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base: rust:remove_dyn
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:column-trait-2
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

1 Commits
remove_dyn ... column-tra

Author SHA1 Message Date
Paul Masurel
4a072e3c18 Introducing a column trait 2022-09-02 11:24:04 +09:00
354 changed files with 13831 additions and 32434 deletions
Expand all files Collapse all files

1
.gitattributes vendored Normal file
View File

@@ -0,0 +1 @@
cpp/* linguist-vendored

6
.github/workflows/coverage.yml vendored
View File

@@ -2,9 +2,9 @@ name: Coverage
on:
push:
branches: [main]
branches: [ main ]
pull_request:
branches: [main]
branches: [ main ]
jobs:
coverage:
@@ -16,7 +16,7 @@ jobs:
- uses: Swatinem/rust-cache@v2
- uses: taiki-e/install-action@cargo-llvm-cov
- name: Generate code coverage
run: cargo +nightly llvm-cov --all-features --workspace --doctests --lcov --output-path lcov.info
run: cargo +nightly llvm-cov --all-features --workspace --lcov --output-path lcov.info
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v3
continue-on-error: true

2
.github/workflows/test.yml vendored
View File

@@ -48,7 +48,7 @@ jobs:
strategy:
matrix:
features: [
{ label: "all", flags: "mmap,stopwords,brotli-compression,lz4-compression,snappy-compression,zstd-compression,failpoints" },
{ label: "all", flags: "mmap,brotli-compression,lz4-compression,snappy-compression,zstd-compression,failpoints" },
{ label: "quickwit", flags: "mmap,quickwit,failpoints" }
]

1
.gitignore vendored
View File

@@ -9,6 +9,7 @@ target/release
Cargo.lock
benchmark
.DS_Store
cpp/simdcomp/bitpackingbenchmark
*.bk
.idea
trace.dat

2
ARCHITECTURE.md
View File

@@ -95,7 +95,7 @@ called [`Directory`](src/directory/directory.rs).
Contrary to Lucene however, "files" are quite different from some kind of `io::Read` object.
Check out [`src/directory/directory.rs`](src/directory/directory.rs) trait for more details.
Tantivy ships two main directory implementation: the `MmapDirectory` and the `RamDirectory`,
Tantivy ships two main directory implementation: the `MMapDirectory` and the `RAMDirectory`,
but users can extend tantivy with their own implementation.
## [schema/](src/schema): What are documents?

39
CHANGELOG.md
View File

@@ -1,37 +1,10 @@
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)
- 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)
- 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)
- 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
- [#1610](https://github.com/quickwit-oss/tantivy/pull/1610) (@PSeitz)
- [#1594](https://github.com/quickwit-oss/tantivy/pull/1594) (@PSeitz)
- [#1582](https://github.com/quickwit-oss/tantivy/pull/1582) (@PSeitz)
- [#1611](https://github.com/quickwit-oss/tantivy/pull/1611) (@PSeitz)
- Added a pre-configured stop word filter for various language [#1666](https://github.com/quickwit-oss/tantivy/pull/1666) (@adamreichold)
- 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).
- Remove Searcher pool and make `Searcher` cloneable.
Tantivy 0.18
================================
@@ -49,10 +22,6 @@ Tantivy 0.18
- Add terms aggregation (@PSeitz)
- Add support for zstd compression (@kryesh)
Tantivy 0.18.1
================================
- Hotfix: positions computation. #1629 (@fmassot, @fulmicoton, @PSeitz)
Tantivy 0.17
================================

51
Cargo.toml
View File

@@ -1,6 +1,6 @@
[package]
name = "tantivy"
version = "0.19.0"
version = "0.18.0"
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
license = "MIT"
categories = ["database-implementations", "data-structures"]
@@ -11,36 +11,40 @@ repository = "https://github.com/quickwit-oss/tantivy"
readme = "README.md"
keywords = ["search", "information", "retrieval"]
edition = "2021"
rust-version = "1.62"
[dependencies]
oneshot = "0.1.5"
base64 = "0.21.0"
oneshot = "0.1.3"
base64 = "0.13.0"
byteorder = "1.4.3"
crc32fast = "1.3.2"
once_cell = "1.10.0"
regex = { version = "1.5.5", default-features = false, features = ["std", "unicode"] }
aho-corasick = "0.7"
tantivy-fst = "0.4.0"
tantivy-fst = "0.3.0"
memmap2 = { version = "0.5.3", optional = true }
lz4_flex = { version = "0.10", default-features = false, features = ["checked-decode"], 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 }
snap = { version = "1.0.5", optional = true }
tempfile = { version = "3.3.0", optional = true }
log = "0.4.16"
serde = { version = "1.0.136", features = ["derive"] }
serde_json = "1.0.79"
num_cpus = "1.13.1"
fs2 = { version = "0.4.3", optional = true }
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"] }
census = "0.4.0"
rustc-hash = "1.1.0"
fnv = "1.0.7"
thiserror = "1.0.30"
htmlescape = "0.3.1"
fail = "0.5.0"
@@ -48,21 +52,15 @@ 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"
pretty_assertions = "1.2.1"
serde_cbor = { version = "0.11.2", optional = true }
async-trait = "0.1.53"
arc-swap = "1.5.0"
columnar = { version="0.1", path="./columnar", package ="tantivy-columnar" }
sstable = { version="0.1", path="./sstable", package ="tantivy-sstable", optional = true }
stacker = { version="0.1", path="./stacker", package ="tantivy-stacker" }
query-grammar = { version= "0.19.0", path="./query-grammar", package = "tantivy-query-grammar" }
tantivy-bitpacker = { version= "0.3", path="./bitpacker" }
common = { version= "0.5", path = "./common/", package = "tantivy-common" }
tokenizer-api = { version="0.1", path="./tokenizer-api", package="tantivy-tokenizer-api" }
[target.'cfg(windows)'.dependencies]
winapi = "0.3.9"
@@ -70,14 +68,12 @@ winapi = "0.3.9"
rand = "0.8.5"
maplit = "1.0.2"
matches = "0.1.9"
pretty_assertions = "1.2.1"
proptest = "1.0.0"
criterion = "0.4"
criterion = "0.3.5"
test-log = "0.2.10"
env_logger = "0.10.0"
pprof = { version = "0.11.0", features = ["flamegraph", "criterion"] }
env_logger = "0.9.0"
pprof = { version = "0.10.0", features = ["flamegraph", "criterion"] }
futures = "0.3.21"
paste = "1.0.11"
[dev-dependencies.fail]
version = "0.5.0"
@@ -93,9 +89,8 @@ debug-assertions = true
overflow-checks = true
[features]
default = ["mmap", "stopwords", "lz4-compression"]
default = ["mmap", "lz4-compression" ]
mmap = ["fs2", "tempfile", "memmap2"]
stopwords = []
brotli-compression = ["brotli"]
lz4-compression = ["lz4_flex"]
@@ -105,10 +100,10 @@ zstd-compression = ["zstd"]
failpoints = ["fail/failpoints"]
unstable = [] # useful for benches.
quickwit = ["sstable"]
quickwit = ["serde_cbor"]
[workspace]
members = ["query-grammar", "bitpacker", "common", "ownedbytes", "stacker", "sstable", "tokenizer-api", "columnar"]
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

56
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)
@@ -41,13 +41,13 @@ Your mileage WILL vary depending on the nature of queries and their load.
- SIMD integer compression when the platform/CPU includes the SSE2 instruction set
- Single valued and multivalued u64, i64, and f64 fast fields (equivalent of doc values in Lucene)
- `&[u8]` fast fields
- Text, i64, u64, f64, dates, ip, bool, and hierarchical facet fields
- Compressed document store (LZ4, Zstd, None, Brotli, Snap)
- Text, i64, u64, f64, dates, and hierarchical facet fields
- 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
@@ -58,7 +58,7 @@ Distributed search is out of the scope of Tantivy, but if you are looking for th
# Getting started
Tantivy works on stable Rust and supports Linux, macOS, and Windows.
Tantivy works on stable Rust (>= 1.27) and supports Linux, macOS, and Windows.
- [Tantivy's simple search example](https://tantivy-search.github.io/examples/basic_search.html)
- [tantivy-cli and its tutorial](https://github.com/quickwit-oss/tantivy-cli) - `tantivy-cli` is an actual command-line interface that makes it easy for you to create a search engine,
@@ -80,27 +80,53 @@ There are many ways to support this project.
# Contributing code
We use the GitHub Pull Request workflow: reference a GitHub ticket and/or include a comprehensive commit message when opening a PR.
Feel free to update CHANGELOG.md with your contribution.
## Tokenizer
When implementing a tokenizer for tantivy depend on the `tantivy-tokenizer-api` crate.
## Clone and build locally
Tantivy compiles on stable Rust.
Tantivy compiles on stable Rust but requires `Rust >= 1.27`.
To check out and run tests, you can simply run:
```bash
git clone https://github.com/quickwit-oss/tantivy.git
cd tantivy
cargo test
git clone https://github.com/quickwit-oss/tantivy.git
cd tantivy
cargo build
```
## Run tests
Some tests will not run with just `cargo test` because of `fail-rs`.
To run the tests exhaustively, run `./run-tests.sh`.
## Debug
You might find it useful to step through the programme with a debugger.
### A failing test
Make sure you haven't run `cargo clean` after the most recent `cargo test` or `cargo build` to guarantee that the `target/` directory exists. Use this bash script to find the name of the most recent debug build of Tantivy and run it under `rust-gdb`:
```bash
find target/debug/ -maxdepth 1 -executable -type f -name "tantivy*" -printf '%TY-%Tm-%Td %TT %p\n' | sort -r | cut -d " " -f 3 | xargs -I RECENT_DBG_TANTIVY rust-gdb RECENT_DBG_TANTIVY
```
Now that you are in `rust-gdb`, you can set breakpoints on lines and methods that match your source code and run the debug executable with flags that you normally pass to `cargo test` like this:
```bash
$gdb run --test-threads 1 --test $NAME_OF_TEST
```
### An example
By default, `rustc` compiles everything in the `examples/` directory in debug mode. This makes it easy for you to make examples to reproduce bugs:
```bash
rust-gdb target/debug/examples/$EXAMPLE_NAME
$ gdb run
```
# Companies Using Tantivy
<p align="left">
<img align="center" src="doc/assets/images/etsy.png" alt="Etsy" height="25" width="auto" />&nbsp;
<img align="center" src="doc/assets/images/Nuclia.png#gh-light-mode-only" alt="Nuclia" height="25" width="auto" /> &nbsp;
<img align="center" src="doc/assets/images/humanfirst.png#gh-light-mode-only" alt="Humanfirst.ai" height="30" width="auto" />
<img align="center" src="doc/assets/images/element.io.svg#gh-light-mode-only" alt="Element.io" height="25" width="auto" />

18
TODO.txt
View File

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

12
benches/index-bench.rs
View File

@@ -34,7 +34,7 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
let index = Index::create_in_ram(schema.clone());
let index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
for doc_json in HDFS_LOGS.trim().split("\n") {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
@@ -46,7 +46,7 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
let index = Index::create_in_ram(schema.clone());
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
for doc_json in HDFS_LOGS.trim().split("\n") {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
@@ -59,7 +59,7 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
let index = Index::create_in_ram(schema_with_store.clone());
let index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
for doc_json in HDFS_LOGS.trim().split("\n") {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
@@ -71,7 +71,7 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
let index = Index::create_in_ram(schema_with_store.clone());
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
for doc_json in HDFS_LOGS.trim().split("\n") {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
@@ -85,7 +85,7 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
let json_field = dynamic_schema.get_field("json").unwrap();
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
for doc_json in HDFS_LOGS.trim().split("\n") {
let json_val: serde_json::Map<String, serde_json::Value> =
serde_json::from_str(doc_json).unwrap();
let doc = tantivy::doc!(json_field=>json_val);
@@ -101,7 +101,7 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
let json_field = dynamic_schema.get_field("json").unwrap();
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..NUM_REPEATS {
for doc_json in HDFS_LOGS.trim().split('\n') {
for doc_json in HDFS_LOGS.trim().split("\n") {
let json_val: serde_json::Map<String, serde_json::Value> =
serde_json::from_str(doc_json).unwrap();
let doc = tantivy::doc!(json_field=>json_val);

8
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"
@@ -8,14 +8,8 @@ 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
[dependencies]
[dev-dependencies]
rand = "0.8"
proptest = "1"

36
bitpacker/benches/bench.rs
View File

@@ -4,39 +4,9 @@ extern crate test;
#[cfg(test)]
mod tests {
use rand::seq::IteratorRandom;
use rand::thread_rng;
use tantivy_bitpacker::{BitPacker, BitUnpacker, BlockedBitpacker};
use tantivy_bitpacker::BlockedBitpacker;
use test::Bencher;
#[inline(never)]
fn create_bitpacked_data(bit_width: u8, num_els: u32) -> Vec<u8> {
let mut bitpacker = BitPacker::new();
let mut buffer = Vec::new();
for _ in 0..num_els {
// the values do not matter.
bitpacker.write(0u64, bit_width, &mut buffer).unwrap();
bitpacker.flush(&mut buffer).unwrap();
}
buffer
}
#[bench]
fn bench_bitpacking_read(b: &mut Bencher) {
let bit_width = 3;
let num_els = 1_000_000u32;
let bit_unpacker = BitUnpacker::new(bit_width);
let data = create_bitpacked_data(bit_width, num_els);
let idxs: Vec<u32> = (0..num_els).choose_multiple(&mut thread_rng(), 100_000);
b.iter(|| {
let mut out = 0u64;
for &idx in &idxs {
out = out.wrapping_add(bit_unpacker.get(idx, &data[..]));
}
out
});
}
#[bench]
fn bench_blockedbitp_read(b: &mut Bencher) {
let mut blocked_bitpacker = BlockedBitpacker::new();
@@ -44,9 +14,9 @@ mod tests {
blocked_bitpacker.add(val * val);
}
b.iter(|| {
let mut out = 0u64;
let mut out = 0;
for val in 0..=21500 {
out = out.wrapping_add(blocked_bitpacker.get(val));
out = blocked_bitpacker.get(val);
}
out
});

116
bitpacker/src/bitpacker.rs
View File

@@ -19,20 +19,21 @@ impl BitPacker {
}
#[inline]
pub fn write<TWrite: io::Write + ?Sized>(
pub fn write<TWrite: io::Write>(
&mut self,
val: u64,
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())?;
@@ -43,7 +44,7 @@ impl BitPacker {
Ok(())
}
pub fn flush<TWrite: io::Write + ?Sized>(&mut self, output: &mut TWrite) -> io::Result<()> {
pub fn flush<TWrite: io::Write>(&mut self, output: &mut TWrite) -> io::Result<()> {
if self.mini_buffer_written > 0 {
let num_bytes = (self.mini_buffer_written + 7) / 8;
let bytes = self.mini_buffer.to_le_bytes();
@@ -54,33 +55,29 @@ impl BitPacker {
Ok(())
}
pub fn close<TWrite: io::Write + ?Sized>(&mut self, output: &mut TWrite) -> io::Result<()> {
pub fn close<TWrite: io::Write>(&mut self, output: &mut TWrite) -> io::Result<()> {
self.flush(output)?;
// Padding the write file to simplify reads.
output.write_all(&[0u8; 7])?;
Ok(())
}
}
#[derive(Clone, Debug, Default, Copy)]
#[derive(Clone, Debug, Default)]
pub struct BitUnpacker {
num_bits: u32,
num_bits: u64,
mask: u64,
}
impl BitUnpacker {
/// Creates a bit unpacker, that assumes the same bitwidth for all values.
///
/// The bitunpacker works by doing an unaligned read of 8 bytes.
/// For this reason, values of `num_bits` between
/// [57..63] are forbidden.
pub fn new(num_bits: u8) -> BitUnpacker {
assert!(num_bits <= 7 * 8 || num_bits == 64);
let mask: u64 = if num_bits == 64 {
!0u64
} else {
(1u64 << num_bits) - 1u64
};
BitUnpacker {
num_bits: u32::from(num_bits),
num_bits: u64::from(num_bits),
mask,
}
}
@@ -90,32 +87,22 @@ impl BitUnpacker {
}
#[inline]
pub fn get(&self, idx: u32, data: &[u8]) -> u64 {
let addr_in_bits = idx * self.num_bits;
let addr = (addr_in_bits >> 3) as usize;
if addr + 8 > data.len() {
if self.num_bits == 0 {
return 0;
}
let bit_shift = addr_in_bits & 7;
return self.get_slow_path(addr, bit_shift, data);
pub fn get(&self, idx: u64, data: &[u8]) -> u64 {
if self.num_bits == 0 {
return 0u64;
}
let addr_in_bits = idx * self.num_bits;
let addr = addr_in_bits >> 3;
let bit_shift = addr_in_bits & 7;
let bytes: [u8; 8] = (&data[addr..addr + 8]).try_into().unwrap();
debug_assert!(
addr + 8 <= data.len() as u64,
"The fast field field should have been padded with 7 bytes."
);
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;
val_shifted & self.mask
}
#[inline(never)]
fn get_slow_path(&self, addr: usize, bit_shift: u32, data: &[u8]) -> u64 {
let mut bytes: [u8; 8] = [0u8; 8];
let available_bytes = data.len() - addr;
// This function is meant to only be called if we did not have 8 bytes to load.
debug_assert!(available_bytes < 8);
bytes[..available_bytes].copy_from_slice(&data[addr..]);
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
}
}
@@ -124,7 +111,7 @@ impl BitUnpacker {
mod test {
use super::{BitPacker, BitUnpacker};
fn create_bitpacker(len: usize, num_bits: u8) -> (BitUnpacker, Vec<u64>, Vec<u8>) {
fn create_fastfield_bitpacker(len: usize, num_bits: u8) -> (BitUnpacker, Vec<u64>, Vec<u8>) {
let mut data = Vec::new();
let mut bitpacker = BitPacker::new();
let max_val: u64 = (1u64 << num_bits as u64) - 1u64;
@@ -135,15 +122,15 @@ mod test {
bitpacker.write(val, num_bits, &mut data).unwrap();
}
bitpacker.close(&mut data).unwrap();
assert_eq!(data.len(), ((num_bits as usize) * len + 7) / 8);
assert_eq!(data.len(), ((num_bits as usize) * len + 7) / 8 + 7);
let bitunpacker = BitUnpacker::new(num_bits);
(bitunpacker, vals, data)
}
fn test_bitpacker_util(len: usize, num_bits: u8) {
let (bitunpacker, vals, data) = create_bitpacker(len, num_bits);
let (bitunpacker, vals, data) = create_fastfield_bitpacker(len, num_bits);
for (i, val) in vals.iter().enumerate() {
assert_eq!(bitunpacker.get(i as u32, &data), *val);
assert_eq!(bitunpacker.get(i as u64, &data), *val);
}
}
@@ -155,49 +142,4 @@ mod test {
test_bitpacker_util(6, 14);
test_bitpacker_util(1000, 14);
}
use proptest::prelude::*;
fn num_bits_strategy() -> impl Strategy<Value = u8> {
prop_oneof!(Just(0), Just(1), 2u8..56u8, Just(56), Just(64),)
}
fn vals_strategy() -> impl Strategy<Value = (u8, Vec<u64>)> {
(num_bits_strategy(), 0usize..100usize).prop_flat_map(|(num_bits, len)| {
let max_val = if num_bits == 64 {
u64::MAX
} else {
(1u64 << num_bits as u32) - 1
};
let vals = proptest::collection::vec(0..=max_val, len);
vals.prop_map(move |vals| (num_bits, vals))
})
}
fn test_bitpacker_aux(num_bits: u8, vals: &[u64]) {
let mut buffer: Vec<u8> = Vec::new();
let mut bitpacker = BitPacker::new();
for &val in vals {
bitpacker.write(val, num_bits, &mut buffer).unwrap();
}
bitpacker.flush(&mut buffer).unwrap();
assert_eq!(buffer.len(), (vals.len() * num_bits as usize + 7) / 8);
let bitunpacker = BitUnpacker::new(num_bits);
let max_val = if num_bits == 64 {
u64::MAX
} else {
(1u64 << num_bits) - 1
};
for (i, val) in vals.iter().copied().enumerate() {
assert!(val <= max_val);
assert_eq!(bitunpacker.get(i as u32, &buffer), val);
}
}
proptest::proptest! {
#[test]
fn test_bitpacker_proptest((num_bits, vals) in vals_strategy()) {
test_bitpacker_aux(num_bits, &vals);
}
}
}

8
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();
}
}
@@ -126,11 +126,11 @@ 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,
pos_in_block as u64,
&self.compressed_blocks[metadata.offset() as usize..],
);
unpacked + metadata.base_value()

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)));
}

23
ci/before_deploy.ps1 Normal file
View File

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

33
ci/before_deploy.sh Normal file
View File

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

47
ci/install.sh Normal file
View File

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

30
ci/script.sh Normal file
View File

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

28
columnar/Cargo.toml
View File

@@ -1,28 +0,0 @@
[package]
name = "tantivy-columnar"
version = "0.1.0"
edition = "2021"
license = "MIT"
[dependencies]
itertools = "0.10.5"
log = "0.4.17"
fnv = "1.0.7"
fastdivide = "0.4.0"
rand = { version = "0.8.5", optional = true }
measure_time = { version = "0.8.2", optional = true }
prettytable-rs = { version = "0.10.0", optional = true }
stacker = { path = "../stacker", package="tantivy-stacker"}
sstable = { path = "../sstable", package = "tantivy-sstable" }
common = { path = "../common", package = "tantivy-common" }
tantivy-bitpacker = { version= "0.3", path = "../bitpacker/" }
serde = "1.0.152"
[dev-dependencies]
proptest = "1"
more-asserts = "0.3.1"
rand = "0.8.5"
[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

