = "tantivy-columnar"
version = "0.1.0"
version = "0.3.0"
edition = "2021"
license = "MIT"
homepage = "https://github.com/quickwit-oss/tantivy"
repository = "https://github.com/quickwit-oss/tantivy"
description = "column oriented storage for tantivy"
categories = ["database-implementations", "data-structures", "compression"]
[dependencies]
itertools = "0.10.5"
log = "0.4.17"
fnv = "1.0.7"
itertools = "0.14.0"
fastdivide = "0.4.0"
rand = { version = "0.8.5", optional = true }
measure_time = { version = "0.8.2", optional = true }
prettytable-rs = { version = "0.10.0", optional = true }
stacker = { path = "../stacker", package="tantivy-stacker"}
sstable = { path = "../sstable", package = "tantivy-sstable" }
common = { path = "../common", package = "tantivy-common" }
tantivy-bitpacker = { version= "0.3", path = "../bitpacker/" }
stacker = { version= "0.3", path = "../stacker", package="tantivy-stacker"}
sstable = { version= "0.3", path = "../sstable", package = "tantivy-sstable" }
common = { version= "0.7", path = "../common", package = "tantivy-common" }
tantivy-bitpacker = { version= "0.6", path = "../bitpacker/" }
serde = "1.0.152"
downcast-rs = "2.0.1"
[dev-dependencies]
proptest = "1.0.0"
proptest = "1"
more-asserts = "0.3.1"
rand = "0.8.5"
rand = "0.8"
binggan = "0.14.0"
[[bench]]
name = "bench_merge"
harness = false
[[bench]]
name = "bench_access"
harness = false
[features]
unstable = []

2
columnar/README.md
View File

@@ -31,7 +31,7 @@ restriction on 50% of the values (e.g. a 64-bit hash). On the other hand, a lot
# Columnar format
This columnar format may have more than one column (with different types) associated to the same `column_name` (see [Coercion rules](#coercion-rules) above).
The `(column_name, columne_type)` couple however uniquely identifies a column.
The `(column_name, column_type)` couple however uniquely identifies a column.
That couple is serialized as a column `column_key`. The format of that key is:
`[column_name][ZERO_BYTE][column_type_header: u8]`

67
columnar/benches/bench_access.rs Normal file
View File

@@ -0,0 +1,67 @@
use binggan::{black_box, InputGroup};
use common::*;
use tantivy_columnar::Column;
pub mod common;
const NUM_DOCS: u32 = 2_000_000;
pub fn generate_columnar_and_open(card: Card, num_docs: u32) -> Column {
let reader = generate_columnar_with_name(card, num_docs, "price");
reader.read_columns("price").unwrap()[0]
.open_u64_lenient()
.unwrap()
.unwrap()
}
fn main() {
let mut inputs = Vec::new();
let mut add_card = |card1: Card| {
inputs.push((
format!("{card1}"),
generate_columnar_and_open(card1, NUM_DOCS),
));
};
add_card(Card::MultiSparse);
add_card(Card::Multi);
add_card(Card::Sparse);
add_card(Card::Dense);
add_card(Card::Full);
bench_group(InputGroup::new_with_inputs(inputs));
}
fn bench_group(mut runner: InputGroup<Column>) {
runner.register("access_values_for_doc", |column| {
let mut sum = 0;
for i in 0..NUM_DOCS {
for value in column.values_for_doc(i) {
sum += value;
}
}
black_box(sum);
});
runner.register("access_first_vals", |column| {
let mut sum = 0;
const BLOCK_SIZE: usize = 32;
let mut docs = vec![0; BLOCK_SIZE];
let mut buffer = vec![None; BLOCK_SIZE];
for i in (0..NUM_DOCS).step_by(BLOCK_SIZE) {
// fill docs
for idx in 0..BLOCK_SIZE {
docs[idx] = idx as u32 + i;
}
column.first_vals(&docs, &mut buffer);
for val in buffer.iter() {
let Some(val) = val else { continue };
sum += *val;
}
}
black_box(sum);
});
runner.run();
}

155
columnar/benches/bench_first_vals.rs Normal file
View File

@@ -0,0 +1,155 @@
#![feature(test)]
extern crate test;
use std::sync::Arc;
use rand::prelude::*;
use tantivy_columnar::column_values::{serialize_and_load_u64_based_column_values, CodecType};
use tantivy_columnar::*;
use test::{black_box, Bencher};
struct Columns {
pub optional: Column,
pub full: Column,
pub multi: Column,
}
fn get_test_columns() -> Columns {
let data = generate_permutation();
let mut dataframe_writer = ColumnarWriter::default();
for (idx, val) in data.iter().enumerate() {
dataframe_writer.record_numerical(idx as u32, "full_values", NumericalValue::U64(*val));
if idx % 2 == 0 {
dataframe_writer.record_numerical(
idx as u32,
"optional_values",
NumericalValue::U64(*val),
);
}
dataframe_writer.record_numerical(idx as u32, "multi_values", NumericalValue::U64(*val));
dataframe_writer.record_numerical(idx as u32, "multi_values", NumericalValue::U64(*val));
}
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer
.serialize(data.len() as u32, &mut buffer)
.unwrap();
let columnar = ColumnarReader::open(buffer).unwrap();
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("optional_values").unwrap();
assert_eq!(cols.len(), 1);
let optional = cols[0].open_u64_lenient().unwrap().unwrap();
assert_eq!(optional.index.get_cardinality(), Cardinality::Optional);
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("full_values").unwrap();
assert_eq!(cols.len(), 1);
let column_full = cols[0].open_u64_lenient().unwrap().unwrap();
assert_eq!(column_full.index.get_cardinality(), Cardinality::Full);
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("multi_values").unwrap();
assert_eq!(cols.len(), 1);
let multi = cols[0].open_u64_lenient().unwrap().unwrap();
assert_eq!(multi.index.get_cardinality(), Cardinality::Multivalued);
Columns {
optional,
full: column_full,
multi,
}
}
const NUM_VALUES: u64 = 100_000;
fn generate_permutation() -> Vec<u64> {
let mut permutation: Vec<u64> = (0u64..NUM_VALUES).collect();
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
permutation
}
pub fn serialize_and_load(column: &[u64], codec_type: CodecType) -> Arc<dyn ColumnValues<u64>> {
serialize_and_load_u64_based_column_values(&column, &[codec_type])
}
fn run_bench_on_column_full_scan(b: &mut Bencher, column: Column) {
let num_iter = black_box(NUM_VALUES);
b.iter(|| {
let mut sum = 0u64;
for i in 0..num_iter as u32 {
let val = column.first(i);
sum += val.unwrap_or(0);
}
sum
});
}
fn run_bench_on_column_block_fetch(b: &mut Bencher, column: Column) {
let mut block: Vec<Option<u64>> = vec![None; 64];
let fetch_docids = (0..64).collect::<Vec<_>>();
b.iter(move || {
column.first_vals(&fetch_docids, &mut block);
block[0]
});
}
fn run_bench_on_column_block_single_calls(b: &mut Bencher, column: Column) {
let mut block: Vec<Option<u64>> = vec![None; 64];
let fetch_docids = (0..64).collect::<Vec<_>>();
b.iter(move || {
for i in 0..fetch_docids.len() {
block[i] = column.first(fetch_docids[i]);
}
block[0]
});
}
/// Column first method
#[bench]
fn bench_get_first_on_full_column_full_scan(b: &mut Bencher) {
let column = get_test_columns().full;
run_bench_on_column_full_scan(b, column);
}
#[bench]
fn bench_get_first_on_optional_column_full_scan(b: &mut Bencher) {
let column = get_test_columns().optional;
run_bench_on_column_full_scan(b, column);
}
#[bench]
fn bench_get_first_on_multi_column_full_scan(b: &mut Bencher) {
let column = get_test_columns().multi;
run_bench_on_column_full_scan(b, column);
}
/// Block fetch column accessor
#[bench]
fn bench_get_block_first_on_optional_column(b: &mut Bencher) {
let column = get_test_columns().optional;
run_bench_on_column_block_fetch(b, column);
}
#[bench]
fn bench_get_block_first_on_multi_column(b: &mut Bencher) {
let column = get_test_columns().multi;
run_bench_on_column_block_fetch(b, column);
}
#[bench]
fn bench_get_block_first_on_full_column(b: &mut Bencher) {
let column = get_test_columns().full;
run_bench_on_column_block_fetch(b, column);
}
#[bench]
fn bench_get_block_first_on_optional_column_single_calls(b: &mut Bencher) {
let column = get_test_columns().optional;
run_bench_on_column_block_single_calls(b, column);
}
#[bench]
fn bench_get_block_first_on_multi_column_single_calls(b: &mut Bencher) {
let column = get_test_columns().multi;
run_bench_on_column_block_single_calls(b, column);
}
#[bench]
fn bench_get_block_first_on_full_column_single_calls(b: &mut Bencher) {
let column = get_test_columns().full;
run_bench_on_column_block_single_calls(b, column);
}

49
columnar/benches/bench_merge.rs Normal file
View File

@@ -0,0 +1,49 @@
pub mod common;
use binggan::BenchRunner;
use common::{generate_columnar_with_name, Card};
use tantivy_columnar::*;
const NUM_DOCS: u32 = 100_000;
fn main() {
let mut inputs = Vec::new();
let mut add_combo = |card1: Card, card2: Card| {
inputs.push((
format!("merge_{card1}_and_{card2}"),
vec![
generate_columnar_with_name(card1, NUM_DOCS, "price"),
generate_columnar_with_name(card2, NUM_DOCS, "price"),
],
));
};
add_combo(Card::Multi, Card::Multi);
add_combo(Card::MultiSparse, Card::MultiSparse);
add_combo(Card::Dense, Card::Dense);
add_combo(Card::Sparse, Card::Sparse);
add_combo(Card::Sparse, Card::Dense);
add_combo(Card::MultiSparse, Card::Dense);
add_combo(Card::MultiSparse, Card::Sparse);
add_combo(Card::Multi, Card::Dense);
add_combo(Card::Multi, Card::Sparse);
let mut runner: BenchRunner = BenchRunner::new();
let mut group = runner.new_group();
for (input_name, columnar_readers) in inputs.iter() {
group.register_with_input(
input_name,
columnar_readers,
move |columnar_readers: &Vec<ColumnarReader>| {
let mut out = Vec::new();
let columnar_readers = columnar_readers.iter().collect::<Vec<_>>();
let merge_row_order = StackMergeOrder::stack(&columnar_readers[..]);
merge_columnar(&columnar_readers, &[], merge_row_order.into(), &mut out).unwrap();
Some(out.len() as u64)
},
);
}
group.run();
}

124
columnar/benches/bench_values_u128.rs Normal file
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@@ -0,0 +1,124 @@
#![feature(test)]
use std::ops::RangeInclusive;
use std::sync::Arc;
use common::OwnedBytes;
use rand::rngs::StdRng;
use rand::seq::SliceRandom;
use rand::{random, Rng, SeedableRng};
use tantivy_columnar::ColumnValues;
use test::Bencher;
extern crate test;
// TODO does this make sense for IPv6 ?
fn generate_random() -> Vec<u64> {
let mut permutation: Vec<u64> = (0u64..100_000u64)
.map(|el| el + random::<u16>() as u64)
.collect();
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
permutation
}
fn get_u128_column_random() -> Arc<dyn ColumnValues<u128>> {
let permutation = generate_random();
let permutation = permutation.iter().map(|el| *el as u128).collect::<Vec<_>>();
get_u128_column_from_data(&permutation)
}
fn get_u128_column_from_data(data: &[u128]) -> Arc<dyn ColumnValues<u128>> {
let mut out = vec![];
tantivy_columnar::column_values::serialize_column_values_u128(&data, &mut out).unwrap();
let out = OwnedBytes::new(out);
tantivy_columnar::column_values::open_u128_mapped::<u128>(out).unwrap()
}
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
const SINGLE_ITEM: u64 = 90;
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
fn get_data_50percent_item() -> Vec<u128> {
let mut rng = StdRng::from_seed([1u8; 32]);
let mut data = vec![];
for _ in 0..300_000 {
let val = rng.gen_range(1..=100);
data.push(val);
}
data.push(SINGLE_ITEM);
data.shuffle(&mut rng);
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
data
}
#[bench]
fn bench_intfastfield_getrange_u128_50percent_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let column = get_u128_column_from_data(&data);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(
*FIFTY_PERCENT_RANGE.start() as u128..=*FIFTY_PERCENT_RANGE.end() as u128,
0..data.len() as u32,
&mut positions,
);
positions
});
}
#[bench]
fn bench_intfastfield_getrange_u128_single_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let column = get_u128_column_from_data(&data);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(
*SINGLE_ITEM_RANGE.start() as u128..=*SINGLE_ITEM_RANGE.end() as u128,
0..data.len() as u32,
&mut positions,
);
positions
});
}
#[bench]
fn bench_intfastfield_getrange_u128_hit_all(b: &mut Bencher) {
let data = get_data_50percent_item();
let column = get_u128_column_from_data(&data);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(0..=u128::MAX, 0..data.len() as u32, &mut positions);
positions
});
}
// U128 RANGE END
#[bench]
fn bench_intfastfield_scan_all_fflookup_u128(b: &mut Bencher) {
let column = get_u128_column_random();
b.iter(|| {
let mut a = 0u128;
for i in 0u64..column.num_vals() as u64 {
a += column.get_val(i as u32);
}
a
});
}
#[bench]
fn bench_intfastfield_jumpy_stride5_u128(b: &mut Bencher) {
let column = get_u128_column_random();
b.iter(|| {
let n = column.num_vals();
let mut a = 0u128;
for i in (0..n / 5).map(|val| val * 5) {
a += column.get_val(i);
}
a
});
}

203
columnar/benches/bench_values_u64.rs Normal file
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@@ -0,0 +1,203 @@
#![feature(test)]
extern crate test;
use std::ops::RangeInclusive;
use std::sync::Arc;
use rand::prelude::*;
use tantivy_columnar::column_values::{serialize_and_load_u64_based_column_values, CodecType};
use tantivy_columnar::*;
use test::Bencher;
// Warning: this generates the same permutation at each call
fn generate_permutation() -> Vec<u64> {
let mut permutation: Vec<u64> = (0u64..100_000u64).collect();
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
permutation
}
// Warning: this generates the same permutation at each call
fn generate_permutation_gcd() -> Vec<u64> {
let mut permutation: Vec<u64> = (1u64..100_000u64).map(|el| el * 1000).collect();
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
permutation
}
pub fn serialize_and_load(column: &[u64], codec_type: CodecType) -> Arc<dyn ColumnValues<u64>> {
serialize_and_load_u64_based_column_values(&column, &[codec_type])
}
#[bench]
fn bench_intfastfield_jumpy_veclookup(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
b.iter(|| {
let mut a = 0u64;
for _ in 0..n {
a = permutation[a as usize];
}
a
});
}
#[bench]
fn bench_intfastfield_jumpy_fflookup_bitpacked(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
b.iter(|| {
let mut a = 0u64;
for _ in 0..n {
a = column.get_val(a as u32);
}
a
});
}
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
const SINGLE_ITEM: u64 = 90;
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
const ONE_PERCENT_ITEM_RANGE: RangeInclusive<u64> = 49..=49;
fn get_data_50percent_item() -> Vec<u128> {
let mut rng = StdRng::from_seed([1u8; 32]);
let mut data = vec![];
for _ in 0..300_000 {
let val = rng.gen_range(1..=100);
data.push(val);
}
data.push(SINGLE_ITEM);
data.shuffle(&mut rng);
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
data
}
// U64 RANGE START
#[bench]
fn bench_intfastfield_getrange_u64_50percent_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(
FIFTY_PERCENT_RANGE,
0..data.len() as u32,
&mut positions,
);
positions
});
}
#[bench]
fn bench_intfastfield_getrange_u64_1percent_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(
ONE_PERCENT_ITEM_RANGE,
0..data.len() as u32,
&mut positions,
);
positions
});
}
#[bench]
fn bench_intfastfield_getrange_u64_single_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(SINGLE_ITEM_RANGE, 0..data.len() as u32, &mut positions);
positions
});
}
#[bench]
fn bench_intfastfield_getrange_u64_hit_all(b: &mut Bencher) {
let data = get_data_50percent_item();
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(0..=u64::MAX, 0..data.len() as u32, &mut positions);
positions
});
}
// U64 RANGE END
#[bench]
fn bench_intfastfield_stride7_vec(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
b.iter(|| {
let mut a = 0u64;
for i in (0..n / 7).map(|val| val * 7) {
a += permutation[i as usize];
}
a
});
}
#[bench]
fn bench_intfastfield_stride7_fflookup(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
b.iter(|| {
let mut a = 0;
for i in (0..n / 7).map(|val| val * 7) {
a += column.get_val(i as u32);
}
a
});
}
#[bench]
fn bench_intfastfield_scan_all_fflookup(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
let column_ref = column.as_ref();
b.iter(|| {
let mut a = 0u64;
for i in 0u32..n as u32 {
a += column_ref.get_val(i);
}
a
});
}
#[bench]
fn bench_intfastfield_scan_all_fflookup_gcd(b: &mut Bencher) {
let permutation = generate_permutation_gcd();
let n = permutation.len();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
b.iter(|| {
let mut a = 0u64;
for i in 0..n {
a += column.get_val(i as u32);
}
a
});
}
#[bench]
fn bench_intfastfield_scan_all_vec(b: &mut Bencher) {
let permutation = generate_permutation();
b.iter(|| {
let mut a = 0u64;
for i in 0..permutation.len() {
a += permutation[i as usize] as u64;
}
a
});
}

59
columnar/benches/common.rs Normal file
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@@ -0,0 +1,59 @@
extern crate tantivy_columnar;
use core::fmt;
use std::fmt::{Display, Formatter};
use tantivy_columnar::{ColumnarReader, ColumnarWriter};
pub enum Card {
MultiSparse,
Multi,
Sparse,
Dense,
Full,
}
impl Display for Card {
fn fmt(&self, f: &mut Formatter) -> fmt::Result {
match self {
Card::MultiSparse => write!(f, "multi sparse 1/13"),
Card::Multi => write!(f, "multi 2x"),
Card::Sparse => write!(f, "sparse 1/13"),
Card::Dense => write!(f, "dense 1/12"),
Card::Full => write!(f, "full"),
}
}
}
pub fn generate_columnar_with_name(card: Card, num_docs: u32, column_name: &str) -> ColumnarReader {
let mut columnar_writer = ColumnarWriter::default();
if let Card::MultiSparse = card {
columnar_writer.record_numerical(0, column_name, 10u64);
columnar_writer.record_numerical(0, column_name, 10u64);
}
for i in 0..num_docs {
match card {
Card::MultiSparse | Card::Sparse => {
if i % 13 == 0 {
columnar_writer.record_numerical(i, column_name, i as u64);
}
}
Card::Dense => {
if i % 12 == 0 {
columnar_writer.record_numerical(i, column_name, i as u64);
}
}
Card::Full => {
columnar_writer.record_numerical(i, column_name, i as u64);
}
Card::Multi => {
columnar_writer.record_numerical(i, column_name, i as u64);
columnar_writer.record_numerical(i, column_name, i as u64);
}
}
}
let mut wrt: Vec<u8> = Vec::new();
columnar_writer.serialize(num_docs, &mut wrt).unwrap();
ColumnarReader::open(wrt).unwrap()
}

18
columnar/columnar-cli-inspect/Cargo.toml Normal file
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@@ -0,0 +1,18 @@
[package]
name = "tantivy-columnar-inspect"
version = "0.1.0"
edition = "2021"
license = "MIT"
[dependencies]
tantivy = {path="../..", package="tantivy"}
columnar = {path="../", package="tantivy-columnar"}
common = {path="../../common", package="tantivy-common"}
[workspace]
members = []
[profile.release]
debug = true
#debug-assertions = true
#overflow-checks = true

54
columnar/columnar-cli-inspect/src/main.rs Normal file
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@@ -0,0 +1,54 @@
use columnar::ColumnarReader;
use common::file_slice::{FileSlice, WrapFile};
use std::io;
use std::path::Path;
use tantivy::directory::footer::Footer;
fn main() -> io::Result<()> {
println!("Opens a columnar file written by tantivy and validates it.");
let path = std::env::args().nth(1).unwrap();
let path = Path::new(&path);
println!("Reading {:?}", path);
let _reader = open_and_validate_columnar(path.to_str().unwrap())?;
Ok(())
}
pub fn validate_columnar_reader(reader: &ColumnarReader) {
let num_rows = reader.num_rows();
println!("num_rows: {}", num_rows);
let columns = reader.list_columns().unwrap();
println!("num columns: {:?}", columns.len());
for (col_name, dynamic_column_handle) in columns {
let col = dynamic_column_handle.open().unwrap();
match col {
columnar::DynamicColumn::Bool(_)
| columnar::DynamicColumn::I64(_)
| columnar::DynamicColumn::U64(_)
| columnar::DynamicColumn::F64(_)
| columnar::DynamicColumn::IpAddr(_)
| columnar::DynamicColumn::DateTime(_)
| columnar::DynamicColumn::Bytes(_) => {}
columnar::DynamicColumn::Str(str_column) => {
let num_vals = str_column.ords().values.num_vals();
let num_terms_dict = str_column.num_terms() as u64;
let max_ord = str_column.ords().values.iter().max().unwrap_or_default();
println!("{col_name:35} num_vals {num_vals:10} \t num_terms_dict {num_terms_dict:8} max_ord: {max_ord:8}",);
for ord in str_column.ords().values.iter() {
assert!(ord < num_terms_dict);
}
}
}
}
}
/// Opens a columnar file that was written by tantivy and validates it.
pub fn open_and_validate_columnar(path: &str) -> io::Result<ColumnarReader> {
let wrap_file = WrapFile::new(std::fs::File::open(path)?)?;
let slice = FileSlice::new(std::sync::Arc::new(wrap_file));
let (_footer, slice) = Footer::extract_footer(slice.clone()).unwrap();
let reader = ColumnarReader::open(slice).unwrap();
validate_columnar_reader(&reader);
Ok(reader)
}

16
columnar/columnar-cli/Cargo.toml Normal file
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@@ -0,0 +1,16 @@
[package]
name = "tantivy-columnar-cli"
version = "0.1.0"
edition = "2021"
license = "MIT"
[dependencies]
columnar = {path="../", package="tantivy-columnar"}
serde_json = "1"
serde_json_borrow = {git="https://github.com/PSeitz/serde_json_borrow/"}
[workspace]
members = []
[profile.release]
debug = true

134
columnar/columnar-cli/src/main.rs Normal file
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@@ -0,0 +1,134 @@
use columnar::ColumnarWriter;
use columnar::NumericalValue;
use serde_json_borrow;
use std::fs::File;
use std::io;
use std::io::BufRead;
use std::io::BufReader;
use std::time::Instant;
#[derive(Default)]
struct JsonStack {
path: String,
stack: Vec<usize>,
}
impl JsonStack {
fn push(&mut self, seg: &str) {
let len = self.path.len();
self.stack.push(len);
self.path.push('.');
self.path.push_str(seg);
}
fn pop(&mut self) {
if let Some(len) = self.stack.pop() {
self.path.truncate(len);
}
}
fn path(&self) -> &str {
&self.path[1..]
}
}
fn append_json_to_columnar(
doc: u32,
json_value: &serde_json_borrow::Value,
columnar: &mut ColumnarWriter,
stack: &mut JsonStack,
) -> usize {
let mut count = 0;
match json_value {
serde_json_borrow::Value::Null => {}
serde_json_borrow::Value::Bool(val) => {
columnar.record_numerical(
doc,
stack.path(),
NumericalValue::from(if *val { 1u64 } else { 0u64 }),
);
count += 1;
}
serde_json_borrow::Value::Number(num) => {
let numerical_value: NumericalValue = if let Some(num_i64) = num.as_i64() {
num_i64.into()
} else if let Some(num_u64) = num.as_u64() {
num_u64.into()
} else if let Some(num_f64) = num.as_f64() {
num_f64.into()
} else {
panic!();
};
count += 1;
columnar.record_numerical(
doc,
stack.path(),
numerical_value,
);
}
serde_json_borrow::Value::Str(msg) => {
columnar.record_str(
doc,
stack.path(),
msg,
);
count += 1;
},
serde_json_borrow::Value::Array(vals) => {
for val in vals {
count += append_json_to_columnar(doc, val, columnar, stack);
}
},
serde_json_borrow::Value::Object(json_map) => {
for (child_key, child_val) in json_map {
stack.push(child_key);
count += append_json_to_columnar(doc, child_val, columnar, stack);
stack.pop();
}
},
}
count
}
fn main() -> io::Result<()> {
let file = File::open("gh_small.json")?;
let mut reader = BufReader::new(file);
let mut line = String::with_capacity(100);
let mut columnar = columnar::ColumnarWriter::default();
let mut doc = 0;
let start = Instant::now();
let mut stack = JsonStack::default();
let mut total_count = 0;
let start_build = Instant::now();
loop {
line.clear();
let len = reader.read_line(&mut line)?;
if len == 0 {
break;
}
let Ok(json_value) = serde_json::from_str::<serde_json_borrow::Value>(&line) else { continue; };
total_count += append_json_to_columnar(doc, &json_value, &mut columnar, &mut stack);
doc += 1;
}
println!("Build in {:?}", start_build.elapsed());
println!("value count {total_count}");
let mut buffer = Vec::new();
let start_serialize = Instant::now();
columnar.serialize(doc, None, &mut buffer)?;
println!("Serialized in {:?}", start_serialize.elapsed());
println!("num docs: {doc}, {:?}", start.elapsed());
println!("buffer len {} MB", buffer.len() / 1_000_000);
let columnar = columnar::ColumnarReader::open(buffer)?;
for (column_name, dynamic_column) in columnar.list_columns()? {
let num_bytes = dynamic_column.num_bytes();
let typ = dynamic_column.column_type();
if num_bytes > 1_000_000 {
println!("{column_name} {typ:?} {} KB", num_bytes / 1_000);
}
}
println!("{} columns", columnar.num_columns());
Ok(())
}

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31
columnar/src/TODO.md
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@@ -1,22 +1,21 @@
# zero to one
* merges
* full still needs a num_values
* replug u128
* add dictionary encoded stuff
* fix multivalued
* find a way to make columnar work with strict types
* plug to tantivy
- indexing
- aggregations
- merge
* revisit line codec
* add columns from schema on merge
* Plugging JSON
* replug examples
* move datetime to quickwit common
* switch to nanos
* reintroduce the gcd map.
# Perf and Size
* remove alloc in `ord_to_term`
+ multivaued range queries restart from the beginning all of the time.
* re-add ZSTD compression for dictionaries
no systematic monotonic mapping
consider removing multilinear
f32?
adhoc solution for bool?
add metrics helper for aggregate. sum(row_id)
review inline absence/presence
improv perf of select using PDEP
@@ -31,16 +30,18 @@ investigate if should have better errors? io::Error is overused at the moment.
rename rank/select in unit tests
Review the public API via cargo doc
go through TODOs
remove all doc_id occurences -> row_id
remove all doc_id occurrences -> row_id
use the rank & select naming in unit tests branch.
multi-linear -> blockwise
linear codec -> simply a multiplication for the index column
rename columnar to something more explicit, like column_dictionary or columnar_table
rename fastfield -> column
document changes
rationalization FastFieldValue, HasColumnType
isolate u128_based and uniform naming
# Other
fix enhance column-cli
# Santa claus
# Santa Claus
autodetect datetime ipaddr, plug customizable tokenizer.

158
columnar/src/block_accessor.rs Normal file
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@@ -0,0 +1,158 @@
use std::cmp::Ordering;
use crate::{Column, DocId, RowId};
#[derive(Debug, Default, Clone)]
pub struct ColumnBlockAccessor<T> {
val_cache: Vec<T>,
docid_cache: Vec<DocId>,
missing_docids_cache: Vec<DocId>,
row_id_cache: Vec<RowId>,
}
impl<T: PartialOrd + Copy + std::fmt::Debug + Send + Sync + 'static + Default>
ColumnBlockAccessor<T>
{
#[inline]
pub fn fetch_block<'a>(&'a mut self, docs: &'a [u32], accessor: &Column<T>) {
if accessor.index.get_cardinality().is_full() {
self.val_cache.resize(docs.len(), T::default());
accessor.values.get_vals(docs, &mut self.val_cache);
} else {
self.docid_cache.clear();
self.row_id_cache.clear();
accessor.row_ids_for_docs(docs, &mut self.docid_cache, &mut self.row_id_cache);
self.val_cache.resize(self.row_id_cache.len(), T::default());
accessor
.values
.get_vals(&self.row_id_cache, &mut self.val_cache);
}
}
#[inline]
pub fn fetch_block_with_missing(&mut self, docs: &[u32], accessor: &Column<T>, missing: T) {
self.fetch_block(docs, accessor);
// no missing values
if accessor.index.get_cardinality().is_full() {
return;
}
// We can compare docid_cache length with docs to find missing docs
// For multi value columns we can't rely on the length and always need to scan
if accessor.index.get_cardinality().is_multivalue() || docs.len() != self.docid_cache.len()
{
self.missing_docids_cache.clear();
find_missing_docs(docs, &self.docid_cache, |doc| {
self.missing_docids_cache.push(doc);
self.val_cache.push(missing);
});
self.docid_cache
.extend_from_slice(&self.missing_docids_cache);
}
}
#[inline]
pub fn iter_vals(&self) -> impl Iterator<Item = T> + '_ {
self.val_cache.iter().cloned()
}
#[inline]
/// Returns an iterator over the docids and values
/// The passed in `docs` slice needs to be the same slice that was passed to `fetch_block` or
/// `fetch_block_with_missing`.
///
/// The docs is used if the column is full (each docs has exactly one value), otherwise the
/// internal docid vec is used for the iterator, which e.g. may contain duplicate docs.
pub fn iter_docid_vals<'a>(
&'a self,
docs: &'a [u32],
accessor: &Column<T>,
) -> impl Iterator<Item = (DocId, T)> + 'a {
if accessor.index.get_cardinality().is_full() {
docs.iter().cloned().zip(self.val_cache.iter().cloned())
} else {
self.docid_cache
.iter()
.cloned()
.zip(self.val_cache.iter().cloned())
}
}
}
/// Given two sorted lists of docids `docs` and `hits`, hits is a subset of `docs`.
/// Return all docs that are not in `hits`.
fn find_missing_docs<F>(docs: &[u32], hits: &[u32], mut callback: F)
where F: FnMut(u32) {
let mut docs_iter = docs.iter();
let mut hits_iter = hits.iter();
let mut doc = docs_iter.next();
let mut hit = hits_iter.next();
while let (Some(&current_doc), Some(&current_hit)) = (doc, hit) {
match current_doc.cmp(&current_hit) {
Ordering::Less => {
callback(current_doc);
doc = docs_iter.next();
}
Ordering::Equal => {
doc = docs_iter.next();
hit = hits_iter.next();
}
Ordering::Greater => {
hit = hits_iter.next();
}
}
}
while let Some(&current_doc) = doc {
callback(current_doc);
doc = docs_iter.next();
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_find_missing_docs() {
let docs: Vec<u32> = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
let hits: Vec<u32> = vec![2, 4, 6, 8, 10];
let mut missing_docs: Vec<u32> = Vec::new();
find_missing_docs(&docs, &hits, |missing_doc| {
missing_docs.push(missing_doc);
});
assert_eq!(missing_docs, vec![1, 3, 5, 7, 9]);
}
#[test]
fn test_find_missing_docs_empty() {
let docs: Vec<u32> = Vec::new();
let hits: Vec<u32> = vec![2, 4, 6, 8, 10];
let mut missing_docs: Vec<u32> = Vec::new();
find_missing_docs(&docs, &hits, |missing_doc| {
missing_docs.push(missing_doc);
});
assert_eq!(missing_docs, vec![]);
}
#[test]
fn test_find_missing_docs_all_missing() {
let docs: Vec<u32> = vec![1, 2, 3, 4, 5];
let hits: Vec<u32> = Vec::new();
let mut missing_docs: Vec<u32> = Vec::new();
find_missing_docs(&docs, &hits, |missing_doc| {
missing_docs.push(missing_doc);
});
assert_eq!(missing_docs, vec![1, 2, 3, 4, 5]);
}
}

55
columnar/src/column/dictionary_encoded.rs
View File

@@ -1,6 +1,6 @@
use std::io;
use std::ops::Deref;
use std::sync::Arc;
use std::{fmt, io};
use sstable::{Dictionary, VoidSSTable};
@@ -21,7 +21,22 @@ pub struct BytesColumn {
pub(crate) term_ord_column: Column<u64>,
}
impl fmt::Debug for BytesColumn {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("BytesColumn")
.field("term_ord_column", &self.term_ord_column)
.finish()
}
}
impl BytesColumn {
pub fn empty(num_docs: u32) -> BytesColumn {
BytesColumn {
dictionary: Arc::new(Dictionary::empty()),
term_ord_column: Column::build_empty_column(num_docs),
}
}
/// Fills the given `output` buffer with the term associated to the ordinal `ord`.
///
/// Returns `false` if the term does not exist (e.g. `term_ord` is greater or equal to the
@@ -32,30 +47,58 @@ impl BytesColumn {
/// Returns the number of rows in the column.
pub fn num_rows(&self) -> RowId {
self.term_ord_column.num_rows()
self.term_ord_column.num_docs()
}
pub fn term_ords(&self, row_id: RowId) -> impl Iterator<Item = u64> + '_ {
self.term_ord_column.values_for_doc(row_id)
}
/// Returns the column of ordinals
pub fn ords(&self) -> &Column<u64> {
&self.term_ord_column
}
pub fn num_terms(&self) -> usize {
self.dictionary.num_terms()
}
pub fn dictionary(&self) -> &Dictionary<VoidSSTable> {
self.dictionary.as_ref()
}
}
#[derive(Clone)]
pub struct StrColumn(BytesColumn);
impl From<BytesColumn> for StrColumn {
fn from(bytes_col: BytesColumn) -> Self {
StrColumn(bytes_col)
impl fmt::Debug for StrColumn {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
write!(f, "{:?}", self.term_ord_column)
}
}
impl From<StrColumn> for BytesColumn {
fn from(str_column: StrColumn) -> BytesColumn {
str_column.0
}
}
impl StrColumn {
pub fn wrap(bytes_column: BytesColumn) -> StrColumn {
StrColumn(bytes_column)
}
pub fn dictionary(&self) -> &Dictionary<VoidSSTable> {
self.0.dictionary.as_ref()
}
/// Fills the buffer
pub fn ord_to_str(&self, term_ord: u64, output: &mut String) -> io::Result<bool> {
unsafe {
let buf = output.as_mut_vec();
self.0.dictionary.ord_to_term(term_ord, buf)?;
if !self.0.dictionary.ord_to_term(term_ord, buf)? {
return Ok(false);
}
// TODO consider remove checks if it hurts performance.
if std::str::from_utf8(buf.as_slice()).is_err() {
buf.clear();

174
columnar/src/column/mod.rs
View File

@@ -1,35 +1,76 @@
mod dictionary_encoded;
mod serialize;
use std::ops::Deref;
use std::fmt::{self, Debug};
use std::io::Write;
use std::ops::{Range, RangeInclusive};
use std::sync::Arc;
use common::BinarySerializable;
pub use dictionary_encoded::{BytesColumn, StrColumn};
pub use serialize::{
open_column_bytes, open_column_u128, open_column_u64, serialize_column_mappable_to_u128,
serialize_column_mappable_to_u64,
open_column_bytes, open_column_str, open_column_u128, open_column_u128_as_compact_u64,
open_column_u64, serialize_column_mappable_to_u128, serialize_column_mappable_to_u64,
};
use crate::column_index::ColumnIndex;
use crate::column_values::ColumnValues;
use crate::{Cardinality, RowId};
use crate::column_index::{ColumnIndex, Set};
use crate::column_values::monotonic_mapping::StrictlyMonotonicMappingToInternal;
use crate::column_values::{monotonic_map_column, ColumnValues};
use crate::{Cardinality, DocId, EmptyColumnValues, MonotonicallyMappableToU64, RowId};
#[derive(Clone)]
pub struct Column<T> {
pub idx: ColumnIndex<'static>,
pub struct Column<T = u64> {
pub index: ColumnIndex,
pub values: Arc<dyn ColumnValues<T>>,
}
impl<T: PartialOrd> Column<T> {
pub fn num_rows(&self) -> RowId {
match &self.idx {
ColumnIndex::Full => self.values.num_vals() as u32,
ColumnIndex::Optional(optional_index) => optional_index.num_rows(),
impl<T: Debug + PartialOrd + Send + Sync + Copy + 'static> Debug for Column<T> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
let num_docs = self.num_docs();
let entries = (0..num_docs)
.map(|i| (i, self.values_for_doc(i).collect::<Vec<_>>()))
.filter(|(_, vals)| !vals.is_empty());
f.debug_map().entries(entries).finish()
}
}
impl<T: PartialOrd + Default> Column<T> {
pub fn build_empty_column(num_docs: u32) -> Column<T> {
Column {
index: ColumnIndex::Empty { num_docs },
values: Arc::new(EmptyColumnValues),
}
}
}
impl<T: MonotonicallyMappableToU64> Column<T> {
pub fn to_u64_monotonic(self) -> Column<u64> {
let values = Arc::new(monotonic_map_column(
self.values,
StrictlyMonotonicMappingToInternal::<T>::new(),
));
Column {
index: self.index,
values,
}
}
}
impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
#[inline]
pub fn get_cardinality(&self) -> Cardinality {
self.index.get_cardinality()
}
pub fn num_docs(&self) -> RowId {
match &self.index {
ColumnIndex::Empty { num_docs } => *num_docs,
ColumnIndex::Full => self.values.num_vals(),
ColumnIndex::Optional(optional_index) => optional_index.num_docs(),
ColumnIndex::Multivalued(col_index) => {
// The multivalued index contains all value start row_id,
// and one extra value at the end with the overall number of rows.
col_index.num_vals() - 1
col_index.num_docs()
}
}
}
@@ -37,21 +78,94 @@ impl<T: PartialOrd> Column<T> {
pub fn min_value(&self) -> T {
self.values.min_value()
}
pub fn max_value(&self) -> T {
self.values.max_value()
}
}
impl<T: PartialOrd + Copy + Send + Sync + 'static> Column<T> {
#[inline]
pub fn first(&self, row_id: RowId) -> Option<T> {
self.values(row_id).next()
self.values_for_doc(row_id).next()
}
pub fn values(&self, row_id: RowId) -> impl Iterator<Item = T> + '_ {
self.value_row_ids(row_id)
/// Load the first value for each docid in the provided slice.
#[inline]
pub fn first_vals(&self, docids: &[DocId], output: &mut [Option<T>]) {
match &self.index {
ColumnIndex::Empty { .. } => {}
ColumnIndex::Full => self.values.get_vals_opt(docids, output),
ColumnIndex::Optional(optional_index) => {
for (i, docid) in docids.iter().enumerate() {
output[i] = optional_index
.rank_if_exists(*docid)
.map(|rowid| self.values.get_val(rowid));
}
}
ColumnIndex::Multivalued(multivalued_index) => {
for (i, docid) in docids.iter().enumerate() {
let range = multivalued_index.range(*docid);
let is_empty = range.start == range.end;
if !is_empty {
output[i] = Some(self.values.get_val(range.start));
}
}
}
}
}
/// Translates a block of docis to row_ids.
///
/// returns the row_ids and the matching docids on the same index
/// e.g.
/// DocId In: [0, 5, 6]
/// DocId Out: [0, 0, 6, 6]
/// RowId Out: [0, 1, 2, 3]
#[inline]
pub fn row_ids_for_docs(
&self,
doc_ids: &[DocId],
doc_ids_out: &mut Vec<DocId>,
row_ids: &mut Vec<RowId>,
) {
self.index.docids_to_rowids(doc_ids, doc_ids_out, row_ids)
}
pub fn values_for_doc(&self, doc_id: DocId) -> impl Iterator<Item = T> + '_ {
self.index
.value_row_ids(doc_id)
.map(|value_row_id: RowId| self.values.get_val(value_row_id))
}
/// Get the docids of values which are in the provided value and docid range.
#[inline]
pub fn get_docids_for_value_range(
&self,
value_range: RangeInclusive<T>,
selected_docid_range: Range<u32>,
doc_ids: &mut Vec<u32>,
) {
// convert passed docid range to row id range
let rowid_range = self
.index
.docid_range_to_rowids(selected_docid_range.clone());
// Load rows
self.values
.get_row_ids_for_value_range(value_range, rowid_range, doc_ids);
// Convert rows to docids
self.index
.select_batch_in_place(selected_docid_range.start, doc_ids);
}
/// Fills the output vector with the (possibly multiple values that are associated_with
/// `row_id`.
///
/// This method clears the `output` vector.
pub fn fill_vals(&self, row_id: RowId, output: &mut Vec<T>) {
output.clear();
output.extend(self.values_for_doc(row_id));
}
pub fn first_or_default_col(self, default_value: T) -> Arc<dyn ColumnValues<T>> {
Arc::new(FirstValueWithDefault {
column: self,
@@ -60,16 +174,8 @@ impl<T: PartialOrd + Copy + Send + Sync + 'static> Column<T> {
}
}
impl<T> Deref for Column<T> {
type Target = ColumnIndex<'static>;
fn deref(&self) -> &Self::Target {
&self.idx
}
}
impl BinarySerializable for Cardinality {
fn serialize<W: std::io::Write>(&self, writer: &mut W) -> std::io::Result<()> {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> std::io::Result<()> {
self.to_code().serialize(writer)
}
@@ -86,7 +192,10 @@ struct FirstValueWithDefault<T: Copy> {
default_value: T,
}
impl<T: PartialOrd + Send + Sync + Copy + 'static> ColumnValues<T> for FirstValueWithDefault<T> {
impl<T: PartialOrd + Debug + Send + Sync + Copy + 'static> ColumnValues<T>
for FirstValueWithDefault<T>
{
#[inline(always)]
fn get_val(&self, idx: u32) -> T {
self.column.first(idx).unwrap_or(self.default_value)
}
@@ -100,10 +209,11 @@ impl<T: PartialOrd + Send + Sync + Copy + 'static> ColumnValues<T> for FirstValu
}
fn num_vals(&self) -> u32 {
match &self.column.idx {
match &self.column.index {
ColumnIndex::Empty { .. } => 0u32,
ColumnIndex::Full => self.column.values.num_vals(),
ColumnIndex::Optional(optional_idx) => optional_idx.num_rows(),
ColumnIndex::Multivalued(_) => todo!(),
ColumnIndex::Optional(optional_idx) => optional_idx.num_docs(),
ColumnIndex::Multivalued(multivalue_idx) => multivalue_idx.num_docs(),
}
}
}

114
columnar/src/column/serialize.rs
View File

@@ -7,69 +7,42 @@ use sstable::Dictionary;
use crate::column::{BytesColumn, Column};
use crate::column_index::{serialize_column_index, SerializableColumnIndex};
use crate::column_values::serialize::serialize_column_values_u128;
use crate::column_values::{
serialize_column_values, ColumnValues, FastFieldCodecType, MonotonicallyMappableToU128,
MonotonicallyMappableToU64,
load_u64_based_column_values, serialize_column_values_u128, serialize_u64_based_column_values,
CodecType, MonotonicallyMappableToU128, MonotonicallyMappableToU64,
};
use crate::iterable::Iterable;
use crate::{StrColumn, Version};
pub fn serialize_column_mappable_to_u128<
F: Fn() -> I,
I: Iterator<Item = T>,
T: MonotonicallyMappableToU128,
>(
pub fn serialize_column_mappable_to_u128<T: MonotonicallyMappableToU128>(
column_index: SerializableColumnIndex<'_>,
column_values: F,
num_vals: u32,
iterable: &dyn Iterable<T>,
output: &mut impl Write,
) -> io::Result<()> {
let column_index_num_bytes = serialize_column_index(column_index, output)?;
serialize_column_values_u128(
|| column_values().map(|val| val.to_u128()),
num_vals,
output,
)?;
serialize_column_values_u128(iterable, output)?;
output.write_all(&column_index_num_bytes.to_le_bytes())?;
Ok(())
}
pub fn serialize_column_mappable_to_u64<T: MonotonicallyMappableToU64>(
column_index: SerializableColumnIndex<'_>,
column_values: &impl ColumnValues<T>,
column_values: &impl Iterable<T>,
output: &mut impl Write,
) -> io::Result<()> {
let column_index_num_bytes = serialize_column_index(column_index, output)?;
serialize_column_values(
serialize_u64_based_column_values(
column_values,
&[
FastFieldCodecType::Bitpacked,
FastFieldCodecType::BlockwiseLinear,
],
&[CodecType::Bitpacked, CodecType::BlockwiseLinear],
output,
)?;
output.write_all(&column_index_num_bytes.to_le_bytes())?;
Ok(())
}
pub fn open_column_u64<T: MonotonicallyMappableToU64>(bytes: OwnedBytes) -> io::Result<Column<T>> {
let (body, column_index_num_bytes_payload) = bytes.rsplit(4);
let column_index_num_bytes = u32::from_le_bytes(
column_index_num_bytes_payload
.as_slice()
.try_into()
.unwrap(),
);
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
let column_index = crate::column_index::open_column_index(column_index_data)?;
let column_values = crate::column_values::open_u64_mapped(column_values_data)?;
Ok(Column {
idx: column_index,
values: column_values,
})
}
pub fn open_column_u128<T: MonotonicallyMappableToU128>(
pub fn open_column_u64<T: MonotonicallyMappableToU64>(
bytes: OwnedBytes,
format_version: Version,
) -> io::Result<Column<T>> {
let (body, column_index_num_bytes_payload) = bytes.rsplit(4);
let column_index_num_bytes = u32::from_le_bytes(
@@ -79,23 +52,70 @@ pub fn open_column_u128<T: MonotonicallyMappableToU128>(
.unwrap(),
);
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
let column_index = crate::column_index::open_column_index(column_index_data)?;
let column_values = crate::column_values::open_u128_mapped(column_values_data)?;
let column_index = crate::column_index::open_column_index(column_index_data, format_version)?;
let column_values = load_u64_based_column_values(column_values_data)?;
Ok(Column {
idx: column_index,
index: column_index,
values: column_values,
})
}
pub fn open_column_bytes<T: From<BytesColumn>>(data: OwnedBytes) -> io::Result<T> {
pub fn open_column_u128<T: MonotonicallyMappableToU128>(
bytes: OwnedBytes,
format_version: Version,
) -> io::Result<Column<T>> {
let (body, column_index_num_bytes_payload) = bytes.rsplit(4);
let column_index_num_bytes = u32::from_le_bytes(
column_index_num_bytes_payload
.as_slice()
.try_into()
.unwrap(),
);
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
let column_index = crate::column_index::open_column_index(column_index_data, format_version)?;
let column_values = crate::column_values::open_u128_mapped(column_values_data)?;
Ok(Column {
index: column_index,
values: column_values,
})
}
/// Open the column as u64.
///
/// See [`open_u128_as_compact_u64`] for more details.
pub fn open_column_u128_as_compact_u64(
bytes: OwnedBytes,
format_version: Version,
) -> io::Result<Column<u64>> {
let (body, column_index_num_bytes_payload) = bytes.rsplit(4);
let column_index_num_bytes = u32::from_le_bytes(
column_index_num_bytes_payload
.as_slice()
.try_into()
.unwrap(),
);
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
let column_index = crate::column_index::open_column_index(column_index_data, format_version)?;
let column_values = crate::column_values::open_u128_as_compact_u64(column_values_data)?;
Ok(Column {
index: column_index,
values: column_values,
})
}
pub fn open_column_bytes(data: OwnedBytes, format_version: Version) -> io::Result<BytesColumn> {
let (body, dictionary_len_bytes) = data.rsplit(4);
let dictionary_len = u32::from_le_bytes(dictionary_len_bytes.as_slice().try_into().unwrap());
let (dictionary_bytes, column_bytes) = body.split(dictionary_len as usize);
let dictionary = Arc::new(Dictionary::from_bytes(dictionary_bytes)?);
let term_ord_column = crate::column::open_column_u64::<u64>(column_bytes)?;
let bytes_column = BytesColumn {
let term_ord_column = crate::column::open_column_u64::<u64>(column_bytes, format_version)?;
Ok(BytesColumn {
dictionary,
term_ord_column,
};
Ok(bytes_column.into())
})
}
pub fn open_column_str(data: OwnedBytes, format_version: Version) -> io::Result<StrColumn> {
let bytes_column = open_column_bytes(data, format_version)?;
Ok(StrColumn::wrap(bytes_column))
}

