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base: rust:columnar-main
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:optional_column
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

4 Commits
columnar-m ... optional_c

Author SHA1 Message Date
Pascal Seitz
208e5fd61a remove superfluous impls 2022-11-21 10:50:53 +08:00
PSeitz
caca4cdda4 Apply suggestions from code review 
Co-authored-by: Paul Masurel <paul@quickwit.io>
 2022-11-21 03:42:52 +01:00
Pascal Seitz
191fe5101b migrate to OptionalColumn 2022-11-18 15:09:15 +08:00
Pascal Seitz
c4474fef3a add OptionalColumn 2022-11-18 12:30:03 +08:00
229 changed files with 2803 additions and 15139 deletions
Expand all files Collapse all files

17
CHANGELOG.md
View File

@@ -1,29 +1,26 @@
Tantivy 0.19
================================
#### Bugfixes
- Fix missing fieldnorms for u64, i64, f64, bool, bytes and date [#1620](https://github.com/quickwit-oss/tantivy/pull/1620) (@PSeitz)
- Fix interpolation overflow in linear interpolation fastfield codec [#1480](https://github.com/quickwit-oss/tantivy/pull/1480) (@PSeitz @fulmicoton)
#### Features/Improvements
- Add support for `IN` in queryparser , e.g. `field: IN [val1 val2 val3]` [#1683](https://github.com/quickwit-oss/tantivy/pull/1683) (@trinity-1686a)
- Skip score calculation, when no scoring is required [#1646](https://github.com/quickwit-oss/tantivy/pull/1646) (@PSeitz)
- Limit fast fields to u32 (`get_val(u32)`) [#1644](https://github.com/quickwit-oss/tantivy/pull/1644) (@PSeitz)
- The `DateTime` type has been updated to hold timestamps with microseconds precision.
`DateOptions` and `DatePrecision` have been added to configure Date fields. The precision is used to hint on fast values compression. Otherwise, seconds precision is used everywhere else (i.e terms, indexing) [#1396](https://github.com/quickwit-oss/tantivy/pull/1396) (@evanxg852000)
- Major bugfix: Fix missing fieldnorms for u64, i64, f64, bool, bytes and date [#1620](https://github.com/quickwit-oss/tantivy/pull/1620) (@PSeitz)
- Updated [Date Field Type](https://github.com/quickwit-oss/tantivy/pull/1396)
The `DateTime` type has been updated to hold timestamps with microseconds precision.
`DateOptions` and `DatePrecision` have been added to configure Date fields. The precision is used to hint on fast values compression. Otherwise, seconds precision is used everywhere else (i.e terms, indexing). (@evanxg852000)
- Add IP address field type [#1553](https://github.com/quickwit-oss/tantivy/pull/1553) (@PSeitz)
- Add boolean field type [#1382](https://github.com/quickwit-oss/tantivy/pull/1382) (@boraarslan)
- Remove Searcher pool and make `Searcher` cloneable. (@PSeitz)
- Validate settings on create [#1570](https://github.com/quickwit-oss/tantivy/pull/1570) (@PSeitz)
- Validate settings on create [#1570](https://github.com/quickwit-oss/tantivy/pull/1570 (@PSeitz)
- Fix interpolation overflow in linear interpolation fastfield codec [#1480](https://github.com/quickwit-oss/tantivy/pull/1480 (@PSeitz @fulmicoton)
- Detect and apply gcd on fastfield codecs [#1418](https://github.com/quickwit-oss/tantivy/pull/1418) (@PSeitz)
- Doc store
- use separate thread to compress block store [#1389](https://github.com/quickwit-oss/tantivy/pull/1389) [#1510](https://github.com/quickwit-oss/tantivy/pull/1510) (@PSeitz @fulmicoton)
- use separate thread to compress block store [#1389](https://github.com/quickwit-oss/tantivy/pull/1389) [#1510](https://github.com/quickwit-oss/tantivy/pull/1510 (@PSeitz @fulmicoton)
- Expose doc store cache size [#1403](https://github.com/quickwit-oss/tantivy/pull/1403) (@PSeitz)
- Enable compression levels for doc store [#1378](https://github.com/quickwit-oss/tantivy/pull/1378) (@PSeitz)
- Make block size configurable [#1374](https://github.com/quickwit-oss/tantivy/pull/1374) (@kryesh)
- Make `tantivy::TantivyError` cloneable [#1402](https://github.com/quickwit-oss/tantivy/pull/1402) (@PSeitz)
- 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 aggregation bucket limit [#1363](https://github.com/quickwit-oss/tantivy/pull/1363) (@PSeitz)
- Faster indexing

29
Cargo.toml
View File

@@ -1,6 +1,6 @@
[package]
name = "tantivy"
version = "0.19.0"
version = "0.19.0-dev"
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
license = "MIT"
categories = ["database-implementations", "data-structures"]
@@ -15,7 +15,7 @@ rust-version = "1.62"
[dependencies]
oneshot = "0.1.5"
base64 = "0.21.0"
base64 = "0.13.0"
byteorder = "1.4.3"
crc32fast = "1.3.2"
once_cell = "1.10.0"
@@ -25,7 +25,7 @@ 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 }
zstd = { version = "0.11", optional = true, default-features = false }
snap = { version = "1.0.5", optional = true }
tempfile = { version = "3.3.0", optional = true }
log = "0.4.16"
@@ -36,6 +36,12 @@ fs2 = { version = "0.4.3", optional = true }
levenshtein_automata = "0.2.1"
uuid = { version = "1.0.0", features = ["v4", "serde"] }
crossbeam-channel = "0.5.4"
tantivy-query-grammar = { version="0.18.0", path="./query-grammar" }
tantivy-bitpacker = { version="0.2", path="./bitpacker" }
common = { version = "0.3", path = "./common/", package = "tantivy-common" }
fastfield_codecs = { version="0.2", path="./fastfield_codecs", default-features = false }
ownedbytes = { version="0.3", path="./ownedbytes" }
stable_deref_trait = "1.2.0"
rust-stemmers = "1.2.0"
downcast-rs = "1.2.0"
bitpacking = { version = "0.8.4", default-features = false, features = ["bitpacker4x"] }
@@ -48,21 +54,14 @@ murmurhash32 = "0.2.0"
time = { version = "0.3.10", features = ["serde-well-known"] }
smallvec = "1.8.0"
rayon = "1.5.2"
lru = "0.9.0"
lru = "0.7.5"
fastdivide = "0.4.0"
itertools = "0.10.3"
measure_time = "0.8.2"
ciborium = { version = "0.2", optional = true}
async-trait = "0.1.53"
arc-swap = "1.5.0"
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" }
[target.'cfg(windows)'.dependencies]
winapi = "0.3.9"
@@ -74,7 +73,7 @@ pretty_assertions = "1.2.1"
proptest = "1.0.0"
criterion = "0.4"
test-log = "0.2.10"
env_logger = "0.10.0"
env_logger = "0.9.0"
pprof = { version = "0.11.0", features = ["flamegraph", "criterion"] }
futures = "0.3.21"
@@ -104,10 +103,10 @@ zstd-compression = ["zstd"]
failpoints = ["fail/failpoints"]
unstable = [] # useful for benches.
quickwit = ["sstable"]
quickwit = ["ciborium"]
[workspace]
members = ["query-grammar", "bitpacker", "common", "fastfield_codecs", "ownedbytes", "stacker", "sstable", "tokenizer-api"]
members = ["query-grammar", "bitpacker", "common", "fastfield_codecs", "ownedbytes"]
# Following the "fail" crate best practises, we isolate
# tests that define specific behavior in fail check points

10
README.md
View File

@@ -29,7 +29,7 @@ Your mileage WILL vary depending on the nature of queries and their load.
# 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))
- 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))
- Fast (check out the :racehorse: :sparkles: [benchmark](https://tantivy-search.github.io/bench/) :sparkles: :racehorse:)
- Tiny startup time (<10ms), perfect for command-line tools
- BM25 scoring (the same as Lucene)
@@ -42,12 +42,12 @@ 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, and hierarchical facet fields
- Compressed document store (LZ4, Zstd, None, Brotli, Snap)
- LZ4 compressed document store
- Range queries
- Faceted search
- Configurable indexing (optional term frequency and position indexing)
- JSON Field
- Aggregation Collector: histogram, range buckets, average, and stats metrics
- Aggregation Collector: range buckets, average, and stats metrics
- LogMergePolicy with deletes
- Searcher Warmer API
- Cheesy logo with a horse
@@ -81,10 +81,6 @@ There are many ways to support this project.
We use the GitHub Pull Request workflow: reference a GitHub ticket and/or include a comprehensive commit message when opening a PR.
## Tokenizer
When implementing a tokenizer for tantivy depend on the `tantivy-tokenizer-api` crate.
## Minimum supported Rust version
Tantivy currently requires at least Rust 1.62 or later to compile.

4
bitpacker/Cargo.toml
View File

@@ -1,6 +1,6 @@
[package]
name = "tantivy-bitpacker"
version = "0.3.0"
version = "0.2.0"
edition = "2021"
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
license = "MIT"
@@ -8,8 +8,6 @@ categories = []
description = """Tantivy-sub crate: bitpacking"""
repository = "https://github.com/quickwit-oss/tantivy"
keywords = []
documentation = "https://docs.rs/tantivy-bitpacker/latest/tantivy_bitpacker"
homepage = "https://github.com/quickwit-oss/tantivy"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

17
bitpacker/src/bitpacker.rs
View File

@@ -25,14 +25,15 @@ impl BitPacker {
num_bits: u8,
output: &mut TWrite,
) -> io::Result<()> {
let val_u64 = val as u64;
let num_bits = num_bits as usize;
if self.mini_buffer_written + num_bits > 64 {
self.mini_buffer |= val.wrapping_shl(self.mini_buffer_written as u32);
self.mini_buffer |= val_u64.wrapping_shl(self.mini_buffer_written as u32);
output.write_all(self.mini_buffer.to_le_bytes().as_ref())?;
self.mini_buffer = val.wrapping_shr((64 - self.mini_buffer_written) as u32);
self.mini_buffer = val_u64.wrapping_shr((64 - self.mini_buffer_written) as u32);
self.mini_buffer_written = self.mini_buffer_written + num_bits - 64;
} else {
self.mini_buffer |= val << self.mini_buffer_written;
self.mini_buffer |= val_u64 << self.mini_buffer_written;
self.mini_buffer_written += num_bits;
if self.mini_buffer_written == 64 {
output.write_all(self.mini_buffer.to_le_bytes().as_ref())?;
@@ -91,15 +92,17 @@ impl BitUnpacker {
return 0u64;
}
let addr_in_bits = idx * self.num_bits as u32;
let addr = (addr_in_bits >> 3) as usize;
let addr = addr_in_bits >> 3;
let bit_shift = addr_in_bits & 7;
debug_assert!(
addr + 8 <= data.len(),
addr + 8 <= data.len() as u32,
"The fast field field should have been padded with 7 bytes."
);
let bytes: [u8; 8] = (&data[addr..addr + 8]).try_into().unwrap();
let bytes: [u8; 8] = (&data[(addr as usize)..(addr as usize) + 8])
.try_into()
.unwrap();
let val_unshifted_unmasked: u64 = u64::from_le_bytes(bytes);
let val_shifted = val_unshifted_unmasked >> bit_shift;
let val_shifted = (val_unshifted_unmasked >> bit_shift) as u64;
val_shifted & self.mask
}
}

6
bitpacker/src/blocked_bitpacker.rs
View File

@@ -84,7 +84,7 @@ impl BlockedBitpacker {
#[inline]
pub fn add(&mut self, val: u64) {
self.buffer.push(val);
if self.buffer.len() == BLOCK_SIZE {
if self.buffer.len() == BLOCK_SIZE as usize {
self.flush();
}
}
@@ -126,8 +126,8 @@ impl BlockedBitpacker {
}
#[inline]
pub fn get(&self, idx: usize) -> u64 {
let metadata_pos = idx / BLOCK_SIZE;
let pos_in_block = idx % BLOCK_SIZE;
let metadata_pos = idx / BLOCK_SIZE as usize;
let pos_in_block = idx % BLOCK_SIZE as usize;
if let Some(metadata) = self.offset_and_bits.get(metadata_pos) {
let unpacked = BitUnpacker::new(metadata.num_bits()).get(
pos_in_block as u32,

128
bitpacker/src/lib.rs
View File

@@ -1,8 +1,6 @@
mod bitpacker;
mod blocked_bitpacker;
use std::cmp::Ordering;
pub use crate::bitpacker::{BitPacker, BitUnpacker};
pub use crate::blocked_bitpacker::BlockedBitpacker;
@@ -39,104 +37,44 @@ pub fn compute_num_bits(n: u64) -> u8 {
}
}
/// Computes the (min, max) of an iterator of `PartialOrd` values.
///
/// For values implementing `Ord` (in a way consistent to their `PartialOrd` impl),
/// this function behaves as expected.
///
/// For values with partial ordering, the behavior is non-trivial and may
/// depends on the order of the values.
/// For floats however, it simply returns the same results as if NaN were
/// skipped.
pub fn minmax<I, T>(mut vals: I) -> Option<(T, T)>
where
I: Iterator<Item = T>,
T: Copy + PartialOrd,
T: Copy + Ord,
{
let first_el = vals.find(|val| {
// We use this to make sure we skip all NaN values when
// working with a float type.
val.partial_cmp(val) == Some(Ordering::Equal)
})?;
let mut min_so_far: T = first_el;
let mut max_so_far: T = first_el;
for val in vals {
if val.partial_cmp(&min_so_far) == Some(Ordering::Less) {
min_so_far = val;
}
if val.partial_cmp(&max_so_far) == Some(Ordering::Greater) {
max_so_far = val;
}
if let Some(first_el) = vals.next() {
return Some(vals.fold((first_el, first_el), |(min_val, max_val), el| {
(min_val.min(el), max_val.max(el))
}));
}
Some((min_so_far, max_so_far))
None
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_compute_num_bits() {
assert_eq!(compute_num_bits(1), 1u8);
assert_eq!(compute_num_bits(0), 0u8);
assert_eq!(compute_num_bits(2), 2u8);
assert_eq!(compute_num_bits(3), 2u8);
assert_eq!(compute_num_bits(4), 3u8);
assert_eq!(compute_num_bits(255), 8u8);
assert_eq!(compute_num_bits(256), 9u8);
assert_eq!(compute_num_bits(5_000_000_000), 33u8);
}
#[test]
fn test_minmax_empty() {
let vals: Vec<u32> = vec![];
assert_eq!(minmax(vals.into_iter()), None);
}
#[test]
fn test_minmax_one() {
assert_eq!(minmax(vec![1].into_iter()), Some((1, 1)));
}
#[test]
fn test_minmax_two() {
assert_eq!(minmax(vec![1, 2].into_iter()), Some((1, 2)));
assert_eq!(minmax(vec![2, 1].into_iter()), Some((1, 2)));
}
#[test]
fn test_minmax_nan() {
assert_eq!(
minmax(vec![f64::NAN, 1f64, 2f64].into_iter()),
Some((1f64, 2f64))
);
assert_eq!(
minmax(vec![2f64, f64::NAN, 1f64].into_iter()),
Some((1f64, 2f64))
);
assert_eq!(
minmax(vec![2f64, 1f64, f64::NAN].into_iter()),
Some((1f64, 2f64))
);
}
#[test]
fn test_minmax_inf() {
assert_eq!(
minmax(vec![f64::INFINITY, 1f64, 2f64].into_iter()),
Some((1f64, f64::INFINITY))
);
assert_eq!(
minmax(vec![-f64::INFINITY, 1f64, 2f64].into_iter()),
Some((-f64::INFINITY, 2f64))
);
assert_eq!(
minmax(vec![2f64, f64::INFINITY, 1f64].into_iter()),
Some((1f64, f64::INFINITY))
);
assert_eq!(
minmax(vec![2f64, 1f64, -f64::INFINITY].into_iter()),
Some((-f64::INFINITY, 2f64))
);
}
#[test]
fn test_compute_num_bits() {
assert_eq!(compute_num_bits(1), 1u8);
assert_eq!(compute_num_bits(0), 0u8);
assert_eq!(compute_num_bits(2), 2u8);
assert_eq!(compute_num_bits(3), 2u8);
assert_eq!(compute_num_bits(4), 3u8);
assert_eq!(compute_num_bits(255), 8u8);
assert_eq!(compute_num_bits(256), 9u8);
assert_eq!(compute_num_bits(5_000_000_000), 33u8);
}
#[test]
fn test_minmax_empty() {
let vals: Vec<u32> = vec![];
assert_eq!(minmax(vals.into_iter()), None);
}
#[test]
fn test_minmax_one() {
assert_eq!(minmax(vec![1].into_iter()), Some((1, 1)));
}
#[test]
fn test_minmax_two() {
assert_eq!(minmax(vec![1, 2].into_iter()), Some((1, 2)));
assert_eq!(minmax(vec![2, 1].into_iter()), Some((1, 2)));
}

32
columnar/Cargo.toml
View File

@@ -1,32 +0,0 @@
[package]
name = "tantivy-columnar"
version = "0.1.0"
edition = "2021"
license = "MIT"
[dependencies]
stacker = { path = "../stacker", package="tantivy-stacker"}
serde_json = "1"
thiserror = "1"
fnv = "1"
sstable = { path = "../sstable", package = "tantivy-sstable" }
common = { path = "../common", package = "tantivy-common" }
itertools = "0.10"
log = "0.4"
tantivy-bitpacker = { version= "0.3", path = "../bitpacker/" }
prettytable-rs = {version="0.10.0", optional= true}
rand = {version="0.8.3", optional= true}
fastdivide = "0.4"
measure_time = { version="0.8.2", optional=true}
[dev-dependencies]
proptest = "1"
more-asserts = "0.3.0"
rand = "0.8.3"
# temporary
[workspace]
members = []
[features]
unstable = []

109
columnar/README.md
View File

@@ -1,109 +0,0 @@
# Columnar format
This crate describes columnar format used in tantivy.
## Goals
This format is special in the following way.
- it needs to be compact
- accessing a specific column does not require to load the entire columnar. It can be done in 2 to 3 random access.
- columns of several types can be associated with the same column name.
- it needs to support columns with different types `(str, u64, i64, f64)`
and different cardinality `(required, optional, multivalued)`.
- columns, once loaded, offer cheap random access.
- it is designed to allow range queries.
# Coercion rules
Users can create a columnar by inserting rows to a `ColumnarWriter`,
and serializing it into a `Write` object.
Nothing prevents a user from recording values with different type to the same `column_name`.
In that case, `tantivy-columnar`'s behavior is as follows:
- JsonValues are grouped into 3 types (String, Number, bool).
Values that corresponds to different groups are mapped to different columns. For instance, String values are treated independently
from Number or boolean values. `tantivy-columnar` will simply emit several columns associated to a given column_name.
- Only one column for a given json value type is emitted. If number values with different number types are recorded (e.g. u64, i64, f64),
`tantivy-columnar` will pick the first type that can represents the set of appended value, with the following prioriy order (`i64`, `u64`, `f64`).
`i64` is picked over `u64` as it is likely to yield less change of types. Most use cases strictly requiring `u64` show the
restriction on 50% of the values (e.g. a 64-bit hash). On the other hand, a lot of use cases can show rare negative value.
# 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.
That couple is serialized as a column `column_key`. The format of that key is:
`[column_name][ZERO_BYTE][column_type_header: u8]`
```
COLUMNAR:=
[COLUMNAR_DATA]
[COLUMNAR_KEY_TO_DATA_INDEX]
[COLUMNAR_FOOTER];
# Columns are sorted by their column key.
COLUMNAR_DATA:=
[COLUMN_DATA]+;
COLUMNAR_FOOTER := [RANGE_SSTABLE_BYTES_LEN: 8 bytes little endian]
```
The columnar file starts by the actual column data, concatenated one after the other,
sorted by column key.
A sstable associates
`(column name, column_cardinality, column_type) to range of bytes.
Column name may not contain the zero byte `\0`.
Listing all columns associated to `column_name` can therefore
be done by listing all keys prefixed by
`[column_name][ZERO_BYTE]`
The associated range of bytes refer to a range of bytes
This crate exposes a columnar format for tantivy.
This format is described in README.md
The crate introduces the following concepts.
`Columnar` is an equivalent of a dataframe.
It maps `column_key` to `Column`.
A `Column<T>` asssociates a `RowId` (u32) to any
number of values.
This is made possible by wrapping a `ColumnIndex` and a `ColumnValue` object.
The `ColumnValue<T>` represents a mapping that associates each `RowId` to
exactly one single value.
The `ColumnIndex` then maps each RowId to a set of `RowId` in the
`ColumnValue`.
For optimization, and compression purposes, the `ColumnIndex` has three
possible representation, each for different cardinalities.
- Full
All RowId have exactly one value. The ColumnIndex is the trivial mapping.
- Optional
All RowIds can have at most one value. The ColumnIndex is the trivial mapping `ColumnRowId -> Option<ColumnValueRowId>`.
- Multivalued
All RowIds can have any number of values.
The column index is mapping values to a range.
All these objects are implemented an unit tested independently
in their own module:
- columnar
- column_index
- column_values
- column

45
columnar/src/TODO.md
View File

@@ -1,45 +0,0 @@
# 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
# Perf and Size
* 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
compare with roaring bitmap/elias fano etc etc.
SIMD range? (see blog post)
Add alignment?
Consider another codec to bridge the gap between few and 5k elements
# Cleanup and rationalization
in benchmark, unify percent vs ratio, f32 vs f64.
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
use the rank & select naming in unit tests branch.
multi-linear -> blockwise
linear codec -> simply a multiplication for the index column
# Other
fix enhance column-cli
# Santa claus
autodetect datetime ipaddr, plug customizable tokenizer.

40
columnar/src/column/dictionary_encoded.rs
View File

@@ -1,40 +0,0 @@
use std::io;
use std::ops::Deref;
use std::sync::Arc;
use sstable::{Dictionary, VoidSSTable};
use crate::column::Column;
use crate::column_index::ColumnIndex;
/// Dictionary encoded column.
#[derive(Clone)]
pub struct BytesColumn {
pub(crate) dictionary: Arc<Dictionary<VoidSSTable>>,
pub(crate) term_ord_column: Column<u64>,
}
impl BytesColumn {
/// Returns `false` if the term does not exist (e.g. `term_ord` is greater or equal to the
/// overll number of terms).
pub fn term_ord_to_str(&self, term_ord: u64, output: &mut Vec<u8>) -> io::Result<bool> {
self.dictionary.ord_to_term(term_ord, output)
}
pub fn term_ords(&self) -> &Column<u64> {
&self.term_ord_column
}
}
impl Deref for BytesColumn {
type Target = ColumnIndex<'static>;
fn deref(&self) -> &Self::Target {
&**self.term_ords()
}
}
#[cfg(test)]
mod tests {
use crate::{ColumnarReader, ColumnarWriter};
}

56
columnar/src/column/mod.rs
View File

@@ -1,56 +0,0 @@
mod dictionary_encoded;
mod serialize;
use std::ops::Deref;
use std::sync::Arc;
use common::BinarySerializable;
pub use dictionary_encoded::BytesColumn;
pub use serialize::{open_column_bytes, open_column_u64, serialize_column_u64};
use crate::column_index::ColumnIndex;
use crate::column_values::ColumnValues;
use crate::{Cardinality, RowId};
#[derive(Clone)]
pub struct Column<T> {
pub idx: ColumnIndex<'static>,
pub values: Arc<dyn ColumnValues<T>>,
}
use crate::column_index::Set;
impl<T: PartialOrd> Column<T> {
pub fn first(&self, row_id: RowId) -> Option<T> {
match &self.idx {
ColumnIndex::Full => Some(self.values.get_val(row_id)),
ColumnIndex::Optional(opt_idx) => {
let value_row_idx = opt_idx.rank_if_exists(row_id)?;
Some(self.values.get_val(value_row_idx))
}
ColumnIndex::Multivalued(_multivalued_index) => {
todo!();
}
}
}
}
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<()> {
self.to_code().serialize(writer)
}
fn deserialize<R: std::io::Read>(reader: &mut R) -> std::io::Result<Self> {
let cardinality_code = u8::deserialize(reader)?;
let cardinality = Cardinality::try_from_code(cardinality_code)?;
Ok(cardinality)
}
}

54
columnar/src/column/serialize.rs
View File

@@ -1,54 +0,0 @@
use std::io;
use std::io::Write;
use std::sync::Arc;
use common::{CountingWriter, OwnedBytes};
use sstable::Dictionary;
use crate::column::{BytesColumn, Column};
use crate::column_index::{serialize_column_index, SerializableColumnIndex};
use crate::column_values::{
serialize_column_values, ColumnValues, MonotonicallyMappableToU64, ALL_CODEC_TYPES,
};
pub fn serialize_column_u64<T: MonotonicallyMappableToU64>(
column_index: SerializableColumnIndex<'_>,
column_values: &impl ColumnValues<T>,
output: &mut impl Write,
) -> io::Result<()> {
let mut counting_writer = CountingWriter::wrap(output);
serialize_column_index(column_index, &mut counting_writer)?;
let column_index_num_bytes = counting_writer.written_bytes() as u32;
let output = counting_writer.finish();
serialize_column_values(column_values, &ALL_CODEC_TYPES[..], 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_bytes(data: OwnedBytes) -> 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)?;
Ok(BytesColumn {
dictionary,
term_ord_column,
})
}

40
columnar/src/column_index/mod.rs
View File

@@ -1,40 +0,0 @@
mod multivalued_index;
mod optional_index;
mod serialize;
use std::sync::Arc;
pub use optional_index::{OptionalIndex, SerializableOptionalIndex, Set};
pub use serialize::{open_column_index, serialize_column_index, SerializableColumnIndex};
use crate::column_values::ColumnValues;
use crate::{Cardinality, RowId};
#[derive(Clone)]
pub enum ColumnIndex<'a> {
Full,
Optional(OptionalIndex),
// TODO remove the Arc<dyn> apart from serialization this is not
// dynamic at all.
Multivalued(Arc<dyn ColumnValues<RowId> + 'a>),
}
impl<'a> ColumnIndex<'a> {
pub fn get_cardinality(&self) -> Cardinality {
match self {
ColumnIndex::Full => Cardinality::Full,
ColumnIndex::Optional(_) => Cardinality::Optional,
ColumnIndex::Multivalued(_) => Cardinality::Multivalued,
}
}
pub fn num_rows(&self) -> RowId {
match self {
ColumnIndex::Full => {
todo!()
}
ColumnIndex::Optional(optional_index) => optional_index.num_rows(),
ColumnIndex::Multivalued(multivalued_index) => multivalued_index.num_vals() - 1,
}
}
}

27
columnar/src/column_index/multivalued_index.rs
View File

@@ -1,27 +0,0 @@
use std::io;
use std::io::Write;
use std::sync::Arc;
use common::OwnedBytes;
use crate::column_values::{ColumnValues, FastFieldCodecType};
use crate::RowId;
#[derive(Clone)]
pub struct MultivaluedIndex(Arc<dyn ColumnValues<RowId>>);
pub fn serialize_multivalued_index(
multivalued_index: MultivaluedIndex,
output: &mut impl Write,
) -> io::Result<()> {
crate::column_values::serialize_column_values(
&*multivalued_index.0,
&[FastFieldCodecType::Bitpacked, FastFieldCodecType::Linear],
output,
)?;
Ok(())
}
pub fn open_multivalued_index(bytes: OwnedBytes) -> io::Result<Arc<dyn ColumnValues<RowId>>> {
todo!();
}

453
columnar/src/column_index/optional_index/mod.rs
View File

@@ -1,453 +0,0 @@
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 set_block::{
DenseBlock, DenseBlockCodec, SparseBlock, SparseBlockCodec, DENSE_BLOCK_NUM_BYTES,
};
use crate::{InvalidData, RowId};
/// The threshold for for number of elements after which we switch to dense block encoding.
///
/// We simply pick the value that minimize the size of the blocks.
const DENSE_BLOCK_THRESHOLD: u32 =
set_block::DENSE_BLOCK_NUM_BYTES / std::mem::size_of::<u16>() as u32; //< 5_120
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,
start_byte_offset: u32,
block_variant: BlockVariant,
}
#[derive(Clone, Copy, Debug)]
enum BlockVariant {
Dense,
Sparse { num_vals: u16 },
}
impl BlockVariant {
pub fn empty() -> Self {
Self::Sparse { num_vals: 0 }
}
pub fn num_bytes_in_block(&self) -> u32 {
match *self {
BlockVariant::Dense => set_block::DENSE_BLOCK_NUM_BYTES,
BlockVariant::Sparse { num_vals } => num_vals as u32 * 2,
}
}
}
/// This codec is inspired by roaring bitmaps.
/// In the dense blocks, however, in order to accelerate `select`
/// we interleave an offset over two bytes. (more on this lower)
///
/// The lower 16 bits of doc ids are stored as u16 while the upper 16 bits are given by the block
/// id. Each block contains 1<<16 docids.
///
/// # Serialized Data Layout
/// The data starts with the block data. Each block is either dense or sparse encoded, depending on
/// the number of values in the block. A block is sparse when it contains less than
/// DENSE_BLOCK_THRESHOLD (6144) values.
/// [Sparse data block | dense data block, .. #repeat*; Desc: Either a sparse or dense encoded
/// block]
/// ### Sparse block data
/// [u16 LE, .. #repeat*; Desc: Positions with values in a block]
/// ### Dense block data
/// [Dense codec for the whole block; Desc: Similar to a bitvec(0..ELEMENTS_PER_BLOCK) + Metadata
/// for faster lookups. See dense.rs]
///
/// The data is followed by block metadata, to know which area of the raw block data belongs to
/// which block. Only metadata for blocks with elements is recorded to
/// keep the overhead low for scenarios with many very sparse columns. The block metadata consists
/// of the block index and the number of values in the block. Since we don't store empty blocks
/// num_vals is incremented by 1, e.g. 0 means 1 value.
///
/// The last u16 is storing the number of metadata blocks.
/// [u16 LE, .. #repeat*; Desc: Positions with values in a block][(u16 LE, u16 LE), .. #repeat*;
/// Desc: (Block Id u16, Num Elements u16)][u16 LE; Desc: num blocks with values u16]
///
/// # Opening
/// When opening the data layout, the data is expanded to `Vec<SparseCodecBlockVariant>`, where the
/// index is the block index. For each block `byte_start` and `offset` is computed.
#[derive(Clone)]
pub struct OptionalIndex {
num_rows: RowId,
num_non_null_rows: RowId,
block_data: OwnedBytes,
block_metas: Arc<[BlockMeta]>,
}
impl OptionalIndex {
pub fn num_rows(&self) -> RowId {
self.num_rows
}
pub fn num_non_nulls(&self) -> RowId {
self.num_non_null_rows
}
}
/// Splits a value address into lower and upper 16bits.
/// The lower 16 bits are the value in the block
/// The upper 16 bits are the block index
#[derive(Copy, Debug, Clone)]
struct RowAddr {
block_id: u16,
in_block_row_id: u16,
}
#[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,
}
}
impl Set<RowId> for OptionalIndex {
// Check if value at position is not null.
#[inline]
fn contains(&self, row_id: RowId) -> bool {
let RowAddr {
block_id,
in_block_row_id,
} = row_addr_from_row_id(row_id);
let block_meta = self.block_metas[block_id as usize];
match self.block(block_meta) {
Block::Dense(dense_block) => dense_block.contains(in_block_row_id),
Block::Sparse(sparse_block) => sparse_block.contains(in_block_row_id),
}
}
#[inline]
fn rank_if_exists(&self, row_id: RowId) -> Option<RowId> {
let RowAddr {
block_id,
in_block_row_id,
} = row_addr_from_row_id(row_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_if_exists(in_block_row_id),
Block::Sparse(sparse_block) => sparse_block.rank_if_exists(in_block_row_id),
}? as u32;
Some(block_meta.non_null_rows_before_block + block_offset_row_id)
}
#[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_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;
let in_block_rank = match block {
Block::Dense(dense_block) => dense_block.select(index_in_block),
Block::Sparse(sparse_block) => sparse_block.select(index_in_block),
};
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;
}
}
};
}
}
}
impl OptionalIndex {
#[inline]
fn block<'a>(&'a self, block_meta: BlockMeta) -> Block<'a> {
let BlockMeta {
start_byte_offset,
block_variant,
..
} = block_meta;
let start_byte_offset = start_byte_offset as usize;
let bytes = self.block_data.as_slice();
match block_variant {
BlockVariant::Dense => Block::Dense(DenseBlockCodec::open(
&bytes[start_byte_offset..start_byte_offset + DENSE_BLOCK_NUM_BYTES as usize],
)),
BlockVariant::Sparse { num_vals } => {
let end_byte_offset = start_byte_offset + num_vals as usize * 2;
let sparse_bytes = &bytes[start_byte_offset..end_byte_offset];
Block::Sparse(SparseBlockCodec::open(sparse_bytes))
}
}
}
#[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 {
let offset = self.block_metas[block_pos as usize].non_null_rows_before_block;
if offset > dense_idx {
return block_pos - 1;
}
}
self.block_metas.len() as u32 - 1u32
}
// TODO Add a good API for the codec_idx to original_idx translation.
// The Iterator API is a probably a bad idea
}
#[derive(Copy, Clone)]
enum Block<'a> {
Dense(DenseBlock<'a>),
Sparse(SparseBlock<'a>),
}
#[derive(Debug, Copy, Clone)]
enum OptionalIndexCodec {
Dense = 0,
Sparse = 1,
}
impl OptionalIndexCodec {
fn to_code(self) -> u8 {
self as u8
}
fn try_from_code(code: u8) -> Result<Self, InvalidData> {
match code {
0 => Ok(Self::Dense),
1 => Ok(Self::Sparse),
_ => Err(InvalidData),
}
}
}
impl BinarySerializable for OptionalIndexCodec {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_all(&[self.to_code()])
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let optional_codec_code = u8::deserialize(reader)?;
let optional_codec = Self::try_from_code(optional_codec_code)?;
Ok(optional_codec)
}
}
fn serialize_optional_index_block(block_els: &[u16], out: &mut impl io::Write) -> io::Result<()> {
let is_sparse = is_sparse(block_els.len() as u32);
if is_sparse {
SparseBlockCodec::serialize(block_els.iter().copied(), out)?;
} else {
DenseBlockCodec::serialize(block_els.iter().copied(), out)?;
}
Ok(())
}
pub fn serialize_optional_index<'a, W: io::Write>(
serializable_optional_index: &dyn SerializableOptionalIndex<'a>,
output: &mut W,
) -> io::Result<()> {
VInt(serializable_optional_index.num_rows() as u64).serialize(output)?;
let mut rows_it = serializable_optional_index.non_null_rows();
let mut block_metadata: Vec<SerializedBlockMeta> = Vec::new();
let mut current_block = Vec::new();
// This if-statement for the first element ensures that
// `block_metadata` is not empty in the loop below.
let Some(idx) = rows_it.next() else {
output.write_all(&0u16.to_le_bytes())?;
return Ok(());
};
let row_addr = row_addr_from_row_id(idx);
let mut current_block_id = row_addr.block_id;
current_block.push(row_addr.in_block_row_id);
for idx in rows_it {
let value_addr = row_addr_from_row_id(idx);
if current_block_id != value_addr.block_id {
serialize_optional_index_block(&current_block[..], output)?;
block_metadata.push(SerializedBlockMeta {
block_id: current_block_id,
num_non_null_rows: current_block.len() as u32,
});
current_block.clear();
current_block_id = value_addr.block_id;
}
current_block.push(value_addr.in_block_row_id);
}
// handle last block
serialize_optional_index_block(&current_block[..], output)?;
block_metadata.push(SerializedBlockMeta {
block_id: current_block_id,
num_non_null_rows: current_block.len() as u32,
});
for block in &block_metadata {
output.write_all(&block.to_bytes())?;
}
output.write_all((block_metadata.len() as u16).to_le_bytes().as_ref())?;
Ok(())
}
const SERIALIZED_BLOCK_META_NUM_BYTES: usize = 4;
#[derive(Clone, Copy, Debug)]
struct SerializedBlockMeta {
block_id: u16,
num_non_null_rows: u32, //< takes values in 1..=u16::MAX
}
// TODO unit tests
impl SerializedBlockMeta {
#[inline]
fn from_bytes(bytes: [u8; SERIALIZED_BLOCK_META_NUM_BYTES]) -> SerializedBlockMeta {
let block_id = u16::from_le_bytes(bytes[0..2].try_into().unwrap());
let num_non_null_rows: u32 =
u16::from_le_bytes(bytes[2..4].try_into().unwrap()) as u32 + 1u32;
SerializedBlockMeta {
block_id,
num_non_null_rows,
}
}
#[inline]
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());
// We don't store empty blocks, therefore we can subtract 1.
// This way we will be able to use u16 when the number of elements is 1 << 16 or u16::MAX+1
bytes[2..4].copy_from_slice(&((self.num_non_null_rows - 1u32) as u16).to_le_bytes());
bytes
}
}
#[inline]
fn is_sparse(num_rows_in_block: u32) -> bool {
num_rows_in_block < DENSE_BLOCK_THRESHOLD as u32
}
fn deserialize_optional_index_block_metadatas(
data: &[u8],
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 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) {
let block_meta_bytes: [u8; SERIALIZED_BLOCK_META_NUM_BYTES] =
block_meta_bytes.try_into().unwrap();
let SerializedBlockMeta {
block_id,
num_non_null_rows,
} = SerializedBlockMeta::from_bytes(block_meta_bytes);
block_metas.resize(
block_id as usize,
BlockMeta {
non_null_rows_before_block,
start_byte_offset,
block_variant: BlockVariant::empty(),
},
);
let block_variant = if is_sparse(num_non_null_rows) {
BlockVariant::Sparse {
num_vals: num_non_null_rows as u16,
}
} else {
BlockVariant::Dense
};
block_metas.push(BlockMeta {
non_null_rows_before_block,
start_byte_offset,
block_variant,
});
start_byte_offset += block_variant.num_bytes_in_block();
non_null_rows_before_block += num_non_null_rows as u32;
}
block_metas.resize(
((num_rows + BLOCK_SIZE - 1) / BLOCK_SIZE) as usize,
BlockMeta {
non_null_rows_before_block,
start_byte_offset,
block_variant: BlockVariant::empty(),
},
);
(block_metas.into_boxed_slice(), non_null_rows_before_block)
}
pub fn open_optional_index(bytes: OwnedBytes) -> io::Result<OptionalIndex> {
let (mut bytes, num_non_empty_blocks_bytes) = bytes.rsplit(2);
let num_non_empty_block_bytes =
u16::from_le_bytes(num_non_empty_blocks_bytes.as_slice().try_into().unwrap());
let num_rows = VInt::deserialize_u64(&mut bytes)? as u32;
let block_metas_num_bytes =
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();
let optional_index = OptionalIndex {
num_rows,
num_non_null_rows,
block_data,
block_metas: block_metas.into(),
};
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;