124
columnar/benches/bench_u128.rs
View File

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

211
columnar/benches/bench_u64.rs
View File

@@ -1,211 +0,0 @@
#![feature(test)]
extern crate test;
use std::ops::RangeInclusive;
use std::sync::Arc;
use rand::prelude::*;
use tantivy_columnar::column_values::{serialize_and_load_u64_based_column_values, CodecType};
use tantivy_columnar::*;
use test::Bencher;
// Warning: this generates the same permutation at each call
fn generate_permutation() -> Vec<u64> {
let mut permutation: Vec<u64> = (0u64..100_000u64).collect();
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
permutation
}
fn generate_random() -> Vec<u64> {
let mut permutation: Vec<u64> = (0u64..100_000u64)
.map(|el| el + random::<u16>() as u64)
.collect();
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
permutation
}
// Warning: this generates the same permutation at each call
fn generate_permutation_gcd() -> Vec<u64> {
let mut permutation: Vec<u64> = (1u64..100_000u64).map(|el| el * 1000).collect();
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
permutation
}
pub fn serialize_and_load(column: &[u64], codec_type: CodecType) -> Arc<dyn ColumnValues<u64>> {
serialize_and_load_u64_based_column_values(&column, &[codec_type])
}
#[bench]
fn bench_intfastfield_jumpy_veclookup(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
b.iter(|| {
let mut a = 0u64;
for _ in 0..n {
a = permutation[a as usize];
}
a
});
}
#[bench]
fn bench_intfastfield_jumpy_fflookup_bitpacked(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
b.iter(|| {
let mut a = 0u64;
for _ in 0..n {
a = column.get_val(a as u32);
}
a
});
}
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
const SINGLE_ITEM: u64 = 90;
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
const ONE_PERCENT_ITEM_RANGE: RangeInclusive<u64> = 49..=49;
fn get_data_50percent_item() -> Vec<u128> {
let mut rng = StdRng::from_seed([1u8; 32]);
let mut data = vec![];
for _ in 0..300_000 {
let val = rng.gen_range(1..=100);
data.push(val);
}
data.push(SINGLE_ITEM);
data.shuffle(&mut rng);
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
data
}
// U64 RANGE START
#[bench]
fn bench_intfastfield_getrange_u64_50percent_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(
FIFTY_PERCENT_RANGE,
0..data.len() as u32,
&mut positions,
);
positions
});
}
#[bench]
fn bench_intfastfield_getrange_u64_1percent_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(
ONE_PERCENT_ITEM_RANGE,
0..data.len() as u32,
&mut positions,
);
positions
});
}
#[bench]
fn bench_intfastfield_getrange_u64_single_hit(b: &mut Bencher) {
let data = get_data_50percent_item();
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(SINGLE_ITEM_RANGE, 0..data.len() as u32, &mut positions);
positions
});
}
#[bench]
fn bench_intfastfield_getrange_u64_hit_all(b: &mut Bencher) {
let data = get_data_50percent_item();
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
b.iter(|| {
let mut positions = Vec::new();
column.get_row_ids_for_value_range(0..=u64::MAX, 0..data.len() as u32, &mut positions);
positions
});
}
// U64 RANGE END
#[bench]
fn bench_intfastfield_stride7_vec(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
b.iter(|| {
let mut a = 0u64;
for i in (0..n / 7).map(|val| val * 7) {
a += permutation[i as usize];
}
a
});
}
#[bench]
fn bench_intfastfield_stride7_fflookup(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
b.iter(|| {
let mut a = 0;
for i in (0..n / 7).map(|val| val * 7) {
a += column.get_val(i as u32);
}
a
});
}
#[bench]
fn bench_intfastfield_scan_all_fflookup(b: &mut Bencher) {
let permutation = generate_permutation();
let n = permutation.len();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
let column_ref = column.as_ref();
b.iter(|| {
let mut a = 0u64;
for i in 0u32..n as u32 {
a += column_ref.get_val(i);
}
a
});
}
#[bench]
fn bench_intfastfield_scan_all_fflookup_gcd(b: &mut Bencher) {
let permutation = generate_permutation_gcd();
let n = permutation.len();
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
b.iter(|| {
let mut a = 0u64;
for i in 0..n {
a += column.get_val(i as u32);
}
a
});
}
#[bench]
fn bench_intfastfield_scan_all_vec(b: &mut Bencher) {
let permutation = generate_permutation();
b.iter(|| {
let mut a = 0u64;
for i in 0..permutation.len() {
a += permutation[i as usize] as u64;
}
a
});
}

17
columnar/columnar-cli/Cargo.toml
View File

@@ -1,17 +0,0 @@
[package]
name = "tantivy-columnar-cli"
version = "0.1.0"
edition = "2021"
license = "MIT"
[dependencies]
columnar = {path="../", package="tantivy-columnar"}
serde_json = "1"
serde_json_borrow = {git="https://github.com/PSeitz/serde_json_borrow/"}
serde = "1"
[workspace]
members = []
[profile.release]
debug = true

134
columnar/columnar-cli/src/main.rs
View File

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

47
columnar/src/TODO.md
View File

@@ -1,47 +0,0 @@
# zero to one
* revisit line codec
* add columns from schema on merge
* Plugging JSON
* replug examples
* move datetime to quickwit common
* switch to nanos
* reintroduce the gcd map.
# Perf and Size
* remove alloc in `ord_to_term`
+ multivaued range queries restrat frm the beginning all of the time.
* re-add ZSTD compression for dictionaries
no systematic monotonic mapping
consider removing multilinear
f32?
adhoc solution for bool?
add metrics helper for aggregate. sum(row_id)
review inline absence/presence
improv perf of select using PDEP
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
rename columnar to something more explicit, like column_dictionary or columnar_table
rename fastfield -> column
document changes
rationalization FastFieldValue, HasColumnType
isolate u128_based and uniform naming
# Other
fix enhance column-cli
# Santa claus
autodetect datetime ipaddr, plug customizable tokenizer.

100
columnar/src/column/dictionary_encoded.rs
View File

@@ -1,100 +0,0 @@
use std::io;
use std::ops::Deref;
use std::sync::Arc;
use sstable::{Dictionary, VoidSSTable};
use crate::column::Column;
use crate::RowId;
/// Dictionary encoded column.
///
/// The column simply gives access to a regular u64-column that, in
/// which the values are term-ordinals.
///
/// These ordinals are ids uniquely identify the bytes that are stored in
/// the column. These ordinals are small, and sorted in the same order
/// as the term_ord_column.
#[derive(Clone)]
pub struct BytesColumn {
pub(crate) dictionary: Arc<Dictionary<VoidSSTable>>,
pub(crate) term_ord_column: Column<u64>,
}
impl BytesColumn {
/// Fills the given `output` buffer with the term associated to the ordinal `ord`.
///
/// Returns `false` if the term does not exist (e.g. `term_ord` is greater or equal to the
/// overll number of terms).
pub fn ord_to_bytes(&self, ord: u64, output: &mut Vec<u8>) -> io::Result<bool> {
self.dictionary.ord_to_term(ord, output)
}
/// Returns the number of rows in the column.
pub fn num_rows(&self) -> RowId {
self.term_ord_column.num_docs()
}
pub fn term_ords(&self, row_id: RowId) -> impl Iterator<Item = u64> + '_ {
self.term_ord_column.values_for_doc(row_id)
}
/// Returns the column of ordinals
pub fn ords(&self) -> &Column<u64> {
&self.term_ord_column
}
pub fn num_terms(&self) -> usize {
self.dictionary.num_terms()
}
pub fn dictionary(&self) -> &Dictionary<VoidSSTable> {
self.dictionary.as_ref()
}
}
#[derive(Clone)]
pub struct StrColumn(BytesColumn);
impl From<StrColumn> for BytesColumn {
fn from(str_column: StrColumn) -> BytesColumn {
str_column.0
}
}
impl StrColumn {
pub(crate) fn wrap(bytes_column: BytesColumn) -> StrColumn {
StrColumn(bytes_column)
}
pub fn dictionary(&self) -> &Dictionary<VoidSSTable> {
self.0.dictionary.as_ref()
}
/// Fills the buffer
pub fn ord_to_str(&self, term_ord: u64, output: &mut String) -> io::Result<bool> {
unsafe {
let buf = output.as_mut_vec();
if !self.0.dictionary.ord_to_term(term_ord, buf)? {
return Ok(false);
}
// TODO consider remove checks if it hurts performance.
if std::str::from_utf8(buf.as_slice()).is_err() {
buf.clear();
return Err(io::Error::new(
io::ErrorKind::InvalidData,
"Not valid utf-8",
));
}
}
Ok(true)
}
}
impl Deref for StrColumn {
type Target = BytesColumn;
fn deref(&self) -> &Self::Target {
&self.0
}
}

161
columnar/src/column/mod.rs
View File

@@ -1,161 +0,0 @@
mod dictionary_encoded;
mod serialize;
use std::fmt::Debug;
use std::io::Write;
use std::ops::{Deref, Range, RangeInclusive};
use std::sync::Arc;
use common::BinarySerializable;
pub use dictionary_encoded::{BytesColumn, StrColumn};
pub use serialize::{
open_column_bytes, open_column_str, open_column_u128, open_column_u64,
serialize_column_mappable_to_u128, serialize_column_mappable_to_u64,
};
use crate::column_index::ColumnIndex;
use crate::column_values::monotonic_mapping::StrictlyMonotonicMappingToInternal;
use crate::column_values::{monotonic_map_column, ColumnValues};
use crate::{Cardinality, MonotonicallyMappableToU64, RowId};
#[derive(Clone)]
pub struct Column<T = u64> {
pub idx: ColumnIndex,
pub values: Arc<dyn ColumnValues<T>>,
}
impl<T: MonotonicallyMappableToU64> Column<T> {
pub fn to_u64_monotonic(self) -> Column<u64> {
let values = Arc::new(monotonic_map_column(
self.values,
StrictlyMonotonicMappingToInternal::<T>::new(),
));
Column {
idx: self.idx,
values,
}
}
}
impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
#[inline]
pub fn get_cardinality(&self) -> Cardinality {
self.idx.get_cardinality()
}
pub fn num_docs(&self) -> RowId {
match &self.idx {
ColumnIndex::Empty { num_docs } => *num_docs,
ColumnIndex::Full => self.values.num_vals(),
ColumnIndex::Optional(optional_index) => optional_index.num_docs(),
ColumnIndex::Multivalued(col_index) => {
// The multivalued index contains all value start row_id,
// and one extra value at the end with the overall number of rows.
col_index.num_docs()
}
}
}
pub fn min_value(&self) -> T {
self.values.min_value()
}
pub fn max_value(&self) -> T {
self.values.max_value()
}
pub fn first(&self, row_id: RowId) -> Option<T> {
self.values_for_doc(row_id).next()
}
pub fn values_for_doc(&self, row_id: RowId) -> impl Iterator<Item = T> + '_ {
self.value_row_ids(row_id)
.map(|value_row_id: RowId| self.values.get_val(value_row_id))
}
/// Get the docids of values which are in the provided value range.
#[inline]
pub fn get_docids_for_value_range(
&self,
value_range: RangeInclusive<T>,
selected_docid_range: Range<u32>,
doc_ids: &mut Vec<u32>,
) {
// convert passed docid range to row id range
let rowid_range = self.idx.docid_range_to_rowids(selected_docid_range.clone());
// Load rows
self.values
.get_row_ids_for_value_range(value_range, rowid_range, doc_ids);
// Convert rows to docids
self.idx
.select_batch_in_place(selected_docid_range.start, doc_ids);
}
/// Fils the output vector with the (possibly multiple values that are associated_with
/// `row_id`.
///
/// This method clears the `output` vector.
pub fn fill_vals(&self, row_id: RowId, output: &mut Vec<T>) {
output.clear();
output.extend(self.values_for_doc(row_id));
}
pub fn first_or_default_col(self, default_value: T) -> Arc<dyn ColumnValues<T>> {
Arc::new(FirstValueWithDefault {
column: self,
default_value,
})
}
}
impl<T> Deref for Column<T> {
type Target = ColumnIndex;
fn deref(&self) -> &Self::Target {
&self.idx
}
}
impl BinarySerializable for Cardinality {
fn serialize<W: Write + ?Sized>(&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)
}
}
// TODO simplify or optimize
struct FirstValueWithDefault<T: Copy> {
column: Column<T>,
default_value: T,
}
impl<T: PartialOrd + Debug + Send + Sync + Copy + 'static> ColumnValues<T>
for FirstValueWithDefault<T>
{
fn get_val(&self, idx: u32) -> T {
self.column.first(idx).unwrap_or(self.default_value)
}
fn min_value(&self) -> T {
self.column.values.min_value()
}
fn max_value(&self) -> T {
self.column.values.max_value()
}
fn num_vals(&self) -> u32 {
match &self.column.idx {
ColumnIndex::Empty { .. } => 0u32,
ColumnIndex::Full => self.column.values.num_vals(),
ColumnIndex::Optional(optional_idx) => optional_idx.num_docs(),
ColumnIndex::Multivalued(multivalue_idx) => multivalue_idx.num_docs(),
}
}
}

94
columnar/src/column/serialize.rs
View File

@@ -1,94 +0,0 @@
use std::io;
use std::io::Write;
use std::sync::Arc;
use common::OwnedBytes;
use sstable::Dictionary;
use crate::column::{BytesColumn, Column};
use crate::column_index::{serialize_column_index, SerializableColumnIndex};
use crate::column_values::{
load_u64_based_column_values, serialize_column_values_u128, serialize_u64_based_column_values,
CodecType, MonotonicallyMappableToU128, MonotonicallyMappableToU64,
};
use crate::iterable::Iterable;
use crate::StrColumn;
pub fn serialize_column_mappable_to_u128<T: MonotonicallyMappableToU128>(
column_index: SerializableColumnIndex<'_>,
iterable: &dyn Iterable<T>,
output: &mut impl Write,
) -> io::Result<()> {
let column_index_num_bytes = serialize_column_index(column_index, output)?;
serialize_column_values_u128(iterable, output)?;
output.write_all(&column_index_num_bytes.to_le_bytes())?;
Ok(())
}
pub fn serialize_column_mappable_to_u64<T: MonotonicallyMappableToU64>(
column_index: SerializableColumnIndex<'_>,
column_values: &impl Iterable<T>,
output: &mut impl Write,
) -> io::Result<()> {
let column_index_num_bytes = serialize_column_index(column_index, output)?;
serialize_u64_based_column_values(
column_values,
&[CodecType::Bitpacked, CodecType::BlockwiseLinear],
output,
)?;
output.write_all(&column_index_num_bytes.to_le_bytes())?;
Ok(())
}
pub fn open_column_u64<T: MonotonicallyMappableToU64>(bytes: OwnedBytes) -> io::Result<Column<T>> {
let (body, column_index_num_bytes_payload) = bytes.rsplit(4);
let column_index_num_bytes = u32::from_le_bytes(
column_index_num_bytes_payload
.as_slice()
.try_into()
.unwrap(),
);
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
let column_index = crate::column_index::open_column_index(column_index_data)?;
let column_values = load_u64_based_column_values(column_values_data)?;
Ok(Column {
idx: column_index,
values: column_values,
})
}
pub fn open_column_u128<T: MonotonicallyMappableToU128>(
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_u128_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,
})
}
pub fn open_column_str(data: OwnedBytes) -> io::Result<StrColumn> {
let bytes_column = open_column_bytes(data)?;
Ok(StrColumn::wrap(bytes_column))
}

136
columnar/src/column_index/merge/mod.rs
View File

@@ -1,136 +0,0 @@
mod shuffled;
mod stacked;
use shuffled::merge_column_index_shuffled;
use stacked::merge_column_index_stacked;
use crate::column_index::SerializableColumnIndex;
use crate::{Cardinality, ColumnIndex, MergeRowOrder};
// For simplification, we never have cardinality go down due to deletes.
fn detect_cardinality(columns: &[Option<ColumnIndex>]) -> Cardinality {
columns
.iter()
.flatten()
.map(ColumnIndex::get_cardinality)
.max()
.unwrap_or(Cardinality::Full)
}
pub fn merge_column_index<'a>(
columns: &'a [Option<ColumnIndex>],
merge_row_order: &'a MergeRowOrder,
) -> SerializableColumnIndex<'a> {
// For simplification, we do not try to detect whether the cardinality could be
// downgraded thanks to deletes.
let cardinality_after_merge = detect_cardinality(columns);
match merge_row_order {
MergeRowOrder::Stack(stack_merge_order) => {
merge_column_index_stacked(columns, cardinality_after_merge, stack_merge_order)
}
MergeRowOrder::Shuffled(complex_merge_order) => {
merge_column_index_shuffled(columns, cardinality_after_merge, complex_merge_order)
}
}
}
// TODO actually, the shuffled code path is a bit too general.
// In practise, we do not really shuffle everything.
// The merge order restricted to a specific column keeps the original row order.
//
// This may offer some optimization that we have not explored yet.
#[cfg(test)]
mod tests {
use crate::column_index::merge::detect_cardinality;
use crate::column_index::multivalued_index::MultiValueIndex;
use crate::column_index::{merge_column_index, OptionalIndex, SerializableColumnIndex};
use crate::{Cardinality, ColumnIndex, MergeRowOrder, RowAddr, RowId, ShuffleMergeOrder};
#[test]
fn test_detect_cardinality() {
assert_eq!(detect_cardinality(&[]), Cardinality::Full);
let optional_index: ColumnIndex = OptionalIndex::for_test(1, &[]).into();
let multivalued_index: ColumnIndex = MultiValueIndex::for_test(&[0, 1]).into();
assert_eq!(
detect_cardinality(&[Some(optional_index.clone()), None]),
Cardinality::Optional
);
assert_eq!(
detect_cardinality(&[Some(optional_index.clone()), Some(ColumnIndex::Full)]),
Cardinality::Optional
);
assert_eq!(
detect_cardinality(&[Some(multivalued_index.clone()), None]),
Cardinality::Multivalued
);
assert_eq!(
detect_cardinality(&[
Some(multivalued_index.clone()),
Some(optional_index.clone())
]),
Cardinality::Multivalued
);
assert_eq!(
detect_cardinality(&[Some(optional_index), Some(multivalued_index)]),
Cardinality::Multivalued
);
}
#[test]
fn test_merge_index_multivalued_sorted() {
let column_indexes: Vec<Option<ColumnIndex>> =
vec![Some(MultiValueIndex::for_test(&[0, 2, 5]).into())];
let merge_row_order: MergeRowOrder = ShuffleMergeOrder::for_test(
&[2],
vec![
RowAddr {
segment_ord: 0u32,
row_id: 1u32,
},
RowAddr {
segment_ord: 0u32,
row_id: 0u32,
},
],
)
.into();
let merged_column_index = merge_column_index(&column_indexes[..], &merge_row_order);
let SerializableColumnIndex::Multivalued(start_index_iterable) = merged_column_index
else { panic!("Excpected a multivalued index") };
let start_indexes: Vec<RowId> = start_index_iterable.boxed_iter().collect();
assert_eq!(&start_indexes, &[0, 3, 5]);
}
#[test]
fn test_merge_index_multivalued_sorted_several_segment() {
let column_indexes: Vec<Option<ColumnIndex>> = vec![
Some(MultiValueIndex::for_test(&[0, 2, 5]).into()),
None,
Some(MultiValueIndex::for_test(&[0, 1, 4]).into()),
];
let merge_row_order: MergeRowOrder = ShuffleMergeOrder::for_test(
&[2, 0, 2],
vec![
RowAddr {
segment_ord: 2u32,
row_id: 1u32,
},
RowAddr {
segment_ord: 0u32,
row_id: 0u32,
},
RowAddr {
segment_ord: 2u32,
row_id: 0u32,
},
],
)
.into();
let merged_column_index = merge_column_index(&column_indexes[..], &merge_row_order);
let SerializableColumnIndex::Multivalued(start_index_iterable) = merged_column_index
else { panic!("Excpected a multivalued index") };
let start_indexes: Vec<RowId> = start_index_iterable.boxed_iter().collect();
assert_eq!(&start_indexes, &[0, 3, 5, 6]);
}
}