223
columnar/src/column_index/merge/mod.rs Normal file
View File

@@ -0,0 +1,223 @@
mod shuffled;
mod stacked;
use common::ReadOnlyBitSet;
use shuffled::merge_column_index_shuffled;
use stacked::merge_column_index_stacked;
use crate::column_index::SerializableColumnIndex;
use crate::{Cardinality, ColumnIndex, MergeRowOrder};
fn detect_cardinality_single_column_index(
column_index: &ColumnIndex,
alive_bitset_opt: &Option<ReadOnlyBitSet>,
) -> Cardinality {
let Some(alive_bitset) = alive_bitset_opt else {
return column_index.get_cardinality();
};
let cardinality_before_deletes = column_index.get_cardinality();
if cardinality_before_deletes == Cardinality::Full {
// The columnar cardinality can only become more restrictive in the presence of deletes
// (where cardinality sorted from the more restrictive to the least restrictive are Full,
// Optional, Multivalued)
//
// If we are already "Full", we are guaranteed to stay "Full" after deletes.
return Cardinality::Full;
}
let mut cardinality_so_far = Cardinality::Full;
for doc_id in alive_bitset.iter() {
let num_values = column_index.value_row_ids(doc_id).len();
let row_cardinality = match num_values {
0 => Cardinality::Optional,
1 => Cardinality::Full,
_ => Cardinality::Multivalued,
};
cardinality_so_far = cardinality_so_far.max(row_cardinality);
if cardinality_so_far >= cardinality_before_deletes {
// There won't be any improvement in the cardinality.
// We can early exit.
return cardinality_before_deletes;
}
}
cardinality_so_far
}
fn detect_cardinality(
column_indexes: &[ColumnIndex],
merge_row_order: &MergeRowOrder,
) -> Cardinality {
match merge_row_order {
MergeRowOrder::Stack(_) => column_indexes
.iter()
.map(ColumnIndex::get_cardinality)
.max()
.unwrap_or(Cardinality::Full),
MergeRowOrder::Shuffled(shuffle_merge_order) => {
let mut merged_cardinality = Cardinality::Full;
for (column_index, alive_bitset_opt) in column_indexes
.iter()
.zip(shuffle_merge_order.alive_bitsets.iter())
{
let cardinality: Cardinality =
detect_cardinality_single_column_index(column_index, alive_bitset_opt);
if cardinality == Cardinality::Multivalued {
return cardinality;
}
merged_cardinality = merged_cardinality.max(cardinality);
}
merged_cardinality
}
}
}
pub fn merge_column_index<'a>(
columns: &'a [ColumnIndex],
merge_row_order: &'a MergeRowOrder,
) -> SerializableColumnIndex<'a> {
// For simplification, we do not try to detect whether the cardinality could be
// downgraded thanks to deletes.
let cardinality_after_merge = detect_cardinality(columns, merge_row_order);
match merge_row_order {
MergeRowOrder::Stack(stack_merge_order) => {
merge_column_index_stacked(columns, cardinality_after_merge, stack_merge_order)
}
MergeRowOrder::Shuffled(complex_merge_order) => {
merge_column_index_shuffled(columns, cardinality_after_merge, complex_merge_order)
}
}
}
// TODO actually, the shuffled code path is a bit too general.
// In practise, we do not really shuffle everything.
// The merge order restricted to a specific column keeps the original row order.
//
// This may offer some optimization that we have not explored yet.
#[cfg(test)]
mod tests {
use common::OwnedBytes;
use crate::column_index::merge::detect_cardinality;
use crate::column_index::multivalued_index::{
open_multivalued_index, serialize_multivalued_index, MultiValueIndex,
};
use crate::column_index::{merge_column_index, OptionalIndex, SerializableColumnIndex};
use crate::{
Cardinality, ColumnIndex, MergeRowOrder, RowAddr, RowId, ShuffleMergeOrder, StackMergeOrder,
};
#[test]
fn test_detect_cardinality() {
assert_eq!(
detect_cardinality(&[], &StackMergeOrder::stack_for_test(&[]).into()),
Cardinality::Full
);
let optional_index: ColumnIndex = OptionalIndex::for_test(1, &[]).into();
let multivalued_index: ColumnIndex = MultiValueIndex::for_test(&[0, 1]).into();
assert_eq!(
detect_cardinality(
&[optional_index.clone(), ColumnIndex::Empty { num_docs: 0 }],
&StackMergeOrder::stack_for_test(&[1, 0]).into()
),
Cardinality::Optional
);
assert_eq!(
detect_cardinality(
&[optional_index.clone(), ColumnIndex::Full],
&StackMergeOrder::stack_for_test(&[1, 1]).into()
),
Cardinality::Optional
);
assert_eq!(
detect_cardinality(
&[
multivalued_index.clone(),
ColumnIndex::Empty { num_docs: 0 }
],
&StackMergeOrder::stack_for_test(&[1, 0]).into()
),
Cardinality::Multivalued
);
assert_eq!(
detect_cardinality(
&[multivalued_index.clone(), optional_index.clone()],
&StackMergeOrder::stack_for_test(&[1, 1]).into()
),
Cardinality::Multivalued
);
assert_eq!(
detect_cardinality(
&[optional_index, multivalued_index],
&StackMergeOrder::stack_for_test(&[1, 1]).into()
),
Cardinality::Multivalued
);
}
#[test]
fn test_merge_index_multivalued_sorted() {
let column_indexes: Vec<ColumnIndex> = vec![MultiValueIndex::for_test(&[0, 2, 5]).into()];
let merge_row_order: MergeRowOrder = ShuffleMergeOrder::for_test(
&[2],
vec![
RowAddr {
segment_ord: 0u32,
row_id: 1u32,
},
RowAddr {
segment_ord: 0u32,
row_id: 0u32,
},
],
)
.into();
let merged_column_index = merge_column_index(&column_indexes[..], &merge_row_order);
let SerializableColumnIndex::Multivalued(start_index_iterable) = merged_column_index else {
panic!("Expected a multivalued index")
};
let mut output = Vec::new();
serialize_multivalued_index(&start_index_iterable, &mut output).unwrap();
let multivalue =
open_multivalued_index(OwnedBytes::new(output), crate::Version::V2).unwrap();
let start_indexes: Vec<RowId> = multivalue.get_start_index_column().iter().collect();
assert_eq!(&start_indexes, &[0, 3, 5]);
}
#[test]
fn test_merge_index_multivalued_sorted_several_segment() {
let column_indexes: Vec<ColumnIndex> = vec![
MultiValueIndex::for_test(&[0, 2, 5]).into(),
ColumnIndex::Empty { num_docs: 0 },
MultiValueIndex::for_test(&[0, 1, 4]).into(),
];
let merge_row_order: MergeRowOrder = ShuffleMergeOrder::for_test(
&[2, 0, 2],
vec![
RowAddr {
segment_ord: 2u32,
row_id: 1u32,
},
RowAddr {
segment_ord: 0u32,
row_id: 0u32,
},
RowAddr {
segment_ord: 2u32,
row_id: 0u32,
},
],
)
.into();
let merged_column_index = merge_column_index(&column_indexes[..], &merge_row_order);
let SerializableColumnIndex::Multivalued(start_index_iterable) = merged_column_index else {
panic!("Expected a multivalued index")
};
let mut output = Vec::new();
serialize_multivalued_index(&start_index_iterable, &mut output).unwrap();
let multivalue =
open_multivalued_index(OwnedBytes::new(output), crate::Version::V2).unwrap();
let start_indexes: Vec<RowId> = multivalue.get_start_index_column().iter().collect();
assert_eq!(&start_indexes, &[0, 3, 5, 6]);
}
}

189
columnar/src/column_index/merge/shuffled.rs Normal file
View File

@@ -0,0 +1,189 @@
use std::iter;
use crate::column_index::{
SerializableColumnIndex, SerializableMultivalueIndex, SerializableOptionalIndex, Set,
};
use crate::iterable::Iterable;
use crate::{Cardinality, ColumnIndex, RowId, ShuffleMergeOrder};
pub fn merge_column_index_shuffled<'a>(
column_indexes: &'a [ColumnIndex],
cardinality_after_merge: Cardinality,
shuffle_merge_order: &'a ShuffleMergeOrder,
) -> SerializableColumnIndex<'a> {
match cardinality_after_merge {
Cardinality::Full => SerializableColumnIndex::Full,
Cardinality::Optional => {
let non_null_row_ids =
merge_column_index_shuffled_optional(column_indexes, shuffle_merge_order);
SerializableColumnIndex::Optional(SerializableOptionalIndex {
non_null_row_ids,
num_rows: shuffle_merge_order.num_rows(),
})
}
Cardinality::Multivalued => {
let non_null_row_ids =
merge_column_index_shuffled_optional(column_indexes, shuffle_merge_order);
SerializableColumnIndex::Multivalued(SerializableMultivalueIndex {
doc_ids_with_values: SerializableOptionalIndex {
non_null_row_ids,
num_rows: shuffle_merge_order.num_rows(),
},
start_offsets: merge_column_index_shuffled_multivalued(
column_indexes,
shuffle_merge_order,
),
})
}
}
}
/// Merge several column indexes into one, ordering rows according to the merge_order passed as
/// argument. While it is true that the `merge_order` may imply deletes and hence could in theory a
/// multivalued index into an optional one, this is not supported today for simplification.
///
/// In other words the column_indexes passed as argument may NOT be multivalued.
fn merge_column_index_shuffled_optional<'a>(
column_indexes: &'a [ColumnIndex],
merge_order: &'a ShuffleMergeOrder,
) -> Box<dyn Iterable<RowId> + 'a> {
Box::new(ShuffledIndex {
column_indexes,
merge_order,
})
}
struct ShuffledIndex<'a> {
column_indexes: &'a [ColumnIndex],
merge_order: &'a ShuffleMergeOrder,
}
impl Iterable<u32> for ShuffledIndex<'_> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
Box::new(
self.merge_order
.iter_new_to_old_row_addrs()
.enumerate()
.filter_map(|(new_row_id, old_row_addr)| {
let column_index = &self.column_indexes[old_row_addr.segment_ord as usize];
let row_id = new_row_id as u32;
if column_index.has_value(old_row_addr.row_id) {
Some(row_id)
} else {
None
}
}),
)
}
}
fn merge_column_index_shuffled_multivalued<'a>(
column_indexes: &'a [ColumnIndex],
merge_order: &'a ShuffleMergeOrder,
) -> Box<dyn Iterable<RowId> + 'a> {
Box::new(ShuffledMultivaluedIndex {
column_indexes,
merge_order,
})
}
struct ShuffledMultivaluedIndex<'a> {
column_indexes: &'a [ColumnIndex],
merge_order: &'a ShuffleMergeOrder,
}
fn iter_num_values<'a>(
column_indexes: &'a [ColumnIndex],
merge_order: &'a ShuffleMergeOrder,
) -> impl Iterator<Item = u32> + 'a {
merge_order.iter_new_to_old_row_addrs().map(|row_addr| {
let column_index = &column_indexes[row_addr.segment_ord as usize];
match column_index {
ColumnIndex::Empty { .. } => 0u32,
ColumnIndex::Full => 1,
ColumnIndex::Optional(optional_index) => {
u32::from(optional_index.contains(row_addr.row_id))
}
ColumnIndex::Multivalued(multivalued_index) => {
multivalued_index.range(row_addr.row_id).len() as u32
}
}
})
}
/// Transforms an iterator containing the number of vals per row (with `num_rows` elements)
/// into a `start_offset` iterator starting at 0 and (with `num_rows + 1` element)
///
/// This will filter values with 0 values as these are covered by the optional index in the
/// multivalue index.
fn integrate_num_vals(num_vals: impl Iterator<Item = u32>) -> impl Iterator<Item = RowId> {
iter::once(0u32).chain(
num_vals
.filter(|num_vals| *num_vals != 0)
.scan(0, |state, num_vals| {
*state += num_vals;
Some(*state)
}),
)
}
impl Iterable<u32> for ShuffledMultivaluedIndex<'_> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
let num_vals_per_row = iter_num_values(self.column_indexes, self.merge_order);
Box::new(integrate_num_vals(num_vals_per_row))
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_index::OptionalIndex;
use crate::RowAddr;
#[test]
fn test_integrate_num_vals_empty() {
assert!(integrate_num_vals(iter::empty()).eq(iter::once(0)));
}
#[test]
fn test_integrate_num_vals_one_el() {
assert!(integrate_num_vals(iter::once(10)).eq([0, 10].into_iter()));
}
#[test]
fn test_integrate_num_vals_several() {
assert!(integrate_num_vals([3, 0, 10, 20].into_iter()).eq([0, 3, 13, 33].into_iter()));
}
#[test]
fn test_merge_column_index_optional_shuffle() {
let optional_index: ColumnIndex = OptionalIndex::for_test(2, &[0]).into();
let column_indexes = [optional_index, ColumnIndex::Full];
let row_addrs = vec![
RowAddr {
segment_ord: 0u32,
row_id: 1u32,
},
RowAddr {
segment_ord: 1u32,
row_id: 0u32,
},
];
let shuffle_merge_order = ShuffleMergeOrder::for_test(&[2, 1], row_addrs);
let serializable_index = merge_column_index_shuffled(
&column_indexes[..],
Cardinality::Optional,
&shuffle_merge_order,
);
let SerializableColumnIndex::Optional(SerializableOptionalIndex {
non_null_row_ids,
num_rows,
}) = serializable_index
else {
panic!()
};
assert_eq!(num_rows, 2);
let non_null_rows: Vec<RowId> = non_null_row_ids.boxed_iter().collect();
assert_eq!(&non_null_rows, &[1]);
}
}

192
columnar/src/column_index/merge/stacked.rs Normal file
View File

@@ -0,0 +1,192 @@
use std::ops::Range;
use crate::column_index::multivalued_index::{MultiValueIndex, SerializableMultivalueIndex};
use crate::column_index::serialize::SerializableOptionalIndex;
use crate::column_index::SerializableColumnIndex;
use crate::iterable::Iterable;
use crate::{Cardinality, ColumnIndex, RowId, StackMergeOrder};
/// Simple case:
/// The new mapping just consists in stacking the different column indexes.
///
/// There are no sort nor deletes involved.
pub fn merge_column_index_stacked<'a>(
columns: &'a [ColumnIndex],
cardinality_after_merge: Cardinality,
stack_merge_order: &'a StackMergeOrder,
) -> SerializableColumnIndex<'a> {
match cardinality_after_merge {
Cardinality::Full => SerializableColumnIndex::Full,
Cardinality::Optional => SerializableColumnIndex::Optional(SerializableOptionalIndex {
non_null_row_ids: Box::new(StackedOptionalIndex {
columns,
stack_merge_order,
}),
num_rows: stack_merge_order.num_rows(),
}),
Cardinality::Multivalued => {
let serializable_multivalue_index =
make_serializable_multivalued_index(columns, stack_merge_order);
SerializableColumnIndex::Multivalued(serializable_multivalue_index)
}
}
}
struct StackedDocIdsWithValues<'a> {
column_indexes: &'a [ColumnIndex],
stack_merge_order: &'a StackMergeOrder,
}
impl Iterable<u32> for StackedDocIdsWithValues<'_> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
Box::new((0..self.column_indexes.len()).flat_map(|i| {
let column_index = &self.column_indexes[i];
let doc_range = self.stack_merge_order.columnar_range(i);
get_doc_ids_with_values(column_index, doc_range)
}))
}
}
fn get_doc_ids_with_values<'a>(
column_index: &'a ColumnIndex,
doc_range: Range<u32>,
) -> Box<dyn Iterator<Item = u32> + 'a> {
match column_index {
ColumnIndex::Empty { .. } => Box::new(0..0),
ColumnIndex::Full => Box::new(doc_range),
ColumnIndex::Optional(optional_index) => Box::new(
optional_index
.iter_rows()
.map(move |row| row + doc_range.start),
),
ColumnIndex::Multivalued(multivalued_index) => match multivalued_index {
MultiValueIndex::MultiValueIndexV1(multivalued_index) => {
Box::new((0..multivalued_index.num_docs()).filter_map(move |docid| {
let range = multivalued_index.range(docid);
if range.is_empty() {
None
} else {
Some(docid + doc_range.start)
}
}))
}
MultiValueIndex::MultiValueIndexV2(multivalued_index) => Box::new(
multivalued_index
.optional_index
.iter_rows()
.map(move |row| row + doc_range.start),
),
},
}
}
fn stack_doc_ids_with_values<'a>(
column_indexes: &'a [ColumnIndex],
stack_merge_order: &'a StackMergeOrder,
) -> SerializableOptionalIndex<'a> {
let num_rows = stack_merge_order.num_rows();
SerializableOptionalIndex {
non_null_row_ids: Box::new(StackedDocIdsWithValues {
column_indexes,
stack_merge_order,
}),
num_rows,
}
}
struct StackedStartOffsets<'a> {
column_indexes: &'a [ColumnIndex],
stack_merge_order: &'a StackMergeOrder,
}
fn get_num_values_iterator<'a>(
column_index: &'a ColumnIndex,
num_docs: u32,
) -> Box<dyn Iterator<Item = u32> + 'a> {
match column_index {
ColumnIndex::Empty { .. } => Box::new(std::iter::empty()),
ColumnIndex::Full => Box::new(std::iter::repeat(1u32).take(num_docs as usize)),
ColumnIndex::Optional(optional_index) => {
Box::new(std::iter::repeat(1u32).take(optional_index.num_non_nulls() as usize))
}
ColumnIndex::Multivalued(multivalued_index) => Box::new(
multivalued_index
.get_start_index_column()
.iter()
.scan(0u32, |previous_start_offset, current_start_offset| {
let num_vals = current_start_offset - *previous_start_offset;
*previous_start_offset = current_start_offset;
Some(num_vals)
})
.skip(1),
),
}
}
impl Iterable<u32> for StackedStartOffsets<'_> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
let num_values_it = (0..self.column_indexes.len()).flat_map(|columnar_id| {
let num_docs = self.stack_merge_order.columnar_range(columnar_id).len() as u32;
let column_index = &self.column_indexes[columnar_id];
get_num_values_iterator(column_index, num_docs)
});
Box::new(std::iter::once(0u32).chain(num_values_it.into_iter().scan(
0u32,
|cumulated, el| {
*cumulated += el;
Some(*cumulated)
},
)))
}
}
fn stack_start_offsets<'a>(
column_indexes: &'a [ColumnIndex],
stack_merge_order: &'a StackMergeOrder,
) -> Box<dyn Iterable<u32> + 'a> {
Box::new(StackedStartOffsets {
column_indexes,
stack_merge_order,
})
}
fn make_serializable_multivalued_index<'a>(
columns: &'a [ColumnIndex],
stack_merge_order: &'a StackMergeOrder,
) -> SerializableMultivalueIndex<'a> {
SerializableMultivalueIndex {
doc_ids_with_values: stack_doc_ids_with_values(columns, stack_merge_order),
start_offsets: stack_start_offsets(columns, stack_merge_order),
}
}
struct StackedOptionalIndex<'a> {
columns: &'a [ColumnIndex],
stack_merge_order: &'a StackMergeOrder,
}
impl<'a> Iterable<RowId> for StackedOptionalIndex<'a> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = RowId> + 'a> {
Box::new(
self.columns
.iter()
.enumerate()
.flat_map(|(columnar_id, column_index_opt)| {
let columnar_row_range = self.stack_merge_order.columnar_range(columnar_id);
let rows_it: Box<dyn Iterator<Item = RowId>> = match column_index_opt {
ColumnIndex::Full => Box::new(columnar_row_range),
ColumnIndex::Optional(optional_index) => Box::new(
optional_index
.iter_rows()
.map(move |row_id: RowId| columnar_row_range.start + row_id),
),
ColumnIndex::Multivalued(_) => {
panic!("No multivalued index is allowed when stacking column index");
}
ColumnIndex::Empty { .. } => Box::new(std::iter::empty()),
};
rows_it
}),
)
}
}

200
columnar/src/column_index/mod.rs
View File

@@ -1,54 +1,210 @@
//! # `column_index`
//!
//! `column_index` provides rank and select operations to associate positions when not all
//! documents have exactly one element.
mod merge;
mod multivalued_index;
mod optional_index;
mod serialize;
use std::ops::Range;
use std::sync::Arc;
pub use optional_index::{OptionalIndex, SerializableOptionalIndex, Set};
pub use serialize::{open_column_index, serialize_column_index, SerializableColumnIndex};
pub use merge::merge_column_index;
pub(crate) use multivalued_index::SerializableMultivalueIndex;
pub use optional_index::{OptionalIndex, Set};
pub use serialize::{
open_column_index, serialize_column_index, SerializableColumnIndex, SerializableOptionalIndex,
};
use crate::column_values::ColumnValues;
use crate::{Cardinality, RowId};
use crate::column_index::multivalued_index::MultiValueIndex;
use crate::{Cardinality, DocId, RowId};
#[derive(Clone)]
pub enum ColumnIndex<'a> {
#[derive(Clone, Debug)]
pub enum ColumnIndex {
Empty {
num_docs: u32,
},
Full,
Optional(OptionalIndex),
// TODO Remove the static by fixing the codec if possible.
/// The column values enclosed contains for all row_id,
/// the value start_index.
///
/// In addition, at index num_rows, an extra value is added
/// containing the overal number of values.
Multivalued(Arc<dyn ColumnValues<RowId> + 'a>),
/// containing the overall number of values.
Multivalued(MultiValueIndex),
}
impl<'a> ColumnIndex<'a> {
impl From<OptionalIndex> for ColumnIndex {
fn from(optional_index: OptionalIndex) -> ColumnIndex {
ColumnIndex::Optional(optional_index)
}
}
impl From<MultiValueIndex> for ColumnIndex {
fn from(multi_value_index: MultiValueIndex) -> ColumnIndex {
ColumnIndex::Multivalued(multi_value_index)
}
}
impl ColumnIndex {
/// Returns the cardinality of the column index.
///
/// By convention, if the column contains no docs, we consider that it is
/// full.
#[inline]
pub fn get_cardinality(&self) -> Cardinality {
match self {
ColumnIndex::Full => Cardinality::Full,
ColumnIndex::Empty { num_docs: 0 } | ColumnIndex::Full => Cardinality::Full,
ColumnIndex::Empty { .. } => Cardinality::Optional,
ColumnIndex::Optional(_) => Cardinality::Optional,
ColumnIndex::Multivalued(_) => Cardinality::Multivalued,
}
}
pub fn value_row_ids(&self, row_id: RowId) -> Range<RowId> {
/// Returns true if and only if there are at least one value associated to the row.
pub fn has_value(&self, doc_id: DocId) -> bool {
match self {
ColumnIndex::Full => row_id..row_id + 1,
ColumnIndex::Empty { .. } => false,
ColumnIndex::Full => true,
ColumnIndex::Optional(optional_index) => optional_index.contains(doc_id),
ColumnIndex::Multivalued(multivalued_index) => {
!multivalued_index.range(doc_id).is_empty()
}
}
}
pub fn value_row_ids(&self, doc_id: DocId) -> Range<RowId> {
match self {
ColumnIndex::Empty { .. } => 0..0,
ColumnIndex::Full => doc_id..doc_id + 1,
ColumnIndex::Optional(optional_index) => {
if let Some(val) = optional_index.rank_if_exists(row_id) {
if let Some(val) = optional_index.rank_if_exists(doc_id) {
val..val + 1
} else {
0..0
}
}
ColumnIndex::Multivalued(multivalued_index) => multivalued_index.range(doc_id),
}
}
/// Translates a block of docis to row_ids.
///
/// returns the row_ids and the matching docids on the same index
/// e.g.
/// DocId In: [0, 5, 6]
/// DocId Out: [0, 0, 6, 6]
/// RowId Out: [0, 1, 2, 3]
#[inline]
pub fn docids_to_rowids(
&self,
doc_ids: &[DocId],
doc_ids_out: &mut Vec<DocId>,
row_ids: &mut Vec<RowId>,
) {
match self {
ColumnIndex::Empty { .. } => {}
ColumnIndex::Full => {
doc_ids_out.extend_from_slice(doc_ids);
row_ids.extend_from_slice(doc_ids);
}
ColumnIndex::Optional(optional_index) => {
for doc_id in doc_ids {
if let Some(row_id) = optional_index.rank_if_exists(*doc_id) {
doc_ids_out.push(*doc_id);
row_ids.push(row_id);
}
}
}
ColumnIndex::Multivalued(multivalued_index) => {
let multivalued_index_ref = &**multivalued_index;
let start: u32 = multivalued_index_ref.get_val(row_id);
let end: u32 = multivalued_index_ref.get_val(row_id + 1);
start..end
for doc_id in doc_ids {
for row_id in multivalued_index.range(*doc_id) {
doc_ids_out.push(*doc_id);
row_ids.push(row_id);
}
}
}
}
}
pub fn docid_range_to_rowids(&self, doc_id_range: Range<DocId>) -> Range<RowId> {
match self {
ColumnIndex::Empty { .. } => 0..0,
ColumnIndex::Full => doc_id_range,
ColumnIndex::Optional(optional_index) => {
let row_start = optional_index.rank(doc_id_range.start);
let row_end = optional_index.rank(doc_id_range.end);
row_start..row_end
}
ColumnIndex::Multivalued(multivalued_index) => match multivalued_index {
MultiValueIndex::MultiValueIndexV1(index) => {
let row_start = index.start_index_column.get_val(doc_id_range.start);
let row_end = index.start_index_column.get_val(doc_id_range.end);
row_start..row_end
}
MultiValueIndex::MultiValueIndexV2(index) => {
// In this case we will use the optional_index select the next values
// that are valid. There are different cases to consider:
// Not exists below means does not exist in the optional
// index, because it has no values.
// * doc_id_range may cover a range of docids which are non existent
// => rank
// will give us the next document outside the range with a value. They both
// get the same rank and therefore return a zero range
//
// * doc_id_range.start and doc_id_range.end may not exist, but docids in
// between may have values
// => rank will give us the next document outside the range with a value.
//
// * doc_id_range.start may be not existent but doc_id_range.end may exist
// * doc_id_range.start may exist but doc_id_range.end may not exist
// * doc_id_range.start and doc_id_range.end may exist
// => rank on doc_id_range.end will give use the next value, which matches
// how the `start_index_column` works, so we get the value start of the next
// docid which we use to create the exclusive range.
//
let rank_start = index.optional_index.rank(doc_id_range.start);
let row_start = index.start_index_column.get_val(rank_start);
let rank_end = index.optional_index.rank(doc_id_range.end);
let row_end = index.start_index_column.get_val(rank_end);
row_start..row_end
}
},
}
}
pub fn select_batch_in_place(&self, doc_id_start: DocId, rank_ids: &mut Vec<RowId>) {
match self {
ColumnIndex::Empty { .. } => {
rank_ids.clear();
}
ColumnIndex::Full => {
// No need to do anything:
// value_idx and row_idx are the same.
}
ColumnIndex::Optional(optional_index) => {
optional_index.select_batch(&mut rank_ids[..]);
}
ColumnIndex::Multivalued(multivalued_index) => {
multivalued_index.select_batch_in_place(doc_id_start, rank_ids)
}
}
}
}
#[cfg(test)]
mod tests {
use crate::{Cardinality, ColumnIndex};
#[test]
fn test_column_index_get_cardinality() {
assert_eq!(
ColumnIndex::Empty { num_docs: 0 }.get_cardinality(),
Cardinality::Full
);
assert_eq!(ColumnIndex::Full.get_cardinality(), Cardinality::Full);
assert_eq!(
ColumnIndex::Empty { num_docs: 1 }.get_cardinality(),
Cardinality::Optional
);
}
}

397
columnar/src/column_index/multivalued_index.rs
View File

@@ -1,29 +1,400 @@
use std::io;
use std::io::Write;
use std::ops::Range;
use std::sync::Arc;
use common::OwnedBytes;
use common::{CountingWriter, OwnedBytes};
use crate::column_values::{ColumnValues, FastFieldCodecType};
use crate::RowId;
use super::optional_index::{open_optional_index, serialize_optional_index};
use super::{OptionalIndex, SerializableOptionalIndex, Set};
use crate::column_values::{
load_u64_based_column_values, serialize_u64_based_column_values, CodecType, ColumnValues,
};
use crate::iterable::Iterable;
use crate::{DocId, RowId, Version};
#[derive(Clone)]
pub struct MultivaluedIndex(Arc<dyn ColumnValues<RowId>>);
pub struct SerializableMultivalueIndex<'a> {
pub doc_ids_with_values: SerializableOptionalIndex<'a>,
pub start_offsets: Box<dyn Iterable<u32> + 'a>,
}
pub fn serialize_multivalued_index(
multivalued_index: &dyn ColumnValues<RowId>,
multivalued_index: &SerializableMultivalueIndex,
output: &mut impl Write,
) -> io::Result<()> {
crate::column_values::serialize_column_values(
&*multivalued_index,
&[FastFieldCodecType::Bitpacked, FastFieldCodecType::Linear],
let SerializableMultivalueIndex {
doc_ids_with_values,
start_offsets,
} = multivalued_index;
let mut count_writer = CountingWriter::wrap(output);
let SerializableOptionalIndex {
non_null_row_ids,
num_rows,
} = doc_ids_with_values;
serialize_optional_index(&**non_null_row_ids, *num_rows, &mut count_writer)?;
let optional_len = count_writer.written_bytes() as u32;
let output = count_writer.finish();
serialize_u64_based_column_values(
&**start_offsets,
&[CodecType::Bitpacked, CodecType::Linear],
output,
)?;
output.write_all(&optional_len.to_le_bytes())?;
Ok(())
}
pub fn open_multivalued_index(bytes: OwnedBytes) -> io::Result<Arc<dyn ColumnValues<RowId>>> {
let start_index_column: Arc<dyn ColumnValues<RowId>> =
crate::column_values::open_u64_mapped(bytes)?;
Ok(start_index_column)
pub fn open_multivalued_index(
bytes: OwnedBytes,
format_version: Version,
) -> io::Result<MultiValueIndex> {
match format_version {
Version::V1 => {
let start_index_column: Arc<dyn ColumnValues<RowId>> =
load_u64_based_column_values(bytes)?;
Ok(MultiValueIndex::MultiValueIndexV1(MultiValueIndexV1 {
start_index_column,
}))
}
Version::V2 => {
let (body_bytes, optional_index_len) = bytes.rsplit(4);
let optional_index_len =
u32::from_le_bytes(optional_index_len.as_slice().try_into().unwrap());
let (optional_index_bytes, start_index_bytes) =
body_bytes.split(optional_index_len as usize);
let optional_index = open_optional_index(optional_index_bytes)?;
let start_index_column: Arc<dyn ColumnValues<RowId>> =
load_u64_based_column_values(start_index_bytes)?;
Ok(MultiValueIndex::MultiValueIndexV2(MultiValueIndexV2 {
optional_index,
start_index_column,
}))
}
}
}
#[derive(Clone)]
/// Index to resolve value range for given doc_id.
/// Starts at 0.
pub enum MultiValueIndex {
MultiValueIndexV1(MultiValueIndexV1),
MultiValueIndexV2(MultiValueIndexV2),
}
#[derive(Clone)]
/// Index to resolve value range for given doc_id.
/// Starts at 0.
pub struct MultiValueIndexV1 {
pub start_index_column: Arc<dyn crate::ColumnValues<RowId>>,
}
impl MultiValueIndexV1 {
/// Returns `[start, end)`, such that the values associated with
/// the given document are `start..end`.
#[inline]
pub(crate) fn range(&self, doc_id: DocId) -> Range<RowId> {
if doc_id >= self.num_docs() {
return 0..0;
}
let start = self.start_index_column.get_val(doc_id);
let end = self.start_index_column.get_val(doc_id + 1);
start..end
}
/// Returns the number of documents in the index.
#[inline]
pub fn num_docs(&self) -> u32 {
self.start_index_column.num_vals() - 1
}
/// Converts a list of ranks (row ids of values) in a 1:n index to the corresponding list of
/// docids. Positions are converted inplace to docids.
///
/// Since there is no index for value pos -> docid, but docid -> value pos range, we scan the
/// index.
///
/// Correctness: positions needs to be sorted. idx_reader needs to contain monotonically
/// increasing positions.
///
/// TODO: Instead of a linear scan we can employ a exponential search into binary search to
/// match a docid to its value position.
pub(crate) fn select_batch_in_place(&self, docid_start: DocId, ranks: &mut Vec<u32>) {
if ranks.is_empty() {
return;
}
let mut cur_doc = docid_start;
let mut last_doc = None;
assert!(self.start_index_column.get_val(docid_start) <= ranks[0]);
let mut write_doc_pos = 0;
for i in 0..ranks.len() {
let pos = ranks[i];
loop {
let end = self.start_index_column.get_val(cur_doc + 1);
if end > pos {
ranks[write_doc_pos] = cur_doc;
write_doc_pos += if last_doc == Some(cur_doc) { 0 } else { 1 };
last_doc = Some(cur_doc);
break;
}
cur_doc += 1;
}
}
ranks.truncate(write_doc_pos);
}
}
#[derive(Clone)]
/// Index to resolve value range for given doc_id.
/// Starts at 0.
pub struct MultiValueIndexV2 {
pub optional_index: OptionalIndex,
pub start_index_column: Arc<dyn crate::ColumnValues<RowId>>,
}
impl std::fmt::Debug for MultiValueIndex {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
let index = match self {
MultiValueIndex::MultiValueIndexV1(idx) => &idx.start_index_column,
MultiValueIndex::MultiValueIndexV2(idx) => &idx.start_index_column,
};
f.debug_struct("MultiValuedIndex")
.field("num_rows", &index.num_vals())
.finish_non_exhaustive()
}
}
impl MultiValueIndex {
pub fn for_test(start_offsets: &[RowId]) -> MultiValueIndex {
assert!(!start_offsets.is_empty());
assert_eq!(start_offsets[0], 0);
let mut doc_with_values = Vec::new();
let mut compact_start_offsets: Vec<u32> = vec![0];
for doc in 0..start_offsets.len() - 1 {
if start_offsets[doc] < start_offsets[doc + 1] {
doc_with_values.push(doc as RowId);
compact_start_offsets.push(start_offsets[doc + 1]);
}
}
let serializable_multivalued_index = SerializableMultivalueIndex {
doc_ids_with_values: SerializableOptionalIndex {
non_null_row_ids: Box::new(&doc_with_values[..]),
num_rows: start_offsets.len() as u32 - 1,
},
start_offsets: Box::new(&compact_start_offsets[..]),
};
let mut buffer = Vec::new();
serialize_multivalued_index(&serializable_multivalued_index, &mut buffer).unwrap();
let bytes = OwnedBytes::new(buffer);
open_multivalued_index(bytes, Version::V2).unwrap()
}
pub fn get_start_index_column(&self) -> &Arc<dyn crate::ColumnValues<RowId>> {
match self {
MultiValueIndex::MultiValueIndexV1(idx) => &idx.start_index_column,
MultiValueIndex::MultiValueIndexV2(idx) => &idx.start_index_column,
}
}
/// Returns `[start, end)` values range, such that the values associated with
/// the given document are `start..end`.
#[inline]
pub(crate) fn range(&self, doc_id: DocId) -> Range<RowId> {
match self {
MultiValueIndex::MultiValueIndexV1(idx) => idx.range(doc_id),
MultiValueIndex::MultiValueIndexV2(idx) => idx.range(doc_id),
}
}
/// Returns the number of documents in the index.
#[inline]
pub fn num_docs(&self) -> u32 {
match self {
MultiValueIndex::MultiValueIndexV1(idx) => idx.start_index_column.num_vals() - 1,
MultiValueIndex::MultiValueIndexV2(idx) => idx.optional_index.num_docs(),
}
}
/// Converts a list of ranks (row ids of values) in a 1:n index to the corresponding list of
/// docids. Positions are converted inplace to docids.
///
/// Since there is no index for value pos -> docid, but docid -> value pos range, we scan the
/// index.
///
/// Correctness: positions needs to be sorted. idx_reader needs to contain monotonically
/// increasing positions.
///
/// TODO: Instead of a linear scan we can employ a exponential search into binary search to
/// match a docid to its value position.
pub(crate) fn select_batch_in_place(&self, docid_start: DocId, ranks: &mut Vec<u32>) {
match self {
MultiValueIndex::MultiValueIndexV1(idx) => {
idx.select_batch_in_place(docid_start, ranks)
}
MultiValueIndex::MultiValueIndexV2(idx) => {
idx.select_batch_in_place(docid_start, ranks)
}
}
}
}
impl MultiValueIndexV2 {
/// Returns `[start, end)`, such that the values associated with
/// the given document are `start..end`.
#[inline]
pub(crate) fn range(&self, doc_id: DocId) -> Range<RowId> {
let Some(rank) = self.optional_index.rank_if_exists(doc_id) else {
return 0..0;
};
let start = self.start_index_column.get_val(rank);
let end = self.start_index_column.get_val(rank + 1);
start..end
}
/// Returns the number of documents in the index.
#[inline]
pub fn num_docs(&self) -> u32 {
self.optional_index.num_docs()
}
/// Converts a list of ranks (row ids of values) in a 1:n index to the corresponding list of
/// docids. Positions are converted inplace to docids.
///
/// Since there is no index for value pos -> docid, but docid -> value pos range, we scan the
/// index.
///
/// Correctness: positions needs to be sorted. idx_reader needs to contain monotonically
/// increasing positions.
///
/// TODO: Instead of a linear scan we can employ a exponential search into binary search to
/// match a docid to its value position.
pub(crate) fn select_batch_in_place(&self, docid_start: DocId, ranks: &mut Vec<u32>) {
if ranks.is_empty() {
return;
}
let mut cur_pos_in_idx = self.optional_index.rank(docid_start);
let mut last_doc = None;
assert!(cur_pos_in_idx <= ranks[0]);
let mut write_doc_pos = 0;
for i in 0..ranks.len() {
let pos = ranks[i];
loop {
let end = self.start_index_column.get_val(cur_pos_in_idx + 1);
if end > pos {
ranks[write_doc_pos] = cur_pos_in_idx;
write_doc_pos += if last_doc == Some(cur_pos_in_idx) {
0
} else {
1
};
last_doc = Some(cur_pos_in_idx);
break;
}
cur_pos_in_idx += 1;
}
}
ranks.truncate(write_doc_pos);
for rank in ranks.iter_mut() {
*rank = self.optional_index.select(*rank);
}
}
}
#[cfg(test)]
mod tests {
use std::ops::Range;
use super::MultiValueIndex;
use crate::{ColumnarReader, DynamicColumn};
fn index_to_pos_helper(
index: &MultiValueIndex,
doc_id_range: Range<u32>,
positions: &[u32],
) -> Vec<u32> {
let mut positions = positions.to_vec();
index.select_batch_in_place(doc_id_range.start, &mut positions);
positions
}
#[test]
fn test_positions_to_docid() {
let index = MultiValueIndex::for_test(&[0, 10, 12, 15, 22, 23]);
assert_eq!(index.num_docs(), 5);
let positions = &[10u32, 11, 15, 20, 21, 22];
assert_eq!(index_to_pos_helper(&index, 0..5, positions), vec![1, 3, 4]);
assert_eq!(index_to_pos_helper(&index, 1..5, positions), vec![1, 3, 4]);
assert_eq!(index_to_pos_helper(&index, 0..5, &[9]), vec![0]);
assert_eq!(index_to_pos_helper(&index, 1..5, &[10]), vec![1]);
assert_eq!(index_to_pos_helper(&index, 1..5, &[11]), vec![1]);
assert_eq!(index_to_pos_helper(&index, 2..5, &[12]), vec![2]);
assert_eq!(index_to_pos_helper(&index, 2..5, &[12, 14]), vec![2]);
assert_eq!(index_to_pos_helper(&index, 2..5, &[12, 14, 15]), vec![2, 3]);
}
#[test]
fn test_range_to_rowids() {
use crate::ColumnarWriter;
let mut columnar_writer = ColumnarWriter::default();
// This column gets coerced to u64
columnar_writer.record_numerical(1, "full", u64::MAX);
columnar_writer.record_numerical(1, "full", u64::MAX);
columnar_writer.record_numerical(5, "full", u64::MAX);
columnar_writer.record_numerical(5, "full", u64::MAX);
let mut wrt: Vec<u8> = Vec::new();
columnar_writer.serialize(7, &mut wrt).unwrap();
let reader = ColumnarReader::open(wrt).unwrap();
// Open the column as u64
let column = reader.read_columns("full").unwrap()[0]
.open()
.unwrap()
.coerce_numerical(crate::NumericalType::U64)
.unwrap();
let DynamicColumn::U64(column) = column else {
panic!();
};
let row_id_range = column.index.docid_range_to_rowids(1..2);
assert_eq!(row_id_range, 0..2);
let row_id_range = column.index.docid_range_to_rowids(0..2);
assert_eq!(row_id_range, 0..2);
let row_id_range = column.index.docid_range_to_rowids(0..4);
assert_eq!(row_id_range, 0..2);
let row_id_range = column.index.docid_range_to_rowids(3..4);
assert_eq!(row_id_range, 2..2);
let row_id_range = column.index.docid_range_to_rowids(1..6);
assert_eq!(row_id_range, 0..4);
let row_id_range = column.index.docid_range_to_rowids(3..6);
assert_eq!(row_id_range, 2..4);
let row_id_range = column.index.docid_range_to_rowids(0..6);
assert_eq!(row_id_range, 0..4);
let row_id_range = column.index.docid_range_to_rowids(0..6);
assert_eq!(row_id_range, 0..4);
let check = |range, expected| {
let full_range = 0..=u64::MAX;
let mut docids = Vec::new();
column.get_docids_for_value_range(full_range, range, &mut docids);
assert_eq!(docids, expected);
};
// check(0..1, vec![]);
// check(0..2, vec![1]);
check(1..2, vec![1]);
}
}

233
columnar/src/column_index/optional_index/mod.rs
View File

@@ -1,17 +1,17 @@
use std::io::{self, Write};
use std::ops::Range;
use std::sync::Arc;
mod set;
mod set_block;
use common::{BinarySerializable, GroupByIteratorExtended, OwnedBytes, VInt};
pub use set::{Set, SetCodec};
use common::{BinarySerializable, OwnedBytes, VInt};
pub use set::{SelectCursor, Set, SetCodec};
use set_block::{
DenseBlock, DenseBlockCodec, SparseBlock, SparseBlockCodec, DENSE_BLOCK_NUM_BYTES,
};
use crate::{InvalidData, RowId};
use crate::iterable::Iterable;
use crate::{DocId, InvalidData, RowId};
/// The threshold for for number of elements after which we switch to dense block encoding.
///
@@ -21,8 +21,6 @@ const DENSE_BLOCK_THRESHOLD: u32 =
const ELEMENTS_PER_BLOCK: u32 = u16::MAX as u32 + 1;
const BLOCK_SIZE: RowId = 1 << 16;
#[derive(Copy, Clone, Debug)]
struct BlockMeta {
non_null_rows_before_block: u32,
@@ -88,13 +86,18 @@ pub struct OptionalIndex {
block_metas: Arc<[BlockMeta]>,
}
impl OptionalIndex {
pub fn num_rows(&self) -> RowId {
self.num_rows
impl Iterable<u32> for &OptionalIndex {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
Box::new(self.iter_rows())
}
}
pub fn num_non_nulls(&self) -> RowId {
self.num_non_null_rows
impl std::fmt::Debug for OptionalIndex {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
f.debug_struct("OptionalIndex")
.field("num_rows", &self.num_rows)
.field("num_non_null_rows", &self.num_non_null_rows)
.finish_non_exhaustive()
}
}
@@ -110,12 +113,70 @@ struct RowAddr {
#[inline(always)]
fn row_addr_from_row_id(row_id: RowId) -> RowAddr {
RowAddr {
block_id: (row_id / BLOCK_SIZE) as u16,
in_block_row_id: (row_id % BLOCK_SIZE) as u16,
block_id: (row_id / ELEMENTS_PER_BLOCK) as u16,
in_block_row_id: (row_id % ELEMENTS_PER_BLOCK) as u16,
}
}
enum BlockSelectCursor<'a> {
Dense(<DenseBlock<'a> as Set<u16>>::SelectCursor<'a>),
Sparse(<SparseBlock<'a> as Set<u16>>::SelectCursor<'a>),
}
impl BlockSelectCursor<'_> {
fn select(&mut self, rank: u16) -> u16 {
match self {
BlockSelectCursor::Dense(dense_select_cursor) => dense_select_cursor.select(rank),
BlockSelectCursor::Sparse(sparse_select_cursor) => sparse_select_cursor.select(rank),
}
}
}
pub struct OptionalIndexSelectCursor<'a> {
current_block_cursor: BlockSelectCursor<'a>,
current_block_id: u16,
// The current block is guaranteed to contain ranks < end_rank.
current_block_end_rank: RowId,
optional_index: &'a OptionalIndex,
block_doc_idx_start: RowId,
num_null_rows_before_block: RowId,
}
impl OptionalIndexSelectCursor<'_> {
fn search_and_load_block(&mut self, rank: RowId) {
if rank < self.current_block_end_rank {
// we are already in the right block
return;
}
self.current_block_id = self.optional_index.find_block(rank, self.current_block_id);
self.current_block_end_rank = self
.optional_index
.block_metas
.get(self.current_block_id as usize + 1)
.map(|block_meta| block_meta.non_null_rows_before_block)
.unwrap_or(u32::MAX);
self.block_doc_idx_start = (self.current_block_id as u32) * ELEMENTS_PER_BLOCK;
let block_meta = self.optional_index.block_metas[self.current_block_id as usize];
self.num_null_rows_before_block = block_meta.non_null_rows_before_block;
let block: Block<'_> = self.optional_index.block(block_meta);
self.current_block_cursor = match block {
Block::Dense(dense_block) => BlockSelectCursor::Dense(dense_block.select_cursor()),
Block::Sparse(sparse_block) => BlockSelectCursor::Sparse(sparse_block.select_cursor()),
};
}
}
impl SelectCursor<RowId> for OptionalIndexSelectCursor<'_> {
fn select(&mut self, rank: RowId) -> RowId {
self.search_and_load_block(rank);
let index_in_block = (rank - self.num_null_rows_before_block) as u16;
self.current_block_cursor.select(index_in_block) as RowId + self.block_doc_idx_start
}
}
impl Set<RowId> for OptionalIndex {
type SelectCursor<'b>
= OptionalIndexSelectCursor<'b>
where Self: 'b;
// Check if value at position is not null.
#[inline]
fn contains(&self, row_id: RowId) -> bool {
@@ -130,14 +191,37 @@ impl Set<RowId> for OptionalIndex {
}
}
/// Any value doc_id is allowed.
/// In particular, doc_id = num_rows.
#[inline]
fn rank_if_exists(&self, row_id: RowId) -> Option<RowId> {
fn rank(&self, doc_id: DocId) -> RowId {
if doc_id >= self.num_docs() {
return self.num_non_nulls();
}
let RowAddr {
block_id,
in_block_row_id,
} = row_addr_from_row_id(row_id);
} = row_addr_from_row_id(doc_id);
let block_meta = self.block_metas[block_id as usize];
let block = self.block(block_meta);
let block_offset_row_id = match block {
Block::Dense(dense_block) => dense_block.rank(in_block_row_id),
Block::Sparse(sparse_block) => sparse_block.rank(in_block_row_id),
} as u32;
block_meta.non_null_rows_before_block + block_offset_row_id
}
/// Any value doc_id is allowed.
/// In particular, doc_id = num_rows.
#[inline]
fn rank_if_exists(&self, doc_id: DocId) -> Option<RowId> {
let RowAddr {
block_id,
in_block_row_id,
} = row_addr_from_row_id(doc_id);
let block_meta = *self.block_metas.get(block_id as usize)?;
let block = self.block(block_meta);
let block_offset_row_id = match block {
Block::Dense(dense_block) => dense_block.rank_if_exists(in_block_row_id),
Block::Sparse(sparse_block) => sparse_block.rank_if_exists(in_block_row_id),
@@ -148,7 +232,7 @@ impl Set<RowId> for OptionalIndex {
#[inline]
fn select(&self, rank: RowId) -> RowId {
let block_pos = self.find_block(rank, 0);
let block_doc_idx_start = block_pos * ELEMENTS_PER_BLOCK;
let block_doc_idx_start = (block_pos as u32) * ELEMENTS_PER_BLOCK;
let block_meta = self.block_metas[block_pos as usize];
let block: Block<'_> = self.block(block_meta);
let index_in_block = (rank - block_meta.non_null_rows_before_block) as u16;
@@ -159,41 +243,55 @@ impl Set<RowId> for OptionalIndex {
block_doc_idx_start + in_block_rank as u32
}
fn select_batch(&self, ranks: &[u32], output_idxs: &mut [u32]) {
let mut block_pos = 0u32;
let mut start = 0;
let group_by_it = ranks.iter().copied().group_by(move |codec_idx| {
block_pos = self.find_block(*codec_idx, block_pos);
block_pos
});
for (block_pos, block_iter) in group_by_it {
let block_doc_idx_start = block_pos * ELEMENTS_PER_BLOCK;
let block_meta = self.block_metas[block_pos as usize];
let block: Block<'_> = self.block(block_meta);
let offset = block_meta.non_null_rows_before_block;
let indexes_in_block_iter =
block_iter.map(move |codec_idx| (codec_idx - offset) as u16);
match block {
Block::Dense(dense_block) => {
for in_offset in dense_block.select_iter(indexes_in_block_iter) {
output_idxs[start] = in_offset as u32 + block_doc_idx_start;
start += 1;
}
}
Block::Sparse(sparse_block) => {
for in_offset in sparse_block.select_iter(indexes_in_block_iter) {
output_idxs[start] = in_offset as u32 + block_doc_idx_start;
start += 1;
}
}
};
fn select_cursor(&self) -> OptionalIndexSelectCursor<'_> {
OptionalIndexSelectCursor {
current_block_cursor: BlockSelectCursor::Sparse(
SparseBlockCodec::open(b"").select_cursor(),
),
current_block_id: 0u16,
current_block_end_rank: 0u32, //< this is sufficient to force the first load
optional_index: self,
block_doc_idx_start: 0u32,
num_null_rows_before_block: 0u32,
}
}
}
impl OptionalIndex {
pub fn for_test(num_rows: RowId, row_ids: &[RowId]) -> OptionalIndex {
assert!(row_ids
.last()
.copied()
.map(|last_row_id| last_row_id < num_rows)
.unwrap_or(true));
let mut buffer = Vec::new();
serialize_optional_index(&row_ids, num_rows, &mut buffer).unwrap();
let bytes = OwnedBytes::new(buffer);
open_optional_index(bytes).unwrap()
}
pub fn num_docs(&self) -> RowId {
self.num_rows
}
pub fn num_non_nulls(&self) -> RowId {
self.num_non_null_rows
}
pub fn iter_rows(&self) -> impl Iterator<Item = RowId> + '_ {
// TODO optimize
let mut select_batch = self.select_cursor();
(0..self.num_non_null_rows).map(move |rank| select_batch.select(rank))
}
pub fn select_batch(&self, ranks: &mut [RowId]) {
let mut select_cursor = self.select_cursor();
for rank in ranks.iter_mut() {
*rank = select_cursor.select(*rank);
}
}
#[inline]
fn block<'a>(&'a self, block_meta: BlockMeta) -> Block<'a> {
fn block(&self, block_meta: BlockMeta) -> Block<'_> {
let BlockMeta {
start_byte_offset,
block_variant,
@@ -214,14 +312,14 @@ impl OptionalIndex {
}
#[inline]
fn find_block(&self, dense_idx: u32, start_block_pos: u32) -> u32 {
for block_pos in start_block_pos..self.block_metas.len() as u32 {
fn find_block(&self, dense_idx: u32, start_block_pos: u16) -> u16 {
for block_pos in start_block_pos..self.block_metas.len() as u16 {
let offset = self.block_metas[block_pos as usize].non_null_rows_before_block;
if offset > dense_idx {
return block_pos - 1;
return block_pos - 1u16;
}
}
self.block_metas.len() as u32 - 1u32
self.block_metas.len() as u16 - 1u16
}
// TODO Add a good API for the codec_idx to original_idx translation.
@@ -255,7 +353,7 @@ impl OptionalIndexCodec {
}
impl BinarySerializable for OptionalIndexCodec {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
writer.write_all(&[self.to_code()])
}
@@ -276,13 +374,14 @@ fn serialize_optional_index_block(block_els: &[u16], out: &mut impl io::Write) -
Ok(())
}
pub fn serialize_optional_index<'a, W: io::Write>(
serializable_optional_index: &dyn SerializableOptionalIndex<'a>,
pub fn serialize_optional_index<W: io::Write>(
non_null_rows: &dyn Iterable<RowId>,
num_rows: RowId,
output: &mut W,
) -> io::Result<()> {
VInt(serializable_optional_index.num_rows() as u64).serialize(output)?;
VInt(num_rows as u64).serialize(output)?;
let mut rows_it = serializable_optional_index.non_null_rows();
let mut rows_it = non_null_rows.boxed_iter();
let mut block_metadata: Vec<SerializedBlockMeta> = Vec::new();
let mut current_block = Vec::new();
@@ -351,7 +450,7 @@ impl SerializedBlockMeta {
}
#[inline]
fn to_bytes(&self) -> [u8; SERIALIZED_BLOCK_META_NUM_BYTES] {
fn to_bytes(self) -> [u8; SERIALIZED_BLOCK_META_NUM_BYTES] {
assert!(self.num_non_null_rows > 0);
let mut bytes = [0u8; SERIALIZED_BLOCK_META_NUM_BYTES];
bytes[0..2].copy_from_slice(&self.block_id.to_le_bytes());
@@ -364,7 +463,7 @@ impl SerializedBlockMeta {
#[inline]
fn is_sparse(num_rows_in_block: u32) -> bool {
num_rows_in_block < DENSE_BLOCK_THRESHOLD as u32
num_rows_in_block < DENSE_BLOCK_THRESHOLD
}
fn deserialize_optional_index_block_metadatas(
@@ -372,7 +471,7 @@ fn deserialize_optional_index_block_metadatas(
num_rows: u32,
) -> (Box<[BlockMeta]>, u32) {
let num_blocks = data.len() / SERIALIZED_BLOCK_META_NUM_BYTES;
let mut block_metas = Vec::with_capacity(num_blocks as usize + 1);
let mut block_metas = Vec::with_capacity(num_blocks + 1);
let mut start_byte_offset = 0;
let mut non_null_rows_before_block = 0;
for block_meta_bytes in data.chunks_exact(SERIALIZED_BLOCK_META_NUM_BYTES) {
@@ -403,10 +502,10 @@ fn deserialize_optional_index_block_metadatas(
block_variant,
});
start_byte_offset += block_variant.num_bytes_in_block();
non_null_rows_before_block += num_non_null_rows as u32;
non_null_rows_before_block += num_non_null_rows;
}
block_metas.resize(
((num_rows + BLOCK_SIZE - 1) / BLOCK_SIZE) as usize,
num_rows.div_ceil(ELEMENTS_PER_BLOCK) as usize,
BlockMeta {
non_null_rows_before_block,
start_byte_offset,
@@ -425,7 +524,7 @@ pub fn open_optional_index(bytes: OwnedBytes) -> io::Result<OptionalIndex> {
num_non_empty_block_bytes as usize * SERIALIZED_BLOCK_META_NUM_BYTES;
let (block_data, block_metas) = bytes.rsplit(block_metas_num_bytes);
let (block_metas, num_non_null_rows) =
deserialize_optional_index_block_metadatas(block_metas.as_slice(), num_rows).into();
deserialize_optional_index_block_metadatas(block_metas.as_slice(), num_rows);
let optional_index = OptionalIndex {
num_rows,
num_non_null_rows,
@@ -435,19 +534,5 @@ pub fn open_optional_index(bytes: OwnedBytes) -> io::Result<OptionalIndex> {
Ok(optional_index)
}
pub trait SerializableOptionalIndex<'a> {
fn num_rows(&self) -> RowId;
fn non_null_rows(&self) -> Box<dyn Iterator<Item = RowId> + 'a>;
}
impl SerializableOptionalIndex<'static> for Range<u32> {
fn num_rows(&self) -> RowId {
self.end
}
fn non_null_rows(&self) -> Box<dyn Iterator<Item = RowId> + 'static> {
Box::new(self.clone())
}
}
#[cfg(test)]
mod tests;