38
columnar/src/column_index/optional_index/set.rs
View File

@@ -1,38 +0,0 @@
use std::io;
/// A codec makes it possible to serialize a set of
/// elements, and open the resulting Set representation.
pub trait SetCodec {
type Item: Copy + TryFrom<usize> + Eq + std::hash::Hash + std::fmt::Debug;
type Reader<'a>: Set<Self::Item>;
/// Serializes a set of unique sorted u16 elements.
///
/// 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>;
}
pub trait Set<T> {
/// 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.
/// If the set does not contain the element, it returns `None`.
fn rank_if_exists(&self, el: T) -> Option<T>;
/// Return the rank-th value stored in this bitmap.
///
/// # Panics
///
/// May panic if rank is greater than 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]);
}

8
columnar/src/column_index/optional_index/set_block/mod.rs
View File

@@ -1,8 +0,0 @@
mod set_block;
mod sparse;
pub use set_block::{DenseBlock, DenseBlockCodec, DENSE_BLOCK_NUM_BYTES};
pub use sparse::{SparseBlock, SparseBlockCodec};
#[cfg(test)]
mod tests;

271
columnar/src/column_index/optional_index/set_block/set_block.rs
View File

@@ -1,271 +0,0 @@
use std::convert::TryInto;
use std::io::{self, Write};
use common::BinarySerializable;
use crate::column_index::optional_index::{Set, SetCodec, ELEMENTS_PER_BLOCK};
#[inline(always)]
fn get_bit_at(input: u64, n: u16) -> bool {
input & (1 << n) != 0
}
#[inline]
fn set_bit_at(input: &mut u64, n: u16) {
*input |= 1 << n;
}
/// For the `DenseCodec`, `data` which contains the encoded blocks.
/// Each block consists of [u8; 12]. The first 8 bytes is a bitvec for 64 elements.
/// The last 4 bytes are the offset, the number of set bits so far.
///
/// When translating the original index to a dense index, the correct block can be computed
/// directly `orig_idx/64`. Inside the block the position is `orig_idx%64`.
///
/// 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;
pub const MINI_BLOCK_NUM_BYTES: usize = MINI_BLOCK_BITVEC_NUM_BYTES + MINI_BLOCK_OFFSET_NUM_BYTES;
/// 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;
pub struct DenseBlockCodec;
impl SetCodec for DenseBlockCodec {
type Item = u16;
type Reader<'a> = DenseBlock<'a>;
fn serialize(els: impl Iterator<Item = u16>, wrt: impl io::Write) -> io::Result<()> {
serialize_dense_codec(els, wrt)
}
#[inline]
fn open<'a>(data: &'a [u8]) -> Self::Reader<'a> {
assert_eq!(data.len(), DENSE_BLOCK_NUM_BYTES as usize);
DenseBlock(data)
}
}
/// Interpreting the bitvec as a set of integer within 0..=63
/// and given an element, returns the number of elements in the
/// set lesser than the element.
///
/// # Panics
///
/// May panic or return a wrong result if el <= 64.
#[inline(always)]
fn rank_u64(bitvec: u64, el: u16) -> u16 {
debug_assert!(el < 64);
let mask = (1u64 << el) - 1;
let masked_bitvec = bitvec & mask;
masked_bitvec.count_ones() as u16
}
#[inline(always)]
fn select_u64(mut bitvec: u64, rank: u16) -> u16 {
for _ in 0..rank {
bitvec &= bitvec - 1;
}
bitvec.trailing_zeros() as u16
}
// TODO test the following solution on Intel... on Ryzen Zen <3 it is a catastrophy.
// #[target_feature(enable = "bmi2")]
// unsafe fn select_bitvec_unsafe(bitvec: u64, rank: u16) -> u16 {
// let pdep = _pdep_u64(1u64 << rank, bitvec);
// pdep.trailing_zeros() as u16
// }
#[derive(Clone, Copy, Debug)]
struct DenseMiniBlock {
bitvec: u64,
rank: u16,
}
impl DenseMiniBlock {
fn from_bytes(data: [u8; MINI_BLOCK_NUM_BYTES]) -> Self {
let bitvec = u64::from_le_bytes(data[..MINI_BLOCK_BITVEC_NUM_BYTES].try_into().unwrap());
let rank = u16::from_le_bytes(data[MINI_BLOCK_BITVEC_NUM_BYTES..].try_into().unwrap());
Self { bitvec, rank }
}
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());
bytes
}
}
#[derive(Copy, Clone)]
pub struct DenseBlock<'a>(&'a [u8]);
impl<'a> Set<u16> for DenseBlock<'a> {
#[inline(always)]
fn contains(&self, el: u16) -> bool {
let mini_block_id = el / ELEMENTS_PER_MINI_BLOCK;
let bitvec = self.mini_block(mini_block_id).bitvec;
let pos_in_bitvec = el % ELEMENTS_PER_MINI_BLOCK;
get_bit_at(bitvec, pos_in_bitvec)
}
#[inline(always)]
fn rank_if_exists(&self, el: u16) -> Option<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);
let rank = index_block.rank + ones_in_block;
if get_bit_at(index_block.bitvec, pos_in_block_bit_vec) {
Some(rank)
} else {
None
}
}
#[inline(always)]
fn select(&self, rank: u16) -> u16 {
let block_id = self.find_miniblock_containing_rank(rank, 0).unwrap();
let index_block = self.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)
}
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;
}
}
}
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> {
#[inline]
fn mini_block(&self, mini_block_id: u16) -> DenseMiniBlock {
let data_start_pos = mini_block_id as usize * MINI_BLOCK_NUM_BYTES;
DenseMiniBlock::from_bytes(
self.0[data_start_pos..data_start_pos + MINI_BLOCK_NUM_BYTES]
.try_into()
.unwrap(),
)
}
#[inline]
fn iter_miniblocks(
&self,
from_block_id: u16,
) -> impl Iterator<Item = (u16, DenseMiniBlock)> + '_ {
self.0
.chunks_exact(MINI_BLOCK_NUM_BYTES)
.enumerate()
.skip(from_block_id as usize)
.map(|(block_id, bytes)| {
let mini_block = DenseMiniBlock::from_bytes(bytes.try_into().unwrap());
(block_id as u16, mini_block)
})
}
/// Finds the block position containing the dense_idx.
///
/// # Correctness
/// dense_idx needs to be smaller than the number of values in the index
///
/// The last offset number is equal to the number of values in the index.
#[inline]
fn find_miniblock_containing_rank(&self, rank: u16, from_block_id: u16) -> Option<u16> {
self.iter_miniblocks(from_block_id)
.take_while(|(_, block)| block.rank <= rank)
.map(|(block_id, _)| block_id)
.last()
}
}
/// Iterator over all values, true if set, otherwise false
pub fn serialize_dense_codec(
els: impl Iterator<Item = u16>,
mut output: impl Write,
) -> io::Result<()> {
let mut non_null_rows_before: u16 = 0u16;
let mut block = 0u64;
let mut current_block_id = 0u16;
for el in els {
let block_id = el / ELEMENTS_PER_MINI_BLOCK;
let in_offset = el % ELEMENTS_PER_MINI_BLOCK;
while block_id > current_block_id {
let dense_mini_block = DenseMiniBlock {
bitvec: block,
rank: non_null_rows_before as u16,
};
output.write_all(&dense_mini_block.to_bytes())?;
non_null_rows_before += block.count_ones() as u16;
block = 0u64;
current_block_id += 1u16;
}
set_bit_at(&mut block, in_offset);
}
while current_block_id <= u16::MAX / ELEMENTS_PER_MINI_BLOCK {
block.serialize(&mut output)?;
non_null_rows_before.serialize(&mut output)?;
// This will overflow to 0 exactly if all bits are set.
// This is however not problem as we won't use this last value.
non_null_rows_before = non_null_rows_before.wrapping_add(block.count_ones() as u16);
block = 0u64;
current_block_id += 1u16;
}
Ok(())
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_select_bitvec() {
assert_eq!(select_u64(1u64, 0), 0);
assert_eq!(select_u64(2u64, 0), 1);
assert_eq!(select_u64(4u64, 0), 2);
assert_eq!(select_u64(8u64, 0), 3);
assert_eq!(select_u64(1 | 8u64, 0), 0);
assert_eq!(select_u64(1 | 8u64, 1), 3);
}
#[test]
fn test_count_ones() {
for i in 0..=63 {
assert_eq!(rank_u64(u64::MAX, i), i);
}
}
#[test]
fn test_dense() {
assert_eq!(DENSE_BLOCK_NUM_BYTES, 10_240);
}
}

112
columnar/src/column_index/optional_index/set_block/sparse.rs
View File

@@ -1,112 +0,0 @@
use crate::column_index::optional_index::{Set, SetCodec};
pub struct SparseBlockCodec;
impl SetCodec for SparseBlockCodec {
type Item = u16;
type Reader<'a> = SparseBlock<'a>;
fn serialize(
els: impl Iterator<Item = u16>,
mut wrt: impl std::io::Write,
) -> std::io::Result<()> {
for el in els {
wrt.write_all(&el.to_le_bytes())?;
}
Ok(())
}
fn open<'a>(data: &'a [u8]) -> Self::Reader<'a> {
SparseBlock(data)
}
}
#[derive(Copy, Clone)]
pub struct SparseBlock<'a>(&'a [u8]);
impl<'a> Set<u16> for SparseBlock<'a> {
#[inline(always)]
fn contains(&self, el: u16) -> bool {
self.binary_search(el).is_ok()
}
#[inline(always)]
fn rank_if_exists(&self, el: u16) -> Option<u16> {
self.binary_search(el).ok()
}
#[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 get_u16(data: &[u8], byte_position: usize) -> u16 {
let bytes: [u8; 2] = data[byte_position..byte_position + 2].try_into().unwrap();
u16::from_le_bytes(bytes)
}
impl<'a> SparseBlock<'a> {
#[inline(always)]
fn value_at_idx(&self, data: &[u8], idx: u16) -> u16 {
let start_offset: usize = idx as usize * 2;
get_u16(data, start_offset)
}
#[inline]
fn num_vals(&self) -> u16 {
(self.0.len() / 2) as u16
}
#[inline]
#[allow(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;
let mut size = self.num_vals();
let mut left = 0;
let mut right = size;
// TODO try different implem.
// e.g. exponential search into binary search
while left < right {
let mid = left + size / 2;
// TODO do boundary check only once, and then use an
// unsafe `value_at_idx`
let mid_val = self.value_at_idx(data, mid);
if target > mid_val {
left = mid + 1;
} else if target < mid_val {
right = mid;
} else {
return Ok(mid);
}
size = right - left;
}
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())
})
}
}

110
columnar/src/column_index/optional_index/set_block/tests.rs
View File

@@ -1,110 +0,0 @@
use std::collections::HashMap;
use crate::column_index::optional_index::set_block::set_block::DENSE_BLOCK_NUM_BYTES;
use crate::column_index::optional_index::set_block::{DenseBlockCodec, SparseBlockCodec};
use crate::column_index::optional_index::{Set, SetCodec};
fn test_set_helper<C: SetCodec<Item = u16>>(vals: &[u16]) -> usize {
let mut buffer = Vec::new();
C::serialize(vals.iter().copied(), &mut buffer).unwrap();
let tested_set = C::open(buffer.as_slice());
let hash_set: HashMap<C::Item, C::Item> = vals
.iter()
.copied()
.enumerate()
.map(|(ord, val)| (val, C::Item::try_from(ord).ok().unwrap()))
.collect();
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());
}
for rank in 0..vals.len() {
assert_eq!(tested_set.select(rank as u16), vals[rank]);
}
buffer.len()
}
#[test]
fn test_dense_block_set_u16_empty() {
let buffer_len = test_set_helper::<DenseBlockCodec>(&[]);
assert_eq!(buffer_len, DENSE_BLOCK_NUM_BYTES as usize);
}
#[test]
fn test_dense_block_set_u16_max() {
let buffer_len = test_set_helper::<DenseBlockCodec>(&[u16::MAX]);
assert_eq!(buffer_len, DENSE_BLOCK_NUM_BYTES as usize);
}
#[test]
fn test_sparse_block_set_u16_empty() {
let buffer_len = test_set_helper::<SparseBlockCodec>(&[]);
assert_eq!(buffer_len, 0);
}
#[test]
fn test_sparse_block_set_u16_max() {
let buffer_len = test_set_helper::<SparseBlockCodec>(&[u16::MAX]);
assert_eq!(buffer_len, 2);
}
use proptest::prelude::*;
proptest! {
#[test]
fn test_prop_test_dense(els in proptest::collection::btree_set(0..=u16::MAX, 0..=u16::MAX as usize)) {
let vals: Vec<u16> = els.into_iter().collect();
let buffer_len = test_set_helper::<DenseBlockCodec>(&vals);
assert_eq!(buffer_len, DENSE_BLOCK_NUM_BYTES as usize);
}
#[test]
fn test_prop_test_sparse(els in proptest::collection::btree_set(0..=u16::MAX, 0..=u16::MAX as usize)) {
let vals: Vec<u16> = els.into_iter().collect();
let buffer_len = test_set_helper::<SparseBlockCodec>(&vals);
assert_eq!(buffer_len, vals.len() * 2);
}
}
#[test]
fn test_simple_translate_codec_codec_idx_to_original_idx_dense() {
let mut buffer = Vec::new();
DenseBlockCodec::serialize([1, 3, 17, 32, 30_000, 30_001].iter().copied(), &mut buffer)
.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]
);
}
#[test]
fn test_simple_translate_codec_idx_to_original_idx_sparse() {
let mut buffer = Vec::new();
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]
);
}
#[test]
fn test_simple_translate_codec_idx_to_original_idx_dense() {
let mut buffer = Vec::new();
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
);
}

327
columnar/src/column_index/optional_index/tests.rs
View File

@@ -1,327 +0,0 @@
use proptest::prelude::{any, prop, *};
use proptest::strategy::Strategy;
use proptest::{prop_oneof, proptest};
use super::*;
#[test]
fn test_dense_block_threshold() {
assert_eq!(super::DENSE_BLOCK_THRESHOLD, 5_120);
}
fn random_bitvec() -> BoxedStrategy<Vec<bool>> {
prop_oneof![
1 => prop::collection::vec(proptest::bool::weighted(1.0), 0..100),
1 => prop::collection::vec(proptest::bool::weighted(0.00), 0..(ELEMENTS_PER_BLOCK as usize * 3)), // empty blocks
1 => prop::collection::vec(proptest::bool::weighted(1.00), 0..(ELEMENTS_PER_BLOCK as usize + 10)), // full block
1 => prop::collection::vec(proptest::bool::weighted(0.01), 0..100),
1 => prop::collection::vec(proptest::bool::weighted(0.01), 0..u16::MAX as usize),
8 => vec![any::<bool>()],
]
.boxed()
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(50))]
#[test]
fn test_with_random_bitvecs(bitvec1 in random_bitvec(), bitvec2 in random_bitvec(), bitvec3 in random_bitvec()) {
let mut bitvec = Vec::new();
bitvec.extend_from_slice(&bitvec1);
bitvec.extend_from_slice(&bitvec2);
bitvec.extend_from_slice(&bitvec3);
test_null_index(&bitvec[..]);
}
}
#[test]
fn test_with_random_sets_simple() {
let vals = 10..BLOCK_SIZE * 2;
let mut out: Vec<u8> = Vec::new();
serialize_optional_index(&vals.clone(), &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);
}
#[test]
fn test_optional_index_trailing_empty_blocks() {
test_null_index(&[false]);
}
#[test]
fn test_optional_index_one_block_false() {
let mut iter = vec![false; ELEMENTS_PER_BLOCK as usize];
iter.push(true);
test_null_index(&iter[..]);
}
#[test]
fn test_optional_index_one_block_true() {
let mut iter = vec![true; ELEMENTS_PER_BLOCK as usize];
iter.push(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> {
Box::new(
self.iter()
.cloned()
.enumerate()
.filter(|(_pos, val)| *val)
.map(|(pos, _val)| pos as u32),
)
}
}
fn test_null_index(data: &[bool]) {
let mut out: Vec<u8> = Vec::new();
serialize_optional_index(&data, &mut out).unwrap();
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
let orig_idx_with_value: Vec<u32> = data
.iter()
.enumerate()
.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 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) {
assert_eq!(null_index.rank_if_exists(*orig_idx), Some(dense_idx as u32));
}
// 100 samples
let step_size = (data.len() / 100).max(1);
for (pos, value) in data.iter().enumerate().step_by(step_size) {
assert_eq!(null_index.contains(pos as u32), *value);
}
}
#[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]);
}
#[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));
}
#[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));
}
#[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 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));
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use test::Bencher;
use super::*;
const TOTAL_NUM_VALUES: u32 = 1_000_000;
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)
.map(|_| rng.gen_bool(fill_ratio))
.collect();
serialize_optional_index(&&vals[..], &mut out).unwrap();
let codec = open_optional_index(OwnedBytes::new(out)).unwrap();
codec
}
fn random_range_iterator(
start: u32,
end: u32,
avg_step_size: u32,
avg_deviation: u32,
) -> impl Iterator<Item = u32> {
let mut rng: StdRng = StdRng::from_seed([1u8; 32]);
let mut current = start;
std::iter::from_fn(move || {
current += rng.gen_range(avg_step_size - avg_deviation..=avg_step_size + avg_deviation);
if current >= end {
None
} else {
Some(current)
}
})
}
fn n_percent_step_iterator(percent: f32, num_values: u32) -> impl Iterator<Item = u32> {
let ratio = percent as f32 / 100.0;
let step_size = (1f32 / ratio) as u32;
let deviation = step_size - 1;
random_range_iterator(0, num_values, step_size, deviation)
}
fn walk_over_data(codec: &OptionalIndex, avg_step_size: u32) -> Option<u32> {
walk_over_data_from_positions(
codec,
random_range_iterator(0, TOTAL_NUM_VALUES, avg_step_size, 0),
)
}
fn walk_over_data_from_positions(
codec: &OptionalIndex,
positions: impl Iterator<Item = u32>,
) -> Option<u32> {
let mut dense_idx: Option<u32> = None;
for idx in positions {
dense_idx = dense_idx.or(codec.rank_if_exists(idx));
}
dense_idx
}
#[bench]
fn bench_translate_orig_to_codec_1percent_filled_10percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_5percent_filled_10percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.05f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_5percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.05f64);
bench.iter(|| walk_over_data(&codec, 1000));
}
#[bench]
fn bench_translate_orig_to_codec_full_scan_1percent_filled(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
}
#[bench]
fn bench_translate_orig_to_codec_full_scan_10percent_filled(bench: &mut Bencher) {
let codec = gen_bools(0.1f64);
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
}
#[bench]
fn bench_translate_orig_to_codec_full_scan_90percent_filled(bench: &mut Bencher) {
let codec = gen_bools(0.9f64);
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
}
#[bench]
fn bench_translate_orig_to_codec_10percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.1f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_50percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.5f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_90percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.9f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_codec_to_orig_1percent_filled_0comma005percent_hit(bench: &mut Bencher) {
bench_translate_codec_to_orig_util(0.01f64, 0.005f32, bench);
}
#[bench]
fn bench_translate_codec_to_orig_10percent_filled_0comma005percent_hit(bench: &mut Bencher) {
bench_translate_codec_to_orig_util(0.1f64, 0.005f32, bench);
}
#[bench]
fn bench_translate_codec_to_orig_1percent_filled_10percent_hit(bench: &mut Bencher) {
bench_translate_codec_to_orig_util(0.01f64, 10f32, bench);
}
#[bench]
fn bench_translate_codec_to_orig_1percent_filled_full_scan(bench: &mut Bencher) {
bench_translate_codec_to_orig_util(0.01f64, 100f32, bench);
}
fn bench_translate_codec_to_orig_util(
percent_filled: f64,
percent_hit: f32,
bench: &mut Bencher,
) {
let codec = gen_bools(percent_filled);
let num_non_nulls = codec.num_non_nulls();
let idxs: Vec<u32> = if percent_hit == 100.0f32 {
(0..num_non_nulls).collect()
} else {
n_percent_step_iterator(percent_hit, num_non_nulls).collect()
};
let mut output = vec![0u32; idxs.len()];
bench.iter(|| {
codec.select_batch(&idxs[..], &mut output);
});
}
#[bench]
fn bench_translate_codec_to_orig_90percent_filled_0comma005percent_hit(bench: &mut Bencher) {
bench_translate_codec_to_orig_util(0.9f64, 0.005, bench);
}
#[bench]
fn bench_translate_codec_to_orig_90percent_filled_full_scan(bench: &mut Bencher) {
bench_translate_codec_to_orig_util(0.9f64, 100.0f32, bench);
}
}

70
columnar/src/column_index/serialize.rs
View File

@@ -1,70 +0,0 @@
use std::io;
use std::io::Write;
use common::OwnedBytes;
use crate::column_index::multivalued_index::{serialize_multivalued_index, MultivaluedIndex};
use crate::column_index::optional_index::serialize_optional_index;
use crate::column_index::{ColumnIndex, SerializableOptionalIndex};
use crate::Cardinality;
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(MultivaluedIndex),
}
impl<'a> SerializableColumnIndex<'a> {
pub fn get_cardinality(&self) -> Cardinality {
match self {
SerializableColumnIndex::Full => Cardinality::Full,
SerializableColumnIndex::Optional(_) => Cardinality::Optional,
SerializableColumnIndex::Multivalued(_) => Cardinality::Multivalued,
}
}
}
pub fn serialize_column_index(
column_index: SerializableColumnIndex,
output: &mut impl Write,
) -> io::Result<()> {
let cardinality = column_index.get_cardinality().to_code();
output.write_all(&[cardinality])?;
match column_index {
SerializableColumnIndex::Full => {}
SerializableColumnIndex::Optional(optional_index) => {
serialize_optional_index(&*optional_index, output)?
}
SerializableColumnIndex::Multivalued(multivalued_index) => {
serialize_multivalued_index(multivalued_index, output)?
}
}
Ok(())
}
pub fn open_column_index(mut bytes: OwnedBytes) -> io::Result<ColumnIndex<'static>> {
if bytes.is_empty() {
return Err(io::Error::new(
io::ErrorKind::UnexpectedEof,
"Failed to deserialize column index. Empty buffer.",
));
}
let cardinality_code = bytes[0];
let cardinality = Cardinality::try_from_code(cardinality_code)?;
bytes.advance(1);
match cardinality {
Cardinality::Full => Ok(ColumnIndex::Full),
Cardinality::Optional => {
let optional_index = super::optional_index::open_optional_index(bytes)?;
Ok(ColumnIndex::Optional(optional_index))
}
Cardinality::Multivalued => {
let multivalued_index = super::multivalued_index::open_multivalued_index(bytes)?;
Ok(ColumnIndex::Multivalued(multivalued_index))
}
}
}
// TODO unit tests

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
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@@ -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
}
}

350
columnar/src/column_values/column.rs
View File

@@ -1,350 +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<'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/>.
//

43
columnar/src/column_values/compact_space/blank_range.rs
View File

@@ -1,43 +0,0 @@
use std::ops::RangeInclusive;
/// The range of a blank in value space.
///
/// A blank is an unoccupied space in the data.
/// Use try_into() to construct.
/// A range has to have at least length of 3. Invalid ranges will be rejected.
///
/// Ordered by range length.
#[derive(Debug, Eq, PartialEq, Clone)]
pub(crate) struct BlankRange {
blank_range: RangeInclusive<u128>,
}
impl TryFrom<RangeInclusive<u128>> for BlankRange {
type Error = &'static str;
fn try_from(range: RangeInclusive<u128>) -> Result<Self, Self::Error> {
let blank_size = range.end().saturating_sub(*range.start());
if blank_size < 2 {
Err("invalid range")
} else {
Ok(BlankRange { blank_range: range })
}
}
}
impl BlankRange {
pub(crate) fn blank_size(&self) -> u128 {
self.blank_range.end() - self.blank_range.start() + 1
}
pub(crate) fn blank_range(&self) -> RangeInclusive<u128> {
self.blank_range.clone()
}
}
impl Ord for BlankRange {
fn cmp(&self, other: &Self) -> std::cmp::Ordering {
self.blank_size().cmp(&other.blank_size())
}
}
impl PartialOrd for BlankRange {
fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
Some(self.blank_size().cmp(&other.blank_size()))
}
}

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
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@@ -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);
}
}

222
columnar/src/column_values/line.rs
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@@ -1,222 +0,0 @@
use std::io;
use std::num::NonZeroU32;
use common::{BinarySerializable, VInt};
use crate::column_values::ColumnValues;
const MID_POINT: u64 = (1u64 << 32) - 1u64;
/// `Line` describes a line function `y: ax + b` using integer
/// arithmetics.
///
/// The slope is in fact a decimal split into a 32 bit integer value,
/// and a 32-bit decimal value.
///
/// The multiplication then becomes.
/// `y = m * x >> 32 + b`
#[derive(Debug, Clone, Copy, Default)]
pub struct Line {
slope: u64,
intercept: u64,
}
/// Compute the line slope.
///
/// This function has the nice property of being
/// invariant by translation.
/// `
/// compute_slope(y0, y1)
/// = compute_slope(y0 + X % 2^64, y1 + X % 2^64)
/// `
fn compute_slope(y0: u64, y1: u64, num_vals: NonZeroU32) -> u64 {
let dy = y1.wrapping_sub(y0);
let sign = dy <= (1 << 63);
let abs_dy = if sign {
y1.wrapping_sub(y0)
} else {
y0.wrapping_sub(y1)
};
if abs_dy >= 1 << 32 {
// This is outside of realm we handle.
// Let's just bail.
return 0u64;
}
let abs_slope = (abs_dy << 32) / num_vals.get() as u64;
if sign {
abs_slope
} else {
// The complement does indeed create the
// opposite decreasing slope...
//
// Intuitively (without the bitshifts and % u64::MAX)
// ```
// (x + shift)*(u64::MAX - abs_slope)
// - (x * (u64::MAX - abs_slope))
// = - shift * abs_slope
// ```
u64::MAX - abs_slope
}
}
impl Line {
#[inline(always)]
pub fn eval(&self, x: u32) -> u64 {
let linear_part = ((x as u64).wrapping_mul(self.slope) >> 32) as i32 as u64;
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(
first_val: u64,
last_val: u64,
num_vals: u32,
positions_and_values: impl Iterator<Item = (u64, u64)>,
) -> Self {
// TODO replace with let else
let idx_last_val = if let Some(idx_last_val) = NonZeroU32::new(num_vals - 1) {
idx_last_val
} else {
return Line::default();
};
let y0 = first_val;
let y1 = last_val;
// We first independently pick our slope.
let slope = compute_slope(y0, y1, idx_last_val);
// We picked our slope. Note that it does not have to be perfect.
// Now we need to compute the best intercept.
//
// Intuitively, the best intercept is such that line passes through one of the
// `(i, ys[])`.
//
// The best intercept therefore has the form
// `y[i] - line.eval(i)` (using wrapping arithmetics).
// In other words, the best intercept is one of the `y - Line::eval(ys[i])`
// and our task is just to pick the one that minimizes our error.
//
// Without sorting our values, this is a difficult problem.
// We however rely on the following trick...
//
// We only focus on the case where the interpolation is half decent.
// If the line interpolation is doing its job on a dataset suited for it,
// we can hope that the maximum error won't be larger than `u64::MAX / 2`.
//
// In other words, even without the intercept the values `y - Line::eval(ys[i])` will all be
// within an interval that takes less than half of the modulo space of `u64`.
//
// Our task is therefore to identify this interval.
// Here we simply translate all of our values by `y0 - 2^63` and pick the min.
let mut line = Line {
slope,
intercept: 0,
};
let heuristic_shift = y0.wrapping_sub(MID_POINT);
line.intercept = positions_and_values
.map(|(pos, y)| y.wrapping_sub(line.eval(pos as u32)))
.min_by_key(|&val| val.wrapping_sub(heuristic_shift))
.unwrap_or(0u64); //< Never happens.
line
}
/// Returns a line that attemps 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()).
/// - 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)
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();
Self::train_from(
first_val,
last_val,
ys.num_vals(),
ys.iter().enumerate().map(|(pos, val)| (pos as u64, val)),
)
}
}
impl BinarySerializable for Line {
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.slope).serialize(writer)?;
VInt(self.intercept).serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let slope = VInt::deserialize(reader)?.0;
let intercept = VInt::deserialize(reader)?.0;
Ok(Line { slope, intercept })
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_values::VecColumn;
/// Test training a line and ensuring that the maximum difference between
/// the data points and the line is `expected`.
///
/// This function operates translation over the data for better coverage.
#[track_caller]
fn test_line_interpol_with_translation(ys: &[u64], expected: Option<u64>) {
let mut translations = vec![0, 100, u64::MAX / 2, u64::MAX, u64::MAX - 1];
translations.extend_from_slice(ys);
for translation in translations {
let translated_ys: Vec<u64> = ys
.iter()
.copied()
.map(|y| y.wrapping_add(translation))
.collect();
let largest_err = test_eval_max_err(&translated_ys);
assert_eq!(largest_err, expected);
}
}
fn test_eval_max_err(ys: &[u64]) -> Option<u64> {
let line = Line::train(&VecColumn::from(&ys));
ys.iter()
.enumerate()
.map(|(x, y)| y.wrapping_sub(line.eval(x as u32)))
.max()
}
#[test]
fn test_train() {
test_line_interpol_with_translation(&[11, 11, 11, 12, 12, 13], Some(1));
test_line_interpol_with_translation(&[13, 12, 12, 11, 11, 11], Some(1));
test_line_interpol_with_translation(&[13, 13, 12, 11, 11, 11], Some(1));
test_line_interpol_with_translation(&[13, 13, 12, 11, 11, 11], Some(1));
test_line_interpol_with_translation(&[u64::MAX - 1, 0, 0, 1], Some(1));
test_line_interpol_with_translation(&[u64::MAX - 1, u64::MAX, 0, 1], Some(0));
test_line_interpol_with_translation(&[0, 1, 2, 3, 5], Some(0));
test_line_interpol_with_translation(&[1, 2, 3, 4], Some(0));
let data: Vec<u64> = (0..255).collect();
test_line_interpol_with_translation(&data, Some(0));
let data: Vec<u64> = (0..255).map(|el| el * 2).collect();
test_line_interpol_with_translation(&data, Some(0));
}
}

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))
}

333
columnar/src/column_values/mod.rs
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@@ -1,333 +0,0 @@
#![warn(missing_docs)]
#![cfg_attr(all(feature = "unstable", test), feature(test))]
//! # `fastfield_codecs`
//!
//! - Columnar storage of data for tantivy [`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::sync::Arc;
use common::{BinarySerializable, OwnedBytes};
use compact_space::CompactSpaceDecompressor;
use monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
StrictlyMonotonicMappingToInternalBaseval, StrictlyMonotonicMappingToInternalGCDBaseval,
};
use serialize::{Header, U128Header};
mod bitpacked;
mod blockwise_linear;
mod compact_space;
mod line;
mod linear;
pub(crate) mod monotonic_mapping;
// mod monotonic_mapping_u128;
mod column;
mod column_with_cardinality;
mod gcd;
pub mod serialize;
pub use self::column::{monotonic_map_column, ColumnValues, IterColumn, VecColumn};
pub use self::monotonic_mapping::{MonotonicallyMappableToU64, StrictlyMonotonicFn};
// pub use self::monotonic_mapping_u128::MonotonicallyMappableToU128;
pub use self::serialize::{serialize_and_load, serialize_column_values, NormalizedHeader};
use crate::column_values::bitpacked::BitpackedCodec;
use crate::column_values::blockwise_linear::BlockwiseLinearCodec;
use crate::column_values::linear::LinearCodec;
#[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,
}
impl BinarySerializable for FastFieldCodecType {
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 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,
}
}
}
#[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,
}
}
}
/// Returns the correct codec reader wrapped in the `Arc` for the data.
// pub fn open_u128<Item: MonotonicallyMappableToU128>(
// bytes: OwnedBytes,
// ) -> io::Result<Arc<dyn Column<Item>>> {
// todo!();
// // let (bytes, _format_version) = read_format_version(bytes)?;
// // let (mut bytes, _null_index_footer) = read_null_index_footer(bytes)?;
// // let header = U128Header::deserialize(&mut bytes)?;
// // assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
// // let reader = CompactSpaceDecompressor::open(bytes)?;
// // let inverted: StrictlyMonotonicMappingInverter<StrictlyMonotonicMappingToInternal<Item>> =
// // StrictlyMonotonicMappingToInternal::<Item>::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)
}
}
}
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)))
}
}
/// 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;
type Reader: ColumnValues<u64> + 'static;
/// Reads the metadata and returns the CodecReader
fn open_from_bytes(bytes: OwnedBytes, header: NormalizedHeader) -> io::Result<Self::Reader>;
/// 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<()>;
/// 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>;
}
/// The list of all available codecs for u64 convertible data.
pub const ALL_CODEC_TYPES: [FastFieldCodecType; 3] = [
FastFieldCodecType::Bitpacked,
FastFieldCodecType::BlockwiseLinear,
FastFieldCodecType::Linear,
];
#[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);
}
}

264
columnar/src/column_values/monotonic_mapping.rs
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@@ -1,264 +0,0 @@
use std::marker::PhantomData;
use fastdivide::DividerU64;
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 {
/// Converts a value to u64.
///
/// Internally all fast field values are encoded as u64.
fn to_u64(self) -> u64;
/// Converts a value from u64
///
/// Internally all fast field values are encoded as u64.
/// **Note: To be used for converting encoded Term, Posting values.**
fn from_u64(val: u64) -> Self;
}
/// Values need to be strictly monotonic mapped to a `Internal` value (u64 or u128) that can be
/// used in fast field codecs.
///
/// The monotonic mapping is required so that `PartialOrd` can be used on `Internal` without
/// converting to `External`.
///
/// All strictly monotonic functions are invertible because they are guaranteed to have a one-to-one
/// mapping from their range to their domain. The `inverse` method is required when opening a codec,
/// so a value can be converted back to its original domain (e.g. ip address or f64) from its
/// internal representation.
pub trait StrictlyMonotonicFn<External, Internal> {
/// Strictly monotonically maps the value from External to Internal.
fn mapping(&self, inp: External) -> Internal;
/// Inverse of `mapping`. Maps the value from Internal to External.
fn inverse(&self, out: Internal) -> External;
}
/// Inverts a strictly monotonic mapping from `StrictlyMonotonicFn<A, B>` to
/// `StrictlyMonotonicFn<B, A>`.
///
/// # Warning
///
/// This type comes with a footgun. A type being strictly monotonic does not impose that the inverse
/// mapping is strictly monotonic over the entire space External. e.g. a -> a * 2. Use at your own
/// risks.
pub(crate) struct StrictlyMonotonicMappingInverter<T> {
orig_mapping: T,
}
impl<T> From<T> for StrictlyMonotonicMappingInverter<T> {
fn from(orig_mapping: T) -> Self {
Self { orig_mapping }
}
}
impl<From, To, T> StrictlyMonotonicFn<To, From> for StrictlyMonotonicMappingInverter<T>
where T: StrictlyMonotonicFn<From, To>
{
#[inline(always)]
fn mapping(&self, val: To) -> From {
self.orig_mapping.inverse(val)
}
#[inline(always)]
fn inverse(&self, val: From) -> To {
self.orig_mapping.mapping(val)
}
}
/// Applies the strictly monotonic mapping from `T` without any additional changes.
pub(crate) struct StrictlyMonotonicMappingToInternal<T> {
_phantom: PhantomData<T>,
}
impl<T> StrictlyMonotonicMappingToInternal<T> {
pub(crate) fn new() -> StrictlyMonotonicMappingToInternal<T> {
Self {
_phantom: PhantomData,
}
}
}
// TODO
// impl<External: MonotonicallyMappableToU128, T: MonotonicallyMappableToU128>
// StrictlyMonotonicFn<External, u128> for StrictlyMonotonicMappingToInternal<T>
// where T: MonotonicallyMappableToU128
// {
// #[inline(always)]
// fn mapping(&self, inp: External) -> u128 {
// External::to_u128(inp)
// }
// #[inline(always)]
// fn inverse(&self, out: u128) -> External {
// External::from_u128(out)
// }
// }
impl<External: MonotonicallyMappableToU64, T: MonotonicallyMappableToU64>
StrictlyMonotonicFn<External, u64> for StrictlyMonotonicMappingToInternal<T>
where T: MonotonicallyMappableToU64
{
#[inline(always)]
fn mapping(&self, inp: External) -> u64 {
External::to_u64(inp)
}
#[inline(always)]
fn inverse(&self, out: u64) -> External {
External::from_u64(out)
}
}
/// 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 {
self
}
#[inline(always)]
fn from_u64(val: u64) -> Self {
val
}
}
impl MonotonicallyMappableToU64 for i64 {
#[inline(always)]
fn to_u64(self) -> u64 {
common::i64_to_u64(self)
}
#[inline(always)]
fn from_u64(val: u64) -> Self {
common::u64_to_i64(val)
}
}
impl MonotonicallyMappableToU64 for bool {
#[inline(always)]
fn to_u64(self) -> u64 {
u64::from(self)
}
#[inline(always)]
fn from_u64(val: u64) -> Self {
val > 0
}
}
impl MonotonicallyMappableToU64 for RowId {
#[inline(always)]
fn to_u64(self) -> u64 {
u64::from(self)
}
#[inline(always)]
fn from_u64(val: u64) -> RowId {
val as RowId
}
}
// TODO remove me.
// Tantivy should refuse NaN values and work with NotNaN internally.
impl MonotonicallyMappableToU64 for f64 {
#[inline(always)]
fn to_u64(self) -> u64 {
common::f64_to_u64(self)
}
#[inline(always)]
fn from_u64(val: u64) -> Self {
common::u64_to_f64(val)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn strictly_monotonic_test() {
// identity mapping
test_round_trip(&StrictlyMonotonicMappingToInternal::<u64>::new(), 100u64);
// round trip to i64
test_round_trip(&StrictlyMonotonicMappingToInternal::<i64>::new(), 100u64);
// 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>(
mapping: &T,
test_val: K,
) {
assert_eq!(mapping.inverse(mapping.mapping(test_val)), test_val);
}
}

40
columnar/src/column_values/monotonic_mapping_u128.rs
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@@ -1,40 +0,0 @@
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 {
/// Converts a value to u128.
///
/// Internally all fast field values are encoded as u64.
fn to_u128(self) -> u128;
/// Converts a value from u128
///
/// Internally all fast field values are encoded as u64.
/// **Note: To be used for converting encoded Term, Posting values.**
fn from_u128(val: u128) -> Self;
}
impl MonotonicallyMappableToU128 for u128 {
fn to_u128(self) -> u128 {
self
}
fn from_u128(val: u128) -> Self {
val
}
}
impl MonotonicallyMappableToU128 for Ipv6Addr {
fn to_u128(self) -> u128 {
ip_to_u128(self)
}
fn from_u128(val: u128) -> Self {
Ipv6Addr::from(val.to_be_bytes())
}
}
fn ip_to_u128(ip_addr: Ipv6Addr) -> u128 {
u128::from_be_bytes(ip_addr.octets())
}