168
columnar/src/column_index/merge/shuffled.rs
View File

@@ -1,168 +0,0 @@
use std::iter;
use crate::column_index::{SerializableColumnIndex, Set};
use crate::iterable::Iterable;
use crate::{Cardinality, ColumnIndex, RowId, ShuffleMergeOrder};
pub fn merge_column_index_shuffled<'a>(
column_indexes: &'a [Option<ColumnIndex>],
cardinality_after_merge: Cardinality,
shuffle_merge_order: &'a ShuffleMergeOrder,
) -> SerializableColumnIndex<'a> {
match cardinality_after_merge {
Cardinality::Full => SerializableColumnIndex::Full,
Cardinality::Optional => {
let non_null_row_ids =
merge_column_index_shuffled_optional(column_indexes, shuffle_merge_order);
SerializableColumnIndex::Optional {
non_null_row_ids,
num_rows: shuffle_merge_order.num_rows(),
}
}
Cardinality::Multivalued => {
let multivalue_start_index =
merge_column_index_shuffled_multivalued(column_indexes, shuffle_merge_order);
SerializableColumnIndex::Multivalued(multivalue_start_index)
}
}
}
/// Merge several column indexes into one, ordering rows according to the merge_order passed as
/// argument. While it is true that the `merge_order` may imply deletes and hence could in theory a
/// multivalued index into an optional one, this is not supported today for simplification.
///
/// In other words the column_indexes passed as argument may NOT be multivalued.
fn merge_column_index_shuffled_optional<'a>(
column_indexes: &'a [Option<ColumnIndex>],
merge_order: &'a ShuffleMergeOrder,
) -> Box<dyn Iterable<RowId> + 'a> {
Box::new(ShuffledOptionalIndex {
column_indexes,
merge_order,
})
}
struct ShuffledOptionalIndex<'a> {
column_indexes: &'a [Option<ColumnIndex>],
merge_order: &'a ShuffleMergeOrder,
}
impl<'a> Iterable<u32> for ShuffledOptionalIndex<'a> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
Box::new(self.merge_order
.iter_new_to_old_row_addrs()
.enumerate()
.filter_map(|(new_row_id, old_row_addr)| {
let Some(column_index) = &self.column_indexes[old_row_addr.segment_ord as usize] else {
return None;
};
let row_id = new_row_id as u32;
if column_index.has_value(old_row_addr.row_id) {
Some(row_id)
} else {
None
}
}))
}
}
fn merge_column_index_shuffled_multivalued<'a>(
column_indexes: &'a [Option<ColumnIndex>],
merge_order: &'a ShuffleMergeOrder,
) -> Box<dyn Iterable<RowId> + 'a> {
Box::new(ShuffledMultivaluedIndex {
column_indexes,
merge_order,
})
}
struct ShuffledMultivaluedIndex<'a> {
column_indexes: &'a [Option<ColumnIndex>],
merge_order: &'a ShuffleMergeOrder,
}
fn iter_num_values<'a>(
column_indexes: &'a [Option<ColumnIndex>],
merge_order: &'a ShuffleMergeOrder,
) -> impl Iterator<Item = u32> + 'a {
merge_order.iter_new_to_old_row_addrs().map(|row_addr| {
let Some(column_index) = &column_indexes[row_addr.segment_ord as usize] else {
// No values in the entire column. It surely means there are 0 values associated to this row.
return 0u32;
};
match column_index {
ColumnIndex::Empty { .. } => 0u32,
ColumnIndex::Full => 1,
ColumnIndex::Optional(optional_index) => {
u32::from(optional_index.contains(row_addr.row_id))
}
ColumnIndex::Multivalued(multivalued_index) => {
multivalued_index.range(row_addr.row_id).len() as u32
}
}
})
}
/// Transforms an iterator containing the number of vals per row (with `num_rows` elements)
/// into a `start_offset` iterator starting at 0 and (with `num_rows + 1` element)
fn integrate_num_vals(num_vals: impl Iterator<Item = u32>) -> impl Iterator<Item = RowId> {
iter::once(0u32).chain(num_vals.scan(0, |state, num_vals| {
*state += num_vals;
Some(*state)
}))
}
impl<'a> Iterable<u32> for ShuffledMultivaluedIndex<'a> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
let num_vals_per_row = iter_num_values(self.column_indexes, self.merge_order);
Box::new(integrate_num_vals(num_vals_per_row))
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_index::OptionalIndex;
use crate::RowAddr;
#[test]
fn test_integrate_num_vals_empty() {
assert!(integrate_num_vals(iter::empty()).eq(iter::once(0)));
}
#[test]
fn test_integrate_num_vals_one_el() {
assert!(integrate_num_vals(iter::once(10)).eq([0, 10].into_iter()));
}
#[test]
fn test_integrate_num_vals_several() {
assert!(integrate_num_vals([3, 0, 10, 20].into_iter()).eq([0, 3, 3, 13, 33].into_iter()));
}
#[test]
fn test_merge_column_index_optional_shuffle() {
let optional_index: ColumnIndex = OptionalIndex::for_test(2, &[0]).into();
let column_indexes = vec![Some(optional_index), Some(ColumnIndex::Full)];
let row_addrs = vec![
RowAddr {
segment_ord: 0u32,
row_id: 1u32,
},
RowAddr {
segment_ord: 1u32,
row_id: 0u32,
},
];
let shuffle_merge_order = ShuffleMergeOrder::for_test(&[2, 1], row_addrs);
let serializable_index = merge_column_index_shuffled(
&column_indexes[..],
Cardinality::Optional,
&shuffle_merge_order,
);
let SerializableColumnIndex::Optional { non_null_row_ids, num_rows } = serializable_index else { panic!() };
assert_eq!(num_rows, 2);
let non_null_rows: Vec<RowId> = non_null_row_ids.boxed_iter().collect();
assert_eq!(&non_null_rows, &[1]);
}
}

156
columnar/src/column_index/merge/stacked.rs
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@@ -1,156 +0,0 @@
use std::iter;
use crate::column_index::{SerializableColumnIndex, Set};
use crate::iterable::Iterable;
use crate::{Cardinality, ColumnIndex, RowId, StackMergeOrder};
/// Simple case:
/// The new mapping just consists in stacking the different column indexes.
///
/// There are no sort nor deletes involved.
pub fn merge_column_index_stacked<'a>(
columns: &'a [Option<ColumnIndex>],
cardinality_after_merge: Cardinality,
stack_merge_order: &'a StackMergeOrder,
) -> SerializableColumnIndex<'a> {
match cardinality_after_merge {
Cardinality::Full => SerializableColumnIndex::Full,
Cardinality::Optional => SerializableColumnIndex::Optional {
non_null_row_ids: Box::new(StackedOptionalIndex {
columns,
stack_merge_order,
}),
num_rows: stack_merge_order.num_rows(),
},
Cardinality::Multivalued => {
let stacked_multivalued_index = StackedMultivaluedIndex {
columns,
stack_merge_order,
};
SerializableColumnIndex::Multivalued(Box::new(stacked_multivalued_index))
}
}
}
struct StackedOptionalIndex<'a> {
columns: &'a [Option<ColumnIndex>],
stack_merge_order: &'a StackMergeOrder,
}
impl<'a> Iterable<RowId> for StackedOptionalIndex<'a> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = RowId> + 'a> {
Box::new(
self.columns
.iter()
.enumerate()
.flat_map(|(columnar_id, column_index_opt)| {
let columnar_row_range = self.stack_merge_order.columnar_range(columnar_id);
let rows_it: Box<dyn Iterator<Item = RowId>> = match column_index_opt {
Some(ColumnIndex::Full) => Box::new(columnar_row_range),
Some(ColumnIndex::Optional(optional_index)) => Box::new(
optional_index
.iter_rows()
.map(move |row_id: RowId| columnar_row_range.start + row_id),
),
Some(ColumnIndex::Multivalued(_)) => {
panic!("No multivalued index is allowed when stacking column index");
}
None | Some(ColumnIndex::Empty { .. }) => Box::new(std::iter::empty()),
};
rows_it
}),
)
}
}
#[derive(Clone, Copy)]
struct StackedMultivaluedIndex<'a> {
columns: &'a [Option<ColumnIndex>],
stack_merge_order: &'a StackMergeOrder,
}
fn convert_column_opt_to_multivalued_index<'a>(
column_index_opt: Option<&'a ColumnIndex>,
num_rows: RowId,
) -> Box<dyn Iterator<Item = RowId> + 'a> {
match column_index_opt {
None | Some(ColumnIndex::Empty { .. }) => {
Box::new(iter::repeat(0u32).take(num_rows as usize + 1))
}
Some(ColumnIndex::Full) => Box::new(0..num_rows + 1),
Some(ColumnIndex::Optional(optional_index)) => {
Box::new(
(0..num_rows)
// TODO optimize
.map(|row_id| optional_index.rank(row_id))
.chain(std::iter::once(optional_index.num_non_nulls())),
)
}
Some(ColumnIndex::Multivalued(multivalued_index)) => {
multivalued_index.start_index_column.iter()
}
}
}
impl<'a> Iterable<RowId> for StackedMultivaluedIndex<'a> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = RowId> + '_> {
let multivalued_indexes =
self.columns
.iter()
.map(Option::as_ref)
.enumerate()
.map(|(columnar_id, column_opt)| {
let num_rows =
self.stack_merge_order.columnar_range(columnar_id).len() as RowId;
convert_column_opt_to_multivalued_index(column_opt, num_rows)
});
stack_multivalued_indexes(multivalued_indexes)
}
}
// Refactor me
fn stack_multivalued_indexes<'a>(
mut multivalued_indexes: impl Iterator<Item = Box<dyn Iterator<Item = RowId> + 'a>> + 'a,
) -> Box<dyn Iterator<Item = RowId> + 'a> {
let mut offset = 0;
let mut last_row_id = 0;
let mut current_it = multivalued_indexes.next();
Box::new(std::iter::from_fn(move || loop {
let Some(multivalued_index) = current_it.as_mut() else {
return None;
};
if let Some(row_id) = multivalued_index.next() {
last_row_id = offset + row_id;
return Some(last_row_id);
}
offset = last_row_id;
loop {
current_it = multivalued_indexes.next();
if current_it.as_mut()?.next().is_some() {
break;
}
}
}))
}
#[cfg(test)]
mod tests {
use crate::RowId;
fn it<'a>(row_ids: &'a [RowId]) -> Box<dyn Iterator<Item = RowId> + 'a> {
Box::new(row_ids.iter().copied())
}
#[test]
fn test_stack() {
let columns = [
it(&[0u32, 0u32]),
it(&[0u32, 1u32, 1u32, 4u32]),
it(&[0u32, 3u32, 5u32]),
it(&[0u32, 4u32]),
]
.into_iter();
let start_offsets: Vec<RowId> = super::stack_multivalued_indexes(columns).collect();
assert_eq!(start_offsets, &[0, 0, 1, 1, 4, 7, 9, 13]);
}
}

115
columnar/src/column_index/mod.rs
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@@ -1,115 +0,0 @@
mod merge;
mod multivalued_index;
mod optional_index;
mod serialize;
use std::ops::Range;
pub use merge::merge_column_index;
pub use optional_index::{OptionalIndex, Set};
pub use serialize::{open_column_index, serialize_column_index, SerializableColumnIndex};
use crate::column_index::multivalued_index::MultiValueIndex;
use crate::{Cardinality, DocId, RowId};
#[derive(Clone)]
pub enum ColumnIndex {
Empty {
num_docs: u32,
},
Full,
Optional(OptionalIndex),
/// In addition, at index num_rows, an extra value is added
/// containing the overal number of values.
Multivalued(MultiValueIndex),
}
impl From<OptionalIndex> for ColumnIndex {
fn from(optional_index: OptionalIndex) -> ColumnIndex {
ColumnIndex::Optional(optional_index)
}
}
impl From<MultiValueIndex> for ColumnIndex {
fn from(multi_value_index: MultiValueIndex) -> ColumnIndex {
ColumnIndex::Multivalued(multi_value_index)
}
}
impl ColumnIndex {
#[inline]
pub fn get_cardinality(&self) -> Cardinality {
match self {
ColumnIndex::Empty { .. } => Cardinality::Optional,
ColumnIndex::Full => Cardinality::Full,
ColumnIndex::Optional(_) => Cardinality::Optional,
ColumnIndex::Multivalued(_) => Cardinality::Multivalued,
}
}
/// Returns true if and only if there are at least one value associated to the row.
pub fn has_value(&self, doc_id: DocId) -> bool {
match self {
ColumnIndex::Empty { .. } => false,
ColumnIndex::Full => true,
ColumnIndex::Optional(optional_index) => optional_index.contains(doc_id),
ColumnIndex::Multivalued(multivalued_index) => {
!multivalued_index.range(doc_id).is_empty()
}
}
}
pub fn value_row_ids(&self, doc_id: DocId) -> Range<RowId> {
match self {
ColumnIndex::Empty { .. } => 0..0,
ColumnIndex::Full => doc_id..doc_id + 1,
ColumnIndex::Optional(optional_index) => {
if let Some(val) = optional_index.rank_if_exists(doc_id) {
val..val + 1
} else {
0..0
}
}
ColumnIndex::Multivalued(multivalued_index) => multivalued_index.range(doc_id),
}
}
pub fn docid_range_to_rowids(&self, doc_id: Range<DocId>) -> Range<RowId> {
match self {
ColumnIndex::Empty { .. } => 0..0,
ColumnIndex::Full => doc_id,
ColumnIndex::Optional(optional_index) => {
let row_start = optional_index.rank(doc_id.start);
let row_end = optional_index.rank(doc_id.end);
row_start..row_end
}
ColumnIndex::Multivalued(multivalued_index) => {
let end_docid = doc_id.end.min(multivalued_index.num_docs() - 1) + 1;
let start_docid = doc_id.start.min(end_docid);
let row_start = multivalued_index.start_index_column.get_val(start_docid);
let row_end = multivalued_index.start_index_column.get_val(end_docid);
row_start..row_end
}
}
}
pub fn select_batch_in_place(&self, doc_id_start: DocId, rank_ids: &mut Vec<RowId>) {
match self {
ColumnIndex::Empty { .. } => {
rank_ids.clear();
}
ColumnIndex::Full => {
// No need to do anything:
// value_idx and row_idx are the same.
}
ColumnIndex::Optional(optional_index) => {
optional_index.select_batch(&mut rank_ids[..]);
}
ColumnIndex::Multivalued(multivalued_index) => {
multivalued_index.select_batch_in_place(doc_id_start, rank_ids)
}
}
}
}

141
columnar/src/column_index/multivalued_index.rs
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@@ -1,141 +0,0 @@
use std::io;
use std::io::Write;
use std::ops::Range;
use std::sync::Arc;
use common::OwnedBytes;
use crate::column_values::{
load_u64_based_column_values, serialize_u64_based_column_values, CodecType, ColumnValues,
};
use crate::iterable::Iterable;
use crate::{DocId, RowId};
pub fn serialize_multivalued_index(
multivalued_index: &dyn Iterable<RowId>,
output: &mut impl Write,
) -> io::Result<()> {
serialize_u64_based_column_values(
multivalued_index,
&[CodecType::Bitpacked, CodecType::Linear],
output,
)?;
Ok(())
}
pub fn open_multivalued_index(bytes: OwnedBytes) -> io::Result<MultiValueIndex> {
let start_index_column: Arc<dyn ColumnValues<RowId>> = load_u64_based_column_values(bytes)?;
Ok(MultiValueIndex { start_index_column })
}
#[derive(Clone)]
/// Index to resolve value range for given doc_id.
/// Starts at 0.
pub struct MultiValueIndex {
pub start_index_column: Arc<dyn crate::ColumnValues<RowId>>,
}
impl From<Arc<dyn ColumnValues<RowId>>> for MultiValueIndex {
fn from(start_index_column: Arc<dyn ColumnValues<RowId>>) -> Self {
MultiValueIndex { start_index_column }
}
}
impl MultiValueIndex {
pub fn for_test(start_offsets: &[RowId]) -> MultiValueIndex {
let mut buffer = Vec::new();
serialize_multivalued_index(&start_offsets, &mut buffer).unwrap();
let bytes = OwnedBytes::new(buffer);
open_multivalued_index(bytes).unwrap()
}
/// Returns `[start, end)`, such that the values associated with
/// the given document are `start..end`.
#[inline]
pub(crate) fn range(&self, doc_id: DocId) -> Range<RowId> {
let start = self.start_index_column.get_val(doc_id);
let end = self.start_index_column.get_val(doc_id + 1);
start..end
}
/// Returns the number of documents in the index.
#[inline]
pub fn num_docs(&self) -> u32 {
self.start_index_column.num_vals() - 1
}
/// Converts a list of ranks (row ids of values) in a 1:n index to the corresponding list of
/// docids. Positions are converted inplace to docids.
///
/// Since there is no index for value pos -> docid, but docid -> value pos range, we scan the
/// index.
///
/// Correctness: positions needs to be sorted. idx_reader needs to contain monotonically
/// increasing positions.
///
/// TODO: Instead of a linear scan we can employ a exponential search into binary search to
/// match a docid to its value position.
#[allow(clippy::bool_to_int_with_if)]
pub(crate) fn select_batch_in_place(&self, docid_start: DocId, ranks: &mut Vec<u32>) {
if ranks.is_empty() {
return;
}
let mut cur_doc = docid_start;
let mut last_doc = None;
assert!(self.start_index_column.get_val(docid_start) <= ranks[0]);
let mut write_doc_pos = 0;
for i in 0..ranks.len() {
let pos = ranks[i];
loop {
let end = self.start_index_column.get_val(cur_doc + 1);
if end > pos {
ranks[write_doc_pos] = cur_doc;
write_doc_pos += if last_doc == Some(cur_doc) { 0 } else { 1 };
last_doc = Some(cur_doc);
break;
}
cur_doc += 1;
}
}
ranks.truncate(write_doc_pos);
}
}
#[cfg(test)]
mod tests {
use std::ops::Range;
use std::sync::Arc;
use super::MultiValueIndex;
use crate::column_values::IterColumn;
use crate::{ColumnValues, RowId};
fn index_to_pos_helper(
index: &MultiValueIndex,
doc_id_range: Range<u32>,
positions: &[u32],
) -> Vec<u32> {
let mut positions = positions.to_vec();
index.select_batch_in_place(doc_id_range.start, &mut positions);
positions
}
#[test]
fn test_positions_to_docid() {
let offsets: Vec<RowId> = vec![0, 10, 12, 15, 22, 23]; // docid values are [0..10, 10..12, 12..15, etc.]
let column: Arc<dyn ColumnValues<RowId>> = Arc::new(IterColumn::from(offsets.into_iter()));
let index = MultiValueIndex::from(column);
assert_eq!(index.num_docs(), 5);
let positions = &[10u32, 11, 15, 20, 21, 22];
assert_eq!(index_to_pos_helper(&index, 0..5, positions), vec![1, 3, 4]);
assert_eq!(index_to_pos_helper(&index, 1..5, positions), vec![1, 3, 4]);
assert_eq!(index_to_pos_helper(&index, 0..5, &[9]), vec![0]);
assert_eq!(index_to_pos_helper(&index, 1..5, &[10]), vec![1]);
assert_eq!(index_to_pos_helper(&index, 1..5, &[11]), vec![1]);
assert_eq!(index_to_pos_helper(&index, 2..5, &[12]), vec![2]);
assert_eq!(index_to_pos_helper(&index, 2..5, &[12, 14]), vec![2]);
assert_eq!(index_to_pos_helper(&index, 2..5, &[12, 14, 15]), vec![2, 3]);
}
}

515
columnar/src/column_index/optional_index/mod.rs
View File

@@ -1,515 +0,0 @@
use std::io::{self, Write};
use std::sync::Arc;
mod set;
mod set_block;
use common::{BinarySerializable, OwnedBytes, VInt};
pub use set::{SelectCursor, Set, SetCodec};
use set_block::{
DenseBlock, DenseBlockCodec, SparseBlock, SparseBlockCodec, DENSE_BLOCK_NUM_BYTES,
};
use crate::iterable::Iterable;
use crate::{DocId, 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]>,
}
/// 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,
}
}
enum BlockSelectCursor<'a> {
Dense(<DenseBlock<'a> as Set<u16>>::SelectCursor<'a>),
Sparse(<SparseBlock<'a> as Set<u16>>::SelectCursor<'a>),
}
impl<'a> BlockSelectCursor<'a> {
fn select(&mut self, rank: u16) -> u16 {
match self {
BlockSelectCursor::Dense(dense_select_cursor) => dense_select_cursor.select(rank),
BlockSelectCursor::Sparse(sparse_select_cursor) => sparse_select_cursor.select(rank),
}
}
}
pub struct OptionalIndexSelectCursor<'a> {
current_block_cursor: BlockSelectCursor<'a>,
current_block_id: u16,
// The current block is guaranteed to contain ranks < end_rank.
current_block_end_rank: RowId,
optional_index: &'a OptionalIndex,
block_doc_idx_start: RowId,
num_null_rows_before_block: RowId,
}
impl<'a> OptionalIndexSelectCursor<'a> {
fn search_and_load_block(&mut self, rank: RowId) {
if rank < self.current_block_end_rank {
// we are already in the right block
return;
}
self.current_block_id = self.optional_index.find_block(rank, self.current_block_id);
self.current_block_end_rank = self
.optional_index
.block_metas
.get(self.current_block_id as usize + 1)
.map(|block_meta| block_meta.non_null_rows_before_block)
.unwrap_or(u32::MAX);
self.block_doc_idx_start = (self.current_block_id as u32) * ELEMENTS_PER_BLOCK;
let block_meta = self.optional_index.block_metas[self.current_block_id as usize];
self.num_null_rows_before_block = block_meta.non_null_rows_before_block;
let block: Block<'_> = self.optional_index.block(block_meta);
self.current_block_cursor = match block {
Block::Dense(dense_block) => BlockSelectCursor::Dense(dense_block.select_cursor()),
Block::Sparse(sparse_block) => BlockSelectCursor::Sparse(sparse_block.select_cursor()),
};
}
}
impl<'a> SelectCursor<RowId> for OptionalIndexSelectCursor<'a> {
fn select(&mut self, rank: RowId) -> RowId {
self.search_and_load_block(rank);
let index_in_block = (rank - self.num_null_rows_before_block) as u16;
self.current_block_cursor.select(index_in_block) as RowId + self.block_doc_idx_start
}
}
impl Set<RowId> for OptionalIndex {
type SelectCursor<'b> = OptionalIndexSelectCursor<'b> where Self: 'b;
// Check if value at position is not null.
#[inline]
fn contains(&self, row_id: RowId) -> bool {
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(&self, doc_id: DocId) -> RowId {
let RowAddr {
block_id,
in_block_row_id,
} = row_addr_from_row_id(doc_id);
let block_meta = self.block_metas[block_id as usize];
let block = self.block(block_meta);
let block_offset_row_id = match block {
Block::Dense(dense_block) => dense_block.rank(in_block_row_id),
Block::Sparse(sparse_block) => sparse_block.rank(in_block_row_id),
} as u32;
block_meta.non_null_rows_before_block + block_offset_row_id
}
#[inline]
fn rank_if_exists(&self, doc_id: DocId) -> Option<RowId> {
let RowAddr {
block_id,
in_block_row_id,
} = row_addr_from_row_id(doc_id);
let block_meta = self.block_metas[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 as u32) * ELEMENTS_PER_BLOCK;
let block_meta = self.block_metas[block_pos as usize];
let block: Block<'_> = self.block(block_meta);
let index_in_block = (rank - block_meta.non_null_rows_before_block) as u16;
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_cursor(&self) -> OptionalIndexSelectCursor<'_> {
OptionalIndexSelectCursor {
current_block_cursor: BlockSelectCursor::Sparse(
SparseBlockCodec::open(b"").select_cursor(),
),
current_block_id: 0u16,
current_block_end_rank: 0u32, //< this is sufficient to force the first load
optional_index: self,
block_doc_idx_start: 0u32,
num_null_rows_before_block: 0u32,
}
}
}
impl OptionalIndex {
pub fn for_test(num_rows: RowId, row_ids: &[RowId]) -> OptionalIndex {
assert!(row_ids
.last()
.copied()
.map(|last_row_id| last_row_id < num_rows)
.unwrap_or(true));
let mut buffer = Vec::new();
serialize_optional_index(&row_ids, num_rows, &mut buffer).unwrap();
let bytes = OwnedBytes::new(buffer);
open_optional_index(bytes).unwrap()
}
pub fn num_docs(&self) -> RowId {
self.num_rows
}
pub fn num_non_nulls(&self) -> RowId {
self.num_non_null_rows
}
pub fn iter_rows(&self) -> impl Iterator<Item = RowId> + '_ {
// TODO optimize
let mut select_batch = self.select_cursor();
(0..self.num_non_null_rows).map(move |rank| select_batch.select(rank))
}
pub fn select_batch(&self, ranks: &mut [RowId]) {
let mut select_cursor = self.select_cursor();
for rank in ranks.iter_mut() {
*rank = select_cursor.select(*rank);
}
}
#[inline]
fn block(&self, block_meta: BlockMeta) -> Block<'_> {
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: u16) -> u16 {
for block_pos in start_block_pos..self.block_metas.len() as u16 {
let offset = self.block_metas[block_pos as usize].non_null_rows_before_block;
if offset > dense_idx {
return block_pos - 1u16;
}
}
self.block_metas.len() as u16 - 1u16
}
// 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 + ?Sized>(&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<W: io::Write>(
non_null_rows: &dyn Iterable<RowId>,
num_rows: RowId,
output: &mut W,
) -> io::Result<()> {
VInt(num_rows as u64).serialize(output)?;
let mut rows_it = non_null_rows.boxed_iter();
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
}
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 + 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;
}
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);
let optional_index = OptionalIndex {
num_rows,
num_non_null_rows,
block_data,
block_metas: block_metas.into(),
};
Ok(optional_index)
}
#[cfg(test)]
mod tests;