31
columnar/src/column_index/optional_index/set.rs
View File

@@ -10,14 +10,29 @@ pub trait SetCodec {
///
/// May panic if the elements are not sorted.
fn serialize(els: impl Iterator<Item = Self::Item>, wrt: impl io::Write) -> io::Result<()>;
fn open<'a>(data: &'a [u8]) -> Self::Reader<'a>;
fn open(data: &[u8]) -> Self::Reader<'_>;
}
/// Stateful object that makes it possible to compute several select in a row,
/// provided the rank passed as argument are increasing.
pub trait SelectCursor<T> {
// May panic if rank is greater than the number of elements in the Set,
// or if rank is < than value provided in the previous call.
fn select(&mut self, rank: T) -> T;
}
pub trait Set<T> {
type SelectCursor<'b>: SelectCursor<T>
where Self: 'b;
/// Returns true if the elements is contained in the Set
fn contains(&self, el: T) -> bool;
/// If the set contains `el` returns its position in the sortd set of elements.
/// Returns the element's rank (its position in the set).
/// If the set does not contain the element, it will return the next existing elements rank.
fn rank(&self, el: T) -> T;
/// If the set contains `el`, returns the element's rank (its position in the set).
/// If the set does not contain the element, it returns `None`.
fn rank_if_exists(&self, el: T) -> Option<T>;
@@ -25,14 +40,10 @@ pub trait Set<T> {
///
/// # Panics
///
/// May panic if rank is greater than the number of elements in the Set.
/// May panic if rank is greater or equal to the number of
/// elements in the Set.
fn select(&self, rank: T) -> T;
/// Batch version of select.
/// `ranks` is assumed to be sorted.
///
/// # Panics
///
/// May panic if rank is greater than the number of elements in the Set.
fn select_batch(&self, ranks: &[T], outputs: &mut [T]);
/// Creates a brand new select cursor.
fn select_cursor(&self) -> Self::SelectCursor<'_>;
}

77
columnar/src/column_index/optional_index/set_block/dense.rs
View File

@@ -1,9 +1,8 @@
use std::convert::TryInto;
use std::io::{self, Write};
use common::BinarySerializable;
use crate::column_index::optional_index::{Set, SetCodec, ELEMENTS_PER_BLOCK};
use crate::column_index::optional_index::{SelectCursor, Set, SetCodec, ELEMENTS_PER_BLOCK};
#[inline(always)]
fn get_bit_at(input: u64, n: u16) -> bool {
@@ -24,7 +23,6 @@ fn set_bit_at(input: &mut u64, n: u16) {
///
/// When translating a dense index to the original index, we can use the offset to find the correct
/// block. Direct computation is not possible, but we can employ a linear or binary search.
const ELEMENTS_PER_MINI_BLOCK: u16 = 64;
const MINI_BLOCK_BITVEC_NUM_BYTES: usize = 8;
const MINI_BLOCK_OFFSET_NUM_BYTES: usize = 2;
@@ -32,7 +30,7 @@ pub const MINI_BLOCK_NUM_BYTES: usize = MINI_BLOCK_BITVEC_NUM_BYTES + MINI_BLOCK
/// Number of bytes in a dense block.
pub const DENSE_BLOCK_NUM_BYTES: u32 =
(ELEMENTS_PER_BLOCK as u32 / ELEMENTS_PER_MINI_BLOCK as u32) * MINI_BLOCK_NUM_BYTES as u32;
(ELEMENTS_PER_BLOCK / ELEMENTS_PER_MINI_BLOCK as u32) * MINI_BLOCK_NUM_BYTES as u32;
pub struct DenseBlockCodec;
@@ -45,7 +43,7 @@ impl SetCodec for DenseBlockCodec {
}
#[inline]
fn open<'a>(data: &'a [u8]) -> Self::Reader<'a> {
fn open(data: &[u8]) -> Self::Reader<'_> {
assert_eq!(data.len(), DENSE_BLOCK_NUM_BYTES as usize);
DenseBlock(data)
}
@@ -94,7 +92,7 @@ impl DenseMiniBlock {
Self { bitvec, rank }
}
fn to_bytes(&self) -> [u8; MINI_BLOCK_NUM_BYTES] {
fn to_bytes(self) -> [u8; MINI_BLOCK_NUM_BYTES] {
let mut bytes = [0u8; MINI_BLOCK_NUM_BYTES];
bytes[..MINI_BLOCK_BITVEC_NUM_BYTES].copy_from_slice(&self.bitvec.to_le_bytes());
bytes[MINI_BLOCK_BITVEC_NUM_BYTES..].copy_from_slice(&self.rank.to_le_bytes());
@@ -105,7 +103,29 @@ impl DenseMiniBlock {
#[derive(Copy, Clone)]
pub struct DenseBlock<'a>(&'a [u8]);
pub struct DenseBlockSelectCursor<'a> {
block_id: u16,
dense_block: DenseBlock<'a>,
}
impl SelectCursor<u16> for DenseBlockSelectCursor<'_> {
#[inline]
fn select(&mut self, rank: u16) -> u16 {
self.block_id = self
.dense_block
.find_miniblock_containing_rank(rank, self.block_id)
.unwrap();
let index_block = self.dense_block.mini_block(self.block_id);
let in_block_rank = rank - index_block.rank;
self.block_id * ELEMENTS_PER_MINI_BLOCK + select_u64(index_block.bitvec, in_block_rank)
}
}
impl<'a> Set<u16> for DenseBlock<'a> {
type SelectCursor<'b>
= DenseBlockSelectCursor<'a>
where Self: 'b;
#[inline(always)]
fn contains(&self, el: u16) -> bool {
let mini_block_id = el / ELEMENTS_PER_MINI_BLOCK;
@@ -128,6 +148,15 @@ impl<'a> Set<u16> for DenseBlock<'a> {
}
}
#[inline(always)]
fn rank(&self, el: u16) -> u16 {
let block_pos = el / ELEMENTS_PER_MINI_BLOCK;
let index_block = self.mini_block(block_pos);
let pos_in_block_bit_vec = el % ELEMENTS_PER_MINI_BLOCK;
let ones_in_block = rank_u64(index_block.bitvec, pos_in_block_bit_vec);
index_block.rank + ones_in_block
}
#[inline(always)]
fn select(&self, rank: u16) -> u16 {
let block_id = self.find_miniblock_containing_rank(rank, 0).unwrap();
@@ -136,38 +165,16 @@ impl<'a> Set<u16> for DenseBlock<'a> {
block_id * ELEMENTS_PER_MINI_BLOCK + select_u64(index_block.bitvec, in_block_rank)
}
fn select_batch(&self, ranks: &[u16], outputs: &mut [u16]) {
let orig_ids = self.select_iter(ranks.iter().copied());
for (output, original_id) in outputs.iter_mut().zip(orig_ids) {
*output = original_id;
#[inline(always)]
fn select_cursor(&self) -> Self::SelectCursor<'_> {
DenseBlockSelectCursor {
block_id: 0,
dense_block: *self,
}
}
}
impl<'a> DenseBlock<'a> {
/// Iterator verison of select.
///
/// # Panics
/// Panics if one of the rank is higher than the number of elements in the set.
pub fn select_iter<'b>(
&self,
rank_it: impl Iterator<Item = u16> + 'b,
) -> impl Iterator<Item = u16> + 'b
where
Self: 'b,
{
let mut block_id = 0u16;
let me = *self;
rank_it.map(move |rank| {
block_id = me.find_miniblock_containing_rank(rank, block_id).unwrap();
let index_block = me.mini_block(block_id);
let in_block_rank = rank - index_block.rank;
block_id * ELEMENTS_PER_MINI_BLOCK + select_u64(index_block.bitvec, in_block_rank)
})
}
}
impl<'a> DenseBlock<'a> {
impl DenseBlock<'_> {
#[inline]
fn mini_block(&self, mini_block_id: u16) -> DenseMiniBlock {
let data_start_pos = mini_block_id as usize * MINI_BLOCK_NUM_BYTES;
@@ -222,7 +229,7 @@ pub fn serialize_dense_codec(
while block_id > current_block_id {
let dense_mini_block = DenseMiniBlock {
bitvec: block,
rank: non_null_rows_before as u16,
rank: non_null_rows_before,
};
output.write_all(&dense_mini_block.to_bytes())?;
non_null_rows_before += block.count_ones() as u16;

47
columnar/src/column_index/optional_index/set_block/sparse.rs
View File

@@ -1,4 +1,4 @@
use crate::column_index::optional_index::{Set, SetCodec};
use crate::column_index::optional_index::{SelectCursor, Set, SetCodec};
pub struct SparseBlockCodec;
@@ -16,7 +16,7 @@ impl SetCodec for SparseBlockCodec {
Ok(())
}
fn open<'a>(data: &'a [u8]) -> Self::Reader<'a> {
fn open(data: &[u8]) -> Self::Reader<'_> {
SparseBlock(data)
}
}
@@ -24,7 +24,18 @@ impl SetCodec for SparseBlockCodec {
#[derive(Copy, Clone)]
pub struct SparseBlock<'a>(&'a [u8]);
impl<'a> Set<u16> for SparseBlock<'a> {
impl<'a> SelectCursor<u16> for SparseBlock<'a> {
#[inline]
fn select(&mut self, rank: u16) -> u16 {
<SparseBlock<'a> as Set<u16>>::select(self, rank)
}
}
impl Set<u16> for SparseBlock<'_> {
type SelectCursor<'b>
= Self
where Self: 'b;
#[inline(always)]
fn contains(&self, el: u16) -> bool {
self.binary_search(el).is_ok()
@@ -35,17 +46,20 @@ impl<'a> Set<u16> for SparseBlock<'a> {
self.binary_search(el).ok()
}
#[inline(always)]
fn rank(&self, el: u16) -> u16 {
self.binary_search(el).unwrap_or_else(|el| el)
}
#[inline(always)]
fn select(&self, rank: u16) -> u16 {
let offset = rank as usize * 2;
u16::from_le_bytes(self.0[offset..offset + 2].try_into().unwrap())
}
fn select_batch(&self, ranks: &[u16], outputs: &mut [u16]) {
let orig_ids = self.select_iter(ranks.iter().copied());
for (output, original_id) in outputs.iter_mut().zip(orig_ids) {
*output = original_id;
}
#[inline(always)]
fn select_cursor(&self) -> Self::SelectCursor<'_> {
*self
}
}
@@ -55,7 +69,7 @@ fn get_u16(data: &[u8], byte_position: usize) -> u16 {
u16::from_le_bytes(bytes)
}
impl<'a> SparseBlock<'a> {
impl SparseBlock<'_> {
#[inline(always)]
fn value_at_idx(&self, data: &[u8], idx: u16) -> u16 {
let start_offset: usize = idx as usize * 2;
@@ -68,7 +82,7 @@ impl<'a> SparseBlock<'a> {
}
#[inline]
#[allow(clippy::comparison_chain)]
#[expect(clippy::comparison_chain)]
// Looks for the element in the block. Returns the positions if found.
fn binary_search(&self, target: u16) -> Result<u16, u16> {
let data = &self.0;
@@ -96,17 +110,4 @@ impl<'a> SparseBlock<'a> {
}
Err(left)
}
pub fn select_iter<'b>(
&self,
iter: impl Iterator<Item = u16> + 'b,
) -> impl Iterator<Item = u16> + 'b
where
Self: 'b,
{
iter.map(|codec_id| {
let offset = codec_id as usize * 2;
u16::from_le_bytes(self.0[offset..offset + 2].try_into().unwrap())
})
}
}

83
columnar/src/column_index/optional_index/set_block/tests.rs
View File

@@ -1,9 +1,8 @@
use std::collections::HashMap;
use crate::column_index::optional_index::set_block::{
DenseBlockCodec, SparseBlockCodec, DENSE_BLOCK_NUM_BYTES,
};
use crate::column_index::optional_index::{Set, SetCodec};
use crate::column_index::optional_index::set_block::dense::DENSE_BLOCK_NUM_BYTES;
use crate::column_index::optional_index::set_block::{DenseBlockCodec, SparseBlockCodec};
use crate::column_index::optional_index::{SelectCursor, Set, SetCodec};
fn test_set_helper<C: SetCodec<Item = u16>>(vals: &[u16]) -> usize {
let mut buffer = Vec::new();
@@ -18,9 +17,13 @@ fn test_set_helper<C: SetCodec<Item = u16>>(vals: &[u16]) -> usize {
for val in 0u16..=u16::MAX {
assert_eq!(tested_set.contains(val), hash_set.contains_key(&val));
assert_eq!(tested_set.rank_if_exists(val), hash_set.get(&val).copied());
assert_eq!(
tested_set.rank(val),
vals.iter().cloned().take_while(|v| *v < val).count() as u16
);
}
for rank in 0..vals.len() {
assert_eq!(tested_set.select(rank as u16), vals[rank]);
for (rank, val) in vals.iter().enumerate() {
assert_eq!(tested_set.select(rank as u16), *val);
}
buffer.len()
}
@@ -75,12 +78,10 @@ fn test_simple_translate_codec_codec_idx_to_original_idx_dense() {
.unwrap();
let tested_set = DenseBlockCodec::open(buffer.as_slice());
assert!(tested_set.contains(1));
assert_eq!(
&tested_set
.select_iter([0, 1, 2, 5].iter().copied())
.collect::<Vec<u16>>(),
&[1, 3, 17, 30_001]
);
let mut select_cursor = tested_set.select_cursor();
assert_eq!(select_cursor.select(0), 1);
assert_eq!(select_cursor.select(1), 3);
assert_eq!(select_cursor.select(2), 17);
}
#[test]
@@ -89,12 +90,10 @@ fn test_simple_translate_codec_idx_to_original_idx_sparse() {
SparseBlockCodec::serialize([1, 3, 17].iter().copied(), &mut buffer).unwrap();
let tested_set = SparseBlockCodec::open(buffer.as_slice());
assert!(tested_set.contains(1));
assert_eq!(
&tested_set
.select_iter([0, 1, 2].iter().copied())
.collect::<Vec<u16>>(),
&[1, 3, 17]
);
let mut select_cursor = tested_set.select_cursor();
assert_eq!(SelectCursor::select(&mut select_cursor, 0), 1);
assert_eq!(SelectCursor::select(&mut select_cursor, 1), 3);
assert_eq!(SelectCursor::select(&mut select_cursor, 2), 17);
}
#[test]
@@ -103,10 +102,46 @@ fn test_simple_translate_codec_idx_to_original_idx_dense() {
DenseBlockCodec::serialize(0u16..150u16, &mut buffer).unwrap();
let tested_set = DenseBlockCodec::open(buffer.as_slice());
assert!(tested_set.contains(1));
let rg = 0u16..150u16;
let els: Vec<u16> = rg.clone().collect();
assert_eq!(
&tested_set.select_iter(rg.clone()).collect::<Vec<u16>>(),
&els
);
let mut select_cursor = tested_set.select_cursor();
for i in 0..150 {
assert_eq!(i, select_cursor.select(i));
}
}
#[test]
fn test_simple_translate_idx_to_value_idx_dense() {
let mut buffer = Vec::new();
DenseBlockCodec::serialize([1, 10].iter().copied(), &mut buffer).unwrap();
let tested_set = DenseBlockCodec::open(buffer.as_slice());
assert!(tested_set.contains(1));
assert!(!tested_set.contains(2));
assert_eq!(tested_set.rank(0), 0);
assert_eq!(tested_set.rank(1), 0);
for rank in 2..10 {
// ranks that don't exist select the next highest one
assert_eq!(tested_set.rank_if_exists(rank), None);
assert_eq!(tested_set.rank(rank), 1);
}
assert_eq!(tested_set.rank(10), 1);
}
#[test]
fn test_simple_translate_idx_to_value_idx_sparse() {
let mut buffer = Vec::new();
SparseBlockCodec::serialize([1, 10].iter().copied(), &mut buffer).unwrap();
let tested_set = SparseBlockCodec::open(buffer.as_slice());
assert!(tested_set.contains(1));
assert!(!tested_set.contains(2));
assert_eq!(tested_set.rank(0), 0);
assert_eq!(tested_set.select(tested_set.rank(0)), 1);
assert_eq!(tested_set.rank(1), 0);
assert_eq!(tested_set.select(tested_set.rank(1)), 1);
for rank in 2..10 {
// ranks that don't exist select the next highest one
assert_eq!(tested_set.rank_if_exists(rank), None);
assert_eq!(tested_set.rank(rank), 1);
assert_eq!(tested_set.select(tested_set.rank(rank)), 10);
}
assert_eq!(tested_set.rank(10), 1);
assert_eq!(tested_set.select(tested_set.rank(10)), 10);
}

179
columnar/src/column_index/optional_index/tests.rs
View File

@@ -1,8 +1,29 @@
use proptest::prelude::{any, prop, *};
use proptest::strategy::Strategy;
use proptest::prelude::*;
use proptest::{prop_oneof, proptest};
use super::*;
use crate::{ColumnarReader, ColumnarWriter, DynamicColumnHandle};
#[test]
fn test_optional_index_bug_2293() {
// tests for panic in docid_range_to_rowids for docid == num_docs
test_optional_index_with_num_docs(ELEMENTS_PER_BLOCK - 1);
test_optional_index_with_num_docs(ELEMENTS_PER_BLOCK);
test_optional_index_with_num_docs(ELEMENTS_PER_BLOCK + 1);
}
fn test_optional_index_with_num_docs(num_docs: u32) {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_numerical(100, "score", 80i64);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(num_docs, &mut buffer).unwrap();
let columnar = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar.num_columns(), 1);
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("score").unwrap();
assert_eq!(cols.len(), 1);
let col = cols[0].open().unwrap();
col.column_index().docid_range_to_rowids(0..num_docs);
}
#[test]
fn test_dense_block_threshold() {
@@ -35,15 +56,16 @@ proptest! {
#[test]
fn test_with_random_sets_simple() {
let vals = 10..BLOCK_SIZE * 2;
let vals = 10..ELEMENTS_PER_BLOCK * 2;
let mut out: Vec<u8> = Vec::new();
serialize_optional_index(&vals.clone(), &mut out).unwrap();
serialize_optional_index(&vals, 100, &mut out).unwrap();
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
let ranks: Vec<u32> = (65_472u32..65_473u32).collect();
let els: Vec<u32> = ranks.iter().copied().map(|rank| rank + 10).collect();
let mut output = vec![0u32; ranks.len()];
null_index.select_batch(&ranks[..], &mut output[..]);
assert_eq!(&output, &els);
let mut select_cursor = null_index.select_cursor();
for (rank, el) in ranks.iter().copied().zip(els.iter().copied()) {
assert_eq!(select_cursor.select(rank), el);
}
}
#[test]
@@ -65,12 +87,8 @@ fn test_optional_index_one_block_true() {
test_null_index(&iter[..]);
}
impl<'a> SerializableOptionalIndex<'a> for &'a [bool] {
fn num_rows(&self) -> RowId {
self.len() as u32
}
fn non_null_rows(&self) -> Box<dyn Iterator<Item = RowId> + 'a> {
impl<'a> Iterable<RowId> for &'a [bool] {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = RowId> + 'a> {
Box::new(
self.iter()
.cloned()
@@ -83,7 +101,7 @@ impl<'a> SerializableOptionalIndex<'a> for &'a [bool] {
fn test_null_index(data: &[bool]) {
let mut out: Vec<u8> = Vec::new();
serialize_optional_index(&data, &mut out).unwrap();
serialize_optional_index(&data, data.len() as RowId, &mut out).unwrap();
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
let orig_idx_with_value: Vec<u32> = data
.iter()
@@ -91,11 +109,10 @@ fn test_null_index(data: &[bool]) {
.filter(|(_pos, val)| **val)
.map(|(pos, _val)| pos as u32)
.collect();
let ids: Vec<u32> = (0..orig_idx_with_value.len() as u32).collect();
let mut output = vec![0u32; ids.len()];
null_index.select_batch(&ids[..], &mut output);
// assert_eq!(&output[0..100], &orig_idx_with_value[0..100]);
assert_eq!(output, orig_idx_with_value);
let mut select_iter = null_index.select_cursor();
for (i, expected) in orig_idx_with_value.iter().enumerate() {
assert_eq!(select_iter.select(i as u32), *expected);
}
let step_size = (orig_idx_with_value.len() / 100).max(1);
for (dense_idx, orig_idx) in orig_idx_with_value.iter().enumerate().step_by(step_size) {
@@ -111,51 +128,96 @@ fn test_null_index(data: &[bool]) {
#[test]
fn test_optional_index_test_translation() {
let mut out = vec![];
let iter = &[true, false, true, false];
serialize_optional_index(&&iter[..], &mut out).unwrap();
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
let mut output = vec![0u32; 2];
null_index.select_batch(&[0, 1], &mut output);
assert_eq!(output, &[0, 2]);
let optional_index = OptionalIndex::for_test(4, &[0, 2]);
let mut select_cursor = optional_index.select_cursor();
assert_eq!(select_cursor.select(0), 0);
assert_eq!(select_cursor.select(1), 2);
}
#[test]
fn test_optional_index_translate() {
let mut out = vec![];
let iter = &[true, false, true, false];
serialize_optional_index(&&iter[..], &mut out).unwrap();
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
assert_eq!(null_index.rank_if_exists(0), Some(0));
assert_eq!(null_index.rank_if_exists(2), Some(1));
let optional_index = OptionalIndex::for_test(4, &[0, 2]);
assert_eq!(optional_index.rank_if_exists(0), Some(0));
assert_eq!(optional_index.rank_if_exists(2), Some(1));
}
#[test]
fn test_optional_index_small() {
let mut out = vec![];
let iter = &[true, false, true, false];
serialize_optional_index(&&iter[..], &mut out).unwrap();
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
assert!(null_index.contains(0));
assert!(!null_index.contains(1));
assert!(null_index.contains(2));
assert!(!null_index.contains(3));
let optional_index = OptionalIndex::for_test(4, &[0, 2]);
assert!(optional_index.contains(0));
assert!(!optional_index.contains(1));
assert!(optional_index.contains(2));
assert!(!optional_index.contains(3));
}
#[test]
fn test_optional_index_large() {
let mut docs = vec![];
docs.extend((0..ELEMENTS_PER_BLOCK).map(|_idx| false));
docs.extend((0..=1).map(|_idx| true));
let row_ids = &[ELEMENTS_PER_BLOCK, ELEMENTS_PER_BLOCK + 1];
let optional_index = OptionalIndex::for_test(ELEMENTS_PER_BLOCK + 2, row_ids);
assert!(!optional_index.contains(0));
assert!(!optional_index.contains(100));
assert!(!optional_index.contains(ELEMENTS_PER_BLOCK - 1));
assert!(optional_index.contains(ELEMENTS_PER_BLOCK));
assert!(optional_index.contains(ELEMENTS_PER_BLOCK + 1));
}
let mut out = vec![];
serialize_optional_index(&&docs[..], &mut out).unwrap();
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
assert!(!null_index.contains(0));
assert!(!null_index.contains(100));
assert!(!null_index.contains(ELEMENTS_PER_BLOCK - 1));
assert!(null_index.contains(ELEMENTS_PER_BLOCK));
assert!(null_index.contains(ELEMENTS_PER_BLOCK + 1));
fn test_optional_index_iter_aux(row_ids: &[RowId], num_rows: RowId) {
let optional_index = OptionalIndex::for_test(num_rows, row_ids);
assert_eq!(optional_index.num_docs(), num_rows);
assert!(optional_index.iter_rows().eq(row_ids.iter().copied()));
}
#[test]
fn test_optional_index_iter_empty() {
test_optional_index_iter_aux(&[], 0u32);
}
fn test_optional_index_rank_aux(row_ids: &[RowId]) {
let num_rows = row_ids.last().copied().unwrap_or(0u32) + 1;
let null_index = OptionalIndex::for_test(num_rows, row_ids);
assert_eq!(null_index.num_docs(), num_rows);
for (row_id, row_val) in row_ids.iter().copied().enumerate() {
assert_eq!(null_index.rank(row_val), row_id as u32);
assert_eq!(null_index.rank_if_exists(row_val), Some(row_id as u32));
if row_val > 0 && !null_index.contains(&row_val - 1) {
assert_eq!(null_index.rank(row_val - 1), row_id as u32);
}
assert_eq!(null_index.rank(row_val + 1), row_id as u32 + 1);
}
}
#[test]
fn test_optional_index_rank() {
test_optional_index_rank_aux(&[1u32]);
test_optional_index_rank_aux(&[0u32, 1u32]);
let mut block = Vec::new();
block.push(3u32);
block.extend((0..ELEMENTS_PER_BLOCK).map(|i| i + ELEMENTS_PER_BLOCK + 1));
test_optional_index_rank_aux(&block);
}
#[test]
fn test_optional_index_iter_empty_one() {
test_optional_index_iter_aux(&[1], 2u32);
test_optional_index_iter_aux(&[100_000], 200_000u32);
}
#[test]
fn test_optional_index_iter_dense_block() {
let mut block = Vec::new();
block.push(3u32);
block.extend((0..ELEMENTS_PER_BLOCK).map(|i| i + ELEMENTS_PER_BLOCK + 1));
test_optional_index_iter_aux(&block, 3 * ELEMENTS_PER_BLOCK);
}
#[test]
fn test_optional_index_for_tests() {
let optional_index = OptionalIndex::for_test(4, &[1, 2]);
assert!(!optional_index.contains(0));
assert!(optional_index.contains(1));
assert!(optional_index.contains(2));
assert!(!optional_index.contains(3));
assert_eq!(optional_index.num_docs(), 4);
}
#[cfg(all(test, feature = "unstable"))]
@@ -171,13 +233,15 @@ mod bench {
fn gen_bools(fill_ratio: f64) -> OptionalIndex {
let mut out = Vec::new();
let mut rng: StdRng = StdRng::from_seed([1u8; 32]);
let vals: Vec<bool> = (0..TOTAL_NUM_VALUES)
let vals: Vec<RowId> = (0..TOTAL_NUM_VALUES)
.map(|_| rng.gen_bool(fill_ratio))
.enumerate()
.filter(|(_pos, val)| *val)
.map(|(pos, _)| pos as RowId)
.collect();
serialize_optional_index(&&vals[..], &mut out).unwrap();
serialize_optional_index(&&vals[..], TOTAL_NUM_VALUES, &mut out).unwrap();
let codec = open_optional_index(OwnedBytes::new(out)).unwrap();
codec
open_optional_index(OwnedBytes::new(out)).unwrap()
}
fn random_range_iterator(
@@ -199,7 +263,7 @@ mod bench {
}
fn n_percent_step_iterator(percent: f32, num_values: u32) -> impl Iterator<Item = u32> {
let ratio = percent as f32 / 100.0;
let ratio = percent / 100.0;
let step_size = (1f32 / ratio) as u32;
let deviation = step_size - 1;
random_range_iterator(0, num_values, step_size, deviation)
@@ -311,7 +375,8 @@ mod bench {
};
let mut output = vec![0u32; idxs.len()];
bench.iter(|| {
codec.select_batch(&idxs[..], &mut output);
output.copy_from_slice(&idxs[..]);
codec.select_batch(&mut output);
});
}

51
columnar/src/column_index/serialize.rs
View File

@@ -3,21 +3,35 @@ use std::io::Write;
use common::{CountingWriter, OwnedBytes};
use super::multivalued_index::SerializableMultivalueIndex;
use super::OptionalIndex;
use crate::column_index::multivalued_index::serialize_multivalued_index;
use crate::column_index::optional_index::serialize_optional_index;
use crate::column_index::{ColumnIndex, SerializableOptionalIndex};
use crate::column_values::ColumnValues;
use crate::{Cardinality, RowId};
use crate::column_index::ColumnIndex;
use crate::iterable::Iterable;
use crate::{Cardinality, RowId, Version};
pub struct SerializableOptionalIndex<'a> {
pub non_null_row_ids: Box<dyn Iterable<RowId> + 'a>,
pub num_rows: RowId,
}
impl<'a> From<&'a OptionalIndex> for SerializableOptionalIndex<'a> {
fn from(optional_index: &'a OptionalIndex) -> Self {
SerializableOptionalIndex {
non_null_row_ids: Box::new(optional_index),
num_rows: optional_index.num_docs(),
}
}
}
pub enum SerializableColumnIndex<'a> {
Full,
Optional(Box<dyn SerializableOptionalIndex<'a> + 'a>),
// TODO remove the Arc<dyn> apart from serialization this is not
// dynamic at all.
Multivalued(Box<dyn ColumnValues<RowId> + 'a>),
Optional(SerializableOptionalIndex<'a>),
Multivalued(SerializableMultivalueIndex<'a>),
}
impl<'a> SerializableColumnIndex<'a> {
impl SerializableColumnIndex<'_> {
pub fn get_cardinality(&self) -> Cardinality {
match self {
SerializableColumnIndex::Full => Cardinality::Full,
@@ -27,6 +41,7 @@ impl<'a> SerializableColumnIndex<'a> {
}
}
/// Serialize a column index.
pub fn serialize_column_index(
column_index: SerializableColumnIndex,
output: &mut impl Write,
@@ -36,18 +51,23 @@ pub fn serialize_column_index(
output.write_all(&[cardinality])?;
match column_index {
SerializableColumnIndex::Full => {}
SerializableColumnIndex::Optional(optional_index) => {
serialize_optional_index(&*optional_index, &mut output)?
}
SerializableColumnIndex::Optional(SerializableOptionalIndex {
non_null_row_ids,
num_rows,
}) => serialize_optional_index(non_null_row_ids.as_ref(), num_rows, &mut output)?,
SerializableColumnIndex::Multivalued(multivalued_index) => {
serialize_multivalued_index(&*multivalued_index, &mut output)?
serialize_multivalued_index(&multivalued_index, &mut output)?
}
}
let column_index_num_bytes = output.written_bytes() as u32;
Ok(column_index_num_bytes)
}
pub fn open_column_index(mut bytes: OwnedBytes) -> io::Result<ColumnIndex<'static>> {
/// Open a serialized column index.
pub fn open_column_index(
mut bytes: OwnedBytes,
format_version: Version,
) -> io::Result<ColumnIndex> {
if bytes.is_empty() {
return Err(io::Error::new(
io::ErrorKind::UnexpectedEof,
@@ -64,8 +84,9 @@ pub fn open_column_index(mut bytes: OwnedBytes) -> io::Result<ColumnIndex<'stati
Ok(ColumnIndex::Optional(optional_index))
}
Cardinality::Multivalued => {
let multivalued_index = super::multivalued_index::open_multivalued_index(bytes)?;
Ok(ColumnIndex::Multivalued(multivalued_index))
let multivalue_index =
super::multivalued_index::open_multivalued_index(bytes, format_version)?;
Ok(ColumnIndex::Multivalued(multivalue_index))
}
}
}

139
columnar/src/column_values/bench.rs Normal file
View File

@@ -0,0 +1,139 @@
use std::sync::Arc;
use common::OwnedBytes;
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use test::{self, Bencher};
use super::*;
use crate::column_values::u64_based::*;
fn get_data() -> Vec<u64> {
let mut rng = StdRng::seed_from_u64(2u64);
let mut data: Vec<_> = (100..55000_u64)
.map(|num| num + rng.gen::<u8>() as u64)
.collect();
data.push(99_000);
data.insert(1000, 2000);
data.insert(2000, 100);
data.insert(3000, 4100);
data.insert(4000, 100);
data.insert(5000, 800);
data
}
fn compute_stats(vals: impl Iterator<Item = u64>) -> ColumnStats {
let mut stats_collector = StatsCollector::default();
for val in vals {
stats_collector.collect(val);
}
stats_collector.stats()
}
#[inline(never)]
fn value_iter() -> impl Iterator<Item = u64> {
0..20_000
}
fn get_reader_for_bench<Codec: ColumnCodec>(data: &[u64]) -> Codec::ColumnValues {
let mut bytes = Vec::new();
let stats = compute_stats(data.iter().cloned());
let mut codec_serializer = Codec::estimator();
for val in data {
codec_serializer.collect(*val);
}
codec_serializer
.serialize(&stats, Box::new(data.iter().copied()).as_mut(), &mut bytes)
.unwrap();
Codec::load(OwnedBytes::new(bytes)).unwrap()
}
fn bench_get<Codec: ColumnCodec>(b: &mut Bencher, data: &[u64]) {
let col = get_reader_for_bench::<Codec>(data);
b.iter(|| {
let mut sum = 0u64;
for pos in value_iter() {
let val = col.get_val(pos as u32);
sum = sum.wrapping_add(val);
}
sum
});
}
#[inline(never)]
fn bench_get_dynamic_helper(b: &mut Bencher, col: Arc<dyn ColumnValues>) {
b.iter(|| {
let mut sum = 0u64;
for pos in value_iter() {
let val = col.get_val(pos as u32);
sum = sum.wrapping_add(val);
}
sum
});
}
fn bench_get_dynamic<Codec: ColumnCodec>(b: &mut Bencher, data: &[u64]) {
let col = Arc::new(get_reader_for_bench::<Codec>(data));
bench_get_dynamic_helper(b, col);
}
fn bench_create<Codec: ColumnCodec>(b: &mut Bencher, data: &[u64]) {
let stats = compute_stats(data.iter().cloned());
let mut bytes = Vec::new();
b.iter(|| {
bytes.clear();
let mut codec_serializer = Codec::estimator();
for val in data.iter().take(1024) {
codec_serializer.collect(*val);
}
codec_serializer.serialize(&stats, Box::new(data.iter().copied()).as_mut(), &mut bytes)
});
}
#[bench]
fn bench_fastfield_bitpack_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<BitpackedCodec>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<LinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<BlockwiseLinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_bitpack_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<BitpackedCodec>(b, &data);
}
#[bench]
fn bench_fastfield_bitpack_get_dynamic(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get_dynamic::<BitpackedCodec>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<LinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_get_dynamic(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get_dynamic::<LinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<BlockwiseLinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_get_dynamic(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get_dynamic::<BlockwiseLinearCodec>(b, &data);
}

115
columnar/src/column_values/bitpacked.rs
View File

@@ -1,115 +0,0 @@
use std::io::{self, Write};
use common::OwnedBytes;
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use super::serialize::NormalizedHeader;
use super::{ColumnValues, FastFieldCodec, FastFieldCodecType};
/// Depending on the field type, a different
/// fast field is required.
#[derive(Clone)]
pub struct BitpackedReader {
data: OwnedBytes,
bit_unpacker: BitUnpacker,
normalized_header: NormalizedHeader,
}
impl ColumnValues for BitpackedReader {
#[inline]
fn get_val(&self, doc: u32) -> u64 {
self.bit_unpacker.get(doc, &self.data)
}
#[inline]
fn min_value(&self) -> u64 {
// The BitpackedReader assumes a normalized vector.
0
}
#[inline]
fn max_value(&self) -> u64 {
self.normalized_header.max_value
}
#[inline]
fn num_vals(&self) -> u32 {
self.normalized_header.num_vals
}
}
pub struct BitpackedCodec;
impl FastFieldCodec for BitpackedCodec {
/// The CODEC_TYPE is an enum value used for serialization.
const CODEC_TYPE: FastFieldCodecType = FastFieldCodecType::Bitpacked;
type Reader = BitpackedReader;
/// Opens a fast field given a file.
fn open_from_bytes(
data: OwnedBytes,
normalized_header: NormalizedHeader,
) -> io::Result<Self::Reader> {
let num_bits = compute_num_bits(normalized_header.max_value);
let bit_unpacker = BitUnpacker::new(num_bits);
Ok(BitpackedReader {
data,
bit_unpacker,
normalized_header,
})
}
/// Serializes data with the BitpackedFastFieldSerializer.
///
/// The bitpacker assumes that the column has been normalized.
/// i.e. It has already been shifted by its minimum value, so that its
/// current minimum value is 0.
///
/// Ideally, we made a shift upstream on the column so that `col.min_value() == 0`.
fn serialize(column: &dyn ColumnValues, write: &mut impl Write) -> io::Result<()> {
assert_eq!(column.min_value(), 0u64);
let num_bits = compute_num_bits(column.max_value());
let mut bit_packer = BitPacker::new();
for val in column.iter() {
bit_packer.write(val, num_bits, write)?;
}
bit_packer.close(write)?;
Ok(())
}
fn estimate(column: &dyn ColumnValues) -> Option<f32> {
let num_bits = compute_num_bits(column.max_value());
let num_bits_uncompressed = 64;
Some(num_bits as f32 / num_bits_uncompressed as f32)
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_values::tests::create_and_validate;
fn create_and_validate_bitpacked_codec(data: &[u64], name: &str) {
create_and_validate::<BitpackedCodec>(data, name);
}
#[test]
fn test_with_codec_data_sets() {
let data_sets = crate::column_values::tests::get_codec_test_datasets();
for (mut data, name) in data_sets {
create_and_validate_bitpacked_codec(&data, name);
data.reverse();
create_and_validate::<BitpackedCodec>(&data, name);
}
}
#[test]
fn bitpacked_fast_field_rand() {
for _ in 0..500 {
let mut data = (0..1 + rand::random::<u8>() as usize)
.map(|_| rand::random::<i64>() as u64 / 2)
.collect::<Vec<_>>();
create_and_validate_bitpacked_codec(&data, "rand");
data.reverse();
create_and_validate::<BitpackedCodec>(&data, "rand");
}
}
}

188
columnar/src/column_values/blockwise_linear.rs
View File

@@ -1,188 +0,0 @@
use std::sync::Arc;
use std::{io, iter};
use common::{BinarySerializable, CountingWriter, DeserializeFrom, OwnedBytes};
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::column_values::line::Line;
use crate::column_values::serialize::NormalizedHeader;
use crate::column_values::{ColumnValues, FastFieldCodec, FastFieldCodecType, VecColumn};
const CHUNK_SIZE: usize = 512;
#[derive(Debug, Default)]
struct Block {
line: Line,
bit_unpacker: BitUnpacker,
data_start_offset: usize,
}
impl BinarySerializable for Block {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
self.line.serialize(writer)?;
self.bit_unpacker.bit_width().serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let line = Line::deserialize(reader)?;
let bit_width = u8::deserialize(reader)?;
Ok(Block {
line,
bit_unpacker: BitUnpacker::new(bit_width),
data_start_offset: 0,
})
}
}
fn compute_num_blocks(num_vals: u32) -> usize {
(num_vals as usize + CHUNK_SIZE - 1) / CHUNK_SIZE
}
pub struct BlockwiseLinearCodec;
impl FastFieldCodec for BlockwiseLinearCodec {
const CODEC_TYPE: FastFieldCodecType = FastFieldCodecType::BlockwiseLinear;
type Reader = BlockwiseLinearReader;
fn open_from_bytes(
bytes: common::OwnedBytes,
normalized_header: NormalizedHeader,
) -> io::Result<Self::Reader> {
let footer_len: u32 = (&bytes[bytes.len() - 4..]).deserialize()?;
let footer_offset = bytes.len() - 4 - footer_len as usize;
let (data, mut footer) = bytes.split(footer_offset);
let num_blocks = compute_num_blocks(normalized_header.num_vals);
let mut blocks: Vec<Block> = iter::repeat_with(|| Block::deserialize(&mut footer))
.take(num_blocks)
.collect::<io::Result<_>>()?;
let mut start_offset = 0;
for block in &mut blocks {
block.data_start_offset = start_offset;
start_offset += (block.bit_unpacker.bit_width() as usize) * CHUNK_SIZE / 8;
}
Ok(BlockwiseLinearReader {
blocks: Arc::new(blocks),
data,
normalized_header,
})
}
// Estimate first_chunk and extrapolate
fn estimate(column: &dyn ColumnValues) -> Option<f32> {
if column.num_vals() < 10 * CHUNK_SIZE as u32 {
return None;
}
let mut first_chunk: Vec<u64> = column.iter().take(CHUNK_SIZE).collect();
let line = Line::train(&VecColumn::from(&first_chunk));
for (i, buffer_val) in first_chunk.iter_mut().enumerate() {
let interpolated_val = line.eval(i as u32);
*buffer_val = buffer_val.wrapping_sub(interpolated_val);
}
let estimated_bit_width = first_chunk
.iter()
.map(|el| ((el + 1) as f32 * 3.0) as u64)
.map(compute_num_bits)
.max()
.unwrap();
let metadata_per_block = {
let mut out = vec![];
Block::default().serialize(&mut out).unwrap();
out.len()
};
let num_bits = estimated_bit_width as u64 * column.num_vals() as u64
// function metadata per block
+ metadata_per_block as u64 * (column.num_vals() as u64 / CHUNK_SIZE as u64);
let num_bits_uncompressed = 64 * column.num_vals();
Some(num_bits as f32 / num_bits_uncompressed as f32)
}
fn serialize(column: &dyn ColumnValues, wrt: &mut impl io::Write) -> io::Result<()> {
// The BitpackedReader assumes a normalized vector.
assert_eq!(column.min_value(), 0);
let mut buffer = Vec::with_capacity(CHUNK_SIZE);
let num_vals = column.num_vals();
let num_blocks = compute_num_blocks(num_vals);
let mut blocks = Vec::with_capacity(num_blocks);
let mut vals = column.iter();
let mut bit_packer = BitPacker::new();
for _ in 0..num_blocks {
buffer.clear();
buffer.extend((&mut vals).take(CHUNK_SIZE));
let line = Line::train(&VecColumn::from(&buffer));
assert!(!buffer.is_empty());
for (i, buffer_val) in buffer.iter_mut().enumerate() {
let interpolated_val = line.eval(i as u32);
*buffer_val = buffer_val.wrapping_sub(interpolated_val);
}
let bit_width = buffer.iter().copied().map(compute_num_bits).max().unwrap();
for &buffer_val in &buffer {
bit_packer.write(buffer_val, bit_width, wrt)?;
}
blocks.push(Block {
line,
bit_unpacker: BitUnpacker::new(bit_width),
data_start_offset: 0,
});
}
bit_packer.close(wrt)?;
assert_eq!(blocks.len(), compute_num_blocks(num_vals));
let mut counting_wrt = CountingWriter::wrap(wrt);
for block in &blocks {
block.serialize(&mut counting_wrt)?;
}
let footer_len = counting_wrt.written_bytes();
(footer_len as u32).serialize(&mut counting_wrt)?;
Ok(())
}
}
#[derive(Clone)]
pub struct BlockwiseLinearReader {
blocks: Arc<Vec<Block>>,
normalized_header: NormalizedHeader,
data: OwnedBytes,
}
impl ColumnValues for BlockwiseLinearReader {
#[inline(always)]
fn get_val(&self, idx: u32) -> u64 {
let block_id = (idx / CHUNK_SIZE as u32) as usize;
let idx_within_block = idx % (CHUNK_SIZE as u32);
let block = &self.blocks[block_id];
let interpoled_val: u64 = block.line.eval(idx_within_block);
let block_bytes = &self.data[block.data_start_offset..];
let bitpacked_diff = block.bit_unpacker.get(idx_within_block, block_bytes);
interpoled_val.wrapping_add(bitpacked_diff)
}
#[inline(always)]
fn min_value(&self) -> u64 {
// The BlockwiseLinearReader assumes a normalized vector.
0u64
}
#[inline(always)]
fn max_value(&self) -> u64 {
self.normalized_header.max_value
}
#[inline(always)]
fn num_vals(&self) -> u32 {
self.normalized_header.num_vals
}
}