343
columnar/src/column_values/serialize.rs
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@@ -1,343 +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 std::sync::Arc;
use common::{BinarySerializable, OwnedBytes, 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, VecColumn, ALL_CODEC_TYPES,
};
/// 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,
})
}
}
/// Return estimated compression for given codec in the value range [0.0..1.0], where 1.0 means no
/// compression.
pub(crate) fn estimate<T: MonotonicallyMappableToU64>(
typed_column: impl ColumnValues<T>,
codec_type: FastFieldCodecType,
) -> Option<f32> {
let column = monotonic_map_column(typed_column, StrictlyMonotonicMappingToInternal::<T>::new());
let min_value = column.min_value();
let gcd = super::gcd::find_gcd(column.iter().map(|val| val - min_value))
.filter(|gcd| gcd.get() > 1u64);
let mapping = StrictlyMonotonicMappingToInternalGCDBaseval::new(
gcd.map(|gcd| gcd.get()).unwrap_or(1u64),
min_value,
);
let normalized_column = monotonic_map_column(&column, mapping);
match codec_type {
FastFieldCodecType::Bitpacked => BitpackedCodec::estimate(&normalized_column),
FastFieldCodecType::Linear => LinearCodec::estimate(&normalized_column),
FastFieldCodecType::BlockwiseLinear => BlockwiseLinearCodec::estimate(&normalized_column),
}
}
// TODO
/// Serializes u128 values with the compact space codec.
// pub fn serialize_u128_new<F: Fn() -> I, I: Iterator<Item = u128>>(
// value_index: ColumnIndex,
// 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).unwrap();
// let null_index_footer = ColumnFooter {
// cardinality: value_index.get_cardinality(),
// null_index_codec: NullIndexCodec::Full,
// null_index_byte_range: 0..0,
// };
// append_null_index_footer(output, null_index_footer)?;
// append_format_version(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(())
}
/// 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();
super::serialize_column_values(&VecColumn::from(&column), &ALL_CODEC_TYPES, &mut buffer)
.unwrap();
super::open_u64_mapped(OwnedBytes::new(buffer)).unwrap()
}
#[cfg(test)]
mod tests {
use super::*;
#[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 + 7);
}
#[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 + 7);
}
#[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) + 7);
}
}

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);
}

126
columnar/src/columnar/column_type.rs
View File

@@ -1,126 +0,0 @@
use crate::utils::{place_bits, select_bits};
use crate::value::NumericalType;
use crate::InvalidData;
/// The column type represents the column type and can fit on 6-bits.
///
/// - bits[0..3]: Column category type.
/// - bits[3..6]: Numerical type if necessary.
#[derive(Hash, Eq, PartialEq, Debug, Clone, Copy)]
pub enum ColumnType {
Bytes,
Numerical(NumericalType),
Bool,
}
impl ColumnType {
/// Encoded over 6 bits.
pub(crate) fn to_code(self) -> u8 {
let column_type_category;
let numerical_type_code: u8;
match self {
ColumnType::Bytes => {
column_type_category = ColumnTypeCategory::Str;
numerical_type_code = 0u8;
}
ColumnType::Numerical(numerical_type) => {
column_type_category = ColumnTypeCategory::Numerical;
numerical_type_code = numerical_type.to_code();
}
ColumnType::Bool => {
column_type_category = ColumnTypeCategory::Bool;
numerical_type_code = 0u8;
}
}
place_bits::<0, 3>(column_type_category.to_code()) | place_bits::<3, 6>(numerical_type_code)
}
pub(crate) fn try_from_code(code: u8) -> Result<ColumnType, InvalidData> {
if select_bits::<6, 8>(code) != 0u8 {
return Err(InvalidData);
}
let column_type_category_code = select_bits::<0, 3>(code);
let numerical_type_code = select_bits::<3, 6>(code);
let column_type_category = ColumnTypeCategory::try_from_code(column_type_category_code)?;
match column_type_category {
ColumnTypeCategory::Bool => {
if numerical_type_code != 0u8 {
return Err(InvalidData);
}
Ok(ColumnType::Bool)
}
ColumnTypeCategory::Str => {
if numerical_type_code != 0u8 {
return Err(InvalidData);
}
Ok(ColumnType::Bytes)
}
ColumnTypeCategory::Numerical => {
let numerical_type = NumericalType::try_from_code(numerical_type_code)?;
Ok(ColumnType::Numerical(numerical_type))
}
}
}
}
/// Column types are grouped into different categories that
/// corresponds to the different types of `JsonValue` types.
///
/// The columnar writer will apply coercion rules to make sure that
/// at most one column exist per `ColumnTypeCategory`.
///
/// See also [README.md].
#[derive(Copy, Clone, Ord, PartialOrd, Eq, PartialEq, Debug)]
#[repr(u8)]
pub(crate) enum ColumnTypeCategory {
Bool = 0u8,
Str = 1u8,
Numerical = 2u8,
}
impl ColumnTypeCategory {
pub fn to_code(self) -> u8 {
self as u8
}
pub fn try_from_code(code: u8) -> Result<Self, InvalidData> {
match code {
0u8 => Ok(Self::Bool),
1u8 => Ok(Self::Str),
2u8 => Ok(Self::Numerical),
_ => Err(InvalidData),
}
}
}
#[cfg(test)]
mod tests {
use std::collections::HashSet;
use super::*;
use crate::Cardinality;
#[test]
fn test_column_type_to_code() {
let mut column_type_set: HashSet<ColumnType> = HashSet::new();
for code in u8::MIN..=u8::MAX {
if let Ok(column_type) = ColumnType::try_from_code(code) {
assert_eq!(column_type.to_code(), code);
assert!(column_type_set.insert(column_type));
}
}
assert_eq!(column_type_set.len(), 2 + 3);
}
#[test]
fn test_cardinality_to_code() {
let mut num_cardinality = 0;
for code in u8::MIN..=u8::MAX {
if let Ok(cardinality) = Cardinality::try_from_code(code) {
assert_eq!(cardinality.to_code(), code);
num_cardinality += 1;
}
}
assert_eq!(num_cardinality, 3);
}
}

73
columnar/src/columnar/format_version.rs
View File

@@ -1,73 +0,0 @@
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];
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[4..8].copy_from_slice(&MAGIC_BYTES[..]);
footer_bytes
}
pub fn parse_footer(footer_bytes: [u8; VERSION_FOOTER_NUM_BYTES]) -> Result<Version, InvalidData> {
if footer_bytes[4..8] != MAGIC_BYTES {
return Err(InvalidData);
}
Version::try_from_bytes(footer_bytes[0..4].try_into().unwrap())
}
#[derive(Debug, Copy, Clone, Eq, PartialEq)]
#[repr(u32)]
pub enum Version {
V1 = 1u32,
}
impl Version {
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),
_ => Err(InvalidData),
}
}
}
#[cfg(test)]
mod tests {
use std::collections::HashSet;
use super::*;
#[test]
fn test_footer_dserialization() {
let parsed_version: Version = parse_footer(footer()).unwrap();
assert_eq!(Version::V1, parsed_version);
}
#[test]
fn test_version_serialization() {
let version_to_tests: Vec<u32> = [0, 1 << 8, 1 << 16, 1 << 24]
.iter()
.copied()
.flat_map(|offset| (0..255).map(move |el| el + offset))
.collect();
let mut valid_versions: HashSet<u32> = HashSet::default();
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);
}
}

28
columnar/src/columnar/mod.rs
View File

@@ -1,28 +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/>.
//
mod column_type;
mod format_version;
mod reader;
mod writer;
pub use column_type::ColumnType;
pub use reader::ColumnarReader;
pub use writer::ColumnarWriter;

121
columnar/src/columnar/reader/mod.rs
View File

@@ -1,121 +0,0 @@
use std::{io, mem};
use common::file_slice::FileSlice;
use common::BinarySerializable;
use sstable::{Dictionary, RangeSSTable};
use crate::columnar::{format_version, ColumnType};
use crate::dynamic_column::DynamicColumnHandle;
fn io_invalid_data(msg: String) -> io::Error {
io::Error::new(io::ErrorKind::InvalidData, msg)
}
/// The ColumnarReader makes it possible to access a set of columns
/// associated to field names.
pub struct ColumnarReader {
column_dictionary: Dictionary<RangeSSTable>,
column_data: FileSlice,
}
impl ColumnarReader {
/// Opens a new Columnar file.
pub fn open<F>(file_slice: F) -> io::Result<ColumnarReader>
where FileSlice: From<F> {
Self::open_inner(file_slice.into())
}
fn open_inner(file_slice: FileSlice) -> io::Result<ColumnarReader> {
let (file_slice_without_sstable_len, footer_slice) = file_slice
.split_from_end(mem::size_of::<u64>() + format_version::VERSION_FOOTER_NUM_BYTES);
let footer_bytes = footer_slice.read_bytes()?;
let (mut sstable_len_bytes, version_footer_bytes) =
footer_bytes.rsplit(format_version::VERSION_FOOTER_NUM_BYTES);
let version_footer_bytes: [u8; format_version::VERSION_FOOTER_NUM_BYTES] =
version_footer_bytes.as_slice().try_into().unwrap();
let _version = format_version::parse_footer(version_footer_bytes)?;
let sstable_len = u64::deserialize(&mut sstable_len_bytes)?;
let (column_data, sstable) =
file_slice_without_sstable_len.split_from_end(sstable_len as usize);
let column_dictionary = Dictionary::open(sstable)?;
Ok(ColumnarReader {
column_dictionary,
column_data,
})
}
// TODO fix ugly API
pub fn list_columns(&self) -> io::Result<Vec<(String, DynamicColumnHandle)>> {
let mut stream = self.column_dictionary.stream()?;
let mut results = Vec::new();
while stream.advance() {
let key_bytes: &[u8] = stream.key();
let column_code: u8 = key_bytes.last().cloned().unwrap();
let column_type: ColumnType = ColumnType::try_from_code(column_code)
.map_err(|_| io_invalid_data(format!("Unknown column code `{column_code}`")))?;
let range = stream.value().clone();
let column_name =
String::from_utf8_lossy(&key_bytes[..key_bytes.len() - 1]).to_string();
let file_slice = self
.column_data
.slice(range.start as usize..range.end as usize);
let column_handle = DynamicColumnHandle {
file_slice,
column_type,
};
results.push((column_name, column_handle));
}
Ok(results)
}
/// Get all columns for the given column name.
///
/// There can be more than one column associated to a given column name, provided they have
/// different types.
// TODO fix ugly API
pub fn read_columns(&self, column_name: &str) -> io::Result<Vec<DynamicColumnHandle>> {
// Each column is a associated to a given `column_key`,
// that starts by `column_name\0column_header`.
//
// Listing the columns associated to the given column name is therefore equivalent to
// listing `column_key` with the prefix `column_name\0`.
//
// This is in turn equivalent to searching for the range
// `[column_name,\0`..column_name\1)`.
// TODO can we get some more generic `prefix(..)` logic in the dictioanry.
let mut start_key = column_name.to_string();
start_key.push('\0');
let mut end_key = column_name.to_string();
end_key.push(1u8 as char);
let mut stream = self
.column_dictionary
.range()
.ge(start_key.as_bytes())
.lt(end_key.as_bytes())
.into_stream()?;
let mut results = Vec::new();
while stream.advance() {
let key_bytes: &[u8] = stream.key();
assert!(key_bytes.starts_with(start_key.as_bytes()));
let column_code: u8 = key_bytes.last().cloned().unwrap();
let column_type = ColumnType::try_from_code(column_code)
.map_err(|_| io_invalid_data(format!("Unknown column code `{column_code}`")))?;
let range = stream.value().clone();
let file_slice = self
.column_data
.slice(range.start as usize..range.end as usize);
let dynamic_column_handle = DynamicColumnHandle {
file_slice,
column_type,
};
results.push(dynamic_column_handle);
}
Ok(results)
}
/// Return the number of columns in the columnar.
pub fn num_columns(&self) -> usize {
self.column_dictionary.num_terms()
}
}

346
columnar/src/columnar/writer/column_operation.rs
View File

@@ -1,346 +0,0 @@
use crate::dictionary::UnorderedId;
use crate::utils::{place_bits, pop_first_byte, select_bits};
use crate::value::NumericalValue;
use crate::{InvalidData, NumericalType, RowId};
/// When we build a columnar dataframe, we first just group
/// all mutations per column, and appends them in append-only buffer
/// in the stacker.
///
/// These ColumnOperation<T> are therefore serialize/deserialized
/// in memory.
///
/// We represents all of these operations as `ColumnOperation`.
#[derive(Eq, PartialEq, Debug, Clone, Copy)]
pub(super) enum ColumnOperation<T> {
NewDoc(RowId),
Value(T),
}
#[derive(Copy, Clone, Eq, PartialEq, Debug)]
struct ColumnOperationMetadata {
op_type: ColumnOperationType,
len: u8,
}
impl ColumnOperationMetadata {
fn to_code(self) -> u8 {
place_bits::<0, 4>(self.len) | place_bits::<4, 8>(self.op_type.to_code())
}
fn try_from_code(code: u8) -> Result<Self, InvalidData> {
let len = select_bits::<0, 4>(code);
let typ_code = select_bits::<4, 8>(code);
let column_type = ColumnOperationType::try_from_code(typ_code)?;
Ok(ColumnOperationMetadata {
op_type: column_type,
len,
})
}
}
#[derive(Copy, Clone, Eq, PartialEq, Debug)]
#[repr(u8)]
enum ColumnOperationType {
NewDoc = 0u8,
AddValue = 1u8,
}
impl ColumnOperationType {
pub fn to_code(self) -> u8 {
self as u8
}
pub fn try_from_code(code: u8) -> Result<Self, InvalidData> {
match code {
0 => Ok(Self::NewDoc),
1 => Ok(Self::AddValue),
_ => Err(InvalidData),
}
}
}
impl<V: SymbolValue> ColumnOperation<V> {
pub(super) fn serialize(self) -> impl AsRef<[u8]> {
let mut minibuf = MiniBuffer::default();
let column_op_metadata = match self {
ColumnOperation::NewDoc(new_doc) => {
let symbol_len = new_doc.serialize(&mut minibuf.bytes[1..]);
ColumnOperationMetadata {
op_type: ColumnOperationType::NewDoc,
len: symbol_len,
}
}
ColumnOperation::Value(val) => {
let symbol_len = val.serialize(&mut minibuf.bytes[1..]);
ColumnOperationMetadata {
op_type: ColumnOperationType::AddValue,
len: symbol_len,
}
}
};
minibuf.bytes[0] = column_op_metadata.to_code();
// +1 for the metadata
minibuf.len = 1 + column_op_metadata.len;
minibuf
}
/// Deserialize a colummn operation.
/// Returns None if the buffer is empty.
///
/// Panics if the payload is invalid:
/// this deserialize method is meant to target in memory.
pub(super) fn deserialize(bytes: &mut &[u8]) -> Option<Self> {
let column_op_metadata_byte = pop_first_byte(bytes)?;
let column_op_metadata = ColumnOperationMetadata::try_from_code(column_op_metadata_byte)
.expect("Invalid op metadata byte");
let symbol_bytes: &[u8];
(symbol_bytes, *bytes) = bytes.split_at(column_op_metadata.len as usize);
match column_op_metadata.op_type {
ColumnOperationType::NewDoc => {
let new_doc = u32::deserialize(symbol_bytes);
Some(ColumnOperation::NewDoc(new_doc))
}
ColumnOperationType::AddValue => {
let value = V::deserialize(symbol_bytes);
Some(ColumnOperation::Value(value))
}
}
}
}
impl<T> From<T> for ColumnOperation<T> {
fn from(value: T) -> Self {
ColumnOperation::Value(value)
}
}
// Serialization trait very local to the writer.
// As we write fast fields, we accumulate them in "in memory".
// In order to limit memory usage, and in order
// to benefit from the stacker, we do this by serialization our data
// as "Symbols".
#[allow(clippy::from_over_into)]
pub(super) trait SymbolValue: Clone + Copy {
// Serializes the symbol into the given buffer.
// Returns the number of bytes written into the buffer.
/// # Panics
/// May not exceed 9bytes
fn serialize(self, buffer: &mut [u8]) -> u8;
// Panics if invalid
fn deserialize(bytes: &[u8]) -> Self;
}
impl SymbolValue for bool {
fn serialize(self, buffer: &mut [u8]) -> u8 {
buffer[0] = u8::from(self);
1u8
}
fn deserialize(bytes: &[u8]) -> Self {
bytes[0] == 1u8
}
}
#[derive(Default)]
struct MiniBuffer {
pub bytes: [u8; 10],
pub len: u8,
}
impl AsRef<[u8]> for MiniBuffer {
fn as_ref(&self) -> &[u8] {
&self.bytes[..self.len as usize]
}
}
impl SymbolValue for NumericalValue {
fn deserialize(mut bytes: &[u8]) -> Self {
let type_code = pop_first_byte(&mut bytes).unwrap();
let symbol_type = NumericalType::try_from_code(type_code).unwrap();
let mut octet: [u8; 8] = [0u8; 8];
octet[..bytes.len()].copy_from_slice(bytes);
match symbol_type {
NumericalType::U64 => {
let val: u64 = u64::from_le_bytes(octet);
NumericalValue::U64(val)
}
NumericalType::I64 => {
let encoded: u64 = u64::from_le_bytes(octet);
let val: i64 = decode_zig_zag(encoded);
NumericalValue::I64(val)
}
NumericalType::F64 => {
debug_assert_eq!(bytes.len(), 8);
let val: f64 = f64::from_le_bytes(octet);
NumericalValue::F64(val)
}
}
}
/// F64: Serialize with a fixed size of 9 bytes
/// U64: Serialize without leading zeroes
/// I64: ZigZag encoded and serialize without leading zeroes
fn serialize(self, output: &mut [u8]) -> u8 {
match self {
NumericalValue::F64(val) => {
output[0] = NumericalType::F64 as u8;
output[1..9].copy_from_slice(&val.to_le_bytes());
9u8
}
NumericalValue::U64(val) => {
let len = compute_num_bytes_for_u64(val) as u8;
output[0] = NumericalType::U64 as u8;
output[1..9].copy_from_slice(&val.to_le_bytes());
len + 1u8
}
NumericalValue::I64(val) => {
let zig_zag_encoded = encode_zig_zag(val);
let len = compute_num_bytes_for_u64(zig_zag_encoded) as u8;
output[0] = NumericalType::I64 as u8;
output[1..9].copy_from_slice(&zig_zag_encoded.to_le_bytes());
len + 1u8
}
}
}
}
impl SymbolValue for u32 {
fn serialize(self, output: &mut [u8]) -> u8 {
let len = compute_num_bytes_for_u64(self as u64);
output[0..4].copy_from_slice(&self.to_le_bytes());
len as u8
}
fn deserialize(bytes: &[u8]) -> Self {
let mut quartet: [u8; 4] = [0u8; 4];
quartet[..bytes.len()].copy_from_slice(bytes);
u32::from_le_bytes(quartet)
}
}
impl SymbolValue for UnorderedId {
fn serialize(self, output: &mut [u8]) -> u8 {
self.0.serialize(output)
}
fn deserialize(bytes: &[u8]) -> Self {
UnorderedId(u32::deserialize(bytes))
}
}
fn compute_num_bytes_for_u64(val: u64) -> usize {
let msb = (64u32 - val.leading_zeros()) as usize;
(msb + 7) / 8
}
fn encode_zig_zag(n: i64) -> u64 {
((n << 1) ^ (n >> 63)) as u64
}
fn decode_zig_zag(n: u64) -> i64 {
((n >> 1) as i64) ^ (-((n & 1) as i64))
}
#[cfg(test)]
mod tests {
use super::*;
#[track_caller]
fn test_zig_zag_aux(val: i64) {
let encoded = super::encode_zig_zag(val);
assert_eq!(decode_zig_zag(encoded), val);
if let Some(abs_val) = val.checked_abs() {
let abs_val = abs_val as u64;
assert!(encoded <= abs_val * 2);
}
}
#[test]
fn test_zig_zag() {
assert_eq!(encode_zig_zag(0i64), 0u64);
assert_eq!(encode_zig_zag(-1i64), 1u64);
assert_eq!(encode_zig_zag(1i64), 2u64);
test_zig_zag_aux(0i64);
test_zig_zag_aux(i64::MIN);
test_zig_zag_aux(i64::MAX);
}
use proptest::prelude::any;
use proptest::proptest;
proptest! {
#[test]
fn test_proptest_zig_zag(val in any::<i64>()) {
test_zig_zag_aux(val);
}
}
#[test]
fn test_column_op_metadata_byte_serialization() {
for len in 0..=15 {
for op_type in [ColumnOperationType::AddValue, ColumnOperationType::NewDoc] {
let column_op_metadata = ColumnOperationMetadata { op_type, len };
let column_op_metadata_code = column_op_metadata.to_code();
let serdeser_metadata =
ColumnOperationMetadata::try_from_code(column_op_metadata_code).unwrap();
assert_eq!(column_op_metadata, serdeser_metadata);
}
}
}
#[track_caller]
fn ser_deser_symbol(column_op: ColumnOperation<NumericalValue>) {
let buf = column_op.serialize();
let mut buffer = buf.as_ref().to_vec();
buffer.extend_from_slice(b"234234");
let mut bytes = &buffer[..];
let serdeser_symbol = ColumnOperation::deserialize(&mut bytes).unwrap();
assert_eq!(bytes.len() + buf.as_ref().len() as usize, buffer.len());
assert_eq!(column_op, serdeser_symbol);
}
#[test]
fn test_compute_num_bytes_for_u64() {
assert_eq!(compute_num_bytes_for_u64(0), 0);
assert_eq!(compute_num_bytes_for_u64(1), 1);
assert_eq!(compute_num_bytes_for_u64(255), 1);
assert_eq!(compute_num_bytes_for_u64(256), 2);
assert_eq!(compute_num_bytes_for_u64((1 << 16) - 1), 2);
assert_eq!(compute_num_bytes_for_u64(1 << 16), 3);
}
#[test]
fn test_symbol_serialization() {
ser_deser_symbol(ColumnOperation::NewDoc(0));
ser_deser_symbol(ColumnOperation::NewDoc(3));
ser_deser_symbol(ColumnOperation::Value(NumericalValue::I64(0i64)));
ser_deser_symbol(ColumnOperation::Value(NumericalValue::I64(1i64)));
ser_deser_symbol(ColumnOperation::Value(NumericalValue::U64(257u64)));
ser_deser_symbol(ColumnOperation::Value(NumericalValue::I64(-257i64)));
ser_deser_symbol(ColumnOperation::Value(NumericalValue::I64(i64::MIN)));
ser_deser_symbol(ColumnOperation::Value(NumericalValue::U64(0u64)));
ser_deser_symbol(ColumnOperation::Value(NumericalValue::U64(u64::MIN)));
ser_deser_symbol(ColumnOperation::Value(NumericalValue::U64(u64::MAX)));
}
fn test_column_operation_unordered_aux(val: u32, expected_len: usize) {
let column_op = ColumnOperation::Value(UnorderedId(val));
let minibuf = column_op.serialize();
assert_eq!(minibuf.as_ref().len() as usize, expected_len);
let mut buf = minibuf.as_ref().to_vec();
buf.extend_from_slice(&[2, 2, 2, 2, 2, 2]);
let mut cursor = &buf[..];
let column_op_serdeser: ColumnOperation<UnorderedId> =
ColumnOperation::deserialize(&mut cursor).unwrap();
assert_eq!(column_op_serdeser, ColumnOperation::Value(UnorderedId(val)));
assert_eq!(cursor.len() + expected_len, buf.len());
}
#[test]
fn test_column_operation_unordered() {
test_column_operation_unordered_aux(300u32, 3);
test_column_operation_unordered_aux(1u32, 2);
test_column_operation_unordered_aux(0u32, 1);
}
}

265
columnar/src/columnar/writer/column_writers.rs
View File

@@ -1,265 +0,0 @@
use std::cmp::Ordering;
use stacker::{ExpUnrolledLinkedList, MemoryArena};
use crate::columnar::writer::column_operation::{ColumnOperation, SymbolValue};
use crate::dictionary::{DictionaryBuilder, UnorderedId};
use crate::{Cardinality, NumericalType, NumericalValue, RowId};
#[derive(Copy, Clone, Debug, Eq, PartialEq)]
#[repr(u8)]
enum DocumentStep {
Same = 0,
Next = 1,
Skipped = 2,
}
#[inline(always)]
fn delta_with_last_doc(last_doc_opt: Option<u32>, doc: u32) -> DocumentStep {
let expected_next_doc = last_doc_opt.map(|last_doc| last_doc + 1).unwrap_or(0u32);
match doc.cmp(&expected_next_doc) {
Ordering::Less => DocumentStep::Same,
Ordering::Equal => DocumentStep::Next,
Ordering::Greater => DocumentStep::Skipped,
}
}
#[derive(Copy, Clone, Default)]
pub struct ColumnWriter {
// Detected cardinality of the column so far.
cardinality: Cardinality,
// Last document inserted.
// None if no doc has been added yet.
last_doc_opt: Option<u32>,
// Buffer containing the serialized values.
values: ExpUnrolledLinkedList,
}
impl ColumnWriter {
/// Returns an iterator over the Symbol that have been recorded
/// for the given column.
pub(super) fn operation_iterator<'a, V: SymbolValue>(
&self,
arena: &MemoryArena,
buffer: &'a mut Vec<u8>,
) -> impl Iterator<Item = ColumnOperation<V>> + 'a {
buffer.clear();
self.values.read_to_end(arena, buffer);
let mut cursor: &[u8] = &buffer[..];
std::iter::from_fn(move || ColumnOperation::deserialize(&mut cursor))
}
/// Records a change of the document being recorded.
///
/// This function will also update the cardinality of the column
/// if necessary.
pub(super) fn record<S: SymbolValue>(&mut self, doc: RowId, value: S, arena: &mut MemoryArena) {
// Difference between `doc` and the last doc.
match delta_with_last_doc(self.last_doc_opt, doc) {
DocumentStep::Same => {
// This is the last encounterred document.
self.cardinality = Cardinality::Multivalued;
}
DocumentStep::Next => {
self.last_doc_opt = Some(doc);
self.write_symbol::<S>(ColumnOperation::NewDoc(doc), arena);
}
DocumentStep::Skipped => {
self.cardinality = self.cardinality.max(Cardinality::Optional);
self.last_doc_opt = Some(doc);
self.write_symbol::<S>(ColumnOperation::NewDoc(doc), arena);
}
}
self.write_symbol(ColumnOperation::Value(value), arena);
}
// Get the cardinality.
// The overall number of docs in the column is necessary to
// deal with the case where the all docs contain 1 value, except some documents
// at the end of the column.
pub(crate) fn get_cardinality(&self, num_docs: RowId) -> Cardinality {
match delta_with_last_doc(self.last_doc_opt, num_docs) {
DocumentStep::Same | DocumentStep::Next => self.cardinality,
DocumentStep::Skipped => self.cardinality.max(Cardinality::Optional),
}
}
/// Appends a new symbol to the `ColumnWriter`.
fn write_symbol<V: SymbolValue>(
&mut self,
column_operation: ColumnOperation<V>,
arena: &mut MemoryArena,
) {
self.values
.writer(arena)
.extend_from_slice(column_operation.serialize().as_ref());
}
}
#[derive(Clone, Copy, Default)]
pub(crate) struct NumericalColumnWriter {
compatible_numerical_types: CompatibleNumericalTypes,
column_writer: ColumnWriter,
}
/// State used to store what types are still acceptable
/// after having seen a set of numerical values.
#[derive(Clone, Copy)]
struct CompatibleNumericalTypes {
all_values_within_i64_range: bool,
all_values_within_u64_range: bool,
// f64 is always acceptable.
}
impl Default for CompatibleNumericalTypes {
fn default() -> CompatibleNumericalTypes {
CompatibleNumericalTypes {
all_values_within_i64_range: true,
all_values_within_u64_range: true,
}
}
}
impl CompatibleNumericalTypes {
fn accept_value(&mut self, numerical_value: NumericalValue) {
match numerical_value {
NumericalValue::I64(val_i64) => {
let value_within_u64_range = val_i64 >= 0i64;
self.all_values_within_u64_range &= value_within_u64_range;
}
NumericalValue::U64(val_u64) => {
let value_within_i64_range = val_u64 < i64::MAX as u64;
self.all_values_within_i64_range &= value_within_i64_range;
}
NumericalValue::F64(_) => {
self.all_values_within_i64_range = false;
self.all_values_within_u64_range = false;
}
}
}
pub fn to_numerical_type(self) -> NumericalType {
if self.all_values_within_i64_range {
NumericalType::I64
} else if self.all_values_within_u64_range {
NumericalType::U64
} else {
NumericalType::F64
}
}
}
impl NumericalColumnWriter {
pub fn column_type_and_cardinality(&self, num_docs: RowId) -> (NumericalType, Cardinality) {
let numerical_type = self.compatible_numerical_types.to_numerical_type();
let cardinality = self.column_writer.get_cardinality(num_docs);
(numerical_type, cardinality)
}
pub fn record_numerical_value(
&mut self,
doc: RowId,
value: NumericalValue,
arena: &mut MemoryArena,
) {
self.compatible_numerical_types.accept_value(value);
self.column_writer.record(doc, value, arena);
}
pub(super) fn operation_iterator<'a>(
self,
arena: &MemoryArena,
buffer: &'a mut Vec<u8>,
) -> impl Iterator<Item = ColumnOperation<NumericalValue>> + 'a {
self.column_writer.operation_iterator(arena, buffer)
}
}
#[derive(Copy, Clone, Default)]
pub(crate) struct StrColumnWriter {
pub(crate) dictionary_id: u32,
pub(crate) column_writer: ColumnWriter,
}
impl StrColumnWriter {
pub(crate) fn with_dictionary_id(dictionary_id: u32) -> StrColumnWriter {
StrColumnWriter {
dictionary_id,
column_writer: Default::default(),
}
}
pub(crate) fn record_bytes(
&mut self,
doc: RowId,
bytes: &[u8],
dictionaries: &mut [DictionaryBuilder],
arena: &mut MemoryArena,
) {
let unordered_id = dictionaries[self.dictionary_id as usize].get_or_allocate_id(bytes);
self.column_writer.record(doc, unordered_id, arena);
}
pub(super) fn operation_iterator<'a>(
&self,
arena: &MemoryArena,
byte_buffer: &'a mut Vec<u8>,
) -> impl Iterator<Item = ColumnOperation<UnorderedId>> + 'a {
self.column_writer.operation_iterator(arena, byte_buffer)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_delta_with_last_doc() {
assert_eq!(delta_with_last_doc(None, 0u32), DocumentStep::Next);
assert_eq!(delta_with_last_doc(None, 1u32), DocumentStep::Skipped);
assert_eq!(delta_with_last_doc(None, 2u32), DocumentStep::Skipped);
assert_eq!(delta_with_last_doc(Some(0u32), 0u32), DocumentStep::Same);
assert_eq!(delta_with_last_doc(Some(1u32), 1u32), DocumentStep::Same);
assert_eq!(delta_with_last_doc(Some(1u32), 2u32), DocumentStep::Next);
assert_eq!(delta_with_last_doc(Some(1u32), 3u32), DocumentStep::Skipped);
assert_eq!(delta_with_last_doc(Some(1u32), 4u32), DocumentStep::Skipped);
}
#[track_caller]
fn test_column_writer_coercion_iter_aux(
values: impl Iterator<Item = NumericalValue>,
expected_numerical_type: NumericalType,
) {
let mut compatible_numerical_types = CompatibleNumericalTypes::default();
for value in values {
compatible_numerical_types.accept_value(value);
}
assert_eq!(
compatible_numerical_types.to_numerical_type(),
expected_numerical_type
);
}
#[track_caller]
fn test_column_writer_coercion_aux(
values: &[NumericalValue],
expected_numerical_type: NumericalType,
) {
test_column_writer_coercion_iter_aux(values.iter().copied(), expected_numerical_type);
test_column_writer_coercion_iter_aux(values.iter().rev().copied(), expected_numerical_type);
}
#[test]
fn test_column_writer_coercion() {
test_column_writer_coercion_aux(&[], NumericalType::I64);
test_column_writer_coercion_aux(&[1i64.into()], NumericalType::I64);
test_column_writer_coercion_aux(&[1u64.into()], NumericalType::I64);
// We don't detect exact integer at the moment. We could!
test_column_writer_coercion_aux(&[1f64.into()], NumericalType::F64);
test_column_writer_coercion_aux(&[u64::MAX.into()], NumericalType::U64);
test_column_writer_coercion_aux(&[(i64::MAX as u64).into()], NumericalType::U64);
test_column_writer_coercion_aux(&[(1u64 << 63).into()], NumericalType::U64);
test_column_writer_coercion_aux(&[1i64.into(), 1u64.into()], NumericalType::I64);
test_column_writer_coercion_aux(&[u64::MAX.into(), (-1i64).into()], NumericalType::F64);
}
}