47
columnar/src/column_index/optional_index/set.rs
View File

@@ -1,47 +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(data: &[u8]) -> Self::Reader<'_>;
}
/// Stateful object that makes it possible to compute several select in a row,
/// provided the rank passed as argument are increasing.
pub trait SelectCursor<T> {
// May panic if rank is greater than the number of elements in the Set,
// or if rank is < than value provided in the previous call.
fn select(&mut self, rank: T) -> T;
}
pub trait Set<T> {
type SelectCursor<'b>: SelectCursor<T>
where Self: 'b;
/// Returns true if the elements is contained in the Set
fn contains(&self, el: T) -> bool;
/// Returns the number of rows in the set that are < `el`
fn rank(&self, el: T) -> T;
/// If the set contains `el` returns the element rank.
/// 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;
/// Creates a brand new select cursor.
fn select_cursor(&self) -> Self::SelectCursor<'_>;
}

278
columnar/src/column_index/optional_index/set_block/dense.rs
View File

@@ -1,278 +0,0 @@
use std::convert::TryInto;
use std::io::{self, Write};
use common::BinarySerializable;
use crate::column_index::optional_index::{SelectCursor, 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 / 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(data: &[u8]) -> Self::Reader<'_> {
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]);
pub struct DenseBlockSelectCursor<'a> {
block_id: u16,
dense_block: DenseBlock<'a>,
}
impl<'a> SelectCursor<u16> for DenseBlockSelectCursor<'a> {
#[inline]
fn select(&mut self, rank: u16) -> u16 {
self.block_id = self
.dense_block
.find_miniblock_containing_rank(rank, self.block_id)
.unwrap();
let index_block = self.dense_block.mini_block(self.block_id);
let in_block_rank = rank - index_block.rank;
self.block_id * ELEMENTS_PER_MINI_BLOCK + select_u64(index_block.bitvec, in_block_rank)
}
}
impl<'a> Set<u16> for DenseBlock<'a> {
type SelectCursor<'b> = DenseBlockSelectCursor<'a> where Self: 'b;
#[inline(always)]
fn contains(&self, el: u16) -> bool {
let mini_block_id = el / ELEMENTS_PER_MINI_BLOCK;
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 rank(&self, el: u16) -> u16 {
let block_pos = el / ELEMENTS_PER_MINI_BLOCK;
let index_block = self.mini_block(block_pos);
let pos_in_block_bit_vec = el % ELEMENTS_PER_MINI_BLOCK;
let ones_in_block = rank_u64(index_block.bitvec, pos_in_block_bit_vec);
index_block.rank + ones_in_block
}
#[inline(always)]
fn select(&self, rank: u16) -> u16 {
let block_id = self.find_miniblock_containing_rank(rank, 0).unwrap();
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)
}
#[inline(always)]
fn select_cursor(&self) -> Self::SelectCursor<'_> {
DenseBlockSelectCursor {
block_id: 0,
dense_block: *self,
}
}
}
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,
};
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);
}
}

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

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

111
columnar/src/column_index/optional_index/set_block/sparse.rs
View File

@@ -1,111 +0,0 @@
use crate::column_index::optional_index::{SelectCursor, 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(data: &[u8]) -> Self::Reader<'_> {
SparseBlock(data)
}
}
#[derive(Copy, Clone)]
pub struct SparseBlock<'a>(&'a [u8]);
impl<'a> SelectCursor<u16> for SparseBlock<'a> {
#[inline]
fn select(&mut self, rank: u16) -> u16 {
<SparseBlock<'a> as Set<u16>>::select(self, rank)
}
}
impl<'a> Set<u16> for SparseBlock<'a> {
type SelectCursor<'b> = Self where Self: 'b;
#[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 rank(&self, el: u16) -> u16 {
self.binary_search(el).unwrap_or_else(|el| el)
}
#[inline(always)]
fn select(&self, rank: u16) -> u16 {
let offset = rank as usize * 2;
u16::from_le_bytes(self.0[offset..offset + 2].try_into().unwrap())
}
#[inline(always)]
fn select_cursor(&self) -> Self::SelectCursor<'_> {
*self
}
}
#[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)
}
}

109
columnar/src/column_index/optional_index/set_block/tests.rs
View File

@@ -1,109 +0,0 @@
use std::collections::HashMap;
use crate::column_index::optional_index::set_block::dense::DENSE_BLOCK_NUM_BYTES;
use crate::column_index::optional_index::set_block::{DenseBlockCodec, SparseBlockCodec};
use crate::column_index::optional_index::{SelectCursor, Set, SetCodec};
fn test_set_helper<C: SetCodec<Item = u16>>(vals: &[u16]) -> usize {
let mut buffer = Vec::new();
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());
assert_eq!(
tested_set.rank(val),
vals.iter().cloned().take_while(|v| *v < val).count() as u16
);
}
for rank in 0..vals.len() {
assert_eq!(tested_set.select(rank as u16), vals[rank]);
}
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! {
#![proptest_config(ProptestConfig::with_cases(1))]
#[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));
let mut select_cursor = tested_set.select_cursor();
assert_eq!(select_cursor.select(0), 1);
assert_eq!(select_cursor.select(1), 3);
assert_eq!(select_cursor.select(2), 17);
}
#[test]
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));
let mut select_cursor = tested_set.select_cursor();
assert_eq!(SelectCursor::select(&mut select_cursor, 0), 1);
assert_eq!(SelectCursor::select(&mut select_cursor, 1), 3);
assert_eq!(SelectCursor::select(&mut select_cursor, 2), 17);
}
#[test]
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 mut select_cursor = tested_set.select_cursor();
for i in 0..150 {
assert_eq!(i, select_cursor.select(i));
}
}

371
columnar/src/column_index/optional_index/tests.rs
View File

@@ -1,371 +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, 100, &mut out).unwrap();
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
let ranks: Vec<u32> = (65_472u32..65_473u32).collect();
let els: Vec<u32> = ranks.iter().copied().map(|rank| rank + 10).collect();
let mut select_cursor = null_index.select_cursor();
for (rank, el) in ranks.iter().copied().zip(els.iter().copied()) {
assert_eq!(select_cursor.select(rank), el);
}
}
#[test]
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> Iterable<RowId> for &'a [bool] {
fn boxed_iter(&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, data.len() as RowId, &mut out).unwrap();
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
let orig_idx_with_value: Vec<u32> = data
.iter()
.enumerate()
.filter(|(_pos, val)| **val)
.map(|(pos, _val)| pos as u32)
.collect();
let mut select_iter = null_index.select_cursor();
for i in 0..orig_idx_with_value.len() {
assert_eq!(select_iter.select(i as u32), orig_idx_with_value[i]);
}
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 optional_index = OptionalIndex::for_test(4, &[0, 2]);
let mut select_cursor = optional_index.select_cursor();
assert_eq!(select_cursor.select(0), 0);
assert_eq!(select_cursor.select(1), 2);
}
#[test]
fn test_optional_index_translate() {
let optional_index = OptionalIndex::for_test(4, &[0, 2]);
assert_eq!(optional_index.rank_if_exists(0), Some(0));
assert_eq!(optional_index.rank_if_exists(2), Some(1));
}
#[test]
fn test_optional_index_small() {
let optional_index = OptionalIndex::for_test(4, &[0, 2]);
assert!(optional_index.contains(0));
assert!(!optional_index.contains(1));
assert!(optional_index.contains(2));
assert!(!optional_index.contains(3));
}
#[test]
fn test_optional_index_large() {
let row_ids = &[ELEMENTS_PER_BLOCK, ELEMENTS_PER_BLOCK + 1];
let optional_index = OptionalIndex::for_test(ELEMENTS_PER_BLOCK + 2, row_ids);
assert!(!optional_index.contains(0));
assert!(!optional_index.contains(100));
assert!(!optional_index.contains(ELEMENTS_PER_BLOCK - 1));
assert!(optional_index.contains(ELEMENTS_PER_BLOCK));
assert!(optional_index.contains(ELEMENTS_PER_BLOCK + 1));
}
fn test_optional_index_iter_aux(row_ids: &[RowId], num_rows: RowId) {
let optional_index = OptionalIndex::for_test(num_rows, row_ids);
assert_eq!(optional_index.num_docs(), num_rows);
assert!(optional_index.iter_rows().eq(row_ids.iter().copied()));
}
#[test]
fn test_optional_index_iter_empty() {
test_optional_index_iter_aux(&[], 0u32);
}
fn test_optional_index_rank_aux(row_ids: &[RowId]) {
let num_rows = row_ids.last().copied().unwrap_or(0u32) + 1;
let null_index = OptionalIndex::for_test(num_rows, row_ids);
assert_eq!(null_index.num_docs(), num_rows);
for (row_id, row_val) in row_ids.iter().copied().enumerate() {
assert_eq!(null_index.rank(row_val), row_id as u32);
assert_eq!(null_index.rank_if_exists(row_val), Some(row_id as u32));
if row_val > 0 && !null_index.contains(&row_val - 1) {
assert_eq!(null_index.rank(row_val - 1), row_id as u32);
}
assert_eq!(null_index.rank(row_val + 1), row_id as u32 + 1);
}
}
#[test]
fn test_optional_index_rank() {
test_optional_index_rank_aux(&[1u32]);
test_optional_index_rank_aux(&[0u32, 1u32]);
let mut block = Vec::new();
block.push(3u32);
block.extend((0..BLOCK_SIZE).map(|i| i + BLOCK_SIZE + 1));
test_optional_index_rank_aux(&block);
}
#[test]
fn test_optional_index_iter_empty_one() {
test_optional_index_iter_aux(&[1], 2u32);
test_optional_index_iter_aux(&[100_000], 200_000u32);
}
#[test]
fn test_optional_index_iter_dense_block() {
let mut block = Vec::new();
block.push(3u32);
block.extend((0..BLOCK_SIZE).map(|i| i + BLOCK_SIZE + 1));
test_optional_index_iter_aux(&block, 3 * BLOCK_SIZE);
}
#[test]
fn test_optional_index_for_tests() {
let optional_index = OptionalIndex::for_test(4, &[1, 2]);
assert!(!optional_index.contains(0));
assert!(optional_index.contains(1));
assert!(optional_index.contains(2));
assert!(!optional_index.contains(3));
assert_eq!(optional_index.num_docs(), 4);
}
#[cfg(all(test, feature = "unstable"))]
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<RowId> = (0..TOTAL_NUM_VALUES)
.map(|_| rng.gen_bool(fill_ratio))
.enumerate()
.filter(|(pos, val)| *val)
.map(|(pos, _)| pos as RowId)
.collect();
serialize_optional_index(&&vals[..], TOTAL_NUM_VALUES, &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(|| {
output.copy_from_slice(&idxs[..]);
codec.select_batch(&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);
}
}

77
columnar/src/column_index/serialize.rs
View File

@@ -1,77 +0,0 @@
use std::io;
use std::io::Write;
use common::{CountingWriter, OwnedBytes};
use crate::column_index::multivalued_index::serialize_multivalued_index;
use crate::column_index::optional_index::serialize_optional_index;
use crate::column_index::ColumnIndex;
use crate::iterable::Iterable;
use crate::{Cardinality, RowId};
pub enum SerializableColumnIndex<'a> {
Full,
Optional {
non_null_row_ids: Box<dyn Iterable<RowId> + 'a>,
num_rows: RowId,
},
// TODO remove the Arc<dyn> apart from serialization this is not
// dynamic at all.
Multivalued(Box<dyn Iterable<RowId> + 'a>),
}
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<u32> {
let mut output = CountingWriter::wrap(output);
let cardinality = column_index.get_cardinality().to_code();
output.write_all(&[cardinality])?;
match column_index {
SerializableColumnIndex::Full => {}
SerializableColumnIndex::Optional {
non_null_row_ids,
num_rows,
} => serialize_optional_index(non_null_row_ids.as_ref(), num_rows, &mut output)?,
SerializableColumnIndex::Multivalued(multivalued_index) => {
serialize_multivalued_index(&*multivalued_index, &mut output)?
}
}
let column_index_num_bytes = output.written_bytes() as u32;
Ok(column_index_num_bytes)
}
pub fn open_column_index(mut bytes: OwnedBytes) -> io::Result<ColumnIndex> {
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 multivalue_index = super::multivalued_index::open_multivalued_index(bytes)?;
Ok(ColumnIndex::Multivalued(multivalue_index))
}
}
}
// TODO unit tests

135
columnar/src/column_values/bench.rs
View File

@@ -1,135 +0,0 @@
use std::sync::Arc;
use common::OwnedBytes;
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use test::{self, Bencher};
use super::*;
use crate::column_values::u64_based::*;
fn get_data() -> Vec<u64> {
let mut rng = StdRng::seed_from_u64(2u64);
let mut data: Vec<_> = (100..55000_u64)
.map(|num| num + rng.gen::<u8>() as u64)
.collect();
data.push(99_000);
data.insert(1000, 2000);
data.insert(2000, 100);
data.insert(3000, 4100);
data.insert(4000, 100);
data.insert(5000, 800);
data
}
fn compute_stats(vals: impl Iterator<Item = u64>) -> ColumnStats {
let mut stats_collector = StatsCollector::default();
for val in vals {
stats_collector.collect(val);
}
stats_collector.stats()
}
#[inline(never)]
fn value_iter() -> impl Iterator<Item = u64> {
0..20_000
}
fn get_reader_for_bench<Codec: ColumnCodec>(data: &[u64]) -> Codec::ColumnValues {
let mut bytes = Vec::new();
let stats = compute_stats(data.iter().cloned());
let mut codec_serializer = Codec::estimator();
for val in data {
codec_serializer.collect(*val);
}
codec_serializer.serialize(&stats, Box::new(data.iter().copied()).as_mut(), &mut bytes);
Codec::load(OwnedBytes::new(bytes)).unwrap()
}
fn bench_get<Codec: ColumnCodec>(b: &mut Bencher, data: &[u64]) {
let col = get_reader_for_bench::<Codec>(data);
b.iter(|| {
let mut sum = 0u64;
for pos in value_iter() {
let val = col.get_val(pos as u32);
sum = sum.wrapping_add(val);
}
sum
});
}
#[inline(never)]
fn bench_get_dynamic_helper(b: &mut Bencher, col: Arc<dyn ColumnValues>) {
b.iter(|| {
let mut sum = 0u64;
for pos in value_iter() {
let val = col.get_val(pos as u32);
sum = sum.wrapping_add(val);
}
sum
});
}
fn bench_get_dynamic<Codec: ColumnCodec>(b: &mut Bencher, data: &[u64]) {
let col = Arc::new(get_reader_for_bench::<Codec>(data));
bench_get_dynamic_helper(b, col);
}
fn bench_create<Codec: ColumnCodec>(b: &mut Bencher, data: &[u64]) {
let stats = compute_stats(data.iter().cloned());
let mut bytes = Vec::new();
b.iter(|| {
bytes.clear();
let mut codec_serializer = Codec::estimator();
for val in data.iter().take(1024) {
codec_serializer.collect(*val);
}
codec_serializer.serialize(&stats, Box::new(data.iter().copied()).as_mut(), &mut bytes)
});
}
#[bench]
fn bench_fastfield_bitpack_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<BitpackedCodec>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<LinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<BlockwiseLinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_bitpack_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<BitpackedCodec>(b, &data);
}
#[bench]
fn bench_fastfield_bitpack_get_dynamic(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get_dynamic::<BitpackedCodec>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<LinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_get_dynamic(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get_dynamic::<LinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<BlockwiseLinearCodec>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_get_dynamic(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get_dynamic::<BlockwiseLinearCodec>(b, &data);
}

41
columnar/src/column_values/merge.rs
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@@ -1,41 +0,0 @@
use std::fmt::Debug;
use std::sync::Arc;
use crate::iterable::Iterable;
use crate::{ColumnIndex, ColumnValues, MergeRowOrder};
pub(crate) struct MergedColumnValues<'a, T> {
pub(crate) column_indexes: &'a [Option<ColumnIndex>],
pub(crate) column_values: &'a [Option<Arc<dyn ColumnValues<T>>>],
pub(crate) merge_row_order: &'a MergeRowOrder,
}
impl<'a, T: Copy + PartialOrd + Debug> Iterable<T> for MergedColumnValues<'a, T> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
match self.merge_row_order {
MergeRowOrder::Stack(_) => Box::new(
self.column_values
.iter()
.flatten()
.flat_map(|column_value| column_value.iter()),
),
MergeRowOrder::Shuffled(shuffle_merge_order) => Box::new(
shuffle_merge_order
.iter_new_to_old_row_addrs()
.flat_map(|row_addr| {
let column_index =
self.column_indexes[row_addr.segment_ord as usize].as_ref()?;
let column_values =
self.column_values[row_addr.segment_ord as usize].as_ref()?;
let value_range = column_index.value_row_ids(row_addr.row_id);
Some((value_range, column_values))
})
.flat_map(|(value_range, column_values)| {
value_range
.into_iter()
.map(|val| column_values.get_val(val))
}),
),
}
}
}

220
columnar/src/column_values/mod.rs
View File

@@ -1,220 +0,0 @@
#![warn(missing_docs)]
//! # `fastfield_codecs`
//!
//! - Columnar storage of data for tantivy [`Column`].
//! - Encode data in different codecs.
//! - Monotonically map values to u64/u128
use std::fmt::Debug;
use std::ops::{Range, RangeInclusive};
use std::sync::Arc;
pub use monotonic_mapping::{MonotonicallyMappableToU64, StrictlyMonotonicFn};
pub use monotonic_mapping_u128::MonotonicallyMappableToU128;
mod merge;
pub(crate) mod monotonic_mapping;
pub(crate) mod monotonic_mapping_u128;
mod stats;
mod u128_based;
mod u64_based;
mod vec_column;
mod monotonic_column;
pub(crate) use merge::MergedColumnValues;
pub use stats::ColumnStats;
pub use u128_based::{open_u128_mapped, serialize_column_values_u128};
pub use u64_based::{
load_u64_based_column_values, serialize_and_load_u64_based_column_values,
serialize_u64_based_column_values, CodecType, ALL_U64_CODEC_TYPES,
};
pub use vec_column::VecColumn;
pub use self::monotonic_column::monotonic_map_column;
use crate::RowId;
/// `ColumnValues` provides access to a dense field column.
///
/// `Column` are just a wrapper over `ColumnValues` and a `ColumnIndex`.
///
/// Any methods with a default and specialized implementation need to be called in the
/// wrappers that implement the trait: Arc and MonotonicMappingColumn
pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync {
/// 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;
/// Allows to push down multiple fetch calls, to avoid dynamic dispatch overhead.
///
/// idx and output should have the same length
///
/// # Panics
///
/// May panic if `idx` is greater than the column length.
fn get_vals(&self, idx: &[u32], output: &mut [T]) {
assert!(idx.len() == output.len());
for (out, idx) in output.iter_mut().zip(idx.iter()) {
*out = self.get_val(*idx as u32);
}
}
/// Fills an output buffer with the fast field values
/// associated with the `DocId` going from
/// `start` to `start + output.len()`.
///
/// # Panics
///
/// Must panic if `start + output.len()` is greater than
/// the segment's `maxdoc`.
#[inline(always)]
fn get_range(&self, start: u64, output: &mut [T]) {
for (out, idx) in output.iter_mut().zip(start..) {
*out = self.get_val(idx as u32);
}
}
/// Get the row ids of values which are in the provided value range.
///
/// Note that position == docid for single value fast fields
#[inline(always)]
fn get_row_ids_for_value_range(
&self,
value_range: RangeInclusive<T>,
row_id_range: Range<RowId>,
row_id_hits: &mut Vec<RowId>,
) {
let row_id_range = row_id_range.start..row_id_range.end.min(self.num_vals());
for idx in row_id_range.start..row_id_range.end {
let val = self.get_val(idx);
if value_range.contains(&val) {
row_id_hits.push(idx);
}
}
}
/// Returns the minimum value for this fast field.
///
/// This min_value may not be exact.
/// For instance, the min value does not take in account of possible
/// deleted document. All values are however guaranteed to be higher than
/// `.min_value()`.
fn min_value(&self) -> T;
/// Returns the maximum value for this fast field.
///
/// This max_value may not be exact.
/// For instance, the max value does not take in account of possible
/// deleted document. All values are however guaranteed to be higher than
/// `.max_value()`.
fn max_value(&self) -> T;
/// The number of values in the column.
fn num_vals(&self) -> u32;
/// Returns a iterator over the data
fn iter<'a>(&'a self) -> Box<dyn Iterator<Item = T> + 'a> {
Box::new((0..self.num_vals()).map(|idx| self.get_val(idx)))
}
}
impl<T: Copy + PartialOrd + Debug> ColumnValues<T> for Arc<dyn ColumnValues<T>> {
#[inline(always)]
fn get_val(&self, idx: u32) -> T {
self.as_ref().get_val(idx)
}
#[inline(always)]
fn min_value(&self) -> T {
self.as_ref().min_value()
}
#[inline(always)]
fn max_value(&self) -> T {
self.as_ref().max_value()
}
#[inline(always)]
fn num_vals(&self) -> u32 {
self.as_ref().num_vals()
}
#[inline(always)]
fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
self.as_ref().iter()
}
#[inline(always)]
fn get_range(&self, start: u64, output: &mut [T]) {
self.as_ref().get_range(start, output)
}
#[inline(always)]
fn get_row_ids_for_value_range(
&self,
range: RangeInclusive<T>,
doc_id_range: Range<u32>,
positions: &mut Vec<u32>,
) {
self.as_ref()
.get_row_ids_for_value_range(range, doc_id_range, positions)
}
}
/// Wraps an cloneable 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 + Debug,
{
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(all(test, feature = "unstable"))]
mod bench;
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_range_as_col() {
let col = IterColumn::from(10..100);
assert_eq!(col.num_vals(), 90);
assert_eq!(col.max_value(), 99);
}
}