376
columnar/src/column_values/column.rs
View File

@@ -1,376 +0,0 @@
use std::marker::PhantomData;
use std::ops::{Range, RangeInclusive};
use tantivy_bitpacker::minmax;
use crate::column_values::monotonic_mapping::StrictlyMonotonicFn;
/// `ColumnValues` provides access to a dense field column.
///
/// `Column` are just a wrapper over `ColumnValues` and a `ColumnIndex`.
pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync {
/// Return the value associated with the given idx.
///
/// This accessor should return as fast as possible.
///
/// # Panics
///
/// May panic if `idx` is greater than the column length.
fn get_val(&self, idx: u32) -> T;
/// Fills an output buffer with the fast field values
/// associated with the `DocId` going from
/// `start` to `start + output.len()`.
///
/// # Panics
///
/// Must panic if `start + output.len()` is greater than
/// the segment's `maxdoc`.
#[inline]
fn get_range(&self, start: u64, output: &mut [T]) {
for (out, idx) in output.iter_mut().zip(start..) {
*out = self.get_val(idx as u32);
}
}
/// Get the positions of values which are in the provided value range.
///
/// Note that position == docid for single value fast fields
#[inline]
fn get_docids_for_value_range(
&self,
value_range: RangeInclusive<T>,
doc_id_range: Range<u32>,
positions: &mut Vec<u32>,
) {
let doc_id_range = doc_id_range.start..doc_id_range.end.min(self.num_vals());
for idx in doc_id_range.start..doc_id_range.end {
let val = self.get_val(idx);
if value_range.contains(&val) {
positions.push(idx);
}
}
}
/// Returns the minimum value for this fast field.
///
/// This min_value may not be exact.
/// For instance, the min value does not take in account of possible
/// deleted document. All values are however guaranteed to be higher than
/// `.min_value()`.
fn min_value(&self) -> T;
/// Returns the maximum value for this fast field.
///
/// This max_value may not be exact.
/// For instance, the max value does not take in account of possible
/// deleted document. All values are however guaranteed to be higher than
/// `.max_value()`.
fn max_value(&self) -> T;
/// The number of values in the column.
fn num_vals(&self) -> u32;
/// Returns a iterator over the data
fn iter<'a>(&'a self) -> Box<dyn Iterator<Item = T> + 'a> {
Box::new((0..self.num_vals()).map(|idx| self.get_val(idx)))
}
}
impl<T: Copy + PartialOrd> ColumnValues<T> for std::sync::Arc<dyn ColumnValues<T>> {
fn get_val(&self, idx: u32) -> T {
self.as_ref().get_val(idx)
}
fn min_value(&self) -> T {
self.as_ref().min_value()
}
fn max_value(&self) -> T {
self.as_ref().max_value()
}
fn num_vals(&self) -> u32 {
self.as_ref().num_vals()
}
fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
self.as_ref().iter()
}
fn get_range(&self, start: u64, output: &mut [T]) {
self.as_ref().get_range(start, output)
}
}
impl<'a, C: ColumnValues<T> + ?Sized, T: Copy + PartialOrd> ColumnValues<T> for &'a C {
fn get_val(&self, idx: u32) -> T {
(*self).get_val(idx)
}
fn min_value(&self) -> T {
(*self).min_value()
}
fn max_value(&self) -> T {
(*self).max_value()
}
fn num_vals(&self) -> u32 {
(*self).num_vals()
}
fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
(*self).iter()
}
fn get_range(&self, start: u64, output: &mut [T]) {
(*self).get_range(start, output)
}
}
/// VecColumn provides `Column` over a slice.
pub struct VecColumn<'a, T = u64> {
pub(crate) values: &'a [T],
pub(crate) min_value: T,
pub(crate) max_value: T,
}
impl<'a, T: Copy + PartialOrd + Send + Sync> ColumnValues<T> for VecColumn<'a, T> {
fn get_val(&self, position: u32) -> T {
self.values[position as usize]
}
fn iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
Box::new(self.values.iter().copied())
}
fn min_value(&self) -> T {
self.min_value
}
fn max_value(&self) -> T {
self.max_value
}
fn num_vals(&self) -> u32 {
self.values.len() as u32
}
fn get_range(&self, start: u64, output: &mut [T]) {
output.copy_from_slice(&self.values[start as usize..][..output.len()])
}
}
impl<'a, T: Copy + PartialOrd + Default, V> From<&'a V> for VecColumn<'a, T>
where V: AsRef<[T]> + ?Sized
{
fn from(values: &'a V) -> Self {
let values = values.as_ref();
let (min_value, max_value) = minmax(values.iter().copied()).unwrap_or_default();
Self {
values,
min_value,
max_value,
}
}
}
struct MonotonicMappingColumn<C, T, Input> {
from_column: C,
monotonic_mapping: T,
_phantom: PhantomData<Input>,
}
/// Creates a view of a column transformed by a strictly monotonic mapping. See
/// [`StrictlyMonotonicFn`].
///
/// E.g. apply a gcd monotonic_mapping([100, 200, 300]) == [1, 2, 3]
/// monotonic_mapping.mapping() is expected to be injective, and we should always have
/// monotonic_mapping.inverse(monotonic_mapping.mapping(el)) == el
///
/// The inverse of the mapping is required for:
/// `fn get_positions_for_value_range(&self, range: RangeInclusive<T>) -> Vec<u64> `
/// The user provides the original value range and we need to monotonic map them in the same way the
/// serialization does before calling the underlying column.
///
/// Note that when opening a codec, the monotonic_mapping should be the inverse of the mapping
/// during serialization. And therefore the monotonic_mapping_inv when opening is the same as
/// monotonic_mapping during serialization.
pub fn monotonic_map_column<C, T, Input, Output>(
from_column: C,
monotonic_mapping: T,
) -> impl ColumnValues<Output>
where
C: ColumnValues<Input>,
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
Input: PartialOrd + Send + Sync + Clone,
Output: PartialOrd + Send + Sync + Clone,
{
MonotonicMappingColumn {
from_column,
monotonic_mapping,
_phantom: PhantomData,
}
}
impl<C, T, Input, Output> ColumnValues<Output> for MonotonicMappingColumn<C, T, Input>
where
C: ColumnValues<Input>,
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
Input: PartialOrd + Send + Sync + Clone,
Output: PartialOrd + Send + Sync + Clone,
{
#[inline]
fn get_val(&self, idx: u32) -> Output {
let from_val = self.from_column.get_val(idx);
self.monotonic_mapping.mapping(from_val)
}
fn min_value(&self) -> Output {
let from_min_value = self.from_column.min_value();
self.monotonic_mapping.mapping(from_min_value)
}
fn max_value(&self) -> Output {
let from_max_value = self.from_column.max_value();
self.monotonic_mapping.mapping(from_max_value)
}
fn num_vals(&self) -> u32 {
self.from_column.num_vals()
}
fn iter(&self) -> Box<dyn Iterator<Item = Output> + '_> {
Box::new(
self.from_column
.iter()
.map(|el| self.monotonic_mapping.mapping(el)),
)
}
fn get_docids_for_value_range(
&self,
range: RangeInclusive<Output>,
doc_id_range: Range<u32>,
positions: &mut Vec<u32>,
) {
self.from_column.get_docids_for_value_range(
self.monotonic_mapping.inverse(range.start().clone())
..=self.monotonic_mapping.inverse(range.end().clone()),
doc_id_range,
positions,
)
}
// We voluntarily do not implement get_range as it yields a regression,
// and we do not have any specialized implementation anyway.
}
/// Wraps an iterator into a `Column`.
pub struct IterColumn<T>(T);
impl<T> From<T> for IterColumn<T>
where T: Iterator + Clone + ExactSizeIterator
{
fn from(iter: T) -> Self {
IterColumn(iter)
}
}
impl<T> ColumnValues<T::Item> for IterColumn<T>
where
T: Iterator + Clone + ExactSizeIterator + Send + Sync,
T::Item: PartialOrd,
{
fn get_val(&self, idx: u32) -> T::Item {
self.0.clone().nth(idx as usize).unwrap()
}
fn min_value(&self) -> T::Item {
self.0.clone().next().unwrap()
}
fn max_value(&self) -> T::Item {
self.0.clone().last().unwrap()
}
fn num_vals(&self) -> u32 {
self.0.len() as u32
}
fn iter(&self) -> Box<dyn Iterator<Item = T::Item> + '_> {
Box::new(self.0.clone())
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_values::monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternalBaseval,
StrictlyMonotonicMappingToInternalGCDBaseval,
};
#[test]
fn test_monotonic_mapping() {
let vals = &[3u64, 5u64][..];
let col = VecColumn::from(vals);
let mapped = monotonic_map_column(col, StrictlyMonotonicMappingToInternalBaseval::new(2));
assert_eq!(mapped.min_value(), 1u64);
assert_eq!(mapped.max_value(), 3u64);
assert_eq!(mapped.num_vals(), 2);
assert_eq!(mapped.num_vals(), 2);
assert_eq!(mapped.get_val(0), 1);
assert_eq!(mapped.get_val(1), 3);
}
#[test]
fn test_range_as_col() {
let col = IterColumn::from(10..100);
assert_eq!(col.num_vals(), 90);
assert_eq!(col.max_value(), 99);
}
#[test]
fn test_monotonic_mapping_iter() {
let vals: Vec<u64> = (10..110u64).map(|el| el * 10).collect();
let col = VecColumn::from(&vals);
let mapped = monotonic_map_column(
col,
StrictlyMonotonicMappingInverter::from(
StrictlyMonotonicMappingToInternalGCDBaseval::new(10, 100),
),
);
let val_i64s: Vec<u64> = mapped.iter().collect();
for i in 0..100 {
assert_eq!(val_i64s[i as usize], mapped.get_val(i));
}
}
#[test]
fn test_monotonic_mapping_get_range() {
let vals: Vec<u64> = (0..100u64).map(|el| el * 10).collect();
let col = VecColumn::from(&vals);
let mapped = monotonic_map_column(
col,
StrictlyMonotonicMappingInverter::from(
StrictlyMonotonicMappingToInternalGCDBaseval::new(10, 0),
),
);
assert_eq!(mapped.min_value(), 0u64);
assert_eq!(mapped.max_value(), 9900u64);
assert_eq!(mapped.num_vals(), 100);
let val_u64s: Vec<u64> = mapped.iter().collect();
assert_eq!(val_u64s.len(), 100);
for i in 0..100 {
assert_eq!(val_u64s[i as usize], mapped.get_val(i));
assert_eq!(val_u64s[i as usize], vals[i as usize] * 10);
}
let mut buf = [0u64; 20];
mapped.get_range(7, &mut buf[..]);
assert_eq!(&val_u64s[7..][..20], &buf);
}
}

19
columnar/src/column_values/column_with_cardinality.rs
View File

@@ -1,19 +0,0 @@
// Copyright (C) 2022 Quickwit, Inc.
//
// Quickwit is offered under the AGPL v3.0 and as commercial software.
// For commercial licensing, contact us at hello@quickwit.io.
//
// AGPL:
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU Affero General Public License as
// published by the Free Software Foundation, either version 3 of the
// License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU Affero General Public License for more details.
//
// You should have received a copy of the GNU Affero General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
//

231
columnar/src/column_values/compact_space/build_compact_space.rs
View File

@@ -1,231 +0,0 @@
use std::collections::{BTreeSet, BinaryHeap};
use std::iter;
use std::ops::RangeInclusive;
use itertools::Itertools;
use super::blank_range::BlankRange;
use super::{CompactSpace, RangeMapping};
/// Put the blanks for the sorted values into a binary heap
fn get_blanks(values_sorted: &BTreeSet<u128>) -> BinaryHeap<BlankRange> {
let mut blanks: BinaryHeap<BlankRange> = BinaryHeap::new();
for (first, second) in values_sorted.iter().tuple_windows() {
// Correctness Overflow: the values are deduped and sorted (BTreeSet property), that means
// there's always space between two values.
let blank_range = first + 1..=second - 1;
let blank_range: Result<BlankRange, _> = blank_range.try_into();
if let Ok(blank_range) = blank_range {
blanks.push(blank_range);
}
}
blanks
}
struct BlankCollector {
blanks: Vec<BlankRange>,
staged_blanks_sum: u128,
}
impl BlankCollector {
fn new() -> Self {
Self {
blanks: vec![],
staged_blanks_sum: 0,
}
}
fn stage_blank(&mut self, blank: BlankRange) {
self.staged_blanks_sum += blank.blank_size();
self.blanks.push(blank);
}
fn drain(&mut self) -> impl Iterator<Item = BlankRange> + '_ {
self.staged_blanks_sum = 0;
self.blanks.drain(..)
}
fn staged_blanks_sum(&self) -> u128 {
self.staged_blanks_sum
}
fn num_staged_blanks(&self) -> usize {
self.blanks.len()
}
}
fn num_bits(val: u128) -> u8 {
(128u32 - val.leading_zeros()) as u8
}
/// Will collect blanks and add them to compact space if more bits are saved than cost from
/// metadata.
pub fn get_compact_space(
values_deduped_sorted: &BTreeSet<u128>,
total_num_values: u32,
cost_per_blank: usize,
) -> CompactSpace {
let mut compact_space_builder = CompactSpaceBuilder::new();
if values_deduped_sorted.is_empty() {
return compact_space_builder.finish();
}
let mut blanks: BinaryHeap<BlankRange> = get_blanks(values_deduped_sorted);
// Replace after stabilization of https://github.com/rust-lang/rust/issues/62924
// We start by space that's limited to min_value..=max_value
let min_value = *values_deduped_sorted.iter().next().unwrap_or(&0);
let max_value = *values_deduped_sorted.iter().last().unwrap_or(&0);
// +1 for null, in case min and max covers the whole space, we are off by one.
let mut amplitude_compact_space = (max_value - min_value).saturating_add(1);
if min_value != 0 {
compact_space_builder.add_blanks(iter::once(0..=min_value - 1));
}
if max_value != u128::MAX {
compact_space_builder.add_blanks(iter::once(max_value + 1..=u128::MAX));
}
let mut amplitude_bits: u8 = num_bits(amplitude_compact_space);
let mut blank_collector = BlankCollector::new();
// We will stage blanks until they reduce the compact space by at least 1 bit and then flush
// them if the metadata cost is lower than the total number of saved bits.
// Binary heap to process the gaps by their size
while let Some(blank_range) = blanks.pop() {
blank_collector.stage_blank(blank_range);
let staged_spaces_sum: u128 = blank_collector.staged_blanks_sum();
let amplitude_new_compact_space = amplitude_compact_space - staged_spaces_sum;
let amplitude_new_bits = num_bits(amplitude_new_compact_space);
if amplitude_bits == amplitude_new_bits {
continue;
}
let saved_bits = (amplitude_bits - amplitude_new_bits) as usize * total_num_values as usize;
// TODO: Maybe calculate exact cost of blanks and run this more expensive computation only,
// when amplitude_new_bits changes
let cost = blank_collector.num_staged_blanks() * cost_per_blank;
if cost >= saved_bits {
// Continue here, since although we walk over the blanks by size,
// we can potentially save a lot at the last bits, which are smaller blanks
//
// E.g. if the first range reduces the compact space by 1000 from 2000 to 1000, which
// saves 11-10=1 bit and the next range reduces the compact space by 950 to
// 50, which saves 10-6=4 bit
continue;
}
amplitude_compact_space = amplitude_new_compact_space;
amplitude_bits = amplitude_new_bits;
compact_space_builder.add_blanks(blank_collector.drain().map(|blank| blank.blank_range()));
}
// special case, when we don't collected any blanks because:
// * the data is empty (early exit)
// * the algorithm did decide it's not worth the cost, which can be the case for single values
//
// We drain one collected blank unconditionally, so the empty case is reserved for empty
// data, and therefore empty compact_space means the data is empty and no data is covered
// (conversely to all data) and we can assign null to it.
if compact_space_builder.is_empty() {
compact_space_builder.add_blanks(
blank_collector
.drain()
.map(|blank| blank.blank_range())
.take(1),
);
}
let compact_space = compact_space_builder.finish();
if max_value - min_value != u128::MAX {
debug_assert_eq!(
compact_space.amplitude_compact_space(),
amplitude_compact_space
);
}
compact_space
}
#[derive(Debug, Clone, Eq, PartialEq)]
struct CompactSpaceBuilder {
blanks: Vec<RangeInclusive<u128>>,
}
impl CompactSpaceBuilder {
/// Creates a new compact space builder which will initially cover the whole space.
fn new() -> Self {
Self { blanks: Vec::new() }
}
/// Assumes that repeated add_blank calls don't overlap and are not adjacent,
/// e.g. [3..=5, 5..=10] is not allowed
///
/// Both of those assumptions are true when blanks are produced from sorted values.
fn add_blanks(&mut self, blank: impl Iterator<Item = RangeInclusive<u128>>) {
self.blanks.extend(blank);
}
fn is_empty(&self) -> bool {
self.blanks.is_empty()
}
/// Convert blanks to covered space and assign null value
fn finish(mut self) -> CompactSpace {
// sort by start. ranges are not allowed to overlap
self.blanks.sort_unstable_by_key(|blank| *blank.start());
let mut covered_space = Vec::with_capacity(self.blanks.len());
// begining of the blanks
if let Some(first_blank_start) = self.blanks.first().map(RangeInclusive::start) {
if *first_blank_start != 0 {
covered_space.push(0..=first_blank_start - 1);
}
}
// Between the blanks
let between_blanks = self.blanks.iter().tuple_windows().map(|(left, right)| {
assert!(
left.end() < right.start(),
"overlapping or adjacent ranges detected"
);
*left.end() + 1..=*right.start() - 1
});
covered_space.extend(between_blanks);
// end of the blanks
if let Some(last_blank_end) = self.blanks.last().map(RangeInclusive::end) {
if *last_blank_end != u128::MAX {
covered_space.push(last_blank_end + 1..=u128::MAX);
}
}
if covered_space.is_empty() {
covered_space.push(0..=0); // empty data case
};
let mut compact_start: u64 = 1; // 0 is reserved for `null`
let mut ranges_mapping: Vec<RangeMapping> = Vec::with_capacity(covered_space.len());
for cov in covered_space {
let range_mapping = super::RangeMapping {
value_range: cov,
compact_start,
};
let covered_range_len = range_mapping.range_length();
ranges_mapping.push(range_mapping);
compact_start += covered_range_len;
}
// println!("num ranges {}", ranges_mapping.len());
CompactSpace { ranges_mapping }
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_binary_heap_pop_order() {
let mut blanks: BinaryHeap<BlankRange> = BinaryHeap::new();
blanks.push((0..=10).try_into().unwrap());
blanks.push((100..=200).try_into().unwrap());
blanks.push((100..=110).try_into().unwrap());
assert_eq!(blanks.pop().unwrap().blank_size(), 101);
assert_eq!(blanks.pop().unwrap().blank_size(), 11);
}
}

813
columnar/src/column_values/compact_space/mod.rs
View File

@@ -1,813 +0,0 @@
/// This codec takes a large number space (u128) and reduces it to a compact number space.
///
/// It will find spaces in the number range. For example:
///
/// 100, 101, 102, 103, 104, 50000, 50001
/// could be mapped to
/// 100..104 -> 0..4
/// 50000..50001 -> 5..6
///
/// Compact space 0..=6 requires much less bits than 100..=50001
///
/// The codec is created to compress ip addresses, but may be employed in other use cases.
use std::{
cmp::Ordering,
collections::BTreeSet,
io::{self, Write},
ops::{Range, RangeInclusive},
};
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt, VIntU128};
use tantivy_bitpacker::{self, BitPacker, BitUnpacker};
use crate::column_values::compact_space::build_compact_space::get_compact_space;
use crate::column_values::ColumnValues;
mod blank_range;
mod build_compact_space;
/// The cost per blank is quite hard actually, since blanks are delta encoded, the actual cost of
/// blanks depends on the number of blanks.
///
/// The number is taken by looking at a real dataset. It is optimized for larger datasets.
const COST_PER_BLANK_IN_BITS: usize = 36;
#[derive(Debug, Clone, Eq, PartialEq)]
pub struct CompactSpace {
ranges_mapping: Vec<RangeMapping>,
}
/// Maps the range from the original space to compact_start + range.len()
#[derive(Debug, Clone, Eq, PartialEq)]
struct RangeMapping {
value_range: RangeInclusive<u128>,
compact_start: u64,
}
impl RangeMapping {
fn range_length(&self) -> u64 {
(self.value_range.end() - self.value_range.start()) as u64 + 1
}
// The last value of the compact space in this range
fn compact_end(&self) -> u64 {
self.compact_start + self.range_length() - 1
}
}
impl BinarySerializable for CompactSpace {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.ranges_mapping.len() as u64).serialize(writer)?;
let mut prev_value = 0;
for value_range in self
.ranges_mapping
.iter()
.map(|range_mapping| &range_mapping.value_range)
{
let blank_delta_start = value_range.start() - prev_value;
VIntU128(blank_delta_start).serialize(writer)?;
prev_value = *value_range.start();
let blank_delta_end = value_range.end() - prev_value;
VIntU128(blank_delta_end).serialize(writer)?;
prev_value = *value_range.end();
}
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let num_ranges = VInt::deserialize(reader)?.0;
let mut ranges_mapping: Vec<RangeMapping> = vec![];
let mut value = 0u128;
let mut compact_start = 1u64; // 0 is reserved for `null`
for _ in 0..num_ranges {
let blank_delta_start = VIntU128::deserialize(reader)?.0;
value += blank_delta_start;
let blank_start = value;
let blank_delta_end = VIntU128::deserialize(reader)?.0;
value += blank_delta_end;
let blank_end = value;
let range_mapping = RangeMapping {
value_range: blank_start..=blank_end,
compact_start,
};
let range_length = range_mapping.range_length();
ranges_mapping.push(range_mapping);
compact_start += range_length;
}
Ok(Self { ranges_mapping })
}
}
impl CompactSpace {
/// Amplitude is the value range of the compact space including the sentinel value used to
/// identify null values. The compact space is 0..=amplitude .
///
/// It's only used to verify we don't exceed u64 number space, which would indicate a bug.
fn amplitude_compact_space(&self) -> u128 {
self.ranges_mapping
.last()
.map(|last_range| last_range.compact_end() as u128)
.unwrap_or(1) // compact space starts at 1, 0 == null
}
fn get_range_mapping(&self, pos: usize) -> &RangeMapping {
&self.ranges_mapping[pos]
}
/// Returns either Ok(the value in the compact space) or if it is outside the compact space the
/// Err(position where it would be inserted)
fn u128_to_compact(&self, value: u128) -> Result<u64, usize> {
self.ranges_mapping
.binary_search_by(|probe| {
let value_range = &probe.value_range;
if value < *value_range.start() {
Ordering::Greater
} else if value > *value_range.end() {
Ordering::Less
} else {
Ordering::Equal
}
})
.map(|pos| {
let range_mapping = &self.ranges_mapping[pos];
let pos_in_range = (value - range_mapping.value_range.start()) as u64;
range_mapping.compact_start + pos_in_range
})
}
/// Unpacks a value from compact space u64 to u128 space
fn compact_to_u128(&self, compact: u64) -> u128 {
let pos = self
.ranges_mapping
.binary_search_by_key(&compact, |range_mapping| range_mapping.compact_start)
// Correctness: Overflow. The first range starts at compact space 0, the error from
// binary search can never be 0
.map_or_else(|e| e - 1, |v| v);
let range_mapping = &self.ranges_mapping[pos];
let diff = compact - range_mapping.compact_start;
range_mapping.value_range.start() + diff as u128
}
}
pub struct CompactSpaceCompressor {
params: IPCodecParams,
}
#[derive(Debug, Clone)]
pub struct IPCodecParams {
compact_space: CompactSpace,
bit_unpacker: BitUnpacker,
min_value: u128,
max_value: u128,
num_vals: u32,
num_bits: u8,
}
impl CompactSpaceCompressor {
/// Taking the vals as Vec may cost a lot of memory. It is used to sort the vals.
pub fn train_from(iter: impl Iterator<Item = u128>, num_vals: u32) -> Self {
let mut values_sorted = BTreeSet::new();
values_sorted.extend(iter);
let total_num_values = num_vals;
let compact_space =
get_compact_space(&values_sorted, total_num_values, COST_PER_BLANK_IN_BITS);
let amplitude_compact_space = compact_space.amplitude_compact_space();
assert!(
amplitude_compact_space <= u64::MAX as u128,
"case unsupported."
);
let num_bits = tantivy_bitpacker::compute_num_bits(amplitude_compact_space as u64);
let min_value = *values_sorted.iter().next().unwrap_or(&0);
let max_value = *values_sorted.iter().last().unwrap_or(&0);
assert_eq!(
compact_space
.u128_to_compact(max_value)
.expect("could not convert max value to compact space"),
amplitude_compact_space as u64
);
CompactSpaceCompressor {
params: IPCodecParams {
compact_space,
bit_unpacker: BitUnpacker::new(num_bits),
min_value,
max_value,
num_vals: total_num_values,
num_bits,
},
}
}
fn write_footer(self, writer: &mut impl Write) -> io::Result<()> {
let writer = &mut CountingWriter::wrap(writer);
self.params.serialize(writer)?;
let footer_len = writer.written_bytes() as u32;
footer_len.serialize(writer)?;
Ok(())
}
pub fn compress_into(
self,
vals: impl Iterator<Item = u128>,
write: &mut impl Write,
) -> io::Result<()> {
let mut bitpacker = BitPacker::default();
for val in vals {
let compact = self
.params
.compact_space
.u128_to_compact(val)
.map_err(|_| {
io::Error::new(
io::ErrorKind::InvalidData,
"Could not convert value to compact_space. This is a bug.",
)
})?;
bitpacker.write(compact, self.params.num_bits, write)?;
}
bitpacker.close(write)?;
self.write_footer(write)?;
Ok(())
}
}
#[derive(Debug, Clone)]
pub struct CompactSpaceDecompressor {
data: OwnedBytes,
params: IPCodecParams,
}
impl BinarySerializable for IPCodecParams {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
// header flags for future optional dictionary encoding
let footer_flags = 0u64;
footer_flags.serialize(writer)?;
VIntU128(self.min_value).serialize(writer)?;
VIntU128(self.max_value).serialize(writer)?;
VIntU128(self.num_vals as u128).serialize(writer)?;
self.num_bits.serialize(writer)?;
self.compact_space.serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let _header_flags = u64::deserialize(reader)?;
let min_value = VIntU128::deserialize(reader)?.0;
let max_value = VIntU128::deserialize(reader)?.0;
let num_vals = VIntU128::deserialize(reader)?.0 as u32;
let num_bits = u8::deserialize(reader)?;
let compact_space = CompactSpace::deserialize(reader)?;
Ok(Self {
compact_space,
bit_unpacker: BitUnpacker::new(num_bits),
min_value,
max_value,
num_vals,
num_bits,
})
}
}
impl ColumnValues<u128> for CompactSpaceDecompressor {
#[inline]
fn get_val(&self, doc: u32) -> u128 {
self.get(doc)
}
fn min_value(&self) -> u128 {
self.min_value()
}
fn max_value(&self) -> u128 {
self.max_value()
}
fn num_vals(&self) -> u32 {
self.params.num_vals
}
#[inline]
fn iter(&self) -> Box<dyn Iterator<Item = u128> + '_> {
Box::new(self.iter())
}
#[inline]
fn get_docids_for_value_range(
&self,
value_range: RangeInclusive<u128>,
positions_range: Range<u32>,
positions: &mut Vec<u32>,
) {
self.get_positions_for_value_range(value_range, positions_range, positions)
}
}
impl CompactSpaceDecompressor {
pub fn open(data: OwnedBytes) -> io::Result<CompactSpaceDecompressor> {
let (data_slice, footer_len_bytes) = data.split_at(data.len() - 4);
let footer_len = u32::deserialize(&mut &footer_len_bytes[..])?;
let data_footer = &data_slice[data_slice.len() - footer_len as usize..];
let params = IPCodecParams::deserialize(&mut &data_footer[..])?;
let decompressor = CompactSpaceDecompressor { data, params };
Ok(decompressor)
}
/// Converting to compact space for the decompressor is more complex, since we may get values
/// which are outside the compact space. e.g. if we map
/// 1000 => 5
/// 2000 => 6
///
/// and we want a mapping for 1005, there is no equivalent compact space. We instead return an
/// error with the index of the next range.
fn u128_to_compact(&self, value: u128) -> Result<u64, usize> {
self.params.compact_space.u128_to_compact(value)
}
fn compact_to_u128(&self, compact: u64) -> u128 {
self.params.compact_space.compact_to_u128(compact)
}
/// Comparing on compact space: Random dataset 0,24 (50% random hit) - 1.05 GElements/s
/// Comparing on compact space: Real dataset 1.08 GElements/s
///
/// Comparing on original space: Real dataset .06 GElements/s (not completely optimized)
#[inline]
pub fn get_positions_for_value_range(
&self,
value_range: RangeInclusive<u128>,
position_range: Range<u32>,
positions: &mut Vec<u32>,
) {
if value_range.start() > value_range.end() {
return;
}
let position_range = position_range.start..position_range.end.min(self.num_vals());
let from_value = *value_range.start();
let to_value = *value_range.end();
assert!(to_value >= from_value);
let compact_from = self.u128_to_compact(from_value);
let compact_to = self.u128_to_compact(to_value);
// Quick return, if both ranges fall into the same non-mapped space, the range can't cover
// any values, so we can early exit
match (compact_to, compact_from) {
(Err(pos1), Err(pos2)) if pos1 == pos2 => return,
_ => {}
}
let compact_from = compact_from.unwrap_or_else(|pos| {
// Correctness: Out of bounds, if this value is Err(last_index + 1), we early exit,
// since the to_value also mapps into the same non-mapped space
let range_mapping = self.params.compact_space.get_range_mapping(pos);
range_mapping.compact_start
});
// If there is no compact space, we go to the closest upperbound compact space
let compact_to = compact_to.unwrap_or_else(|pos| {
// Correctness: Overflow, if this value is Err(0), we early exit,
// since the from_value also mapps into the same non-mapped space
// Get end of previous range
let pos = pos - 1;
let range_mapping = self.params.compact_space.get_range_mapping(pos);
range_mapping.compact_end()
});
let range = compact_from..=compact_to;
let scan_num_docs = position_range.end - position_range.start;
let step_size = 4;
let cutoff = position_range.start + scan_num_docs - scan_num_docs % step_size;
let mut push_if_in_range = |idx, val| {
if range.contains(&val) {
positions.push(idx);
}
};
let get_val = |idx| self.params.bit_unpacker.get(idx, &self.data);
// unrolled loop
for idx in (position_range.start..cutoff).step_by(step_size as usize) {
let idx1 = idx;
let idx2 = idx + 1;
let idx3 = idx + 2;
let idx4 = idx + 3;
let val1 = get_val(idx1);
let val2 = get_val(idx2);
let val3 = get_val(idx3);
let val4 = get_val(idx4);
push_if_in_range(idx1, val1);
push_if_in_range(idx2, val2);
push_if_in_range(idx3, val3);
push_if_in_range(idx4, val4);
}
// handle rest
for idx in cutoff..position_range.end {
push_if_in_range(idx, get_val(idx));
}
}
#[inline]
fn iter_compact(&self) -> impl Iterator<Item = u64> + '_ {
(0..self.params.num_vals).map(move |idx| self.params.bit_unpacker.get(idx, &self.data))
}
#[inline]
fn iter(&self) -> impl Iterator<Item = u128> + '_ {
// TODO: Performance. It would be better to iterate on the ranges and check existence via
// the bit_unpacker.
self.iter_compact()
.map(|compact| self.compact_to_u128(compact))
}
#[inline]
pub fn get(&self, idx: u32) -> u128 {
let compact = self.params.bit_unpacker.get(idx, &self.data);
self.compact_to_u128(compact)
}
pub fn min_value(&self) -> u128 {
self.params.min_value
}
pub fn max_value(&self) -> u128 {
self.params.max_value
}
}
// TODO reenable what can be reenabled.
// #[cfg(test)]
// mod tests {
//
// use super::*;
// use crate::column::format_version::read_format_version;
// use crate::column::column_footer::read_null_index_footer;
// use crate::column::serialize::U128Header;
// use crate::column::{open_u128, serialize_u128};
//
// #[test]
// fn compact_space_test() {
// let ips = &[
// 2u128, 4u128, 1000, 1001, 1002, 1003, 1004, 1005, 1008, 1010, 1012, 1260,
// ]
// .into_iter()
// .collect();
// let compact_space = get_compact_space(ips, ips.len() as u32, 11);
// let amplitude = compact_space.amplitude_compact_space();
// assert_eq!(amplitude, 17);
// assert_eq!(1, compact_space.u128_to_compact(2).unwrap());
// assert_eq!(2, compact_space.u128_to_compact(3).unwrap());
// assert_eq!(compact_space.u128_to_compact(100).unwrap_err(), 1);
//
// for (num1, num2) in (0..3).tuple_windows() {
// assert_eq!(
// compact_space.get_range_mapping(num1).compact_end() + 1,
// compact_space.get_range_mapping(num2).compact_start
// );
// }
//
// let mut output: Vec<u8> = Vec::new();
// compact_space.serialize(&mut output).unwrap();
//
// assert_eq!(
// compact_space,
// CompactSpace::deserialize(&mut &output[..]).unwrap()
// );
//
// for ip in ips {
// let compact = compact_space.u128_to_compact(*ip).unwrap();
// assert_eq!(compact_space.compact_to_u128(compact), *ip);
// }
// }
//
// #[test]
// fn compact_space_amplitude_test() {
// let ips = &[100000u128, 1000000].into_iter().collect();
// let compact_space = get_compact_space(ips, ips.len() as u32, 1);
// let amplitude = compact_space.amplitude_compact_space();
// assert_eq!(amplitude, 2);
// }
//
// fn test_all(mut data: OwnedBytes, expected: &[u128]) {
// let _header = U128Header::deserialize(&mut data);
// let decompressor = CompactSpaceDecompressor::open(data).unwrap();
// for (idx, expected_val) in expected.iter().cloned().enumerate() {
// let val = decompressor.get(idx as u32);
// assert_eq!(val, expected_val);
//
// let test_range = |range: RangeInclusive<u128>| {
// let expected_positions = expected
// .iter()
// .positions(|val| range.contains(val))
// .map(|pos| pos as u32)
// .collect::<Vec<_>>();
// let mut positions = Vec::new();
// decompressor.get_positions_for_value_range(
// range,
// 0..decompressor.num_vals(),
// &mut positions,
// );
// assert_eq!(positions, expected_positions);
// };
//
// test_range(expected_val.saturating_sub(1)..=expected_val);
// test_range(expected_val..=expected_val);
// test_range(expected_val..=expected_val.saturating_add(1));
// test_range(expected_val.saturating_sub(1)..=expected_val.saturating_add(1));
// }
// }
//
// fn test_aux_vals(u128_vals: &[u128]) -> OwnedBytes {
// let mut out = Vec::new();
// serialize_u128(
// || u128_vals.iter().cloned(),
// u128_vals.len() as u32,
// &mut out,
// )
// .unwrap();
//
// let data = OwnedBytes::new(out);
// let (data, _format_version) = read_format_version(data).unwrap();
// let (data, _null_index_footer) = read_null_index_footer(data).unwrap();
// test_all(data.clone(), u128_vals);
//
// data
// }
//
// #[test]
// fn test_range_1() {
// let vals = &[
// 1u128,
// 100u128,
// 3u128,
// 99999u128,
// 100000u128,
// 100001u128,
// 4_000_211_221u128,
// 4_000_211_222u128,
// 333u128,
// ];
// let mut data = test_aux_vals(vals);
//
// let _header = U128Header::deserialize(&mut data);
// let decomp = CompactSpaceDecompressor::open(data).unwrap();
// let complete_range = 0..vals.len() as u32;
// for (pos, val) in vals.iter().enumerate() {
// let val = *val;
// let pos = pos as u32;
// let mut positions = Vec::new();
// decomp.get_positions_for_value_range(val..=val, pos..pos + 1, &mut positions);
// assert_eq!(positions, vec![pos]);
// }
//
// handle docid range out of bounds
// let positions: Vec<u32> = get_positions_for_value_range_helper(&decomp, 0..=1, 1..u32::MAX);
// assert!(positions.is_empty());
//
// let positions =
// get_positions_for_value_range_helper(&decomp, 0..=1, complete_range.clone());
// assert_eq!(positions, vec![0]);
// let positions =
// get_positions_for_value_range_helper(&decomp, 0..=2, complete_range.clone());
// assert_eq!(positions, vec![0]);
// let positions =
// get_positions_for_value_range_helper(&decomp, 0..=3, complete_range.clone());
// assert_eq!(positions, vec![0, 2]);
// assert_eq!(
// get_positions_for_value_range_helper(
// &decomp,
// 99999u128..=99999u128,
// complete_range.clone()
// ),
// vec![3]
// );
// assert_eq!(
// get_positions_for_value_range_helper(
// &decomp,
// 99999u128..=100000u128,
// complete_range.clone()
// ),
// vec![3, 4]
// );
// assert_eq!(
// get_positions_for_value_range_helper(
// &decomp,
// 99998u128..=100000u128,
// complete_range.clone()
// ),
// vec![3, 4]
// );
// assert_eq!(
// &get_positions_for_value_range_helper(
// &decomp,
// 99998u128..=99999u128,
// complete_range.clone()
// ),
// &[3]
// );
// assert!(get_positions_for_value_range_helper(
// &decomp,
// 99998u128..=99998u128,
// complete_range.clone()
// )
// .is_empty());
// assert_eq!(
// &get_positions_for_value_range_helper(
// &decomp,
// 333u128..=333u128,
// complete_range.clone()
// ),
// &[8]
// );
// assert_eq!(
// &get_positions_for_value_range_helper(
// &decomp,
// 332u128..=333u128,
// complete_range.clone()
// ),
// &[8]
// );
// assert_eq!(
// &get_positions_for_value_range_helper(
// &decomp,
// 332u128..=334u128,
// complete_range.clone()
// ),
// &[8]
// );
// assert_eq!(
// &get_positions_for_value_range_helper(
// &decomp,
// 333u128..=334u128,
// complete_range.clone()
// ),
// &[8]
// );
//
// assert_eq!(
// &get_positions_for_value_range_helper(
// &decomp,
// 4_000_211_221u128..=5_000_000_000u128,
// complete_range
// ),
// &[6, 7]
// );
// }
//
// #[test]
// fn test_empty() {
// let vals = &[];
// let data = test_aux_vals(vals);
// let _decomp = CompactSpaceDecompressor::open(data).unwrap();
// }
//
// #[test]
// fn test_range_2() {
// let vals = &[
// 100u128,
// 99999u128,
// 100000u128,
// 100001u128,
// 4_000_211_221u128,
// 4_000_211_222u128,
// 333u128,
// ];
// let mut data = test_aux_vals(vals);
// let _header = U128Header::deserialize(&mut data);
// let decomp = CompactSpaceDecompressor::open(data).unwrap();
// let complete_range = 0..vals.len() as u32;
// assert!(
// &get_positions_for_value_range_helper(&decomp, 0..=5, complete_range.clone())
// .is_empty(),
// );
// assert_eq!(
// &get_positions_for_value_range_helper(&decomp, 0..=100, complete_range.clone()),
// &[0]
// );
// assert_eq!(
// &get_positions_for_value_range_helper(&decomp, 0..=105, complete_range),
// &[0]
// );
// }
//
// fn get_positions_for_value_range_helper<C: Column<T> + ?Sized, T: PartialOrd>(
// column: &C,
// value_range: RangeInclusive<T>,
// doc_id_range: Range<u32>,
// ) -> Vec<u32> {
// let mut positions = Vec::new();
// column.get_docids_for_value_range(value_range, doc_id_range, &mut positions);
// positions
// }
//
// #[test]
// fn test_range_3() {
// let vals = &[
// 200u128,
// 201,
// 202,
// 203,
// 204,
// 204,
// 206,
// 207,
// 208,
// 209,
// 210,
// 1_000_000,
// 5_000_000_000,
// ];
// let mut out = Vec::new();
// serialize_u128(|| vals.iter().cloned(), vals.len() as u32, &mut out).unwrap();
// let decomp = open_u128::<u128>(OwnedBytes::new(out)).unwrap();
// let complete_range = 0..vals.len() as u32;
//
// assert_eq!(
// get_positions_for_value_range_helper(&*decomp, 199..=200, complete_range.clone()),
// vec![0]
// );
//
// assert_eq!(
// get_positions_for_value_range_helper(&*decomp, 199..=201, complete_range.clone()),
// vec![0, 1]
// );
//
// assert_eq!(
// get_positions_for_value_range_helper(&*decomp, 200..=200, complete_range.clone()),
// vec![0]
// );
//
// assert_eq!(
// get_positions_for_value_range_helper(&*decomp, 1_000_000..=1_000_000, complete_range),
// vec![11]
// );
// }
//
// #[test]
// fn test_bug1() {
// let vals = &[9223372036854775806];
// let _data = test_aux_vals(vals);
// }
//
// #[test]
// fn test_bug2() {
// let vals = &[340282366920938463463374607431768211455u128];
// let _data = test_aux_vals(vals);
// }
//
// #[test]
// fn test_bug3() {
// let vals = &[340282366920938463463374607431768211454];
// let _data = test_aux_vals(vals);
// }
//
// #[test]
// fn test_bug4() {
// let vals = &[340282366920938463463374607431768211455, 0];
// let _data = test_aux_vals(vals);
// }
//
// #[test]
// fn test_first_large_gaps() {
// let vals = &[1_000_000_000u128; 100];
// let _data = test_aux_vals(vals);
// }
// use itertools::Itertools;
// use proptest::prelude::*;
//
// fn num_strategy() -> impl Strategy<Value = u128> {
// prop_oneof![
// 1 => prop::num::u128::ANY.prop_map(|num| u128::MAX - (num % 10) ),
// 1 => prop::num::u128::ANY.prop_map(|num| i64::MAX as u128 + 5 - (num % 10) ),
// 1 => prop::num::u128::ANY.prop_map(|num| i128::MAX as u128 + 5 - (num % 10) ),
// 1 => prop::num::u128::ANY.prop_map(|num| num % 10 ),
// 20 => prop::num::u128::ANY,
// ]
// }
//
// proptest! {
// #![proptest_config(ProptestConfig::with_cases(10))]
//
// #[test]
// fn compress_decompress_random(vals in proptest::collection::vec(num_strategy()
// , 1..1000)) {
// let _data = test_aux_vals(&vals);
// }
// }
// }
//

75
columnar/src/column_values/gcd.rs
View File

@@ -1,75 +0,0 @@
use std::num::NonZeroU64;
use fastdivide::DividerU64;
/// Compute the gcd of two non null numbers.
///
/// It is recommended, but not required, to feed values such that `large >= small`.
fn compute_gcd(mut large: NonZeroU64, mut small: NonZeroU64) -> NonZeroU64 {
loop {
let rem: u64 = large.get() % small;
if let Some(new_small) = NonZeroU64::new(rem) {
(large, small) = (small, new_small);
} else {
return small;
}
}
}
// Find GCD for iterator of numbers
pub fn find_gcd(numbers: impl Iterator<Item = u64>) -> Option<NonZeroU64> {
let mut numbers = numbers.flat_map(NonZeroU64::new);
let mut gcd: NonZeroU64 = numbers.next()?;
if gcd.get() == 1 {
return Some(gcd);
}
let mut gcd_divider = DividerU64::divide_by(gcd.get());
for val in numbers {
let remainder = val.get() - (gcd_divider.divide(val.get())) * gcd.get();
if remainder == 0 {
continue;
}
gcd = compute_gcd(val, gcd);
if gcd.get() == 1 {
return Some(gcd);
}
gcd_divider = DividerU64::divide_by(gcd.get());
}
Some(gcd)
}
#[cfg(test)]
mod tests {
use std::num::NonZeroU64;
use crate::column_values::gcd::{compute_gcd, find_gcd};
#[test]
fn test_compute_gcd() {
let test_compute_gcd_aux = |large, small, expected| {
let large = NonZeroU64::new(large).unwrap();
let small = NonZeroU64::new(small).unwrap();
let expected = NonZeroU64::new(expected).unwrap();
assert_eq!(compute_gcd(small, large), expected);
assert_eq!(compute_gcd(large, small), expected);
};
test_compute_gcd_aux(1, 4, 1);
test_compute_gcd_aux(2, 4, 2);
test_compute_gcd_aux(10, 25, 5);
test_compute_gcd_aux(25, 25, 25);
}
#[test]
fn find_gcd_test() {
assert_eq!(find_gcd([0].into_iter()), None);
assert_eq!(find_gcd([0, 10].into_iter()), NonZeroU64::new(10));
assert_eq!(find_gcd([10, 0].into_iter()), NonZeroU64::new(10));
assert_eq!(find_gcd([].into_iter()), None);
assert_eq!(find_gcd([15, 30, 5, 10].into_iter()), NonZeroU64::new(5));
assert_eq!(find_gcd([15, 16, 10].into_iter()), NonZeroU64::new(1));
assert_eq!(find_gcd([0, 5, 5, 5].into_iter()), NonZeroU64::new(5));
assert_eq!(find_gcd([0, 0].into_iter()), None);
}
}

230
columnar/src/column_values/linear.rs
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@@ -1,230 +0,0 @@
use std::io::{self, Write};
use common::{BinarySerializable, OwnedBytes};
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use super::line::Line;
use super::serialize::NormalizedHeader;
use super::{ColumnValues, FastFieldCodec, FastFieldCodecType};
/// Depending on the field type, a different
/// fast field is required.
#[derive(Clone)]
pub struct LinearReader {
data: OwnedBytes,
linear_params: LinearParams,
header: NormalizedHeader,
}
impl ColumnValues for LinearReader {
#[inline]
fn get_val(&self, doc: u32) -> u64 {
let interpoled_val: u64 = self.linear_params.line.eval(doc);
let bitpacked_diff = self.linear_params.bit_unpacker.get(doc, &self.data);
interpoled_val.wrapping_add(bitpacked_diff)
}
#[inline(always)]
fn min_value(&self) -> u64 {
// The LinearReader assumes a normalized vector.
0u64
}
#[inline(always)]
fn max_value(&self) -> u64 {
self.header.max_value
}
#[inline]
fn num_vals(&self) -> u32 {
self.header.num_vals
}
}
/// Fastfield serializer, which tries to guess values by linear interpolation
/// and stores the difference bitpacked.
pub struct LinearCodec;
#[derive(Debug, Clone)]
struct LinearParams {
line: Line,
bit_unpacker: BitUnpacker,
}
impl BinarySerializable for LinearParams {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
self.line.serialize(writer)?;
self.bit_unpacker.bit_width().serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let line = Line::deserialize(reader)?;
let bit_width = u8::deserialize(reader)?;
Ok(Self {
line,
bit_unpacker: BitUnpacker::new(bit_width),
})
}
}
impl FastFieldCodec for LinearCodec {
const CODEC_TYPE: FastFieldCodecType = FastFieldCodecType::Linear;
type Reader = LinearReader;
/// Opens a fast field given a file.
fn open_from_bytes(mut data: OwnedBytes, header: NormalizedHeader) -> io::Result<Self::Reader> {
let linear_params = LinearParams::deserialize(&mut data)?;
Ok(LinearReader {
data,
linear_params,
header,
})
}
/// Creates a new fast field serializer.
fn serialize(column: &dyn ColumnValues, write: &mut impl Write) -> io::Result<()> {
assert_eq!(column.min_value(), 0);
let line = Line::train(column);
let max_offset_from_line = column
.iter()
.enumerate()
.map(|(pos, actual_value)| {
let calculated_value = line.eval(pos as u32);
actual_value.wrapping_sub(calculated_value)
})
.max()
.unwrap();
let num_bits = compute_num_bits(max_offset_from_line);
let linear_params = LinearParams {
line,
bit_unpacker: BitUnpacker::new(num_bits),
};
linear_params.serialize(write)?;
let mut bit_packer = BitPacker::new();
for (pos, actual_value) in column.iter().enumerate() {
let calculated_value = line.eval(pos as u32);
let offset = actual_value.wrapping_sub(calculated_value);
bit_packer.write(offset, num_bits, write)?;
}
bit_packer.close(write)?;
Ok(())
}
/// estimation for linear interpolation is hard because, you don't know
/// where the local maxima for the deviation of the calculated value are and
/// the offset to shift all values to >=0 is also unknown.
#[allow(clippy::question_mark)]
fn estimate(column: &dyn ColumnValues) -> Option<f32> {
if column.num_vals() < 3 {
return None; // disable compressor for this case
}
let limit_num_vals = column.num_vals().min(100_000);
let num_samples = 100;
let step_size = (limit_num_vals / num_samples).max(1); // 20 samples
let mut sample_positions_and_values: Vec<_> = Vec::new();
for (pos, val) in column.iter().enumerate().step_by(step_size as usize) {
sample_positions_and_values.push((pos as u64, val));
}
let line = Line::estimate(&sample_positions_and_values);
let estimated_bit_width = sample_positions_and_values
.into_iter()
.map(|(pos, actual_value)| {
let interpolated_val = line.eval(pos as u32);
actual_value.wrapping_sub(interpolated_val)
})
.map(|diff| ((diff as f32 * 1.5) * 2.0) as u64)
.map(compute_num_bits)
.max()
.unwrap_or(0);
// Extrapolate to whole column
let num_bits = (estimated_bit_width as u64 * column.num_vals() as u64) + 64;
let num_bits_uncompressed = 64 * column.num_vals();
Some(num_bits as f32 / num_bits_uncompressed as f32)
}
}
#[cfg(test)]
mod tests {
use rand::RngCore;
use super::*;
use crate::column_values::tests;
fn create_and_validate(data: &[u64], name: &str) -> Option<(f32, f32)> {
tests::create_and_validate::<LinearCodec>(data, name)
}
#[test]
fn test_compression() {
let data = (10..=6_000_u64).collect::<Vec<_>>();
let (estimate, actual_compression) =
create_and_validate(&data, "simple monotonically large").unwrap();
assert_le!(actual_compression, 0.001);
assert_le!(estimate, 0.02);
}
#[test]
fn test_with_codec_datasets() {
let data_sets = tests::get_codec_test_datasets();
for (mut data, name) in data_sets {
create_and_validate(&data, name);
data.reverse();
create_and_validate(&data, name);
}
}
#[test]
fn linear_interpol_fast_field_test_large_amplitude() {
let data = vec![
i64::MAX as u64 / 2,
i64::MAX as u64 / 3,
i64::MAX as u64 / 2,
];
create_and_validate(&data, "large amplitude");
}
#[test]
fn overflow_error_test() {
let data = vec![1572656989877777, 1170935903116329, 720575940379279, 0];
create_and_validate(&data, "overflow test");
}
#[test]
fn linear_interpol_fast_concave_data() {
let data = vec![0, 1, 2, 5, 8, 10, 20, 50];
create_and_validate(&data, "concave data");
}
#[test]
fn linear_interpol_fast_convex_data() {
let data = vec![0, 40, 60, 70, 75, 77];
create_and_validate(&data, "convex data");
}
#[test]
fn linear_interpol_fast_field_test_simple() {
let data = (10..=20_u64).collect::<Vec<_>>();
create_and_validate(&data, "simple monotonically");
}
#[test]
fn linear_interpol_fast_field_rand() {
let mut rng = rand::thread_rng();
for _ in 0..50 {
let mut data = (0..10_000).map(|_| rng.next_u64()).collect::<Vec<_>>();
create_and_validate(&data, "random");
data.reverse();
create_and_validate(&data, "random");
}
}
}