543
columnar/src/columnar/writer/mod.rs
View File

@@ -1,543 +0,0 @@
mod column_operation;
mod column_writers;
mod serializer;
mod value_index;
use std::io;
use column_operation::ColumnOperation;
use common::CountingWriter;
use serializer::ColumnarSerializer;
use stacker::{Addr, ArenaHashMap, MemoryArena};
use crate::column_index::SerializableColumnIndex;
use crate::column_values::{ColumnValues, MonotonicallyMappableToU64, VecColumn};
use crate::columnar::column_type::{ColumnType, ColumnTypeCategory};
use crate::columnar::writer::column_writers::{
ColumnWriter, NumericalColumnWriter, StrColumnWriter,
};
use crate::columnar::writer::value_index::{IndexBuilder, PreallocatedIndexBuilders};
use crate::dictionary::{DictionaryBuilder, TermIdMapping, UnorderedId};
use crate::value::{Coerce, NumericalType, NumericalValue};
use crate::{Cardinality, RowId};
/// This is a set of buffers that are used to temporarily write the values into before passing them
/// to the fast field codecs.
#[derive(Default)]
struct SpareBuffers {
value_index_builders: PreallocatedIndexBuilders,
i64_values: Vec<i64>,
u64_values: Vec<u64>,
f64_values: Vec<f64>,
bool_values: Vec<bool>,
}
/// Makes it possible to create a new columnar.
///
/// ```rust
/// use tantivy_columnar::ColumnarWriter;
///
/// let mut columnar_writer = ColumnarWriter::default();
/// columnar_writer.record_str(0u32 /* doc id */, "product_name", "Red backpack");
/// columnar_writer.record_numerical(0u32 /* doc id */, "price", 10u64);
/// columnar_writer.record_str(1u32 /* doc id */, "product_name", "Apple");
/// columnar_writer.record_numerical(0u32 /* doc id */, "price", 10.5f64); //< uh oh we ended up mixing integer and floats.
/// let mut wrt: Vec<u8> = Vec::new();
/// columnar_writer.serialize(2u32, &mut wrt).unwrap();
/// ```
pub struct ColumnarWriter {
numerical_field_hash_map: ArenaHashMap,
bool_field_hash_map: ArenaHashMap,
bytes_field_hash_map: ArenaHashMap,
arena: MemoryArena,
// Dictionaries used to store dictionary-encoded values.
dictionaries: Vec<DictionaryBuilder>,
buffers: SpareBuffers,
}
impl Default for ColumnarWriter {
fn default() -> Self {
ColumnarWriter {
numerical_field_hash_map: ArenaHashMap::new(10_000),
bool_field_hash_map: ArenaHashMap::new(10_000),
bytes_field_hash_map: ArenaHashMap::new(10_000),
dictionaries: Vec::new(),
arena: MemoryArena::default(),
buffers: SpareBuffers::default(),
}
}
}
impl ColumnarWriter {
pub fn record_numerical<T: Into<NumericalValue> + Copy>(
&mut self,
doc: RowId,
column_name: &str,
numerical_value: T,
) {
assert!(
!column_name.as_bytes().contains(&0u8),
"key may not contain the 0 byte"
);
let (hash_map, arena) = (&mut self.numerical_field_hash_map, &mut self.arena);
hash_map.mutate_or_create(
column_name.as_bytes(),
|column_opt: Option<NumericalColumnWriter>| {
let mut column: NumericalColumnWriter = column_opt.unwrap_or_default();
column.record_numerical_value(doc, numerical_value.into(), arena);
column
},
);
}
pub fn record_bool(&mut self, doc: RowId, column_name: &str, val: bool) {
assert!(
!column_name.as_bytes().contains(&0u8),
"key may not contain the 0 byte"
);
let (hash_map, arena) = (&mut self.bool_field_hash_map, &mut self.arena);
hash_map.mutate_or_create(
column_name.as_bytes(),
|column_opt: Option<ColumnWriter>| {
let mut column: ColumnWriter = column_opt.unwrap_or_default();
column.record(doc, val, arena);
column
},
);
}
pub fn record_str(&mut self, doc: RowId, column_name: &str, value: &str) {
assert!(
!column_name.as_bytes().contains(&0u8),
"key may not contain the 0 byte"
);
let (hash_map, arena, dictionaries) = (
&mut self.bytes_field_hash_map,
&mut self.arena,
&mut self.dictionaries,
);
hash_map.mutate_or_create(
column_name.as_bytes(),
|column_opt: Option<StrColumnWriter>| {
let mut column: StrColumnWriter = column_opt.unwrap_or_else(|| {
// Each column has its own dictionary
let dictionary_id = dictionaries.len() as u32;
dictionaries.push(DictionaryBuilder::default());
StrColumnWriter::with_dictionary_id(dictionary_id)
});
column.record_bytes(doc, value.as_bytes(), dictionaries, arena);
column
},
);
}
pub fn serialize(&mut self, num_docs: RowId, wrt: &mut dyn io::Write) -> io::Result<()> {
let mut serializer = ColumnarSerializer::new(wrt);
let mut field_columns: Vec<(&[u8], ColumnTypeCategory, Addr)> = self
.numerical_field_hash_map
.iter()
.map(|(term, addr, _)| (term, ColumnTypeCategory::Numerical, addr))
.collect();
field_columns.extend(
self.bytes_field_hash_map
.iter()
.map(|(term, addr, _)| (term, ColumnTypeCategory::Str, addr)),
);
field_columns.extend(
self.bool_field_hash_map
.iter()
.map(|(term, addr, _)| (term, ColumnTypeCategory::Bool, addr)),
);
field_columns.sort_unstable_by_key(|(column_name, col_type, _)| (*column_name, *col_type));
let (arena, buffers, dictionaries) = (&self.arena, &mut self.buffers, &self.dictionaries);
let mut symbol_byte_buffer: Vec<u8> = Vec::new();
for (column_name, bytes_or_numerical, addr) in field_columns {
match bytes_or_numerical {
ColumnTypeCategory::Bool => {
let column_writer: ColumnWriter = self.bool_field_hash_map.read(addr);
let cardinality = column_writer.get_cardinality(num_docs);
let mut column_serializer =
serializer.serialize_column(column_name, ColumnType::Bool);
serialize_bool_column(
cardinality,
num_docs,
column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
buffers,
&mut column_serializer,
)?;
}
ColumnTypeCategory::Str => {
let str_column_writer: StrColumnWriter = self.bytes_field_hash_map.read(addr);
let dictionary_builder =
&dictionaries[str_column_writer.dictionary_id as usize];
let cardinality = str_column_writer.column_writer.get_cardinality(num_docs);
let mut column_serializer =
serializer.serialize_column(column_name, ColumnType::Bytes);
serialize_bytes_column(
cardinality,
num_docs,
dictionary_builder,
str_column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
buffers,
&mut column_serializer,
)?;
}
ColumnTypeCategory::Numerical => {
let numerical_column_writer: NumericalColumnWriter =
self.numerical_field_hash_map.read(addr);
let (numerical_type, cardinality) =
numerical_column_writer.column_type_and_cardinality(num_docs);
let mut column_serializer = serializer
.serialize_column(column_name, ColumnType::Numerical(numerical_type));
serialize_numerical_column(
cardinality,
num_docs,
numerical_type,
numerical_column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
buffers,
&mut column_serializer,
)?;
}
};
}
serializer.finalize()?;
Ok(())
}
}
fn serialize_bytes_column(
cardinality: Cardinality,
num_docs: RowId,
dictionary_builder: &DictionaryBuilder,
operation_it: impl Iterator<Item = ColumnOperation<UnorderedId>>,
buffers: &mut SpareBuffers,
wrt: impl io::Write,
) -> io::Result<()> {
let SpareBuffers {
value_index_builders,
u64_values,
..
} = buffers;
let mut counting_writer = CountingWriter::wrap(wrt);
let term_id_mapping: TermIdMapping = dictionary_builder.serialize(&mut counting_writer)?;
let dictionary_num_bytes: u32 = counting_writer.written_bytes() as u32;
let mut wrt = counting_writer.finish();
let operation_iterator = operation_it.map(|symbol: ColumnOperation<UnorderedId>| {
// We map unordered ids to ordered ids.
match symbol {
ColumnOperation::Value(unordered_id) => {
let ordered_id = term_id_mapping.to_ord(unordered_id);
ColumnOperation::Value(ordered_id.0 as u64)
}
ColumnOperation::NewDoc(doc) => ColumnOperation::NewDoc(doc),
}
});
serialize_column(
operation_iterator,
cardinality,
num_docs,
value_index_builders,
u64_values,
&mut wrt,
)?;
wrt.write_all(&dictionary_num_bytes.to_le_bytes()[..])?;
Ok(())
}
fn serialize_numerical_column(
cardinality: Cardinality,
num_docs: RowId,
numerical_type: NumericalType,
op_iterator: impl Iterator<Item = ColumnOperation<NumericalValue>>,
buffers: &mut SpareBuffers,
wrt: &mut impl io::Write,
) -> io::Result<()> {
let SpareBuffers {
value_index_builders,
u64_values,
i64_values,
f64_values,
..
} = buffers;
match numerical_type {
NumericalType::I64 => {
serialize_column(
coerce_numerical_symbol::<i64>(op_iterator),
cardinality,
num_docs,
value_index_builders,
i64_values,
wrt,
)?;
}
NumericalType::U64 => {
serialize_column(
coerce_numerical_symbol::<u64>(op_iterator),
cardinality,
num_docs,
value_index_builders,
u64_values,
wrt,
)?;
}
NumericalType::F64 => {
serialize_column(
coerce_numerical_symbol::<f64>(op_iterator),
cardinality,
num_docs,
value_index_builders,
f64_values,
wrt,
)?;
}
};
Ok(())
}
fn serialize_bool_column(
cardinality: Cardinality,
num_docs: RowId,
column_operations_it: impl Iterator<Item = ColumnOperation<bool>>,
buffers: &mut SpareBuffers,
wrt: &mut impl io::Write,
) -> io::Result<()> {
let SpareBuffers {
value_index_builders,
bool_values,
..
} = buffers;
serialize_column(
column_operations_it,
cardinality,
num_docs,
value_index_builders,
bool_values,
wrt,
)?;
Ok(())
}
fn serialize_column<
T: Copy + Default + std::fmt::Debug + Send + Sync + MonotonicallyMappableToU64 + PartialOrd,
>(
op_iterator: impl Iterator<Item = ColumnOperation<T>>,
cardinality: Cardinality,
num_docs: RowId,
value_index_builders: &mut PreallocatedIndexBuilders,
values: &mut Vec<T>,
mut wrt: impl io::Write,
) -> io::Result<()>
where
for<'a> VecColumn<'a, T>: ColumnValues<T>,
{
values.clear();
let serializable_column_index = match cardinality {
Cardinality::Full => {
consume_operation_iterator(
op_iterator,
value_index_builders.borrow_required_index_builder(),
values,
);
SerializableColumnIndex::Full
}
Cardinality::Optional => {
let optional_index_builder = value_index_builders.borrow_optional_index_builder();
consume_operation_iterator(op_iterator, optional_index_builder, values);
let optional_index = optional_index_builder.finish(num_docs);
SerializableColumnIndex::Optional(Box::new(optional_index))
}
Cardinality::Multivalued => {
let multivalued_index_builder = value_index_builders.borrow_multivalued_index_builder();
consume_operation_iterator(op_iterator, multivalued_index_builder, values);
let multivalued_index = multivalued_index_builder.finish(num_docs);
todo!();
// SerializableColumnIndex::Multivalued(Box::new(multivalued_index))
}
};
crate::column::serialize_column_u64(
serializable_column_index,
&VecColumn::from(&values[..]),
&mut wrt,
)?;
Ok(())
}
fn coerce_numerical_symbol<T>(
operation_iterator: impl Iterator<Item = ColumnOperation<NumericalValue>>,
) -> impl Iterator<Item = ColumnOperation<T>>
where T: Coerce {
operation_iterator.map(|symbol| match symbol {
ColumnOperation::NewDoc(doc) => ColumnOperation::NewDoc(doc),
ColumnOperation::Value(numerical_value) => {
ColumnOperation::Value(Coerce::coerce(numerical_value))
}
})
}
fn consume_operation_iterator<T: std::fmt::Debug, TIndexBuilder: IndexBuilder>(
operation_iterator: impl Iterator<Item = ColumnOperation<T>>,
index_builder: &mut TIndexBuilder,
values: &mut Vec<T>,
) {
for symbol in operation_iterator {
match symbol {
ColumnOperation::NewDoc(doc) => {
index_builder.record_row(doc);
}
ColumnOperation::Value(value) => {
index_builder.record_value();
values.push(value);
}
}
}
}
// /// Serializes the column with the codec with the best estimate on the data.
// fn serialize_numerical<T: MonotonicallyMappableToU64>(
// value_index: ValueIndexInfo,
// typed_column: impl Column<T>,
// output: &mut impl io::Write,
// codecs: &[FastFieldCodecType],
// ) -> io::Result<()> {
// let counting_writer = CountingWriter::wrap(output);
// serialize_value_index(value_index, output)?;
// let value_index_len = counting_writer.written_bytes();
// let output = counting_writer.finish();
// serialize_column(value_index, output)?;
// let column = monotonic_map_column(
// typed_column,
// crate::column::monotonic_mapping::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)?;
// let column_header = ColumnFooter {
// value_index_len: todo!(),
// cardinality: todo!(),
// };
// let null_index_footer = NullIndexFooter {
// cardinality: value_index.get_cardinality(),
// null_index_codec: NullIndexCodec::Full,
// null_index_byte_range: 0..0,
// };
// append_null_index_footer(output, null_index_footer)?;
// Ok(())
// }
#[cfg(test)]
mod tests {
use column_operation::ColumnOperation;
use stacker::MemoryArena;
use super::*;
use crate::value::NumericalValue;
#[test]
fn test_column_writer_required_simple() {
let mut arena = MemoryArena::default();
let mut column_writer = super::ColumnWriter::default();
column_writer.record(0u32, NumericalValue::from(14i64), &mut arena);
column_writer.record(1u32, NumericalValue::from(15i64), &mut arena);
column_writer.record(2u32, NumericalValue::from(-16i64), &mut arena);
assert_eq!(column_writer.get_cardinality(3), Cardinality::Full);
let mut buffer = Vec::new();
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
.operation_iterator(&mut arena, &mut buffer)
.collect();
assert_eq!(symbols.len(), 6);
assert!(matches!(symbols[0], ColumnOperation::NewDoc(0u32)));
assert!(matches!(
symbols[1],
ColumnOperation::Value(NumericalValue::I64(14i64))
));
assert!(matches!(symbols[2], ColumnOperation::NewDoc(1u32)));
assert!(matches!(
symbols[3],
ColumnOperation::Value(NumericalValue::I64(15i64))
));
assert!(matches!(symbols[4], ColumnOperation::NewDoc(2u32)));
assert!(matches!(
symbols[5],
ColumnOperation::Value(NumericalValue::I64(-16i64))
));
}
#[test]
fn test_column_writer_optional_cardinality_missing_first() {
let mut arena = MemoryArena::default();
let mut column_writer = super::ColumnWriter::default();
column_writer.record(1u32, NumericalValue::from(15i64), &mut arena);
column_writer.record(2u32, NumericalValue::from(-16i64), &mut arena);
assert_eq!(column_writer.get_cardinality(3), Cardinality::Optional);
let mut buffer = Vec::new();
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
.operation_iterator(&mut arena, &mut buffer)
.collect();
assert_eq!(symbols.len(), 4);
assert!(matches!(symbols[0], ColumnOperation::NewDoc(1u32)));
assert!(matches!(
symbols[1],
ColumnOperation::Value(NumericalValue::I64(15i64))
));
assert!(matches!(symbols[2], ColumnOperation::NewDoc(2u32)));
assert!(matches!(
symbols[3],
ColumnOperation::Value(NumericalValue::I64(-16i64))
));
}
#[test]
fn test_column_writer_optional_cardinality_missing_last() {
let mut arena = MemoryArena::default();
let mut column_writer = super::ColumnWriter::default();
column_writer.record(0u32, NumericalValue::from(15i64), &mut arena);
assert_eq!(column_writer.get_cardinality(2), Cardinality::Optional);
let mut buffer = Vec::new();
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
.operation_iterator(&mut arena, &mut buffer)
.collect();
assert_eq!(symbols.len(), 2);
assert!(matches!(symbols[0], ColumnOperation::NewDoc(0u32)));
assert!(matches!(
symbols[1],
ColumnOperation::Value(NumericalValue::I64(15i64))
));
}
#[test]
fn test_column_writer_multivalued() {
let mut arena = MemoryArena::default();
let mut column_writer = super::ColumnWriter::default();
column_writer.record(0u32, NumericalValue::from(16i64), &mut arena);
column_writer.record(0u32, NumericalValue::from(17i64), &mut arena);
assert_eq!(column_writer.get_cardinality(1), Cardinality::Multivalued);
let mut buffer = Vec::new();
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
.operation_iterator(&mut arena, &mut buffer)
.collect();
assert_eq!(symbols.len(), 3);
assert!(matches!(symbols[0], ColumnOperation::NewDoc(0u32)));
assert!(matches!(
symbols[1],
ColumnOperation::Value(NumericalValue::I64(16i64))
));
assert!(matches!(
symbols[2],
ColumnOperation::Value(NumericalValue::I64(17i64))
));
}
}

106
columnar/src/columnar/writer/serializer.rs
View File

@@ -1,106 +0,0 @@
use std::io;
use std::io::Write;
use common::CountingWriter;
use sstable::value::RangeValueWriter;
use sstable::RangeSSTable;
use crate::columnar::ColumnType;
pub struct ColumnarSerializer<W: io::Write> {
wrt: CountingWriter<W>,
sstable_range: sstable::Writer<Vec<u8>, RangeValueWriter>,
prepare_key_buffer: Vec<u8>,
}
/// Returns a key consisting of the concatenation of the key and the column_type_and_cardinality
/// code.
fn prepare_key(key: &[u8], column_type: ColumnType, buffer: &mut Vec<u8>) {
buffer.clear();
buffer.extend_from_slice(key);
buffer.push(0u8);
buffer.push(column_type.to_code());
}
impl<W: io::Write> ColumnarSerializer<W> {
pub(crate) fn new(wrt: W) -> ColumnarSerializer<W> {
let sstable_range: sstable::Writer<Vec<u8>, RangeValueWriter> =
sstable::Dictionary::<RangeSSTable>::builder(Vec::with_capacity(100_000)).unwrap();
ColumnarSerializer {
wrt: CountingWriter::wrap(wrt),
sstable_range,
prepare_key_buffer: Vec::new(),
}
}
pub fn serialize_column<'a>(
&'a mut self,
column_name: &[u8],
column_type: ColumnType,
) -> impl io::Write + 'a {
let start_offset = self.wrt.written_bytes();
prepare_key(column_name, column_type, &mut self.prepare_key_buffer);
ColumnSerializer {
columnar_serializer: self,
start_offset,
}
}
pub(crate) fn finalize(mut self) -> io::Result<()> {
let sstable_bytes: Vec<u8> = self.sstable_range.finish()?;
let sstable_num_bytes: u64 = sstable_bytes.len() as u64;
self.wrt.write_all(&sstable_bytes)?;
self.wrt.write_all(&sstable_num_bytes.to_le_bytes()[..])?;
self.wrt
.write_all(&super::super::format_version::footer())?;
self.wrt.flush()?;
Ok(())
}
}
struct ColumnSerializer<'a, W: io::Write> {
columnar_serializer: &'a mut ColumnarSerializer<W>,
start_offset: u64,
}
impl<'a, W: io::Write> Drop for ColumnSerializer<'a, W> {
fn drop(&mut self) {
let end_offset: u64 = self.columnar_serializer.wrt.written_bytes();
let byte_range = self.start_offset..end_offset;
self.columnar_serializer.sstable_range.insert_cannot_fail(
&self.columnar_serializer.prepare_key_buffer[..],
&byte_range,
);
self.columnar_serializer.prepare_key_buffer.clear();
}
}
impl<'a, W: io::Write> io::Write for ColumnSerializer<'a, W> {
fn write(&mut self, buf: &[u8]) -> io::Result<usize> {
self.columnar_serializer.wrt.write(buf)
}
fn flush(&mut self) -> io::Result<()> {
self.columnar_serializer.wrt.flush()
}
fn write_all(&mut self, buf: &[u8]) -> io::Result<()> {
self.columnar_serializer.wrt.write_all(buf)
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::columnar::column_type::ColumnType;
#[test]
fn test_prepare_key_bytes() {
let mut buffer: Vec<u8> = b"somegarbage".to_vec();
prepare_key(b"root\0child", ColumnType::Bytes, &mut buffer);
assert_eq!(buffer.len(), 12);
assert_eq!(&buffer[..10], b"root\0child");
assert_eq!(buffer[10], 0u8);
assert_eq!(buffer[11], ColumnType::Bytes.to_code());
}
}

205
columnar/src/columnar/writer/value_index.rs
View File

@@ -1,205 +0,0 @@
use crate::column_index::SerializableOptionalIndex;
use crate::column_values::{ColumnValues, VecColumn};
use crate::RowId;
/// The `IndexBuilder` interprets a sequence of
/// calls of the form:
/// (record_doc,record_value+)*
/// and can then serialize the results into an index to associate docids with their value[s].
///
/// It has different implementation depending on whether the
/// cardinality is required, optional, or multivalued.
pub(crate) trait IndexBuilder {
fn record_row(&mut self, doc: RowId);
#[inline]
fn record_value(&mut self) {}
}
/// The FullIndexBuilder does nothing.
#[derive(Default)]
pub struct FullIndexBuilder;
impl IndexBuilder for FullIndexBuilder {
#[inline(always)]
fn record_row(&mut self, _doc: RowId) {}
}
#[derive(Default)]
pub struct OptionalIndexBuilder {
docs: Vec<RowId>,
}
struct SingleValueArrayIndex<'a> {
// RowIds with a value, in a strictly increasing order
row_ids: &'a [RowId],
num_rows: RowId,
}
impl<'a> SerializableOptionalIndex<'a> for SingleValueArrayIndex<'a> {
fn num_rows(&self) -> RowId {
self.num_rows
}
fn non_null_rows(&self) -> Box<dyn Iterator<Item = RowId> + 'a> {
Box::new(self.row_ids.iter().copied())
}
}
impl OptionalIndexBuilder {
fn num_non_nulls(&self) -> u32 {
self.docs.len() as u32
}
fn iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
Box::new(self.docs.iter().copied())
}
}
impl OptionalIndexBuilder {
pub fn finish<'a>(&'a mut self, num_rows: RowId) -> impl SerializableOptionalIndex + 'a {
debug_assert!(self
.docs
.last()
.copied()
.map(|last_doc| last_doc < num_rows)
.unwrap_or(true));
SingleValueArrayIndex {
row_ids: &self.docs[..],
num_rows,
}
}
fn reset(&mut self) {
self.docs.clear();
}
}
impl IndexBuilder for OptionalIndexBuilder {
#[inline(always)]
fn record_row(&mut self, doc: RowId) {
debug_assert!(self
.docs
.last()
.copied()
.map(|prev_doc| doc > prev_doc)
.unwrap_or(true));
self.docs.push(doc);
}
}
#[derive(Default)]
pub struct MultivaluedIndexBuilder {
start_offsets: Vec<RowId>,
total_num_vals_seen: u32,
}
impl MultivaluedIndexBuilder {
pub fn finish(&mut self, num_docs: RowId) -> impl ColumnValues<u32> + '_ {
self.start_offsets
.resize(num_docs as usize, self.total_num_vals_seen);
VecColumn {
values: &&self.start_offsets[..],
min_value: 0,
max_value: self.start_offsets.last().copied().unwrap_or(0),
}
}
fn reset(&mut self) {
self.start_offsets.clear();
self.start_offsets.push(0u32);
self.total_num_vals_seen = 0;
}
}
impl IndexBuilder for MultivaluedIndexBuilder {
fn record_row(&mut self, row_id: RowId) {
self.start_offsets
.resize(row_id as usize + 1, self.total_num_vals_seen);
}
fn record_value(&mut self) {
self.total_num_vals_seen += 1;
}
}
/// The `SpareIndexBuilders` is there to avoid allocating a
/// new index builder for every single column.
#[derive(Default)]
pub struct PreallocatedIndexBuilders {
required_index_builder: FullIndexBuilder,
optional_index_builder: OptionalIndexBuilder,
multivalued_index_builder: MultivaluedIndexBuilder,
}
impl PreallocatedIndexBuilders {
pub fn borrow_required_index_builder(&mut self) -> &mut FullIndexBuilder {
&mut self.required_index_builder
}
pub fn borrow_optional_index_builder(&mut self) -> &mut OptionalIndexBuilder {
self.optional_index_builder.reset();
&mut self.optional_index_builder
}
pub fn borrow_multivalued_index_builder(&mut self) -> &mut MultivaluedIndexBuilder {
self.multivalued_index_builder.reset();
&mut self.multivalued_index_builder
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_optional_value_index_builder() {
let mut opt_value_index_builder = OptionalIndexBuilder::default();
opt_value_index_builder.record_row(0u32);
opt_value_index_builder.record_value();
assert_eq!(
&opt_value_index_builder
.finish(1u32)
.non_null_rows()
.collect::<Vec<u32>>(),
&[0]
);
opt_value_index_builder.reset();
opt_value_index_builder.record_row(1u32);
opt_value_index_builder.record_value();
assert_eq!(
&opt_value_index_builder
.finish(2u32)
.non_null_rows()
.collect::<Vec<u32>>(),
&[1]
);
}
#[test]
fn test_multivalued_value_index_builder() {
let mut multivalued_value_index_builder = MultivaluedIndexBuilder::default();
multivalued_value_index_builder.record_row(1u32);
multivalued_value_index_builder.record_value();
multivalued_value_index_builder.record_value();
multivalued_value_index_builder.record_row(2u32);
multivalued_value_index_builder.record_value();
assert_eq!(
multivalued_value_index_builder
.finish(4u32)
.iter()
.collect::<Vec<u32>>(),
vec![0, 0, 2, 3]
);
multivalued_value_index_builder.reset();
multivalued_value_index_builder.record_row(2u32);
multivalued_value_index_builder.record_value();
multivalued_value_index_builder.record_value();
assert_eq!(
multivalued_value_index_builder
.finish(4u32)
.iter()
.collect::<Vec<u32>>(),
vec![0, 0, 0, 2]
);
}
}

84
columnar/src/dictionary.rs
View File

@@ -1,84 +0,0 @@
use std::io;
use fnv::FnvHashMap;
use sstable::SSTable;
pub(crate) struct TermIdMapping {
unordered_to_ord: Vec<OrderedId>,
}
impl TermIdMapping {
pub fn to_ord(&self, unordered: UnorderedId) -> OrderedId {
self.unordered_to_ord[unordered.0 as usize]
}
}
/// When we add values, we cannot know their ordered id yet.
/// For this reason, we temporarily assign them a `UnorderedId`
/// that will be mapped to an `OrderedId` upon serialization.
#[derive(Clone, Copy, Debug, Hash, PartialEq, Eq)]
pub struct UnorderedId(pub u32);
#[derive(Clone, Copy, Hash, PartialEq, Eq, Debug)]
pub struct OrderedId(pub u32);
/// `DictionaryBuilder` for dictionary encoding.
///
/// It stores the different terms encounterred and assigns them a temporary value
/// we call unordered id.
///
/// Upon serialization, we will sort the ids and hence build a `UnorderedId -> Term ordinal`
/// mapping.
#[derive(Default)]
pub(crate) struct DictionaryBuilder {
dict: FnvHashMap<Vec<u8>, UnorderedId>,
}
impl DictionaryBuilder {
/// Get or allocate an unordered id.
/// (This ID is simply an auto-incremented id.)
pub fn get_or_allocate_id(&mut self, term: &[u8]) -> UnorderedId {
if let Some(term_id) = self.dict.get(term) {
return *term_id;
}
let new_id = UnorderedId(self.dict.len() as u32);
self.dict.insert(term.to_vec(), new_id);
new_id
}
/// Serialize the dictionary into an fst, and returns the
/// `UnorderedId -> TermOrdinal` map.
pub fn serialize<'a, W: io::Write + 'a>(&self, wrt: &mut W) -> io::Result<TermIdMapping> {
let mut terms: Vec<(&[u8], UnorderedId)> =
self.dict.iter().map(|(k, v)| (k.as_slice(), *v)).collect();
terms.sort_unstable_by_key(|(key, _)| *key);
// TODO Remove the allocation.
let mut unordered_to_ord: Vec<OrderedId> = vec![OrderedId(0u32); terms.len()];
let mut sstable_builder = sstable::VoidSSTable::writer(wrt);
for (ord, (key, unordered_id)) in terms.into_iter().enumerate() {
let ordered_id = OrderedId(ord as u32);
sstable_builder.insert(key, &())?;
unordered_to_ord[unordered_id.0 as usize] = ordered_id;
}
sstable_builder.finish()?;
Ok(TermIdMapping { unordered_to_ord })
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_dictionary_builder() {
let mut dictionary_builder = DictionaryBuilder::default();
let hello_uid = dictionary_builder.get_or_allocate_id(b"hello");
let happy_uid = dictionary_builder.get_or_allocate_id(b"happy");
let tax_uid = dictionary_builder.get_or_allocate_id(b"tax");
let mut buffer = Vec::new();
let id_mapping = dictionary_builder.serialize(&mut buffer).unwrap();
assert_eq!(id_mapping.to_ord(hello_uid), OrderedId(1));
assert_eq!(id_mapping.to_ord(happy_uid), OrderedId(0));
assert_eq!(id_mapping.to_ord(tax_uid), OrderedId(2));
}
}

95
columnar/src/dynamic_column.rs
View File

@@ -1,95 +0,0 @@
use std::io;
use std::net::IpAddr;
use common::file_slice::FileSlice;
use common::{HasLen, OwnedBytes};
use crate::column::{BytesColumn, Column};
use crate::columnar::ColumnType;
use crate::DateTime;
#[derive(Clone)]
pub enum DynamicColumn {
Bool(Column<bool>),
I64(Column<i64>),
U64(Column<u64>),
F64(Column<f64>),
IpAddr(Column<IpAddr>),
DateTime(Column<DateTime>),
Str(BytesColumn),
}
impl From<Column<i64>> for DynamicColumn {
fn from(column_i64: Column<i64>) -> Self {
DynamicColumn::I64(column_i64)
}
}
impl From<Column<u64>> for DynamicColumn {
fn from(column_u64: Column<u64>) -> Self {
DynamicColumn::U64(column_u64)
}
}
impl From<Column<f64>> for DynamicColumn {
fn from(column_f64: Column<f64>) -> Self {
DynamicColumn::F64(column_f64)
}
}
impl From<Column<bool>> for DynamicColumn {
fn from(bool_column: Column<bool>) -> Self {
DynamicColumn::Bool(bool_column)
}
}
impl From<BytesColumn> for DynamicColumn {
fn from(dictionary_encoded_col: BytesColumn) -> Self {
DynamicColumn::Str(dictionary_encoded_col)
}
}
#[derive(Clone)]
pub struct DynamicColumnHandle {
pub(crate) file_slice: FileSlice,
pub(crate) column_type: ColumnType,
}
impl DynamicColumnHandle {
pub fn open(&self) -> io::Result<DynamicColumn> {
let column_bytes: OwnedBytes = self.file_slice.read_bytes()?;
self.open_internal(column_bytes)
}
pub async fn open_async(&self) -> io::Result<DynamicColumn> {
let column_bytes: OwnedBytes = self.file_slice.read_bytes_async().await?;
self.open_internal(column_bytes)
}
fn open_internal(&self, column_bytes: OwnedBytes) -> io::Result<DynamicColumn> {
let dynamic_column: DynamicColumn = match self.column_type {
ColumnType::Bytes => crate::column::open_column_bytes(column_bytes)?.into(),
ColumnType::Numerical(numerical_type) => match numerical_type {
crate::NumericalType::I64 => {
crate::column::open_column_u64::<i64>(column_bytes)?.into()
}
crate::NumericalType::U64 => {
crate::column::open_column_u64::<u64>(column_bytes)?.into()
}
crate::NumericalType::F64 => {
crate::column::open_column_u64::<f64>(column_bytes)?.into()
}
},
ColumnType::Bool => crate::column::open_column_u64::<bool>(column_bytes)?.into(),
};
Ok(dynamic_column)
}
pub fn num_bytes(&self) -> usize {
self.file_slice.len()
}
pub fn column_type(&self) -> ColumnType {
self.column_type
}
}

75
columnar/src/lib.rs
View File

@@ -1,75 +0,0 @@
#![cfg_attr(all(feature = "unstable", test), feature(test))]
#[cfg(test)]
#[macro_use]
extern crate more_asserts;
#[cfg(all(test, feature = "unstable"))]
extern crate test;
use std::io;
mod column;
mod column_index;
mod column_values;
mod columnar;
mod dictionary;
mod dynamic_column;
pub(crate) mod utils;
mod value;
pub use columnar::{ColumnarReader, ColumnarWriter};
pub use value::{NumericalType, NumericalValue};
// pub use self::dynamic_column::DynamicColumnHandle;
pub type RowId = u32;
#[derive(Clone, Copy)]
pub struct DateTime {
timestamp_micros: i64,
}
#[derive(Copy, Clone, Debug)]
pub struct InvalidData;
impl From<InvalidData> for io::Error {
fn from(_: InvalidData) -> Self {
io::Error::new(io::ErrorKind::InvalidData, "Invalid data")
}
}
/// Enum describing the number of values that can exist per document
/// (or per row if you will).
///
/// The cardinality must fit on 2 bits.
#[derive(Clone, Copy, Hash, Default, Debug, PartialEq, Eq, PartialOrd, Ord)]
#[repr(u8)]
pub 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.
Optional = 1,
/// All documents may contain any number of values.
Multivalued = 2,
}
impl Cardinality {
pub(crate) fn to_code(self) -> u8 {
self as u8
}
pub(crate) fn try_from_code(code: u8) -> Result<Cardinality, InvalidData> {
match code {
0 => Ok(Cardinality::Full),
1 => Ok(Cardinality::Optional),
2 => Ok(Cardinality::Multivalued),
_ => Err(InvalidData),
}
}
}
#[cfg(test)]
mod tests;

84
columnar/src/tests.rs
View File

@@ -1,84 +0,0 @@
use crate::columnar::ColumnType;
use crate::dynamic_column::{DynamicColumn, DynamicColumnHandle};
use crate::value::NumericalValue;
use crate::{Cardinality, ColumnarReader, ColumnarWriter};
#[test]
fn test_dataframe_writer_bytes() {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_str(1u32, "my_string", "hello");
dataframe_writer.record_str(3u32, "my_string", "helloeee");
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(5, &mut buffer).unwrap();
let columnar = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar.num_columns(), 1);
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("my_string").unwrap();
assert_eq!(cols.len(), 1);
assert_eq!(cols[0].num_bytes(), 165);
}
#[test]
fn test_dataframe_writer_bool() {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_bool(1u32, "bool.value", false);
dataframe_writer.record_bool(3u32, "bool.value", true);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(5, &mut buffer).unwrap();
let columnar = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar.num_columns(), 1);
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("bool.value").unwrap();
assert_eq!(cols.len(), 1);
assert_eq!(cols[0].num_bytes(), 29);
assert_eq!(cols[0].column_type(), ColumnType::Bool);
let dyn_bool_col = cols[0].open().unwrap();
let DynamicColumn::Bool(bool_col) = dyn_bool_col else { panic!(); };
let vals: Vec<Option<bool>> = (0..5).map(|row_id| bool_col.first(row_id)).collect();
assert_eq!(&vals, &[None, Some(false), None, Some(true), None,]);
}
#[test]
fn test_dataframe_writer_numerical() {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_numerical(1u32, "srical.value", NumericalValue::U64(12u64));
dataframe_writer.record_numerical(2u32, "srical.value", NumericalValue::U64(13u64));
dataframe_writer.record_numerical(4u32, "srical.value", NumericalValue::U64(15u64));
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(6, &mut buffer).unwrap();
let columnar = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar.num_columns(), 1);
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("srical.value").unwrap();
assert_eq!(cols.len(), 1);
// Right now this 31 bytes are spent as follows
//
// - header 14 bytes
// - vals 8 //< due to padding? could have been 1byte?.
// - null footer 6 bytes
assert_eq!(cols[0].num_bytes(), 40);
let column = cols[0].open().unwrap();
let DynamicColumn::I64(column_i64) = column else { panic!(); };
assert_eq!(column_i64.idx.get_cardinality(), Cardinality::Optional);
assert_eq!(column_i64.first(0), None);
assert_eq!(column_i64.first(1), Some(12i64));
assert_eq!(column_i64.first(2), Some(13i64));
assert_eq!(column_i64.first(3), None);
assert_eq!(column_i64.first(4), Some(15i64));
assert_eq!(column_i64.first(5), None);
assert_eq!(column_i64.first(6), None); //< we can change the spec for that one.
}
#[test]
fn test_dictionary_encoded() {
let mut buffer = Vec::new();
let mut columnar_writer = ColumnarWriter::default();
columnar_writer.record_str(1, "my.column", "my.key");
columnar_writer.record_str(3, "my.column", "my.key2");
columnar_writer.record_str(3, "my.column2", "different_column!");
columnar_writer.serialize(5, &mut buffer).unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_columns(), 2);
let col_handles = columnar_reader.read_columns("my.column").unwrap();
assert_eq!(col_handles.len(), 1);
let DynamicColumn::Str(str_col) = col_handles[0].open().unwrap() else { panic!(); };
assert_eq!(str_col.num_rows(), 5);
// let term_ords = (0..)
}

76
columnar/src/utils.rs
View File

@@ -1,76 +0,0 @@
const fn compute_mask(num_bits: u8) -> u8 {
if num_bits == 8 {
u8::MAX
} else {
(1u8 << num_bits) - 1
}
}
#[inline(always)]
#[must_use]
pub(crate) fn select_bits<const START: u8, const END: u8>(code: u8) -> u8 {
assert!(START <= END);
assert!(END <= 8);
let num_bits: u8 = END - START;
let mask: u8 = compute_mask(num_bits);
(code >> START) & mask
}
#[inline(always)]
#[must_use]
pub(crate) fn place_bits<const START: u8, const END: u8>(code: u8) -> u8 {
assert!(START <= END);
assert!(END <= 8);
let num_bits: u8 = END - START;
let mask: u8 = compute_mask(num_bits);
assert!(code <= mask);
code << START
}
/// Pop-front one bytes from a slice of bytes.
#[inline(always)]
pub fn pop_first_byte(bytes: &mut &[u8]) -> Option<u8> {
if bytes.is_empty() {
return None;
}
let first_byte = bytes[0];
*bytes = &bytes[1..];
Some(first_byte)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_select_bits() {
assert_eq!(255u8, select_bits::<0, 8>(255u8));
assert_eq!(0u8, select_bits::<0, 0>(255u8));
assert_eq!(8u8, select_bits::<0, 4>(8u8));
assert_eq!(4u8, select_bits::<1, 4>(8u8));
assert_eq!(0u8, select_bits::<1, 3>(8u8));
}
#[test]
fn test_place_bits() {
assert_eq!(255u8, place_bits::<0, 8>(255u8));
assert_eq!(4u8, place_bits::<2, 3>(1u8));
assert_eq!(0u8, place_bits::<2, 2>(0u8));
}
#[test]
#[should_panic]
fn test_place_bits_overflows() {
let _ = place_bits::<1, 4>(8u8);
}
#[test]
fn test_pop_first_byte() {
let mut cursor: &[u8] = &b"abcd"[..];
assert_eq!(pop_first_byte(&mut cursor), Some(b'a'));
assert_eq!(pop_first_byte(&mut cursor), Some(b'b'));
assert_eq!(pop_first_byte(&mut cursor), Some(b'c'));
assert_eq!(pop_first_byte(&mut cursor), Some(b'd'));
assert_eq!(pop_first_byte(&mut cursor), None);
}
}

124
columnar/src/value.rs
View File

@@ -1,124 +0,0 @@
use crate::InvalidData;
#[derive(Copy, Clone, Debug, PartialEq)]
pub enum NumericalValue {
I64(i64),
U64(u64),
F64(f64),
}
impl From<u64> for NumericalValue {
fn from(val: u64) -> NumericalValue {
NumericalValue::U64(val)
}
}
impl From<i64> for NumericalValue {
fn from(val: i64) -> Self {
NumericalValue::I64(val)
}
}
impl From<f64> for NumericalValue {
fn from(val: f64) -> Self {
NumericalValue::F64(val)
}
}
impl NumericalValue {
pub fn numerical_type(&self) -> NumericalType {
match self {
NumericalValue::F64(_) => NumericalType::F64,
NumericalValue::I64(_) => NumericalType::I64,
NumericalValue::U64(_) => NumericalType::U64,
}
}
}
impl Eq for NumericalValue {}
#[derive(Clone, Copy, Debug, Default, Hash, Eq, PartialEq)]
#[repr(u8)]
pub enum NumericalType {
#[default]
I64 = 0,
U64 = 1,
F64 = 2,
}
impl NumericalType {
pub fn to_code(self) -> u8 {
self as u8
}
pub fn try_from_code(code: u8) -> Result<NumericalType, InvalidData> {
match code {
0 => Ok(NumericalType::I64),
1 => Ok(NumericalType::U64),
2 => Ok(NumericalType::F64),
_ => Err(InvalidData),
}
}
}
/// We voluntarily avoid using `Into` here to keep this
/// implementation quirk as private as possible.
///
/// # Panics
/// This coercion trait actually panics if it is used
/// to convert a loose types to a stricter type.
///
/// The level is strictness is somewhat arbitrary.
/// - i64
/// - u64
/// - f64.
pub(crate) trait Coerce {
fn coerce(numerical_value: NumericalValue) -> Self;
}
impl Coerce for i64 {
fn coerce(value: NumericalValue) -> Self {
match value {
NumericalValue::I64(val) => val,
NumericalValue::U64(val) => val as i64,
NumericalValue::F64(_) => unreachable!(),
}
}
}
impl Coerce for u64 {
fn coerce(value: NumericalValue) -> Self {
match value {
NumericalValue::I64(val) => val as u64,
NumericalValue::U64(val) => val,
NumericalValue::F64(_) => unreachable!(),
}
}
}
impl Coerce for f64 {
fn coerce(value: NumericalValue) -> Self {
match value {
NumericalValue::I64(val) => val as f64,
NumericalValue::U64(val) => val as f64,
NumericalValue::F64(val) => val,
}
}
}
#[cfg(test)]
mod tests {
use super::NumericalType;
#[test]
fn test_numerical_type_code() {
let mut num_numerical_type = 0;
for code in u8::MIN..=u8::MAX {
if let Ok(numerical_type) = NumericalType::try_from_code(code) {
assert_eq!(numerical_type.to_code(), code);
num_numerical_type += 1;
}
}
assert_eq!(num_numerical_type, 3);
}
}

9
common/Cargo.toml
View File

@@ -1,21 +1,16 @@
[package]
name = "tantivy-common"
version = "0.5.0"
version = "0.3.0"
authors = ["Paul Masurel <paul@quickwit.io>", "Pascal Seitz <pascal@quickwit.io>"]
license = "MIT"
edition = "2021"
description = "common traits and utility functions used by multiple tantivy subcrates"
documentation = "https://docs.rs/tantivy_common/"
homepage = "https://github.com/quickwit-oss/tantivy"
repository = "https://github.com/quickwit-oss/tantivy"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]
byteorder = "1.4.3"
ownedbytes = { version= "0.5", path="../ownedbytes" }
async-trait = "0.1"
ownedbytes = { version="0.3", path="../ownedbytes" }
[dev-dependencies]
proptest = "1.0.0"

6
common/src/bitset.rs
View File

@@ -151,7 +151,7 @@ impl TinySet {
if self.is_empty() {
None
} else {
let lowest = self.0.trailing_zeros();
let lowest = self.0.trailing_zeros() as u32;
self.0 ^= TinySet::singleton(lowest).0;
Some(lowest)
}
@@ -421,7 +421,7 @@ mod tests {
bitset.serialize(&mut out).unwrap();
let bitset = ReadOnlyBitSet::open(OwnedBytes::new(out));
assert_eq!(bitset.len(), i as usize);
assert_eq!(bitset.len() as usize, i as usize);
}
}
@@ -432,7 +432,7 @@ mod tests {
bitset.serialize(&mut out).unwrap();
let bitset = ReadOnlyBitSet::open(OwnedBytes::new(out));
assert_eq!(bitset.len(), 64);
assert_eq!(bitset.len() as usize, 64);
}
#[test]