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

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

211
columnar/src/column_values/monotonic_mapping.rs
View File

@@ -1,211 +0,0 @@
use std::fmt::Debug;
use std::marker::PhantomData;
use common::DateTime;
use super::MonotonicallyMappableToU128;
use crate::RowId;
/// Monotonic maps a value to u64 value space.
/// Monotonic mapping enables `PartialOrd` on u64 space without conversion to original space.
pub trait MonotonicallyMappableToU64: 'static + PartialOrd + Debug + 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,
}
}
}
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)
}
}
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 DateTime {
#[inline(always)]
fn to_u64(self) -> u64 {
common::i64_to_u64(self.into_timestamp_micros())
}
#[inline(always)]
fn from_u64(val: u64) -> Self {
DateTime::from_timestamp_micros(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);
}
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);
}
}

41
columnar/src/column_values/monotonic_mapping_u128.rs
View File

@@ -1,41 +0,0 @@
use std::fmt::Debug;
use std::net::Ipv6Addr;
/// Montonic maps a value to u128 value space
/// Monotonic mapping enables `PartialOrd` on u128 space without conversion to original space.
pub trait MonotonicallyMappableToU128: 'static + PartialOrd + Copy + Debug + 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())
}

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

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

43
columnar/src/column_values/u128_based/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/u128_based/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);
}
}

809
columnar/src/column_values/u128_based/compact_space/mod.rs
View File

@@ -1,809 +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},
};
mod blank_range;
mod build_compact_space;
use build_compact_space::get_compact_space;
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt, VIntU128};
use tantivy_bitpacker::{self, BitPacker, BitUnpacker};
use crate::column_values::ColumnValues;
use crate::RowId;
/// The cost per blank is quite hard actually, since blanks are delta encoded, the actual cost of
/// blanks depends on the number of blanks.
///
/// 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 + ?Sized>(&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: RowId,
num_bits: u8,
}
impl CompactSpaceCompressor {
pub fn num_vals(&self) -> RowId {
self.params.num_vals
}
/// Taking the vals as Vec may cost a lot of memory. It is used to sort the vals.
pub fn train_from(iter: impl Iterator<Item = u128>) -> Self {
let mut values_sorted = BTreeSet::new();
let mut total_num_values = 0u32;
for val in iter {
total_num_values += 1u32;
values_sorted.insert(val);
}
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 + ?Sized>(&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_row_ids_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
}
}
#[cfg(test)]
mod tests {
use itertools::Itertools;
use super::*;
use crate::column_values::u128_based::U128Header;
use crate::column_values::{open_u128_mapped, serialize_column_values_u128};
#[test]
fn compact_space_test() {
let ips = &[
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_column_values_u128(&u128_vals, &mut out).unwrap();
let data = OwnedBytes::new(out);
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: ColumnValues<T> + ?Sized, T: PartialOrd>(
column: &C,
value_range: RangeInclusive<T>,
doc_id_range: Range<u32>,
) -> Vec<u32> {
let mut positions = Vec::new();
column.get_row_ids_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_column_values_u128(&&vals[..], &mut out).unwrap();
let decomp = open_u128_mapped(OwnedBytes::new(out)).unwrap();
let complete_range = 0..vals.len() as u32;
assert_eq!(
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 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);
}
}
}

178
columnar/src/column_values/u128_based/mod.rs
View File

@@ -1,178 +0,0 @@
use std::fmt::Debug;
use std::io;
use std::io::Write;
use std::sync::Arc;
mod compact_space;
use common::{BinarySerializable, OwnedBytes, VInt};
use compact_space::{CompactSpaceCompressor, CompactSpaceDecompressor};
use crate::column_values::monotonic_map_column;
use crate::column_values::monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
};
use crate::iterable::Iterable;
use crate::{ColumnValues, MonotonicallyMappableToU128};
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
pub(crate) struct U128Header {
pub num_vals: u32,
pub codec_type: U128FastFieldCodecType,
}
impl BinarySerializable for U128Header {
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.num_vals as u64).serialize(writer)?;
self.codec_type.serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let num_vals = VInt::deserialize(reader)?.0 as u32;
let codec_type = U128FastFieldCodecType::deserialize(reader)?;
Ok(U128Header {
num_vals,
codec_type,
})
}
}
/// Serializes u128 values with the compact space codec.
pub fn serialize_column_values_u128<T: MonotonicallyMappableToU128>(
iterable: &dyn Iterable<T>,
output: &mut impl io::Write,
) -> io::Result<()> {
let compressor = CompactSpaceCompressor::train_from(
iterable
.boxed_iter()
.map(MonotonicallyMappableToU128::to_u128),
);
let header = U128Header {
num_vals: compressor.num_vals(),
codec_type: U128FastFieldCodecType::CompactSpace,
};
header.serialize(output)?;
compressor.compress_into(
iterable
.boxed_iter()
.map(MonotonicallyMappableToU128::to_u128),
output,
)?;
Ok(())
}
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
#[repr(u8)]
/// Available codecs to use to encode the u128 (via [`MonotonicallyMappableToU128`]) converted data.
pub(crate) enum U128FastFieldCodecType {
/// This codec takes a large number space (u128) and reduces it to a compact number space, by
/// removing the holes.
CompactSpace = 1,
}
impl BinarySerializable for U128FastFieldCodecType {
fn serialize<W: Write + ?Sized>(&self, wrt: &mut W) -> io::Result<()> {
self.to_code().serialize(wrt)
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let code = u8::deserialize(reader)?;
let codec_type: Self = Self::from_code(code)
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Unknown code `{code}.`"))?;
Ok(codec_type)
}
}
impl U128FastFieldCodecType {
pub(crate) fn to_code(self) -> u8 {
self as u8
}
pub(crate) fn from_code(code: u8) -> Option<Self> {
match code {
1 => Some(Self::CompactSpace),
_ => None,
}
}
}
/// Returns the correct codec reader wrapped in the `Arc` for the data.
pub fn open_u128_mapped<T: MonotonicallyMappableToU128 + Debug>(
mut bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
let header = U128Header::deserialize(&mut bytes)?;
assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
let reader = CompactSpaceDecompressor::open(bytes)?;
let inverted: StrictlyMonotonicMappingInverter<StrictlyMonotonicMappingToInternal<T>> =
StrictlyMonotonicMappingToInternal::<T>::new().into();
Ok(Arc::new(monotonic_map_column(reader, inverted)))
}
#[cfg(test)]
pub mod tests {
use super::*;
use crate::column_values::u64_based::{
serialize_and_load_u64_based_column_values, serialize_u64_based_column_values,
ALL_U64_CODEC_TYPES,
};
use crate::column_values::CodecType;
#[test]
fn test_serialize_deserialize_u128_header() {
let original = U128Header {
num_vals: 11,
codec_type: U128FastFieldCodecType::CompactSpace,
};
let mut out = Vec::new();
original.serialize(&mut out).unwrap();
let restored = U128Header::deserialize(&mut &out[..]).unwrap();
assert_eq!(restored, original);
}
#[test]
fn test_serialize_deserialize() {
let original = [1u64, 5u64, 10u64];
let restored: Vec<u64> =
serialize_and_load_u64_based_column_values(&&original[..], &ALL_U64_CODEC_TYPES)
.iter()
.collect();
assert_eq!(&restored, &original[..]);
}
#[test]
fn test_fastfield_bool_size_bitwidth_1() {
let mut buffer = Vec::new();
serialize_u64_based_column_values::<bool>(
&&[false, true][..],
&ALL_U64_CODEC_TYPES,
&mut buffer,
)
.unwrap();
// TODO put the header as a footer so that it serves as a padding.
// 5 bytes of header, 1 byte of value, 7 bytes of padding.
assert_eq!(buffer.len(), 5 + 1);
}
#[test]
fn test_fastfield_bool_bit_size_bitwidth_0() {
let mut buffer = Vec::new();
serialize_u64_based_column_values::<bool>(
&&[false, true][..],
&ALL_U64_CODEC_TYPES,
&mut buffer,
)
.unwrap();
// 6 bytes of header, 0 bytes of value, 7 bytes of padding.
assert_eq!(buffer.len(), 6);
}
#[test]
fn test_fastfield_gcd() {
let mut buffer = Vec::new();
let vals: Vec<u64> = (0..80).map(|val| (val % 7) * 1_000u64).collect();
serialize_u64_based_column_values(&&vals[..], &[CodecType::Bitpacked], &mut buffer)
.unwrap();
// Values are stored over 3 bits.
assert_eq!(buffer.len(), 6 + (3 * 80 / 8));
}
}

127
columnar/src/column_values/u64_based/bitpacked.rs
View File

@@ -1,127 +0,0 @@
use std::io::{self, Write};
use common::{BinarySerializable, OwnedBytes};
use fastdivide::DividerU64;
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
use crate::{ColumnValues, RowId};
/// Depending on the field type, a different
/// fast field is required.
#[derive(Clone)]
pub struct BitpackedReader {
data: OwnedBytes,
bit_unpacker: BitUnpacker,
stats: ColumnStats,
}
impl ColumnValues for BitpackedReader {
#[inline(always)]
fn get_val(&self, doc: u32) -> u64 {
self.stats.min_value + self.stats.gcd.get() * self.bit_unpacker.get(doc, &self.data)
}
#[inline]
fn min_value(&self) -> u64 {
self.stats.min_value
}
#[inline]
fn max_value(&self) -> u64 {
self.stats.max_value
}
#[inline]
fn num_vals(&self) -> RowId {
self.stats.num_rows
}
}
fn num_bits(stats: &ColumnStats) -> u8 {
compute_num_bits(stats.amplitude() / stats.gcd)
}
#[derive(Default)]
pub struct BitpackedCodecEstimator;
impl ColumnCodecEstimator for BitpackedCodecEstimator {
fn collect(&mut self, _value: u64) {}
fn estimate(&self, stats: &ColumnStats) -> Option<u64> {
let num_bits_per_value = num_bits(stats);
Some(stats.num_bytes() + (stats.num_rows as u64 * (num_bits_per_value as u64) + 7) / 8)
}
fn serialize(
&self,
stats: &ColumnStats,
vals: &mut dyn Iterator<Item = u64>,
wrt: &mut dyn Write,
) -> io::Result<()> {
stats.serialize(wrt)?;
let num_bits = num_bits(stats);
let mut bit_packer = BitPacker::new();
let divider = DividerU64::divide_by(stats.gcd.get());
for val in vals {
bit_packer.write(divider.divide(val - stats.min_value), num_bits, wrt)?;
}
bit_packer.close(wrt)?;
Ok(())
}
}
pub struct BitpackedCodec;
impl ColumnCodec for BitpackedCodec {
type ColumnValues = BitpackedReader;
type Estimator = BitpackedCodecEstimator;
/// Opens a fast field given a file.
fn load(mut data: OwnedBytes) -> io::Result<Self::ColumnValues> {
let stats = ColumnStats::deserialize(&mut data)?;
let num_bits = num_bits(&stats);
let bit_unpacker = BitUnpacker::new(num_bits);
Ok(BitpackedReader {
data,
bit_unpacker,
stats,
})
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_values::u64_based::tests::create_and_validate;
#[test]
fn test_with_codec_data_sets_simple() {
create_and_validate::<BitpackedCodec>(&[4, 3, 12], "name");
}
#[test]
fn test_with_codec_data_sets_simple_gcd() {
create_and_validate::<BitpackedCodec>(&[1000, 2000, 3000], "name");
}
#[test]
fn test_with_codec_data_sets() {
let data_sets = crate::column_values::u64_based::tests::get_codec_test_datasets();
for (mut data, name) in data_sets {
create_and_validate::<BitpackedCodec>(&data, name);
data.reverse();
create_and_validate::<BitpackedCodec>(&data, name);
}
}
#[test]
fn bitpacked_fast_field_rand() {
for _ in 0..500 {
let mut data = (0..1 + rand::random::<u8>() as usize)
.map(|_| rand::random::<i64>() as u64 / 2)
.collect::<Vec<_>>();
create_and_validate::<BitpackedCodec>(&data, "rand");
data.reverse();
create_and_validate::<BitpackedCodec>(&data, "rand");
}
}
}

281
columnar/src/column_values/u64_based/blockwise_linear.rs
View File

@@ -1,281 +0,0 @@
use std::io::Write;
use std::sync::Arc;
use std::{io, iter};
use common::{BinarySerializable, CountingWriter, DeserializeFrom, OwnedBytes};
use fastdivide::DividerU64;
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::column_values::u64_based::line::Line;
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
use crate::column_values::{ColumnValues, VecColumn};
use crate::MonotonicallyMappableToU64;
const BLOCK_SIZE: u32 = 512u32;
#[derive(Debug, Default)]
struct Block {
line: Line,
bit_unpacker: BitUnpacker,
data_start_offset: usize,
}
impl BinarySerializable for Block {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
self.line.serialize(writer)?;
self.bit_unpacker.bit_width().serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let line = Line::deserialize(reader)?;
let bit_width = u8::deserialize(reader)?;
Ok(Block {
line,
bit_unpacker: BitUnpacker::new(bit_width),
data_start_offset: 0,
})
}
}
fn compute_num_blocks(num_vals: u32) -> u32 {
(num_vals + BLOCK_SIZE - 1) / BLOCK_SIZE
}
pub struct BlockwiseLinearEstimator {
block: Vec<u64>,
values_num_bytes: u64,
meta_num_bytes: u64,
}
impl Default for BlockwiseLinearEstimator {
fn default() -> Self {
Self {
block: Vec::with_capacity(BLOCK_SIZE as usize),
values_num_bytes: 0u64,
meta_num_bytes: 0u64,
}
}
}
impl BlockwiseLinearEstimator {
fn flush_block_estimate(&mut self) {
if self.block.is_empty() {
return;
}
let line = Line::train(&VecColumn::from(&self.block));
let mut max_value = 0u64;
for (i, buffer_val) in self.block.iter().enumerate() {
let interpolated_val = line.eval(i as u32);
let val = buffer_val.wrapping_sub(interpolated_val);
max_value = val.max(max_value);
}
let bit_width = compute_num_bits(max_value) as usize;
self.values_num_bytes += (bit_width * self.block.len() + 7) as u64 / 8;
self.meta_num_bytes += 1 + line.num_bytes();
}
}
impl ColumnCodecEstimator for BlockwiseLinearEstimator {
fn collect(&mut self, value: u64) {
self.block.push(value);
if self.block.len() == BLOCK_SIZE as usize {
self.flush_block_estimate();
self.block.clear();
}
}
fn estimate(&self, stats: &ColumnStats) -> Option<u64> {
let mut estimate = 4 + stats.num_bytes() + self.meta_num_bytes + self.values_num_bytes;
if stats.gcd.get() > 1 {
let estimate_gain_from_gcd =
(stats.gcd.get() as f32).log2().floor() * stats.num_rows as f32 / 8.0f32;
estimate = estimate.saturating_sub(estimate_gain_from_gcd as u64);
}
Some(estimate)
}
fn finalize(&mut self) {
self.flush_block_estimate();
}
fn serialize(
&self,
stats: &ColumnStats,
mut vals: &mut dyn Iterator<Item = u64>,
wrt: &mut dyn Write,
) -> io::Result<()> {
stats.serialize(wrt)?;
let mut buffer = Vec::with_capacity(BLOCK_SIZE as usize);
let num_blocks = compute_num_blocks(stats.num_rows) as usize;
let mut blocks = Vec::with_capacity(num_blocks);
let mut bit_packer = BitPacker::new();
let gcd_divider = DividerU64::divide_by(stats.gcd.get());
for _ in 0..num_blocks {
buffer.clear();
buffer.extend(
(&mut vals)
.map(MonotonicallyMappableToU64::to_u64)
.take(BLOCK_SIZE as usize),
);
for buffer_val in buffer.iter_mut() {
*buffer_val = gcd_divider.divide(*buffer_val - stats.min_value);
}
let line = Line::train(&VecColumn::from(&buffer));
assert!(!buffer.is_empty());
for (i, buffer_val) in buffer.iter_mut().enumerate() {
let interpolated_val = line.eval(i as u32);
*buffer_val = buffer_val.wrapping_sub(interpolated_val);
}
let bit_width = buffer.iter().copied().map(compute_num_bits).max().unwrap();
for &buffer_val in &buffer {
bit_packer.write(buffer_val, bit_width, wrt)?;
}
blocks.push(Block {
line,
bit_unpacker: BitUnpacker::new(bit_width),
data_start_offset: 0,
});
}
bit_packer.close(wrt)?;
assert_eq!(blocks.len(), num_blocks);
let mut counting_wrt = CountingWriter::wrap(wrt);
for block in &blocks {
block.serialize(&mut counting_wrt)?;
}
let footer_len = counting_wrt.written_bytes();
(footer_len as u32).serialize(&mut counting_wrt)?;
Ok(())
}
}
pub struct BlockwiseLinearCodec;
impl ColumnCodec<u64> for BlockwiseLinearCodec {
type ColumnValues = BlockwiseLinearReader;
type Estimator = BlockwiseLinearEstimator;
fn load(mut bytes: OwnedBytes) -> io::Result<Self::ColumnValues> {
let stats = ColumnStats::deserialize(&mut bytes)?;
let footer_len: u32 = (&bytes[bytes.len() - 4..]).deserialize()?;
let footer_offset = bytes.len() - 4 - footer_len as usize;
let (data, mut footer) = bytes.split(footer_offset);
let num_blocks = compute_num_blocks(stats.num_rows);
let mut blocks: Vec<Block> = iter::repeat_with(|| Block::deserialize(&mut footer))
.take(num_blocks as usize)
.collect::<io::Result<_>>()?;
let mut start_offset = 0;
for block in &mut blocks {
block.data_start_offset = start_offset;
start_offset += (block.bit_unpacker.bit_width() as usize) * BLOCK_SIZE as usize / 8;
}
Ok(BlockwiseLinearReader {
blocks: blocks.into_boxed_slice().into(),
data,
stats,
})
}
}
#[derive(Clone)]
pub struct BlockwiseLinearReader {
blocks: Arc<[Block]>,
data: OwnedBytes,
stats: ColumnStats,
}
impl ColumnValues for BlockwiseLinearReader {
#[inline(always)]
fn get_val(&self, idx: u32) -> u64 {
let block_id = (idx / BLOCK_SIZE) as usize;
let idx_within_block = idx % BLOCK_SIZE;
let block = &self.blocks[block_id];
let interpoled_val: u64 = block.line.eval(idx_within_block);
let block_bytes = &self.data[block.data_start_offset..];
let bitpacked_diff = block.bit_unpacker.get(idx_within_block, block_bytes);
// TODO optimize me! the line parameters could be tweaked to include the multiplication and
// remove the dependency.
self.stats.min_value
+ self
.stats
.gcd
.get()
.wrapping_mul(interpoled_val.wrapping_add(bitpacked_diff))
}
#[inline(always)]
fn min_value(&self) -> u64 {
self.stats.min_value
}
#[inline(always)]
fn max_value(&self) -> u64 {
self.stats.max_value
}
#[inline(always)]
fn num_vals(&self) -> u32 {
self.stats.num_rows
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::column_values::u64_based::tests::create_and_validate;
#[test]
fn test_with_codec_data_sets_simple() {
create_and_validate::<BlockwiseLinearCodec>(
&[11, 20, 40, 20, 10, 10, 10, 10, 10, 10],
"simple test",
)
.unwrap();
}
#[test]
fn test_with_codec_data_sets_simple_gcd() {
let (_, actual_compression_rate) = create_and_validate::<BlockwiseLinearCodec>(
&[10, 20, 40, 20, 10, 10, 10, 10, 10, 10],
"name",
)
.unwrap();
assert_eq!(actual_compression_rate, 0.175);
}
#[test]
fn test_with_codec_data_sets() {
let data_sets = crate::column_values::u64_based::tests::get_codec_test_datasets();
for (mut data, name) in data_sets {
create_and_validate::<BlockwiseLinearCodec>(&data, name);
data.reverse();
create_and_validate::<BlockwiseLinearCodec>(&data, name);
}
}
#[test]
fn test_blockwise_linear_fast_field_rand() {
for _ in 0..500 {
let mut data = (0..1 + rand::random::<u8>() as usize)
.map(|_| rand::random::<i64>() as u64 / 2)
.collect::<Vec<_>>();
create_and_validate::<BlockwiseLinearCodec>(&data, "rand");
data.reverse();
create_and_validate::<BlockwiseLinearCodec>(&data, "rand");
}
}
}