222
columnar/src/column_values/main.rs
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@@ -1,222 +0,0 @@
#[macro_use]
extern crate prettytable;
use std::collections::HashSet;
use std::env;
use std::io::BufRead;
use std::net::{IpAddr, Ipv6Addr};
use std::str::FromStr;
use common::OwnedBytes;
use fastfield_codecs::{open_u128, serialize_u128, Column, FastFieldCodecType, VecColumn};
use itertools::Itertools;
use measure_time::print_time;
use prettytable::{Cell, Row, Table};
fn print_set_stats(ip_addrs: &[u128]) {
println!("NumIps\t{}", ip_addrs.len());
let ip_addr_set: HashSet<u128> = ip_addrs.iter().cloned().collect();
println!("NumUniqueIps\t{}", ip_addr_set.len());
let ratio_unique = ip_addr_set.len() as f64 / ip_addrs.len() as f64;
println!("RatioUniqueOverTotal\t{ratio_unique:.4}");
// histogram
let mut ip_addrs = ip_addrs.to_vec();
ip_addrs.sort();
let mut cnts: Vec<usize> = ip_addrs
.into_iter()
.dedup_with_count()
.map(|(cnt, _)| cnt)
.collect();
cnts.sort();
let top_256_cnt: usize = cnts.iter().rev().take(256).sum();
let top_128_cnt: usize = cnts.iter().rev().take(128).sum();
let top_64_cnt: usize = cnts.iter().rev().take(64).sum();
let top_8_cnt: usize = cnts.iter().rev().take(8).sum();
let total: usize = cnts.iter().sum();
println!("{}", total);
println!("{}", top_256_cnt);
println!("{}", top_128_cnt);
println!("Percentage Top8 {:02}", top_8_cnt as f32 / total as f32);
println!("Percentage Top64 {:02}", top_64_cnt as f32 / total as f32);
println!("Percentage Top128 {:02}", top_128_cnt as f32 / total as f32);
println!("Percentage Top256 {:02}", top_256_cnt as f32 / total as f32);
let mut cnts: Vec<(usize, usize)> = cnts.into_iter().dedup_with_count().collect();
cnts.sort_by(|a, b| {
if a.1 == b.1 {
a.0.cmp(&b.0)
} else {
b.1.cmp(&a.1)
}
});
}
fn ip_dataset() -> Vec<u128> {
let mut ip_addr_v4 = 0;
let stdin = std::io::stdin();
let ip_addrs: Vec<u128> = stdin
.lock()
.lines()
.flat_map(|line| {
let line = line.unwrap();
let line = line.trim();
let ip_addr = IpAddr::from_str(line.trim()).ok()?;
if ip_addr.is_ipv4() {
ip_addr_v4 += 1;
}
let ip_addr_v6: Ipv6Addr = match ip_addr {
IpAddr::V4(v4) => v4.to_ipv6_mapped(),
IpAddr::V6(v6) => v6,
};
Some(ip_addr_v6)
})
.map(|ip_v6| u128::from_be_bytes(ip_v6.octets()))
.collect();
println!("IpAddrsAny\t{}", ip_addrs.len());
println!("IpAddrsV4\t{}", ip_addr_v4);
ip_addrs
}
fn bench_ip() {
let dataset = ip_dataset();
print_set_stats(&dataset);
// Chunks
{
let mut data = vec![];
for dataset in dataset.chunks(500_000) {
serialize_u128(|| dataset.iter().cloned(), dataset.len() as u32, &mut data).unwrap();
}
let compression = data.len() as f64 / (dataset.len() * 16) as f64;
println!("Compression 50_000 chunks {:.4}", compression);
println!(
"Num Bits per elem {:.2}",
(data.len() * 8) as f32 / dataset.len() as f32
);
}
let mut data = vec![];
{
print_time!("creation");
serialize_u128(|| dataset.iter().cloned(), dataset.len() as u32, &mut data).unwrap();
}
let compression = data.len() as f64 / (dataset.len() * 16) as f64;
println!("Compression {:.2}", compression);
println!(
"Num Bits per elem {:.2}",
(data.len() * 8) as f32 / dataset.len() as f32
);
let decompressor = open_u128::<u128>(OwnedBytes::new(data)).unwrap();
// Sample some ranges
let mut doc_values = Vec::new();
for value in dataset.iter().take(1110).skip(1100).cloned() {
doc_values.clear();
print_time!("get range");
decompressor.get_docids_for_value_range(
value..=value,
0..decompressor.num_vals(),
&mut doc_values,
);
println!("{:?}", doc_values.len());
}
}
fn main() {
if env::args().nth(1).unwrap() == "bench_ip" {
bench_ip();
return;
}
let mut table = Table::new();
// Add a row per time
table.add_row(row!["", "Compression Ratio", "Compression Estimation"]);
for (data, data_set_name) in get_codec_test_data_sets() {
let results: Vec<(f32, f32, FastFieldCodecType)> = [
serialize_with_codec(&data, FastFieldCodecType::Bitpacked),
serialize_with_codec(&data, FastFieldCodecType::Linear),
serialize_with_codec(&data, FastFieldCodecType::BlockwiseLinear),
]
.into_iter()
.flatten()
.collect();
let best_compression_ratio_codec = results
.iter()
.min_by(|&res1, &res2| res1.partial_cmp(res2).unwrap())
.cloned()
.unwrap();
table.add_row(Row::new(vec![Cell::new(data_set_name).style_spec("Bbb")]));
for (est, comp, codec_type) in results {
let est_cell = est.to_string();
let ratio_cell = comp.to_string();
let style = if comp == best_compression_ratio_codec.1 {
"Fb"
} else {
""
};
table.add_row(Row::new(vec![
Cell::new(&format!("{codec_type:?}")).style_spec("bFg"),
Cell::new(&ratio_cell).style_spec(style),
Cell::new(&est_cell).style_spec(""),
]));
}
}
table.printstd();
}
pub fn get_codec_test_data_sets() -> Vec<(Vec<u64>, &'static str)> {
let mut data_and_names = vec![];
let data = (1000..=200_000_u64).collect::<Vec<_>>();
data_and_names.push((data, "Autoincrement"));
let mut current_cumulative = 0;
let data = (1..=200_000_u64)
.map(|num| {
let num = (num as f32 + num as f32).log10() as u64;
current_cumulative += num;
current_cumulative
})
.collect::<Vec<_>>();
// let data = (1..=200000_u64).map(|num| num + num).collect::<Vec<_>>();
data_and_names.push((data, "Monotonically increasing concave"));
let mut current_cumulative = 0;
let data = (1..=200_000_u64)
.map(|num| {
let num = (200_000.0 - num as f32).log10() as u64;
current_cumulative += num;
current_cumulative
})
.collect::<Vec<_>>();
data_and_names.push((data, "Monotonically increasing convex"));
let data = (1000..=200_000_u64)
.map(|num| num + rand::random::<u8>() as u64)
.collect::<Vec<_>>();
data_and_names.push((data, "Almost monotonically increasing"));
data_and_names
}
pub fn serialize_with_codec(
data: &[u64],
codec_type: FastFieldCodecType,
) -> Option<(f32, f32, FastFieldCodecType)> {
let col = VecColumn::from(data);
let estimation = fastfield_codecs::estimate(&col, codec_type)?;
let mut out = Vec::new();
fastfield_codecs::serialize(&col, &mut out, &[codec_type]).ok()?;
let actual_compression = out.len() as f32 / (col.num_vals() * 8) as f32;
Some((estimation, actual_compression, codec_type))
}

40
columnar/src/column_values/merge.rs Normal file
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@@ -0,0 +1,40 @@
use std::fmt::Debug;
use std::sync::Arc;
use crate::iterable::Iterable;
use crate::{ColumnIndex, ColumnValues, MergeRowOrder};
pub(crate) struct MergedColumnValues<'a, T> {
pub(crate) column_indexes: &'a [ColumnIndex],
pub(crate) column_values: &'a [Option<Arc<dyn ColumnValues<T>>>],
pub(crate) merge_row_order: &'a MergeRowOrder,
}
impl<T: Copy + PartialOrd + Debug + 'static> Iterable<T> for MergedColumnValues<'_, T> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
match self.merge_row_order {
MergeRowOrder::Stack(_) => Box::new(
self.column_values
.iter()
.flatten()
.flat_map(|column_value| column_value.iter()),
),
MergeRowOrder::Shuffled(shuffle_merge_order) => Box::new(
shuffle_merge_order
.iter_new_to_old_row_addrs()
.flat_map(|row_addr| {
let column_index = &self.column_indexes[row_addr.segment_ord as usize];
let column_values =
self.column_values[row_addr.segment_ord as usize].as_ref()?;
let value_range = column_index.value_row_ids(row_addr.row_id);
Some((value_range, column_values))
})
.flat_map(|(value_range, column_values)| {
value_range
.into_iter()
.map(|val| column_values.get_val(val))
}),
),
}
}
}

499
columnar/src/column_values/mod.rs
View File

@@ -1,326 +1,247 @@
#![warn(missing_docs)]
#![cfg_attr(all(feature = "unstable", test), feature(test))]
//! # `fastfield_codecs`
//!
//! - Columnar storage of data for tantivy [`Column`].
//! - Columnar storage of data for tantivy [`crate::Column`].
//! - Encode data in different codecs.
//! - Monotonically map values to u64/u128
#[cfg(test)]
mod tests;
use std::io;
use std::io::Write;
use std::fmt::Debug;
use std::ops::{Range, RangeInclusive};
use std::sync::Arc;
use common::{BinarySerializable, OwnedBytes};
use compact_space::CompactSpaceDecompressor;
use monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
StrictlyMonotonicMappingToInternalBaseval, StrictlyMonotonicMappingToInternalGCDBaseval,
};
use serialize::{Header, U128Header};
use downcast_rs::DowncastSync;
pub use monotonic_mapping::{MonotonicallyMappableToU64, StrictlyMonotonicFn};
pub use monotonic_mapping_u128::MonotonicallyMappableToU128;
mod bitpacked;
mod blockwise_linear;
mod compact_space;
mod line;
mod linear;
mod merge;
pub(crate) mod monotonic_mapping;
pub(crate) mod monotonic_mapping_u128;
mod stats;
mod u128_based;
mod u64_based;
mod vec_column;
mod column;
mod column_with_cardinality;
mod gcd;
pub mod serialize;
mod monotonic_column;
pub use self::column::{monotonic_map_column, ColumnValues, IterColumn, VecColumn};
pub use self::monotonic_mapping::{MonotonicallyMappableToU64, StrictlyMonotonicFn};
pub use self::monotonic_mapping_u128::MonotonicallyMappableToU128;
#[cfg(test)]
pub use self::serialize::tests::serialize_and_load;
pub use self::serialize::{serialize_column_values, NormalizedHeader};
use crate::column_values::bitpacked::BitpackedCodec;
use crate::column_values::blockwise_linear::BlockwiseLinearCodec;
use crate::column_values::linear::LinearCodec;
pub(crate) use merge::MergedColumnValues;
pub use stats::ColumnStats;
pub use u128_based::{
open_u128_as_compact_u64, open_u128_mapped, serialize_column_values_u128,
CompactSpaceU64Accessor,
};
pub use u64_based::{
load_u64_based_column_values, serialize_and_load_u64_based_column_values,
serialize_u64_based_column_values, CodecType, ALL_U64_CODEC_TYPES,
};
pub use vec_column::VecColumn;
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
#[repr(u8)]
/// Available codecs to use to encode the u64 (via [`MonotonicallyMappableToU64`]) converted data.
pub enum FastFieldCodecType {
/// Bitpack all values in the value range. The number of bits is defined by the amplitude
/// `column.max_value() - column.min_value()`
Bitpacked = 1,
/// Linear interpolation puts a line between the first and last value and then bitpacks the
/// values by the offset from the line. The number of bits is defined by the max deviation from
/// the line.
Linear = 2,
/// Same as [`FastFieldCodecType::Linear`], but encodes in blocks of 512 elements.
BlockwiseLinear = 3,
}
pub use self::monotonic_column::monotonic_map_column;
use crate::RowId;
impl BinarySerializable for FastFieldCodecType {
fn serialize<W: Write>(&self, wrt: &mut W) -> io::Result<()> {
self.to_code().serialize(wrt)
}
/// `ColumnValues` provides access to a dense field column.
///
/// `Column` are just a wrapper over `ColumnValues` and a `ColumnIndex`.
///
/// Any methods with a default and specialized implementation need to be called in the
/// wrappers that implement the trait: Arc and MonotonicMappingColumn
pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync + DowncastSync {
/// Return the value associated with the given idx.
///
/// This accessor should return as fast as possible.
///
/// # Panics
///
/// May panic if `idx` is greater than the column length.
fn get_val(&self, idx: u32) -> T;
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let code = u8::deserialize(reader)?;
let codec_type: Self = Self::from_code(code)
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Unknown code `{code}.`"))?;
Ok(codec_type)
}
}
/// Allows to push down multiple fetch calls, to avoid dynamic dispatch overhead.
///
/// idx and output should have the same length
///
/// # Panics
///
/// May panic if `idx` is greater than the column length.
fn get_vals(&self, indexes: &[u32], output: &mut [T]) {
assert!(indexes.len() == output.len());
let out_and_idx_chunks = output.chunks_exact_mut(4).zip(indexes.chunks_exact(4));
for (out_x4, idx_x4) in out_and_idx_chunks {
out_x4[0] = self.get_val(idx_x4[0]);
out_x4[1] = self.get_val(idx_x4[1]);
out_x4[2] = self.get_val(idx_x4[2]);
out_x4[3] = self.get_val(idx_x4[3]);
}
impl FastFieldCodecType {
pub(crate) fn to_code(self) -> u8 {
self as u8
}
pub(crate) fn from_code(code: u8) -> Option<Self> {
match code {
1 => Some(Self::Bitpacked),
2 => Some(Self::Linear),
3 => Some(Self::BlockwiseLinear),
_ => None,
let out_and_idx_chunks = output
.chunks_exact_mut(4)
.into_remainder()
.iter_mut()
.zip(indexes.chunks_exact(4).remainder());
for (out, idx) in out_and_idx_chunks {
*out = self.get_val(*idx);
}
}
}
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
#[repr(u8)]
/// Available codecs to use to encode the u128 (via [`MonotonicallyMappableToU128`]) converted data.
pub enum U128FastFieldCodecType {
/// This codec takes a large number space (u128) and reduces it to a compact number space, by
/// removing the holes.
CompactSpace = 1,
}
impl BinarySerializable for U128FastFieldCodecType {
fn serialize<W: Write>(&self, wrt: &mut W) -> io::Result<()> {
self.to_code().serialize(wrt)
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let code = u8::deserialize(reader)?;
let codec_type: Self = Self::from_code(code)
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Unknown code `{code}.`"))?;
Ok(codec_type)
}
}
impl U128FastFieldCodecType {
pub(crate) fn to_code(self) -> u8 {
self as u8
}
pub(crate) fn from_code(code: u8) -> Option<Self> {
match code {
1 => Some(Self::CompactSpace),
_ => None,
/// Allows to push down multiple fetch calls, to avoid dynamic dispatch overhead.
/// The slightly weird `Option<T>` in output allows pushdown to full columns.
///
/// idx and output should have the same length
///
/// # Panics
///
/// May panic if `idx` is greater than the column length.
fn get_vals_opt(&self, indexes: &[u32], output: &mut [Option<T>]) {
assert!(indexes.len() == output.len());
let out_and_idx_chunks = output.chunks_exact_mut(4).zip(indexes.chunks_exact(4));
for (out_x4, idx_x4) in out_and_idx_chunks {
out_x4[0] = Some(self.get_val(idx_x4[0]));
out_x4[1] = Some(self.get_val(idx_x4[1]));
out_x4[2] = Some(self.get_val(idx_x4[2]));
out_x4[3] = Some(self.get_val(idx_x4[3]));
}
let out_and_idx_chunks = output
.chunks_exact_mut(4)
.into_remainder()
.iter_mut()
.zip(indexes.chunks_exact(4).remainder());
for (out, idx) in out_and_idx_chunks {
*out = Some(self.get_val(*idx));
}
}
}
/// Returns the correct codec reader wrapped in the `Arc` for the data.
pub fn open_u128_mapped<T: MonotonicallyMappableToU128>(
mut bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
let header = U128Header::deserialize(&mut bytes)?;
assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
let reader = CompactSpaceDecompressor::open(bytes)?;
let inverted: StrictlyMonotonicMappingInverter<StrictlyMonotonicMappingToInternal<T>> =
StrictlyMonotonicMappingToInternal::<T>::new().into();
Ok(Arc::new(monotonic_map_column(reader, inverted)))
}
/// Returns the correct codec reader wrapped in the `Arc` for the data.
pub fn open_u64_mapped<T: MonotonicallyMappableToU64>(
mut bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
let header = Header::deserialize(&mut bytes)?;
match header.codec_type {
FastFieldCodecType::Bitpacked => open_specific_codec::<BitpackedCodec, _>(bytes, &header),
FastFieldCodecType::Linear => open_specific_codec::<LinearCodec, _>(bytes, &header),
FastFieldCodecType::BlockwiseLinear => {
open_specific_codec::<BlockwiseLinearCodec, _>(bytes, &header)
/// Fills an output buffer with the fast field values
/// associated with the `DocId` going from
/// `start` to `start + output.len()`.
///
/// # Panics
///
/// Must panic if `start + output.len()` is greater than
/// the segment's `maxdoc`.
#[inline(always)]
fn get_range(&self, start: u64, output: &mut [T]) {
for (out, idx) in output.iter_mut().zip(start..) {
*out = self.get_val(idx as u32);
}
}
}
fn open_specific_codec<C: FastFieldCodec, Item: MonotonicallyMappableToU64>(
bytes: OwnedBytes,
header: &Header,
) -> io::Result<Arc<dyn ColumnValues<Item>>> {
let normalized_header = header.normalized();
let reader = C::open_from_bytes(bytes, normalized_header)?;
let min_value = header.min_value;
if let Some(gcd) = header.gcd {
let mapping = StrictlyMonotonicMappingInverter::from(
StrictlyMonotonicMappingToInternalGCDBaseval::new(gcd.get(), min_value),
);
Ok(Arc::new(monotonic_map_column(reader, mapping)))
} else {
let mapping = StrictlyMonotonicMappingInverter::from(
StrictlyMonotonicMappingToInternalBaseval::new(min_value),
);
Ok(Arc::new(monotonic_map_column(reader, mapping)))
/// Get the row ids of values which are in the provided value range.
///
/// Note that position == docid for single value fast fields
fn get_row_ids_for_value_range(
&self,
value_range: RangeInclusive<T>,
row_id_range: Range<RowId>,
row_id_hits: &mut Vec<RowId>,
) {
let row_id_range = row_id_range.start..row_id_range.end.min(self.num_vals());
for idx in row_id_range {
let val = self.get_val(idx);
if value_range.contains(&val) {
row_id_hits.push(idx);
}
}
}
/// Returns a lower bound for this column of values.
///
/// All values are guaranteed to be higher than `.min_value()`
/// but this value is not necessary the best boundary value.
///
/// We have
/// ∀i < self.num_vals(), self.get_val(i) >= self.min_value()
/// But we don't have necessarily
/// ∃i < self.num_vals(), self.get_val(i) == self.min_value()
fn min_value(&self) -> T;
/// Returns an upper bound for this column of values.
///
/// All values are guaranteed to be lower than `.max_value()`
/// but this value is not necessary the best boundary value.
///
/// We have
/// ∀i < self.num_vals(), self.get_val(i) <= self.max_value()
/// But we don't have necessarily
/// ∃i < self.num_vals(), self.get_val(i) == self.max_value()
fn max_value(&self) -> T;
/// The number of values in the column.
fn num_vals(&self) -> u32;
/// Returns a iterator over the data
fn iter<'a>(&'a self) -> Box<dyn Iterator<Item = T> + 'a> {
Box::new((0..self.num_vals()).map(|idx| self.get_val(idx)))
}
}
downcast_rs::impl_downcast!(sync ColumnValues<T> where T: PartialOrd);
/// Empty column of values.
pub struct EmptyColumnValues;
impl<T: PartialOrd + Default> ColumnValues<T> for EmptyColumnValues {
fn get_val(&self, _idx: u32) -> T {
panic!("Internal Error: Called get_val of empty column.")
}
fn min_value(&self) -> T {
T::default()
}
fn max_value(&self) -> T {
T::default()
}
fn num_vals(&self) -> u32 {
0
}
}
/// The FastFieldSerializerEstimate trait is required on all variants
/// of fast field compressions, to decide which one to choose.
pub(crate) trait FastFieldCodec: 'static {
/// A codex needs to provide a unique name and id, which is
/// used for debugging and de/serialization.
const CODEC_TYPE: FastFieldCodecType;
impl<T: Copy + PartialOrd + Debug + 'static> ColumnValues<T> for Arc<dyn ColumnValues<T>> {
#[inline(always)]
fn get_val(&self, idx: u32) -> T {
self.as_ref().get_val(idx)
}
type Reader: ColumnValues<u64> + 'static;
#[inline(always)]
fn get_vals_opt(&self, indexes: &[u32], output: &mut [Option<T>]) {
self.as_ref().get_vals_opt(indexes, output)
}
/// Reads the metadata and returns the CodecReader
fn open_from_bytes(bytes: OwnedBytes, header: NormalizedHeader) -> io::Result<Self::Reader>;
#[inline(always)]
fn min_value(&self) -> T {
self.as_ref().min_value()
}
/// Serializes the data using the serializer into write.
///
/// The column iterator should be preferred over using column `get_val` method for
/// performance reasons.
fn serialize(column: &dyn ColumnValues, write: &mut impl Write) -> io::Result<()>;
#[inline(always)]
fn max_value(&self) -> T {
self.as_ref().max_value()
}
/// Returns an estimate of the compression ratio.
/// If the codec is not applicable, returns `None`.
///
/// The baseline is uncompressed 64bit data.
///
/// It could make sense to also return a value representing
/// computational complexity.
fn estimate(column: &dyn ColumnValues) -> Option<f32>;
#[inline(always)]
fn num_vals(&self) -> u32 {
self.as_ref().num_vals()
}
#[inline(always)]
fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
self.as_ref().iter()
}
#[inline(always)]
fn get_range(&self, start: u64, output: &mut [T]) {
self.as_ref().get_range(start, output)
}
#[inline(always)]
fn get_row_ids_for_value_range(
&self,
range: RangeInclusive<T>,
doc_id_range: Range<u32>,
positions: &mut Vec<u32>,
) {
self.as_ref()
.get_row_ids_for_value_range(range, doc_id_range, positions)
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use std::sync::Arc;
use common::OwnedBytes;
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use test::{self, Bencher};
use super::*;
fn get_data() -> Vec<u64> {
let mut rng = StdRng::seed_from_u64(2u64);
let mut data: Vec<_> = (100..55000_u64)
.map(|num| num + rng.gen::<u8>() as u64)
.collect();
data.push(99_000);
data.insert(1000, 2000);
data.insert(2000, 100);
data.insert(3000, 4100);
data.insert(4000, 100);
data.insert(5000, 800);
data
}
#[inline(never)]
fn value_iter() -> impl Iterator<Item = u64> {
0..20_000
}
fn get_reader_for_bench<Codec: FastFieldCodec>(data: &[u64]) -> Codec::Reader {
let mut bytes = Vec::new();
let min_value = *data.iter().min().unwrap();
let data = data.iter().map(|el| *el - min_value).collect::<Vec<_>>();
let col = VecColumn::from(&data);
let normalized_header = NormalizedHeader {
num_vals: col.num_vals(),
max_value: col.max_value(),
};
Codec::serialize(&VecColumn::from(&data), &mut bytes).unwrap();
Codec::open_from_bytes(OwnedBytes::new(bytes), normalized_header).unwrap()
}
fn bench_get<Codec: FastFieldCodec>(b: &mut Bencher, data: &[u64]) {
let col = get_reader_for_bench::<Codec>(data);
b.iter(|| {
let mut sum = 0u64;
for pos in value_iter() {
let val = col.get_val(pos as u32);
sum = sum.wrapping_add(val);
}
sum
});
}
#[inline(never)]
fn bench_get_dynamic_helper(b: &mut Bencher, col: Arc<dyn ColumnValues>) {
b.iter(|| {
let mut sum = 0u64;
for pos in value_iter() {
let val = col.get_val(pos as u32);
sum = sum.wrapping_add(val);
}
sum
});
}
fn bench_get_dynamic<Codec: FastFieldCodec>(b: &mut Bencher, data: &[u64]) {
let col = Arc::new(get_reader_for_bench::<Codec>(data));
bench_get_dynamic_helper(b, col);
}
fn bench_create<Codec: FastFieldCodec>(b: &mut Bencher, data: &[u64]) {
let min_value = *data.iter().min().unwrap();
let data = data.iter().map(|el| *el - min_value).collect::<Vec<_>>();
let mut bytes = Vec::new();
b.iter(|| {
bytes.clear();
Codec::serialize(&VecColumn::from(&data), &mut bytes).unwrap();
});
}
#[bench]
fn bench_fastfield_bitpack_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<BitpackedCodec>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<LinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<BlockwiseLinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_bitpack_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<BitpackedCodec>(b, &data);
}
#[bench]
fn bench_fastfield_bitpack_get_dynamic(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get_dynamic::<BitpackedCodec>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<LinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_get_dynamic(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get_dynamic::<LinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<BlockwiseLinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_get_dynamic(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get_dynamic::<BlockwiseLinearCodec>(b, &data);
}
}
mod bench;

120
columnar/src/column_values/monotonic_column.rs Normal file
View File

@@ -0,0 +1,120 @@
use std::fmt::Debug;
use std::marker::PhantomData;
use std::ops::{Range, RangeInclusive};
use crate::column_values::monotonic_mapping::StrictlyMonotonicFn;
use crate::ColumnValues;
struct MonotonicMappingColumn<C, T, Input> {
from_column: C,
monotonic_mapping: T,
_phantom: PhantomData<Input>,
}
/// Creates a view of a column transformed by a strictly monotonic mapping. See
/// [`StrictlyMonotonicFn`].
///
/// E.g. apply a gcd monotonic_mapping([100, 200, 300]) == [1, 2, 3]
/// monotonic_mapping.mapping() is expected to be injective, and we should always have
/// monotonic_mapping.inverse(monotonic_mapping.mapping(el)) == el
///
/// The inverse of the mapping is required for:
/// `fn get_positions_for_value_range(&self, range: RangeInclusive<T>) -> Vec<u64> `
/// The user provides the original value range and we need to monotonic map them in the same way the
/// serialization does before calling the underlying column.
///
/// Note that when opening a codec, the monotonic_mapping should be the inverse of the mapping
/// during serialization. And therefore the monotonic_mapping_inv when opening is the same as
/// monotonic_mapping during serialization.
pub fn monotonic_map_column<C, T, Input, Output>(
from_column: C,
monotonic_mapping: T,
) -> impl ColumnValues<Output>
where
C: ColumnValues<Input> + 'static,
T: StrictlyMonotonicFn<Input, Output> + Send + Sync + 'static,
Input: PartialOrd + Debug + Send + Sync + Clone + 'static,
Output: PartialOrd + Debug + Send + Sync + Clone + 'static,
{
MonotonicMappingColumn {
from_column,
monotonic_mapping,
_phantom: PhantomData,
}
}
impl<C, T, Input, Output> ColumnValues<Output> for MonotonicMappingColumn<C, T, Input>
where
C: ColumnValues<Input> + 'static,
T: StrictlyMonotonicFn<Input, Output> + Send + Sync + 'static,
Input: PartialOrd + Send + Debug + Sync + Clone + 'static,
Output: PartialOrd + Send + Debug + Sync + Clone + 'static,
{
#[inline(always)]
fn get_val(&self, idx: u32) -> Output {
let from_val = self.from_column.get_val(idx);
self.monotonic_mapping.mapping(from_val)
}
fn min_value(&self) -> Output {
let from_min_value = self.from_column.min_value();
self.monotonic_mapping.mapping(from_min_value)
}
fn max_value(&self) -> Output {
let from_max_value = self.from_column.max_value();
self.monotonic_mapping.mapping(from_max_value)
}
fn num_vals(&self) -> u32 {
self.from_column.num_vals()
}
fn iter(&self) -> Box<dyn Iterator<Item = Output> + '_> {
Box::new(
self.from_column
.iter()
.map(|el| self.monotonic_mapping.mapping(el)),
)
}
fn get_row_ids_for_value_range(
&self,
range: RangeInclusive<Output>,
doc_id_range: Range<u32>,
positions: &mut Vec<u32>,
) {
self.from_column.get_row_ids_for_value_range(
self.monotonic_mapping.inverse(range.start().clone())
..=self.monotonic_mapping.inverse(range.end().clone()),
doc_id_range,
positions,
)
}
// We voluntarily do not implement get_range as it yields a regression,
// and we do not have any specialized implementation anyway.
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_values::monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
};
use crate::column_values::VecColumn;
#[test]
fn test_monotonic_mapping_iter() {
let vals: Vec<u64> = (0..100u64).map(|el| el * 10).collect();
let col = VecColumn::from(vals);
let mapped = monotonic_map_column(
col,
StrictlyMonotonicMappingInverter::from(StrictlyMonotonicMappingToInternal::<i64>::new()),
);
let val_i64s: Vec<u64> = mapped.iter().collect();
for i in 0..100 {
assert_eq!(val_i64s[i as usize], mapped.get_val(i));
}
}
}

79
columnar/src/column_values/monotonic_mapping.rs
View File

@@ -1,13 +1,14 @@
use std::fmt::Debug;
use std::marker::PhantomData;
use fastdivide::DividerU64;
use common::DateTime;
use super::MonotonicallyMappableToU128;
use crate::RowId;
/// Monotonic maps a value to u64 value space.
/// Monotonic mapping enables `PartialOrd` on u64 space without conversion to original space.
pub trait MonotonicallyMappableToU64: 'static + PartialOrd + Copy + Send + Sync {
pub trait MonotonicallyMappableToU64: 'static + PartialOrd + Debug + Copy + Send + Sync {
/// Converts a value to u64.
///
/// Internally all fast field values are encoded as u64.
@@ -111,65 +112,6 @@ where T: MonotonicallyMappableToU64
}
}
/// Mapping dividing by gcd and a base value.
///
/// The function is assumed to be only called on values divided by passed
/// gcd value. (It is necessary for the function to be monotonic.)
pub(crate) struct StrictlyMonotonicMappingToInternalGCDBaseval {
gcd_divider: DividerU64,
gcd: u64,
min_value: u64,
}
impl StrictlyMonotonicMappingToInternalGCDBaseval {
pub(crate) fn new(gcd: u64, min_value: u64) -> Self {
let gcd_divider = DividerU64::divide_by(gcd);
Self {
gcd_divider,
gcd,
min_value,
}
}
}
impl<External: MonotonicallyMappableToU64> StrictlyMonotonicFn<External, u64>
for StrictlyMonotonicMappingToInternalGCDBaseval
{
#[inline(always)]
fn mapping(&self, inp: External) -> u64 {
self.gcd_divider
.divide(External::to_u64(inp) - self.min_value)
}
#[inline(always)]
fn inverse(&self, out: u64) -> External {
External::from_u64(self.min_value + out * self.gcd)
}
}
/// Strictly monotonic mapping with a base value.
pub(crate) struct StrictlyMonotonicMappingToInternalBaseval {
min_value: u64,
}
impl StrictlyMonotonicMappingToInternalBaseval {
#[inline(always)]
pub(crate) fn new(min_value: u64) -> Self {
Self { min_value }
}
}
impl<External: MonotonicallyMappableToU64> StrictlyMonotonicFn<External, u64>
for StrictlyMonotonicMappingToInternalBaseval
{
#[inline(always)]
fn mapping(&self, val: External) -> u64 {
External::to_u64(val) - self.min_value
}
#[inline(always)]
fn inverse(&self, val: u64) -> External {
External::from_u64(self.min_value + val)
}
}
impl MonotonicallyMappableToU64 for u64 {
#[inline(always)]
fn to_u64(self) -> u64 {
@@ -194,17 +136,15 @@ impl MonotonicallyMappableToU64 for i64 {
}
}
impl MonotonicallyMappableToU64 for crate::DateTime {
impl MonotonicallyMappableToU64 for DateTime {
#[inline(always)]
fn to_u64(self) -> u64 {
common::i64_to_u64(self.timestamp_micros)
common::i64_to_u64(self.into_timestamp_nanos())
}
#[inline(always)]
fn from_u64(val: u64) -> Self {
crate::DateTime {
timestamp_micros: common::u64_to_i64(val),
}
DateTime::from_timestamp_nanos(common::u64_to_i64(val))
}
}
@@ -260,13 +200,6 @@ mod tests {
// TODO
// identity mapping
// test_round_trip(&StrictlyMonotonicMappingToInternal::<u128>::new(), 100u128);
// base value to i64 round trip
let mapping = StrictlyMonotonicMappingToInternalBaseval::new(100);
test_round_trip::<_, _, u64>(&mapping, 100i64);
// base value and gcd to u64 round trip
let mapping = StrictlyMonotonicMappingToInternalGCDBaseval::new(10, 100);
test_round_trip::<_, _, u64>(&mapping, 100u64);
}
fn test_round_trip<T: StrictlyMonotonicFn<K, L>, K: std::fmt::Debug + Eq + Copy, L>(

3
columnar/src/column_values/monotonic_mapping_u128.rs
View File

@@ -1,8 +1,9 @@
use std::fmt::Debug;
use std::net::Ipv6Addr;
/// Montonic maps a value to u128 value space
/// Monotonic mapping enables `PartialOrd` on u128 space without conversion to original space.
pub trait MonotonicallyMappableToU128: 'static + PartialOrd + Copy + Send + Sync {
pub trait MonotonicallyMappableToU128: 'static + PartialOrd + Copy + Debug + Send + Sync {
/// Converts a value to u128.
///
/// Internally all fast field values are encoded as u64.

320
columnar/src/column_values/serialize.rs
View File

@@ -1,320 +0,0 @@
// Copyright (C) 2022 Quickwit, Inc.
//
// Quickwit is offered under the AGPL v3.0 and as commercial software.
// For commercial licensing, contact us at hello@quickwit.io.
//
// AGPL:
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU Affero General Public License as
// published by the Free Software Foundation, either version 3 of the
// License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU Affero General Public License for more details.
//
// You should have received a copy of the GNU Affero General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
use std::io;
use std::num::NonZeroU64;
use common::{BinarySerializable, VInt};
use log::warn;
use super::bitpacked::BitpackedCodec;
use super::blockwise_linear::BlockwiseLinearCodec;
use super::linear::LinearCodec;
use super::monotonic_mapping::{
StrictlyMonotonicFn, StrictlyMonotonicMappingToInternal,
StrictlyMonotonicMappingToInternalGCDBaseval,
};
use super::{
monotonic_map_column, ColumnValues, FastFieldCodec, FastFieldCodecType,
MonotonicallyMappableToU64, U128FastFieldCodecType,
};
use crate::column_values::compact_space::CompactSpaceCompressor;
/// The normalized header gives some parameters after applying the following
/// normalization of the vector:
/// `val -> (val - min_value) / gcd`
///
/// By design, after normalization, `min_value = 0` and `gcd = 1`.
#[derive(Debug, Copy, Clone)]
pub struct NormalizedHeader {
/// The number of values in the underlying column.
pub num_vals: u32,
/// The max value of the underlying column.
pub max_value: u64,
}
#[derive(Debug, Copy, Clone)]
pub(crate) struct Header {
pub num_vals: u32,
pub min_value: u64,
pub max_value: u64,
pub gcd: Option<NonZeroU64>,
pub codec_type: FastFieldCodecType,
}
impl Header {
pub fn normalized(self) -> NormalizedHeader {
let gcd = self.gcd.map(|gcd| gcd.get()).unwrap_or(1);
let gcd_min_val_mapping =
StrictlyMonotonicMappingToInternalGCDBaseval::new(gcd, self.min_value);
let max_value = gcd_min_val_mapping.mapping(self.max_value);
NormalizedHeader {
num_vals: self.num_vals,
max_value,
}
}
pub(crate) fn normalize_column<C: ColumnValues>(&self, from_column: C) -> impl ColumnValues {
normalize_column(from_column, self.min_value, self.gcd)
}
pub fn compute_header(
column: impl ColumnValues<u64>,
codecs: &[FastFieldCodecType],
) -> Option<Header> {
let num_vals = column.num_vals();
let min_value = column.min_value();
let max_value = column.max_value();
let gcd = super::gcd::find_gcd(column.iter().map(|val| val - min_value))
.filter(|gcd| gcd.get() > 1u64);
let normalized_column = normalize_column(column, min_value, gcd);
let codec_type = detect_codec(normalized_column, codecs)?;
Some(Header {
num_vals,
min_value,
max_value,
gcd,
codec_type,
})
}
}
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
pub(crate) struct U128Header {
pub num_vals: u32,
pub codec_type: U128FastFieldCodecType,
}
impl BinarySerializable for U128Header {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.num_vals as u64).serialize(writer)?;
self.codec_type.serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let num_vals = VInt::deserialize(reader)?.0 as u32;
let codec_type = U128FastFieldCodecType::deserialize(reader)?;
Ok(U128Header {
num_vals,
codec_type,
})
}
}
fn normalize_column<C: ColumnValues>(
from_column: C,
min_value: u64,
gcd: Option<NonZeroU64>,
) -> impl ColumnValues {
let gcd = gcd.map(|gcd| gcd.get()).unwrap_or(1);
let mapping = StrictlyMonotonicMappingToInternalGCDBaseval::new(gcd, min_value);
monotonic_map_column(from_column, mapping)
}
impl BinarySerializable for Header {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.num_vals as u64).serialize(writer)?;
VInt(self.min_value).serialize(writer)?;
VInt(self.max_value - self.min_value).serialize(writer)?;
if let Some(gcd) = self.gcd {
VInt(gcd.get()).serialize(writer)?;
} else {
VInt(0u64).serialize(writer)?;
}
self.codec_type.serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let num_vals = VInt::deserialize(reader)?.0 as u32;
let min_value = VInt::deserialize(reader)?.0;
let amplitude = VInt::deserialize(reader)?.0;
let max_value = min_value + amplitude;
let gcd_u64 = VInt::deserialize(reader)?.0;
let codec_type = FastFieldCodecType::deserialize(reader)?;
Ok(Header {
num_vals,
min_value,
max_value,
gcd: NonZeroU64::new(gcd_u64),
codec_type,
})
}
}
/// Serializes u128 values with the compact space codec.
pub fn serialize_column_values_u128<F: Fn() -> I, I: Iterator<Item = u128>>(
iter_gen: F,
num_vals: u32,
output: &mut impl io::Write,
) -> io::Result<()> {
let header = U128Header {
num_vals,
codec_type: U128FastFieldCodecType::CompactSpace,
};
header.serialize(output)?;
let compressor = CompactSpaceCompressor::train_from(iter_gen(), num_vals);
compressor.compress_into(iter_gen(), output)?;
Ok(())
}
/// Serializes the column with the codec with the best estimate on the data.
pub fn serialize_column_values<T: MonotonicallyMappableToU64>(
typed_column: impl ColumnValues<T>,
codecs: &[FastFieldCodecType],
output: &mut impl io::Write,
) -> io::Result<()> {
let column = monotonic_map_column(typed_column, StrictlyMonotonicMappingToInternal::<T>::new());
let header = Header::compute_header(&column, codecs).ok_or_else(|| {
io::Error::new(
io::ErrorKind::InvalidInput,
format!(
"Data cannot be serialized with this list of codec. {:?}",
codecs
),
)
})?;
header.serialize(output)?;
let normalized_column = header.normalize_column(column);
assert_eq!(normalized_column.min_value(), 0u64);
serialize_given_codec(normalized_column, header.codec_type, output)?;
Ok(())
}
fn detect_codec(
column: impl ColumnValues<u64>,
codecs: &[FastFieldCodecType],
) -> Option<FastFieldCodecType> {
let mut estimations = Vec::new();
for &codec in codecs {
let estimation_opt = match codec {
FastFieldCodecType::Bitpacked => BitpackedCodec::estimate(&column),
FastFieldCodecType::Linear => LinearCodec::estimate(&column),
FastFieldCodecType::BlockwiseLinear => BlockwiseLinearCodec::estimate(&column),
};
if let Some(estimation) = estimation_opt {
estimations.push((estimation, codec));
}
}
if let Some(broken_estimation) = estimations.iter().find(|estimation| estimation.0.is_nan()) {
warn!(
"broken estimation for fast field codec {:?}",
broken_estimation.1
);
}
// removing nan values for codecs with broken calculations, and max values which disables
// codecs
estimations.retain(|estimation| !estimation.0.is_nan() && estimation.0 != f32::MAX);
estimations.sort_by(|(score_left, _), (score_right, _)| score_left.total_cmp(score_right));
Some(estimations.first()?.1)
}
pub(crate) fn serialize_given_codec(
column: impl ColumnValues<u64>,
codec_type: FastFieldCodecType,
output: &mut impl io::Write,
) -> io::Result<()> {
match codec_type {
FastFieldCodecType::Bitpacked => {
BitpackedCodec::serialize(&column, output)?;
}
FastFieldCodecType::Linear => {
LinearCodec::serialize(&column, output)?;
}
FastFieldCodecType::BlockwiseLinear => {
BlockwiseLinearCodec::serialize(&column, output)?;
}
}
Ok(())
}
#[cfg(test)]
pub mod tests {
use std::sync::Arc;
use common::OwnedBytes;
use super::*;
use crate::column_values::{open_u64_mapped, VecColumn};
const ALL_CODEC_TYPES: [FastFieldCodecType; 3] = [
FastFieldCodecType::Bitpacked,
FastFieldCodecType::Linear,
FastFieldCodecType::BlockwiseLinear,
];
/// Helper function to serialize a column (autodetect from all codecs) and then open it
pub fn serialize_and_load<T: MonotonicallyMappableToU64 + Ord + Default>(
column: &[T],
) -> Arc<dyn ColumnValues<T>> {
let mut buffer = Vec::new();
serialize_column_values(&VecColumn::from(&column), &ALL_CODEC_TYPES, &mut buffer).unwrap();
open_u64_mapped(OwnedBytes::new(buffer)).unwrap()
}
#[test]
fn test_serialize_deserialize_u128_header() {
let original = U128Header {
num_vals: 11,
codec_type: U128FastFieldCodecType::CompactSpace,
};
let mut out = Vec::new();
original.serialize(&mut out).unwrap();
let restored = U128Header::deserialize(&mut &out[..]).unwrap();
assert_eq!(restored, original);
}
#[test]
fn test_serialize_deserialize() {
let original = [1u64, 5u64, 10u64];
let restored: Vec<u64> = serialize_and_load(&original[..]).iter().collect();
assert_eq!(&restored, &original[..]);
}
#[test]
fn test_fastfield_bool_size_bitwidth_1() {
let mut buffer = Vec::new();
let col = VecColumn::from(&[false, true][..]);
serialize_column_values(&col, &ALL_CODEC_TYPES, &mut buffer).unwrap();
// TODO put the header as a footer so that it serves as a padding.
// 5 bytes of header, 1 byte of value, 7 bytes of padding.
assert_eq!(buffer.len(), 5 + 1);
}
#[test]
fn test_fastfield_bool_bit_size_bitwidth_0() {
let mut buffer = Vec::new();
let col = VecColumn::from(&[true][..]);
serialize_column_values(&col, &ALL_CODEC_TYPES, &mut buffer).unwrap();
// 5 bytes of header, 0 bytes of value, 7 bytes of padding.
assert_eq!(buffer.len(), 5);
}
#[test]
fn test_fastfield_gcd() {
let mut buffer = Vec::new();
let vals: Vec<u64> = (0..80).map(|val| (val % 7) * 1_000u64).collect();
let col = VecColumn::from(&vals[..]);
serialize_column_values(&col, &[FastFieldCodecType::Bitpacked], &mut buffer).unwrap();
// Values are stored over 3 bits.
assert_eq!(buffer.len(), 7 + (3 * 80 / 8));
}
}

103
columnar/src/column_values/stats.rs Normal file
View File

@@ -0,0 +1,103 @@
use std::io;
use std::io::Write;
use std::num::NonZeroU64;
use common::{BinarySerializable, VInt};
use crate::RowId;
/// Column statistics.
#[derive(Debug, Clone, Eq, PartialEq)]
pub struct ColumnStats {
/// GCD of the elements `el - min(column)`.
pub gcd: NonZeroU64,
/// Minimum value of the column.
pub min_value: u64,
/// Maximum value of the column.
pub max_value: u64,
/// Number of rows in the column.
pub num_rows: RowId,
}
impl ColumnStats {
/// Amplitude of value.
/// Difference between the maximum and the minimum value.
pub fn amplitude(&self) -> u64 {
self.max_value - self.min_value
}
}
impl BinarySerializable for ColumnStats {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.min_value).serialize(writer)?;
VInt(self.gcd.get()).serialize(writer)?;
VInt(self.amplitude() / self.gcd).serialize(writer)?;
VInt(self.num_rows as u64).serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let min_value = VInt::deserialize(reader)?.0;
let gcd = VInt::deserialize(reader)?.0;
let gcd = NonZeroU64::new(gcd)
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "GCD of 0 is forbidden"))?;
let amplitude = VInt::deserialize(reader)?.0 * gcd.get();
let max_value = min_value + amplitude;
let num_rows = VInt::deserialize(reader)?.0 as RowId;
Ok(ColumnStats {
min_value,
max_value,
num_rows,
gcd,
})
}
}
#[cfg(test)]
mod tests {
use std::num::NonZeroU64;
use common::BinarySerializable;
use crate::column_values::ColumnStats;
#[track_caller]
fn test_stats_ser_deser_aux(stats: &ColumnStats, num_bytes: usize) {
let mut buffer: Vec<u8> = Vec::new();
stats.serialize(&mut buffer).unwrap();
assert_eq!(buffer.len(), num_bytes);
let deser_stats = ColumnStats::deserialize(&mut &buffer[..]).unwrap();
assert_eq!(stats, &deser_stats);
}
#[test]
fn test_stats_serialization() {
test_stats_ser_deser_aux(
&(ColumnStats {
gcd: NonZeroU64::new(3).unwrap(),
min_value: 1,
max_value: 3001,
num_rows: 10,
}),
5,
);
test_stats_ser_deser_aux(
&(ColumnStats {
gcd: NonZeroU64::new(1_000).unwrap(),
min_value: 1,
max_value: 3001,
num_rows: 10,
}),
5,
);
test_stats_ser_deser_aux(
&(ColumnStats {
gcd: NonZeroU64::new(1).unwrap(),
min_value: 0,
max_value: 0,
num_rows: 0,
}),
4,
);
}
}