166
common/src/group_by.rs
View File

@@ -1,166 +0,0 @@
use std::cell::RefCell;
use std::iter::Peekable;
use std::rc::Rc;
pub trait GroupByIteratorExtended: Iterator {
/// Return an `Iterator` that groups iterator elements. Consecutive elements that map to the
/// same key are assigned to the same group.
///
/// The returned Iterator item is `(K, impl Iterator)`, where Iterator are the items of the
/// group.
///
/// ```
/// use tantivy_common::GroupByIteratorExtended;
///
/// // group data into blocks of larger than zero or not.
/// let data: Vec<i32> = vec![1, 3, -2, -2, 1, 0, 1, 2];
/// // groups: |---->|------>|--------->|
///
/// let mut data_grouped = Vec::new();
/// // Note: group is an iterator
/// for (key, group) in data.into_iter().group_by(|val| *val >= 0) {
/// data_grouped.push((key, group.collect()));
/// }
/// assert_eq!(data_grouped, vec![(true, vec![1, 3]), (false, vec![-2, -2]), (true, vec![1, 0, 1, 2])]);
/// ```
fn group_by<K, F>(self, key: F) -> GroupByIterator<Self, F, K>
where
Self: Sized,
F: FnMut(&Self::Item) -> K,
K: PartialEq + Copy,
Self::Item: Copy,
{
GroupByIterator::new(self, key)
}
}
impl<I: Iterator> GroupByIteratorExtended for I {}
pub struct GroupByIterator<I, F, K: Copy>
where
I: Iterator,
F: FnMut(&I::Item) -> K,
{
// I really would like to avoid the Rc<RefCell>, but the Iterator is shared between
// `GroupByIterator` and `GroupIter`. In practice they are used consecutive and
// `GroupByIter` is finished before calling next on `GroupByIterator`. I'm not sure there
// is a solution with lifetimes for that, because we would need to enforce it in the usage
// somehow.
//
// One potential solution would be to replace the iterator approach with something similar.
inner: Rc<RefCell<GroupByShared<I, F, K>>>,
}
struct GroupByShared<I, F, K: Copy>
where
I: Iterator,
F: FnMut(&I::Item) -> K,
{
iter: Peekable<I>,
group_by_fn: F,
}
impl<I, F, K> GroupByIterator<I, F, K>
where
I: Iterator,
F: FnMut(&I::Item) -> K,
K: Copy,
{
fn new(inner: I, group_by_fn: F) -> Self {
let inner = GroupByShared {
iter: inner.peekable(),
group_by_fn,
};
Self {
inner: Rc::new(RefCell::new(inner)),
}
}
}
impl<I, F, K> Iterator for GroupByIterator<I, F, K>
where
I: Iterator,
I::Item: Copy,
F: FnMut(&I::Item) -> K,
K: Copy,
{
type Item = (K, GroupIterator<I, F, K>);
fn next(&mut self) -> Option<Self::Item> {
let mut inner = self.inner.borrow_mut();
let value = *inner.iter.peek()?;
let key = (inner.group_by_fn)(&value);
let inner = self.inner.clone();
let group_iter = GroupIterator {
inner,
group_key: key,
};
Some((key, group_iter))
}
}
pub struct GroupIterator<I, F, K: Copy>
where
I: Iterator,
F: FnMut(&I::Item) -> K,
{
inner: Rc<RefCell<GroupByShared<I, F, K>>>,
group_key: K,
}
impl<I, F, K: PartialEq + Copy> Iterator for GroupIterator<I, F, K>
where
I: Iterator,
I::Item: Copy,
F: FnMut(&I::Item) -> K,
{
type Item = I::Item;
fn next(&mut self) -> Option<Self::Item> {
let mut inner = self.inner.borrow_mut();
// peek if next value is in group
let peek_val = *inner.iter.peek()?;
if (inner.group_by_fn)(&peek_val) == self.group_key {
inner.iter.next()
} else {
None
}
}
}
#[cfg(test)]
mod tests {
use super::*;
fn group_by_collect<I: Iterator<Item = u32>>(iter: I) -> Vec<(I::Item, Vec<I::Item>)> {
iter.group_by(|val| val / 10)
.map(|(el, iter)| (el, iter.collect::<Vec<_>>()))
.collect::<Vec<_>>()
}
#[test]
fn group_by_two_groups() {
let vals = vec![1u32, 4, 15];
let grouped_vals = group_by_collect(vals.into_iter());
assert_eq!(grouped_vals, vec![(0, vec![1, 4]), (1, vec![15])]);
}
#[test]
fn group_by_test_empty() {
let vals = vec![];
let grouped_vals = group_by_collect(vals.into_iter());
assert_eq!(grouped_vals, vec![]);
}
#[test]
fn group_by_three_groups() {
let vals = vec![1u32, 4, 15, 1];
let grouped_vals = group_by_collect(vals.into_iter());
assert_eq!(
grouped_vals,
vec![(0, vec![1, 4]), (1, vec![15]), (0, vec![1])]
);
}
}

5
common/src/lib.rs
View File

@@ -5,14 +5,11 @@ use std::ops::Deref;
pub use byteorder::LittleEndian as Endianness;
mod bitset;
pub mod file_slice;
mod group_by;
mod serialize;
mod vint;
mod writer;
pub use bitset::*;
pub use group_by::GroupByIteratorExtended;
pub use ownedbytes::{OwnedBytes, StableDeref};
pub use serialize::{BinarySerializable, DeserializeFrom, FixedSize};
pub use vint::{
deserialize_vint_u128, read_u32_vint, read_u32_vint_no_advance, serialize_vint_u128,

14
common/src/serialize.rs
View File

@@ -94,20 +94,6 @@ impl FixedSize for u32 {
const SIZE_IN_BYTES: usize = 4;
}
impl BinarySerializable for u16 {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_u16::<Endianness>(*self)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<u16> {
reader.read_u16::<Endianness>()
}
}
impl FixedSize for u16 {
const SIZE_IN_BYTES: usize = 2;
}
impl BinarySerializable for u64 {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_u64::<Endianness>(*self)

2
examples/aggregation.rs
View File

@@ -118,7 +118,7 @@ fn main() -> tantivy::Result<()> {
.into_iter()
.collect();
let collector = AggregationCollector::from_aggs(agg_req_1, None, index.schema());
let collector = AggregationCollector::from_aggs(agg_req_1, None);
let searcher = reader.searcher();
let agg_res: AggregationResults = searcher.search(&term_query, &collector).unwrap();

26
examples/custom_collector.rs
View File

@@ -9,12 +9,12 @@
use std::sync::Arc;
use fastfield_codecs::Column;
use fastfield_codecs::OptionalColumn;
// ---
// Importing tantivy...
use tantivy::collector::{Collector, SegmentCollector};
use tantivy::query::QueryParser;
use tantivy::schema::{Schema, FAST, INDEXED, TEXT};
use tantivy::schema::{Field, Schema, FAST, INDEXED, TEXT};
use tantivy::{doc, Index, Score, SegmentReader};
#[derive(Default)]
@@ -52,11 +52,11 @@ impl Stats {
}
struct StatsCollector {
field: String,
field: Field,
}
impl StatsCollector {
fn with_field(field: String) -> StatsCollector {
fn with_field(field: Field) -> StatsCollector {
StatsCollector { field }
}
}
@@ -73,7 +73,7 @@ impl Collector for StatsCollector {
_segment_local_id: u32,
segment_reader: &SegmentReader,
) -> tantivy::Result<StatsSegmentCollector> {
let fast_field_reader = segment_reader.fast_fields().u64(&self.field)?;
let fast_field_reader = segment_reader.fast_fields().u64(self.field)?;
Ok(StatsSegmentCollector {
fast_field_reader,
stats: Stats::default(),
@@ -97,7 +97,7 @@ impl Collector for StatsCollector {
}
struct StatsSegmentCollector {
fast_field_reader: Arc<dyn Column<u64>>,
fast_field_reader: Arc<dyn OptionalColumn<u64>>,
stats: Stats,
}
@@ -105,10 +105,12 @@ impl SegmentCollector for StatsSegmentCollector {
type Fruit = Option<Stats>;
fn collect(&mut self, doc: u32, _score: Score) {
let value = self.fast_field_reader.get_val(doc) as f64;
self.stats.count += 1;
self.stats.sum += value;
self.stats.squared_sum += value * value;
if let Some(value) = self.fast_field_reader.get_val(doc) {
let value = value as f64;
self.stats.count += 1;
self.stats.sum += value;
self.stats.squared_sum += value * value;
}
}
fn harvest(self) -> <Self as SegmentCollector>::Fruit {
@@ -171,9 +173,7 @@ fn main() -> tantivy::Result<()> {
// here we want to get a hit on the 'ken' in Frankenstein
let query = query_parser.parse_query("broom")?;
if let Some(stats) =
searcher.search(&query, &StatsCollector::with_field("price".to_string()))?
{
if let Some(stats) = searcher.search(&query, &StatsCollector::with_field(price))? {
println!("count: {}", stats.count());
println!("mean: {}", stats.mean());
println!("standard deviation: {}", stats.standard_deviation());

20
examples/faceted_search.rs
View File

@@ -1,17 +1,15 @@
// # Faceted Search
// # Basic Example
//
// This example covers the faceted search functionalities of
// This example covers the basic functionalities of
// tantivy.
//
// We will :
// - define a text field "name" in our schema
// - define a facet field "classification" in our schema
// - create an index in memory
// - index few documents with respective facets in our index
// - search and count the number of documents that the classifications start the facet "/Felidae"
// - Search the facet "/Felidae/Pantherinae" and count the number of documents that the
// classifications include the facet.
//
// - define our schema
// = create an index in a directory
// - index few documents in our index
// - search for the best document matchings "sea whale"
// - retrieve the best document original content.
// ---
// Importing tantivy...
use tantivy::collector::FacetCollector;
@@ -23,7 +21,7 @@ fn main() -> tantivy::Result<()> {
// Let's create a temporary directory for the sake of this example
let mut schema_builder = Schema::builder();
let name = schema_builder.add_text_field("name", TEXT | STORED);
let name = schema_builder.add_text_field("felin_name", TEXT | STORED);
// this is our faceted field: its scientific classification
let classification = schema_builder.add_facet_field("classification", FacetOptions::default());

2
examples/integer_range_search.rs
View File

@@ -27,7 +27,7 @@ fn main() -> Result<()> {
reader.reload()?;
let searcher = reader.searcher();
// The end is excluded i.e. here we are searching up to 1969
let docs_in_the_sixties = RangeQuery::new_u64("year".to_string(), 1960..1970);
let docs_in_the_sixties = RangeQuery::new_u64(year_field, 1960..1970);
// Uses a Count collector to sum the total number of docs in the range
let num_60s_books = searcher.search(&docs_in_the_sixties, &Count)?;
assert_eq!(num_60s_books, 10);

73
examples/ip_field.rs
View File

@@ -1,73 +0,0 @@
// # IP Address example
//
// This example shows how the ip field can be used
// with IpV6 and IpV4.
use tantivy::collector::{Count, TopDocs};
use tantivy::query::QueryParser;
use tantivy::schema::{Schema, FAST, INDEXED, STORED, STRING};
use tantivy::Index;
fn main() -> tantivy::Result<()> {
// # Defining the schema
let mut schema_builder = Schema::builder();
let event_type = schema_builder.add_text_field("event_type", STRING | STORED);
let ip = schema_builder.add_ip_addr_field("ip", STORED | INDEXED | FAST);
let schema = schema_builder.build();
// # Indexing documents
let index = Index::create_in_ram(schema.clone());
let mut index_writer = index.writer(50_000_000)?;
let doc = schema.parse_document(
r#"{
"ip": "192.168.0.33",
"event_type": "login"
}"#,
)?;
index_writer.add_document(doc)?;
let doc = schema.parse_document(
r#"{
"ip": "192.168.0.80",
"event_type": "checkout"
}"#,
)?;
index_writer.add_document(doc)?;
let doc = schema.parse_document(
r#"{
"ip": "2001:0db8:85a3:0000:0000:8a2e:0370:7334",
"event_type": "checkout"
}"#,
)?;
index_writer.add_document(doc)?;
index_writer.commit()?;
let reader = index.reader()?;
let searcher = reader.searcher();
let query_parser = QueryParser::for_index(&index, vec![event_type, ip]);
{
let query = query_parser.parse_query("ip:[192.168.0.0 TO 192.168.0.100]")?;
let count_docs = searcher.search(&*query, &TopDocs::with_limit(5))?;
assert_eq!(count_docs.len(), 2);
}
{
let query = query_parser.parse_query("ip:[192.168.1.0 TO 192.168.1.100]")?;
let count_docs = searcher.search(&*query, &TopDocs::with_limit(2))?;
assert_eq!(count_docs.len(), 0);
}
{
let query = query_parser.parse_query("ip:192.168.0.80")?;
let count_docs = searcher.search(&*query, &Count)?;
assert_eq!(count_docs, 1);
}
{
// IpV6 needs to be escaped because it contains `:`
let query = query_parser.parse_query("ip:\"2001:0db8:85a3:0000:0000:8a2e:0370:7334\"")?;
let count_docs = searcher.search(&*query, &Count)?;
assert_eq!(count_docs, 1);
}
Ok(())
}

12
examples/warmer.rs
View File

@@ -4,7 +4,7 @@ use std::sync::{Arc, RwLock, Weak};
use tantivy::collector::TopDocs;
use tantivy::query::QueryParser;
use tantivy::schema::{Schema, FAST, TEXT};
use tantivy::schema::{Field, Schema, FAST, TEXT};
use tantivy::{
doc, DocAddress, DocId, Index, IndexReader, Opstamp, Searcher, SearcherGeneration, SegmentId,
SegmentReader, Warmer,
@@ -25,13 +25,13 @@ pub trait PriceFetcher: Send + Sync + 'static {
}
struct DynamicPriceColumn {
field: String,
field: Field,
price_cache: RwLock<HashMap<(SegmentId, Option<Opstamp>), Arc<Vec<Price>>>>,
price_fetcher: Box<dyn PriceFetcher>,
}
impl DynamicPriceColumn {
pub fn with_product_id_field<T: PriceFetcher>(field: String, price_fetcher: T) -> Self {
pub fn with_product_id_field<T: PriceFetcher>(field: Field, price_fetcher: T) -> Self {
DynamicPriceColumn {
field,
price_cache: Default::default(),
@@ -48,10 +48,10 @@ impl Warmer for DynamicPriceColumn {
fn warm(&self, searcher: &Searcher) -> tantivy::Result<()> {
for segment in searcher.segment_readers() {
let key = (segment.segment_id(), segment.delete_opstamp());
let product_id_reader = segment.fast_fields().u64(&self.field)?;
let product_id_reader = segment.fast_fields().u64(self.field)?;
let product_ids: Vec<ProductId> = segment
.doc_ids_alive()
.map(|doc| product_id_reader.get_val(doc))
.flat_map(|doc| product_id_reader.get_val(doc))
.collect();
let mut prices_it = self.price_fetcher.fetch_prices(&product_ids).into_iter();
let mut price_vals: Vec<Price> = Vec::new();
@@ -123,7 +123,7 @@ fn main() -> tantivy::Result<()> {
let price_table = ExternalPriceTable::default();
let price_dynamic_column = Arc::new(DynamicPriceColumn::with_product_id_field(
"product_id".to_string(),
product_id,
price_table.clone(),
));
price_table.update_price(OLIVE_OIL, 12);

12
fastfield_codecs/Cargo.toml
View File

@@ -1,20 +1,18 @@
[package]
name = "fastfield_codecs"
version = "0.3.0"
version = "0.2.0"
authors = ["Pascal Seitz <pascal@quickwit.io>"]
license = "MIT"
edition = "2021"
description = "Fast field codecs used by tantivy"
documentation = "https://docs.rs/fastfield_codecs/"
homepage = "https://github.com/quickwit-oss/tantivy"
repository = "https://github.com/quickwit-oss/tantivy"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]
common = { version = "0.5", path = "../common/", package = "tantivy-common" }
tantivy-bitpacker = { version= "0.3", path = "../bitpacker/" }
prettytable-rs = {version="0.10.0", optional= true}
common = { version = "0.3", path = "../common/", package = "tantivy-common" }
tantivy-bitpacker = { version="0.2", path = "../bitpacker/" }
ownedbytes = { version = "0.3.0", path = "../ownedbytes" }
prettytable-rs = {version="0.9.0", optional= true}
rand = {version="0.8.3", optional= true}
fastdivide = "0.4"
log = "0.4"

115
fastfield_codecs/benches/bench.rs
View File

@@ -4,11 +4,11 @@ extern crate test;
#[cfg(test)]
mod tests {
use std::ops::RangeInclusive;
use std::iter;
use std::sync::Arc;
use common::OwnedBytes;
use fastfield_codecs::*;
use ownedbytes::OwnedBytes;
use rand::prelude::*;
use test::Bencher;
@@ -41,7 +41,7 @@ mod tests {
) -> Arc<dyn Column<T>> {
let mut buffer = Vec::new();
serialize(VecColumn::from(&column), &mut buffer, &ALL_CODEC_TYPES).unwrap();
open(OwnedBytes::new(buffer)).unwrap()
open(OwnedBytes::new(buffer)).unwrap().to_full().unwrap()
}
#[bench]
@@ -71,24 +71,27 @@ mod tests {
});
}
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]);
fn get_exp_data() -> Vec<u64> {
let mut data = vec![];
for _ in 0..300_000 {
let val = rng.gen_range(1..=100);
data.push(val);
for i in 0..100 {
let num = i * i;
data.extend(iter::repeat(i as u64).take(num));
}
data.push(SINGLE_ITEM);
data.shuffle(&mut StdRng::from_seed([1u8; 32]));
data.shuffle(&mut rng);
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
// lengt = 328350
data
}
fn get_data_50percent_item() -> (u128, u128, Vec<u128>) {
let mut permutation = get_exp_data();
let major_item = 20;
let minor_item = 10;
permutation.extend(iter::repeat(major_item).take(permutation.len()));
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
let permutation = permutation.iter().map(|el| *el as u128).collect::<Vec<_>>();
(major_item as u128, minor_item as u128, permutation)
}
fn get_u128_column_random() -> Arc<dyn Column<u128>> {
let permutation = generate_random();
let permutation = permutation.iter().map(|el| *el as u128).collect::<Vec<_>>();
@@ -100,85 +103,18 @@ mod tests {
let iter_gen = || data.iter().cloned();
serialize_u128(iter_gen, data.len() as u32, &mut out).unwrap();
let out = OwnedBytes::new(out);
open_u128::<u128>(out).unwrap()
open_u128::<u128>(out).unwrap().to_full().unwrap()
}
// 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 Column<u64>> = serialize_and_load(&data);
b.iter(|| {
let mut positions = Vec::new();
column.get_docids_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 Column<u64>> = serialize_and_load(&data);
b.iter(|| {
let mut positions = Vec::new();
column.get_docids_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 Column<u64>> = serialize_and_load(&data);
b.iter(|| {
let mut positions = Vec::new();
column.get_docids_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 Column<u64>> = serialize_and_load(&data);
b.iter(|| {
let mut positions = Vec::new();
column.get_docids_for_value_range(0..=u64::MAX, 0..data.len() as u32, &mut positions);
positions
});
}
// U64 RANGE END
// U128 RANGE START
#[bench]
fn bench_intfastfield_getrange_u128_50percent_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let (major_item, _minor_item, data) = get_data_50percent_item();
let column = get_u128_column_from_data(&data);
b.iter(|| {
let mut positions = Vec::new();
column.get_docids_for_value_range(
*FIFTY_PERCENT_RANGE.start() as u128..=*FIFTY_PERCENT_RANGE.end() as u128,
major_item..=major_item,
0..data.len() as u32,
&mut positions,
);
@@ -188,13 +124,13 @@ mod tests {
#[bench]
fn bench_intfastfield_getrange_u128_single_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let (_major_item, minor_item, data) = get_data_50percent_item();
let column = get_u128_column_from_data(&data);
b.iter(|| {
let mut positions = Vec::new();
column.get_docids_for_value_range(
*SINGLE_ITEM_RANGE.start() as u128..=*SINGLE_ITEM_RANGE.end() as u128,
minor_item..=minor_item,
0..data.len() as u32,
&mut positions,
);
@@ -204,7 +140,7 @@ mod tests {
#[bench]
fn bench_intfastfield_getrange_u128_hit_all(b: &mut Bencher) {
let data = get_data_50percent_item();
let (_major_item, _minor_item, data) = get_data_50percent_item();
let column = get_u128_column_from_data(&data);
b.iter(|| {
@@ -213,7 +149,6 @@ mod tests {
positions
});
}
// U128 RANGE END
#[bench]
fn bench_intfastfield_scan_all_fflookup_u128(b: &mut Bencher) {

2
fastfield_codecs/src/bitpacked.rs
View File

@@ -1,6 +1,6 @@
use std::io::{self, Write};
use common::OwnedBytes;
use ownedbytes::OwnedBytes;
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::serialize::NormalizedHeader;

10
fastfield_codecs/src/blockwise_linear.rs
View File

@@ -1,7 +1,8 @@
use std::sync::Arc;
use std::{io, iter};
use common::{BinarySerializable, CountingWriter, DeserializeFrom, OwnedBytes};
use common::{BinarySerializable, CountingWriter, DeserializeFrom};
use ownedbytes::OwnedBytes;
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::line::Line;
@@ -46,7 +47,7 @@ impl FastFieldCodec for BlockwiseLinearCodec {
type Reader = BlockwiseLinearReader;
fn open_from_bytes(
bytes: common::OwnedBytes,
bytes: ownedbytes::OwnedBytes,
normalized_header: NormalizedHeader,
) -> io::Result<Self::Reader> {
let footer_len: u32 = (&bytes[bytes.len() - 4..]).deserialize()?;
@@ -74,7 +75,7 @@ impl FastFieldCodec for BlockwiseLinearCodec {
if column.num_vals() < 10 * CHUNK_SIZE as u32 {
return None;
}
let mut first_chunk: Vec<u64> = column.iter().take(CHUNK_SIZE).collect();
let mut first_chunk: Vec<u64> = column.iter().take(CHUNK_SIZE as usize).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);
@@ -170,18 +171,15 @@ impl Column for BlockwiseLinearReader {
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
}

34
fastfield_codecs/src/column.rs
View File

@@ -1,4 +1,3 @@
use std::fmt::{self, Debug};
use std::marker::PhantomData;
use std::ops::{Range, RangeInclusive};
@@ -7,7 +6,7 @@ use tantivy_bitpacker::minmax;
use crate::monotonic_mapping::StrictlyMonotonicFn;
/// `Column` provides columnar access on a field.
pub trait Column<T: PartialOrd + Debug = u64>: Send + Sync {
pub trait Column<T: PartialOrd = u64>: Send + Sync {
/// Return the value associated with the given idx.
///
/// This accessor should return as fast as possible.
@@ -84,7 +83,7 @@ pub struct VecColumn<'a, T = u64> {
max_value: T,
}
impl<'a, C: Column<T>, T: Copy + PartialOrd + fmt::Debug> Column<T> for &'a C {
impl<'a, C: Column<T>, T: Copy + PartialOrd> Column<T> for &'a C {
fn get_val(&self, idx: u32) -> T {
(*self).get_val(idx)
}
@@ -110,7 +109,7 @@ impl<'a, C: Column<T>, T: Copy + PartialOrd + fmt::Debug> Column<T> for &'a C {
}
}
impl<'a, T: Copy + PartialOrd + Send + Sync + Debug> Column<T> for VecColumn<'a, T> {
impl<'a, T: Copy + PartialOrd + Send + Sync> Column<T> for VecColumn<'a, T> {
fn get_val(&self, position: u32) -> T {
self.values[position as usize]
}
@@ -136,7 +135,7 @@ impl<'a, T: Copy + PartialOrd + Send + Sync + Debug> Column<T> for VecColumn<'a,
}
}
impl<'a, T: Copy + PartialOrd + Default, V> From<&'a V> for VecColumn<'a, T>
impl<'a, T: Copy + Ord + Default, V> From<&'a V> for VecColumn<'a, T>
where V: AsRef<[T]> + ?Sized
{
fn from(values: &'a V) -> Self {
@@ -178,8 +177,8 @@ pub fn monotonic_map_column<C, T, Input, Output>(
where
C: Column<Input>,
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
Input: PartialOrd + Send + Sync + Copy + Debug,
Output: PartialOrd + Send + Sync + Copy + Debug,
Input: PartialOrd + Send + Sync + Clone,
Output: PartialOrd + Send + Sync + Clone,
{
MonotonicMappingColumn {
from_column,
@@ -192,8 +191,8 @@ impl<C, T, Input, Output> Column<Output> for MonotonicMappingColumn<C, T, Input>
where
C: Column<Input>,
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
Input: PartialOrd + Send + Sync + Copy + Debug,
Output: PartialOrd + Send + Sync + Copy + Debug,
Input: PartialOrd + Send + Sync + Clone,
Output: PartialOrd + Send + Sync + Clone,
{
#[inline]
fn get_val(&self, idx: u32) -> Output {
@@ -229,15 +228,12 @@ where
doc_id_range: Range<u32>,
positions: &mut Vec<u32>,
) {
if range.start() > &self.max_value() || range.end() < &self.min_value() {
return;
}
let range = self.monotonic_mapping.inverse_coerce(range);
if range.start() > range.end() {
return;
}
self.from_column
.get_docids_for_value_range(range, doc_id_range, positions)
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,
@@ -258,7 +254,7 @@ where T: Iterator + Clone + ExactSizeIterator
impl<T> Column<T::Item> for IterColumn<T>
where
T: Iterator + Clone + ExactSizeIterator + Send + Sync,
T::Item: PartialOrd + fmt::Debug,
T::Item: PartialOrd,
{
fn get_val(&self, idx: u32) -> T::Item {
self.0.clone().nth(idx as usize).unwrap()

2
fastfield_codecs/src/compact_space/build_compact_space.rs
View File

@@ -208,7 +208,7 @@ impl CompactSpaceBuilder {
};
let covered_range_len = range_mapping.range_length();
ranges_mapping.push(range_mapping);
compact_start += covered_range_len;
compact_start += covered_range_len as u64;
}
// println!("num ranges {}", ranges_mapping.len());
CompactSpace { ranges_mapping }

45
fastfield_codecs/src/compact_space/mod.rs
View File

@@ -17,7 +17,8 @@ use std::{
ops::{Range, RangeInclusive},
};
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt, VIntU128};
use common::{BinarySerializable, CountingWriter, VInt, VIntU128};
use ownedbytes::OwnedBytes;
use tantivy_bitpacker::{self, BitPacker, BitUnpacker};
use crate::compact_space::build_compact_space::get_compact_space;
@@ -96,7 +97,7 @@ impl BinarySerializable for CompactSpace {
};
let range_length = range_mapping.range_length();
ranges_mapping.push(range_mapping);
compact_start += range_length;
compact_start += range_length as u64;
}
Ok(Self { ranges_mapping })
@@ -406,10 +407,10 @@ impl CompactSpaceDecompressor {
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);
let val1 = get_val(idx1 as u32);
let val2 = get_val(idx2 as u32);
let val3 = get_val(idx3 as u32);
let val4 = get_val(idx4 as u32);
push_if_in_range(idx1, val1);
push_if_in_range(idx2, val2);
push_if_in_range(idx3, val3);
@@ -418,13 +419,14 @@ impl CompactSpaceDecompressor {
// handle rest
for idx in cutoff..position_range.end {
push_if_in_range(idx, get_val(idx));
push_if_in_range(idx, get_val(idx as u32));
}
}
#[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))
(0..self.params.num_vals)
.map(move |idx| self.params.bit_unpacker.get(idx, &self.data) as u64)
}
#[inline]
@@ -453,11 +455,7 @@ impl CompactSpaceDecompressor {
#[cfg(test)]
mod tests {
use std::fmt;
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};
@@ -543,10 +541,7 @@ mod tests {
.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
}
@@ -564,12 +559,11 @@ mod tests {
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 val = *val as u128;
let pos = pos as u32;
let mut positions = Vec::new();
decomp.get_positions_for_value_range(val..=val, pos..pos + 1, &mut positions);
@@ -666,7 +660,7 @@ mod tests {
get_positions_for_value_range_helper(
&decomp,
4_000_211_221u128..=5_000_000_000u128,
complete_range
complete_range.clone()
),
vec![6, 7]
);
@@ -703,12 +697,12 @@ mod tests {
vec![0]
);
assert_eq!(
get_positions_for_value_range_helper(&decomp, 0..=105, complete_range),
get_positions_for_value_range_helper(&decomp, 0..=105, complete_range.clone()),
vec![0]
);
}
fn get_positions_for_value_range_helper<C: Column<T> + ?Sized, T: PartialOrd + fmt::Debug>(
fn get_positions_for_value_range_helper<C: Column<T> + ?Sized, T: PartialOrd>(
column: &C,
value_range: RangeInclusive<T>,
doc_id_range: Range<u32>,
@@ -737,7 +731,10 @@ mod tests {
];
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 decomp = open_u128::<u128>(OwnedBytes::new(out))
.unwrap()
.to_full()
.unwrap();
let complete_range = 0..vals.len() as u32;
assert_eq!(
@@ -756,7 +753,11 @@ mod tests {
);
assert_eq!(
get_positions_for_value_range_helper(&*decomp, 1_000_000..=1_000_000, complete_range),
get_positions_for_value_range_helper(
&*decomp,
1_000_000..=1_000_000,
complete_range.clone()
),
vec![11]
);
}

38
fastfield_codecs/src/format_version.rs
View File

@@ -1,38 +0,0 @@
use std::io;
use common::{BinarySerializable, OwnedBytes};
const MAGIC_NUMBER: u16 = 4335u16;
const FASTFIELD_FORMAT_VERSION: u8 = 1;
pub(crate) fn append_format_version(output: &mut impl io::Write) -> io::Result<()> {
FASTFIELD_FORMAT_VERSION.serialize(output)?;
MAGIC_NUMBER.serialize(output)?;
Ok(())
}
pub(crate) fn read_format_version(data: OwnedBytes) -> io::Result<(OwnedBytes, u8)> {
let (data, magic_number_bytes) = data.rsplit(2);
let magic_number = u16::deserialize(&mut magic_number_bytes.as_slice())?;
if magic_number != MAGIC_NUMBER {
return Err(io::Error::new(
io::ErrorKind::InvalidData,
format!("magic number mismatch {} != {}", magic_number, MAGIC_NUMBER),
));
}
let (data, format_version_bytes) = data.rsplit(1);
let format_version = u8::deserialize(&mut format_version_bytes.as_slice())?;
if format_version > FASTFIELD_FORMAT_VERSION {
return Err(io::Error::new(
io::ErrorKind::InvalidData,
format!(
"Unsupported fastfield format version: {}. Max supported version: {}",
format_version, FASTFIELD_FORMAT_VERSION
),
));
}
Ok((data, format_version))
}

22
fastfield_codecs/src/gcd.rs
View File

@@ -45,7 +45,7 @@ mod tests {
use std::io;
use std::num::NonZeroU64;
use common::OwnedBytes;
use ownedbytes::OwnedBytes;
use crate::gcd::{compute_gcd, find_gcd};
use crate::{FastFieldCodecType, VecColumn};
@@ -59,11 +59,11 @@ mod tests {
crate::serialize(VecColumn::from(&vals), &mut buffer, &[codec_type])?;
let buffer = OwnedBytes::new(buffer);
let column = crate::open::<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);
assert_eq!(column.get_val(0), Some(-4000i64));
assert_eq!(column.get_val(1), Some(-3000i64));
assert_eq!(column.get_val(2), Some(-2000i64));
assert_eq!(column.max_value(), Some((num_vals as i64 - 5) * 1000));
assert_eq!(column.min_value(), Some(-4000i64));
// Can't apply gcd
let mut buffer_without_gcd = Vec::new();
@@ -101,11 +101,11 @@ mod tests {
crate::serialize(VecColumn::from(&vals), &mut buffer, &[codec_type])?;
let buffer = OwnedBytes::new(buffer);
let column = crate::open::<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);
assert_eq!(column.get_val(0), Some(1000u64));
assert_eq!(column.get_val(1), Some(2000u64));
assert_eq!(column.get_val(2), Some(3000u64));
assert_eq!(column.max_value(), Some(num_vals as u64 * 1000));
assert_eq!(column.min_value(), Some(1000u64));
// Can't apply gcd
let mut buffer_without_gcd = Vec::new();

62
fastfield_codecs/src/lib.rs
View File

@@ -14,35 +14,33 @@ extern crate more_asserts;
#[cfg(all(test, feature = "unstable"))]
extern crate test;
use std::io;
use std::io::Write;
use std::sync::Arc;
use std::{fmt, io};
use common::{BinarySerializable, OwnedBytes};
use common::BinarySerializable;
use compact_space::CompactSpaceDecompressor;
use format_version::read_format_version;
use monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
StrictlyMonotonicMappingToInternalBaseval, StrictlyMonotonicMappingToInternalGCDBaseval,
};
use null_index_footer::read_null_index_footer;
pub use optional_column::OptionalColumn;
use optional_column::ToOptionalColumn;
use ownedbytes::OwnedBytes;
use serialize::{Header, U128Header};
mod bitpacked;
mod blockwise_linear;
mod compact_space;
mod format_version;
mod line;
mod linear;
mod monotonic_mapping;
mod monotonic_mapping_u128;
#[allow(dead_code)]
mod null_index;
mod null_index_footer;
mod optional_column;
mod column;
mod gcd;
pub mod serialize;
mod serialize;
use self::bitpacked::BitpackedCodec;
use self::blockwise_linear::BlockwiseLinearCodec;
@@ -133,25 +131,24 @@ impl U128FastFieldCodecType {
}
/// Returns the correct codec reader wrapped in the `Arc` for the data.
pub fn open_u128<Item: MonotonicallyMappableToU128 + fmt::Debug>(
bytes: OwnedBytes,
) -> io::Result<Arc<dyn Column<Item>>> {
let (bytes, _format_version) = read_format_version(bytes)?;
let (mut bytes, _null_index_footer) = read_null_index_footer(bytes)?;
pub fn open_u128<Item: MonotonicallyMappableToU128>(
mut bytes: OwnedBytes,
) -> io::Result<Arc<dyn OptionalColumn<Item>>> {
let header = U128Header::deserialize(&mut bytes)?;
assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
let reader = CompactSpaceDecompressor::open(bytes)?;
let inverted: StrictlyMonotonicMappingInverter<StrictlyMonotonicMappingToInternal<Item>> =
StrictlyMonotonicMappingToInternal::<Item>::new().into();
Ok(Arc::new(monotonic_map_column(reader, inverted)))
Ok(Arc::new(ToOptionalColumn::new(Arc::new(
monotonic_map_column(reader, inverted),
))))
}
/// Returns the correct codec reader wrapped in the `Arc` for the data.
pub fn open<T: MonotonicallyMappableToU64 + fmt::Debug>(
bytes: OwnedBytes,
) -> io::Result<Arc<dyn Column<T>>> {
let (bytes, _format_version) = read_format_version(bytes)?;
let (mut bytes, _null_index_footer) = read_null_index_footer(bytes)?;
pub fn open<T: MonotonicallyMappableToU64>(
mut bytes: OwnedBytes,
) -> io::Result<Arc<dyn OptionalColumn<T>>> {
let header = Header::deserialize(&mut bytes)?;
match header.codec_type {
FastFieldCodecType::Bitpacked => open_specific_codec::<BitpackedCodec, _>(bytes, &header),
@@ -162,10 +159,10 @@ pub fn open<T: MonotonicallyMappableToU64 + fmt::Debug>(
}
}
fn open_specific_codec<C: FastFieldCodec, Item: MonotonicallyMappableToU64 + fmt::Debug>(
fn open_specific_codec<C: FastFieldCodec, Item: MonotonicallyMappableToU64>(
bytes: OwnedBytes,
header: &Header,
) -> io::Result<Arc<dyn Column<Item>>> {
) -> io::Result<Arc<dyn OptionalColumn<Item>>> {
let normalized_header = header.normalized();
let reader = C::open_from_bytes(bytes, normalized_header)?;
let min_value = header.min_value;
@@ -173,12 +170,16 @@ fn open_specific_codec<C: FastFieldCodec, Item: MonotonicallyMappableToU64 + fmt
let mapping = StrictlyMonotonicMappingInverter::from(
StrictlyMonotonicMappingToInternalGCDBaseval::new(gcd.get(), min_value),
);
Ok(Arc::new(monotonic_map_column(reader, mapping)))
Ok(Arc::new(ToOptionalColumn::new(Arc::new(
monotonic_map_column(reader, mapping),
))))
} else {
let mapping = StrictlyMonotonicMappingInverter::from(
StrictlyMonotonicMappingToInternalBaseval::new(min_value),
);
Ok(Arc::new(monotonic_map_column(reader, mapping)))
Ok(Arc::new(ToOptionalColumn::new(Arc::new(
monotonic_map_column(reader, mapping),
))))
}
}
@@ -249,8 +250,9 @@ mod tests {
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 \
val,
Some(orig_val),
"val `{val:?}` does not match orig_val {orig_val:?}, in data set {name}, data \
`{data:?}`",
);
}
@@ -323,9 +325,6 @@ mod tests {
pub fn get_codec_test_datasets() -> Vec<(Vec<u64>, &'static str)> {
let mut data_and_names = vec![];
let data = vec![10];
data_and_names.push((data, "minimal test"));
let data = (10..=10_000_u64).collect::<Vec<_>>();
data_and_names.push((data, "simple monotonically increasing"));
@@ -333,9 +332,6 @@ mod tests {
vec![5, 6, 7, 8, 9, 10, 99, 100],
"offset in linear interpol",
));
data_and_names.push((vec![3, 18446744073709551613, 5], "docid range regression"));
data_and_names.push((vec![5, 50, 3, 13, 1, 1000, 35], "rand small"));
data_and_names.push((vec![10], "single value"));
@@ -443,7 +439,7 @@ mod tests {
mod bench {
use std::sync::Arc;
use common::OwnedBytes;
use ownedbytes::OwnedBytes;
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use test::{self, Bencher};

7
fastfield_codecs/src/linear.rs
View File

@@ -1,6 +1,7 @@
use std::io::{self, Write};
use common::{BinarySerializable, OwnedBytes};
use common::BinarySerializable;
use ownedbytes::OwnedBytes;
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::line::Line;
@@ -24,13 +25,13 @@ impl Column for LinearReader {
interpoled_val.wrapping_add(bitpacked_diff)
}
#[inline(always)]
#[inline]
fn min_value(&self) -> u64 {
// The LinearReader assumes a normalized vector.
0u64
}
#[inline(always)]
#[inline]
fn max_value(&self) -> u64 {
self.header.max_value
}

7
fastfield_codecs/src/main.rs
View File

@@ -6,10 +6,10 @@ 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 ownedbytes::OwnedBytes;
use prettytable::{Cell, Row, Table};
fn print_set_stats(ip_addrs: &[u128]) {
@@ -113,7 +113,10 @@ fn bench_ip() {
(data.len() * 8) as f32 / dataset.len() as f32
);
let decompressor = open_u128::<u128>(OwnedBytes::new(data)).unwrap();
let decompressor = open_u128::<u128>(OwnedBytes::new(data))
.unwrap()
.to_full()
.unwrap();
// Sample some ranges
let mut doc_values = Vec::new();
for value in dataset.iter().take(1110).skip(1100).cloned() {