210
columnar/src/column_values/u64_based/line.rs
View File

@@ -1,210 +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 {
pub(crate) slope: u64,
pub(crate) 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)
}
// Intercept is only computed from provided positions
pub 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)
/// TODO USE array
pub fn train(ys: &dyn ColumnValues) -> Self {
let first_val = ys.iter().next().unwrap();
let last_val = ys.iter().nth(ys.num_vals() as usize - 1).unwrap();
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 + ?Sized>(&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));
}
}

277
columnar/src/column_values/u64_based/linear.rs
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@@ -1,277 +0,0 @@
use std::io;
use common::{BinarySerializable, OwnedBytes};
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use super::line::Line;
use super::ColumnValues;
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
use crate::column_values::VecColumn;
use crate::RowId;
const HALF_SPACE: u64 = u64::MAX / 2;
const LINE_ESTIMATION_BLOCK_LEN: usize = 512;
/// Depending on the field type, a different
/// fast field is required.
#[derive(Clone)]
pub struct LinearReader {
data: OwnedBytes,
linear_params: LinearParams,
stats: ColumnStats,
}
impl ColumnValues for LinearReader {
#[inline]
fn get_val(&self, doc: u32) -> u64 {
let interpoled_val: u64 = self.linear_params.line.eval(doc);
let bitpacked_diff = self.linear_params.bit_unpacker.get(doc, &self.data);
interpoled_val.wrapping_add(bitpacked_diff)
}
#[inline(always)]
fn min_value(&self) -> u64 {
self.stats.min_value
}
#[inline(always)]
fn max_value(&self) -> u64 {
self.stats.max_value
}
#[inline]
fn num_vals(&self) -> u32 {
self.stats.num_rows
}
}
/// Fastfield serializer, which tries to guess values by linear interpolation
/// and stores the difference bitpacked.
pub struct LinearCodec;
#[derive(Debug, Clone)]
struct LinearParams {
line: Line,
bit_unpacker: BitUnpacker,
}
impl BinarySerializable for LinearParams {
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
self.line.serialize(writer)?;
self.bit_unpacker.bit_width().serialize(writer)?;
Ok(())
}
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
let line = Line::deserialize(reader)?;
let bit_width = u8::deserialize(reader)?;
Ok(Self {
line,
bit_unpacker: BitUnpacker::new(bit_width),
})
}
}
pub struct LinearCodecEstimator {
block: Vec<u64>,
line: Option<Line>,
row_id: RowId,
min_deviation: u64,
max_deviation: u64,
first_val: u64,
last_val: u64,
}
impl Default for LinearCodecEstimator {
fn default() -> LinearCodecEstimator {
LinearCodecEstimator {
block: Vec::with_capacity(LINE_ESTIMATION_BLOCK_LEN),
line: None,
row_id: 0,
min_deviation: u64::MAX,
max_deviation: u64::MIN,
first_val: 0u64,
last_val: 0u64,
}
}
}
impl ColumnCodecEstimator for LinearCodecEstimator {
fn finalize(&mut self) {
if let Some(line) = self.line.as_mut() {
line.intercept = line
.intercept
.wrapping_add(self.min_deviation)
.wrapping_sub(HALF_SPACE);
}
}
fn estimate(&self, stats: &ColumnStats) -> Option<u64> {
let line = self.line?;
let amplitude = self.max_deviation - self.min_deviation;
let num_bits = compute_num_bits(amplitude);
let linear_params = LinearParams {
line,
bit_unpacker: BitUnpacker::new(num_bits),
};
Some(
stats.num_bytes()
+ linear_params.num_bytes()
+ (num_bits as u64 * stats.num_rows as u64 + 7) / 8,
)
}
fn serialize(
&self,
stats: &ColumnStats,
vals: &mut dyn Iterator<Item = u64>,
wrt: &mut dyn io::Write,
) -> io::Result<()> {
stats.serialize(wrt)?;
let line = self.line.unwrap();
let amplitude = self.max_deviation - self.min_deviation;
let num_bits = compute_num_bits(amplitude);
let linear_params = LinearParams {
line,
bit_unpacker: BitUnpacker::new(num_bits),
};
linear_params.serialize(wrt)?;
let mut bit_packer = BitPacker::new();
for (pos, value) in vals.enumerate() {
let calculated_value = line.eval(pos as u32);
let offset = value.wrapping_sub(calculated_value);
bit_packer.write(offset, num_bits, wrt)?;
}
bit_packer.close(wrt)?;
Ok(())
}
fn collect(&mut self, value: u64) {
if let Some(line) = self.line {
self.collect_after_line_estimation(&line, value);
} else {
self.collect_before_line_estimation(value);
}
}
}
impl LinearCodecEstimator {
#[inline]
fn collect_after_line_estimation(&mut self, line: &Line, value: u64) {
let interpoled_val: u64 = line.eval(self.row_id);
let deviation = value.wrapping_add(HALF_SPACE).wrapping_sub(interpoled_val);
self.min_deviation = self.min_deviation.min(deviation);
self.max_deviation = self.max_deviation.max(deviation);
if self.row_id == 0 {
self.first_val = value;
}
self.last_val = value;
self.row_id += 1u32;
}
#[inline]
fn collect_before_line_estimation(&mut self, value: u64) {
self.block.push(value);
if self.block.len() == LINE_ESTIMATION_BLOCK_LEN {
let line = Line::train(&VecColumn::from(&self.block));
let block = std::mem::take(&mut self.block);
for val in block {
self.collect_after_line_estimation(&line, val);
}
self.line = Some(line);
}
}
}
impl ColumnCodec for LinearCodec {
type ColumnValues = LinearReader;
type Estimator = LinearCodecEstimator;
fn load(mut data: OwnedBytes) -> io::Result<Self::ColumnValues> {
let stats = ColumnStats::deserialize(&mut data)?;
let linear_params = LinearParams::deserialize(&mut data)?;
Ok(LinearReader {
stats,
linear_params,
data,
})
}
}
#[cfg(test)]
mod tests {
use rand::RngCore;
use super::*;
use crate::column_values::u64_based::tests::{create_and_validate, get_codec_test_datasets};
#[test]
fn test_compression_simple() {
let vals = (100u64..)
.take(super::LINE_ESTIMATION_BLOCK_LEN)
.collect::<Vec<_>>();
create_and_validate::<LinearCodec>(&vals, "simple monotonically large").unwrap();
}
#[test]
fn test_compression() {
let data = (10..=6_000_u64).collect::<Vec<_>>();
let (estimate, actual_compression) =
create_and_validate::<LinearCodec>(&data, "simple monotonically large").unwrap();
assert_le!(actual_compression, 0.001);
assert_le!(estimate, 0.02);
}
#[test]
fn test_with_codec_datasets() {
let data_sets = get_codec_test_datasets();
for (mut data, name) in data_sets {
create_and_validate::<LinearCodec>(&data, name);
data.reverse();
create_and_validate::<LinearCodec>(&data, name);
}
}
#[test]
fn linear_interpol_fast_field_test_large_amplitude() {
let data = vec![
i64::MAX as u64 / 2,
i64::MAX as u64 / 3,
i64::MAX as u64 / 2,
];
create_and_validate::<LinearCodec>(&data, "large amplitude");
}
#[test]
fn overflow_error_test() {
let data = vec![1572656989877777, 1170935903116329, 720575940379279, 0];
create_and_validate::<LinearCodec>(&data, "overflow test");
}
#[test]
fn linear_interpol_fast_concave_data() {
let data = vec![0, 1, 2, 5, 8, 10, 20, 50];
create_and_validate::<LinearCodec>(&data, "concave data");
}
#[test]
fn linear_interpol_fast_convex_data() {
let data = vec![0, 40, 60, 70, 75, 77];
create_and_validate::<LinearCodec>(&data, "convex data");
}
#[test]
fn linear_interpol_fast_field_test_simple() {
let data = (10..=20_u64).collect::<Vec<_>>();
create_and_validate::<LinearCodec>(&data, "simple monotonically");
}
#[test]
fn linear_interpol_fast_field_rand() {
let mut rng = rand::thread_rng();
for _ in 0..50 {
let mut data = (0..10_000).map(|_| rng.next_u64()).collect::<Vec<_>>();
create_and_validate::<LinearCodec>(&data, "random");
data.reverse();
create_and_validate::<LinearCodec>(&data, "random");
}
}
}

214
columnar/src/column_values/u64_based/mod.rs
View File

@@ -1,214 +0,0 @@
mod bitpacked;
mod blockwise_linear;
mod line;
mod linear;
mod stats_collector;
use std::io;
use std::io::Write;
use std::sync::Arc;
use common::{BinarySerializable, OwnedBytes};
use crate::column_values::monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
};
pub use crate::column_values::u64_based::bitpacked::BitpackedCodec;
pub use crate::column_values::u64_based::blockwise_linear::BlockwiseLinearCodec;
pub use crate::column_values::u64_based::linear::LinearCodec;
pub use crate::column_values::u64_based::stats_collector::StatsCollector;
use crate::column_values::{monotonic_map_column, ColumnStats};
use crate::iterable::Iterable;
use crate::{ColumnValues, MonotonicallyMappableToU64};
/// A `ColumnCodecEstimator` is in charge of gathering all
/// data required to serialize a column.
///
/// This happens during a first pass on data of the column elements.
/// During that pass, all column estimators receive a call to their
/// `.collect(el)`.
///
/// After this first pass, finalize is called.
/// `.estimate(..)` then should return an accurate estimation of the
/// size of the serialized column (were we to pick this codec.).
/// `.serialize(..)` then serializes the column using this codec.
pub trait ColumnCodecEstimator<T = u64>: 'static {
/// Records a new value for estimation.
/// This method will be called for each element of the column during
/// `estimation`.
fn collect(&mut self, value: u64);
/// Finalizes the first pass phase.
fn finalize(&mut self) {}
/// Returns an accurate estimation of the number of bytes that will
/// be used to represent this column.
fn estimate(&self, stats: &ColumnStats) -> Option<u64>;
/// Serializes the column using the given codec.
/// This constitutes a second pass over the columns values.
fn serialize(
&self,
stats: &ColumnStats,
vals: &mut dyn Iterator<Item = T>,
wrt: &mut dyn io::Write,
) -> io::Result<()>;
}
/// A column codec describes a colunm serialization format.
pub trait ColumnCodec<T: PartialOrd = u64> {
/// Specialized `ColumnValues` type.
type ColumnValues: ColumnValues<T> + 'static;
/// `Estimator` for the given codec.
type Estimator: ColumnCodecEstimator + Default;
/// Loads a column that has been serialized using this codec.
fn load(bytes: OwnedBytes) -> io::Result<Self::ColumnValues>;
/// Returns an estimator.
fn estimator() -> Self::Estimator {
Self::Estimator::default()
}
/// Returns a boxed estimator.
fn boxed_estimator() -> Box<dyn ColumnCodecEstimator> {
Box::new(Self::estimator())
}
}
/// Available codecs to use to encode the u64 (via [`MonotonicallyMappableToU64`]) converted data.
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
#[repr(u8)]
pub enum CodecType {
/// Bitpack all values in the value range. The number of bits is defined by the amplitude
/// `column.max_value() - column.min_value()`
Bitpacked = 0u8,
/// Linear interpolation puts a line between the first and last value and then bitpacks the
/// values by the offset from the line. The number of bits is defined by the max deviation from
/// the line.
Linear = 1u8,
/// Same as [`CodecType::Linear`], but encodes in blocks of 512 elements.
BlockwiseLinear = 2u8,
}
/// List of all available u64-base codecs.
pub const ALL_U64_CODEC_TYPES: [CodecType; 3] = [
CodecType::Bitpacked,
CodecType::Linear,
CodecType::BlockwiseLinear,
];
impl CodecType {
fn to_code(self) -> u8 {
self as u8
}
fn try_from_code(code: u8) -> Option<CodecType> {
match code {
0u8 => Some(CodecType::Bitpacked),
1u8 => Some(CodecType::Linear),
2u8 => Some(CodecType::BlockwiseLinear),
_ => None,
}
}
fn load<T: MonotonicallyMappableToU64>(
&self,
bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
match self {
CodecType::Bitpacked => load_specific_codec::<BitpackedCodec, T>(bytes),
CodecType::Linear => load_specific_codec::<LinearCodec, T>(bytes),
CodecType::BlockwiseLinear => load_specific_codec::<BlockwiseLinearCodec, T>(bytes),
}
}
}
fn load_specific_codec<C: ColumnCodec, T: MonotonicallyMappableToU64>(
bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
let reader = C::load(bytes)?;
let reader_typed = monotonic_map_column(
reader,
StrictlyMonotonicMappingInverter::from(StrictlyMonotonicMappingToInternal::<T>::new()),
);
Ok(Arc::new(reader_typed))
}
impl CodecType {
/// Returns a boxed codec estimator associated to a given `CodecType`.
pub fn estimator(&self) -> Box<dyn ColumnCodecEstimator> {
match self {
CodecType::Bitpacked => BitpackedCodec::boxed_estimator(),
CodecType::Linear => LinearCodec::boxed_estimator(),
CodecType::BlockwiseLinear => BlockwiseLinearCodec::boxed_estimator(),
}
}
}
/// Serializes a given column of u64-mapped values.
pub fn serialize_u64_based_column_values<T: MonotonicallyMappableToU64>(
vals: &dyn Iterable<T>,
codec_types: &[CodecType],
wrt: &mut dyn Write,
) -> io::Result<()> {
let mut stats_collector = StatsCollector::default();
let mut estimators: Vec<(CodecType, Box<dyn ColumnCodecEstimator>)> =
Vec::with_capacity(codec_types.len());
for &codec_type in codec_types {
estimators.push((codec_type, codec_type.estimator()));
}
for val in vals.boxed_iter() {
let val_u64 = val.to_u64();
stats_collector.collect(val_u64);
for (_, estimator) in &mut estimators {
estimator.collect(val_u64);
}
}
for (_, estimator) in &mut estimators {
estimator.finalize();
}
let stats = stats_collector.stats();
let (_, best_codec, best_codec_estimator) = estimators
.into_iter()
.flat_map(|(codec_type, estimator)| {
let num_bytes = estimator.estimate(&stats)?;
Some((num_bytes, codec_type, estimator))
})
.min_by_key(|(num_bytes, _, _)| *num_bytes)
.ok_or_else(|| {
io::Error::new(io::ErrorKind::InvalidData, "No available applicable codec.")
})?;
best_codec.to_code().serialize(wrt)?;
best_codec_estimator.serialize(
&stats,
&mut vals.boxed_iter().map(MonotonicallyMappableToU64::to_u64),
wrt,
)?;
Ok(())
}
/// Load u64-based column values.
///
/// This method first identifies the codec off the first byte.
pub fn load_u64_based_column_values<T: MonotonicallyMappableToU64>(
mut bytes: OwnedBytes,
) -> io::Result<Arc<dyn ColumnValues<T>>> {
let codec_type: CodecType = bytes
.first()
.copied()
.and_then(CodecType::try_from_code)
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Failed to read codec type"))?;
bytes.advance(1);
codec_type.load(bytes)
}
/// Helper function to serialize a column (autodetect from all codecs) and then open it
pub fn serialize_and_load_u64_based_column_values<T: MonotonicallyMappableToU64>(
vals: &dyn Iterable,
codec_types: &[CodecType],
) -> Arc<dyn ColumnValues<T>> {
let mut buffer = Vec::new();
serialize_u64_based_column_values(vals, codec_types, &mut buffer).unwrap();
load_u64_based_column_values::<T>(OwnedBytes::new(buffer)).unwrap()
}
#[cfg(test)]
mod tests;

200
columnar/src/column_values/u64_based/stats_collector.rs
View File

@@ -1,200 +0,0 @@
use std::num::NonZeroU64;
use fastdivide::DividerU64;
use crate::column_values::ColumnStats;
use crate::RowId;
/// Compute the gcd of two non null numbers.
///
/// It is recommended, but not required, to feed values such that `large >= small`.
fn compute_gcd(mut large: NonZeroU64, mut small: NonZeroU64) -> NonZeroU64 {
loop {
let rem: u64 = large.get() % small;
if let Some(new_small) = NonZeroU64::new(rem) {
(large, small) = (small, new_small);
} else {
return small;
}
}
}
#[derive(Default)]
pub struct StatsCollector {
min_max_opt: Option<(u64, u64)>,
num_rows: RowId,
// We measure the GCD of the difference between the values and the minimal value.
// This is the same as computing the difference between the values and the first value.
//
// This way, we can compress i64-converted-to-u64 (e.g. timestamp that were supplied in
// seconds, only to be converted in microseconds).
increment_gcd_opt: Option<(NonZeroU64, DividerU64)>,
first_value_opt: Option<u64>,
}
impl StatsCollector {
pub fn stats(&self) -> ColumnStats {
let (min_value, max_value) = self.min_max_opt.unwrap_or((0u64, 0u64));
let increment_gcd = if let Some((increment_gcd, _)) = self.increment_gcd_opt {
increment_gcd
} else {
NonZeroU64::new(1u64).unwrap()
};
ColumnStats {
min_value,
max_value,
num_rows: self.num_rows,
gcd: increment_gcd,
}
}
#[inline]
fn update_increment_gcd(&mut self, value: u64) {
let Some(first_value) = self.first_value_opt else {
// We set the first value and just quit.
self.first_value_opt = Some(value);
return;
};
let Some(non_zero_value) = NonZeroU64::new(value.abs_diff(first_value)) else {
// We can simply skip 0 values.
return;
};
let Some((gcd, gcd_divider)) = self.increment_gcd_opt else {
self.set_increment_gcd(non_zero_value);
return;
};
if gcd.get() == 1 {
// It won't see any update now.
return;
}
let remainder =
non_zero_value.get() - (gcd_divider.divide(non_zero_value.get())) * gcd.get();
if remainder == 0 {
return;
}
let new_gcd = compute_gcd(non_zero_value, gcd);
self.set_increment_gcd(new_gcd);
}
fn set_increment_gcd(&mut self, gcd: NonZeroU64) {
let new_divider = DividerU64::divide_by(gcd.get());
self.increment_gcd_opt = Some((gcd, new_divider));
}
pub fn collect(&mut self, value: u64) {
self.min_max_opt = Some(if let Some((min, max)) = self.min_max_opt {
(min.min(value), max.max(value))
} else {
(value, value)
});
self.num_rows += 1;
self.update_increment_gcd(value);
}
}
#[cfg(test)]
mod tests {
use std::num::NonZeroU64;
use crate::column_values::u64_based::stats_collector::{compute_gcd, StatsCollector};
use crate::column_values::u64_based::ColumnStats;
fn compute_stats(vals: impl Iterator<Item = u64>) -> ColumnStats {
let mut stats_collector = StatsCollector::default();
for val in vals {
stats_collector.collect(val);
}
stats_collector.stats()
}
fn find_gcd(vals: impl Iterator<Item = u64>) -> u64 {
compute_stats(vals).gcd.get()
}
#[test]
fn test_compute_gcd() {
let test_compute_gcd_aux = |large, small, expected| {
let large = NonZeroU64::new(large).unwrap();
let small = NonZeroU64::new(small).unwrap();
let expected = NonZeroU64::new(expected).unwrap();
assert_eq!(compute_gcd(small, large), expected);
assert_eq!(compute_gcd(large, small), expected);
};
test_compute_gcd_aux(1, 4, 1);
test_compute_gcd_aux(2, 4, 2);
test_compute_gcd_aux(10, 25, 5);
test_compute_gcd_aux(25, 25, 25);
}
#[test]
fn test_gcd() {
assert_eq!(find_gcd([0].into_iter()), 1);
assert_eq!(find_gcd([0, 10].into_iter()), 10);
assert_eq!(find_gcd([10, 0].into_iter()), 10);
assert_eq!(find_gcd([].into_iter()), 1);
assert_eq!(find_gcd([15, 30, 5, 10].into_iter()), 5);
assert_eq!(find_gcd([15, 16, 10].into_iter()), 1);
assert_eq!(find_gcd([0, 5, 5, 5].into_iter()), 5);
assert_eq!(find_gcd([0, 0].into_iter()), 1);
assert_eq!(find_gcd([1, 10, 4, 1, 7, 10].into_iter()), 3);
assert_eq!(find_gcd([1, 10, 0, 4, 1, 7, 10].into_iter()), 1);
}
#[test]
fn test_stats() {
assert_eq!(
compute_stats([].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(1).unwrap(),
min_value: 0,
max_value: 0,
num_rows: 0
}
);
assert_eq!(
compute_stats([0, 1].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(1).unwrap(),
min_value: 0,
max_value: 1,
num_rows: 2
}
);
assert_eq!(
compute_stats([0, 1].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(1).unwrap(),
min_value: 0,
max_value: 1,
num_rows: 2
}
);
assert_eq!(
compute_stats([10, 20, 30].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(10).unwrap(),
min_value: 10,
max_value: 30,
num_rows: 3
}
);
assert_eq!(
compute_stats([10, 50, 10, 30].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(20).unwrap(),
min_value: 10,
max_value: 50,
num_rows: 4
}
);
assert_eq!(
compute_stats([10, 0, 30].into_iter()),
ColumnStats {
gcd: NonZeroU64::new(10).unwrap(),
min_value: 0,
max_value: 30,
num_rows: 3
}
);
}
}