309
columnar/src/column_values/tests.rs
View File

@@ -1,309 +0,0 @@
use proptest::prelude::*;
use proptest::strategy::Strategy;
use proptest::{prop_oneof, proptest};
use super::bitpacked::BitpackedCodec;
use super::blockwise_linear::BlockwiseLinearCodec;
use super::linear::LinearCodec;
use super::serialize::Header;
pub(crate) fn create_and_validate<Codec: FastFieldCodec>(
data: &[u64],
name: &str,
) -> Option<(f32, f32)> {
let col = &VecColumn::from(data);
let header = Header::compute_header(col, &[Codec::CODEC_TYPE])?;
let normalized_col = header.normalize_column(col);
let estimation = Codec::estimate(&normalized_col)?;
let mut out = Vec::new();
let col = VecColumn::from(data);
serialize_column_values(&col, &[Codec::CODEC_TYPE], &mut out).unwrap();
let actual_compression = out.len() as f32 / (data.len() as f32 * 8.0);
let reader = super::open_u64_mapped::<u64>(OwnedBytes::new(out)).unwrap();
assert_eq!(reader.num_vals(), data.len() as u32);
for (doc, orig_val) in data.iter().copied().enumerate() {
let val = reader.get_val(doc as u32);
assert_eq!(
val, orig_val,
"val `{val}` does not match orig_val {orig_val:?}, in data set {name}, data `{data:?}`",
);
}
if !data.is_empty() {
let test_rand_idx = rand::thread_rng().gen_range(0..=data.len() - 1);
let expected_positions: Vec<u32> = data
.iter()
.enumerate()
.filter(|(_, el)| **el == data[test_rand_idx])
.map(|(pos, _)| pos as u32)
.collect();
let mut positions = Vec::new();
reader.get_docids_for_value_range(
data[test_rand_idx]..=data[test_rand_idx],
0..data.len() as u32,
&mut positions,
);
assert_eq!(expected_positions, positions);
}
Some((estimation, actual_compression))
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(100))]
#[test]
fn test_proptest_small_bitpacked(data in proptest::collection::vec(num_strategy(), 1..10)) {
create_and_validate::<BitpackedCodec>(&data, "proptest bitpacked");
}
#[test]
fn test_proptest_small_linear(data in proptest::collection::vec(num_strategy(), 1..10)) {
create_and_validate::<LinearCodec>(&data, "proptest linearinterpol");
}
#[test]
fn test_proptest_small_blockwise_linear(data in proptest::collection::vec(num_strategy(), 1..10)) {
create_and_validate::<BlockwiseLinearCodec>(&data, "proptest multilinearinterpol");
}
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(10))]
#[test]
fn test_proptest_large_bitpacked(data in proptest::collection::vec(num_strategy(), 1..6000)) {
create_and_validate::<BitpackedCodec>(&data, "proptest bitpacked");
}
#[test]
fn test_proptest_large_linear(data in proptest::collection::vec(num_strategy(), 1..6000)) {
create_and_validate::<LinearCodec>(&data, "proptest linearinterpol");
}
#[test]
fn test_proptest_large_blockwise_linear(data in proptest::collection::vec(num_strategy(), 1..6000)) {
create_and_validate::<BlockwiseLinearCodec>(&data, "proptest multilinearinterpol");
}
}
fn num_strategy() -> impl Strategy<Value = u64> {
prop_oneof![
1 => prop::num::u64::ANY.prop_map(|num| u64::MAX - (num % 10) ),
1 => prop::num::u64::ANY.prop_map(|num| num % 10 ),
20 => prop::num::u64::ANY,
]
}
pub fn get_codec_test_datasets() -> Vec<(Vec<u64>, &'static str)> {
let mut data_and_names = vec![];
let data = (10..=10_000_u64).collect::<Vec<_>>();
data_and_names.push((data, "simple monotonically increasing"));
data_and_names.push((
vec![5, 6, 7, 8, 9, 10, 99, 100],
"offset in linear interpol",
));
data_and_names.push((vec![5, 50, 3, 13, 1, 1000, 35], "rand small"));
data_and_names.push((vec![10], "single value"));
data_and_names.push((
vec![1572656989877777, 1170935903116329, 720575940379279, 0],
"overflow error",
));
data_and_names
}
fn test_codec<C: FastFieldCodec>() {
let codec_name = format!("{:?}", C::CODEC_TYPE);
for (data, dataset_name) in get_codec_test_datasets() {
let estimate_actual_opt: Option<(f32, f32)> =
tests::create_and_validate::<C>(&data, dataset_name);
let result = if let Some((estimate, actual)) = estimate_actual_opt {
format!("Estimate `{estimate}` Actual `{actual}`")
} else {
"Disabled".to_string()
};
println!("Codec {codec_name}, DataSet {dataset_name}, {result}");
}
}
#[test]
fn test_codec_bitpacking() {
test_codec::<BitpackedCodec>();
}
#[test]
fn test_codec_interpolation() {
test_codec::<LinearCodec>();
}
#[test]
fn test_codec_multi_interpolation() {
test_codec::<BlockwiseLinearCodec>();
}
use super::*;
#[test]
fn estimation_good_interpolation_case() {
let data = (10..=20000_u64).collect::<Vec<_>>();
let data: VecColumn = data.as_slice().into();
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
assert_le!(linear_interpol_estimation, 0.01);
let multi_linear_interpol_estimation = BlockwiseLinearCodec::estimate(&data).unwrap();
assert_le!(multi_linear_interpol_estimation, 0.2);
assert_lt!(linear_interpol_estimation, multi_linear_interpol_estimation);
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
assert_lt!(linear_interpol_estimation, bitpacked_estimation);
}
#[test]
fn estimation_test_bad_interpolation_case() {
let data: &[u64] = &[200, 10, 10, 10, 10, 1000, 20];
let data: VecColumn = data.into();
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
assert_le!(linear_interpol_estimation, 0.34);
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
assert_lt!(bitpacked_estimation, linear_interpol_estimation);
}
#[test]
fn estimation_prefer_bitpacked() {
let data = VecColumn::from(&[10, 10, 10, 10]);
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
assert_lt!(bitpacked_estimation, linear_interpol_estimation);
}
#[test]
fn estimation_test_bad_interpolation_case_monotonically_increasing() {
let mut data: Vec<u64> = (201..=20000_u64).collect();
data.push(1_000_000);
let data: VecColumn = data.as_slice().into();
// in this case the linear interpolation can't in fact not be worse than bitpacking,
// but the estimator adds some threshold, which leads to estimated worse behavior
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
assert_le!(linear_interpol_estimation, 0.35);
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
assert_le!(bitpacked_estimation, 0.32);
assert_le!(bitpacked_estimation, linear_interpol_estimation);
}
#[test]
fn test_fast_field_codec_type_to_code() {
let mut count_codec = 0;
for code in 0..=255 {
if let Some(codec_type) = FastFieldCodecType::from_code(code) {
assert_eq!(codec_type.to_code(), code);
count_codec += 1;
}
}
assert_eq!(count_codec, 3);
}
fn test_fastfield_gcd_i64_with_codec(
codec_type: FastFieldCodecType,
num_vals: usize,
) -> io::Result<()> {
let mut vals: Vec<i64> = (-4..=(num_vals as i64) - 5).map(|val| val * 1000).collect();
let mut buffer: Vec<u8> = Vec::new();
crate::column_values::serialize_column_values(
&VecColumn::from(&vals),
&[codec_type],
&mut buffer,
)?;
let buffer = OwnedBytes::new(buffer);
let column = crate::column_values::open_u64_mapped::<i64>(buffer.clone())?;
assert_eq!(column.get_val(0), -4000i64);
assert_eq!(column.get_val(1), -3000i64);
assert_eq!(column.get_val(2), -2000i64);
assert_eq!(column.max_value(), (num_vals as i64 - 5) * 1000);
assert_eq!(column.min_value(), -4000i64);
// Can't apply gcd
let mut buffer_without_gcd = Vec::new();
vals.pop();
vals.push(1001i64);
crate::column_values::serialize_column_values(
&VecColumn::from(&vals),
&[codec_type],
&mut buffer_without_gcd,
)?;
let buffer_without_gcd = OwnedBytes::new(buffer_without_gcd);
assert!(buffer_without_gcd.len() > buffer.len());
Ok(())
}
#[test]
fn test_fastfield_gcd_i64() -> io::Result<()> {
for &codec_type in &[
FastFieldCodecType::Bitpacked,
FastFieldCodecType::BlockwiseLinear,
FastFieldCodecType::Linear,
] {
test_fastfield_gcd_i64_with_codec(codec_type, 5500)?;
}
Ok(())
}
fn test_fastfield_gcd_u64_with_codec(
codec_type: FastFieldCodecType,
num_vals: usize,
) -> io::Result<()> {
let mut vals: Vec<u64> = (1..=num_vals).map(|i| i as u64 * 1000u64).collect();
let mut buffer: Vec<u8> = Vec::new();
crate::column_values::serialize_column_values(
&VecColumn::from(&vals),
&[codec_type],
&mut buffer,
)?;
let buffer = OwnedBytes::new(buffer);
let column = crate::column_values::open_u64_mapped::<u64>(buffer.clone())?;
assert_eq!(column.get_val(0), 1000u64);
assert_eq!(column.get_val(1), 2000u64);
assert_eq!(column.get_val(2), 3000u64);
assert_eq!(column.max_value(), num_vals as u64 * 1000);
assert_eq!(column.min_value(), 1000u64);
// Can't apply gcd
let mut buffer_without_gcd = Vec::new();
vals.pop();
vals.push(1001u64);
crate::column_values::serialize_column_values(
&VecColumn::from(&vals),
&[codec_type],
&mut buffer_without_gcd,
)?;
let buffer_without_gcd = OwnedBytes::new(buffer_without_gcd);
assert!(buffer_without_gcd.len() > buffer.len());
Ok(())
}
#[test]
fn test_fastfield_gcd_u64() -> io::Result<()> {
for &codec_type in &[
FastFieldCodecType::Bitpacked,
FastFieldCodecType::BlockwiseLinear,
FastFieldCodecType::Linear,
] {
test_fastfield_gcd_u64_with_codec(codec_type, 5500)?;
}
Ok(())
}
#[test]
pub fn test_fastfield2() {
let test_fastfield = crate::column_values::serialize_and_load(&[100u64, 200u64, 300u64]);
assert_eq!(test_fastfield.get_val(0), 100);
assert_eq!(test_fastfield.get_val(1), 200);
assert_eq!(test_fastfield.get_val(2), 300);
}

2
columnar/src/column_values/compact_space/blank_range.rs → columnar/src/column_values/u128_based/compact_space/blank_range.rs
View File

@@ -38,6 +38,6 @@ impl Ord for BlankRange {
}
impl PartialOrd for BlankRange {
fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
Some(self.blank_size().cmp(&other.blank_size()))
Some(self.cmp(other))
}
}

39
fastfield_codecs/src/compact_space/build_compact_space.rs → columnar/src/column_values/u128_based/compact_space/build_compact_space.rs
View File

@@ -10,7 +10,7 @@ use super::{CompactSpace, RangeMapping};
/// Put the blanks for the sorted values into a binary heap
fn get_blanks(values_sorted: &BTreeSet<u128>) -> BinaryHeap<BlankRange> {
let mut blanks: BinaryHeap<BlankRange> = BinaryHeap::new();
for (first, second) in values_sorted.iter().tuple_windows() {
for (first, second) in values_sorted.iter().copied().tuple_windows() {
// Correctness Overflow: the values are deduped and sorted (BTreeSet property), that means
// there's always space between two values.
let blank_range = first + 1..=second - 1;
@@ -65,12 +65,12 @@ pub fn get_compact_space(
return compact_space_builder.finish();
}
let mut blanks: BinaryHeap<BlankRange> = get_blanks(values_deduped_sorted);
// Replace after stabilization of https://github.com/rust-lang/rust/issues/62924
// We start by space that's limited to min_value..=max_value
let min_value = *values_deduped_sorted.iter().next().unwrap_or(&0);
let max_value = *values_deduped_sorted.iter().last().unwrap_or(&0);
// Replace after stabilization of https://github.com/rust-lang/rust/issues/62924
let min_value = values_deduped_sorted.iter().next().copied().unwrap_or(0);
let max_value = values_deduped_sorted.iter().last().copied().unwrap_or(0);
let mut blanks: BinaryHeap<BlankRange> = get_blanks(values_deduped_sorted);
// +1 for null, in case min and max covers the whole space, we are off by one.
let mut amplitude_compact_space = (max_value - min_value).saturating_add(1);
@@ -84,6 +84,7 @@ pub fn get_compact_space(
let mut amplitude_bits: u8 = num_bits(amplitude_compact_space);
let mut blank_collector = BlankCollector::new();
// We will stage blanks until they reduce the compact space by at least 1 bit and then flush
// them if the metadata cost is lower than the total number of saved bits.
// Binary heap to process the gaps by their size
@@ -93,6 +94,7 @@ pub fn get_compact_space(
let staged_spaces_sum: u128 = blank_collector.staged_blanks_sum();
let amplitude_new_compact_space = amplitude_compact_space - staged_spaces_sum;
let amplitude_new_bits = num_bits(amplitude_new_compact_space);
if amplitude_bits == amplitude_new_bits {
continue;
}
@@ -100,7 +102,16 @@ pub fn get_compact_space(
// TODO: Maybe calculate exact cost of blanks and run this more expensive computation only,
// when amplitude_new_bits changes
let cost = blank_collector.num_staged_blanks() * cost_per_blank;
if cost >= saved_bits {
// We want to end up with a compact space that fits into 32 bits.
// In order to deal with pathological cases, we force the algorithm to keep
// refining the compact space the amplitude bits is lower than 32.
//
// The worst case scenario happens for a large number of u128s regularly
// spread over the full u128 space.
//
// This change will force the algorithm to degenerate into dictionary encoding.
if amplitude_bits <= 32 && cost >= saved_bits {
// Continue here, since although we walk over the blanks by size,
// we can potentially save a lot at the last bits, which are smaller blanks
//
@@ -115,6 +126,8 @@ pub fn get_compact_space(
compact_space_builder.add_blanks(blank_collector.drain().map(|blank| blank.blank_range()));
}
assert!(amplitude_bits <= 32);
// special case, when we don't collected any blanks because:
// * the data is empty (early exit)
// * the algorithm did decide it's not worth the cost, which can be the case for single values
@@ -171,7 +184,7 @@ impl CompactSpaceBuilder {
let mut covered_space = Vec::with_capacity(self.blanks.len());
// begining of the blanks
// beginning of the blanks
if let Some(first_blank_start) = self.blanks.first().map(RangeInclusive::start) {
if *first_blank_start != 0 {
covered_space.push(0..=first_blank_start - 1);
@@ -199,7 +212,7 @@ impl CompactSpaceBuilder {
covered_space.push(0..=0); // empty data case
};
let mut compact_start: u64 = 1; // 0 is reserved for `null`
let mut compact_start: u32 = 1; // 0 is reserved for `null`
let mut ranges_mapping: Vec<RangeMapping> = Vec::with_capacity(covered_space.len());
for cov in covered_space {
let range_mapping = super::RangeMapping {
@@ -218,6 +231,7 @@ impl CompactSpaceBuilder {
#[cfg(test)]
mod tests {
use super::*;
use crate::column_values::u128_based::compact_space::COST_PER_BLANK_IN_BITS;
#[test]
fn test_binary_heap_pop_order() {
@@ -228,4 +242,11 @@ mod tests {
assert_eq!(blanks.pop().unwrap().blank_size(), 101);
assert_eq!(blanks.pop().unwrap().blank_size(), 11);
}
#[test]
fn test_worst_case_scenario() {
let vals: BTreeSet<u128> = (0..8).map(|i| i * ((1u128 << 34) / 8)).collect();
let compact_space = get_compact_space(&vals, vals.len() as u32, COST_PER_BLANK_IN_BITS);
assert!(compact_space.amplitude_compact_space() < u32::MAX as u128);
}
}

355
fastfield_codecs/src/compact_space/mod.rs → columnar/src/column_values/u128_based/compact_space/mod.rs
View File

@@ -17,15 +17,16 @@ use std::{
ops::{Range, RangeInclusive},
};
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt, VIntU128};
use tantivy_bitpacker::{self, BitPacker, BitUnpacker};
use crate::compact_space::build_compact_space::get_compact_space;
use crate::Column;
mod blank_range;
mod build_compact_space;
use build_compact_space::get_compact_space;
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt, VIntU128};
use tantivy_bitpacker::{BitPacker, BitUnpacker};
use crate::column_values::ColumnValues;
use crate::RowId;
/// The cost per blank is quite hard actually, since blanks are delta encoded, the actual cost of
/// blanks depends on the number of blanks.
///
@@ -41,21 +42,21 @@ pub struct CompactSpace {
#[derive(Debug, Clone, Eq, PartialEq)]
struct RangeMapping {
value_range: RangeInclusive<u128>,
compact_start: u64,
compact_start: u32,
}
impl RangeMapping {
fn range_length(&self) -> u64 {
(self.value_range.end() - self.value_range.start()) as u64 + 1
fn range_length(&self) -> u32 {
(self.value_range.end() - self.value_range.start()) as u32 + 1
}
// The last value of the compact space in this range
fn compact_end(&self) -> u64 {
fn compact_end(&self) -> u32 {
self.compact_start + self.range_length() - 1
}
}
impl BinarySerializable for CompactSpace {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.ranges_mapping.len() as u64).serialize(writer)?;
let mut prev_value = 0;
@@ -80,7 +81,7 @@ impl BinarySerializable for CompactSpace {
let num_ranges = VInt::deserialize(reader)?.0;
let mut ranges_mapping: Vec<RangeMapping> = vec![];
let mut value = 0u128;
let mut compact_start = 1u64; // 0 is reserved for `null`
let mut compact_start = 1u32; // 0 is reserved for `null`
for _ in 0..num_ranges {
let blank_delta_start = VIntU128::deserialize(reader)?.0;
value += blank_delta_start;
@@ -121,10 +122,10 @@ impl CompactSpace {
/// Returns either Ok(the value in the compact space) or if it is outside the compact space the
/// Err(position where it would be inserted)
fn u128_to_compact(&self, value: u128) -> Result<u64, usize> {
fn u128_to_compact(&self, value: u128) -> Result<u32, usize> {
self.ranges_mapping
.binary_search_by(|probe| {
let value_range = &probe.value_range;
let value_range: &RangeInclusive<u128> = &probe.value_range;
if value < *value_range.start() {
Ordering::Greater
} else if value > *value_range.end() {
@@ -135,19 +136,19 @@ impl CompactSpace {
})
.map(|pos| {
let range_mapping = &self.ranges_mapping[pos];
let pos_in_range = (value - range_mapping.value_range.start()) as u64;
let pos_in_range: u32 = (value - range_mapping.value_range.start()) as u32;
range_mapping.compact_start + pos_in_range
})
}
/// Unpacks a value from compact space u64 to u128 space
fn compact_to_u128(&self, compact: u64) -> u128 {
/// Unpacks a value from compact space u32 to u128 space
fn compact_to_u128(&self, compact: u32) -> u128 {
let pos = self
.ranges_mapping
.binary_search_by_key(&compact, |range_mapping| range_mapping.compact_start)
// Correctness: Overflow. The first range starts at compact space 0, the error from
// binary search can never be 0
.map_or_else(|e| e - 1, |v| v);
.unwrap_or_else(|e| e - 1);
let range_mapping = &self.ranges_mapping[pos];
let diff = compact - range_mapping.compact_start;
@@ -158,22 +159,33 @@ impl CompactSpace {
pub struct CompactSpaceCompressor {
params: IPCodecParams,
}
#[derive(Debug, Clone)]
pub struct IPCodecParams {
compact_space: CompactSpace,
bit_unpacker: BitUnpacker,
min_value: u128,
max_value: u128,
num_vals: u32,
num_vals: RowId,
num_bits: u8,
}
impl CompactSpaceCompressor {
pub fn num_vals(&self) -> RowId {
self.params.num_vals
}
/// Taking the vals as Vec may cost a lot of memory. It is used to sort the vals.
pub fn train_from(iter: impl Iterator<Item = u128>, num_vals: u32) -> Self {
pub fn train_from(iter: impl Iterator<Item = u128>) -> Self {
let mut values_sorted = BTreeSet::new();
values_sorted.extend(iter);
let total_num_values = num_vals;
// Total number of values, with their redundancy.
let mut total_num_values = 0u32;
for val in iter {
total_num_values += 1u32;
values_sorted.insert(val);
}
let min_value = *values_sorted.iter().next().unwrap_or(&0);
let max_value = *values_sorted.iter().last().unwrap_or(&0);
let compact_space =
get_compact_space(&values_sorted, total_num_values, COST_PER_BLANK_IN_BITS);
@@ -185,13 +197,12 @@ impl CompactSpaceCompressor {
);
let num_bits = tantivy_bitpacker::compute_num_bits(amplitude_compact_space as u64);
let min_value = *values_sorted.iter().next().unwrap_or(&0);
let max_value = *values_sorted.iter().last().unwrap_or(&0);
assert_eq!(
compact_space
.u128_to_compact(max_value)
.expect("could not convert max value to compact space"),
amplitude_compact_space as u64
amplitude_compact_space as u32
);
CompactSpaceCompressor {
params: IPCodecParams {
@@ -232,7 +243,7 @@ impl CompactSpaceCompressor {
"Could not convert value to compact_space. This is a bug.",
)
})?;
bitpacker.write(compact, self.params.num_bits, write)?;
bitpacker.write(compact as u64, self.params.num_bits, write)?;
}
bitpacker.close(write)?;
self.write_footer(write)?;
@@ -247,7 +258,7 @@ pub struct CompactSpaceDecompressor {
}
impl BinarySerializable for IPCodecParams {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
// header flags for future optional dictionary encoding
let footer_flags = 0u64;
footer_flags.serialize(writer)?;
@@ -281,7 +292,64 @@ impl BinarySerializable for IPCodecParams {
}
}
impl Column<u128> for CompactSpaceDecompressor {
/// Exposes the compact space compressed values as u64.
///
/// This allows faster access to the values, as u64 is faster to work with than u128.
/// It also allows to handle u128 values like u64, via the `open_u64_lenient` as a uniform
/// access interface.
///
/// When converting from the internal u64 to u128 `compact_to_u128` can be used.
pub struct CompactSpaceU64Accessor(CompactSpaceDecompressor);
impl CompactSpaceU64Accessor {
pub(crate) fn open(data: OwnedBytes) -> io::Result<CompactSpaceU64Accessor> {
let decompressor = CompactSpaceU64Accessor(CompactSpaceDecompressor::open(data)?);
Ok(decompressor)
}
/// Convert a compact space value to u128
pub fn compact_to_u128(&self, compact: u32) -> u128 {
self.0.compact_to_u128(compact)
}
}
impl ColumnValues<u64> for CompactSpaceU64Accessor {
#[inline]
fn get_val(&self, doc: u32) -> u64 {
let compact = self.0.get_compact(doc);
compact as u64
}
fn min_value(&self) -> u64 {
self.0.u128_to_compact(self.0.min_value()).unwrap() as u64
}
fn max_value(&self) -> u64 {
self.0.u128_to_compact(self.0.max_value()).unwrap() as u64
}
fn num_vals(&self) -> u32 {
self.0.params.num_vals
}
#[inline]
fn iter(&self) -> Box<dyn Iterator<Item = u64> + '_> {
Box::new(self.0.iter_compact().map(|el| el as u64))
}
#[inline]
fn get_row_ids_for_value_range(
&self,
value_range: RangeInclusive<u64>,
position_range: Range<u32>,
positions: &mut Vec<u32>,
) {
let value_range = self.0.compact_to_u128(*value_range.start() as u32)
..=self.0.compact_to_u128(*value_range.end() as u32);
self.0
.get_row_ids_for_value_range(value_range, position_range, positions)
}
}
impl ColumnValues<u128> for CompactSpaceDecompressor {
#[inline]
fn get_val(&self, doc: u32) -> u128 {
self.get(doc)
@@ -305,49 +373,7 @@ impl Column<u128> for CompactSpaceDecompressor {
}
#[inline]
fn get_docids_for_value_range(
&self,
value_range: RangeInclusive<u128>,
positions_range: Range<u32>,
positions: &mut Vec<u32>,
) {
self.get_positions_for_value_range(value_range, positions_range, positions)
}
}
impl CompactSpaceDecompressor {
pub fn open(data: OwnedBytes) -> io::Result<CompactSpaceDecompressor> {
let (data_slice, footer_len_bytes) = data.split_at(data.len() - 4);
let footer_len = u32::deserialize(&mut &footer_len_bytes[..])?;
let data_footer = &data_slice[data_slice.len() - footer_len as usize..];
let params = IPCodecParams::deserialize(&mut &data_footer[..])?;
let decompressor = CompactSpaceDecompressor { data, params };
Ok(decompressor)
}
/// Converting to compact space for the decompressor is more complex, since we may get values
/// which are outside the compact space. e.g. if we map
/// 1000 => 5
/// 2000 => 6
///
/// and we want a mapping for 1005, there is no equivalent compact space. We instead return an
/// error with the index of the next range.
fn u128_to_compact(&self, value: u128) -> Result<u64, usize> {
self.params.compact_space.u128_to_compact(value)
}
fn compact_to_u128(&self, compact: u64) -> u128 {
self.params.compact_space.compact_to_u128(compact)
}
/// Comparing on compact space: Random dataset 0,24 (50% random hit) - 1.05 GElements/s
/// Comparing on compact space: Real dataset 1.08 GElements/s
///
/// Comparing on original space: Real dataset .06 GElements/s (not completely optimized)
#[inline]
pub fn get_positions_for_value_range(
fn get_row_ids_for_value_range(
&self,
value_range: RangeInclusive<u128>,
position_range: Range<u32>,
@@ -387,44 +413,42 @@ impl CompactSpaceDecompressor {
range_mapping.compact_end()
});
let range = compact_from..=compact_to;
let value_range = compact_from..=compact_to;
self.get_positions_for_compact_value_range(value_range, position_range, positions);
}
}
let scan_num_docs = position_range.end - position_range.start;
impl CompactSpaceDecompressor {
pub fn open(data: OwnedBytes) -> io::Result<CompactSpaceDecompressor> {
let (data_slice, footer_len_bytes) = data.split_at(data.len() - 4);
let footer_len = u32::deserialize(&mut &footer_len_bytes[..])?;
let step_size = 4;
let cutoff = position_range.start + scan_num_docs - scan_num_docs % step_size;
let data_footer = &data_slice[data_slice.len() - footer_len as usize..];
let params = IPCodecParams::deserialize(&mut &data_footer[..])?;
let decompressor = CompactSpaceDecompressor { data, params };
let mut push_if_in_range = |idx, val| {
if range.contains(&val) {
positions.push(idx);
}
};
let get_val = |idx| self.params.bit_unpacker.get(idx, &self.data);
// unrolled loop
for idx in (position_range.start..cutoff).step_by(step_size as usize) {
let idx1 = idx;
let idx2 = idx + 1;
let idx3 = idx + 2;
let idx4 = idx + 3;
let val1 = get_val(idx1);
let val2 = get_val(idx2);
let val3 = get_val(idx3);
let val4 = get_val(idx4);
push_if_in_range(idx1, val1);
push_if_in_range(idx2, val2);
push_if_in_range(idx3, val3);
push_if_in_range(idx4, val4);
}
Ok(decompressor)
}
// handle rest
for idx in cutoff..position_range.end {
push_if_in_range(idx, get_val(idx));
}
/// Converting to compact space for the decompressor is more complex, since we may get values
/// which are outside the compact space. e.g. if we map
/// 1000 => 5
/// 2000 => 6
///
/// and we want a mapping for 1005, there is no equivalent compact space. We instead return an
/// error with the index of the next range.
fn u128_to_compact(&self, value: u128) -> Result<u32, usize> {
self.params.compact_space.u128_to_compact(value)
}
fn compact_to_u128(&self, compact: u32) -> u128 {
self.params.compact_space.compact_to_u128(compact)
}
#[inline]
fn iter_compact(&self) -> impl Iterator<Item = u64> + '_ {
(0..self.params.num_vals).map(move |idx| self.params.bit_unpacker.get(idx, &self.data))
fn iter_compact(&self) -> impl Iterator<Item = u32> + '_ {
(0..self.params.num_vals)
.map(move |idx| self.params.bit_unpacker.get(idx, &self.data) as u32)
}
#[inline]
@@ -435,9 +459,14 @@ impl CompactSpaceDecompressor {
.map(|compact| self.compact_to_u128(compact))
}
#[inline]
pub fn get_compact(&self, idx: u32) -> u32 {
self.params.bit_unpacker.get(idx, &self.data) as u32
}
#[inline]
pub fn get(&self, idx: u32) -> u128 {
let compact = self.params.bit_unpacker.get(idx, &self.data);
let compact = self.get_compact(idx);
self.compact_to_u128(compact)
}
@@ -448,27 +477,39 @@ impl CompactSpaceDecompressor {
pub fn max_value(&self) -> u128 {
self.params.max_value
}
fn get_positions_for_compact_value_range(
&self,
value_range: RangeInclusive<u32>,
position_range: Range<u32>,
positions: &mut Vec<u32>,
) {
self.params.bit_unpacker.get_ids_for_value_range(
*value_range.start() as u64..=*value_range.end() as u64,
position_range,
&self.data,
positions,
);
}
}
#[cfg(test)]
mod tests {
use std::fmt;
use itertools::Itertools;
use super::*;
use crate::format_version::read_format_version;
use crate::null_index_footer::read_null_index_footer;
use crate::serialize::U128Header;
use crate::{open_u128, serialize_u128};
use crate::column_values::u128_based::U128Header;
use crate::column_values::{open_u128_mapped, serialize_column_values_u128};
#[test]
fn compact_space_test() {
let ips = &[
let ips: BTreeSet<u128> = [
2u128, 4u128, 1000, 1001, 1002, 1003, 1004, 1005, 1008, 1010, 1012, 1260,
]
.into_iter()
.collect();
let compact_space = get_compact_space(ips, ips.len() as u32, 11);
let compact_space = get_compact_space(&ips, ips.len() as u32, 11);
let amplitude = compact_space.amplitude_compact_space();
assert_eq!(amplitude, 17);
assert_eq!(1, compact_space.u128_to_compact(2).unwrap());
@@ -491,8 +532,8 @@ mod tests {
);
for ip in ips {
let compact = compact_space.u128_to_compact(*ip).unwrap();
assert_eq!(compact_space.compact_to_u128(compact), *ip);
let compact = compact_space.u128_to_compact(ip).unwrap();
assert_eq!(compact_space.compact_to_u128(compact), ip);
}
}
@@ -518,7 +559,7 @@ mod tests {
.map(|pos| pos as u32)
.collect::<Vec<_>>();
let mut positions = Vec::new();
decompressor.get_positions_for_value_range(
decompressor.get_row_ids_for_value_range(
range,
0..decompressor.num_vals(),
&mut positions,
@@ -535,18 +576,9 @@ mod tests {
fn test_aux_vals(u128_vals: &[u128]) -> OwnedBytes {
let mut out = Vec::new();
serialize_u128(
|| u128_vals.iter().cloned(),
u128_vals.len() as u32,
&mut out,
)
.unwrap();
serialize_column_values_u128(&u128_vals, &mut out).unwrap();
let data = OwnedBytes::new(out);
let (data, _format_version) = read_format_version(data).unwrap();
let (data, _null_index_footer) = read_null_index_footer(data).unwrap();
test_all(data.clone(), u128_vals);
data
}
@@ -572,13 +604,13 @@ mod tests {
let val = *val;
let pos = pos as u32;
let mut positions = Vec::new();
decomp.get_positions_for_value_range(val..=val, pos..pos + 1, &mut positions);
decomp.get_row_ids_for_value_range(val..=val, pos..pos + 1, &mut positions);
assert_eq!(positions, vec![pos]);
}
// handle docid range out of bounds
let positions = get_positions_for_value_range_helper(&decomp, 0..=1, 1..u32::MAX);
assert_eq!(positions, vec![]);
let positions: Vec<u32> = get_positions_for_value_range_helper(&decomp, 0..=1, 1..u32::MAX);
assert!(positions.is_empty());
let positions =
get_positions_for_value_range_helper(&decomp, 0..=1, complete_range.clone());
@@ -614,61 +646,59 @@ mod tests {
vec![3, 4]
);
assert_eq!(
get_positions_for_value_range_helper(
&get_positions_for_value_range_helper(
&decomp,
99998u128..=99999u128,
complete_range.clone()
),
vec![3]
&[3]
);
assert!(get_positions_for_value_range_helper(
&decomp,
99998u128..=99998u128,
complete_range.clone()
)
.is_empty());
assert_eq!(
get_positions_for_value_range_helper(
&decomp,
99998u128..=99998u128,
complete_range.clone()
),
vec![]
);
assert_eq!(
get_positions_for_value_range_helper(
&get_positions_for_value_range_helper(
&decomp,
333u128..=333u128,
complete_range.clone()
),
vec![8]
&[8]
);
assert_eq!(
get_positions_for_value_range_helper(
&get_positions_for_value_range_helper(
&decomp,
332u128..=333u128,
complete_range.clone()
),
vec![8]
&[8]
);
assert_eq!(
get_positions_for_value_range_helper(
&get_positions_for_value_range_helper(
&decomp,
332u128..=334u128,
complete_range.clone()
),
vec![8]
&[8]
);
assert_eq!(
get_positions_for_value_range_helper(
&get_positions_for_value_range_helper(
&decomp,
333u128..=334u128,
complete_range.clone()
),
vec![8]
&[8]
);
assert_eq!(
get_positions_for_value_range_helper(
&get_positions_for_value_range_helper(
&decomp,
4_000_211_221u128..=5_000_000_000u128,
complete_range
),
vec![6, 7]
&[6, 7]
);
}
@@ -694,27 +724,27 @@ mod tests {
let _header = U128Header::deserialize(&mut data);
let decomp = CompactSpaceDecompressor::open(data).unwrap();
let complete_range = 0..vals.len() as u32;
assert_eq!(
get_positions_for_value_range_helper(&decomp, 0..=5, complete_range.clone()),
vec![]
assert!(
&get_positions_for_value_range_helper(&decomp, 0..=5, complete_range.clone())
.is_empty(),
);
assert_eq!(
get_positions_for_value_range_helper(&decomp, 0..=100, complete_range.clone()),
vec![0]
&get_positions_for_value_range_helper(&decomp, 0..=100, complete_range.clone()),
&[0]
);
assert_eq!(
get_positions_for_value_range_helper(&decomp, 0..=105, complete_range),
vec![0]
&get_positions_for_value_range_helper(&decomp, 0..=105, complete_range),
&[0]
);
}
fn get_positions_for_value_range_helper<C: Column<T> + ?Sized, T: PartialOrd + fmt::Debug>(
fn get_positions_for_value_range_helper<C: ColumnValues<T> + ?Sized, T: PartialOrd>(
column: &C,
value_range: RangeInclusive<T>,
doc_id_range: Range<u32>,
) -> Vec<u32> {
let mut positions = Vec::new();
column.get_docids_for_value_range(value_range, doc_id_range, &mut positions);
column.get_row_ids_for_value_range(value_range, doc_id_range, &mut positions);
positions
}
@@ -736,8 +766,8 @@ mod tests {
5_000_000_000,
];
let mut out = Vec::new();
serialize_u128(|| vals.iter().cloned(), vals.len() as u32, &mut out).unwrap();
let decomp = open_u128::<u128>(OwnedBytes::new(out)).unwrap();
serialize_column_values_u128(&&vals[..], &mut out).unwrap();
let decomp = open_u128_mapped(OwnedBytes::new(out)).unwrap();
let complete_range = 0..vals.len() as u32;
assert_eq!(
@@ -790,7 +820,7 @@ mod tests {
let vals = &[1_000_000_000u128; 100];
let _data = test_aux_vals(vals);
}
use itertools::Itertools;
use proptest::prelude::*;
fn num_strategy() -> impl Strategy<Value = u128> {
@@ -806,10 +836,9 @@ mod tests {
proptest! {
#![proptest_config(ProptestConfig::with_cases(10))]
#[test]
fn compress_decompress_random(vals in proptest::collection::vec(num_strategy()
, 1..1000)) {
let _data = test_aux_vals(&vals);
}
#[test]
fn compress_decompress_random(vals in proptest::collection::vec(num_strategy() , 1..1000)) {
let _data = test_aux_vals(&vals);
}
}
}

197
columnar/src/column_values/u128_based/mod.rs Normal file
View File

@@ -0,0 +1,197 @@
use std::fmt::Debug;
use std::io;
use std::io::Write;
use std::sync::Arc;
mod compact_space;
use common::{BinarySerializable, OwnedBytes, VInt};
pub use compact_space::{
CompactSpaceCompressor, CompactSpaceDecompressor, CompactSpaceU64Accessor,
};
use crate::column_values::monotonic_map_column;
use crate::column_values::monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
};
use crate::iterable::Iterable;
use crate::{ColumnValues, MonotonicallyMappableToU128};
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
pub(crate) struct U128Header {
pub num_vals: u32,
pub codec_type: U128FastFieldCodecType,
}
impl BinarySerializable for U128Header {
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.num_vals as u64).serialize(writer)?;
self.codec_type.serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let num_vals = VInt::deserialize(reader)?.0 as u32;
let codec_type = U128FastFieldCodecType::deserialize(reader)?;
Ok(U128Header {
num_vals,
codec_type,
})
}
}
/// Serializes u128 values with the compact space codec.
pub fn serialize_column_values_u128<T: MonotonicallyMappableToU128>(
iterable: &dyn Iterable<T>,
output: &mut impl io::Write,
) -> io::Result<()> {
let compressor = CompactSpaceCompressor::train_from(
iterable
.boxed_iter()
.map(MonotonicallyMappableToU128::to_u128),
);
let header = U128Header {
num_vals: compressor.num_vals(),
codec_type: U128FastFieldCodecType::CompactSpace,
};
header.serialize(output)?;
compressor.compress_into(
iterable
.boxed_iter()
.map(MonotonicallyMappableToU128::to_u128),
output,
)?;
Ok(())
}
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
#[repr(u8)]
/// Available codecs to use to encode the u128 (via [`MonotonicallyMappableToU128`]) converted data.
pub(crate) enum U128FastFieldCodecType {
/// This codec takes a large number space (u128) and reduces it to a compact number space, by
/// removing the holes.
CompactSpace = 1,
}
impl BinarySerializable for U128FastFieldCodecType {
fn serialize<W: Write + ?Sized>(&self, wrt: &mut W) -> io::Result<()> {
self.to_code().serialize(wrt)
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let code = u8::deserialize(reader)?;
let codec_type: Self = Self::from_code(code)
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Unknown code `{code}.`"))?;
Ok(codec_type)
}
}
impl U128FastFieldCodecType {
pub(crate) fn to_code(self) -> u8 {
self as u8
}
pub(crate) fn from_code(code: u8) -> Option<Self> {
match code {
1 => Some(Self::CompactSpace),
_ => None,
}
}
}
/// Returns the correct codec reader wrapped in the `Arc` for the data.
pub fn open_u128_mapped<T: MonotonicallyMappableToU128 + Debug>(
mut bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
let header = U128Header::deserialize(&mut bytes)?;
assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
let reader = CompactSpaceDecompressor::open(bytes)?;
let inverted: StrictlyMonotonicMappingInverter<StrictlyMonotonicMappingToInternal<T>> =
StrictlyMonotonicMappingToInternal::<T>::new().into();
Ok(Arc::new(monotonic_map_column(reader, inverted)))
}
/// Returns the u64 representation of the u128 data.
/// The internal representation of the data as u64 is useful for faster processing.
///
/// In order to convert to u128 back cast to `CompactSpaceU64Accessor` and call
/// `compact_to_u128`.
///
/// # Notice
/// In case there are new codecs added, check for usages of `CompactSpaceDecompressorU64` and
/// also handle the new codecs.
pub fn open_u128_as_compact_u64(mut bytes: OwnedBytes) -> io::Result<Arc<dyn ColumnValues<u64>>> {
let header = U128Header::deserialize(&mut bytes)?;
assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
let reader = CompactSpaceU64Accessor::open(bytes)?;
Ok(Arc::new(reader))
}
#[cfg(test)]
pub(crate) mod tests {
use super::*;
use crate::column_values::u64_based::{
serialize_and_load_u64_based_column_values, serialize_u64_based_column_values,
ALL_U64_CODEC_TYPES,
};
use crate::column_values::CodecType;
#[test]
fn test_serialize_deserialize_u128_header() {
let original = U128Header {
num_vals: 11,
codec_type: U128FastFieldCodecType::CompactSpace,
};
let mut out = Vec::new();
original.serialize(&mut out).unwrap();
let restored = U128Header::deserialize(&mut &out[..]).unwrap();
assert_eq!(restored, original);
}
#[test]
fn test_serialize_deserialize() {
let original = [1u64, 5u64, 10u64];
let restored: Vec<u64> =
serialize_and_load_u64_based_column_values(&&original[..], &ALL_U64_CODEC_TYPES)
.iter()
.collect();
assert_eq!(&restored, &original[..]);
}
#[test]
fn test_fastfield_bool_size_bitwidth_1() {
let mut buffer = Vec::new();
serialize_u64_based_column_values::<bool>(
&&[false, true][..],
&ALL_U64_CODEC_TYPES,
&mut buffer,
)
.unwrap();
// TODO put the header as a footer so that it serves as a padding.
// 5 bytes of header, 1 byte of value, 7 bytes of padding.
assert_eq!(buffer.len(), 5 + 1);
}
#[test]
fn test_fastfield_bool_bit_size_bitwidth_0() {
let mut buffer = Vec::new();
serialize_u64_based_column_values::<bool>(
&&[false, true][..],
&ALL_U64_CODEC_TYPES,
&mut buffer,
)
.unwrap();
// 6 bytes of header, 0 bytes of value, 7 bytes of padding.
assert_eq!(buffer.len(), 6);
}
#[test]
fn test_fastfield_gcd() {
let mut buffer = Vec::new();
let vals: Vec<u64> = (0..80).map(|val| (val % 7) * 1_000u64).collect();
serialize_u64_based_column_values(&&vals[..], &[CodecType::Bitpacked], &mut buffer)
.unwrap();
// Values are stored over 3 bits.
assert_eq!(buffer.len(), 6 + (3 * 80 / 8));
}
}

188
columnar/src/column_values/u64_based/bitpacked.rs Normal file
View File

@@ -0,0 +1,188 @@
use std::io::{self, Write};
use std::num::NonZeroU64;
use std::ops::{Range, RangeInclusive};
use common::{BinarySerializable, OwnedBytes};
use fastdivide::DividerU64;
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
use crate::{ColumnValues, RowId};
/// Depending on the field type, a different
/// fast field is required.
#[derive(Clone)]
pub struct BitpackedReader {
data: OwnedBytes,
bit_unpacker: BitUnpacker,
stats: ColumnStats,
}
#[inline(always)]
const fn div_ceil(n: u64, q: NonZeroU64) -> u64 {
// copied from unstable rust standard library.
let d = n / q.get();
let r = n % q.get();
if r > 0 {
d + 1
} else {
d
}
}
// The bitpacked codec applies a linear transformation `f` over data that are bitpacked.
// f is defined by:
// f: bitpacked -> stats.min_value + stats.gcd * bitpacked
//
// In order to run range queries, we invert the transformation.
// `transform_range_before_linear_transformation` returns the range of values
// [min_bipacked_value..max_bitpacked_value] such that
// f(bitpacked) ∈ [min_value, max_value] <=> bitpacked ∈ [min_bitpacked_value, max_bitpacked_value]
fn transform_range_before_linear_transformation(
stats: &ColumnStats,
range: RangeInclusive<u64>,
) -> Option<RangeInclusive<u64>> {
if range.is_empty() {
return None;
}
if stats.min_value > *range.end() {
return None;
}
if stats.max_value < *range.start() {
return None;
}
let shifted_range =
range.start().saturating_sub(stats.min_value)..=range.end().saturating_sub(stats.min_value);
let start_before_gcd_multiplication: u64 = div_ceil(*shifted_range.start(), stats.gcd);
let end_before_gcd_multiplication: u64 = *shifted_range.end() / stats.gcd;
Some(start_before_gcd_multiplication..=end_before_gcd_multiplication)
}
impl ColumnValues for BitpackedReader {
#[inline(always)]
fn get_val(&self, doc: u32) -> u64 {
self.stats.min_value + self.stats.gcd.get() * self.bit_unpacker.get(doc, &self.data)
}
#[inline]
fn min_value(&self) -> u64 {
self.stats.min_value
}
#[inline]
fn max_value(&self) -> u64 {
self.stats.max_value
}
#[inline]
fn num_vals(&self) -> RowId {
self.stats.num_rows
}
fn get_row_ids_for_value_range(
&self,
range: RangeInclusive<u64>,
doc_id_range: Range<u32>,
positions: &mut Vec<u32>,
) {
let Some(transformed_range) =
transform_range_before_linear_transformation(&self.stats, range)
else {
positions.clear();
return;
};
self.bit_unpacker.get_ids_for_value_range(
transformed_range,
doc_id_range,
&self.data,
positions,
);
}
}
fn num_bits(stats: &ColumnStats) -> u8 {
compute_num_bits(stats.amplitude() / stats.gcd)
}
#[derive(Default)]
pub struct BitpackedCodecEstimator;
impl ColumnCodecEstimator for BitpackedCodecEstimator {
fn collect(&mut self, _value: u64) {}
fn estimate(&self, stats: &ColumnStats) -> Option<u64> {
let num_bits_per_value = num_bits(stats);
Some(stats.num_bytes() + (stats.num_rows as u64 * (num_bits_per_value as u64) + 7) / 8)
}
fn serialize(
&self,
stats: &ColumnStats,
vals: &mut dyn Iterator<Item = u64>,
wrt: &mut dyn Write,
) -> io::Result<()> {
stats.serialize(wrt)?;
let num_bits = num_bits(stats);
let mut bit_packer = BitPacker::new();
let divider = DividerU64::divide_by(stats.gcd.get());
for val in vals {
bit_packer.write(divider.divide(val - stats.min_value), num_bits, wrt)?;
}
bit_packer.close(wrt)?;
Ok(())
}
}
pub struct BitpackedCodec;
impl ColumnCodec for BitpackedCodec {
type ColumnValues = BitpackedReader;
type Estimator = BitpackedCodecEstimator;
/// Opens a fast field given a file.
fn load(mut data: OwnedBytes) -> io::Result<Self::ColumnValues> {
let stats = ColumnStats::deserialize(&mut data)?;
let num_bits = num_bits(&stats);
let bit_unpacker = BitUnpacker::new(num_bits);
Ok(BitpackedReader {
data,
bit_unpacker,
stats,
})
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_values::u64_based::tests::create_and_validate;
#[test]
fn test_with_codec_data_sets_simple() {
create_and_validate::<BitpackedCodec>(&[4, 3, 12], "name");
}
#[test]
fn test_with_codec_data_sets_simple_gcd() {
create_and_validate::<BitpackedCodec>(&[1000, 2000, 3000], "name");
}
#[test]
fn test_with_codec_data_sets() {
let data_sets = crate::column_values::u64_based::tests::get_codec_test_datasets();
for (mut data, name) in data_sets {
create_and_validate::<BitpackedCodec>(&data, name);
data.reverse();
create_and_validate::<BitpackedCodec>(&data, name);
}
}
#[test]
fn bitpacked_fast_field_rand() {
for _ in 0..500 {
let mut data = (0..1 + rand::random::<u8>() as usize)
.map(|_| rand::random::<i64>() as u64 / 2)
.collect::<Vec<_>>();
create_and_validate::<BitpackedCodec>(&data, "rand");
data.reverse();
create_and_validate::<BitpackedCodec>(&data, "rand");
}
}
}

284
columnar/src/column_values/u64_based/blockwise_linear.rs Normal file
View File

@@ -0,0 +1,284 @@
use std::io::Write;
use std::sync::Arc;
use std::{io, iter};
use common::{BinarySerializable, CountingWriter, DeserializeFrom, OwnedBytes};
use fastdivide::DividerU64;
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::column_values::u64_based::line::Line;
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
use crate::column_values::{ColumnValues, VecColumn};
use crate::MonotonicallyMappableToU64;
const BLOCK_SIZE: u32 = 512u32;
#[derive(Debug, Default)]
struct Block {
line: Line,
bit_unpacker: BitUnpacker,
data_start_offset: usize,
}
impl BinarySerializable for Block {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
self.line.serialize(writer)?;
self.bit_unpacker.bit_width().serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let line = Line::deserialize(reader)?;
let bit_width = u8::deserialize(reader)?;
Ok(Block {
line,
bit_unpacker: BitUnpacker::new(bit_width),
data_start_offset: 0,
})
}
}
fn compute_num_blocks(num_vals: u32) -> u32 {
num_vals.div_ceil(BLOCK_SIZE)
}
pub struct BlockwiseLinearEstimator {
block: Vec<u64>,
values_num_bytes: u64,
meta_num_bytes: u64,
}
impl Default for BlockwiseLinearEstimator {
fn default() -> Self {
Self {
block: Vec::with_capacity(BLOCK_SIZE as usize),
values_num_bytes: 0u64,
meta_num_bytes: 0u64,
}
}
}
impl BlockwiseLinearEstimator {
fn flush_block_estimate(&mut self) {
if self.block.is_empty() {
return;
}
let column = VecColumn::from(std::mem::take(&mut self.block));
let line = Line::train(&column);
self.block = column.into();
let mut max_value = 0u64;
for (i, buffer_val) in self.block.iter().enumerate() {
let interpolated_val = line.eval(i as u32);
let val = buffer_val.wrapping_sub(interpolated_val);
max_value = val.max(max_value);
}
let bit_width = compute_num_bits(max_value) as usize;
self.values_num_bytes += (bit_width * self.block.len() + 7) as u64 / 8;
self.meta_num_bytes += 1 + line.num_bytes();
}
}
impl ColumnCodecEstimator for BlockwiseLinearEstimator {
fn collect(&mut self, value: u64) {
self.block.push(value);
if self.block.len() == BLOCK_SIZE as usize {
self.flush_block_estimate();
self.block.clear();
}
}
fn estimate(&self, stats: &ColumnStats) -> Option<u64> {
let mut estimate = 4 + stats.num_bytes() + self.meta_num_bytes + self.values_num_bytes;
if stats.gcd.get() > 1 {
let estimate_gain_from_gcd =
(stats.gcd.get() as f32).log2().floor() * stats.num_rows as f32 / 8.0f32;
estimate = estimate.saturating_sub(estimate_gain_from_gcd as u64);
}
Some(estimate)
}
fn finalize(&mut self) {
self.flush_block_estimate();
}
fn serialize(
&self,
stats: &ColumnStats,
mut vals: &mut dyn Iterator<Item = u64>,
wrt: &mut dyn Write,
) -> io::Result<()> {
stats.serialize(wrt)?;
let mut buffer = Vec::with_capacity(BLOCK_SIZE as usize);
let num_blocks = compute_num_blocks(stats.num_rows) as usize;
let mut blocks = Vec::with_capacity(num_blocks);
let mut bit_packer = BitPacker::new();
let gcd_divider = DividerU64::divide_by(stats.gcd.get());
for _ in 0..num_blocks {
buffer.clear();
buffer.extend(
(&mut vals)
.map(MonotonicallyMappableToU64::to_u64)
.take(BLOCK_SIZE as usize),
);
for buffer_val in buffer.iter_mut() {
*buffer_val = gcd_divider.divide(*buffer_val - stats.min_value);
}
let line = Line::train(&VecColumn::from(buffer.to_vec()));
assert!(!buffer.is_empty());
for (i, buffer_val) in buffer.iter_mut().enumerate() {
let interpolated_val = line.eval(i as u32);
*buffer_val = buffer_val.wrapping_sub(interpolated_val);
}
let bit_width = buffer.iter().copied().map(compute_num_bits).max().unwrap();
for &buffer_val in &buffer {
bit_packer.write(buffer_val, bit_width, wrt)?;
}
blocks.push(Block {
line,
bit_unpacker: BitUnpacker::new(bit_width),
data_start_offset: 0,
});
}
bit_packer.close(wrt)?;
assert_eq!(blocks.len(), num_blocks);
let mut counting_wrt = CountingWriter::wrap(wrt);
for block in &blocks {
block.serialize(&mut counting_wrt)?;
}
let footer_len = counting_wrt.written_bytes();
(footer_len as u32).serialize(&mut counting_wrt)?;
Ok(())
}
}
pub struct BlockwiseLinearCodec;
impl ColumnCodec<u64> for BlockwiseLinearCodec {
type ColumnValues = BlockwiseLinearReader;
type Estimator = BlockwiseLinearEstimator;
fn load(mut bytes: OwnedBytes) -> io::Result<Self::ColumnValues> {
let stats = ColumnStats::deserialize(&mut bytes)?;
let footer_len: u32 = (&bytes[bytes.len() - 4..]).deserialize()?;
let footer_offset = bytes.len() - 4 - footer_len as usize;
let (data, mut footer) = bytes.split(footer_offset);
let num_blocks = compute_num_blocks(stats.num_rows);
let mut blocks: Vec<Block> = iter::repeat_with(|| Block::deserialize(&mut footer))
.take(num_blocks as usize)
.collect::<io::Result<_>>()?;
let mut start_offset = 0;
for block in &mut blocks {
block.data_start_offset = start_offset;
start_offset += (block.bit_unpacker.bit_width() as usize) * BLOCK_SIZE as usize / 8;
}
Ok(BlockwiseLinearReader {
blocks: blocks.into_boxed_slice().into(),
data,
stats,
})
}
}
#[derive(Clone)]
pub struct BlockwiseLinearReader {
blocks: Arc<[Block]>,
data: OwnedBytes,
stats: ColumnStats,
}
impl ColumnValues for BlockwiseLinearReader {
#[inline(always)]
fn get_val(&self, idx: u32) -> u64 {
let block_id = (idx / BLOCK_SIZE) as usize;
let idx_within_block = idx % BLOCK_SIZE;
let block = &self.blocks[block_id];
let interpoled_val: u64 = block.line.eval(idx_within_block);
let block_bytes = &self.data[block.data_start_offset..];
let bitpacked_diff = block.bit_unpacker.get(idx_within_block, block_bytes);
// TODO optimize me! the line parameters could be tweaked to include the multiplication and
// remove the dependency.
self.stats.min_value
+ self
.stats
.gcd
.get()
.wrapping_mul(interpoled_val.wrapping_add(bitpacked_diff))
}
#[inline(always)]
fn min_value(&self) -> u64 {
self.stats.min_value
}
#[inline(always)]
fn max_value(&self) -> u64 {
self.stats.max_value
}
#[inline(always)]
fn num_vals(&self) -> u32 {
self.stats.num_rows
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_values::u64_based::tests::create_and_validate;
#[test]
fn test_with_codec_data_sets_simple() {
create_and_validate::<BlockwiseLinearCodec>(
&[11, 20, 40, 20, 10, 10, 10, 10, 10, 10],
"simple test",
)
.unwrap();
}
#[test]
fn test_with_codec_data_sets_simple_gcd() {
let (_, actual_compression_rate) = create_and_validate::<BlockwiseLinearCodec>(
&[10, 20, 40, 20, 10, 10, 10, 10, 10, 10],
"name",
)
.unwrap();
assert_eq!(actual_compression_rate, 0.175);
}
#[test]
fn test_with_codec_data_sets() {
let data_sets = crate::column_values::u64_based::tests::get_codec_test_datasets();
for (mut data, name) in data_sets {
create_and_validate::<BlockwiseLinearCodec>(&data, name);
data.reverse();
create_and_validate::<BlockwiseLinearCodec>(&data, name);
}
}
#[test]
fn test_blockwise_linear_fast_field_rand() {
for _ in 0..500 {
let mut data = (0..1 + rand::random::<u8>() as usize)
.map(|_| rand::random::<i64>() as u64 / 2)
.collect::<Vec<_>>();
create_and_validate::<BlockwiseLinearCodec>(&data, "rand");
data.reverse();
create_and_validate::<BlockwiseLinearCodec>(&data, "rand");
}
}
}