95
fastfield_codecs/src/monotonic_mapping.rs
View File

@@ -1,6 +1,4 @@
use std::fmt;
use std::marker::PhantomData;
use std::ops::RangeInclusive;
use fastdivide::DividerU64;
@@ -8,9 +6,7 @@ use crate::MonotonicallyMappableToU128;
/// 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 + fmt::Debug
{
pub trait MonotonicallyMappableToU64: 'static + PartialOrd + Copy + Send + Sync {
/// Converts a value to u64.
///
/// Internally all fast field values are encoded as u64.
@@ -33,29 +29,11 @@ pub trait MonotonicallyMappableToU64:
/// mapping from their range to their domain. The `inverse` method is required when opening a codec,
/// so a value can be converted back to its original domain (e.g. ip address or f64) from its
/// internal representation.
pub trait StrictlyMonotonicFn<External: Copy, Internal: Copy> {
pub trait StrictlyMonotonicFn<External, Internal> {
/// Strictly monotonically maps the value from External to Internal.
fn mapping(&self, inp: External) -> Internal;
/// Inverse of `mapping`. Maps the value from Internal to External.
fn inverse(&self, out: Internal) -> External;
/// Maps a user provded value from External to Internal.
/// It may be necessary to coerce the value if it is outside the value space.
/// In that case it tries to find the next greater value in the value space.
///
/// Returns a bool to mark if a value was outside the value space and had to be coerced _up_.
/// With that information we can detect if two values in a range both map outside the same value
/// space.
///
/// coerce_up means the next valid upper value in the value space will be chosen if the value
/// has to be coerced.
fn mapping_coerce(&self, inp: RangeInclusive<External>) -> RangeInclusive<Internal> {
self.mapping(*inp.start())..=self.mapping(*inp.end())
}
/// Inverse of `mapping_coerce`.
fn inverse_coerce(&self, out: RangeInclusive<Internal>) -> RangeInclusive<External> {
self.inverse(*out.start())..=self.inverse(*out.end())
}
}
/// Inverts a strictly monotonic mapping from `StrictlyMonotonicFn<A, B>` to
@@ -76,29 +54,15 @@ impl<T> From<T> for StrictlyMonotonicMappingInverter<T> {
}
impl<From, To, T> StrictlyMonotonicFn<To, From> for StrictlyMonotonicMappingInverter<T>
where
T: StrictlyMonotonicFn<From, To>,
From: Copy,
To: Copy,
where T: StrictlyMonotonicFn<From, To>
{
#[inline(always)]
fn mapping(&self, val: To) -> From {
self.orig_mapping.inverse(val)
}
#[inline(always)]
fn inverse(&self, val: From) -> To {
self.orig_mapping.mapping(val)
}
#[inline]
fn mapping_coerce(&self, inp: RangeInclusive<To>) -> RangeInclusive<From> {
self.orig_mapping.inverse_coerce(inp)
}
#[inline]
fn inverse_coerce(&self, out: RangeInclusive<From>) -> RangeInclusive<To> {
self.orig_mapping.mapping_coerce(out)
}
}
/// Applies the strictly monotonic mapping from `T` without any additional changes.
@@ -118,12 +82,10 @@ impl<External: MonotonicallyMappableToU128, T: MonotonicallyMappableToU128>
StrictlyMonotonicFn<External, u128> for StrictlyMonotonicMappingToInternal<T>
where T: MonotonicallyMappableToU128
{
#[inline(always)]
fn mapping(&self, inp: External) -> u128 {
External::to_u128(inp)
}
#[inline(always)]
fn inverse(&self, out: u128) -> External {
External::from_u128(out)
}
@@ -133,12 +95,10 @@ impl<External: MonotonicallyMappableToU64, T: MonotonicallyMappableToU64>
StrictlyMonotonicFn<External, u64> for StrictlyMonotonicMappingToInternal<T>
where T: MonotonicallyMappableToU64
{
#[inline(always)]
fn mapping(&self, inp: External) -> u64 {
External::to_u64(inp)
}
#[inline(always)]
fn inverse(&self, out: u64) -> External {
External::from_u64(out)
}
@@ -166,41 +126,14 @@ impl StrictlyMonotonicMappingToInternalGCDBaseval {
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)
}
#[inline]
#[allow(clippy::reversed_empty_ranges)]
fn mapping_coerce(&self, inp: RangeInclusive<External>) -> RangeInclusive<u64> {
let end = External::to_u64(*inp.end());
if end < self.min_value || inp.end() < inp.start() {
return 1..=0;
}
let map_coerce = |mut inp, coerce_up| {
let inp_lower_bound = self.inverse(0);
if inp < inp_lower_bound {
inp = inp_lower_bound;
}
let val = External::to_u64(inp);
let need_coercion = coerce_up && (val - self.min_value) % self.gcd != 0;
let mut mapped_val = self.mapping(inp);
if need_coercion {
mapped_val += 1;
}
mapped_val
};
let start = map_coerce(*inp.start(), true);
let end = map_coerce(*inp.end(), false);
start..=end
}
}
/// Strictly monotonic mapping with a base value.
@@ -208,7 +141,6 @@ pub(crate) struct StrictlyMonotonicMappingToInternalBaseval {
min_value: u64,
}
impl StrictlyMonotonicMappingToInternalBaseval {
#[inline(always)]
pub(crate) fn new(min_value: u64) -> Self {
Self { min_value }
}
@@ -217,35 +149,20 @@ impl StrictlyMonotonicMappingToInternalBaseval {
impl<External: MonotonicallyMappableToU64> StrictlyMonotonicFn<External, u64>
for StrictlyMonotonicMappingToInternalBaseval
{
#[inline]
#[allow(clippy::reversed_empty_ranges)]
fn mapping_coerce(&self, inp: RangeInclusive<External>) -> RangeInclusive<u64> {
if External::to_u64(*inp.end()) < self.min_value {
return 1..=0;
}
let start = self.mapping(External::to_u64(*inp.start()).max(self.min_value));
let end = self.mapping(External::to_u64(*inp.end()));
start..=end
}
#[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 {
self
}
#[inline(always)]
fn from_u64(val: u64) -> Self {
val
}
@@ -275,15 +192,11 @@ impl MonotonicallyMappableToU64 for bool {
}
}
// TODO remove me.
// Tantivy should refuse NaN values and work with NotNaN internally.
impl MonotonicallyMappableToU64 for f64 {
#[inline(always)]
fn to_u64(self) -> u64 {
common::f64_to_u64(self)
}
#[inline(always)]
fn from_u64(val: u64) -> Self {
common::u64_to_f64(val)
}
@@ -311,7 +224,7 @@ mod tests {
test_round_trip::<_, _, u64>(&mapping, 100u64);
}
fn test_round_trip<T: StrictlyMonotonicFn<K, L>, K: std::fmt::Debug + Eq + Copy, L: Copy>(
fn test_round_trip<T: StrictlyMonotonicFn<K, L>, K: std::fmt::Debug + Eq + Copy, L>(
mapping: &T,
test_val: K,
) {

5
fastfield_codecs/src/monotonic_mapping_u128.rs
View File

@@ -1,11 +1,8 @@
use std::fmt;
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 + fmt::Debug
{
pub trait MonotonicallyMappableToU128: 'static + PartialOrd + Copy + Send + Sync {
/// Converts a value to u128.
///
/// Internally all fast field values are encoded as u64.

500
fastfield_codecs/src/null_index/dense.rs
View File

@@ -1,500 +0,0 @@
use std::convert::TryInto;
use std::io::{self, Write};
use common::{BinarySerializable, OwnedBytes};
use itertools::Itertools;
use super::{get_bit_at, set_bit_at};
/// For the `DenseCodec`, `data` which contains the encoded blocks.
/// Each block consists of [u8; 12]. The first 8 bytes is a bitvec for 64 elements.
/// The last 4 bytes are the offset, the number of set bits so far.
///
/// When translating the original index to a dense index, the correct block can be computed
/// directly `orig_idx/64`. Inside the block the position is `orig_idx%64`.
///
/// 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.
#[derive(Clone)]
pub struct DenseCodec {
// data consists of blocks of 64 bits.
//
// The format is &[(u64, u32)]
// u64 is the bitvec
// u32 is the offset of the block, the number of set bits so far.
//
// At the end one block is appended, to store the number of values in the index in offset.
data: OwnedBytes,
}
const ELEMENTS_PER_BLOCK: u32 = 64;
const BLOCK_BITVEC_SIZE: usize = 8;
const BLOCK_OFFSET_SIZE: usize = 4;
const SERIALIZED_BLOCK_SIZE: usize = BLOCK_BITVEC_SIZE + BLOCK_OFFSET_SIZE;
/// Interpreting the bitvec as a list of 64 bits from the low weight to the
/// high weight.
///
/// This function returns the number of bits set to 1 within
/// `[0..pos_in_vec)`.
#[inline]
fn count_ones(bitvec: u64, pos_in_bitvec: u32) -> u32 {
let mask = (1u64 << pos_in_bitvec) - 1;
let masked_bitvec = bitvec & mask;
masked_bitvec.count_ones()
}
#[derive(Clone, Copy)]
struct DenseIndexBlock {
bitvec: u64,
offset: u32,
}
impl From<[u8; SERIALIZED_BLOCK_SIZE]> for DenseIndexBlock {
fn from(data: [u8; SERIALIZED_BLOCK_SIZE]) -> Self {
let bitvec = u64::from_le_bytes(data[..BLOCK_BITVEC_SIZE].try_into().unwrap());
let offset = u32::from_le_bytes(data[BLOCK_BITVEC_SIZE..].try_into().unwrap());
Self { bitvec, offset }
}
}
impl DenseCodec {
/// Open the DenseCodec from OwnedBytes
pub fn open(data: OwnedBytes) -> Self {
Self { data }
}
#[inline]
/// Check if value at position is not null.
pub fn exists(&self, idx: u32) -> bool {
let block_pos = idx / ELEMENTS_PER_BLOCK;
let bitvec = self.dense_index_block(block_pos).bitvec;
let pos_in_bitvec = idx % ELEMENTS_PER_BLOCK;
get_bit_at(bitvec, pos_in_bitvec)
}
#[inline]
fn dense_index_block(&self, block_pos: u32) -> DenseIndexBlock {
dense_index_block(&self.data, block_pos)
}
/// Return the number of non-null values in an index
pub fn num_non_nulls(&self) -> u32 {
let last_block = (self.data.len() / SERIALIZED_BLOCK_SIZE) - 1;
self.dense_index_block(last_block as u32).offset
}
#[inline]
/// Translate from the original index to the codec index.
pub fn translate_to_codec_idx(&self, idx: u32) -> Option<u32> {
let block_pos = idx / ELEMENTS_PER_BLOCK;
let index_block = self.dense_index_block(block_pos);
let pos_in_block_bit_vec = idx % ELEMENTS_PER_BLOCK;
let ones_in_block = count_ones(index_block.bitvec, pos_in_block_bit_vec);
if get_bit_at(index_block.bitvec, pos_in_block_bit_vec) {
Some(index_block.offset + ones_in_block)
} else {
None
}
}
/// Translate positions from the codec index to the original index.
///
/// # Panics
///
/// May panic if any `idx` is greater than the max codec index.
pub fn translate_codec_idx_to_original_idx<'a>(
&'a self,
iter: impl Iterator<Item = u32> + 'a,
) -> impl Iterator<Item = u32> + 'a {
let mut block_pos = 0u32;
iter.map(move |dense_idx| {
// update block_pos to limit search scope
block_pos = find_block(dense_idx, block_pos, &self.data);
let index_block = self.dense_index_block(block_pos);
// The next offset is higher than dense_idx and therefore:
// dense_idx <= offset + num_set_bits in block
let mut num_set_bits = 0;
for idx_in_bitvec in 0..ELEMENTS_PER_BLOCK {
if get_bit_at(index_block.bitvec, idx_in_bitvec) {
num_set_bits += 1;
}
if num_set_bits == (dense_idx - index_block.offset + 1) {
let orig_idx = block_pos * ELEMENTS_PER_BLOCK + idx_in_bitvec;
return orig_idx;
}
}
panic!("Internal Error: Offset calculation in dense idx seems to be wrong.");
})
}
}
#[inline]
fn dense_index_block(data: &[u8], block_pos: u32) -> DenseIndexBlock {
let data_start_pos = block_pos as usize * SERIALIZED_BLOCK_SIZE;
let block_data: [u8; SERIALIZED_BLOCK_SIZE] = data[data_start_pos..][..SERIALIZED_BLOCK_SIZE]
.try_into()
.unwrap();
block_data.into()
}
#[inline]
/// Finds the block position containing the dense_idx.
///
/// # Correctness
/// dense_idx needs to be smaller than the number of values in the index
///
/// The last offset number is equal to the number of values in the index.
fn find_block(dense_idx: u32, mut block_pos: u32, data: &[u8]) -> u32 {
loop {
let offset = dense_index_block(data, block_pos).offset;
if offset > dense_idx {
return block_pos - 1;
}
block_pos += 1;
}
}
/// Iterator over all values, true if set, otherwise false
pub fn serialize_dense_codec(
iter: impl Iterator<Item = bool>,
mut out: impl Write,
) -> io::Result<()> {
let mut offset: u32 = 0;
for chunk in &iter.chunks(ELEMENTS_PER_BLOCK as usize) {
let mut block: u64 = 0;
for (pos, is_bit_set) in chunk.enumerate() {
if is_bit_set {
set_bit_at(&mut block, pos as u64);
}
}
block.serialize(&mut out)?;
offset.serialize(&mut out)?;
offset += block.count_ones();
}
// Add sentinal block for the offset
let block: u64 = 0;
block.serialize(&mut out)?;
offset.serialize(&mut out)?;
Ok(())
}
#[cfg(test)]
mod tests {
use proptest::prelude::{any, prop, *};
use proptest::strategy::Strategy;
use proptest::{prop_oneof, proptest};
use super::*;
fn random_bitvec() -> BoxedStrategy<Vec<bool>> {
prop_oneof![
1 => prop::collection::vec(proptest::bool::weighted(1.0), 0..100),
1 => prop::collection::vec(proptest::bool::weighted(1.0), 0..64),
1 => prop::collection::vec(proptest::bool::weighted(0.0), 0..100),
1 => prop::collection::vec(proptest::bool::weighted(0.0), 0..64),
8 => vec![any::<bool>()],
2 => prop::collection::vec(any::<bool>(), 0..50),
]
.boxed()
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(500))]
#[test]
fn test_with_random_bitvecs(bitvec1 in random_bitvec(), bitvec2 in random_bitvec(), bitvec3 in random_bitvec()) {
let mut bitvec = Vec::new();
bitvec.extend_from_slice(&bitvec1);
bitvec.extend_from_slice(&bitvec2);
bitvec.extend_from_slice(&bitvec3);
test_null_index(bitvec);
}
}
#[test]
fn dense_codec_test_one_block_false() {
let mut iter = vec![false; 64];
iter.push(true);
test_null_index(iter);
}
fn test_null_index(data: Vec<bool>) {
let mut out = vec![];
serialize_dense_codec(data.iter().cloned(), &mut out).unwrap();
let null_index = DenseCodec::open(OwnedBytes::new(out));
let orig_idx_with_value: Vec<u32> = data
.iter()
.enumerate()
.filter(|(_pos, val)| **val)
.map(|(pos, _val)| pos as u32)
.collect();
assert_eq!(
null_index
.translate_codec_idx_to_original_idx(0..orig_idx_with_value.len() as u32)
.collect_vec(),
orig_idx_with_value
);
for (dense_idx, orig_idx) in orig_idx_with_value.iter().enumerate() {
assert_eq!(
null_index.translate_to_codec_idx(*orig_idx),
Some(dense_idx as u32)
);
}
for (pos, value) in data.iter().enumerate() {
assert_eq!(null_index.exists(pos as u32), *value);
}
}
#[test]
fn dense_codec_test_translation() {
let mut out = vec![];
let iter = ([true, false, true, false]).iter().cloned();
serialize_dense_codec(iter, &mut out).unwrap();
let null_index = DenseCodec::open(OwnedBytes::new(out));
assert_eq!(
null_index
.translate_codec_idx_to_original_idx(0..2)
.collect_vec(),
vec![0, 2]
);
}
#[test]
fn dense_codec_translate() {
let mut out = vec![];
let iter = ([true, false, true, false]).iter().cloned();
serialize_dense_codec(iter, &mut out).unwrap();
let null_index = DenseCodec::open(OwnedBytes::new(out));
assert_eq!(null_index.translate_to_codec_idx(0), Some(0));
assert_eq!(null_index.translate_to_codec_idx(2), Some(1));
}
#[test]
fn dense_codec_test_small() {
let mut out = vec![];
let iter = ([true, false, true, false]).iter().cloned();
serialize_dense_codec(iter, &mut out).unwrap();
let null_index = DenseCodec::open(OwnedBytes::new(out));
assert!(null_index.exists(0));
assert!(!null_index.exists(1));
assert!(null_index.exists(2));
assert!(!null_index.exists(3));
}
#[test]
fn dense_codec_test_large() {
let mut docs = vec![];
docs.extend((0..1000).map(|_idx| false));
docs.extend((0..=1000).map(|_idx| true));
let iter = docs.iter().cloned();
let mut out = vec![];
serialize_dense_codec(iter, &mut out).unwrap();
let null_index = DenseCodec::open(OwnedBytes::new(out));
assert!(!null_index.exists(0));
assert!(!null_index.exists(100));
assert!(!null_index.exists(999));
assert!(null_index.exists(1000));
assert!(null_index.exists(1999));
assert!(null_index.exists(2000));
assert!(!null_index.exists(2001));
}
#[test]
fn test_count_ones() {
let mut block = 0;
set_bit_at(&mut block, 0);
set_bit_at(&mut block, 2);
assert_eq!(count_ones(block, 0), 0);
assert_eq!(count_ones(block, 1), 1);
assert_eq!(count_ones(block, 2), 1);
assert_eq!(count_ones(block, 3), 2);
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use test::Bencher;
use super::*;
const TOTAL_NUM_VALUES: u32 = 1_000_000;
fn gen_bools(fill_ratio: f64) -> DenseCodec {
let mut out = Vec::new();
let mut rng: StdRng = StdRng::from_seed([1u8; 32]);
let bools: Vec<_> = (0..TOTAL_NUM_VALUES)
.map(|_| rng.gen_bool(fill_ratio))
.collect();
serialize_dense_codec(bools.into_iter(), &mut out).unwrap();
let codec = DenseCodec::open(OwnedBytes::new(out));
codec
}
fn random_range_iterator(
start: u32,
end: u32,
avg_step_size: u32,
avg_deviation: u32,
) -> impl Iterator<Item = u32> {
let mut rng: StdRng = StdRng::from_seed([1u8; 32]);
let mut current = start;
std::iter::from_fn(move || {
current += rng.gen_range(avg_step_size - avg_deviation..=avg_step_size + avg_deviation);
if current >= end {
None
} else {
Some(current)
}
})
}
fn n_percent_step_iterator(percent: f32, num_values: u32) -> impl Iterator<Item = u32> {
let ratio = percent as f32 / 100.0;
let step_size = (1f32 / ratio) as u32;
let deviation = step_size - 1;
random_range_iterator(0, num_values, step_size, deviation)
}
fn walk_over_data(codec: &DenseCodec, avg_step_size: u32) -> Option<u32> {
walk_over_data_from_positions(
codec,
random_range_iterator(0, TOTAL_NUM_VALUES, avg_step_size, 0),
)
}
fn walk_over_data_from_positions(
codec: &DenseCodec,
positions: impl Iterator<Item = u32>,
) -> Option<u32> {
let mut dense_idx: Option<u32> = None;
for idx in positions {
dense_idx = dense_idx.or(codec.translate_to_codec_idx(idx));
}
dense_idx
}
#[bench]
fn bench_translate_orig_to_codec_1percent_filled_10percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_5percent_filled_10percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.05f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_5percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.05f64);
bench.iter(|| walk_over_data(&codec, 1000));
}
#[bench]
fn bench_translate_orig_to_codec_full_scan_1percent_filled(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
}
#[bench]
fn bench_translate_orig_to_codec_full_scan_10percent_filled(bench: &mut Bencher) {
let codec = gen_bools(0.1f64);
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
}
#[bench]
fn bench_translate_orig_to_codec_full_scan_90percent_filled(bench: &mut Bencher) {
let codec = gen_bools(0.9f64);
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
}
#[bench]
fn bench_translate_orig_to_codec_10percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.1f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_50percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.5f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_90percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.9f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_codec_to_orig_1percent_filled_0comma005percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
let num_non_nulls = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(n_percent_step_iterator(0.005, num_non_nulls))
.last()
});
}
#[bench]
fn bench_translate_codec_to_orig_1percent_filled_10percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
let num_non_nulls = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(n_percent_step_iterator(10.0, num_non_nulls))
.last()
});
}
#[bench]
fn bench_translate_codec_to_orig_1percent_filled_full_scan(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
let num_vals = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(0..num_vals)
.last()
});
}
#[bench]
fn bench_translate_codec_to_orig_90percent_filled_0comma005percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.90f64);
let num_non_nulls = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(n_percent_step_iterator(0.005, num_non_nulls))
.last()
});
}
#[bench]
fn bench_translate_codec_to_orig_90percent_filled_full_scan(bench: &mut Bencher) {
let codec = gen_bools(0.9f64);
let num_vals = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(0..num_vals)
.last()
});
}
}

14
fastfield_codecs/src/null_index/mod.rs
View File

@@ -1,14 +0,0 @@
pub use dense::{serialize_dense_codec, DenseCodec};
mod dense;
mod sparse;
#[inline]
fn get_bit_at(input: u64, n: u32) -> bool {
input & (1 << n) != 0
}
#[inline]
fn set_bit_at(input: &mut u64, n: u64) {
*input |= 1 << n;
}

768
fastfield_codecs/src/null_index/sparse.rs
View File

@@ -1,768 +0,0 @@
use std::io::{self, Write};
use common::{BitSet, GroupByIteratorExtended, OwnedBytes};
use super::{serialize_dense_codec, DenseCodec};
/// `SparseCodec` is the codec for data, when only few documents have values.
/// In contrast to `DenseCodec` opening a `SparseCodec` causes runtime data to be produced, for
/// faster access.
///
/// The lower 16 bits of doc ids are stored as u16 while the upper 16 bits are given by the block
/// id. Each block contains 1<<16 docids.
///
/// # Serialized Data Layout
/// The data starts with the block data. Each block is either dense or sparse encoded, depending on
/// the number of values in the block. A block is sparse when it contains less than
/// DENSE_BLOCK_THRESHOLD (6144) values.
/// [Sparse data block | dense data block, .. #repeat*; Desc: Either a sparse or dense encoded
/// block]
/// ### Sparse block data
/// [u16 LE, .. #repeat*; Desc: Positions with values in a block]
/// ### Dense block data
/// [Dense codec for the whole block; Desc: Similar to a bitvec(0..ELEMENTS_PER_BLOCK) + Metadata
/// for faster lookups. See dense.rs]
///
/// The data is followed by block metadata, to know which area of the raw block data belongs to
/// which block. Only metadata for blocks with elements is recorded to
/// keep the overhead low for scenarios with many very sparse columns. The block metadata consists
/// of the block index and the number of values in the block. Since we don't store empty blocks
/// num_vals is incremented by 1, e.g. 0 means 1 value.
///
/// The last u16 is storing the number of metadata blocks.
/// [u16 LE, .. #repeat*; Desc: Positions with values in a block][(u16 LE, u16 LE), .. #repeat*;
/// Desc: (Block Id u16, Num Elements u16)][u16 LE; Desc: num blocks with values u16]
///
/// # Opening
/// When opening the data layout, the data is expanded to `Vec<SparseCodecBlockVariant>`, where the
/// index is the block index. For each block `byte_start` and `offset` is computed.
pub struct SparseCodec {
data: OwnedBytes,
blocks: Vec<SparseCodecBlockVariant>,
}
/// The threshold for for number of elements after which we switch to dense block encoding
const DENSE_BLOCK_THRESHOLD: u32 = 6144;
const ELEMENTS_PER_BLOCK: u32 = u16::MAX as u32 + 1;
/// 1.5 bit per Element + 12 bytes for the sentinal block
const NUM_BYTES_DENSE_BLOCK: u32 = (ELEMENTS_PER_BLOCK + ELEMENTS_PER_BLOCK / 2 + 64 + 32) / 8;
#[derive(Clone)]
enum SparseCodecBlockVariant {
Empty { offset: u32 },
Dense(DenseBlock),
Sparse(SparseBlock),
}
impl SparseCodecBlockVariant {
/// The number of non-null values that preceeded that block.
#[inline]
fn offset(&self) -> u32 {
match self {
SparseCodecBlockVariant::Empty { offset } => *offset,
SparseCodecBlockVariant::Dense(dense) => dense.offset,
SparseCodecBlockVariant::Sparse(sparse) => sparse.offset,
}
}
}
/// A block consists of max u16 values
#[derive(Clone)]
struct DenseBlock {
/// The number of values set before the block
offset: u32,
/// The data for the dense encoding
codec: DenseCodec,
}
impl DenseBlock {
#[inline]
pub fn exists(&self, idx: u32) -> bool {
self.codec.exists(idx)
}
#[inline]
pub fn translate_to_codec_idx(&self, idx: u32) -> Option<u32> {
self.codec.translate_to_codec_idx(idx)
}
#[inline]
pub fn translate_codec_idx_to_original_idx_iter<'a>(
&'a self,
iter: impl Iterator<Item = u32> + 'a,
) -> impl Iterator<Item = u32> + 'a {
self.codec.translate_codec_idx_to_original_idx(iter)
}
#[inline]
pub fn translate_codec_idx_to_original_idx(&self, idx: u32) -> u32 {
self.codec
.translate_codec_idx_to_original_idx(idx..=idx)
.next()
.unwrap()
}
}
/// A block consists of max u16 values
#[derive(Debug, Copy, Clone)]
struct SparseBlock {
/// The number of values in the block
num_vals: u32,
/// The number of values set before the block
offset: u32,
/// The start position of the data for the block
byte_start: u32,
}
impl SparseBlock {
fn empty_block(offset: u32) -> Self {
Self {
num_vals: 0,
byte_start: 0,
offset,
}
}
#[inline]
fn value_at_idx(&self, data: &[u8], idx: u16) -> u16 {
let start_offset: usize = self.byte_start as usize + (idx as u32 as usize * 2);
get_u16(data, start_offset)
}
#[inline]
#[allow(clippy::comparison_chain)]
// Looks for the element in the block. Returns the positions if found.
fn binary_search(&self, data: &[u8], target: u16) -> Option<u16> {
let mut size = self.num_vals as u16;
let mut left = 0;
let mut right = size;
// TODO try different implem.
// e.g. exponential search into binary search
while left < right {
let mid = left + size / 2;
// TODO do boundary check only once, and then use an
// unsafe `value_at_idx`
let mid_val = self.value_at_idx(data, mid);
if target > mid_val {
left = mid + 1;
} else if target < mid_val {
right = mid;
} else {
return Some(mid);
}
size = right - left;
}
None
}
}
#[inline]
fn get_u16(data: &[u8], byte_position: usize) -> u16 {
let bytes: [u8; 2] = data[byte_position..byte_position + 2].try_into().unwrap();
u16::from_le_bytes(bytes)
}
const SERIALIZED_BLOCK_METADATA_SIZE: usize = 4;
fn deserialize_sparse_codec_block(data: &OwnedBytes) -> Vec<SparseCodecBlockVariant> {
// The number of vals so far
let mut offset = 0;
let mut sparse_codec_blocks = Vec::new();
let num_blocks = get_u16(data, data.len() - 2);
let block_data_index_start =
data.len() - 2 - num_blocks as usize * SERIALIZED_BLOCK_METADATA_SIZE;
let mut byte_start = 0;
for block_num in 0..num_blocks as usize {
let block_data_index = block_data_index_start + SERIALIZED_BLOCK_METADATA_SIZE * block_num;
let block_idx = get_u16(data, block_data_index);
let num_vals = get_u16(data, block_data_index + 2) as u32 + 1;
sparse_codec_blocks.resize(
block_idx as usize,
SparseCodecBlockVariant::Empty { offset },
);
if is_sparse(num_vals) {
let block = SparseBlock {
num_vals,
offset,
byte_start,
};
sparse_codec_blocks.push(SparseCodecBlockVariant::Sparse(block));
byte_start += 2 * num_vals;
} else {
let block = DenseBlock {
offset,
codec: DenseCodec::open(data.slice(byte_start as usize..data.len()).clone()),
};
sparse_codec_blocks.push(SparseCodecBlockVariant::Dense(block));
// Dense blocks have a fixed size spanning ELEMENTS_PER_BLOCK.
byte_start += NUM_BYTES_DENSE_BLOCK;
}
offset += num_vals;
}
sparse_codec_blocks.push(SparseCodecBlockVariant::Empty { offset });
sparse_codec_blocks
}
/// Splits a value address into lower and upper 16bits.
/// The lower 16 bits are the value in the block
/// The upper 16 bits are the block index
#[derive(Debug, Clone, Copy)]
struct ValueAddr {
block_idx: u16,
value_in_block: u16,
}
/// Splits a idx into block index and value in the block
#[inline]
fn value_addr(idx: u32) -> ValueAddr {
/// Static assert number elements per block this method expects
#[allow(clippy::assertions_on_constants)]
const _: () = assert!(ELEMENTS_PER_BLOCK == (1 << 16));
let value_in_block = idx as u16;
let block_idx = (idx >> 16) as u16;
ValueAddr {
block_idx,
value_in_block,
}
}
impl SparseCodec {
/// Open the SparseCodec from OwnedBytes
pub fn open(data: OwnedBytes) -> Self {
let blocks = deserialize_sparse_codec_block(&data);
Self { data, blocks }
}
#[inline]
/// Check if value at position is not null.
pub fn exists(&self, idx: u32) -> bool {
let value_addr = value_addr(idx);
// There may be trailing nulls without data, those are not stored as blocks. It would be
// possible to create empty blocks, but for that we would need to serialize the number of
// values or pass them when opening
if let Some(block) = self.blocks.get(value_addr.block_idx as usize) {
match block {
SparseCodecBlockVariant::Empty { offset: _ } => false,
SparseCodecBlockVariant::Dense(block) => {
block.exists(value_addr.value_in_block as u32)
}
SparseCodecBlockVariant::Sparse(block) => block
.binary_search(&self.data, value_addr.value_in_block)
.is_some(),
}
} else {
false
}
}
/// Return the number of non-null values in an index
pub fn num_non_nulls(&self) -> u32 {
self.blocks.last().map(|block| block.offset()).unwrap_or(0)
}
#[inline]
/// Translate from the original index to the codec index.
pub fn translate_to_codec_idx(&self, idx: u32) -> Option<u32> {
let value_addr = value_addr(idx);
let block = self.blocks.get(value_addr.block_idx as usize)?;
match block {
SparseCodecBlockVariant::Empty { offset: _ } => None,
SparseCodecBlockVariant::Dense(block) => block
.translate_to_codec_idx(value_addr.value_in_block as u32)
.map(|pos_in_block| pos_in_block + block.offset),
SparseCodecBlockVariant::Sparse(block) => {
let pos_in_block = block.binary_search(&self.data, value_addr.value_in_block);
pos_in_block.map(|pos_in_block: u16| block.offset + pos_in_block as u32)
}
}
}
#[inline]
fn find_block(&self, dense_idx: u32, mut block_pos: u32) -> u32 {
loop {
let offset = self.blocks[block_pos as usize].offset();
if offset > dense_idx {
return block_pos - 1;
}
block_pos += 1;
}
}
/// Translate positions from the codec index to the original index.
/// Correctness: Provided values must be in increasing values
///
/// # Panics
///
/// May panic if any `idx` is greater than the max codec index.
pub fn translate_codec_idx_to_original_idx<'a>(
&'a self,
iter: impl Iterator<Item = u32> + 'a,
) -> impl Iterator<Item = u32> + 'a {
let mut block_pos = 0u32;
iter.group_by(move |codec_idx| {
block_pos = self.find_block(*codec_idx, block_pos);
block_pos
})
.flat_map(move |(block_pos, block_iter)| {
let block_doc_idx_start = block_pos * ELEMENTS_PER_BLOCK;
let block = &self.blocks[block_pos as usize];
let offset = block.offset();
let indexes_in_block_iter = block_iter.map(move |codec_idx| codec_idx - offset);
match block {
SparseCodecBlockVariant::Empty { offset: _ } => {
panic!(
"invalid input, cannot translate to original index. associated empty \
block with dense idx. block_pos {}, idx_in_block {:?}",
block_pos,
indexes_in_block_iter.collect::<Vec<_>>()
)
}
SparseCodecBlockVariant::Dense(dense) => {
Box::new(dense.translate_codec_idx_to_original_idx_iter(indexes_in_block_iter))
as Box<dyn Iterator<Item = u32>>
}
SparseCodecBlockVariant::Sparse(block) => {
Box::new(indexes_in_block_iter.map(move |idx_in_block| {
block.value_at_idx(&self.data, idx_in_block as u16) as u32
}))
}
}
.map(move |idx| idx + block_doc_idx_start)
})
}
}
#[inline]
fn is_sparse(num_elem_in_block: u32) -> bool {
num_elem_in_block < DENSE_BLOCK_THRESHOLD
}
#[derive(Default)]
struct BlockDataSerialized {
block_idx: u16,
num_vals: u32,
}
/// Iterator over positions of set values.
pub fn serialize_sparse_codec<W: Write>(
mut iter: impl Iterator<Item = u32>,
mut out: W,
) -> io::Result<()> {
let mut block_metadata: Vec<BlockDataSerialized> = Vec::new();
let mut current_block = Vec::new();
// This if-statement for the first element ensures that
// `block_metadata` is not empty in the loop below.
if let Some(idx) = iter.next() {
let value_addr = value_addr(idx);
block_metadata.push(BlockDataSerialized {
block_idx: value_addr.block_idx,
num_vals: 1,
});
current_block.push(value_addr.value_in_block);
}
let flush_block = |current_block: &mut Vec<u16>, out: &mut W| -> io::Result<()> {
let is_sparse = is_sparse(current_block.len() as u32);
if is_sparse {
for val_in_block in current_block.iter() {
out.write_all(val_in_block.to_le_bytes().as_ref())?;
}
} else {
let mut bitset = BitSet::with_max_value(ELEMENTS_PER_BLOCK + 1);
for val_in_block in current_block.iter() {
bitset.insert(*val_in_block as u32);
}
let iter = (0..ELEMENTS_PER_BLOCK).map(|idx| bitset.contains(idx));
serialize_dense_codec(iter, out)?;
}
current_block.clear();
Ok(())
};
for idx in iter {
let value_addr = value_addr(idx);
if block_metadata[block_metadata.len() - 1].block_idx == value_addr.block_idx {
let last_idx_metadata = block_metadata.len() - 1;
block_metadata[last_idx_metadata].num_vals += 1;
} else {
// flush prev block
flush_block(&mut current_block, &mut out)?;
block_metadata.push(BlockDataSerialized {
block_idx: value_addr.block_idx,
num_vals: 1,
});
}
current_block.push(value_addr.value_in_block);
}
// handle last block
flush_block(&mut current_block, &mut out)?;
for block in &block_metadata {
out.write_all(block.block_idx.to_le_bytes().as_ref())?;
// We don't store empty blocks, therefore we can subtract 1.
// This way we will be able to use u16 when the number of elements is 1 << 16 or u16::MAX+1
out.write_all(((block.num_vals - 1) as u16).to_le_bytes().as_ref())?;
}
out.write_all((block_metadata.len() as u16).to_le_bytes().as_ref())?;
Ok(())
}
#[cfg(test)]
mod tests {
use itertools::Itertools;
use proptest::prelude::{any, prop, *};
use proptest::strategy::Strategy;
use proptest::{prop_oneof, proptest};
use super::*;
fn random_bitvec() -> BoxedStrategy<Vec<bool>> {
prop_oneof![
1 => prop::collection::vec(proptest::bool::weighted(1.0), 0..100),
1 => prop::collection::vec(proptest::bool::weighted(0.00), 0..(ELEMENTS_PER_BLOCK as usize * 3)), // empty blocks
1 => prop::collection::vec(proptest::bool::weighted(1.00), 0..(ELEMENTS_PER_BLOCK as usize + 10)), // full block
1 => prop::collection::vec(proptest::bool::weighted(0.01), 0..100),
1 => prop::collection::vec(proptest::bool::weighted(0.01), 0..u16::MAX as usize),
8 => vec![any::<bool>()],
]
.boxed()
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(50))]
#[test]
fn test_with_random_bitvecs(bitvec1 in random_bitvec(), bitvec2 in random_bitvec(), bitvec3 in random_bitvec()) {
let mut bitvec = Vec::new();
bitvec.extend_from_slice(&bitvec1);
bitvec.extend_from_slice(&bitvec2);
bitvec.extend_from_slice(&bitvec3);
test_null_index(bitvec);
}
}
#[test]
fn sparse_codec_test_one_block_false() {
let mut iter = vec![false; ELEMENTS_PER_BLOCK as usize];
iter.push(true);
test_null_index(iter);
}
#[test]
fn sparse_codec_test_one_block_true() {
let mut iter = vec![true; ELEMENTS_PER_BLOCK as usize];
iter.push(true);
test_null_index(iter);
}
fn test_null_index(data: Vec<bool>) {
let mut out = vec![];
serialize_sparse_codec(
data.iter()
.cloned()
.enumerate()
.filter(|(_pos, val)| *val)
.map(|(pos, _val)| pos as u32),
&mut out,
)
.unwrap();
let null_index = SparseCodec::open(OwnedBytes::new(out));
let orig_idx_with_value: Vec<u32> = data
.iter()
.enumerate()
.filter(|(_pos, val)| **val)
.map(|(pos, _val)| pos as u32)
.collect();
assert_eq!(
null_index
.translate_codec_idx_to_original_idx(0..orig_idx_with_value.len() as u32)
.collect_vec(),
orig_idx_with_value
);
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) {
assert_eq!(
null_index.translate_to_codec_idx(*orig_idx),
Some(dense_idx as u32)
);
}
// 100 samples
let step_size = (data.len() / 100).max(1);
for (pos, value) in data.iter().enumerate().step_by(step_size) {
assert_eq!(null_index.exists(pos as u32), *value);
}
}
#[test]
fn sparse_codec_test_translation() {
let mut out = vec![];
let iter = ([true, false, true, false]).iter().cloned();
serialize_sparse_codec(
iter.enumerate()
.filter(|(_pos, val)| *val)
.map(|(pos, _val)| pos as u32),
&mut out,
)
.unwrap();
let null_index = SparseCodec::open(OwnedBytes::new(out));
assert_eq!(
null_index
.translate_codec_idx_to_original_idx(0..2)
.collect_vec(),
vec![0, 2]
);
}
#[test]
fn sparse_codec_translate() {
let mut out = vec![];
let iter = ([true, false, true, false]).iter().cloned();
serialize_sparse_codec(
iter.enumerate()
.filter(|(_pos, val)| *val)
.map(|(pos, _val)| pos as u32),
&mut out,
)
.unwrap();
let null_index = SparseCodec::open(OwnedBytes::new(out));
assert_eq!(null_index.translate_to_codec_idx(0), Some(0));
assert_eq!(null_index.translate_to_codec_idx(2), Some(1));
}
#[test]
fn sparse_codec_test_small() {
let mut out = vec![];
let iter = ([true, false, true, false]).iter().cloned();
serialize_sparse_codec(
iter.enumerate()
.filter(|(_pos, val)| *val)
.map(|(pos, _val)| pos as u32),
&mut out,
)
.unwrap();
let null_index = SparseCodec::open(OwnedBytes::new(out));
assert!(null_index.exists(0));
assert!(!null_index.exists(1));
assert!(null_index.exists(2));
assert!(!null_index.exists(3));
}
#[test]
fn sparse_codec_test_large() {
let mut docs = vec![];
docs.extend((0..ELEMENTS_PER_BLOCK).map(|_idx| false));
docs.extend((0..=1).map(|_idx| true));
let iter = docs.iter().cloned();
let mut out = vec![];
serialize_sparse_codec(
iter.enumerate()
.filter(|(_pos, val)| *val)
.map(|(pos, _val)| pos as u32),
&mut out,
)
.unwrap();
let null_index = SparseCodec::open(OwnedBytes::new(out));
assert!(!null_index.exists(0));
assert!(!null_index.exists(100));
assert!(!null_index.exists(ELEMENTS_PER_BLOCK - 1));
assert!(null_index.exists(ELEMENTS_PER_BLOCK));
assert!(null_index.exists(ELEMENTS_PER_BLOCK + 1));
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use test::Bencher;
use super::*;
const TOTAL_NUM_VALUES: u32 = 1_000_000;
fn gen_bools(fill_ratio: f64) -> SparseCodec {
let mut out = Vec::new();
let mut rng: StdRng = StdRng::from_seed([1u8; 32]);
serialize_sparse_codec(
(0..TOTAL_NUM_VALUES)
.map(|_| rng.gen_bool(fill_ratio))
.enumerate()
.filter(|(_pos, val)| *val)
.map(|(pos, _val)| pos as u32),
&mut out,
)
.unwrap();
let codec = SparseCodec::open(OwnedBytes::new(out));
codec
}
fn random_range_iterator(
start: u32,
end: u32,
avg_step_size: u32,
avg_deviation: u32,
) -> impl Iterator<Item = u32> {
let mut rng: StdRng = StdRng::from_seed([1u8; 32]);
let mut current = start;
std::iter::from_fn(move || {
current += rng.gen_range(avg_step_size - avg_deviation..=avg_step_size + avg_deviation);
if current >= end {
None
} else {
Some(current)
}
})
}
fn n_percent_step_iterator(percent: f32, num_values: u32) -> impl Iterator<Item = u32> {
let ratio = percent as f32 / 100.0;
let step_size = (1f32 / ratio) as u32;
let deviation = step_size - 1;
random_range_iterator(0, num_values, step_size, deviation)
}
fn walk_over_data(codec: &SparseCodec, avg_step_size: u32) -> Option<u32> {
walk_over_data_from_positions(
codec,
random_range_iterator(0, TOTAL_NUM_VALUES, avg_step_size, 0),
)
}
fn walk_over_data_from_positions(
codec: &SparseCodec,
positions: impl Iterator<Item = u32>,
) -> Option<u32> {
let mut dense_idx: Option<u32> = None;
for idx in positions {
dense_idx = dense_idx.or(codec.translate_to_codec_idx(idx));
}
dense_idx
}
#[bench]
fn bench_translate_orig_to_codec_1percent_filled_10percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_5percent_filled_10percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.05f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_5percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.05f64);
bench.iter(|| walk_over_data(&codec, 1000));
}
#[bench]
fn bench_translate_orig_to_codec_full_scan_1percent_filled(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
}
#[bench]
fn bench_translate_orig_to_codec_full_scan_10percent_filled(bench: &mut Bencher) {
let codec = gen_bools(0.1f64);
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
}
#[bench]
fn bench_translate_orig_to_codec_full_scan_90percent_filled(bench: &mut Bencher) {
let codec = gen_bools(0.9f64);
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
}
#[bench]
fn bench_translate_orig_to_codec_10percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.1f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_50percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.5f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_orig_to_codec_90percent_filled_1percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.9f64);
bench.iter(|| walk_over_data(&codec, 100));
}
#[bench]
fn bench_translate_codec_to_orig_1percent_filled_0comma005percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
let num_non_nulls = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(n_percent_step_iterator(0.005, num_non_nulls))
.last()
});
}
#[bench]
fn bench_translate_codec_to_orig_1percent_filled_10percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
let num_non_nulls = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(n_percent_step_iterator(10.0, num_non_nulls))
.last()
});
}
#[bench]
fn bench_translate_codec_to_orig_1percent_filled_full_scan(bench: &mut Bencher) {
let codec = gen_bools(0.01f64);
let num_vals = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(0..num_vals)
.last()
});
}
#[bench]
fn bench_translate_codec_to_orig_90percent_filled_0comma005percent_hit(bench: &mut Bencher) {
let codec = gen_bools(0.90f64);
let num_non_nulls = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(n_percent_step_iterator(0.005, num_non_nulls))
.last()
});
}
#[bench]
fn bench_translate_codec_to_orig_90percent_filled_full_scan(bench: &mut Bencher) {
let codec = gen_bools(0.9f64);
let num_vals = codec.num_non_nulls();
bench.iter(|| {
codec
.translate_codec_idx_to_original_idx(0..num_vals)
.last()
});
}
}