401
columnar/src/column_values/u64_based/tests.rs
View File

@@ -1,401 +0,0 @@
use proptest::prelude::*;
use proptest::strategy::Strategy;
use proptest::{num, prop_oneof, proptest};
#[test]
fn test_serialize_and_load_simple() {
let mut buffer = Vec::new();
let vals = &[1u64, 2u64, 5u64];
serialize_u64_based_column_values(
&&vals[..],
&[CodecType::Bitpacked, CodecType::BlockwiseLinear],
&mut buffer,
)
.unwrap();
assert_eq!(buffer.len(), 7);
let col = load_u64_based_column_values::<u64>(OwnedBytes::new(buffer)).unwrap();
assert_eq!(col.num_vals(), 3);
assert_eq!(col.get_val(0), 1);
assert_eq!(col.get_val(1), 2);
assert_eq!(col.get_val(2), 5);
}
#[test]
fn test_empty_column_i64() {
let vals: [i64; 0] = [];
let mut num_acceptable_codecs = 0;
for codec in ALL_U64_CODEC_TYPES {
let mut buffer = Vec::new();
if serialize_u64_based_column_values(&&vals[..], &[codec], &mut buffer).is_err() {
continue;
}
num_acceptable_codecs += 1;
let col = load_u64_based_column_values::<i64>(OwnedBytes::new(buffer)).unwrap();
assert_eq!(col.num_vals(), 0);
assert_eq!(col.min_value(), i64::MIN);
assert_eq!(col.max_value(), i64::MIN);
}
assert!(num_acceptable_codecs > 0);
}
#[test]
fn test_empty_column_u64() {
let vals: [u64; 0] = [];
let mut num_acceptable_codecs = 0;
for codec in ALL_U64_CODEC_TYPES {
let mut buffer = Vec::new();
if serialize_u64_based_column_values(&&vals[..], &[codec], &mut buffer).is_err() {
continue;
}
num_acceptable_codecs += 1;
let col = load_u64_based_column_values::<u64>(OwnedBytes::new(buffer)).unwrap();
assert_eq!(col.num_vals(), 0);
assert_eq!(col.min_value(), u64::MIN);
assert_eq!(col.max_value(), u64::MIN);
}
assert!(num_acceptable_codecs > 0);
}
#[test]
fn test_empty_column_f64() {
let vals: [f64; 0] = [];
let mut num_acceptable_codecs = 0;
for codec in ALL_U64_CODEC_TYPES {
let mut buffer = Vec::new();
if serialize_u64_based_column_values(&&vals[..], &[codec], &mut buffer).is_err() {
continue;
}
num_acceptable_codecs += 1;
let col = load_u64_based_column_values::<f64>(OwnedBytes::new(buffer)).unwrap();
assert_eq!(col.num_vals(), 0);
// FIXME. f64::MIN would be better!
assert!(col.min_value().is_nan());
assert!(col.max_value().is_nan());
}
assert!(num_acceptable_codecs > 0);
}
pub(crate) fn create_and_validate<TColumnCodec: ColumnCodec>(
vals: &[u64],
name: &str,
) -> Option<(f32, f32)> {
let mut stats_collector = StatsCollector::default();
let mut codec_estimator: TColumnCodec::Estimator = Default::default();
for val in vals.boxed_iter() {
stats_collector.collect(val);
codec_estimator.collect(val);
}
codec_estimator.finalize();
let stats = stats_collector.stats();
let estimation = codec_estimator.estimate(&stats)?;
let mut buffer = Vec::new();
codec_estimator
.serialize(&stats, vals.boxed_iter().as_mut(), &mut buffer)
.unwrap();
let actual_compression = buffer.len() as u64;
let reader = TColumnCodec::load(OwnedBytes::new(buffer)).unwrap();
assert_eq!(reader.num_vals(), vals.len() as u32);
for (doc, orig_val) in vals.iter().copied().enumerate() {
let val = reader.get_val(doc as u32);
assert_eq!(
val, orig_val,
"val `{val}` does not match orig_val {orig_val:?}, in data set {name}, data `{vals:?}`",
);
}
if !vals.is_empty() {
let test_rand_idx = rand::thread_rng().gen_range(0..=vals.len() - 1);
let expected_positions: Vec<u32> = vals
.iter()
.enumerate()
.filter(|(_, el)| **el == vals[test_rand_idx])
.map(|(pos, _)| pos as u32)
.collect();
let mut positions = Vec::new();
reader.get_row_ids_for_value_range(
vals[test_rand_idx]..=vals[test_rand_idx],
0..vals.len() as u32,
&mut positions,
);
assert_eq!(expected_positions, positions);
}
if actual_compression > 1000 {
assert!(relative_difference(estimation, actual_compression) < 0.10f32);
}
Some((
compression_rate(estimation, stats.num_rows),
compression_rate(actual_compression, stats.num_rows),
))
}
fn compression_rate(num_bytes: u64, num_values: u32) -> f32 {
num_bytes as f32 / (num_values as f32 * 8.0)
}
fn relative_difference(left: u64, right: u64) -> f32 {
let left = left as f32;
let right = right as f32;
2.0f32 * (left - right).abs() / (left + right)
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(100))]
#[test]
fn test_proptest_small_bitpacked(data in proptest::collection::vec(num_strategy(), 1..10)) {
create_and_validate::<BitpackedCodec>(&data, "proptest bitpacked");
}
#[test]
fn test_proptest_small_linear(data in proptest::collection::vec(num_strategy(), 1..10)) {
create_and_validate::<LinearCodec>(&data, "proptest linearinterpol");
}
#[test]
fn test_proptest_small_blockwise_linear(data in proptest::collection::vec(num_strategy(), 1..10)) {
create_and_validate::<BlockwiseLinearCodec>(&data, "proptest multilinearinterpol");
}
}
#[test]
fn test_small_blockwise_linear_example() {
create_and_validate::<BlockwiseLinearCodec>(
&[9223372036854775808, 9223370937344622593],
"proptest multilinearinterpol",
);
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(10))]
#[test]
fn test_proptest_large_bitpacked(data in proptest::collection::vec(num_strategy(), 1..6000)) {
create_and_validate::<BitpackedCodec>(&data, "proptest bitpacked");
}
#[test]
fn test_proptest_large_linear(data in proptest::collection::vec(num_strategy(), 1..6000)) {
create_and_validate::<LinearCodec>(&data, "proptest linearinterpol");
}
#[test]
fn test_proptest_large_blockwise_linear(data in proptest::collection::vec(num_strategy(), 1..6000)) {
create_and_validate::<BlockwiseLinearCodec>(&data, "proptest multilinearinterpol");
}
}
fn num_strategy() -> impl Strategy<Value = u64> {
prop_oneof![
1 => prop::num::u64::ANY.prop_map(|num| u64::MAX - (num % 10) ),
1 => prop::num::u64::ANY.prop_map(|num| num % 10 ),
20 => prop::num::u64::ANY,
]
}
pub fn get_codec_test_datasets() -> Vec<(Vec<u64>, &'static str)> {
let mut data_and_names = vec![];
let data = (10..=10_000_u64).collect::<Vec<_>>();
data_and_names.push((data, "simple monotonically increasing"));
data_and_names.push((
vec![5, 6, 7, 8, 9, 10, 99, 100],
"offset in linear interpol",
));
data_and_names.push((vec![5, 50, 3, 13, 1, 1000, 35], "rand small"));
data_and_names.push((vec![10], "single value"));
data_and_names.push((
vec![1572656989877777, 1170935903116329, 720575940379279, 0],
"overflow error",
));
data_and_names
}
fn test_codec<C: ColumnCodec>() {
let codec_name = std::any::type_name::<C>();
for (data, dataset_name) in get_codec_test_datasets() {
let estimate_actual_opt: Option<(f32, f32)> =
tests::create_and_validate::<C>(&data, dataset_name);
let result = if let Some((estimate, actual)) = estimate_actual_opt {
format!("Estimate `{estimate}` Actual `{actual}`")
} else {
"Disabled".to_string()
};
println!("Codec {codec_name}, DataSet {dataset_name}, {result}");
}
}
#[test]
fn test_codec_bitpacking() {
test_codec::<BitpackedCodec>();
}
#[test]
fn test_codec_interpolation() {
test_codec::<LinearCodec>();
}
#[test]
fn test_codec_multi_interpolation() {
test_codec::<BlockwiseLinearCodec>();
}
use super::*;
fn estimate<C: ColumnCodec>(vals: &[u64]) -> Option<f32> {
let mut stats_collector = StatsCollector::default();
let mut estimator = C::Estimator::default();
for &val in vals {
stats_collector.collect(val);
estimator.collect(val);
}
estimator.finalize();
let stats = stats_collector.stats();
let num_bytes = estimator.estimate(&stats)?;
if stats.num_rows == 0 {
return None;
}
Some(num_bytes as f32 / (8.0 * stats.num_rows as f32))
}
#[test]
fn estimation_good_interpolation_case() {
let data = (10..=20000_u64).collect::<Vec<_>>();
let linear_interpol_estimation = estimate::<LinearCodec>(&data).unwrap();
assert_le!(linear_interpol_estimation, 0.01);
let multi_linear_interpol_estimation = estimate::<BlockwiseLinearCodec>(&data).unwrap();
assert_le!(multi_linear_interpol_estimation, 0.2);
assert_lt!(linear_interpol_estimation, multi_linear_interpol_estimation);
let bitpacked_estimation = estimate::<BitpackedCodec>(&data).unwrap();
assert_lt!(linear_interpol_estimation, bitpacked_estimation);
}
#[test]
fn estimation_test_bad_interpolation_case_monotonically_increasing() {
let mut data: Vec<u64> = (201..=20000_u64).collect();
data.push(1_000_000);
// in this case the linear interpolation can't in fact not be worse than bitpacking,
// but the estimator adds some threshold, which leads to estimated worse behavior
let linear_interpol_estimation = estimate::<LinearCodec>(&data[..]).unwrap();
assert_le!(linear_interpol_estimation, 0.35);
let bitpacked_estimation = estimate::<BitpackedCodec>(&data).unwrap();
assert_le!(bitpacked_estimation, 0.32);
assert_le!(bitpacked_estimation, linear_interpol_estimation);
}
#[test]
fn test_fast_field_codec_type_to_code() {
let mut count_codec = 0;
for code in 0..=255 {
if let Some(codec_type) = CodecType::try_from_code(code) {
assert_eq!(codec_type.to_code(), code);
count_codec += 1;
}
}
assert_eq!(count_codec, 3);
}
fn test_fastfield_gcd_i64_with_codec(codec_type: CodecType, num_vals: usize) -> io::Result<()> {
let mut vals: Vec<i64> = (-4..=(num_vals as i64) - 5).map(|val| val * 1000).collect();
let mut buffer: Vec<u8> = Vec::new();
crate::column_values::serialize_u64_based_column_values(
&&vals[..],
&[codec_type],
&mut buffer,
)?;
let buffer = OwnedBytes::new(buffer);
let column = crate::column_values::load_u64_based_column_values::<i64>(buffer.clone())?;
assert_eq!(column.get_val(0), -4000i64);
assert_eq!(column.get_val(1), -3000i64);
assert_eq!(column.get_val(2), -2000i64);
assert_eq!(column.max_value(), (num_vals as i64 - 5) * 1000);
assert_eq!(column.min_value(), -4000i64);
// Can't apply gcd
let mut buffer_without_gcd = Vec::new();
vals.pop();
vals.push(1001i64);
crate::column_values::serialize_u64_based_column_values(
&&vals[..],
&[codec_type],
&mut buffer_without_gcd,
)?;
let buffer_without_gcd = OwnedBytes::new(buffer_without_gcd);
assert!(buffer_without_gcd.len() > buffer.len());
Ok(())
}
#[test]
fn test_fastfield_gcd_i64() -> io::Result<()> {
for &codec_type in &[
CodecType::Bitpacked,
CodecType::BlockwiseLinear,
CodecType::Linear,
] {
test_fastfield_gcd_i64_with_codec(codec_type, 5500)?;
}
Ok(())
}
fn test_fastfield_gcd_u64_with_codec(codec_type: CodecType, num_vals: usize) -> io::Result<()> {
let mut vals: Vec<u64> = (1..=num_vals).map(|i| i as u64 * 1000u64).collect();
let mut buffer: Vec<u8> = Vec::new();
crate::column_values::serialize_u64_based_column_values(
&&vals[..],
&[codec_type],
&mut buffer,
)?;
let buffer = OwnedBytes::new(buffer);
let column = crate::column_values::load_u64_based_column_values::<u64>(buffer.clone())?;
assert_eq!(column.get_val(0), 1000u64);
assert_eq!(column.get_val(1), 2000u64);
assert_eq!(column.get_val(2), 3000u64);
assert_eq!(column.max_value(), num_vals as u64 * 1000);
assert_eq!(column.min_value(), 1000u64);
// Can't apply gcd
let mut buffer_without_gcd = Vec::new();
vals.pop();
vals.push(1001u64);
crate::column_values::serialize_u64_based_column_values(
&&vals[..],
&[codec_type],
&mut buffer_without_gcd,
)?;
let buffer_without_gcd = OwnedBytes::new(buffer_without_gcd);
assert!(buffer_without_gcd.len() > buffer.len());
Ok(())
}
#[test]
fn test_fastfield_gcd_u64() -> io::Result<()> {
for &codec_type in &[
CodecType::Bitpacked,
CodecType::BlockwiseLinear,
CodecType::Linear,
] {
test_fastfield_gcd_u64_with_codec(codec_type, 5500)?;
}
Ok(())
}
#[test]
pub fn test_fastfield2() {
let test_fastfield = crate::column_values::serialize_and_load_u64_based_column_values::<u64>(
&&[100u64, 200u64, 300u64][..],
&ALL_U64_CODEC_TYPES,
);
assert_eq!(test_fastfield.get_val(0), 100);
assert_eq!(test_fastfield.get_val(1), 200);
assert_eq!(test_fastfield.get_val(2), 300);
}

52
columnar/src/column_values/vec_column.rs
View File

@@ -1,52 +0,0 @@
use std::fmt::Debug;
use tantivy_bitpacker::minmax;
use crate::ColumnValues;
/// 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 + Debug> 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,
}
}
}

163
columnar/src/columnar/column_type.rs
View File

@@ -1,163 +0,0 @@
use std::fmt::Debug;
use std::net::Ipv6Addr;
use serde::{Deserialize, Serialize};
use crate::value::NumericalType;
use crate::InvalidData;
/// The column type represents the column type.
/// Any changes need to be propagated to `COLUMN_TYPES`.
#[derive(Hash, Eq, PartialEq, Debug, Clone, Copy, Ord, PartialOrd, Serialize, Deserialize)]
#[repr(u8)]
pub enum ColumnType {
I64 = 0u8,
U64 = 1u8,
F64 = 2u8,
Bytes = 3u8,
Str = 4u8,
Bool = 5u8,
IpAddr = 6u8,
DateTime = 7u8,
}
// The order needs to match _exactly_ the order in the enum
const COLUMN_TYPES: [ColumnType; 8] = [
ColumnType::I64,
ColumnType::U64,
ColumnType::F64,
ColumnType::Bytes,
ColumnType::Str,
ColumnType::Bool,
ColumnType::IpAddr,
ColumnType::DateTime,
];
impl ColumnType {
pub fn to_code(self) -> u8 {
self as u8
}
pub(crate) fn try_from_code(code: u8) -> Result<ColumnType, InvalidData> {
COLUMN_TYPES.get(code as usize).copied().ok_or(InvalidData)
}
}
impl From<NumericalType> for ColumnType {
fn from(numerical_type: NumericalType) -> Self {
match numerical_type {
NumericalType::I64 => ColumnType::I64,
NumericalType::U64 => ColumnType::U64,
NumericalType::F64 => ColumnType::F64,
}
}
}
impl ColumnType {
pub fn numerical_type(&self) -> Option<NumericalType> {
match self {
ColumnType::I64 => Some(NumericalType::I64),
ColumnType::U64 => Some(NumericalType::U64),
ColumnType::F64 => Some(NumericalType::F64),
ColumnType::Bytes
| ColumnType::Str
| ColumnType::Bool
| ColumnType::IpAddr
| ColumnType::DateTime => None,
}
}
}
// TODO remove if possible
pub trait HasAssociatedColumnType: 'static + Debug + Send + Sync + Copy + PartialOrd {
fn column_type() -> ColumnType;
fn default_value() -> Self;
}
impl HasAssociatedColumnType for u64 {
fn column_type() -> ColumnType {
ColumnType::U64
}
fn default_value() -> Self {
0u64
}
}
impl HasAssociatedColumnType for i64 {
fn column_type() -> ColumnType {
ColumnType::I64
}
fn default_value() -> Self {
0i64
}
}
impl HasAssociatedColumnType for f64 {
fn column_type() -> ColumnType {
ColumnType::F64
}
fn default_value() -> Self {
Default::default()
}
}
impl HasAssociatedColumnType for bool {
fn column_type() -> ColumnType {
ColumnType::Bool
}
fn default_value() -> Self {
Default::default()
}
}
impl HasAssociatedColumnType for common::DateTime {
fn column_type() -> ColumnType {
ColumnType::DateTime
}
fn default_value() -> Self {
Default::default()
}
}
impl HasAssociatedColumnType for Ipv6Addr {
fn column_type() -> ColumnType {
ColumnType::IpAddr
}
fn default_value() -> Self {
Ipv6Addr::from([0u8; 16])
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::Cardinality;
#[test]
fn test_column_type_to_code() {
for (code, expected_column_type) in super::COLUMN_TYPES.iter().copied().enumerate() {
if let Ok(column_type) = ColumnType::try_from_code(code as u8) {
assert_eq!(column_type, expected_column_type);
}
}
for code in COLUMN_TYPES.len() as u8..=u8::MAX {
assert!(ColumnType::try_from_code(code).is_err());
}
}
#[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
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@@ -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, 66];
pub fn footer() -> [u8; VERSION_FOOTER_NUM_BYTES] {
let mut footer_bytes = [0u8; VERSION_FOOTER_NUM_BYTES];
footer_bytes[0..4].copy_from_slice(&Version::V1.to_bytes());
footer_bytes[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);
}
}

204
columnar/src/columnar/merge/merge_dict_column.rs
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@@ -1,204 +0,0 @@
use std::io::{self, Write};
use common::{BitSet, CountingWriter, ReadOnlyBitSet};
use sstable::{SSTable, TermOrdinal};
use super::term_merger::TermMerger;
use crate::column::serialize_column_mappable_to_u64;
use crate::column_index::SerializableColumnIndex;
use crate::iterable::Iterable;
use crate::{BytesColumn, MergeRowOrder, ShuffleMergeOrder};
// Serialize [Dictionary, Column, dictionary num bytes U32::LE]
// Column: [Column Index, Column Values, column index num bytes U32::LE]
pub fn merge_bytes_or_str_column(
column_index: SerializableColumnIndex<'_>,
bytes_columns: &[Option<BytesColumn>],
merge_row_order: &MergeRowOrder,
output: &mut impl Write,
) -> io::Result<()> {
// Serialize dict and generate mapping for values
let mut output = CountingWriter::wrap(output);
// TODO !!! Remove useless terms.
let term_ord_mapping = serialize_merged_dict(bytes_columns, merge_row_order, &mut output)?;
let dictionary_num_bytes: u32 = output.written_bytes() as u32;
let output = output.finish();
let remapped_term_ordinals_values = RemappedTermOrdinalsValues {
bytes_columns,
term_ord_mapping: &term_ord_mapping,
merge_row_order,
};
serialize_column_mappable_to_u64(column_index, &remapped_term_ordinals_values, output)?;
output.write_all(&dictionary_num_bytes.to_le_bytes())?;
Ok(())
}
struct RemappedTermOrdinalsValues<'a> {
bytes_columns: &'a [Option<BytesColumn>],
term_ord_mapping: &'a TermOrdinalMapping,
merge_row_order: &'a MergeRowOrder,
}
impl<'a> Iterable for RemappedTermOrdinalsValues<'a> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u64> + '_> {
match self.merge_row_order {
MergeRowOrder::Stack(_) => self.boxed_iter_stacked(),
MergeRowOrder::Shuffled(shuffle_merge_order) => {
self.boxed_iter_shuffled(shuffle_merge_order)
}
}
}
}
impl<'a> RemappedTermOrdinalsValues<'a> {
fn boxed_iter_stacked(&self) -> Box<dyn Iterator<Item = u64> + '_> {
let iter = self
.bytes_columns
.iter()
.enumerate()
.flat_map(|(segment_ord, byte_column)| {
let segment_ord = self.term_ord_mapping.get_segment(segment_ord as u32);
byte_column.iter().flat_map(move |bytes_column| {
bytes_column
.ords()
.values
.iter()
.map(move |term_ord| segment_ord[term_ord as usize])
})
});
// TODO see if we can better decompose the mapping / and the stacking
Box::new(iter)
}
fn boxed_iter_shuffled<'b>(
&'b self,
shuffle_merge_order: &'b ShuffleMergeOrder,
) -> Box<dyn Iterator<Item = u64> + 'b> {
Box::new(
shuffle_merge_order
.iter_new_to_old_row_addrs()
.flat_map(move |old_addr| {
let segment_ord = self.term_ord_mapping.get_segment(old_addr.segment_ord);
self.bytes_columns[old_addr.segment_ord as usize]
.as_ref()
.into_iter()
.flat_map(move |bytes_column| {
bytes_column
.term_ords(old_addr.row_id)
.map(|old_term_ord: u64| segment_ord[old_term_ord as usize])
})
}),
)
}
}
fn compute_term_bitset(column: &BytesColumn, row_bitset: &ReadOnlyBitSet) -> BitSet {
let num_terms = column.dictionary().num_terms();
let mut term_bitset = BitSet::with_max_value(num_terms as u32);
for row_id in row_bitset.iter() {
for term_ord in column.term_ord_column.values_for_doc(row_id) {
term_bitset.insert(term_ord as u32);
}
}
term_bitset
}
fn is_term_present(bitsets: &[Option<BitSet>], term_merger: &TermMerger) -> bool {
for (segment_ord, from_term_ord) in term_merger.matching_segments() {
if let Some(bitset) = bitsets[segment_ord].as_ref() {
if bitset.contains(from_term_ord as u32) {
return true;
}
} else {
return true;
}
}
false
}
fn serialize_merged_dict(
bytes_columns: &[Option<BytesColumn>],
merge_row_order: &MergeRowOrder,
output: &mut impl Write,
) -> io::Result<TermOrdinalMapping> {
let mut term_ord_mapping = TermOrdinalMapping::default();
let mut field_term_streams = Vec::new();
for column in bytes_columns.iter().flatten() {
term_ord_mapping.add_segment(column.dictionary.num_terms());
let terms = column.dictionary.stream()?;
field_term_streams.push(terms);
}
let mut merged_terms = TermMerger::new(field_term_streams);
let mut sstable_builder = sstable::VoidSSTable::writer(output);
// TODO support complex `merge_row_order`.
match merge_row_order {
MergeRowOrder::Stack(_) => {
let mut current_term_ord = 0;
while merged_terms.advance() {
let term_bytes: &[u8] = merged_terms.key();
sstable_builder.insert(term_bytes, &())?;
for (segment_ord, from_term_ord) in merged_terms.matching_segments() {
term_ord_mapping.register_from_to(segment_ord, from_term_ord, current_term_ord);
}
current_term_ord += 1;
}
sstable_builder.finish()?;
}
MergeRowOrder::Shuffled(shuffle_merge_order) => {
assert_eq!(shuffle_merge_order.alive_bitsets.len(), bytes_columns.len());
let mut term_bitsets: Vec<Option<BitSet>> = Vec::with_capacity(bytes_columns.len());
for (alive_bitset_opt, bytes_column_opt) in shuffle_merge_order
.alive_bitsets
.iter()
.zip(bytes_columns.iter())
{
match (alive_bitset_opt, bytes_column_opt) {
(Some(alive_bitset), Some(bytes_column)) => {
let term_bitset = compute_term_bitset(bytes_column, alive_bitset);
term_bitsets.push(Some(term_bitset));
}
_ => {
term_bitsets.push(None);
}
}
}
let mut current_term_ord = 0;
while merged_terms.advance() {
let term_bytes: &[u8] = merged_terms.key();
if !is_term_present(&term_bitsets[..], &merged_terms) {
continue;
}
sstable_builder.insert(term_bytes, &())?;
for (segment_ord, from_term_ord) in merged_terms.matching_segments() {
term_ord_mapping.register_from_to(segment_ord, from_term_ord, current_term_ord);
}
current_term_ord += 1;
}
sstable_builder.finish()?;
}
}
Ok(term_ord_mapping)
}
#[derive(Default, Debug)]
struct TermOrdinalMapping {
per_segment_new_term_ordinals: Vec<Vec<TermOrdinal>>,
}
impl TermOrdinalMapping {
fn add_segment(&mut self, max_term_ord: usize) {
self.per_segment_new_term_ordinals
.push(vec![TermOrdinal::default(); max_term_ord]);
}
fn register_from_to(&mut self, segment_ord: usize, from_ord: TermOrdinal, to_ord: TermOrdinal) {
self.per_segment_new_term_ordinals[segment_ord][from_ord as usize] = to_ord;
}
fn get_segment(&self, segment_ord: u32) -> &[TermOrdinal] {
&(self.per_segment_new_term_ordinals[segment_ord as usize])[..]
}
}