31
columnar/src/column_values/line.rs → columnar/src/column_values/u64_based/line.rs
View File

@@ -17,8 +17,8 @@ const MID_POINT: u64 = (1u64 << 32) - 1u64;
/// `y = m * x >> 32 + b`
#[derive(Debug, Clone, Copy, Default)]
pub struct Line {
slope: u64,
intercept: u64,
pub(crate) slope: u64,
pub(crate) intercept: u64,
}
/// Compute the line slope.
@@ -67,21 +67,8 @@ impl Line {
self.intercept.wrapping_add(linear_part)
}
// Same as train, but the intercept is only estimated from provided sample positions
pub fn estimate(sample_positions_and_values: &[(u64, u64)]) -> Self {
let first_val = sample_positions_and_values[0].1;
let last_val = sample_positions_and_values[sample_positions_and_values.len() - 1].1;
let num_vals = sample_positions_and_values[sample_positions_and_values.len() - 1].0 + 1;
Self::train_from(
first_val,
last_val,
num_vals as u32,
sample_positions_and_values.iter().cloned(),
)
}
// Intercept is only computed from provided positions
fn train_from(
pub fn train_from(
first_val: u64,
last_val: u64,
num_vals: u32,
@@ -135,16 +122,16 @@ impl Line {
line
}
/// Returns a line that attemps to approximate a function
/// Returns a line that attempts to approximate a function
/// f: i in 0..[ys.num_vals()) -> ys[i].
///
/// - The approximation is always lower than the actual value.
/// Or more rigorously, formally `f(i).wrapping_sub(ys[i])` is small
/// for any i in [0..ys.len()).
/// - The approximation is always lower than the actual value. Or more rigorously, formally
/// `f(i).wrapping_sub(ys[i])` is small for any i in [0..ys.len()).
/// - It computes without panicking for any value of it.
///
/// This function is only invariable by translation if all of the
/// `ys` are packaged into half of the space. (See heuristic below)
/// TODO USE array
pub fn train(ys: &dyn ColumnValues) -> Self {
let first_val = ys.iter().next().unwrap();
let last_val = ys.iter().nth(ys.num_vals() as usize - 1).unwrap();
@@ -158,7 +145,7 @@ impl Line {
}
impl BinarySerializable for Line {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.slope).serialize(writer)?;
VInt(self.intercept).serialize(writer)?;
Ok(())
@@ -196,7 +183,7 @@ mod tests {
}
fn test_eval_max_err(ys: &[u64]) -> Option<u64> {
let line = Line::train(&VecColumn::from(&ys));
let line = Line::train(&VecColumn::from(ys.to_vec()));
ys.iter()
.enumerate()
.map(|(x, y)| y.wrapping_sub(line.eval(x as u32)))

279
columnar/src/column_values/u64_based/linear.rs Normal file
View File

@@ -0,0 +1,279 @@
use std::io;
use common::{BinarySerializable, OwnedBytes};
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use super::line::Line;
use super::ColumnValues;
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
use crate::column_values::VecColumn;
use crate::RowId;
const HALF_SPACE: u64 = u64::MAX / 2;
const LINE_ESTIMATION_BLOCK_LEN: usize = 512;
/// Depending on the field type, a different
/// fast field is required.
#[derive(Clone)]
pub struct LinearReader {
data: OwnedBytes,
linear_params: LinearParams,
stats: ColumnStats,
}
impl ColumnValues for LinearReader {
#[inline]
fn get_val(&self, doc: u32) -> u64 {
let interpoled_val: u64 = self.linear_params.line.eval(doc);
let bitpacked_diff = self.linear_params.bit_unpacker.get(doc, &self.data);
interpoled_val.wrapping_add(bitpacked_diff)
}
#[inline(always)]
fn min_value(&self) -> u64 {
self.stats.min_value
}
#[inline(always)]
fn max_value(&self) -> u64 {
self.stats.max_value
}
#[inline]
fn num_vals(&self) -> u32 {
self.stats.num_rows
}
}
/// Fastfield serializer, which tries to guess values by linear interpolation
/// and stores the difference bitpacked.
pub struct LinearCodec;
#[derive(Debug, Clone)]
struct LinearParams {
line: Line,
bit_unpacker: BitUnpacker,
}
impl BinarySerializable for LinearParams {
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
self.line.serialize(writer)?;
self.bit_unpacker.bit_width().serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let line = Line::deserialize(reader)?;
let bit_width = u8::deserialize(reader)?;
Ok(Self {
line,
bit_unpacker: BitUnpacker::new(bit_width),
})
}
}
pub struct LinearCodecEstimator {
block: Vec<u64>,
line: Option<Line>,
row_id: RowId,
min_deviation: u64,
max_deviation: u64,
first_val: u64,
last_val: u64,
}
impl Default for LinearCodecEstimator {
fn default() -> LinearCodecEstimator {
LinearCodecEstimator {
block: Vec::with_capacity(LINE_ESTIMATION_BLOCK_LEN),
line: None,
row_id: 0,
min_deviation: u64::MAX,
max_deviation: u64::MIN,
first_val: 0u64,
last_val: 0u64,
}
}
}
impl ColumnCodecEstimator for LinearCodecEstimator {
fn finalize(&mut self) {
if let Some(line) = self.line.as_mut() {
line.intercept = line
.intercept
.wrapping_add(self.min_deviation)
.wrapping_sub(HALF_SPACE);
}
}
fn estimate(&self, stats: &ColumnStats) -> Option<u64> {
let line = self.line?;
let amplitude = self.max_deviation - self.min_deviation;
let num_bits = compute_num_bits(amplitude);
let linear_params = LinearParams {
line,
bit_unpacker: BitUnpacker::new(num_bits),
};
Some(
stats.num_bytes()
+ linear_params.num_bytes()
+ (num_bits as u64 * stats.num_rows as u64 + 7) / 8,
)
}
fn serialize(
&self,
stats: &ColumnStats,
vals: &mut dyn Iterator<Item = u64>,
wrt: &mut dyn io::Write,
) -> io::Result<()> {
stats.serialize(wrt)?;
let line = self.line.unwrap();
let amplitude = self.max_deviation - self.min_deviation;
let num_bits = compute_num_bits(amplitude);
let linear_params = LinearParams {
line,
bit_unpacker: BitUnpacker::new(num_bits),
};
linear_params.serialize(wrt)?;
let mut bit_packer = BitPacker::new();
for (pos, value) in vals.enumerate() {
let calculated_value = line.eval(pos as u32);
let offset = value.wrapping_sub(calculated_value);
bit_packer.write(offset, num_bits, wrt)?;
}
bit_packer.close(wrt)?;
Ok(())
}
fn collect(&mut self, value: u64) {
if let Some(line) = self.line {
self.collect_after_line_estimation(&line, value);
} else {
self.collect_before_line_estimation(value);
}
}
}
impl LinearCodecEstimator {
#[inline]
fn collect_after_line_estimation(&mut self, line: &Line, value: u64) {
let interpoled_val: u64 = line.eval(self.row_id);
let deviation = value.wrapping_add(HALF_SPACE).wrapping_sub(interpoled_val);
self.min_deviation = self.min_deviation.min(deviation);
self.max_deviation = self.max_deviation.max(deviation);
if self.row_id == 0 {
self.first_val = value;
}
self.last_val = value;
self.row_id += 1u32;
}
#[inline]
fn collect_before_line_estimation(&mut self, value: u64) {
self.block.push(value);
if self.block.len() == LINE_ESTIMATION_BLOCK_LEN {
let column = VecColumn::from(std::mem::take(&mut self.block));
let line = Line::train(&column);
self.block = column.into();
let block = std::mem::take(&mut self.block);
for val in block {
self.collect_after_line_estimation(&line, val);
}
self.line = Some(line);
}
}
}
impl ColumnCodec for LinearCodec {
type ColumnValues = LinearReader;
type Estimator = LinearCodecEstimator;
fn load(mut data: OwnedBytes) -> io::Result<Self::ColumnValues> {
let stats = ColumnStats::deserialize(&mut data)?;
let linear_params = LinearParams::deserialize(&mut data)?;
Ok(LinearReader {
stats,
linear_params,
data,
})
}
}
#[cfg(test)]
mod tests {
use rand::RngCore;
use super::*;
use crate::column_values::u64_based::tests::{create_and_validate, get_codec_test_datasets};
#[test]
fn test_compression_simple() {
let vals = (100u64..)
.take(super::LINE_ESTIMATION_BLOCK_LEN)
.collect::<Vec<_>>();
create_and_validate::<LinearCodec>(&vals, "simple monotonically large").unwrap();
}
#[test]
fn test_compression() {
let data = (10..=6_000_u64).collect::<Vec<_>>();
let (estimate, actual_compression) =
create_and_validate::<LinearCodec>(&data, "simple monotonically large").unwrap();
assert_le!(actual_compression, 0.001);
assert_le!(estimate, 0.02);
}
#[test]
fn test_with_codec_datasets() {
let data_sets = get_codec_test_datasets();
for (mut data, name) in data_sets {
create_and_validate::<LinearCodec>(&data, name);
data.reverse();
create_and_validate::<LinearCodec>(&data, name);
}
}
#[test]
fn linear_interpol_fast_field_test_large_amplitude() {
let data = vec![
i64::MAX as u64 / 2,
i64::MAX as u64 / 3,
i64::MAX as u64 / 2,
];
create_and_validate::<LinearCodec>(&data, "large amplitude");
}
#[test]
fn overflow_error_test() {
let data = vec![1572656989877777, 1170935903116329, 720575940379279, 0];
create_and_validate::<LinearCodec>(&data, "overflow test");
}
#[test]
fn linear_interpol_fast_concave_data() {
let data = vec![0, 1, 2, 5, 8, 10, 20, 50];
create_and_validate::<LinearCodec>(&data, "concave data");
}
#[test]
fn linear_interpol_fast_convex_data() {
let data = vec![0, 40, 60, 70, 75, 77];
create_and_validate::<LinearCodec>(&data, "convex data");
}
#[test]
fn linear_interpol_fast_field_test_simple() {
let data = (10..=20_u64).collect::<Vec<_>>();
create_and_validate::<LinearCodec>(&data, "simple monotonically");
}
#[test]
fn linear_interpol_fast_field_rand() {
let mut rng = rand::thread_rng();
for _ in 0..50 {
let mut data = (0..10_000).map(|_| rng.next_u64()).collect::<Vec<_>>();
create_and_validate::<LinearCodec>(&data, "random");
data.reverse();
create_and_validate::<LinearCodec>(&data, "random");
}
}
}

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mod bitpacked;
mod blockwise_linear;
mod line;
mod linear;
mod stats_collector;
use std::io;
use std::io::Write;
use std::sync::Arc;
use common::{BinarySerializable, OwnedBytes};
use crate::column_values::monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
};
pub use crate::column_values::u64_based::bitpacked::BitpackedCodec;
pub use crate::column_values::u64_based::blockwise_linear::BlockwiseLinearCodec;
pub use crate::column_values::u64_based::linear::LinearCodec;
pub use crate::column_values::u64_based::stats_collector::StatsCollector;
use crate::column_values::{monotonic_map_column, ColumnStats};
use crate::iterable::Iterable;
use crate::{ColumnValues, MonotonicallyMappableToU64};
/// A `ColumnCodecEstimator` is in charge of gathering all
/// data required to serialize a column.
///
/// This happens during a first pass on data of the column elements.
/// During that pass, all column estimators receive a call to their
/// `.collect(el)`.
///
/// After this first pass, finalize is called.
/// `.estimate(..)` then should return an accurate estimation of the
/// size of the serialized column (were we to pick this codec.).
/// `.serialize(..)` then serializes the column using this codec.
pub trait ColumnCodecEstimator<T = u64>: 'static {
/// Records a new value for estimation.
/// This method will be called for each element of the column during
/// `estimation`.
fn collect(&mut self, value: u64);
/// Finalizes the first pass phase.
fn finalize(&mut self) {}
/// Returns an accurate estimation of the number of bytes that will
/// be used to represent this column.
fn estimate(&self, stats: &ColumnStats) -> Option<u64>;
/// Serializes the column using the given codec.
/// This constitutes a second pass over the columns values.
fn serialize(
&self,
stats: &ColumnStats,
vals: &mut dyn Iterator<Item = T>,
wrt: &mut dyn io::Write,
) -> io::Result<()>;
}
/// A column codec describes a colunm serialization format.
pub trait ColumnCodec<T: PartialOrd = u64> {
/// Specialized `ColumnValues` type.
type ColumnValues: ColumnValues<T> + 'static;
/// `Estimator` for the given codec.
type Estimator: ColumnCodecEstimator + Default;
/// Loads a column that has been serialized using this codec.
fn load(bytes: OwnedBytes) -> io::Result<Self::ColumnValues>;
/// Returns an estimator.
fn estimator() -> Self::Estimator {
Self::Estimator::default()
}
/// Returns a boxed estimator.
fn boxed_estimator() -> Box<dyn ColumnCodecEstimator> {
Box::new(Self::estimator())
}
}
/// Available codecs to use to encode the u64 (via [`MonotonicallyMappableToU64`]) converted data.
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
#[repr(u8)]
pub enum CodecType {
/// Bitpack all values in the value range. The number of bits is defined by the amplitude
/// `column.max_value() - column.min_value()`
Bitpacked = 0u8,
/// Linear interpolation puts a line between the first and last value and then bitpacks the
/// values by the offset from the line. The number of bits is defined by the max deviation from
/// the line.
Linear = 1u8,
/// Same as [`CodecType::Linear`], but encodes in blocks of 512 elements.
BlockwiseLinear = 2u8,
}
/// List of all available u64-base codecs.
pub const ALL_U64_CODEC_TYPES: [CodecType; 3] = [
CodecType::Bitpacked,
CodecType::Linear,
CodecType::BlockwiseLinear,
];
impl CodecType {
fn to_code(self) -> u8 {
self as u8
}
fn try_from_code(code: u8) -> Option<CodecType> {
match code {
0u8 => Some(CodecType::Bitpacked),
1u8 => Some(CodecType::Linear),
2u8 => Some(CodecType::BlockwiseLinear),
_ => None,
}
}
fn load<T: MonotonicallyMappableToU64>(
&self,
bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
match self {
CodecType::Bitpacked => load_specific_codec::<BitpackedCodec, T>(bytes),
CodecType::Linear => load_specific_codec::<LinearCodec, T>(bytes),
CodecType::BlockwiseLinear => load_specific_codec::<BlockwiseLinearCodec, T>(bytes),
}
}
}
fn load_specific_codec<C: ColumnCodec, T: MonotonicallyMappableToU64>(
bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
let reader = C::load(bytes)?;
let reader_typed = monotonic_map_column(
reader,
StrictlyMonotonicMappingInverter::from(StrictlyMonotonicMappingToInternal::<T>::new()),
);
Ok(Arc::new(reader_typed))
}
impl CodecType {
/// Returns a boxed codec estimator associated to a given `CodecType`.
pub fn estimator(&self) -> Box<dyn ColumnCodecEstimator> {
match self {
CodecType::Bitpacked => BitpackedCodec::boxed_estimator(),
CodecType::Linear => LinearCodec::boxed_estimator(),
CodecType::BlockwiseLinear => BlockwiseLinearCodec::boxed_estimator(),
}
}
}
/// Serializes a given column of u64-mapped values.
pub fn serialize_u64_based_column_values<T: MonotonicallyMappableToU64>(
vals: &dyn Iterable<T>,
codec_types: &[CodecType],
wrt: &mut dyn Write,
) -> io::Result<()> {
let mut stats_collector = StatsCollector::default();
let mut estimators: Vec<(CodecType, Box<dyn ColumnCodecEstimator>)> =
Vec::with_capacity(codec_types.len());
for &codec_type in codec_types {
estimators.push((codec_type, codec_type.estimator()));
}
for val in vals.boxed_iter() {
let val_u64 = val.to_u64();
stats_collector.collect(val_u64);
for (_, estimator) in &mut estimators {
estimator.collect(val_u64);
}
}
for (_, estimator) in &mut estimators {
estimator.finalize();
}
let stats = stats_collector.stats();
let (_, best_codec, best_codec_estimator) = estimators
.into_iter()
.flat_map(|(codec_type, estimator)| {
let num_bytes = estimator.estimate(&stats)?;
Some((num_bytes, codec_type, estimator))
})
.min_by_key(|(num_bytes, _, _)| *num_bytes)
.ok_or_else(|| {
io::Error::new(io::ErrorKind::InvalidData, "No available applicable codec.")
})?;
best_codec.to_code().serialize(wrt)?;
best_codec_estimator.serialize(
&stats,
&mut vals.boxed_iter().map(MonotonicallyMappableToU64::to_u64),
wrt,
)?;
Ok(())
}
/// Load u64-based column values.
///
/// This method first identifies the codec off the first byte.
pub fn load_u64_based_column_values<T: MonotonicallyMappableToU64>(
mut bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
let codec_type: CodecType = bytes
.first()
.copied()
.and_then(CodecType::try_from_code)
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Failed to read codec type"))?;
bytes.advance(1);
codec_type.load(bytes)
}
/// Helper function to serialize a column (autodetect from all codecs) and then open it
pub fn serialize_and_load_u64_based_column_values<T: MonotonicallyMappableToU64>(
vals: &dyn Iterable,
codec_types: &[CodecType],
) -> Arc<dyn ColumnValues<T>> {
let mut buffer = Vec::new();
serialize_u64_based_column_values(vals, codec_types, &mut buffer).unwrap();
load_u64_based_column_values::<T>(OwnedBytes::new(buffer)).unwrap()
}
#[cfg(test)]
mod tests;

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use std::num::NonZeroU64;
use fastdivide::DividerU64;
use crate::column_values::ColumnStats;
use crate::RowId;
/// Compute the gcd of two non null numbers.
///
/// It is recommended, but not required, to feed values such that `large >= small`.
fn compute_gcd(mut large: NonZeroU64, mut small: NonZeroU64) -> NonZeroU64 {
loop {
let rem: u64 = large.get() % small;
if let Some(new_small) = NonZeroU64::new(rem) {
(large, small) = (small, new_small);
} else {
return small;
}
}
}
#[derive(Default)]
pub struct StatsCollector {
min_max_opt: Option<(u64, u64)>,
num_rows: RowId,
// We measure the GCD of the difference between the values and the minimal value.
// This is the same as computing the difference between the values and the first value.
//
// This way, we can compress i64-converted-to-u64 (e.g. timestamp that were supplied in
// seconds, only to be converted in nanoseconds).
increment_gcd_opt: Option<(NonZeroU64, DividerU64)>,
first_value_opt: Option<u64>,
}
impl StatsCollector {
pub fn stats(&self) -> ColumnStats {
let (min_value, max_value) = self.min_max_opt.unwrap_or((0u64, 0u64));
let increment_gcd = if let Some((increment_gcd, _)) = self.increment_gcd_opt {
increment_gcd
} else {
NonZeroU64::new(1u64).unwrap()
};
ColumnStats {
min_value,
max_value,
num_rows: self.num_rows,
gcd: increment_gcd,
}
}
#[inline]
fn update_increment_gcd(&mut self, value: u64) {
let Some(first_value) = self.first_value_opt else {
// We set the first value and just quit.
self.first_value_opt = Some(value);
return;
};
let Some(non_zero_value) = NonZeroU64::new(value.abs_diff(first_value)) else {
// We can simply skip 0 values.
return;
};
let Some((gcd, gcd_divider)) = self.increment_gcd_opt else {
self.set_increment_gcd(non_zero_value);
return;
};
if gcd.get() == 1 {
// It won't see any update now.
return;
}
let remainder =
non_zero_value.get() - (gcd_divider.divide(non_zero_value.get())) * gcd.get();
if remainder == 0 {
return;
}
let new_gcd = compute_gcd(non_zero_value, gcd);
self.set_increment_gcd(new_gcd);
}
fn set_increment_gcd(&mut self, gcd: NonZeroU64) {
let new_divider = DividerU64::divide_by(gcd.get());
self.increment_gcd_opt = Some((gcd, new_divider));
}
pub fn collect(&mut self, value: u64) {
self.min_max_opt = Some(if let Some((min, max)) = self.min_max_opt {
(min.min(value), max.max(value))
} else {
(value, value)
});
self.num_rows += 1;
self.update_increment_gcd(value);
}
}
#[cfg(test)]
mod tests {
use std::num::NonZeroU64;
use crate::column_values::u64_based::stats_collector::{compute_gcd, StatsCollector};
use crate::column_values::u64_based::ColumnStats;
fn compute_stats(vals: impl Iterator<Item = u64>) -> ColumnStats {
let mut stats_collector = StatsCollector::default();
for val in vals {
stats_collector.collect(val);
}
stats_collector.stats()
}
fn find_gcd(vals: impl Iterator<Item = u64>) -> u64 {
compute_stats(vals).gcd.get()
}
#[test]
fn test_compute_gcd() {
let test_compute_gcd_aux = |large, small, expected| {
let large = NonZeroU64::new(large).unwrap();
let small = NonZeroU64::new(small).unwrap();
let expected = NonZeroU64::new(expected).unwrap();
assert_eq!(compute_gcd(small, large), expected);
assert_eq!(compute_gcd(large, small), expected);
};
test_compute_gcd_aux(1, 4, 1);
test_compute_gcd_aux(2, 4, 2);
test_compute_gcd_aux(10, 25, 5);
test_compute_gcd_aux(25, 25, 25);
}
#[test]
fn test_gcd() {
assert_eq!(find_gcd([0].into_iter()), 1);
assert_eq!(find_gcd([0, 10].into_iter()), 10);
assert_eq!(find_gcd([10, 0].into_iter()), 10);
assert_eq!(find_gcd([].into_iter()), 1);
assert_eq!(find_gcd([15, 30, 5, 10].into_iter()), 5);
assert_eq!(find_gcd([15, 16, 10].into_iter()), 1);
assert_eq!(find_gcd([0, 5, 5, 5].into_iter()), 5);
assert_eq!(find_gcd([0, 0].into_iter()), 1);
assert_eq!(find_gcd([1, 10, 4, 1, 7, 10].into_iter()), 3);
assert_eq!(find_gcd([1, 10, 0, 4, 1, 7, 10].into_iter()), 1);
}
#[test]
fn test_stats() {
assert_eq!(
compute_stats([].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(1).unwrap(),
min_value: 0,
max_value: 0,
num_rows: 0
}
);
assert_eq!(
compute_stats([0, 1].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(1).unwrap(),
min_value: 0,
max_value: 1,
num_rows: 2
}
);
assert_eq!(
compute_stats([0, 1].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(1).unwrap(),
min_value: 0,
max_value: 1,
num_rows: 2
}
);
assert_eq!(
compute_stats([10, 20, 30].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(10).unwrap(),
min_value: 10,
max_value: 30,
num_rows: 3
}
);
assert_eq!(
compute_stats([10, 50, 10, 30].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(20).unwrap(),
min_value: 10,
max_value: 50,
num_rows: 4
}
);
assert_eq!(
compute_stats([10, 0, 30].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(10).unwrap(),
min_value: 0,
max_value: 30,
num_rows: 3
}
);
}
}

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use proptest::prelude::*;
use proptest::{prop_oneof, proptest};
#[test]
fn test_serialize_and_load_simple() {
let mut buffer = Vec::new();
let vals = &[1u64, 2u64, 5u64];
serialize_u64_based_column_values(
&&vals[..],
&[CodecType::Bitpacked, CodecType::BlockwiseLinear],
&mut buffer,
)
.unwrap();
assert_eq!(buffer.len(), 7);
let col = load_u64_based_column_values::<u64>(OwnedBytes::new(buffer)).unwrap();
assert_eq!(col.num_vals(), 3);
assert_eq!(col.get_val(0), 1);
assert_eq!(col.get_val(1), 2);
assert_eq!(col.get_val(2), 5);
}
#[test]
fn test_empty_column_i64() {
let vals: [i64; 0] = [];
let mut num_acceptable_codecs = 0;
for codec in ALL_U64_CODEC_TYPES {
let mut buffer = Vec::new();
if serialize_u64_based_column_values(&&vals[..], &[codec], &mut buffer).is_err() {
continue;
}
num_acceptable_codecs += 1;
let col = load_u64_based_column_values::<i64>(OwnedBytes::new(buffer)).unwrap();
assert_eq!(col.num_vals(), 0);
assert_eq!(col.min_value(), i64::MIN);
assert_eq!(col.max_value(), i64::MIN);
}
assert!(num_acceptable_codecs > 0);
}
#[test]
fn test_empty_column_u64() {
let vals: [u64; 0] = [];
let mut num_acceptable_codecs = 0;
for codec in ALL_U64_CODEC_TYPES {
let mut buffer = Vec::new();
if serialize_u64_based_column_values(&&vals[..], &[codec], &mut buffer).is_err() {
continue;
}
num_acceptable_codecs += 1;
let col = load_u64_based_column_values::<u64>(OwnedBytes::new(buffer)).unwrap();
assert_eq!(col.num_vals(), 0);
assert_eq!(col.min_value(), u64::MIN);
assert_eq!(col.max_value(), u64::MIN);
}
assert!(num_acceptable_codecs > 0);
}
#[test]
fn test_empty_column_f64() {
let vals: [f64; 0] = [];
let mut num_acceptable_codecs = 0;
for codec in ALL_U64_CODEC_TYPES {
let mut buffer = Vec::new();
if serialize_u64_based_column_values(&&vals[..], &[codec], &mut buffer).is_err() {
continue;
}
num_acceptable_codecs += 1;
let col = load_u64_based_column_values::<f64>(OwnedBytes::new(buffer)).unwrap();
assert_eq!(col.num_vals(), 0);
// FIXME. f64::MIN would be better!
assert!(col.min_value().is_nan());
assert!(col.max_value().is_nan());
}
assert!(num_acceptable_codecs > 0);
}
pub(crate) fn create_and_validate<TColumnCodec: ColumnCodec>(
vals: &[u64],
name: &str,
) -> Option<(f32, f32)> {
let mut stats_collector = StatsCollector::default();
let mut codec_estimator: TColumnCodec::Estimator = Default::default();
for val in vals.boxed_iter() {
stats_collector.collect(val);
codec_estimator.collect(val);
}
codec_estimator.finalize();
let stats = stats_collector.stats();
let estimation = codec_estimator.estimate(&stats)?;
let mut buffer = Vec::new();
codec_estimator
.serialize(&stats, vals.boxed_iter().as_mut(), &mut buffer)
.unwrap();
let actual_compression = buffer.len() as u64;
let reader = TColumnCodec::load(OwnedBytes::new(buffer)).unwrap();
assert_eq!(reader.num_vals(), vals.len() as u32);
let mut buffer = Vec::new();
for (doc, orig_val) in vals.iter().copied().enumerate() {
let val = reader.get_val(doc as u32);
assert_eq!(
val, orig_val,
"val `{val}` does not match orig_val {orig_val:?}, in data set {name}, data `{vals:?}`",
);
buffer.resize(1, 0);
reader.get_vals(&[doc as u32], &mut buffer);
let val = buffer[0];
assert_eq!(
val, orig_val,
"val `{val}` does not match orig_val {orig_val:?}, in data set {name}, data `{vals:?}`",
);
}
let all_docs: Vec<u32> = (0..vals.len() as u32).collect();
buffer.resize(all_docs.len(), 0);
reader.get_vals(&all_docs, &mut buffer);
assert_eq!(vals, buffer);
if !vals.is_empty() {
let test_rand_idx = rand::thread_rng().gen_range(0..=vals.len() - 1);
let expected_positions: Vec<u32> = vals
.iter()
.enumerate()
.filter(|(_, el)| **el == vals[test_rand_idx])
.map(|(pos, _)| pos as u32)
.collect();
let mut positions = Vec::new();
reader.get_row_ids_for_value_range(
vals[test_rand_idx]..=vals[test_rand_idx],
0..vals.len() as u32,
&mut positions,
);
assert_eq!(expected_positions, positions);
}
if actual_compression > 1000 {
assert!(relative_difference(estimation, actual_compression) < 0.10f32);
}
Some((
compression_rate(estimation, stats.num_rows),
compression_rate(actual_compression, stats.num_rows),
))
}
fn compression_rate(num_bytes: u64, num_values: u32) -> f32 {
num_bytes as f32 / (num_values as f32 * 8.0)
}
fn relative_difference(left: u64, right: u64) -> f32 {
let left = left as f32;
let right = right as f32;
2.0f32 * (left - right).abs() / (left + right)
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(100))]
#[test]
fn test_proptest_small_bitpacked(data in proptest::collection::vec(num_strategy(), 1..10)) {
create_and_validate::<BitpackedCodec>(&data, "proptest bitpacked");
}
#[test]
fn test_proptest_small_linear(data in proptest::collection::vec(num_strategy(), 1..10)) {
create_and_validate::<LinearCodec>(&data, "proptest linearinterpol");
}
#[test]
fn test_proptest_small_blockwise_linear(data in proptest::collection::vec(num_strategy(), 1..10)) {
create_and_validate::<BlockwiseLinearCodec>(&data, "proptest multilinearinterpol");
}
}
#[test]
fn test_small_blockwise_linear_example() {
create_and_validate::<BlockwiseLinearCodec>(
&[9223372036854775808, 9223370937344622593],
"proptest multilinearinterpol",
);
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(10))]
#[test]
fn test_proptest_large_bitpacked(data in proptest::collection::vec(num_strategy(), 1..6000)) {
create_and_validate::<BitpackedCodec>(&data, "proptest bitpacked");
}
#[test]
fn test_proptest_large_linear(data in proptest::collection::vec(num_strategy(), 1..6000)) {
create_and_validate::<LinearCodec>(&data, "proptest linearinterpol");
}
#[test]
fn test_proptest_large_blockwise_linear(data in proptest::collection::vec(num_strategy(), 1..6000)) {
create_and_validate::<BlockwiseLinearCodec>(&data, "proptest multilinearinterpol");
}
}
fn num_strategy() -> impl Strategy<Value = u64> {
prop_oneof![
1 => prop::num::u64::ANY.prop_map(|num| u64::MAX - (num % 10) ),
1 => prop::num::u64::ANY.prop_map(|num| num % 10 ),
20 => prop::num::u64::ANY,
]
}
pub fn get_codec_test_datasets() -> Vec<(Vec<u64>, &'static str)> {
let mut data_and_names = vec![];
let data = (10..=10_000_u64).collect::<Vec<_>>();
data_and_names.push((data, "simple monotonically increasing"));
data_and_names.push((
vec![5, 6, 7, 8, 9, 10, 99, 100],
"offset in linear interpol",
));
data_and_names.push((vec![5, 50, 3, 13, 1, 1000, 35], "rand small"));
data_and_names.push((vec![10], "single value"));
data_and_names.push((
vec![1572656989877777, 1170935903116329, 720575940379279, 0],
"overflow error",
));
data_and_names
}
fn test_codec<C: ColumnCodec>() {
let codec_name = std::any::type_name::<C>();
for (data, dataset_name) in get_codec_test_datasets() {
let estimate_actual_opt: Option<(f32, f32)> =
tests::create_and_validate::<C>(&data, dataset_name);
let result = if let Some((estimate, actual)) = estimate_actual_opt {
format!("Estimate `{estimate}` Actual `{actual}`")
} else {
"Disabled".to_string()
};
println!("Codec {codec_name}, DataSet {dataset_name}, {result}");
}
}
#[test]
fn test_codec_bitpacking() {
test_codec::<BitpackedCodec>();
}
#[test]
fn test_codec_interpolation() {
test_codec::<LinearCodec>();
}
#[test]
fn test_codec_multi_interpolation() {
test_codec::<BlockwiseLinearCodec>();
}
use super::*;
fn estimate<C: ColumnCodec>(vals: &[u64]) -> Option<f32> {
let mut stats_collector = StatsCollector::default();
let mut estimator = C::Estimator::default();
for &val in vals {
stats_collector.collect(val);
estimator.collect(val);
}
estimator.finalize();
let stats = stats_collector.stats();
let num_bytes = estimator.estimate(&stats)?;
if stats.num_rows == 0 {
return None;
}
Some(num_bytes as f32 / (8.0 * stats.num_rows as f32))
}
#[test]
fn estimation_good_interpolation_case() {
let data = (10..=20000_u64).collect::<Vec<_>>();
let linear_interpol_estimation = estimate::<LinearCodec>(&data).unwrap();
assert_le!(linear_interpol_estimation, 0.01);
let multi_linear_interpol_estimation = estimate::<BlockwiseLinearCodec>(&data).unwrap();
assert_le!(multi_linear_interpol_estimation, 0.2);
assert_lt!(linear_interpol_estimation, multi_linear_interpol_estimation);
let bitpacked_estimation = estimate::<BitpackedCodec>(&data).unwrap();
assert_lt!(linear_interpol_estimation, bitpacked_estimation);
}
#[test]
fn estimation_test_bad_interpolation_case_monotonically_increasing() {
let mut data: Vec<u64> = (201..=20000_u64).collect();
data.push(1_000_000);
// in this case the linear interpolation can't in fact not be worse than bitpacking,
// but the estimator adds some threshold, which leads to estimated worse behavior
let linear_interpol_estimation = estimate::<LinearCodec>(&data[..]).unwrap();
assert_le!(linear_interpol_estimation, 0.35);
let bitpacked_estimation = estimate::<BitpackedCodec>(&data).unwrap();
assert_le!(bitpacked_estimation, 0.32);
assert_le!(bitpacked_estimation, linear_interpol_estimation);
}
#[test]
fn test_fast_field_codec_type_to_code() {
let mut count_codec = 0;
for code in 0..=255 {
if let Some(codec_type) = CodecType::try_from_code(code) {
assert_eq!(codec_type.to_code(), code);
count_codec += 1;
}
}
assert_eq!(count_codec, 3);
}
fn test_fastfield_gcd_i64_with_codec(codec_type: CodecType, num_vals: usize) -> io::Result<()> {
let mut vals: Vec<i64> = (-4..=(num_vals as i64) - 5).map(|val| val * 1000).collect();
let mut buffer: Vec<u8> = Vec::new();
crate::column_values::serialize_u64_based_column_values(
&&vals[..],
&[codec_type],
&mut buffer,
)?;
let buffer = OwnedBytes::new(buffer);
let column = crate::column_values::load_u64_based_column_values::<i64>(buffer.clone())?;
assert_eq!(column.get_val(0), -4000i64);
assert_eq!(column.get_val(1), -3000i64);
assert_eq!(column.get_val(2), -2000i64);
assert_eq!(column.max_value(), (num_vals as i64 - 5) * 1000);
assert_eq!(column.min_value(), -4000i64);
// Can't apply gcd
let mut buffer_without_gcd = Vec::new();
vals.pop();
vals.push(1001i64);
crate::column_values::serialize_u64_based_column_values(
&&vals[..],
&[codec_type],
&mut buffer_without_gcd,
)?;
let buffer_without_gcd = OwnedBytes::new(buffer_without_gcd);
assert!(buffer_without_gcd.len() > buffer.len());
Ok(())
}
#[test]
fn test_fastfield_gcd_i64() -> io::Result<()> {
for &codec_type in &[
CodecType::Bitpacked,
CodecType::BlockwiseLinear,
CodecType::Linear,
] {
test_fastfield_gcd_i64_with_codec(codec_type, 5500)?;
}
Ok(())
}
fn test_fastfield_gcd_u64_with_codec(codec_type: CodecType, num_vals: usize) -> io::Result<()> {
let mut vals: Vec<u64> = (1..=num_vals).map(|i| i as u64 * 1000u64).collect();
let mut buffer: Vec<u8> = Vec::new();
crate::column_values::serialize_u64_based_column_values(
&&vals[..],
&[codec_type],
&mut buffer,
)?;
let buffer = OwnedBytes::new(buffer);
let column = crate::column_values::load_u64_based_column_values::<u64>(buffer.clone())?;
assert_eq!(column.get_val(0), 1000u64);
assert_eq!(column.get_val(1), 2000u64);
assert_eq!(column.get_val(2), 3000u64);
assert_eq!(column.max_value(), num_vals as u64 * 1000);
assert_eq!(column.min_value(), 1000u64);
// Can't apply gcd
let mut buffer_without_gcd = Vec::new();
vals.pop();
vals.push(1001u64);
crate::column_values::serialize_u64_based_column_values(
&&vals[..],
&[codec_type],
&mut buffer_without_gcd,
)?;
let buffer_without_gcd = OwnedBytes::new(buffer_without_gcd);
assert!(buffer_without_gcd.len() > buffer.len());
Ok(())
}
#[test]
fn test_fastfield_gcd_u64() -> io::Result<()> {
for &codec_type in &[
CodecType::Bitpacked,
CodecType::BlockwiseLinear,
CodecType::Linear,
] {
test_fastfield_gcd_u64_with_codec(codec_type, 5500)?;
}
Ok(())
}
#[test]
pub fn test_fastfield2() {
let test_fastfield = crate::column_values::serialize_and_load_u64_based_column_values::<u64>(
&&[100u64, 200u64, 300u64][..],
&ALL_U64_CODEC_TYPES,
);
assert_eq!(test_fastfield.get_val(0), 100);
assert_eq!(test_fastfield.get_val(1), 200);
assert_eq!(test_fastfield.get_val(2), 300);
}

54
columnar/src/column_values/vec_column.rs Normal file
View File

@@ -0,0 +1,54 @@
use std::fmt::Debug;
use tantivy_bitpacker::minmax;
use crate::ColumnValues;
/// VecColumn provides `Column` over a `Vec<T>`.
pub struct VecColumn<T = u64> {
pub(crate) values: Vec<T>,
pub(crate) min_value: T,
pub(crate) max_value: T,
}
impl<T: Copy + PartialOrd + Send + Sync + Debug + 'static> ColumnValues<T> for VecColumn<T> {
fn get_val(&self, position: u32) -> T {
self.values[position as usize]
}
fn iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
Box::new(self.values.iter().copied())
}
fn min_value(&self) -> T {
self.min_value
}
fn max_value(&self) -> T {
self.max_value
}
fn num_vals(&self) -> u32 {
self.values.len() as u32
}
fn get_range(&self, start: u64, output: &mut [T]) {
output.copy_from_slice(&self.values[start as usize..][..output.len()])
}
}
impl<T: Copy + PartialOrd + Default> From<Vec<T>> for VecColumn<T> {
fn from(values: Vec<T>) -> Self {
let (min_value, max_value) = minmax(values.iter().copied()).unwrap_or_default();
Self {
values,
min_value,
max_value,
}
}
}
impl From<VecColumn> for Vec<u64> {
fn from(column: VecColumn) -> Self {
column.values
}
}

31
columnar/src/columnar/column_type.rs
View File

@@ -1,11 +1,15 @@
use std::fmt;
use std::fmt::Debug;
use std::net::Ipv6Addr;
use serde::{Deserialize, Serialize};
use crate::value::NumericalType;
use crate::InvalidData;
/// The column type represents the column type.
/// Any changes need to be propagated to `COLUMN_TYPES`.
#[derive(Hash, Eq, PartialEq, Debug, Clone, Copy, Ord, PartialOrd)]
#[derive(Hash, Eq, PartialEq, Debug, Clone, Copy, Ord, PartialOrd, Serialize, Deserialize)]
#[repr(u8)]
pub enum ColumnType {
I64 = 0u8,
@@ -18,6 +22,22 @@ pub enum ColumnType {
DateTime = 7u8,
}
impl fmt::Display for ColumnType {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
let short_str = match self {
ColumnType::I64 => "i64",
ColumnType::U64 => "u64",
ColumnType::F64 => "f64",
ColumnType::Bytes => "bytes",
ColumnType::Str => "str",
ColumnType::Bool => "bool",
ColumnType::IpAddr => "ip",
ColumnType::DateTime => "datetime",
};
write!(f, "{short_str}")
}
}
// The order needs to match _exactly_ the order in the enum
const COLUMN_TYPES: [ColumnType; 8] = [
ColumnType::I64,
@@ -34,6 +54,9 @@ impl ColumnType {
pub fn to_code(self) -> u8 {
self as u8
}
pub fn is_date_time(&self) -> bool {
self == &ColumnType::DateTime
}
pub(crate) fn try_from_code(code: u8) -> Result<ColumnType, InvalidData> {
COLUMN_TYPES.get(code as usize).copied().ok_or(InvalidData)
@@ -66,7 +89,7 @@ impl ColumnType {
}
// TODO remove if possible
pub trait HasAssociatedColumnType: 'static + Send + Sync + Copy + PartialOrd {
pub trait HasAssociatedColumnType: 'static + Debug + Send + Sync + Copy + PartialOrd {
fn column_type() -> ColumnType;
fn default_value() -> Self;
}
@@ -110,7 +133,7 @@ impl HasAssociatedColumnType for bool {
}
}
impl HasAssociatedColumnType for crate::DateTime {
impl HasAssociatedColumnType for common::DateTime {
fn column_type() -> ColumnType {
ColumnType::DateTime
}
@@ -142,7 +165,7 @@ mod tests {
}
}
for code in COLUMN_TYPES.len() as u8..=u8::MAX {
assert!(ColumnType::try_from_code(code as u8).is_err());
assert!(ColumnType::try_from_code(code).is_err());
}
}

31
columnar/src/columnar/format_version.rs
View File

@@ -1,14 +1,17 @@
use core::fmt;
use std::fmt::{Display, Formatter};
use crate::InvalidData;
pub const VERSION_FOOTER_NUM_BYTES: usize = MAGIC_BYTES.len() + std::mem::size_of::<u32>();
/// We end the file by these 4 bytes just to somewhat identify that
/// this is indeed a columnar file.
const MAGIC_BYTES: [u8; 4] = [2, 113, 119, 066];
const MAGIC_BYTES: [u8; 4] = [2, 113, 119, 66];
pub fn footer() -> [u8; VERSION_FOOTER_NUM_BYTES] {
let mut footer_bytes = [0u8; VERSION_FOOTER_NUM_BYTES];
footer_bytes[0..4].copy_from_slice(&Version::V1.to_bytes());
footer_bytes[0..4].copy_from_slice(&CURRENT_VERSION.to_bytes());
footer_bytes[4..8].copy_from_slice(&MAGIC_BYTES[..]);
footer_bytes
}
@@ -20,21 +23,34 @@ pub fn parse_footer(footer_bytes: [u8; VERSION_FOOTER_NUM_BYTES]) -> Result<Vers
Version::try_from_bytes(footer_bytes[0..4].try_into().unwrap())
}
pub const CURRENT_VERSION: Version = Version::V2;
#[derive(Debug, Copy, Clone, Eq, PartialEq)]
#[repr(u32)]
pub enum Version {
V1 = 1u32,
V2 = 2u32,
}
impl Display for Version {
fn fmt(&self, f: &mut Formatter) -> fmt::Result {
match self {
Version::V1 => write!(f, "v1"),
Version::V2 => write!(f, "v2"),
}
}
}
impl Version {
fn to_bytes(&self) -> [u8; 4] {
(*self as u32).to_le_bytes()
fn to_bytes(self) -> [u8; 4] {
(self as u32).to_le_bytes()
}
fn try_from_bytes(bytes: [u8; 4]) -> Result<Version, InvalidData> {
let code = u32::from_le_bytes(bytes);
match code {
1u32 => Ok(Version::V1),
2u32 => Ok(Version::V2),
_ => Err(InvalidData),
}
}
@@ -47,9 +63,9 @@ mod tests {
use super::*;
#[test]
fn test_footer_dserialization() {
fn test_footer_deserialization() {
let parsed_version: Version = parse_footer(footer()).unwrap();
assert_eq!(Version::V1, parsed_version);
assert_eq!(Version::V2, parsed_version);
}
#[test]
@@ -63,11 +79,10 @@ mod tests {
for &i in &version_to_tests {
let version_res = Version::try_from_bytes(i.to_le_bytes());
if let Ok(version) = version_res {
assert_eq!(version, Version::V1);
assert_eq!(version.to_bytes(), i.to_le_bytes());
valid_versions.insert(i);
}
}
assert_eq!(valid_versions.len(), 1);
assert_eq!(valid_versions.len(), 2);
}
}

208
columnar/src/columnar/merge.rs
View File

@@ -1,208 +0,0 @@
use std::collections::HashMap;
use std::io;
use crate::columnar::ColumnarReader;
use crate::dynamic_column::DynamicColumn;
use crate::ColumnType;
pub enum MergeDocOrder {
/// Columnar tables are simply stacked one above the other.
/// If the i-th columnar_readers has n_rows_i rows, then
/// in the resulting columnar,
/// rows [r0..n_row_0) contains the row of columnar_readers[0], in ordder
/// rows [n_row_0..n_row_0 + n_row_1 contains the row of columnar_readers[1], in order.
/// ..
Stack,
/// Some more complex mapping, that can interleaves rows from the different readers and
/// possibly drop rows.
Complex(()),
}
pub fn merge_columnar(
_columnar_readers: &[ColumnarReader],
mapping: MergeDocOrder,
_output: &mut impl io::Write,
) -> io::Result<()> {
match mapping {
MergeDocOrder::Stack => {
// implement me :)
todo!();
}
MergeDocOrder::Complex(_) => {
// for later
todo!();
}
}
}
/// Column types are grouped into different categories.
/// After merge, all columns belonging to the same category are coerced to
/// the same column type.
///
/// In practise, today, only Numerical colummns are coerced into one type today.
///
/// See also [README.md].
#[derive(Copy, Clone, Eq, PartialEq, Hash, Debug)]
#[repr(u8)]
enum ColumnTypeCategory {
Bool,
Str,
Numerical,
DateTime,
Bytes,
IpAddr,
}
impl From<ColumnType> for ColumnTypeCategory {
fn from(column_type: ColumnType) -> Self {
match column_type {
ColumnType::I64 => ColumnTypeCategory::Numerical,
ColumnType::U64 => ColumnTypeCategory::Numerical,
ColumnType::F64 => ColumnTypeCategory::Numerical,
ColumnType::Bytes => ColumnTypeCategory::Bytes,
ColumnType::Str => ColumnTypeCategory::Str,
ColumnType::Bool => ColumnTypeCategory::Bool,
ColumnType::IpAddr => ColumnTypeCategory::IpAddr,
ColumnType::DateTime => ColumnTypeCategory::DateTime,
}
}
}
fn collect_columns(
columnar_readers: &[&ColumnarReader],
) -> io::Result<HashMap<String, HashMap<ColumnTypeCategory, Vec<DynamicColumn>>>> {
// Each column name may have multiple types of column associated.
// For merging we are interested in the same column type category since they can be merged.
let mut field_name_to_group: HashMap<String, HashMap<ColumnTypeCategory, Vec<DynamicColumn>>> =
HashMap::new();
for columnar_reader in columnar_readers {
let column_name_and_handle = columnar_reader.list_columns()?;
for (column_name, handle) in column_name_and_handle {
let column_type_to_handles = field_name_to_group
.entry(column_name.to_string())
.or_default();
let columns = column_type_to_handles
.entry(handle.column_type().into())
.or_default();
columns.push(handle.open()?);
}
}
normalize_columns(&mut field_name_to_group);
Ok(field_name_to_group)
}
/// Coerce numerical type columns to the same type
/// TODO rename to `coerce_columns`
fn normalize_columns(map: &mut HashMap<String, HashMap<ColumnTypeCategory, Vec<DynamicColumn>>>) {
for (_field_name, type_category_to_columns) in map.iter_mut() {
for (type_category, columns) in type_category_to_columns {
if type_category == &ColumnTypeCategory::Numerical {
let casted_columns = cast_to_common_numerical_column(&columns);
*columns = casted_columns;
}
}
}
}
/// Receives a list of columns of numerical types (u64, i64, f64)
///
/// Returns a list of `DynamicColumn` which are all of the same numerical type
fn cast_to_common_numerical_column(columns: &[DynamicColumn]) -> Vec<DynamicColumn> {
assert!(columns
.iter()
.all(|column| column.column_type().numerical_type().is_some()));
let coerce_to_i64: Vec<_> = columns
.iter()
.map(|column| column.clone().coerce_to_i64())
.collect();
if coerce_to_i64.iter().all(|column| column.is_some()) {
return coerce_to_i64
.into_iter()
.map(|column| column.unwrap())
.collect();
}
let coerce_to_u64: Vec<_> = columns
.iter()
.map(|column| column.clone().coerce_to_u64())
.collect();
if coerce_to_u64.iter().all(|column| column.is_some()) {
return coerce_to_u64
.into_iter()
.map(|column| column.unwrap())
.collect();
}
columns
.iter()
.map(|column| {
column
.clone()
.coerce_to_f64()
.expect("couldn't cast column to f64")
})
.collect()
}
#[cfg(test)]
mod tests {
use super::*;
use crate::ColumnarWriter;
#[test]
fn test_column_coercion() {
// i64 type
let columnar1 = {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_numerical(1u32, "numbers", 1i64);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(2, &mut buffer).unwrap();
ColumnarReader::open(buffer).unwrap()
};
// u64 type
let columnar2 = {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_numerical(1u32, "numbers", u64::MAX - 100);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(2, &mut buffer).unwrap();
ColumnarReader::open(buffer).unwrap()
};
// f64 type
let columnar3 = {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_numerical(1u32, "numbers", 30.5);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(2, &mut buffer).unwrap();
ColumnarReader::open(buffer).unwrap()
};
let column_map = collect_columns(&[&columnar1, &columnar2, &columnar3]).unwrap();
assert_eq!(column_map.len(), 1);
let cat_to_columns = column_map.get("numbers").unwrap();
assert_eq!(cat_to_columns.len(), 1);
let numerical = cat_to_columns.get(&ColumnTypeCategory::Numerical).unwrap();
assert!(numerical.iter().all(|column| column.is_f64()));
let column_map = collect_columns(&[&columnar1, &columnar1]).unwrap();
assert_eq!(column_map.len(), 1);
let cat_to_columns = column_map.get("numbers").unwrap();
assert_eq!(cat_to_columns.len(), 1);
let numerical = cat_to_columns.get(&ColumnTypeCategory::Numerical).unwrap();
assert!(numerical.iter().all(|column| column.is_i64()));
let column_map = collect_columns(&[&columnar2, &columnar2]).unwrap();
assert_eq!(column_map.len(), 1);
let cat_to_columns = column_map.get("numbers").unwrap();
assert_eq!(cat_to_columns.len(), 1);
let numerical = cat_to_columns.get(&ColumnTypeCategory::Numerical).unwrap();
assert!(numerical.iter().all(|column| column.is_u64()));
}
}