145
fastfield_codecs/src/null_index_footer.rs
View File

@@ -1,145 +0,0 @@
use std::io::{self, Write};
use std::ops::Range;
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt};
#[derive(Debug, Clone, Copy, Eq, PartialEq)]
pub(crate) enum FastFieldCardinality {
Single = 1,
Multi = 2,
}
impl BinarySerializable for FastFieldCardinality {
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 FastFieldCardinality {
pub(crate) fn to_code(self) -> u8 {
self as u8
}
pub(crate) fn from_code(code: u8) -> Option<Self> {
match code {
1 => Some(Self::Single),
2 => Some(Self::Multi),
_ => None,
}
}
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub(crate) enum NullIndexCodec {
Full = 1,
}
impl BinarySerializable for NullIndexCodec {
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 NullIndexCodec {
pub(crate) fn to_code(self) -> u8 {
self as u8
}
pub(crate) fn from_code(code: u8) -> Option<Self> {
match code {
1 => Some(Self::Full),
_ => None,
}
}
}
#[derive(Debug, Clone, Eq, PartialEq)]
pub(crate) struct NullIndexFooter {
pub(crate) cardinality: FastFieldCardinality,
pub(crate) null_index_codec: NullIndexCodec,
// Unused for NullIndexCodec::Full
pub(crate) null_index_byte_range: Range<u64>,
}
impl BinarySerializable for NullIndexFooter {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
self.cardinality.serialize(writer)?;
self.null_index_codec.serialize(writer)?;
VInt(self.null_index_byte_range.start).serialize(writer)?;
VInt(self.null_index_byte_range.end - self.null_index_byte_range.start)
.serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let cardinality = FastFieldCardinality::deserialize(reader)?;
let null_index_codec = NullIndexCodec::deserialize(reader)?;
let null_index_byte_range_start = VInt::deserialize(reader)?.0;
let null_index_byte_range_end = VInt::deserialize(reader)?.0 + null_index_byte_range_start;
Ok(Self {
cardinality,
null_index_codec,
null_index_byte_range: null_index_byte_range_start..null_index_byte_range_end,
})
}
}
pub(crate) fn append_null_index_footer(
output: &mut impl io::Write,
null_index_footer: NullIndexFooter,
) -> io::Result<()> {
let mut counting_write = CountingWriter::wrap(output);
null_index_footer.serialize(&mut counting_write)?;
let footer_payload_len = counting_write.written_bytes();
BinarySerializable::serialize(&(footer_payload_len as u16), &mut counting_write)?;
Ok(())
}
pub(crate) fn read_null_index_footer(
data: OwnedBytes,
) -> io::Result<(OwnedBytes, NullIndexFooter)> {
let (data, null_footer_length_bytes) = data.rsplit(2);
let footer_length = u16::deserialize(&mut null_footer_length_bytes.as_slice())?;
let (data, null_index_footer_bytes) = data.rsplit(footer_length as usize);
let null_index_footer = NullIndexFooter::deserialize(&mut null_index_footer_bytes.as_ref())?;
Ok((data, null_index_footer))
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn null_index_footer_deser_test() {
let null_index_footer = NullIndexFooter {
cardinality: FastFieldCardinality::Single,
null_index_codec: NullIndexCodec::Full,
null_index_byte_range: 100..120,
};
let mut out = vec![];
null_index_footer.serialize(&mut out).unwrap();
assert_eq!(
null_index_footer,
NullIndexFooter::deserialize(&mut &out[..]).unwrap()
);
}
}

119
fastfield_codecs/src/optional_column.rs Normal file
View File

@@ -0,0 +1,119 @@
use std::ops::{Range, RangeInclusive};
use std::sync::Arc;
use crate::Column;
/// `OptionalColumn` provides columnar access on a field.
pub trait OptionalColumn<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) -> Option<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`.
fn get_range(&self, start: u64, output: &mut [Option<T>]) {
for (out, idx) in output.iter_mut().zip(start..) {
*out = self.get_val(idx as u32);
}
}
/// Return the positions of values which are in the provided range.
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 {
let val = self.get_val(idx);
if let Some(val) = val {
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) -> Option<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) -> Option<T>;
/// The number of values including `None` in the column.
fn num_vals(&self) -> u32;
/// Returns a iterator over the data
fn iter<'a>(&'a self) -> Box<dyn Iterator<Item = Option<T>> + 'a> {
Box::new((0..self.num_vals()).map(|idx| self.get_val(idx)))
}
/// return full column if all values are set and is not empty
fn to_full(&self) -> Option<Arc<dyn Column<T>>> {
None
}
}
/// Temporary wrapper to migrate to optional column
pub(crate) struct ToOptionalColumn<T> {
column: Arc<dyn Column<T>>,
}
impl<T: PartialOrd> ToOptionalColumn<T> {
pub(crate) fn new(column: Arc<dyn Column<T>>) -> Self {
Self { column }
}
}
impl<T: PartialOrd> OptionalColumn<T> for ToOptionalColumn<T> {
#[inline]
fn get_val(&self, idx: u32) -> Option<T> {
let val = self.column.get_val(idx);
Some(val)
}
fn min_value(&self) -> Option<T> {
let min_value = self.column.min_value();
Some(min_value)
}
fn max_value(&self) -> Option<T> {
let max_value = self.column.max_value();
Some(max_value)
}
fn num_vals(&self) -> u32 {
self.column.num_vals()
}
fn iter(&self) -> Box<dyn Iterator<Item = Option<T>> + '_> {
Box::new(self.column.iter().map(|el| Some(el)))
}
/// return full column if all values are set and is not empty
fn to_full(&self) -> Option<Arc<dyn Column<T>>> {
Some(self.column.clone())
}
}

116
fastfield_codecs/src/serialize.rs
View File

@@ -17,25 +17,22 @@
// 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 std::sync::Arc;
use std::{fmt, io};
use common::{BinarySerializable, OwnedBytes, VInt};
use common::{BinarySerializable, VInt};
use log::warn;
use ownedbytes::OwnedBytes;
use crate::bitpacked::BitpackedCodec;
use crate::blockwise_linear::BlockwiseLinearCodec;
use crate::compact_space::CompactSpaceCompressor;
use crate::format_version::append_format_version;
use crate::linear::LinearCodec;
use crate::monotonic_mapping::{
StrictlyMonotonicFn, StrictlyMonotonicMappingToInternal,
StrictlyMonotonicMappingToInternalGCDBaseval,
};
use crate::null_index_footer::{
append_null_index_footer, FastFieldCardinality, NullIndexCodec, NullIndexFooter,
};
use crate::{
monotonic_map_column, Column, FastFieldCodec, FastFieldCodecType, MonotonicallyMappableToU64,
U128FastFieldCodecType, VecColumn, ALL_CODEC_TYPES,
@@ -167,7 +164,7 @@ impl BinarySerializable for Header {
/// Return estimated compression for given codec in the value range [0.0..1.0], where 1.0 means no
/// compression.
pub fn estimate<T: MonotonicallyMappableToU64 + fmt::Debug>(
pub fn estimate<T: MonotonicallyMappableToU64>(
typed_column: impl Column<T>,
codec_type: FastFieldCodecType,
) -> Option<f32> {
@@ -192,69 +189,6 @@ pub fn serialize_u128<F: Fn() -> I, I: Iterator<Item = u128>>(
iter_gen: F,
num_vals: u32,
output: &mut impl io::Write,
) -> io::Result<()> {
serialize_u128_new(ValueIndexInfo::default(), iter_gen, num_vals, output)
}
#[allow(dead_code)]
pub enum ValueIndexInfo<'a> {
MultiValue(Box<dyn MultiValueIndexInfo + 'a>),
SingleValue(Box<dyn SingleValueIndexInfo + 'a>),
}
// TODO Remove me
impl Default for ValueIndexInfo<'static> {
fn default() -> Self {
struct Dummy {}
impl SingleValueIndexInfo for Dummy {
fn num_vals(&self) -> u32 {
todo!()
}
fn num_non_nulls(&self) -> u32 {
todo!()
}
fn iter(&self) -> Box<dyn Iterator<Item = u32>> {
todo!()
}
}
Self::SingleValue(Box::new(Dummy {}))
}
}
impl<'a> ValueIndexInfo<'a> {
fn get_cardinality(&self) -> FastFieldCardinality {
match self {
ValueIndexInfo::MultiValue(_) => FastFieldCardinality::Multi,
ValueIndexInfo::SingleValue(_) => FastFieldCardinality::Single,
}
}
}
pub trait MultiValueIndexInfo {
/// The number of docs in the column.
fn num_docs(&self) -> u32;
/// The number of values in the column.
fn num_vals(&self) -> u32;
/// Return the start index of the values for each doc
fn iter(&self) -> Box<dyn Iterator<Item = u32> + '_>;
}
pub trait SingleValueIndexInfo {
/// The number of values including nulls in the column.
fn num_vals(&self) -> u32;
/// The number of non-null values in the column.
fn num_non_nulls(&self) -> u32;
/// Return a iterator of the positions of docs with a value
fn iter(&self) -> Box<dyn Iterator<Item = u32> + '_>;
}
/// Serializes u128 values with the compact space codec.
pub fn serialize_u128_new<F: Fn() -> I, I: Iterator<Item = u128>>(
value_index: ValueIndexInfo,
iter_gen: F,
num_vals: u32,
output: &mut impl io::Write,
) -> io::Result<()> {
let header = U128Header {
num_vals,
@@ -264,29 +198,11 @@ pub fn serialize_u128_new<F: Fn() -> I, I: Iterator<Item = u128>>(
let compressor = CompactSpaceCompressor::train_from(iter_gen(), num_vals);
compressor.compress_into(iter_gen(), output).unwrap();
let null_index_footer = NullIndexFooter {
cardinality: value_index.get_cardinality(),
null_index_codec: NullIndexCodec::Full,
null_index_byte_range: 0..0,
};
append_null_index_footer(output, null_index_footer)?;
append_format_version(output)?;
Ok(())
}
/// Serializes the column with the codec with the best estimate on the data.
pub fn serialize<T: MonotonicallyMappableToU64 + fmt::Debug>(
typed_column: impl Column<T>,
output: &mut impl io::Write,
codecs: &[FastFieldCodecType],
) -> io::Result<()> {
serialize_new(ValueIndexInfo::default(), typed_column, output, codecs)
}
/// Serializes the column with the codec with the best estimate on the data.
pub fn serialize_new<T: MonotonicallyMappableToU64 + fmt::Debug>(
value_index: ValueIndexInfo,
pub fn serialize<T: MonotonicallyMappableToU64>(
typed_column: impl Column<T>,
output: &mut impl io::Write,
codecs: &[FastFieldCodecType],
@@ -305,15 +221,6 @@ pub fn serialize_new<T: MonotonicallyMappableToU64 + fmt::Debug>(
let normalized_column = header.normalize_column(column);
assert_eq!(normalized_column.min_value(), 0u64);
serialize_given_codec(normalized_column, header.codec_type, output)?;
let null_index_footer = NullIndexFooter {
cardinality: value_index.get_cardinality(),
null_index_codec: NullIndexCodec::Full,
null_index_byte_range: 0..0,
};
append_null_index_footer(output, null_index_footer)?;
append_format_version(output)?;
Ok(())
}
@@ -366,12 +273,15 @@ fn serialize_given_codec(
}
/// Helper function to serialize a column (autodetect from all codecs) and then open it
pub fn serialize_and_load<T: MonotonicallyMappableToU64 + Ord + Default + fmt::Debug>(
pub fn serialize_and_load<T: MonotonicallyMappableToU64 + Ord + Default>(
column: &[T],
) -> Arc<dyn Column<T>> {
let mut buffer = Vec::new();
super::serialize(VecColumn::from(&column), &mut buffer, &ALL_CODEC_TYPES).unwrap();
super::open(OwnedBytes::new(buffer)).unwrap()
super::open(OwnedBytes::new(buffer))
.unwrap()
.to_full()
.unwrap()
}
#[cfg(test)]
@@ -403,7 +313,7 @@ mod tests {
let col = VecColumn::from(&[false, true][..]);
serialize(col, &mut buffer, &ALL_CODEC_TYPES).unwrap();
// 5 bytes of header, 1 byte of value, 7 bytes of padding.
assert_eq!(buffer.len(), 3 + 5 + 8 + 4 + 2);
assert_eq!(buffer.len(), 5 + 8);
}
#[test]
@@ -412,7 +322,7 @@ mod tests {
let col = VecColumn::from(&[true][..]);
serialize(col, &mut buffer, &ALL_CODEC_TYPES).unwrap();
// 5 bytes of header, 0 bytes of value, 7 bytes of padding.
assert_eq!(buffer.len(), 3 + 5 + 7 + 4 + 2);
assert_eq!(buffer.len(), 5 + 7);
}
#[test]
@@ -422,6 +332,6 @@ mod tests {
let col = VecColumn::from(&vals[..]);
serialize(col, &mut buffer, &[FastFieldCodecType::Bitpacked]).unwrap();
// Values are stored over 3 bits.
assert_eq!(buffer.len(), 3 + 7 + (3 * 80 / 8) + 7 + 4 + 2);
assert_eq!(buffer.len(), 7 + (3 * 80 / 8) + 7);
}
}

6
ownedbytes/Cargo.toml
View File

@@ -1,14 +1,10 @@
[package]
authors = ["Paul Masurel <paul@quickwit.io>", "Pascal Seitz <pascal@quickwit.io>"]
name = "ownedbytes"
version = "0.5.0"
version = "0.3.0"
edition = "2021"
description = "Expose data as static slice"
license = "MIT"
documentation = "https://docs.rs/ownedbytes/"
homepage = "https://github.com/quickwit-oss/tantivy"
repository = "https://github.com/quickwit-oss/tantivy"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]

17
ownedbytes/src/lib.rs
View File

@@ -3,7 +3,7 @@ use std::ops::{Deref, Range};
use std::sync::Arc;
use std::{fmt, io, mem};
pub use stable_deref_trait::StableDeref;
use stable_deref_trait::StableDeref;
/// An OwnedBytes simply wraps an object that owns a slice of data and exposes
/// this data as a slice.
@@ -80,21 +80,6 @@ impl OwnedBytes {
(left, right)
}
/// Splits the OwnedBytes into two OwnedBytes `(left, right)`.
///
/// Right will hold `split_len` bytes.
///
/// This operation is cheap and does not require to copy any memory.
/// On the other hand, both `left` and `right` retain a handle over
/// the entire slice of memory. In other words, the memory will only
/// be released when both left and right are dropped.
#[inline]
#[must_use]
pub fn rsplit(self, split_len: usize) -> (OwnedBytes, OwnedBytes) {
let data_len = self.data.len();
self.split(data_len - split_len)
}
/// Splits the right part of the `OwnedBytes` at the given offset.
///
/// `self` is truncated to `split_len`, left with the remaining bytes.

2
query-grammar/Cargo.toml
View File

@@ -1,6 +1,6 @@
[package]
name = "tantivy-query-grammar"
version = "0.19.0"
version = "0.18.0"
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
license = "MIT"
categories = ["database-implementations", "data-structures"]

65
src/aggregation/agg_req.rs
View File

@@ -51,10 +51,7 @@ use serde::{Deserialize, Serialize};
pub use super::bucket::RangeAggregation;
use super::bucket::{HistogramAggregation, TermsAggregation};
use super::metric::{
AverageAggregation, CountAggregation, MaxAggregation, MinAggregation, StatsAggregation,
SumAggregation,
};
use super::metric::{AverageAggregation, StatsAggregation};
use super::VecWithNames;
/// The top-level aggregation request structure, which contains [`Aggregation`] and their user
@@ -240,38 +237,20 @@ impl BucketAggregationType {
/// called multi-value numeric metrics aggregation.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub enum MetricAggregation {
/// Computes the average of the extracted values.
/// Calculates the average.
#[serde(rename = "avg")]
Average(AverageAggregation),
/// Counts the number of extracted values.
#[serde(rename = "value_count")]
Count(CountAggregation),
/// Finds the maximum value.
#[serde(rename = "max")]
Max(MaxAggregation),
/// Finds the minimum value.
#[serde(rename = "min")]
Min(MinAggregation),
/// Computes a collection of statistics (`min`, `max`, `sum`, `count`, and `avg`) over the
/// extracted values.
/// Calculates stats sum, average, min, max, standard_deviation on a field.
#[serde(rename = "stats")]
Stats(StatsAggregation),
/// Computes the sum of the extracted values.
#[serde(rename = "sum")]
Sum(SumAggregation),
}
impl MetricAggregation {
fn get_fast_field_names(&self, fast_field_names: &mut HashSet<String>) {
let fast_field_name = match self {
MetricAggregation::Average(avg) => avg.field_name(),
MetricAggregation::Count(count) => count.field_name(),
MetricAggregation::Max(max) => max.field_name(),
MetricAggregation::Min(min) => min.field_name(),
MetricAggregation::Stats(stats) => stats.field_name(),
MetricAggregation::Sum(sum) => sum.field_name(),
match self {
MetricAggregation::Average(avg) => fast_field_names.insert(avg.field.to_string()),
MetricAggregation::Stats(stats) => fast_field_names.insert(stats.field.to_string()),
};
fast_field_names.insert(fast_field_name.to_string());
}
}
@@ -279,38 +258,6 @@ impl MetricAggregation {
mod tests {
use super::*;
#[test]
fn test_metric_aggregations_deser() {
let agg_req_json = r#"{
"price_avg": { "avg": { "field": "price" } },
"price_count": { "value_count": { "field": "price" } },
"price_max": { "max": { "field": "price" } },
"price_min": { "min": { "field": "price" } },
"price_stats": { "stats": { "field": "price" } },
"price_sum": { "sum": { "field": "price" } }
}"#;
let agg_req: Aggregations = serde_json::from_str(agg_req_json).unwrap();
assert!(
matches!(agg_req.get("price_avg").unwrap(), Aggregation::Metric(MetricAggregation::Average(avg)) if avg.field == "price")
);
assert!(
matches!(agg_req.get("price_count").unwrap(), Aggregation::Metric(MetricAggregation::Count(count)) if count.field == "price")
);
assert!(
matches!(agg_req.get("price_max").unwrap(), Aggregation::Metric(MetricAggregation::Max(max)) if max.field == "price")
);
assert!(
matches!(agg_req.get("price_min").unwrap(), Aggregation::Metric(MetricAggregation::Min(min)) if min.field == "price")
);
assert!(
matches!(agg_req.get("price_stats").unwrap(), Aggregation::Metric(MetricAggregation::Stats(stats)) if stats.field == "price")
);
assert!(
matches!(agg_req.get("price_sum").unwrap(), Aggregation::Metric(MetricAggregation::Sum(sum)) if sum.field == "price")
);
}
#[test]
fn serialize_to_json_test() {
let agg_req1: Aggregations = vec![(

45
src/aggregation/agg_req_with_accessor.rs
View File

@@ -4,17 +4,14 @@ use std::rc::Rc;
use std::sync::atomic::AtomicU32;
use std::sync::Arc;
use fastfield_codecs::Column;
use fastfield_codecs::OptionalColumn;
use super::agg_req::{Aggregation, Aggregations, BucketAggregationType, MetricAggregation};
use super::bucket::{HistogramAggregation, RangeAggregation, TermsAggregation};
use super::metric::{
AverageAggregation, CountAggregation, MaxAggregation, MinAggregation, StatsAggregation,
SumAggregation,
};
use super::metric::{AverageAggregation, StatsAggregation};
use super::segment_agg_result::BucketCount;
use super::VecWithNames;
use crate::fastfield::{type_and_cardinality, MultiValuedFastFieldReader};
use crate::fastfield::{type_and_cardinality, FastType, MultiValuedFastFieldReader};
use crate::schema::{Cardinality, Type};
use crate::{InvertedIndexReader, SegmentReader, TantivyError};
@@ -40,16 +37,16 @@ impl AggregationsWithAccessor {
#[derive(Clone)]
pub(crate) enum FastFieldAccessor {
Multi(MultiValuedFastFieldReader<u64>),
Single(Arc<dyn Column<u64>>),
Single(Arc<dyn OptionalColumn<u64>>),
}
impl FastFieldAccessor {
pub fn as_single(&self) -> Option<&dyn Column<u64>> {
pub fn as_single(&self) -> Option<&dyn OptionalColumn<u64>> {
match self {
FastFieldAccessor::Multi(_) => None,
FastFieldAccessor::Single(reader) => Some(&**reader),
}
}
pub fn into_single(self) -> Option<Arc<dyn Column<u64>>> {
pub fn into_single(self) -> Option<Arc<dyn OptionalColumn<u64>>> {
match self {
FastFieldAccessor::Multi(_) => None,
FastFieldAccessor::Single(reader) => Some(reader),
@@ -94,7 +91,10 @@ impl BucketAggregationWithAccessor {
BucketAggregationType::Terms(TermsAggregation {
field: field_name, ..
}) => {
let field = reader.schema().get_field(field_name)?;
let field = reader
.schema()
.get_field(field_name)
.ok_or_else(|| TantivyError::FieldNotFound(field_name.to_string()))?;
inverted_index = Some(reader.inverted_index(field)?);
get_ff_reader_and_validate(reader, field_name, Cardinality::MultiValues)?
}
@@ -124,7 +124,7 @@ impl BucketAggregationWithAccessor {
pub struct MetricAggregationWithAccessor {
pub metric: MetricAggregation,
pub field_type: Type,
pub accessor: Arc<dyn Column>,
pub accessor: Arc<dyn OptionalColumn>,
}
impl MetricAggregationWithAccessor {
@@ -134,11 +134,7 @@ impl MetricAggregationWithAccessor {
) -> crate::Result<MetricAggregationWithAccessor> {
match &metric {
MetricAggregation::Average(AverageAggregation { field: field_name })
| MetricAggregation::Count(CountAggregation { field: field_name })
| MetricAggregation::Max(MaxAggregation { field: field_name })
| MetricAggregation::Min(MinAggregation { field: field_name })
| MetricAggregation::Stats(StatsAggregation { field: field_name })
| MetricAggregation::Sum(SumAggregation { field: field_name }) => {
| MetricAggregation::Stats(StatsAggregation { field: field_name }) => {
let (accessor, field_type) =
get_ff_reader_and_validate(reader, field_name, Cardinality::SingleValue)?;
@@ -192,10 +188,19 @@ fn get_ff_reader_and_validate(
field_name: &str,
cardinality: Cardinality,
) -> crate::Result<(FastFieldAccessor, Type)> {
let field = reader.schema().get_field(field_name)?;
let field = reader
.schema()
.get_field(field_name)
.ok_or_else(|| TantivyError::FieldNotFound(field_name.to_string()))?;
let field_type = reader.schema().get_field_entry(field).field_type();
if let Some((_ff_type, field_cardinality)) = type_and_cardinality(field_type) {
if let Some((ff_type, field_cardinality)) = type_and_cardinality(field_type) {
if ff_type == FastType::Date {
return Err(TantivyError::InvalidArgument(
"Unsupported field type date in aggregation".to_string(),
));
}
if cardinality != field_cardinality {
return Err(TantivyError::InvalidArgument(format!(
"Invalid field cardinality on field {} expected {:?}, but got {:?}",
@@ -212,10 +217,10 @@ fn get_ff_reader_and_validate(
let ff_fields = reader.fast_fields();
match cardinality {
Cardinality::SingleValue => ff_fields
.u64_lenient(field_name)
.u64_lenient(field)
.map(|field| (FastFieldAccessor::Single(field), field_type.value_type())),
Cardinality::MultiValues => ff_fields
.u64s_lenient(field_name)
.u64s_lenient(field)
.map(|field| (FastFieldAccessor::Multi(field), field_type.value_type())),
}
}

57
src/aggregation/agg_result.rs
View File

@@ -12,7 +12,6 @@ use super::bucket::GetDocCount;
use super::intermediate_agg_result::{IntermediateBucketResult, IntermediateMetricResult};
use super::metric::{SingleMetricResult, Stats};
use super::Key;
use crate::schema::Schema;
use crate::TantivyError;
#[derive(Clone, Default, Debug, PartialEq, Serialize, Deserialize)]
@@ -30,7 +29,7 @@ impl AggregationResults {
} else {
// Validation is be done during request parsing, so we can't reach this state.
Err(TantivyError::InternalError(format!(
"Can't find aggregation {:?} in sub-aggregations",
"Can't find aggregation {:?} in sub_aggregations",
name
)))
}
@@ -70,51 +69,27 @@ impl AggregationResult {
pub enum MetricResult {
/// Average metric result.
Average(SingleMetricResult),
/// Count metric result.
Count(SingleMetricResult),
/// Max metric result.
Max(SingleMetricResult),
/// Min metric result.
Min(SingleMetricResult),
/// Stats metric result.
Stats(Stats),
/// Sum metric result.
Sum(SingleMetricResult),
}
impl MetricResult {
fn get_value(&self, agg_property: &str) -> crate::Result<Option<f64>> {
match self {
MetricResult::Average(avg) => Ok(avg.value),
MetricResult::Count(count) => Ok(count.value),
MetricResult::Max(max) => Ok(max.value),
MetricResult::Min(min) => Ok(min.value),
MetricResult::Stats(stats) => stats.get_value(agg_property),
MetricResult::Sum(sum) => Ok(sum.value),
}
}
}
impl From<IntermediateMetricResult> for MetricResult {
fn from(metric: IntermediateMetricResult) -> Self {
match metric {
IntermediateMetricResult::Average(intermediate_avg) => {
MetricResult::Average(intermediate_avg.finalize().into())
}
IntermediateMetricResult::Count(intermediate_count) => {
MetricResult::Count(intermediate_count.finalize().into())
}
IntermediateMetricResult::Max(intermediate_max) => {
MetricResult::Max(intermediate_max.finalize().into())
}
IntermediateMetricResult::Min(intermediate_min) => {
MetricResult::Min(intermediate_min.finalize().into())
IntermediateMetricResult::Average(avg_data) => {
MetricResult::Average(avg_data.finalize().into())
}
IntermediateMetricResult::Stats(intermediate_stats) => {
MetricResult::Stats(intermediate_stats.finalize())
}
IntermediateMetricResult::Sum(intermediate_sum) => {
MetricResult::Sum(intermediate_sum.finalize().into())
}
}
}
}
@@ -124,13 +99,13 @@ impl From<IntermediateMetricResult> for MetricResult {
#[serde(untagged)]
pub enum BucketResult {
/// This is the range entry for a bucket, which contains a key, count, from, to, and optionally
/// sub-aggregations.
/// sub_aggregations.
Range {
/// The range buckets sorted by range.
buckets: BucketEntries<RangeBucketEntry>,
},
/// This is the histogram entry for a bucket, which contains a key, count, and optionally
/// sub-aggregations.
/// sub_aggregations.
Histogram {
/// The buckets.
///
@@ -154,12 +129,9 @@ pub enum BucketResult {
}
impl BucketResult {
pub(crate) fn empty_from_req(
req: &BucketAggregationInternal,
schema: &Schema,
) -> crate::Result<Self> {
pub(crate) fn empty_from_req(req: &BucketAggregationInternal) -> crate::Result<Self> {
let empty_bucket = IntermediateBucketResult::empty_from_req(&req.bucket_agg);
empty_bucket.into_final_bucket_result(req, schema)
empty_bucket.into_final_bucket_result(req)
}
}
@@ -175,7 +147,7 @@ pub enum BucketEntries<T> {
}
/// This is the default entry for a bucket, which contains a key, count, and optionally
/// sub-aggregations.
/// sub_aggregations.
///
/// # JSON Format
/// ```json
@@ -202,9 +174,6 @@ pub enum BucketEntries<T> {
/// ```
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct BucketEntry {
#[serde(skip_serializing_if = "Option::is_none")]
/// The string representation of the bucket.
pub key_as_string: Option<String>,
/// The identifier of the bucket.
pub key: Key,
/// Number of documents in the bucket.
@@ -225,7 +194,7 @@ impl GetDocCount for BucketEntry {
}
/// This is the range entry for a bucket, which contains a key, count, and optionally
/// sub-aggregations.
/// sub_aggregations.
///
/// # JSON Format
/// ```json
@@ -261,7 +230,7 @@ pub struct RangeBucketEntry {
/// Number of documents in the bucket.
pub doc_count: u64,
#[serde(flatten)]
/// Sub-aggregations in this bucket.
/// sub-aggregations in this bucket.
pub sub_aggregation: AggregationResults,
/// The from range of the bucket. Equals `f64::MIN` when `None`.
#[serde(skip_serializing_if = "Option::is_none")]
@@ -269,10 +238,4 @@ pub struct RangeBucketEntry {
/// The to range of the bucket. Equals `f64::MAX` when `None`.
#[serde(skip_serializing_if = "Option::is_none")]
pub to: Option<f64>,
/// The optional string representation for the `from` range.
#[serde(skip_serializing_if = "Option::is_none")]
pub from_as_string: Option<String>,
/// The optional string representation for the `to` range.
#[serde(skip_serializing_if = "Option::is_none")]
pub to_as_string: Option<String>,
}

262
src/aggregation/bucket/histogram/histogram.rs
View File

@@ -1,7 +1,7 @@
use std::cmp::Ordering;
use std::fmt::Display;
use fastfield_codecs::Column;
use fastfield_codecs::OptionalColumn;
use itertools::Itertools;
use serde::{Deserialize, Serialize};
@@ -10,12 +10,12 @@ use crate::aggregation::agg_req_with_accessor::{
AggregationsWithAccessor, BucketAggregationWithAccessor,
};
use crate::aggregation::agg_result::BucketEntry;
use crate::aggregation::f64_from_fastfield_u64;
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResults, IntermediateBucketResult, IntermediateHistogramBucketEntry,
};
use crate::aggregation::segment_agg_result::SegmentAggregationResultsCollector;
use crate::aggregation::{f64_from_fastfield_u64, format_date};
use crate::schema::{Schema, Type};
use crate::schema::Type;
use crate::{DocId, TantivyError};
/// Histogram is a bucket aggregation, where buckets are created dynamically for given `interval`.
@@ -206,7 +206,6 @@ pub struct SegmentHistogramCollector {
field_type: Type,
interval: f64,
offset: f64,
min_doc_count: u64,
first_bucket_num: i64,
bounds: HistogramBounds,
}
@@ -216,30 +215,6 @@ impl SegmentHistogramCollector {
self,
agg_with_accessor: &BucketAggregationWithAccessor,
) -> crate::Result<IntermediateBucketResult> {
// Compute the number of buckets to validate against max num buckets
// Note: We use min_doc_count here, but it's only an lowerbound here, since were are on the
// intermediate level and after merging the number of documents of a bucket could exceed
// `min_doc_count`.
{
let cut_off_buckets_front = self
.buckets
.iter()
.take_while(|bucket| bucket.doc_count <= self.min_doc_count)
.count();
let cut_off_buckets_back = self.buckets[cut_off_buckets_front..]
.iter()
.rev()
.take_while(|bucket| bucket.doc_count <= self.min_doc_count)
.count();
let estimate_num_buckets =
self.buckets.len() - cut_off_buckets_front - cut_off_buckets_back;
agg_with_accessor
.bucket_count
.add_count(estimate_num_buckets as u32);
agg_with_accessor.bucket_count.validate_bucket_count()?;
}
let mut buckets = Vec::with_capacity(
self.buckets
.iter()
@@ -276,6 +251,11 @@ impl SegmentHistogramCollector {
);
};
agg_with_accessor
.bucket_count
.add_count(buckets.len() as u32);
agg_with_accessor.bucket_count.validate_bucket_count()?;
Ok(IntermediateBucketResult::Histogram { buckets })
}
@@ -283,13 +263,17 @@ impl SegmentHistogramCollector {
req: &HistogramAggregation,
sub_aggregation: &AggregationsWithAccessor,
field_type: Type,
accessor: &dyn Column<u64>,
accessor: &dyn OptionalColumn<u64>,
) -> crate::Result<Self> {
req.validate()?;
let min = f64_from_fastfield_u64(accessor.min_value(), &field_type);
let max = f64_from_fastfield_u64(accessor.max_value(), &field_type);
let min_max_u64 = accessor.min_value().zip(accessor.max_value());
let min_max_f64 = min_max_u64.map(|(min, max)| {
let min = f64_from_fastfield_u64(min, &field_type);
let max = f64_from_fastfield_u64(max, &field_type);
(min, max)
});
let (min, max) = get_req_min_max(req, Some((min, max)));
let (min, max) = get_req_min_max(req, min_max_f64);
// We compute and generate the buckets range (min, max) based on the request and the min
// max in the fast field, but this is likely not ideal when this is a subbucket, where many
@@ -328,7 +312,6 @@ impl SegmentHistogramCollector {
first_bucket_num,
bounds,
sub_aggregations,
min_doc_count: req.min_doc_count(),
})
}
@@ -352,47 +335,58 @@ impl SegmentHistogramCollector {
.expect("unexpected fast field cardinatility");
let mut iter = doc.chunks_exact(4);
for docs in iter.by_ref() {
let val0 = self.f64_from_fastfield_u64(accessor.get_val(docs[0]));
let val1 = self.f64_from_fastfield_u64(accessor.get_val(docs[1]));
let val2 = self.f64_from_fastfield_u64(accessor.get_val(docs[2]));
let val3 = self.f64_from_fastfield_u64(accessor.get_val(docs[3]));
if let Some(val) = accessor.get_val(docs[0]) {
let val = self.f64_from_fastfield_u64(val);
let bucket_pos = get_bucket_num(val);
self.increment_bucket_if_in_bounds(
val,
&bounds,
bucket_pos,
docs[0],
&bucket_with_accessor.sub_aggregation,
)?;
}
let bucket_pos0 = get_bucket_num(val0);
let bucket_pos1 = get_bucket_num(val1);
let bucket_pos2 = get_bucket_num(val2);
let bucket_pos3 = get_bucket_num(val3);
if let Some(val) = accessor.get_val(docs[1]) {
let val = self.f64_from_fastfield_u64(val);
let bucket_pos = get_bucket_num(val);
self.increment_bucket_if_in_bounds(
val,
&bounds,
bucket_pos,
docs[1],
&bucket_with_accessor.sub_aggregation,
)?;
}
self.increment_bucket_if_in_bounds(
val0,
&bounds,
bucket_pos0,
docs[0],
&bucket_with_accessor.sub_aggregation,
)?;
self.increment_bucket_if_in_bounds(
val1,
&bounds,
bucket_pos1,
docs[1],
&bucket_with_accessor.sub_aggregation,
)?;
self.increment_bucket_if_in_bounds(
val2,
&bounds,
bucket_pos2,
docs[2],
&bucket_with_accessor.sub_aggregation,
)?;
self.increment_bucket_if_in_bounds(
val3,
&bounds,
bucket_pos3,
docs[3],
&bucket_with_accessor.sub_aggregation,
)?;
if let Some(val) = accessor.get_val(docs[2]) {
let val = self.f64_from_fastfield_u64(val);
let bucket_pos = get_bucket_num(val);
self.increment_bucket_if_in_bounds(
val,
&bounds,
bucket_pos,
docs[2],
&bucket_with_accessor.sub_aggregation,
)?;
}
if let Some(val) = accessor.get_val(docs[3]) {
let val = self.f64_from_fastfield_u64(val);
let bucket_pos = get_bucket_num(val);
self.increment_bucket_if_in_bounds(
val,
&bounds,
bucket_pos,
docs[3],
&bucket_with_accessor.sub_aggregation,
)?;
}
}
for &doc in iter.remainder() {
let val = f64_from_fastfield_u64(accessor.get_val(doc), &self.field_type);
let Some(val) = accessor.get_val(doc).map(|val|f64_from_fastfield_u64(val, &self.field_type)) else{
continue;
};
if !bounds.contains(val) {
continue;
}
@@ -401,7 +395,7 @@ impl SegmentHistogramCollector {
debug_assert_eq!(
self.buckets[bucket_pos].key,
get_bucket_val(val, self.interval, self.offset)
get_bucket_val(val, self.interval, self.offset) as f64
);
self.increment_bucket(bucket_pos, doc, &bucket_with_accessor.sub_aggregation)?;
}
@@ -428,7 +422,7 @@ impl SegmentHistogramCollector {
if bounds.contains(val) {
debug_assert_eq!(
self.buckets[bucket_pos].key,
get_bucket_val(val, self.interval, self.offset)
get_bucket_val(val, self.interval, self.offset) as f64
);
self.increment_bucket(bucket_pos, doc, bucket_with_accessor)?;
@@ -472,7 +466,6 @@ fn intermediate_buckets_to_final_buckets_fill_gaps(
buckets: Vec<IntermediateHistogramBucketEntry>,
histogram_req: &HistogramAggregation,
sub_aggregation: &AggregationsInternal,
schema: &Schema,
) -> crate::Result<Vec<BucketEntry>> {
// Generate the full list of buckets without gaps.
//
@@ -513,9 +506,7 @@ fn intermediate_buckets_to_final_buckets_fill_gaps(
sub_aggregation: empty_sub_aggregation.clone(),
},
})
.map(|intermediate_bucket| {
intermediate_bucket.into_final_bucket_entry(sub_aggregation, schema)
})
.map(|intermediate_bucket| intermediate_bucket.into_final_bucket_entry(sub_aggregation))
.collect::<crate::Result<Vec<_>>>()
}
@@ -524,41 +515,20 @@ pub(crate) fn intermediate_histogram_buckets_to_final_buckets(
buckets: Vec<IntermediateHistogramBucketEntry>,
histogram_req: &HistogramAggregation,
sub_aggregation: &AggregationsInternal,
schema: &Schema,
) -> crate::Result<Vec<BucketEntry>> {
let mut buckets = if histogram_req.min_doc_count() == 0 {
if histogram_req.min_doc_count() == 0 {
// With min_doc_count != 0, we may need to add buckets, so that there are no
// gaps, since intermediate result does not contain empty buckets (filtered to
// reduce serialization size).
intermediate_buckets_to_final_buckets_fill_gaps(
buckets,
histogram_req,
sub_aggregation,
schema,
)?
intermediate_buckets_to_final_buckets_fill_gaps(buckets, histogram_req, sub_aggregation)
} else {
buckets
.into_iter()
.filter(|histogram_bucket| histogram_bucket.doc_count >= histogram_req.min_doc_count())
.map(|histogram_bucket| {
histogram_bucket.into_final_bucket_entry(sub_aggregation, schema)
})
.collect::<crate::Result<Vec<_>>>()?
};
// If we have a date type on the histogram buckets, we add the `key_as_string` field as rfc339
let field = schema.get_field(&histogram_req.field)?;
if schema.get_field_entry(field).field_type().is_date() {
for bucket in buckets.iter_mut() {
if let crate::aggregation::Key::F64(val) = bucket.key {
let key_as_string = format_date(val as i64)?;
bucket.key_as_string = Some(key_as_string);
}
}
.map(|histogram_bucket| histogram_bucket.into_final_bucket_entry(sub_aggregation))
.collect::<crate::Result<Vec<_>>>()
}
Ok(buckets)
}
/// Applies req extended_bounds/hard_bounds on the min_max value
@@ -1364,6 +1334,7 @@ mod tests {
"min": Value::Null,
"max": Value::Null,
"avg": Value::Null,
"standard_deviation": Value::Null,
}
})
);
@@ -1416,63 +1387,6 @@ mod tests {
Ok(())
}
#[test]
fn histogram_date_test_single_segment() -> crate::Result<()> {
histogram_date_test_with_opt(true)
}
#[test]
fn histogram_date_test_multi_segment() -> crate::Result<()> {
histogram_date_test_with_opt(false)
}
fn histogram_date_test_with_opt(merge_segments: bool) -> crate::Result<()> {
let index = get_test_index_2_segments(merge_segments)?;
let agg_req: Aggregations = vec![(
"histogram".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Histogram(HistogramAggregation {
field: "date".to_string(),
interval: 86400000000.0, // one day in microseconds
..Default::default()
}),
sub_aggregation: Default::default(),
}),
)]
.into_iter()
.collect();
let agg_res = exec_request(agg_req, &index)?;
let res: Value = serde_json::from_str(&serde_json::to_string(&agg_res)?)?;
assert_eq!(res["histogram"]["buckets"][0]["key"], 1546300800000000.0);
assert_eq!(
res["histogram"]["buckets"][0]["key_as_string"],
"2019-01-01T00:00:00Z"
);
assert_eq!(res["histogram"]["buckets"][0]["doc_count"], 1);
assert_eq!(res["histogram"]["buckets"][1]["key"], 1546387200000000.0);
assert_eq!(
res["histogram"]["buckets"][1]["key_as_string"],
"2019-01-02T00:00:00Z"
);
assert_eq!(res["histogram"]["buckets"][1]["doc_count"], 5);
assert_eq!(res["histogram"]["buckets"][2]["key"], 1546473600000000.0);
assert_eq!(
res["histogram"]["buckets"][2]["key_as_string"],
"2019-01-03T00:00:00Z"
);
assert_eq!(res["histogram"]["buckets"][3], Value::Null);
Ok(())
}
#[test]
fn histogram_invalid_request() -> crate::Result<()> {
let index = get_test_index_2_segments(true)?;
@@ -1539,36 +1453,4 @@ mod tests {
Ok(())
}
#[test]
fn histogram_test_max_buckets_segments() -> crate::Result<()> {
let values = vec![0.0, 70000.0];
let index = get_test_index_from_values(true, &values)?;
let agg_req: Aggregations = vec![(
"my_interval".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Histogram(HistogramAggregation {
field: "score_f64".to_string(),
interval: 1.0,
..Default::default()
}),
sub_aggregation: Default::default(),
}),
)]
.into_iter()
.collect();
let res = exec_request(agg_req, &index);
assert_eq!(
res.unwrap_err().to_string(),
"An invalid argument was passed: 'Aborting aggregation because too many buckets were \
created'"
.to_string()
);
Ok(())
}
}

139
src/aggregation/bucket/range.rs
View File

@@ -1,7 +1,6 @@
use std::fmt::Debug;
use std::ops::Range;
use fastfield_codecs::MonotonicallyMappableToU64;
use rustc_hash::FxHashMap;
use serde::{Deserialize, Serialize};
@@ -12,9 +11,7 @@ use crate::aggregation::intermediate_agg_result::{
IntermediateBucketResult, IntermediateRangeBucketEntry, IntermediateRangeBucketResult,
};
use crate::aggregation::segment_agg_result::{BucketCount, SegmentAggregationResultsCollector};
use crate::aggregation::{
f64_from_fastfield_u64, f64_to_fastfield_u64, format_date, Key, SerializedKey,
};
use crate::aggregation::{f64_from_fastfield_u64, f64_to_fastfield_u64, Key, SerializedKey};
use crate::schema::Type;
use crate::{DocId, TantivyError};
@@ -184,7 +181,7 @@ impl SegmentRangeCollector {
.into_iter()
.map(move |range_bucket| {
Ok((
range_to_string(&range_bucket.range, &field_type)?,
range_to_string(&range_bucket.range, &field_type),
range_bucket
.bucket
.into_intermediate_bucket_entry(&agg_with_accessor.sub_aggregation)?,
@@ -212,8 +209,8 @@ impl SegmentRangeCollector {
let key = range
.key
.clone()
.map(|key| Ok(Key::Str(key)))
.unwrap_or_else(|| range_to_key(&range.range, &field_type))?;
.map(Key::Str)
.unwrap_or_else(|| range_to_key(&range.range, &field_type));
let to = if range.range.end == u64::MAX {
None
} else {
@@ -231,7 +228,6 @@ impl SegmentRangeCollector {
sub_aggregation,
)?)
};
Ok(SegmentRangeAndBucketEntry {
range: range.range.clone(),
bucket: SegmentRangeBucketEntry {
@@ -271,20 +267,29 @@ impl SegmentRangeCollector {
let val2 = accessor.get_val(docs[1]);
let val3 = accessor.get_val(docs[2]);
let val4 = accessor.get_val(docs[3]);
let bucket_pos1 = self.get_bucket_pos(val1);
let bucket_pos2 = self.get_bucket_pos(val2);
let bucket_pos3 = self.get_bucket_pos(val3);
let bucket_pos4 = self.get_bucket_pos(val4);
self.increment_bucket(bucket_pos1, docs[0], &bucket_with_accessor.sub_aggregation)?;
self.increment_bucket(bucket_pos2, docs[1], &bucket_with_accessor.sub_aggregation)?;
self.increment_bucket(bucket_pos3, docs[2], &bucket_with_accessor.sub_aggregation)?;
self.increment_bucket(bucket_pos4, docs[3], &bucket_with_accessor.sub_aggregation)?;
if let Some(val) = val1 {
let bucket_pos = self.get_bucket_pos(val);
self.increment_bucket(bucket_pos, docs[0], &bucket_with_accessor.sub_aggregation)?;
}
if let Some(val) = val2 {
let bucket_pos = self.get_bucket_pos(val);
self.increment_bucket(bucket_pos, docs[1], &bucket_with_accessor.sub_aggregation)?;
}
if let Some(val) = val3 {
let bucket_pos = self.get_bucket_pos(val);
self.increment_bucket(bucket_pos, docs[2], &bucket_with_accessor.sub_aggregation)?;
}
if let Some(val) = val4 {
let bucket_pos = self.get_bucket_pos(val);
self.increment_bucket(bucket_pos, docs[3], &bucket_with_accessor.sub_aggregation)?;
}
}
for &doc in iter.remainder() {
let val = accessor.get_val(doc);
let bucket_pos = self.get_bucket_pos(val);
self.increment_bucket(bucket_pos, doc, &bucket_with_accessor.sub_aggregation)?;
if let Some(val) = val {
let bucket_pos = self.get_bucket_pos(val);
self.increment_bucket(bucket_pos, doc, &bucket_with_accessor.sub_aggregation)?;
}
}
if force_flush {
for bucket in &mut self.buckets {
@@ -406,45 +411,34 @@ fn extend_validate_ranges(
Ok(converted_buckets)
}
pub(crate) fn range_to_string(range: &Range<u64>, field_type: &Type) -> crate::Result<String> {
pub(crate) fn range_to_string(range: &Range<u64>, field_type: &Type) -> String {
// is_start is there for malformed requests, e.g. ig the user passes the range u64::MIN..0.0,
// it should be rendered as "*-0" and not "*-*"
let to_str = |val: u64, is_start: bool| {
if (is_start && val == u64::MIN) || (!is_start && val == u64::MAX) {
Ok("*".to_string())
} else if *field_type == Type::Date {
let val = i64::from_u64(val);
format_date(val)
"*".to_string()
} else {
Ok(f64_from_fastfield_u64(val, field_type).to_string())
f64_from_fastfield_u64(val, field_type).to_string()
}
};
Ok(format!(
"{}-{}",
to_str(range.start, true)?,
to_str(range.end, false)?
))
format!("{}-{}", to_str(range.start, true), to_str(range.end, false))
}
pub(crate) fn range_to_key(range: &Range<u64>, field_type: &Type) -> crate::Result<Key> {
Ok(Key::Str(range_to_string(range, field_type)?))
pub(crate) fn range_to_key(range: &Range<u64>, field_type: &Type) -> Key {
Key::Str(range_to_string(range, field_type))
}
#[cfg(test)]
mod tests {
use fastfield_codecs::MonotonicallyMappableToU64;
use serde_json::Value;
use super::*;
use crate::aggregation::agg_req::{
Aggregation, Aggregations, BucketAggregation, BucketAggregationType,
};
use crate::aggregation::tests::{
exec_request, exec_request_with_query, get_test_index_2_segments,
get_test_index_with_num_docs,
};
use crate::aggregation::tests::{exec_request_with_query, get_test_index_with_num_docs};
pub fn get_collector_from_ranges(
ranges: Vec<RangeAggregationRange>,
@@ -582,77 +576,6 @@ mod tests {
Ok(())
}
#[test]
fn range_date_test_single_segment() -> crate::Result<()> {
range_date_test_with_opt(true)
}
#[test]
fn range_date_test_multi_segment() -> crate::Result<()> {
range_date_test_with_opt(false)
}
fn range_date_test_with_opt(merge_segments: bool) -> crate::Result<()> {
let index = get_test_index_2_segments(merge_segments)?;
let agg_req: Aggregations = vec![(
"date_ranges".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Range(RangeAggregation {
field: "date".to_string(),
ranges: vec![
RangeAggregationRange {
key: None,
from: None,
to: Some(1546300800000000.0f64),
},
RangeAggregationRange {
key: None,
from: Some(1546300800000000.0f64),
to: Some(1546387200000000.0f64),
},
],
keyed: false,
}),
sub_aggregation: Default::default(),
}),
)]
.into_iter()
.collect();
let agg_res = exec_request(agg_req, &index)?;
let res: Value = serde_json::from_str(&serde_json::to_string(&agg_res)?)?;
assert_eq!(
res["date_ranges"]["buckets"][0]["from_as_string"],
Value::Null
);
assert_eq!(
res["date_ranges"]["buckets"][0]["key"],
"*-2019-01-01T00:00:00Z"
);
assert_eq!(
res["date_ranges"]["buckets"][1]["from_as_string"],
"2019-01-01T00:00:00Z"
);
assert_eq!(
res["date_ranges"]["buckets"][1]["to_as_string"],
"2019-01-02T00:00:00Z"
);
assert_eq!(
res["date_ranges"]["buckets"][2]["from_as_string"],
"2019-01-02T00:00:00Z"
);
assert_eq!(
res["date_ranges"]["buckets"][2]["to_as_string"],
Value::Null
);
Ok(())
}
#[test]
fn range_custom_key_keyed_buckets_test() -> crate::Result<()> {
let index = get_test_index_with_num_docs(false, 100)?;

7
src/aggregation/collector.rs
View File

@@ -7,7 +7,6 @@ use super::intermediate_agg_result::IntermediateAggregationResults;
use super::segment_agg_result::SegmentAggregationResultsCollector;
use crate::aggregation::agg_req_with_accessor::get_aggs_with_accessor_and_validate;
use crate::collector::{Collector, SegmentCollector};
use crate::schema::Schema;
use crate::{SegmentReader, TantivyError};
/// The default max bucket count, before the aggregation fails.
@@ -17,7 +16,6 @@ pub const MAX_BUCKET_COUNT: u32 = 65000;
///
/// The collector collects all aggregations by the underlying aggregation request.
pub struct AggregationCollector {
schema: Schema,
agg: Aggregations,
max_bucket_count: u32,
}
@@ -27,9 +25,8 @@ impl AggregationCollector {
///
/// Aggregation fails when the total bucket count is higher than max_bucket_count.
/// max_bucket_count will default to `MAX_BUCKET_COUNT` (65000) when unset
pub fn from_aggs(agg: Aggregations, max_bucket_count: Option<u32>, schema: Schema) -> Self {
pub fn from_aggs(agg: Aggregations, max_bucket_count: Option<u32>) -> Self {
Self {
schema,
agg,
max_bucket_count: max_bucket_count.unwrap_or(MAX_BUCKET_COUNT),
}
@@ -116,7 +113,7 @@ impl Collector for AggregationCollector {
segment_fruits: Vec<<Self::Child as SegmentCollector>::Fruit>,
) -> crate::Result<Self::Fruit> {
let res = merge_fruits(segment_fruits)?;
res.into_final_bucket_result(self.agg.clone(), &self.schema)
res.into_final_bucket_result(self.agg.clone())
}
}

18
src/aggregation/date.rs
View File

@@ -1,18 +0,0 @@
use time::format_description::well_known::Rfc3339;
use time::OffsetDateTime;
use crate::TantivyError;
pub(crate) fn format_date(val: i64) -> crate::Result<String> {
let datetime =
OffsetDateTime::from_unix_timestamp_nanos(1_000 * (val as i128)).map_err(|err| {
TantivyError::InvalidArgument(format!(
"Could not convert {:?} to OffsetDateTime, err {:?}",
val, err
))
})?;
let key_as_string = datetime
.format(&Rfc3339)
.map_err(|_err| TantivyError::InvalidArgument("Could not serialize date".to_string()))?;
Ok(key_as_string)
}

143
src/aggregation/intermediate_agg_result.rs
View File

@@ -10,22 +10,18 @@ use serde::{Deserialize, Serialize};
use super::agg_req::{
Aggregations, AggregationsInternal, BucketAggregationInternal, BucketAggregationType,
MetricAggregation, RangeAggregation,
MetricAggregation,
};
use super::agg_result::{AggregationResult, BucketResult, RangeBucketEntry};
use super::bucket::{
cut_off_buckets, get_agg_name_and_property, intermediate_histogram_buckets_to_final_buckets,
GetDocCount, Order, OrderTarget, SegmentHistogramBucketEntry, TermsAggregation,
};
use super::metric::{
IntermediateAverage, IntermediateCount, IntermediateMax, IntermediateMin, IntermediateStats,
IntermediateSum,
};
use super::metric::{IntermediateAverage, IntermediateStats};
use super::segment_agg_result::SegmentMetricResultCollector;
use super::{format_date, Key, SerializedKey, VecWithNames};
use super::{Key, SerializedKey, VecWithNames};
use crate::aggregation::agg_result::{AggregationResults, BucketEntries, BucketEntry};
use crate::aggregation::bucket::TermsAggregationInternal;
use crate::schema::Schema;
/// Contains the intermediate aggregation result, which is optimized to be merged with other
/// intermediate results.
@@ -39,12 +35,8 @@ pub struct IntermediateAggregationResults {
impl IntermediateAggregationResults {
/// Convert intermediate result and its aggregation request to the final result.
pub fn into_final_bucket_result(
self,
req: Aggregations,
schema: &Schema,
) -> crate::Result<AggregationResults> {
self.into_final_bucket_result_internal(&(req.into()), schema)
pub fn into_final_bucket_result(self, req: Aggregations) -> crate::Result<AggregationResults> {
self.into_final_bucket_result_internal(&(req.into()))
}
/// Convert intermediate result and its aggregation request to the final result.
@@ -54,7 +46,6 @@ impl IntermediateAggregationResults {
pub(crate) fn into_final_bucket_result_internal(
self,
req: &AggregationsInternal,
schema: &Schema,
) -> crate::Result<AggregationResults> {
// Important assumption:
// When the tree contains buckets/metric, we expect it to have all buckets/metrics from the
@@ -62,11 +53,11 @@ impl IntermediateAggregationResults {
let mut results: FxHashMap<String, AggregationResult> = FxHashMap::default();
if let Some(buckets) = self.buckets {
convert_and_add_final_buckets_to_result(&mut results, buckets, &req.buckets, schema)?
convert_and_add_final_buckets_to_result(&mut results, buckets, &req.buckets)?
} else {
// When there are no buckets, we create empty buckets, so that the serialized json
// format is constant
add_empty_final_buckets_to_result(&mut results, &req.buckets, schema)?
add_empty_final_buckets_to_result(&mut results, &req.buckets)?
};
if let Some(metrics) = self.metrics {
@@ -167,12 +158,10 @@ fn add_empty_final_metrics_to_result(
fn add_empty_final_buckets_to_result(
results: &mut FxHashMap<String, AggregationResult>,
req_buckets: &VecWithNames<BucketAggregationInternal>,
schema: &Schema,
) -> crate::Result<()> {
let requested_buckets = req_buckets.iter();
for (key, req) in requested_buckets {
let empty_bucket =
AggregationResult::BucketResult(BucketResult::empty_from_req(req, schema)?);
let empty_bucket = AggregationResult::BucketResult(BucketResult::empty_from_req(req)?);
results.insert(key.to_string(), empty_bucket);
}
Ok(())
@@ -182,13 +171,12 @@ fn convert_and_add_final_buckets_to_result(
results: &mut FxHashMap<String, AggregationResult>,
buckets: VecWithNames<IntermediateBucketResult>,
req_buckets: &VecWithNames<BucketAggregationInternal>,
schema: &Schema,
) -> crate::Result<()> {
assert_eq!(buckets.len(), req_buckets.len());
let buckets_with_request = buckets.into_iter().zip(req_buckets.values());
for ((key, bucket), req) in buckets_with_request {
let result = AggregationResult::BucketResult(bucket.into_final_bucket_result(req, schema)?);
let result = AggregationResult::BucketResult(bucket.into_final_bucket_result(req)?);
results.insert(key, result);
}
Ok(())
@@ -206,43 +194,21 @@ pub enum IntermediateAggregationResult {
/// Holds the intermediate data for metric results
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub enum IntermediateMetricResult {
/// Intermediate average result.
/// Average containing intermediate average data result
Average(IntermediateAverage),
/// Intermediate count result.
Count(IntermediateCount),
/// Intermediate max result.
Max(IntermediateMax),
/// Intermediate min result.
Min(IntermediateMin),
/// Intermediate stats result.
/// AverageData variant
Stats(IntermediateStats),
/// Intermediate sum result.
Sum(IntermediateSum),
}
impl From<SegmentMetricResultCollector> for IntermediateMetricResult {
fn from(tree: SegmentMetricResultCollector) -> Self {
match tree {
SegmentMetricResultCollector::Stats(collector) => match collector.collecting_for {
super::metric::SegmentStatsType::Average => IntermediateMetricResult::Average(
IntermediateAverage::from_collector(collector),
),
super::metric::SegmentStatsType::Count => {
IntermediateMetricResult::Count(IntermediateCount::from_collector(collector))
}
super::metric::SegmentStatsType::Max => {
IntermediateMetricResult::Max(IntermediateMax::from_collector(collector))
}
super::metric::SegmentStatsType::Min => {
IntermediateMetricResult::Min(IntermediateMin::from_collector(collector))
}
super::metric::SegmentStatsType::Stats => {
IntermediateMetricResult::Stats(collector.stats)
}
super::metric::SegmentStatsType::Sum => {
IntermediateMetricResult::Sum(IntermediateSum::from_collector(collector))
}
},
SegmentMetricResultCollector::Average(collector) => {
IntermediateMetricResult::Average(IntermediateAverage::from_collector(collector))
}
SegmentMetricResultCollector::Stats(collector) => {
IntermediateMetricResult::Stats(collector.stats)
}
}
}
}
@@ -253,36 +219,18 @@ impl IntermediateMetricResult {
MetricAggregation::Average(_) => {
IntermediateMetricResult::Average(IntermediateAverage::default())
}
MetricAggregation::Count(_) => {
IntermediateMetricResult::Count(IntermediateCount::default())
}
MetricAggregation::Max(_) => IntermediateMetricResult::Max(IntermediateMax::default()),
MetricAggregation::Min(_) => IntermediateMetricResult::Min(IntermediateMin::default()),
MetricAggregation::Stats(_) => {
IntermediateMetricResult::Stats(IntermediateStats::default())
}
MetricAggregation::Sum(_) => IntermediateMetricResult::Sum(IntermediateSum::default()),
}
}
fn merge_fruits(&mut self, other: IntermediateMetricResult) {
match (self, other) {
(
IntermediateMetricResult::Average(avg_left),
IntermediateMetricResult::Average(avg_right),
IntermediateMetricResult::Average(avg_data_left),
IntermediateMetricResult::Average(avg_data_right),
) => {
avg_left.merge_fruits(avg_right);
}
(
IntermediateMetricResult::Count(count_left),
IntermediateMetricResult::Count(count_right),
) => {
count_left.merge_fruits(count_right);
}
(IntermediateMetricResult::Max(max_left), IntermediateMetricResult::Max(max_right)) => {
max_left.merge_fruits(max_right);
}
(IntermediateMetricResult::Min(min_left), IntermediateMetricResult::Min(min_right)) => {
min_left.merge_fruits(min_right);
avg_data_left.merge_fruits(avg_data_right);
}
(
IntermediateMetricResult::Stats(stats_left),
@@ -290,9 +238,6 @@ impl IntermediateMetricResult {
) => {
stats_left.merge_fruits(stats_right);
}
(IntermediateMetricResult::Sum(sum_left), IntermediateMetricResult::Sum(sum_right)) => {
sum_left.merge_fruits(sum_right);
}
_ => {
panic!("incompatible fruit types in tree");
}
@@ -321,21 +266,13 @@ impl IntermediateBucketResult {
pub(crate) fn into_final_bucket_result(
self,
req: &BucketAggregationInternal,
schema: &Schema,
) -> crate::Result<BucketResult> {
match self {
IntermediateBucketResult::Range(range_res) => {
let mut buckets: Vec<RangeBucketEntry> = range_res
.buckets
.into_values()
.map(|bucket| {
bucket.into_final_bucket_entry(
&req.sub_aggregation,
schema,
req.as_range()
.expect("unexpected aggregation, expected histogram aggregation"),
)
})
.into_iter()
.map(|(_, bucket)| bucket.into_final_bucket_entry(&req.sub_aggregation))
.collect::<crate::Result<Vec<_>>>()?;
buckets.sort_by(|left, right| {
@@ -366,7 +303,6 @@ impl IntermediateBucketResult {
req.as_histogram()
.expect("unexpected aggregation, expected histogram aggregation"),
&req.sub_aggregation,
schema,
)?;
let buckets = if req.as_histogram().unwrap().keyed {
@@ -385,7 +321,6 @@ impl IntermediateBucketResult {
req.as_term()
.expect("unexpected aggregation, expected term aggregation"),
&req.sub_aggregation,
schema,
),
}
}
@@ -476,7 +411,6 @@ impl IntermediateTermBucketResult {
self,
req: &TermsAggregation,
sub_aggregation_req: &AggregationsInternal,
schema: &Schema,
) -> crate::Result<BucketResult> {
let req = TermsAggregationInternal::from_req(req);
let mut buckets: Vec<BucketEntry> = self
@@ -485,12 +419,11 @@ impl IntermediateTermBucketResult {
.filter(|bucket| bucket.1.doc_count >= req.min_doc_count)
.map(|(key, entry)| {
Ok(BucketEntry {
key_as_string: None,
key: Key::Str(key),
doc_count: entry.doc_count,
sub_aggregation: entry
.sub_aggregation
.into_final_bucket_result_internal(sub_aggregation_req, schema)?,
.into_final_bucket_result_internal(sub_aggregation_req)?,
})
})
.collect::<crate::Result<_>>()?;
@@ -595,15 +528,13 @@ impl IntermediateHistogramBucketEntry {
pub(crate) fn into_final_bucket_entry(
self,
req: &AggregationsInternal,
schema: &Schema,
) -> crate::Result<BucketEntry> {
Ok(BucketEntry {
key_as_string: None,
key: Key::F64(self.key),
doc_count: self.doc_count,
sub_aggregation: self
.sub_aggregation
.into_final_bucket_result_internal(req, schema)?,
.into_final_bucket_result_internal(req)?,
})
}
}
@@ -640,36 +571,16 @@ impl IntermediateRangeBucketEntry {
pub(crate) fn into_final_bucket_entry(
self,
req: &AggregationsInternal,
schema: &Schema,
range_req: &RangeAggregation,
) -> crate::Result<RangeBucketEntry> {
let mut range_bucket_entry = RangeBucketEntry {
Ok(RangeBucketEntry {
key: self.key,
doc_count: self.doc_count,
sub_aggregation: self
.sub_aggregation
.into_final_bucket_result_internal(req, schema)?,
.into_final_bucket_result_internal(req)?,
to: self.to,
from: self.from,
to_as_string: None,
from_as_string: None,
};
// If we have a date type on the histogram buckets, we add the `key_as_string` field as
// rfc339
let field = schema.get_field(&range_req.field)?;
if schema.get_field_entry(field).field_type().is_date() {
if let Some(val) = range_bucket_entry.to {
let key_as_string = format_date(val as i64)?;
range_bucket_entry.to_as_string = Some(key_as_string);
}
if let Some(val) = range_bucket_entry.from {
let key_as_string = format_date(val as i64)?;
range_bucket_entry.from_as_string = Some(key_as_string);
}
}
Ok(range_bucket_entry)
})
}
}

104
src/aggregation/metric/average.rs
View File

@@ -1,9 +1,13 @@
use std::fmt::Debug;
use fastfield_codecs::OptionalColumn;
use serde::{Deserialize, Serialize};
use super::{IntermediateStats, SegmentStatsCollector};
use crate::aggregation::f64_from_fastfield_u64;
use crate::schema::Type;
use crate::DocId;
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
/// A single-value metric aggregation that computes the average of numeric values that are
/// extracted from the aggregated documents.
/// Supported field types are u64, i64, and f64.
@@ -17,43 +21,101 @@ use super::{IntermediateStats, SegmentStatsCollector};
/// }
/// }
/// ```
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct AverageAggregation {
/// The field name to compute the average on.
/// The field name to compute the stats on.
pub field: String,
}
impl AverageAggregation {
/// Creates a new [`AverageAggregation`] instance from a field name.
/// Create new AverageAggregation from a field.
pub fn from_field_name(field_name: String) -> Self {
Self { field: field_name }
AverageAggregation { field: field_name }
}
/// Returns the field name the aggregation is computed on.
/// Return the field name.
pub fn field_name(&self) -> &str {
&self.field
}
}
/// Intermediate result of the average aggregation that can be combined with other intermediate
/// results.
#[derive(Clone, PartialEq)]
pub(crate) struct SegmentAverageCollector {
pub data: IntermediateAverage,
field_type: Type,
}
impl Debug for SegmentAverageCollector {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("AverageCollector")
.field("data", &self.data)
.finish()
}
}
impl SegmentAverageCollector {
pub fn from_req(field_type: Type) -> Self {
Self {
field_type,
data: Default::default(),
}
}
pub(crate) fn collect_block(&mut self, doc: &[DocId], field: &dyn OptionalColumn<u64>) {
let mut iter = doc.chunks_exact(4);
for docs in iter.by_ref() {
if let Some(val) = field.get_val(docs[0]) {
let val = f64_from_fastfield_u64(val, &self.field_type);
self.data.collect(val);
}
if let Some(val) = field.get_val(docs[1]) {
let val = f64_from_fastfield_u64(val, &self.field_type);
self.data.collect(val);
}
if let Some(val) = field.get_val(docs[2]) {
let val = f64_from_fastfield_u64(val, &self.field_type);
self.data.collect(val);
}
if let Some(val) = field.get_val(docs[3]) {
let val = f64_from_fastfield_u64(val, &self.field_type);
self.data.collect(val);
}
}
for &doc in iter.remainder() {
if let Some(val) = field.get_val(doc) {
let val = f64_from_fastfield_u64(val, &self.field_type);
self.data.collect(val);
}
}
}
}
/// Contains mergeable version of average data.
#[derive(Default, Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct IntermediateAverage {
stats: IntermediateStats,
pub(crate) sum: f64,
pub(crate) doc_count: u64,
}
impl IntermediateAverage {
/// Creates a new [`IntermediateAverage`] instance from a [`SegmentStatsCollector`].
pub(crate) fn from_collector(collector: SegmentStatsCollector) -> Self {
Self {
stats: collector.stats,
pub(crate) fn from_collector(collector: SegmentAverageCollector) -> Self {
collector.data
}
/// Merge average data into this instance.
pub fn merge_fruits(&mut self, other: IntermediateAverage) {
self.sum += other.sum;
self.doc_count += other.doc_count;
}
/// compute final result
pub fn finalize(&self) -> Option<f64> {
if self.doc_count == 0 {
None
} else {
Some(self.sum / self.doc_count as f64)
}
}
/// Merges the other intermediate result into self.
pub fn merge_fruits(&mut self, other: IntermediateAverage) {
self.stats.merge_fruits(other.stats);
}
/// Computes the final average value.
pub fn finalize(&self) -> Option<f64> {
self.stats.finalize().avg
#[inline]
fn collect(&mut self, val: f64) {
self.doc_count += 1;
self.sum += val;
}
}

59
src/aggregation/metric/count.rs
View File

@@ -1,59 +0,0 @@
use std::fmt::Debug;
use serde::{Deserialize, Serialize};
use super::{IntermediateStats, SegmentStatsCollector};
/// A single-value metric aggregation that counts the number of values that are
/// extracted from the aggregated documents.
/// Supported field types are u64, i64, and f64.
/// See [super::SingleMetricResult] for return value.
///
/// # JSON Format
/// ```json
/// {
/// "value_count": {
/// "field": "score",
/// }
/// }
/// ```
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct CountAggregation {
/// The field name to compute the minimum on.
pub field: String,
}
impl CountAggregation {
/// Creates a new [`CountAggregation`] instance from a field name.
pub fn from_field_name(field_name: String) -> Self {
Self { field: field_name }
}
/// Returns the field name the aggregation is computed on.
pub fn field_name(&self) -> &str {
&self.field
}
}
/// Intermediate result of the count aggregation that can be combined with other intermediate
/// results.
#[derive(Default, Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct IntermediateCount {
stats: IntermediateStats,
}
impl IntermediateCount {
/// Creates a new [`IntermediateCount`] instance from a [`SegmentStatsCollector`].
pub(crate) fn from_collector(collector: SegmentStatsCollector) -> Self {
Self {
stats: collector.stats,
}
}
/// Merges the other intermediate result into self.
pub fn merge_fruits(&mut self, other: IntermediateCount) {
self.stats.merge_fruits(other.stats);
}
/// Computes the final minimum value.
pub fn finalize(&self) -> Option<f64> {
Some(self.stats.finalize().count as f64)
}
}

59
src/aggregation/metric/max.rs
View File

@@ -1,59 +0,0 @@
use std::fmt::Debug;
use serde::{Deserialize, Serialize};
use super::{IntermediateStats, SegmentStatsCollector};
/// A single-value metric aggregation that computes the maximum of numeric values that are
/// extracted from the aggregated documents.
/// Supported field types are u64, i64, and f64.
/// See [super::SingleMetricResult] for return value.
///
/// # JSON Format
/// ```json
/// {
/// "max": {
/// "field": "score",
/// }
/// }
/// ```
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct MaxAggregation {
/// The field name to compute the maximum on.
pub field: String,
}
impl MaxAggregation {
/// Creates a new [`MaxAggregation`] instance from a field name.
pub fn from_field_name(field_name: String) -> Self {
Self { field: field_name }
}
/// Returns the field name the aggregation is computed on.
pub fn field_name(&self) -> &str {
&self.field
}
}
/// Intermediate result of the maximum aggregation that can be combined with other intermediate
/// results.
#[derive(Default, Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct IntermediateMax {
stats: IntermediateStats,
}
impl IntermediateMax {
/// Creates a new [`IntermediateMax`] instance from a [`SegmentStatsCollector`].
pub(crate) fn from_collector(collector: SegmentStatsCollector) -> Self {
Self {
stats: collector.stats,
}
}
/// Merges the other intermediate result into self.
pub fn merge_fruits(&mut self, other: IntermediateMax) {
self.stats.merge_fruits(other.stats);
}
/// Computes the final maximum value.
pub fn finalize(&self) -> Option<f64> {
self.stats.finalize().max
}
}

59
src/aggregation/metric/min.rs
View File

@@ -1,59 +0,0 @@
use std::fmt::Debug;
use serde::{Deserialize, Serialize};
use super::{IntermediateStats, SegmentStatsCollector};
/// A single-value metric aggregation that computes the minimum of numeric values that are
/// extracted from the aggregated documents.
/// Supported field types are u64, i64, and f64.
/// See [super::SingleMetricResult] for return value.
///
/// # JSON Format
/// ```json
/// {
/// "min": {
/// "field": "score",
/// }
/// }
/// ```
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct MinAggregation {
/// The field name to compute the minimum on.
pub field: String,
}
impl MinAggregation {
/// Creates a new [`MinAggregation`] instance from a field name.
pub fn from_field_name(field_name: String) -> Self {
Self { field: field_name }
}
/// Returns the field name the aggregation is computed on.
pub fn field_name(&self) -> &str {
&self.field
}
}
/// Intermediate result of the minimum aggregation that can be combined with other intermediate
/// results.
#[derive(Default, Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct IntermediateMin {
stats: IntermediateStats,
}
impl IntermediateMin {
/// Creates a new [`IntermediateMin`] instance from a [`SegmentStatsCollector`].
pub(crate) fn from_collector(collector: SegmentStatsCollector) -> Self {
Self {
stats: collector.stats,
}
}
/// Merges the other intermediate result into self.
pub fn merge_fruits(&mut self, other: IntermediateMin) {
self.stats.merge_fruits(other.stats);
}
/// Computes the final minimum value.
pub fn finalize(&self) -> Option<f64> {
self.stats.finalize().min
}
}

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