210
columnar/src/columnar/merge/merge_dict_column.rs Normal file
View File

@@ -0,0 +1,210 @@
use std::io::{self, Write};
use common::{BitSet, CountingWriter, ReadOnlyBitSet};
use sstable::{SSTable, Streamer, TermOrdinal, VoidSSTable};
use super::term_merger::TermMerger;
use crate::column::serialize_column_mappable_to_u64;
use crate::column_index::SerializableColumnIndex;
use crate::iterable::Iterable;
use crate::{BytesColumn, MergeRowOrder, ShuffleMergeOrder};
// Serialize [Dictionary, Column, dictionary num bytes U32::LE]
// Column: [Column Index, Column Values, column index num bytes U32::LE]
pub fn merge_bytes_or_str_column(
column_index: SerializableColumnIndex<'_>,
bytes_columns: &[Option<BytesColumn>],
merge_row_order: &MergeRowOrder,
output: &mut impl Write,
) -> io::Result<()> {
// Serialize dict and generate mapping for values
let mut output = CountingWriter::wrap(output);
// TODO !!! Remove useless terms.
let term_ord_mapping = serialize_merged_dict(bytes_columns, merge_row_order, &mut output)?;
let dictionary_num_bytes: u32 = output.written_bytes() as u32;
let output = output.finish();
let remapped_term_ordinals_values = RemappedTermOrdinalsValues {
bytes_columns,
term_ord_mapping: &term_ord_mapping,
merge_row_order,
};
serialize_column_mappable_to_u64(column_index, &remapped_term_ordinals_values, output)?;
output.write_all(&dictionary_num_bytes.to_le_bytes())?;
Ok(())
}
struct RemappedTermOrdinalsValues<'a> {
bytes_columns: &'a [Option<BytesColumn>],
term_ord_mapping: &'a TermOrdinalMapping,
merge_row_order: &'a MergeRowOrder,
}
impl Iterable for RemappedTermOrdinalsValues<'_> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u64> + '_> {
match self.merge_row_order {
MergeRowOrder::Stack(_) => self.boxed_iter_stacked(),
MergeRowOrder::Shuffled(shuffle_merge_order) => {
self.boxed_iter_shuffled(shuffle_merge_order)
}
}
}
}
impl RemappedTermOrdinalsValues<'_> {
fn boxed_iter_stacked(&self) -> Box<dyn Iterator<Item = u64> + '_> {
let iter = self
.bytes_columns
.iter()
.enumerate()
.flat_map(|(seg_ord, bytes_column_opt)| {
let bytes_column = bytes_column_opt.as_ref()?;
Some((seg_ord, bytes_column))
})
.flat_map(move |(seg_ord, bytes_column)| {
let term_ord_after_merge_mapping =
self.term_ord_mapping.get_segment(seg_ord as u32);
bytes_column
.ords()
.values
.iter()
.map(move |term_ord| term_ord_after_merge_mapping[term_ord as usize])
});
Box::new(iter)
}
fn boxed_iter_shuffled<'b>(
&'b self,
shuffle_merge_order: &'b ShuffleMergeOrder,
) -> Box<dyn Iterator<Item = u64> + 'b> {
Box::new(
shuffle_merge_order
.iter_new_to_old_row_addrs()
.flat_map(move |old_addr| {
let segment_ord = self.term_ord_mapping.get_segment(old_addr.segment_ord);
self.bytes_columns[old_addr.segment_ord as usize]
.as_ref()
.into_iter()
.flat_map(move |bytes_column| {
bytes_column
.term_ords(old_addr.row_id)
.map(|old_term_ord: u64| segment_ord[old_term_ord as usize])
})
}),
)
}
}
fn compute_term_bitset(column: &BytesColumn, row_bitset: &ReadOnlyBitSet) -> BitSet {
let num_terms = column.dictionary().num_terms();
let mut term_bitset = BitSet::with_max_value(num_terms as u32);
for row_id in row_bitset.iter() {
for term_ord in column.term_ord_column.values_for_doc(row_id) {
term_bitset.insert(term_ord as u32);
}
}
term_bitset
}
fn is_term_present(bitsets: &[Option<BitSet>], term_merger: &TermMerger) -> bool {
for (segment_ord, from_term_ord) in term_merger.matching_segments() {
if let Some(bitset) = bitsets[segment_ord].as_ref() {
if bitset.contains(from_term_ord as u32) {
return true;
}
} else {
return true;
}
}
false
}
fn serialize_merged_dict(
bytes_columns: &[Option<BytesColumn>],
merge_row_order: &MergeRowOrder,
output: &mut impl Write,
) -> io::Result<TermOrdinalMapping> {
let mut term_ord_mapping = TermOrdinalMapping::default();
let mut field_term_streams = Vec::new();
for column_opt in bytes_columns.iter() {
if let Some(column) = column_opt {
term_ord_mapping.add_segment(column.dictionary.num_terms());
let terms: Streamer<VoidSSTable> = column.dictionary.stream()?;
field_term_streams.push(terms);
} else {
term_ord_mapping.add_segment(0);
field_term_streams.push(Streamer::empty());
}
}
let mut merged_terms = TermMerger::new(field_term_streams);
let mut sstable_builder = sstable::VoidSSTable::writer(output);
match merge_row_order {
MergeRowOrder::Stack(_) => {
let mut current_term_ord = 0;
while merged_terms.advance() {
let term_bytes: &[u8] = merged_terms.key();
sstable_builder.insert(term_bytes, &())?;
for (segment_ord, from_term_ord) in merged_terms.matching_segments() {
term_ord_mapping.register_from_to(segment_ord, from_term_ord, current_term_ord);
}
current_term_ord += 1;
}
sstable_builder.finish()?;
}
MergeRowOrder::Shuffled(shuffle_merge_order) => {
assert_eq!(shuffle_merge_order.alive_bitsets.len(), bytes_columns.len());
let mut term_bitsets: Vec<Option<BitSet>> = Vec::with_capacity(bytes_columns.len());
for (alive_bitset_opt, bytes_column_opt) in shuffle_merge_order
.alive_bitsets
.iter()
.zip(bytes_columns.iter())
{
match (alive_bitset_opt, bytes_column_opt) {
(Some(alive_bitset), Some(bytes_column)) => {
let term_bitset = compute_term_bitset(bytes_column, alive_bitset);
term_bitsets.push(Some(term_bitset));
}
_ => {
term_bitsets.push(None);
}
}
}
let mut current_term_ord = 0;
while merged_terms.advance() {
let term_bytes: &[u8] = merged_terms.key();
if !is_term_present(&term_bitsets[..], &merged_terms) {
continue;
}
sstable_builder.insert(term_bytes, &())?;
for (segment_ord, from_term_ord) in merged_terms.matching_segments() {
term_ord_mapping.register_from_to(segment_ord, from_term_ord, current_term_ord);
}
current_term_ord += 1;
}
sstable_builder.finish()?;
}
}
Ok(term_ord_mapping)
}
#[derive(Default, Debug)]
struct TermOrdinalMapping {
per_segment_new_term_ordinals: Vec<Vec<TermOrdinal>>,
}
impl TermOrdinalMapping {
fn add_segment(&mut self, max_term_ord: usize) {
self.per_segment_new_term_ordinals
.push(vec![TermOrdinal::default(); max_term_ord]);
}
fn register_from_to(&mut self, segment_ord: usize, from_ord: TermOrdinal, to_ord: TermOrdinal) {
self.per_segment_new_term_ordinals[segment_ord][from_ord as usize] = to_ord;
}
fn get_segment(&self, segment_ord: u32) -> &[TermOrdinal] {
&(self.per_segment_new_term_ordinals[segment_ord as usize])[..]
}
}

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use std::ops::Range;
use common::{BitSet, OwnedBytes, ReadOnlyBitSet};
use crate::{ColumnarReader, RowAddr, RowId};
pub struct StackMergeOrder {
// This does not start at 0. The first row is the number of
// rows in the first columnar.
cumulated_row_ids: Vec<RowId>,
}
impl StackMergeOrder {
#[cfg(test)]
pub fn stack_for_test(num_rows_per_columnar: &[u32]) -> StackMergeOrder {
let mut cumulated_row_ids: Vec<RowId> = Vec::with_capacity(num_rows_per_columnar.len());
let mut cumulated_row_id = 0;
for &num_rows in num_rows_per_columnar {
cumulated_row_id += num_rows;
cumulated_row_ids.push(cumulated_row_id);
}
StackMergeOrder { cumulated_row_ids }
}
pub fn stack(columnars: &[&ColumnarReader]) -> StackMergeOrder {
let mut cumulated_row_ids: Vec<RowId> = Vec::with_capacity(columnars.len());
let mut cumulated_row_id = 0;
for columnar in columnars {
cumulated_row_id += columnar.num_rows();
cumulated_row_ids.push(cumulated_row_id);
}
StackMergeOrder { cumulated_row_ids }
}
pub fn num_rows(&self) -> RowId {
self.cumulated_row_ids.last().copied().unwrap_or(0)
}
pub fn offset(&self, columnar_id: usize) -> RowId {
if columnar_id == 0 {
return 0;
}
self.cumulated_row_ids[columnar_id - 1]
}
pub fn columnar_range(&self, columnar_id: usize) -> Range<RowId> {
self.offset(columnar_id)..self.offset(columnar_id + 1)
}
}
pub enum MergeRowOrder {
/// Columnar tables are simply stacked one above the other.
/// If the i-th columnar_readers has n_rows_i rows, then
/// in the resulting columnar,
/// rows [r0..n_row_0) contains the row of `columnar_readers[0]`, in ordder
/// rows [n_row_0..n_row_0 + n_row_1 contains the row of `columnar_readers[1]`, in order.
/// ..
/// No documents is deleted.
Stack(StackMergeOrder),
/// Some more complex mapping, that may interleaves rows from the different readers and
/// drop rows, or do both.
Shuffled(ShuffleMergeOrder),
}
impl From<StackMergeOrder> for MergeRowOrder {
fn from(stack_merge_order: StackMergeOrder) -> MergeRowOrder {
MergeRowOrder::Stack(stack_merge_order)
}
}
impl From<ShuffleMergeOrder> for MergeRowOrder {
fn from(shuffle_merge_order: ShuffleMergeOrder) -> MergeRowOrder {
MergeRowOrder::Shuffled(shuffle_merge_order)
}
}
impl MergeRowOrder {
pub fn num_rows(&self) -> RowId {
match self {
MergeRowOrder::Stack(stack_row_order) => stack_row_order.num_rows(),
MergeRowOrder::Shuffled(complex_mapping) => complex_mapping.num_rows(),
}
}
}
pub struct ShuffleMergeOrder {
pub new_row_id_to_old_row_id: Vec<RowAddr>,
pub alive_bitsets: Vec<Option<ReadOnlyBitSet>>,
}
impl ShuffleMergeOrder {
pub fn for_test(
segment_num_rows: &[RowId],
new_row_id_to_old_row_id: Vec<RowAddr>,
) -> ShuffleMergeOrder {
let mut alive_bitsets: Vec<BitSet> = segment_num_rows
.iter()
.map(|&num_rows| BitSet::with_max_value(num_rows))
.collect();
for &RowAddr {
segment_ord,
row_id,
} in &new_row_id_to_old_row_id
{
alive_bitsets[segment_ord as usize].insert(row_id);
}
let alive_bitsets: Vec<Option<ReadOnlyBitSet>> = alive_bitsets
.into_iter()
.map(|alive_bitset| {
let mut buffer = Vec::new();
alive_bitset.serialize(&mut buffer).unwrap();
let data = OwnedBytes::new(buffer);
Some(ReadOnlyBitSet::open(data))
})
.collect();
ShuffleMergeOrder {
new_row_id_to_old_row_id,
alive_bitsets,
}
}
pub fn num_rows(&self) -> RowId {
self.new_row_id_to_old_row_id.len() as RowId
}
pub fn iter_new_to_old_row_addrs(&self) -> impl Iterator<Item = RowAddr> + '_ {
self.new_row_id_to_old_row_id.iter().copied()
}
}

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mod merge_dict_column;
mod merge_mapping;
mod term_merger;
use std::collections::{BTreeMap, HashSet};
use std::io;
use std::net::Ipv6Addr;
use std::sync::Arc;
pub use merge_mapping::{MergeRowOrder, ShuffleMergeOrder, StackMergeOrder};
use super::writer::ColumnarSerializer;
use crate::column::{serialize_column_mappable_to_u128, serialize_column_mappable_to_u64};
use crate::column_values::MergedColumnValues;
use crate::columnar::merge::merge_dict_column::merge_bytes_or_str_column;
use crate::columnar::writer::CompatibleNumericalTypes;
use crate::columnar::ColumnarReader;
use crate::dynamic_column::DynamicColumn;
use crate::{
BytesColumn, Column, ColumnIndex, ColumnType, ColumnValues, DynamicColumnHandle, NumericalType,
NumericalValue,
};
/// Column types are grouped into different categories.
/// After merge, all columns belonging to the same category are coerced to
/// the same column type.
///
/// In practise, today, only Numerical columns are coerced into one type today.
///
/// See also [README.md].
///
/// The ordering has to match the ordering of the variants in [ColumnType].
#[derive(Copy, Clone, Eq, PartialOrd, Ord, PartialEq, Hash, Debug)]
pub(crate) enum ColumnTypeCategory {
Numerical,
Bytes,
Str,
Bool,
IpAddr,
DateTime,
}
impl From<ColumnType> for ColumnTypeCategory {
fn from(column_type: ColumnType) -> Self {
match column_type {
ColumnType::I64 => ColumnTypeCategory::Numerical,
ColumnType::U64 => ColumnTypeCategory::Numerical,
ColumnType::F64 => ColumnTypeCategory::Numerical,
ColumnType::Bytes => ColumnTypeCategory::Bytes,
ColumnType::Str => ColumnTypeCategory::Str,
ColumnType::Bool => ColumnTypeCategory::Bool,
ColumnType::IpAddr => ColumnTypeCategory::IpAddr,
ColumnType::DateTime => ColumnTypeCategory::DateTime,
}
}
}
/// Merge several columnar table together.
///
/// If several columns with the same name are conflicting with the numerical types in the
/// input columnars, the first type compatible out of i64, u64, f64 in that order will be used.
///
/// `require_columns` makes it possible to ensure that some columns will be present in the
/// resulting columnar. When a required column is a numerical column type, one of two things can
/// happen:
/// - If the required column type is compatible with all of the input columnar, the resulting merged
/// columnar will simply coerce the input column and use the required column type.
/// - If the required column type is incompatible with one of the input columnar, the merged will
/// fail with an InvalidData error.
///
/// `merge_row_order` makes it possible to remove or reorder row in the resulting
/// `Columnar` table.
///
/// Reminder: a string and a numerical column may bare the same column name. This is not
/// considered a conflict.
pub fn merge_columnar(
columnar_readers: &[&ColumnarReader],
required_columns: &[(String, ColumnType)],
merge_row_order: MergeRowOrder,
output: &mut impl io::Write,
) -> io::Result<()> {
let mut serializer = ColumnarSerializer::new(output);
let num_rows_per_columnar = columnar_readers
.iter()
.map(|reader| reader.num_rows())
.collect::<Vec<u32>>();
let columns_to_merge =
group_columns_for_merge(columnar_readers, required_columns, &merge_row_order)?;
for res in columns_to_merge {
let ((column_name, _column_type_category), grouped_columns) = res;
let grouped_columns = grouped_columns.open(&merge_row_order)?;
if grouped_columns.is_empty() {
continue;
}
let column_type = grouped_columns.column_type_after_merge();
let mut columns = grouped_columns.columns;
coerce_columns(column_type, &mut columns)?;
let mut column_serializer =
serializer.start_serialize_column(column_name.as_bytes(), column_type);
merge_column(
column_type,
&num_rows_per_columnar,
columns,
&merge_row_order,
&mut column_serializer,
)?;
column_serializer.finalize()?;
}
serializer.finalize(merge_row_order.num_rows())?;
Ok(())
}
fn dynamic_column_to_u64_monotonic(dynamic_column: DynamicColumn) -> Option<Column<u64>> {
match dynamic_column {
DynamicColumn::Bool(column) => Some(column.to_u64_monotonic()),
DynamicColumn::I64(column) => Some(column.to_u64_monotonic()),
DynamicColumn::U64(column) => Some(column.to_u64_monotonic()),
DynamicColumn::F64(column) => Some(column.to_u64_monotonic()),
DynamicColumn::DateTime(column) => Some(column.to_u64_monotonic()),
DynamicColumn::IpAddr(_) | DynamicColumn::Bytes(_) | DynamicColumn::Str(_) => None,
}
}
fn merge_column(
column_type: ColumnType,
num_docs_per_column: &[u32],
columns: Vec<Option<DynamicColumn>>,
merge_row_order: &MergeRowOrder,
wrt: &mut impl io::Write,
) -> io::Result<()> {
match column_type {
ColumnType::I64
| ColumnType::U64
| ColumnType::F64
| ColumnType::DateTime
| ColumnType::Bool => {
let mut column_indexes: Vec<ColumnIndex> = Vec::with_capacity(columns.len());
let mut column_values: Vec<Option<Arc<dyn ColumnValues>>> =
Vec::with_capacity(columns.len());
for (i, dynamic_column_opt) in columns.into_iter().enumerate() {
if let Some(Column { index: idx, values }) =
dynamic_column_opt.and_then(dynamic_column_to_u64_monotonic)
{
column_indexes.push(idx);
column_values.push(Some(values));
} else {
column_indexes.push(ColumnIndex::Empty {
num_docs: num_docs_per_column[i],
});
column_values.push(None);
}
}
let merged_column_index =
crate::column_index::merge_column_index(&column_indexes[..], merge_row_order);
let merge_column_values = MergedColumnValues {
column_indexes: &column_indexes[..],
column_values: &column_values[..],
merge_row_order,
};
serialize_column_mappable_to_u64(merged_column_index, &merge_column_values, wrt)?;
}
ColumnType::IpAddr => {
let mut column_indexes: Vec<ColumnIndex> = Vec::with_capacity(columns.len());
let mut column_values: Vec<Option<Arc<dyn ColumnValues<Ipv6Addr>>>> =
Vec::with_capacity(columns.len());
for (i, dynamic_column_opt) in columns.into_iter().enumerate() {
if let Some(DynamicColumn::IpAddr(Column { index: idx, values })) =
dynamic_column_opt
{
column_indexes.push(idx);
column_values.push(Some(values));
} else {
column_indexes.push(ColumnIndex::Empty {
num_docs: num_docs_per_column[i],
});
column_values.push(None);
}
}
let merged_column_index =
crate::column_index::merge_column_index(&column_indexes[..], merge_row_order);
let merge_column_values = MergedColumnValues {
column_indexes: &column_indexes[..],
column_values: &column_values,
merge_row_order,
};
serialize_column_mappable_to_u128(merged_column_index, &merge_column_values, wrt)?;
}
ColumnType::Bytes | ColumnType::Str => {
let mut column_indexes: Vec<ColumnIndex> = Vec::with_capacity(columns.len());
let mut bytes_columns: Vec<Option<BytesColumn>> = Vec::with_capacity(columns.len());
for (i, dynamic_column_opt) in columns.into_iter().enumerate() {
match dynamic_column_opt {
Some(DynamicColumn::Str(str_column)) => {
column_indexes.push(str_column.term_ord_column.index.clone());
bytes_columns.push(Some(str_column.into()));
}
Some(DynamicColumn::Bytes(bytes_column)) => {
column_indexes.push(bytes_column.term_ord_column.index.clone());
bytes_columns.push(Some(bytes_column));
}
_ => {
column_indexes.push(ColumnIndex::Empty {
num_docs: num_docs_per_column[i],
});
bytes_columns.push(None);
}
}
}
let merged_column_index =
crate::column_index::merge_column_index(&column_indexes[..], merge_row_order);
merge_bytes_or_str_column(merged_column_index, &bytes_columns, merge_row_order, wrt)?;
}
}
Ok(())
}
struct GroupedColumns {
required_column_type: Option<ColumnType>,
columns: Vec<Option<DynamicColumn>>,
}
impl GroupedColumns {
/// Check is column group can be skipped during serialization.
fn is_empty(&self) -> bool {
self.required_column_type.is_none() && self.columns.iter().all(Option::is_none)
}
/// Returns the column type after merge.
///
/// This method does not check if the column types can actually be coerced to
/// this type.
fn column_type_after_merge(&self) -> ColumnType {
if let Some(required_type) = self.required_column_type {
return required_type;
}
let column_type: HashSet<ColumnType> = self
.columns
.iter()
.flatten()
.map(|column| column.column_type())
.collect();
if column_type.len() == 1 {
return column_type.into_iter().next().unwrap();
}
// At the moment, only the numerical categorical column type has more than one possible
// column type.
assert!(self
.columns
.iter()
.flatten()
.all(|el| ColumnTypeCategory::from(el.column_type()) == ColumnTypeCategory::Numerical));
merged_numerical_columns_type(self.columns.iter().flatten()).into()
}
}
struct GroupedColumnsHandle {
required_column_type: Option<ColumnType>,
columns: Vec<Option<DynamicColumnHandle>>,
}
impl GroupedColumnsHandle {
fn new(num_columnars: usize) -> Self {
GroupedColumnsHandle {
required_column_type: None,
columns: vec![None; num_columnars],
}
}
fn open(self, merge_row_order: &MergeRowOrder) -> io::Result<GroupedColumns> {
let mut columns: Vec<Option<DynamicColumn>> = Vec::new();
for (columnar_id, column) in self.columns.iter().enumerate() {
if let Some(column) = column {
let column = column.open()?;
// We skip columns that end up with 0 documents.
// That way, we make sure they don't end up influencing the merge type or
// creating empty columns.
if is_empty_after_merge(merge_row_order, &column, columnar_id) {
columns.push(None);
} else {
columns.push(Some(column));
}
} else {
columns.push(None);
}
}
Ok(GroupedColumns {
required_column_type: self.required_column_type,
columns,
})
}
/// Set the dynamic column for a given columnar.
fn set_column(&mut self, columnar_id: usize, column: DynamicColumnHandle) {
self.columns[columnar_id] = Some(column);
}
/// Force the existence of a column, as well as its type.
fn require_type(&mut self, required_type: ColumnType) -> io::Result<()> {
if let Some(existing_required_type) = self.required_column_type {
if existing_required_type == required_type {
// This was just a duplicate in the `required_columns`.
// Nothing to do.
return Ok(());
} else {
return Err(io::Error::new(
io::ErrorKind::InvalidInput,
"Required column conflicts with another required column of the same type \
category.",
));
}
}
self.required_column_type = Some(required_type);
Ok(())
}
}
/// Returns the type of the merged numerical column.
///
/// This function picks the first numerical type out of i64, u64, f64 (order matters
/// here), that is compatible with all the `columns`.
///
/// # Panics
/// Panics if one of the column is not numerical.
fn merged_numerical_columns_type<'a>(
columns: impl Iterator<Item = &'a DynamicColumn>,
) -> NumericalType {
let mut compatible_numerical_types = CompatibleNumericalTypes::default();
for column in columns {
let (min_value, max_value) =
min_max_if_numerical(column).expect("All columns re required to be numerical");
compatible_numerical_types.accept_value(min_value);
compatible_numerical_types.accept_value(max_value);
}
compatible_numerical_types.to_numerical_type()
}
fn is_empty_after_merge(
merge_row_order: &MergeRowOrder,
column: &DynamicColumn,
columnar_ord: usize,
) -> bool {
if column.num_values() == 0u32 {
// It was empty before the merge.
return true;
}
match merge_row_order {
MergeRowOrder::Stack(_) => {
// If we are stacking the columnar, no rows are being deleted.
false
}
MergeRowOrder::Shuffled(shuffled) => {
if let Some(alive_bitset) = &shuffled.alive_bitsets[columnar_ord] {
let column_index = column.column_index();
match column_index {
ColumnIndex::Empty { .. } => true,
ColumnIndex::Full => alive_bitset.len() == 0,
ColumnIndex::Optional(optional_index) => {
for doc in optional_index.iter_rows() {
if alive_bitset.contains(doc) {
return false;
}
}
true
}
ColumnIndex::Multivalued(multivalued_index) => {
for alive_docid in alive_bitset.iter() {
if !multivalued_index.range(alive_docid).is_empty() {
return false;
}
}
true
}
}
} else {
// No document is being deleted.
// The shuffle is applying a permutation.
false
}
}
}
}
/// Iterates over the columns of the columnar readers, grouped by column name.
/// Key functionality is that `open` of the Columns is done lazy per group.
fn group_columns_for_merge<'a>(
columnar_readers: &'a [&'a ColumnarReader],
required_columns: &'a [(String, ColumnType)],
_merge_row_order: &'a MergeRowOrder,
) -> io::Result<BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle>> {
let mut columns: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> = BTreeMap::new();
for &(ref column_name, column_type) in required_columns {
columns
.entry((column_name.clone(), column_type.into()))
.or_insert_with(|| GroupedColumnsHandle::new(columnar_readers.len()))
.require_type(column_type)?;
}
for (columnar_id, columnar_reader) in columnar_readers.iter().enumerate() {
let column_name_and_handle = columnar_reader.iter_columns()?;
for (column_name, handle) in column_name_and_handle {
let column_category: ColumnTypeCategory = handle.column_type().into();
columns
.entry((column_name, column_category))
.or_insert_with(|| GroupedColumnsHandle::new(columnar_readers.len()))
.set_column(columnar_id, handle);
}
}
Ok(columns)
}
fn coerce_columns(
column_type: ColumnType,
columns: &mut [Option<DynamicColumn>],
) -> io::Result<()> {
for column_opt in columns.iter_mut() {
if let Some(column) = column_opt.take() {
*column_opt = Some(coerce_column(column_type, column)?);
}
}
Ok(())
}
fn coerce_column(column_type: ColumnType, column: DynamicColumn) -> io::Result<DynamicColumn> {
if let Some(numerical_type) = column_type.numerical_type() {
column
.coerce_numerical(numerical_type)
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidInput, ""))
} else {
if column.column_type() != column_type {
return Err(io::Error::new(
io::ErrorKind::InvalidInput,
format!(
"Cannot coerce column of type `{:?}` to `{column_type:?}`",
column.column_type()
),
));
}
Ok(column)
}
}
/// Returns the (min, max) of a column provided it is numerical (i64, u64. f64).
///
/// The min and the max are simply the numerical value as defined by `ColumnValue::min_value()`,
/// and `ColumnValue::max_value()`.
///
/// It is important to note that these values are only guaranteed to be lower/upper bound
/// (as opposed to min/max value).
/// If a column is empty, the min and max values are currently set to 0.
fn min_max_if_numerical(column: &DynamicColumn) -> Option<(NumericalValue, NumericalValue)> {
match column {
DynamicColumn::I64(column) => Some((column.min_value().into(), column.max_value().into())),
DynamicColumn::U64(column) => Some((column.min_value().into(), column.max_value().into())),
DynamicColumn::F64(column) => Some((column.min_value().into(), column.max_value().into())),
DynamicColumn::Bool(_)
| DynamicColumn::IpAddr(_)
| DynamicColumn::DateTime(_)
| DynamicColumn::Bytes(_)
| DynamicColumn::Str(_) => None,
}
}
#[cfg(test)]
mod tests;

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use std::cmp::Ordering;
use std::collections::BinaryHeap;
use sstable::TermOrdinal;
use crate::Streamer;
pub struct HeapItem<'a> {
pub streamer: Streamer<'a>,
pub segment_ord: usize,
}
impl PartialEq for HeapItem<'_> {
fn eq(&self, other: &Self) -> bool {
self.segment_ord == other.segment_ord
}
}
impl Eq for HeapItem<'_> {}
impl<'a> PartialOrd for HeapItem<'a> {
fn partial_cmp(&self, other: &HeapItem<'a>) -> Option<Ordering> {
Some(self.cmp(other))
}
}
impl<'a> Ord for HeapItem<'a> {
fn cmp(&self, other: &HeapItem<'a>) -> Ordering {
(&other.streamer.key(), &other.segment_ord).cmp(&(&self.streamer.key(), &self.segment_ord))
}
}
/// Given a list of sorted term streams,
/// returns an iterator over sorted unique terms.
///
/// The item yield is actually a pair with
/// - the term
/// - a slice with the ordinal of the segments containing the terms.
pub struct TermMerger<'a> {
heap: BinaryHeap<HeapItem<'a>>,
current_streamers: Vec<HeapItem<'a>>,
}
impl<'a> TermMerger<'a> {
/// Stream of merged term dictionary
pub fn new(streams: Vec<Streamer<'a>>) -> TermMerger<'a> {
TermMerger {
heap: BinaryHeap::new(),
current_streamers: streams
.into_iter()
.enumerate()
.map(|(ord, streamer)| HeapItem {
streamer,
segment_ord: ord,
})
.collect(),
}
}
pub(crate) fn matching_segments<'b: 'a>(
&'b self,
) -> impl 'b + Iterator<Item = (usize, TermOrdinal)> {
self.current_streamers
.iter()
.map(|heap_item| (heap_item.segment_ord, heap_item.streamer.term_ord()))
}
fn advance_segments(&mut self) {
let streamers = &mut self.current_streamers;
let heap = &mut self.heap;
for mut heap_item in streamers.drain(..) {
if heap_item.streamer.advance() {
heap.push(heap_item);
}
}
}
/// Advance the term iterator to the next term.
/// Returns true if there is indeed another term
/// False if there is none.
pub fn advance(&mut self) -> bool {
self.advance_segments();
if let Some(head) = self.heap.pop() {
self.current_streamers.push(head);
while let Some(next_streamer) = self.heap.peek() {
if self.current_streamers[0].streamer.key() != next_streamer.streamer.key() {
break;
}
let next_heap_it = self.heap.pop().unwrap(); // safe : we peeked beforehand
self.current_streamers.push(next_heap_it);
}
true
} else {
false
}
}
/// Returns the current term.
///
/// This method may be called
/// if and only if advance() has been called before
/// and "true" was returned.
pub fn key(&self) -> &[u8] {
self.current_streamers[0].streamer.key()
}
}

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use itertools::Itertools;
use super::*;
use crate::{Cardinality, ColumnarWriter, HasAssociatedColumnType, RowId};
fn make_columnar<T: Into<NumericalValue> + HasAssociatedColumnType + Copy>(
column_name: &str,
vals: &[T],
) -> ColumnarReader {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_column_type(column_name, T::column_type(), false);
for (row_id, val) in vals.iter().copied().enumerate() {
dataframe_writer.record_numerical(row_id as RowId, column_name, val.into());
}
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer
.serialize(vals.len() as RowId, &mut buffer)
.unwrap();
ColumnarReader::open(buffer).unwrap()
}
#[test]
fn test_column_coercion_to_u64() {
// i64 type
let columnar1 = make_columnar("numbers", &[1i64]);
// u64 type
let columnar2 = make_columnar("numbers", &[u64::MAX]);
let columnars = &[&columnar1, &columnar2];
let merge_order = StackMergeOrder::stack(columnars).into();
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
group_columns_for_merge(columnars, &[], &merge_order).unwrap();
assert_eq!(column_map.len(), 1);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
}
#[test]
fn test_column_coercion_to_i64() {
let columnar1 = make_columnar("numbers", &[-1i64]);
let columnar2 = make_columnar("numbers", &[2u64]);
let columnars = &[&columnar1, &columnar2];
let merge_order = StackMergeOrder::stack(columnars).into();
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
group_columns_for_merge(columnars, &[], &merge_order).unwrap();
assert_eq!(column_map.len(), 1);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
}
//#[test]
// fn test_impossible_coercion_returns_an_error() {
// let columnar1 = make_columnar("numbers", &[u64::MAX]);
// let merge_order = StackMergeOrder::stack(&[&columnar1]).into();
// let group_error = group_columns_for_merge_iter(
//&[&columnar1],
//&[("numbers".to_string(), ColumnType::I64)],
//&merge_order,
//)
//.unwrap_err();
// assert_eq!(group_error.kind(), io::ErrorKind::InvalidInput);
//}
#[test]
fn test_group_columns_with_required_column() {
let columnar1 = make_columnar("numbers", &[1i64]);
let columnar2 = make_columnar("numbers", &[2u64]);
let columnars = &[&columnar1, &columnar2];
let merge_order = StackMergeOrder::stack(columnars).into();
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
group_columns_for_merge(
&[&columnar1, &columnar2],
&[("numbers".to_string(), ColumnType::U64)],
&merge_order,
)
.unwrap();
assert_eq!(column_map.len(), 1);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
}
#[test]
fn test_group_columns_required_column_with_no_existing_columns() {
let columnar1 = make_columnar("numbers", &[2u64]);
let columnar2 = make_columnar("numbers", &[2u64]);
let columnars = &[&columnar1, &columnar2];
let merge_order = StackMergeOrder::stack(columnars).into();
let column_map: BTreeMap<_, _> = group_columns_for_merge(
columnars,
&[("required_col".to_string(), ColumnType::Str)],
&merge_order,
)
.unwrap();
assert_eq!(column_map.len(), 2);
let columns = &column_map
.get(&("required_col".to_string(), ColumnTypeCategory::Str))
.unwrap()
.columns;
assert_eq!(columns.len(), 2);
assert!(columns[0].is_none());
assert!(columns[1].is_none());
}
#[test]
fn test_group_columns_required_column_is_above_all_columns_have_the_same_type_rule() {
let columnar1 = make_columnar("numbers", &[2i64]);
let columnar2 = make_columnar("numbers", &[2i64]);
let columnars = &[&columnar1, &columnar2];
let merge_order = StackMergeOrder::stack(columnars).into();
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
group_columns_for_merge(
columnars,
&[("numbers".to_string(), ColumnType::U64)],
&merge_order,
)
.unwrap();
assert_eq!(column_map.len(), 1);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
}
#[test]
fn test_missing_column() {
let columnar1 = make_columnar("numbers", &[-1i64]);
let columnar2 = make_columnar("numbers2", &[2u64]);
let columnars = &[&columnar1, &columnar2];
let merge_order = StackMergeOrder::stack(columnars).into();
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
group_columns_for_merge(columnars, &[], &merge_order).unwrap();
assert_eq!(column_map.len(), 2);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
{
let columns = &column_map
.get(&("numbers".to_string(), ColumnTypeCategory::Numerical))
.unwrap()
.columns;
assert!(columns[0].is_some());
assert!(columns[1].is_none());
}
{
let columns = &column_map
.get(&("numbers2".to_string(), ColumnTypeCategory::Numerical))
.unwrap()
.columns;
assert!(columns[0].is_none());
assert!(columns[1].is_some());
}
}
fn make_numerical_columnar_multiple_columns(
columns: &[(&str, &[&[NumericalValue]])],
) -> ColumnarReader {
let mut dataframe_writer = ColumnarWriter::default();
for (column_name, column_values) in columns {
for (row_id, vals) in column_values.iter().enumerate() {
for val in vals.iter() {
dataframe_writer.record_numerical(row_id as u32, column_name, *val);
}
}
}
let num_rows = columns
.iter()
.map(|(_, val_rows)| val_rows.len() as RowId)
.max()
.unwrap_or(0u32);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(num_rows, &mut buffer).unwrap();
ColumnarReader::open(buffer).unwrap()
}
#[track_caller]
fn make_byte_columnar_multiple_columns(
columns: &[(&str, &[&[&[u8]]])],
num_rows: u32,
) -> ColumnarReader {
let mut dataframe_writer = ColumnarWriter::default();
for (column_name, column_values) in columns {
assert_eq!(
column_values.len(),
num_rows as usize,
"All columns must have `{num_rows}` rows"
);
for (row_id, vals) in column_values.iter().enumerate() {
for val in vals.iter() {
dataframe_writer.record_bytes(row_id as u32, column_name, val);
}
}
}
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(num_rows, &mut buffer).unwrap();
ColumnarReader::open(buffer).unwrap()
}
fn make_text_columnar_multiple_columns(columns: &[(&str, &[&[&str]])]) -> ColumnarReader {
let mut dataframe_writer = ColumnarWriter::default();
for (column_name, column_values) in columns {
for (row_id, vals) in column_values.iter().enumerate() {
for val in vals.iter() {
dataframe_writer.record_str(row_id as u32, column_name, val);
}
}
}
let num_rows = columns
.iter()
.map(|(_, val_rows)| val_rows.len() as RowId)
.max()
.unwrap_or(0u32);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(num_rows, &mut buffer).unwrap();
ColumnarReader::open(buffer).unwrap()
}
#[test]
fn test_merge_columnar_numbers() {
let columnar1 =
make_numerical_columnar_multiple_columns(&[("numbers", &[&[NumericalValue::from(-1f64)]])]);
let columnar2 = make_numerical_columnar_multiple_columns(&[(
"numbers",
&[&[], &[NumericalValue::from(-3f64)]],
)]);
let mut buffer = Vec::new();
let columnars = &[&columnar1, &columnar2];
let stack_merge_order = StackMergeOrder::stack(columnars);
crate::columnar::merge_columnar(
columnars,
&[],
MergeRowOrder::Stack(stack_merge_order),
&mut buffer,
)
.unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_rows(), 3);
assert_eq!(columnar_reader.num_columns(), 1);
let cols = columnar_reader.read_columns("numbers").unwrap();
let dynamic_column = cols[0].open().unwrap();
let DynamicColumn::F64(vals) = dynamic_column else {
panic!()
};
assert_eq!(vals.get_cardinality(), Cardinality::Optional);
assert_eq!(vals.first(0u32), Some(-1f64));
assert_eq!(vals.first(1u32), None);
assert_eq!(vals.first(2u32), Some(-3f64));
}
#[test]
fn test_merge_columnar_texts() {
let columnar1 = make_text_columnar_multiple_columns(&[("texts", &[&["a"]])]);
let columnar2 = make_text_columnar_multiple_columns(&[("texts", &[&[], &["b"]])]);
let mut buffer = Vec::new();
let columnars = &[&columnar1, &columnar2];
let stack_merge_order = StackMergeOrder::stack(columnars);
crate::columnar::merge_columnar(
columnars,
&[],
MergeRowOrder::Stack(stack_merge_order),
&mut buffer,
)
.unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_rows(), 3);
assert_eq!(columnar_reader.num_columns(), 1);
let cols = columnar_reader.read_columns("texts").unwrap();
let dynamic_column = cols[0].open().unwrap();
let DynamicColumn::Str(vals) = dynamic_column else {
panic!()
};
assert_eq!(vals.ords().get_cardinality(), Cardinality::Optional);
let get_str_for_ord = |ord| {
let mut out = String::new();
vals.ord_to_str(ord, &mut out).unwrap();
out
};
assert_eq!(vals.dictionary.num_terms(), 2);
assert_eq!(get_str_for_ord(0), "a");
assert_eq!(get_str_for_ord(1), "b");
let get_str_for_row = |row_id| {
let term_ords: Vec<u64> = vals.term_ords(row_id).collect();
assert!(term_ords.len() <= 1);
let mut out = String::new();
if term_ords.len() == 1 {
vals.ord_to_str(term_ords[0], &mut out).unwrap();
}
out
};
assert_eq!(get_str_for_row(0), "a");
assert_eq!(get_str_for_row(1), "");
assert_eq!(get_str_for_row(2), "b");
}
#[test]
fn test_merge_columnar_byte() {
let columnar1 = make_byte_columnar_multiple_columns(&[("bytes", &[&[b"bbbb"], &[b"baaa"]])], 2);
let columnar2 = make_byte_columnar_multiple_columns(&[("bytes", &[&[], &[b"a"]])], 2);
let mut buffer = Vec::new();
let columnars = &[&columnar1, &columnar2];
let stack_merge_order = StackMergeOrder::stack(columnars);
crate::columnar::merge_columnar(
columnars,
&[],
MergeRowOrder::Stack(stack_merge_order),
&mut buffer,
)
.unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_rows(), 4);
assert_eq!(columnar_reader.num_columns(), 1);
let cols = columnar_reader.read_columns("bytes").unwrap();
let dynamic_column = cols[0].open().unwrap();
let DynamicColumn::Bytes(vals) = dynamic_column else {
panic!()
};
let get_bytes_for_ord = |ord| {
let mut out = Vec::new();
vals.ord_to_bytes(ord, &mut out).unwrap();
out
};
assert_eq!(vals.dictionary.num_terms(), 3);
assert_eq!(get_bytes_for_ord(0), b"a");
assert_eq!(get_bytes_for_ord(1), b"baaa");
assert_eq!(get_bytes_for_ord(2), b"bbbb");
let get_bytes_for_row = |row_id| {
let term_ords: Vec<u64> = vals.term_ords(row_id).collect();
assert!(term_ords.len() <= 1);
let mut out = Vec::new();
if term_ords.len() == 1 {
vals.ord_to_bytes(term_ords[0], &mut out).unwrap();
}
out
};
assert_eq!(get_bytes_for_row(0), b"bbbb");
assert_eq!(get_bytes_for_row(1), b"baaa");
assert_eq!(get_bytes_for_row(2), b"");
assert_eq!(get_bytes_for_row(3), b"a");
}
#[test]
fn test_merge_columnar_byte_with_missing() {
let columnar1 = make_byte_columnar_multiple_columns(&[], 3);
let columnar2 = make_byte_columnar_multiple_columns(&[("col", &[&[b"b"], &[]])], 2);
let columnar3 = make_byte_columnar_multiple_columns(
&[
("col", &[&[], &[b"b"], &[b"a", b"b"]]),
("col2", &[&[b"hello"], &[], &[b"a", b"b"]]),
],
3,
);
let mut buffer = Vec::new();
let columnars = &[&columnar1, &columnar2, &columnar3];
let stack_merge_order = StackMergeOrder::stack(columnars);
crate::columnar::merge_columnar(
columnars,
&[],
MergeRowOrder::Stack(stack_merge_order),
&mut buffer,
)
.unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_rows(), 3 + 2 + 3);
assert_eq!(columnar_reader.num_columns(), 2);
let cols = columnar_reader.read_columns("col").unwrap();
let dynamic_column = cols[0].open().unwrap();
let DynamicColumn::Bytes(vals) = dynamic_column else {
panic!()
};
let get_bytes_for_ord = |ord| {
let mut out = Vec::new();
vals.ord_to_bytes(ord, &mut out).unwrap();
out
};
assert_eq!(vals.dictionary.num_terms(), 2);
assert_eq!(get_bytes_for_ord(0), b"a");
assert_eq!(get_bytes_for_ord(1), b"b");
let get_bytes_for_row = |row_id| {
let terms: Vec<Vec<u8>> = vals
.term_ords(row_id)
.map(|term_ord| {
let mut out = Vec::new();
vals.ord_to_bytes(term_ord, &mut out).unwrap();
out
})
.collect();
terms
};
assert!(get_bytes_for_row(0).is_empty());
assert!(get_bytes_for_row(1).is_empty());
assert!(get_bytes_for_row(2).is_empty());
assert_eq!(get_bytes_for_row(3), vec![b"b".to_vec()]);
assert!(get_bytes_for_row(4).is_empty());
assert!(get_bytes_for_row(5).is_empty());
assert_eq!(get_bytes_for_row(6), vec![b"b".to_vec()]);
assert_eq!(get_bytes_for_row(7), vec![b"a".to_vec(), b"b".to_vec()]);
}
#[test]
fn test_merge_columnar_different_types() {
let columnar1 = make_text_columnar_multiple_columns(&[("mixed", &[&["a"]])]);
let columnar2 = make_text_columnar_multiple_columns(&[("mixed", &[&[], &["b"]])]);
let columnar3 = make_columnar("mixed", &[1i64]);
let mut buffer = Vec::new();
let columnars = &[&columnar1, &columnar2, &columnar3];
let stack_merge_order = StackMergeOrder::stack(columnars);
crate::columnar::merge_columnar(
columnars,
&[],
MergeRowOrder::Stack(stack_merge_order),
&mut buffer,
)
.unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_rows(), 4);
assert_eq!(columnar_reader.num_columns(), 2);
let cols = columnar_reader.read_columns("mixed").unwrap();
// numeric column
let dynamic_column = cols[0].open().unwrap();
let DynamicColumn::I64(vals) = dynamic_column else {
panic!()
};
assert_eq!(vals.get_cardinality(), Cardinality::Optional);
assert_eq!(vals.values_for_doc(0).collect_vec(), vec![]);
assert_eq!(vals.values_for_doc(1).collect_vec(), vec![]);
assert_eq!(vals.values_for_doc(2).collect_vec(), vec![]);
assert_eq!(vals.values_for_doc(3).collect_vec(), vec![1]);
assert_eq!(vals.values_for_doc(4).collect_vec(), vec![]);
// text column
let dynamic_column = cols[1].open().unwrap();
let DynamicColumn::Str(vals) = dynamic_column else {
panic!()
};
assert_eq!(vals.ords().get_cardinality(), Cardinality::Optional);
let get_str_for_ord = |ord| {
let mut out = String::new();
vals.ord_to_str(ord, &mut out).unwrap();
out
};
assert_eq!(vals.dictionary.num_terms(), 2);
assert_eq!(get_str_for_ord(0), "a");
assert_eq!(get_str_for_ord(1), "b");
let get_str_for_row = |row_id| {
let term_ords: Vec<String> = vals
.term_ords(row_id)
.map(|el| {
let mut out = String::new();
vals.ord_to_str(el, &mut out).unwrap();
out
})
.collect();
term_ords
};
assert_eq!(get_str_for_row(0), vec!["a".to_string()]);
assert_eq!(get_str_for_row(1), Vec::<String>::new());
assert_eq!(get_str_for_row(2), vec!["b".to_string()]);
assert_eq!(get_str_for_row(3), Vec::<String>::new());
}
#[test]
fn test_merge_columnar_different_empty_cardinality() {
let columnar1 = make_text_columnar_multiple_columns(&[("mixed", &[&["a"]])]);
let columnar2 = make_columnar("mixed", &[1i64]);
let mut buffer = Vec::new();
let columnars = &[&columnar1, &columnar2];
let stack_merge_order = StackMergeOrder::stack(columnars);
crate::columnar::merge_columnar(
columnars,
&[],
MergeRowOrder::Stack(stack_merge_order),
&mut buffer,
)
.unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_rows(), 2);
assert_eq!(columnar_reader.num_columns(), 2);
let cols = columnar_reader.read_columns("mixed").unwrap();
// numeric column
let dynamic_column = cols[0].open().unwrap();
assert_eq!(dynamic_column.get_cardinality(), Cardinality::Optional);
// text column
let dynamic_column = cols[1].open().unwrap();
assert_eq!(dynamic_column.get_cardinality(), Cardinality::Optional);
}

5
columnar/src/columnar/mod.rs
View File

@@ -5,6 +5,9 @@ mod reader;
mod writer;
pub use column_type::{ColumnType, HasAssociatedColumnType};
pub use merge::{merge_columnar, MergeDocOrder};
pub use format_version::{Version, CURRENT_VERSION};
#[cfg(test)]
pub(crate) use merge::ColumnTypeCategory;
pub use merge::{merge_columnar, MergeRowOrder, ShuffleMergeOrder, StackMergeOrder};
pub use reader::ColumnarReader;
pub use writer::ColumnarWriter;

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