118
columnar/src/columnar/merge/merge_mapping.rs
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@@ -1,118 +0,0 @@
use std::ops::Range;
use common::{BitSet, OwnedBytes, ReadOnlyBitSet};
use crate::{ColumnarReader, RowAddr, RowId};
pub struct StackMergeOrder {
// This does not start at 0. The first row is the number of
// rows in the first columnar.
cumulated_row_ids: Vec<RowId>,
}
impl StackMergeOrder {
pub fn stack(columnars: &[&ColumnarReader]) -> StackMergeOrder {
let mut cumulated_row_ids: Vec<RowId> = Vec::with_capacity(columnars.len());
let mut cumulated_row_id = 0;
for columnar in columnars {
cumulated_row_id += columnar.num_rows();
cumulated_row_ids.push(cumulated_row_id);
}
StackMergeOrder { cumulated_row_ids }
}
pub fn num_rows(&self) -> RowId {
self.cumulated_row_ids.last().copied().unwrap_or(0)
}
pub fn offset(&self, columnar_id: usize) -> RowId {
if columnar_id == 0 {
return 0;
}
self.cumulated_row_ids[columnar_id - 1]
}
pub fn columnar_range(&self, columnar_id: usize) -> Range<RowId> {
self.offset(columnar_id)..self.offset(columnar_id + 1)
}
}
pub enum MergeRowOrder {
/// Columnar tables are simply stacked one above the other.
/// If the i-th columnar_readers has n_rows_i rows, then
/// in the resulting columnar,
/// rows [r0..n_row_0) contains the row of columnar_readers[0], in ordder
/// rows [n_row_0..n_row_0 + n_row_1 contains the row of columnar_readers[1], in order.
/// ..
/// No documents is deleted.
Stack(StackMergeOrder),
/// Some more complex mapping, that may interleaves rows from the different readers and
/// drop rows, or do both.
Shuffled(ShuffleMergeOrder),
}
impl From<StackMergeOrder> for MergeRowOrder {
fn from(stack_merge_order: StackMergeOrder) -> MergeRowOrder {
MergeRowOrder::Stack(stack_merge_order)
}
}
impl From<ShuffleMergeOrder> for MergeRowOrder {
fn from(shuffle_merge_order: ShuffleMergeOrder) -> MergeRowOrder {
MergeRowOrder::Shuffled(shuffle_merge_order)
}
}
impl MergeRowOrder {
pub fn num_rows(&self) -> RowId {
match self {
MergeRowOrder::Stack(stack_row_order) => stack_row_order.num_rows(),
MergeRowOrder::Shuffled(complex_mapping) => complex_mapping.num_rows(),
}
}
}
pub struct ShuffleMergeOrder {
pub new_row_id_to_old_row_id: Vec<RowAddr>,
pub alive_bitsets: Vec<Option<ReadOnlyBitSet>>,
}
impl ShuffleMergeOrder {
pub fn for_test(
segment_num_rows: &[RowId],
new_row_id_to_old_row_id: Vec<RowAddr>,
) -> ShuffleMergeOrder {
let mut alive_bitsets: Vec<BitSet> = segment_num_rows
.iter()
.map(|&num_rows| BitSet::with_max_value(num_rows))
.collect();
for &RowAddr {
segment_ord,
row_id,
} in &new_row_id_to_old_row_id
{
alive_bitsets[segment_ord as usize].insert(row_id);
}
let alive_bitsets: Vec<Option<ReadOnlyBitSet>> = alive_bitsets
.into_iter()
.map(|alive_bitset| {
let mut buffer = Vec::new();
alive_bitset.serialize(&mut buffer).unwrap();
let data = OwnedBytes::new(buffer);
Some(ReadOnlyBitSet::open(data))
})
.collect();
ShuffleMergeOrder {
new_row_id_to_old_row_id,
alive_bitsets,
}
}
pub fn num_rows(&self) -> RowId {
self.new_row_id_to_old_row_id.len() as RowId
}
pub fn iter_new_to_old_row_addrs(&self) -> impl Iterator<Item = RowAddr> + '_ {
self.new_row_id_to_old_row_id.iter().copied()
}
}

375
columnar/src/columnar/merge/mod.rs
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@@ -1,375 +0,0 @@
mod merge_dict_column;
mod merge_mapping;
mod term_merger;
use std::collections::{BTreeMap, HashMap, HashSet};
use std::io;
use std::net::Ipv6Addr;
use std::sync::Arc;
pub use merge_mapping::{MergeRowOrder, ShuffleMergeOrder, StackMergeOrder};
use super::writer::ColumnarSerializer;
use crate::column::{serialize_column_mappable_to_u128, serialize_column_mappable_to_u64};
use crate::column_values::MergedColumnValues;
use crate::columnar::merge::merge_dict_column::merge_bytes_or_str_column;
use crate::columnar::writer::CompatibleNumericalTypes;
use crate::columnar::ColumnarReader;
use crate::dynamic_column::DynamicColumn;
use crate::{
BytesColumn, Column, ColumnIndex, ColumnType, ColumnValues, NumericalType, NumericalValue,
};
/// Column types are grouped into different categories.
/// After merge, all columns belonging to the same category are coerced to
/// the same column type.
///
/// In practise, today, only Numerical colummns are coerced into one type today.
///
/// See also [README.md].
#[derive(Copy, Clone, Eq, PartialEq, Hash, Debug)]
enum ColumnTypeCategory {
Bool,
Str,
Numerical,
DateTime,
Bytes,
IpAddr,
}
impl From<ColumnType> for ColumnTypeCategory {
fn from(column_type: ColumnType) -> Self {
match column_type {
ColumnType::I64 => ColumnTypeCategory::Numerical,
ColumnType::U64 => ColumnTypeCategory::Numerical,
ColumnType::F64 => ColumnTypeCategory::Numerical,
ColumnType::Bytes => ColumnTypeCategory::Bytes,
ColumnType::Str => ColumnTypeCategory::Str,
ColumnType::Bool => ColumnTypeCategory::Bool,
ColumnType::IpAddr => ColumnTypeCategory::IpAddr,
ColumnType::DateTime => ColumnTypeCategory::DateTime,
}
}
}
/// Merge several columnar table together.
///
/// If several columns with the same name are conflicting with the numerical types in the
/// input columnars, the first type compatible out of i64, u64, f64 in that order will be used.
///
/// `require_columns` makes it possible to ensure that some columns will be present in the
/// resulting columnar. When a required column is a numerical column type, one of two things can
/// happen:
/// - If the required column type is compatible with all of the input columnar, the resulsting
/// merged
/// columnar will simply coerce the input column and use the required column type.
/// - If the required column type is incompatible with one of the input columnar, the merged
/// will fail with an InvalidData error.
///
/// `merge_row_order` makes it possible to remove or reorder row in the resulting
/// `Columnar` table.
///
/// Reminder: a string and a numerical column may bare the same column name. This is not
/// considered a conflict.
pub fn merge_columnar(
columnar_readers: &[&ColumnarReader],
required_columns: &[(String, ColumnType)],
merge_row_order: MergeRowOrder,
output: &mut impl io::Write,
) -> io::Result<()> {
let mut serializer = ColumnarSerializer::new(output);
let columns_to_merge = group_columns_for_merge(columnar_readers, required_columns)?;
for ((column_name, column_type), columns) in columns_to_merge {
let mut column_serializer =
serializer.serialize_column(column_name.as_bytes(), column_type);
merge_column(
column_type,
columns,
&merge_row_order,
&mut column_serializer,
)?;
}
serializer.finalize(merge_row_order.num_rows())?;
Ok(())
}
fn dynamic_column_to_u64_monotonic(dynamic_column: DynamicColumn) -> Option<Column<u64>> {
match dynamic_column {
DynamicColumn::Bool(column) => Some(column.to_u64_monotonic()),
DynamicColumn::I64(column) => Some(column.to_u64_monotonic()),
DynamicColumn::U64(column) => Some(column.to_u64_monotonic()),
DynamicColumn::F64(column) => Some(column.to_u64_monotonic()),
DynamicColumn::DateTime(column) => Some(column.to_u64_monotonic()),
DynamicColumn::IpAddr(_) | DynamicColumn::Bytes(_) | DynamicColumn::Str(_) => None,
}
}
fn merge_column(
column_type: ColumnType,
columns: Vec<Option<DynamicColumn>>,
merge_row_order: &MergeRowOrder,
wrt: &mut impl io::Write,
) -> io::Result<()> {
match column_type {
ColumnType::I64
| ColumnType::U64
| ColumnType::F64
| ColumnType::DateTime
| ColumnType::Bool => {
let mut column_indexes: Vec<Option<ColumnIndex>> = Vec::with_capacity(columns.len());
let mut column_values: Vec<Option<Arc<dyn ColumnValues>>> =
Vec::with_capacity(columns.len());
for dynamic_column_opt in columns {
if let Some(Column { idx, values }) =
dynamic_column_opt.and_then(dynamic_column_to_u64_monotonic)
{
column_indexes.push(Some(idx));
column_values.push(Some(values));
} else {
column_indexes.push(None);
column_values.push(None);
}
}
let merged_column_index =
crate::column_index::merge_column_index(&column_indexes[..], merge_row_order);
let merge_column_values = MergedColumnValues {
column_indexes: &column_indexes[..],
column_values: &column_values[..],
merge_row_order,
};
serialize_column_mappable_to_u64(merged_column_index, &merge_column_values, wrt)?;
}
ColumnType::IpAddr => {
let mut column_indexes: Vec<Option<ColumnIndex>> = Vec::with_capacity(columns.len());
let mut column_values: Vec<Option<Arc<dyn ColumnValues<Ipv6Addr>>>> =
Vec::with_capacity(columns.len());
for dynamic_column_opt in columns {
if let Some(DynamicColumn::IpAddr(Column { idx, values })) = dynamic_column_opt {
column_indexes.push(Some(idx));
column_values.push(Some(values));
} else {
column_indexes.push(None);
column_values.push(None);
}
}
let merged_column_index =
crate::column_index::merge_column_index(&column_indexes[..], merge_row_order);
let merge_column_values = MergedColumnValues {
column_indexes: &column_indexes[..],
column_values: &column_values,
merge_row_order,
};
serialize_column_mappable_to_u128(merged_column_index, &merge_column_values, wrt)?;
}
ColumnType::Bytes | ColumnType::Str => {
let mut column_indexes: Vec<Option<ColumnIndex>> = Vec::with_capacity(columns.len());
let mut bytes_columns: Vec<Option<BytesColumn>> = Vec::with_capacity(columns.len());
for dynamic_column_opt in columns {
match dynamic_column_opt {
Some(DynamicColumn::Str(str_column)) => {
column_indexes.push(Some(str_column.term_ord_column.idx.clone()));
bytes_columns.push(Some(str_column.into()));
}
Some(DynamicColumn::Bytes(bytes_column)) => {
column_indexes.push(Some(bytes_column.term_ord_column.idx.clone()));
bytes_columns.push(Some(bytes_column));
}
_ => {
column_indexes.push(None);
bytes_columns.push(None);
}
}
}
let merged_column_index =
crate::column_index::merge_column_index(&column_indexes[..], merge_row_order);
merge_bytes_or_str_column(merged_column_index, &bytes_columns, merge_row_order, wrt)?;
}
}
Ok(())
}
struct GroupedColumns {
required_column_type: Option<ColumnType>,
columns: Vec<Option<DynamicColumn>>,
column_category: ColumnTypeCategory,
}
impl GroupedColumns {
fn for_category(column_category: ColumnTypeCategory, num_columnars: usize) -> Self {
GroupedColumns {
required_column_type: None,
columns: vec![None; num_columnars],
column_category,
}
}
/// Set the dynamic column for a given columnar.
fn set_column(&mut self, columnar_id: usize, column: DynamicColumn) {
self.columns[columnar_id] = Some(column);
}
/// Force the existence of a column, as well as its type.
fn require_type(&mut self, required_type: ColumnType) -> io::Result<()> {
if let Some(existing_required_type) = self.required_column_type {
if existing_required_type == required_type {
// This was just a duplicate in the `required_columns`.
// Nothing to do.
return Ok(());
} else {
return Err(io::Error::new(
io::ErrorKind::InvalidInput,
"Required column conflicts with another required column of the same type \
category.",
));
}
}
self.required_column_type = Some(required_type);
Ok(())
}
/// Returns the column type after merge.
///
/// This method does not check if the column types can actually be coerced to
/// this type.
fn column_type_after_merge(&self) -> ColumnType {
if let Some(required_type) = self.required_column_type {
return required_type;
}
let column_type: HashSet<ColumnType> = self
.columns
.iter()
.flatten()
.map(|column| column.column_type())
.collect();
if column_type.len() == 1 {
return column_type.into_iter().next().unwrap();
}
// At the moment, only the numerical categorical column type has more than one possible
// column type.
assert_eq!(self.column_category, ColumnTypeCategory::Numerical);
merged_numerical_columns_type(self.columns.iter().flatten()).into()
}
}
/// Returns the type of the merged numerical column.
///
/// This function picks the first numerical type out of i64, u64, f64 (order matters
/// here), that is compatible with all the `columns`.
///
/// # Panics
/// Panics if one of the column is not numerical.
fn merged_numerical_columns_type<'a>(
columns: impl Iterator<Item = &'a DynamicColumn>,
) -> NumericalType {
let mut compatible_numerical_types = CompatibleNumericalTypes::default();
for column in columns {
let (min_value, max_value) =
min_max_if_numerical(column).expect("All columns re required to be numerical");
compatible_numerical_types.accept_value(min_value);
compatible_numerical_types.accept_value(max_value);
}
compatible_numerical_types.to_numerical_type()
}
#[allow(clippy::type_complexity)]
fn group_columns_for_merge(
columnar_readers: &[&ColumnarReader],
required_columns: &[(String, ColumnType)],
) -> io::Result<BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>>> {
// Each column name may have multiple types of column associated.
// For merging we are interested in the same column type category since they can be merged.
let mut columns_grouped: HashMap<(String, ColumnTypeCategory), GroupedColumns> = HashMap::new();
for &(ref column_name, column_type) in required_columns {
columns_grouped
.entry((column_name.clone(), column_type.into()))
.or_insert_with(|| {
GroupedColumns::for_category(column_type.into(), columnar_readers.len())
})
.require_type(column_type)?;
}
for (columnar_id, columnar_reader) in columnar_readers.iter().enumerate() {
let column_name_and_handle = columnar_reader.list_columns()?;
for (column_name, handle) in column_name_and_handle {
let column_category: ColumnTypeCategory = handle.column_type().into();
let column = handle.open()?;
columns_grouped
.entry((column_name, column_category))
.or_insert_with(|| {
GroupedColumns::for_category(column_category, columnar_readers.len())
})
.set_column(columnar_id, column);
}
}
let mut merge_columns: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
Default::default();
for ((column_name, _), mut grouped_columns) in columns_grouped {
let column_type = grouped_columns.column_type_after_merge();
coerce_columns(column_type, &mut grouped_columns.columns)?;
merge_columns.insert((column_name, column_type), grouped_columns.columns);
}
Ok(merge_columns)
}
fn coerce_columns(
column_type: ColumnType,
columns: &mut [Option<DynamicColumn>],
) -> io::Result<()> {
for column_opt in columns.iter_mut() {
if let Some(column) = column_opt.take() {
*column_opt = Some(coerce_column(column_type, column)?);
}
}
Ok(())
}
fn coerce_column(column_type: ColumnType, column: DynamicColumn) -> io::Result<DynamicColumn> {
if let Some(numerical_type) = column_type.numerical_type() {
column
.coerce_numerical(numerical_type)
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidInput, ""))
} else {
if column.column_type() != column_type {
return Err(io::Error::new(
io::ErrorKind::InvalidInput,
format!(
"Cannot coerce column of type `{:?}` to `{column_type:?}`",
column.column_type()
),
));
}
Ok(column)
}
}
/// Returns the (min, max) of a column provided it is numerical (i64, u64. f64).
///
/// The min and the max are simply the numerical value as defined by `ColumnValue::min_value()`,
/// and `ColumnValue::max_value()`.
///
/// It is important to note that these values are only guaranteed to be lower/upper bound
/// (as opposed to min/max value).
/// If a column is empty, the min and max values are currently set to 0.
fn min_max_if_numerical(column: &DynamicColumn) -> Option<(NumericalValue, NumericalValue)> {
match column {
DynamicColumn::I64(column) => Some((column.min_value().into(), column.max_value().into())),
DynamicColumn::U64(column) => Some((column.min_value().into(), column.min_value().into())),
DynamicColumn::F64(column) => Some((column.min_value().into(), column.min_value().into())),
DynamicColumn::Bool(_)
| DynamicColumn::IpAddr(_)
| DynamicColumn::DateTime(_)
| DynamicColumn::Bytes(_)
| DynamicColumn::Str(_) => None,
}
}
#[cfg(test)]
mod tests;

107
columnar/src/columnar/merge/term_merger.rs
View File

@@ -1,107 +0,0 @@
use std::cmp::Ordering;
use std::collections::BinaryHeap;
use sstable::TermOrdinal;
use crate::Streamer;
pub struct HeapItem<'a> {
pub streamer: Streamer<'a>,
pub segment_ord: usize,
}
impl<'a> PartialEq for HeapItem<'a> {
fn eq(&self, other: &Self) -> bool {
self.segment_ord == other.segment_ord
}
}
impl<'a> Eq for HeapItem<'a> {}
impl<'a> PartialOrd for HeapItem<'a> {
fn partial_cmp(&self, other: &HeapItem<'a>) -> Option<Ordering> {
Some(self.cmp(other))
}
}
impl<'a> Ord for HeapItem<'a> {
fn cmp(&self, other: &HeapItem<'a>) -> Ordering {
(&other.streamer.key(), &other.segment_ord).cmp(&(&self.streamer.key(), &self.segment_ord))
}
}
/// Given a list of sorted term streams,
/// returns an iterator over sorted unique terms.
///
/// The item yield is actually a pair with
/// - the term
/// - a slice with the ordinal of the segments containing
/// the terms.
pub struct TermMerger<'a> {
heap: BinaryHeap<HeapItem<'a>>,
current_streamers: Vec<HeapItem<'a>>,
}
impl<'a> TermMerger<'a> {
/// Stream of merged term dictionary
pub fn new(streams: Vec<Streamer<'a>>) -> TermMerger<'a> {
TermMerger {
heap: BinaryHeap::new(),
current_streamers: streams
.into_iter()
.enumerate()
.map(|(ord, streamer)| HeapItem {
streamer,
segment_ord: ord,
})
.collect(),
}
}
pub(crate) fn matching_segments<'b: 'a>(
&'b self,
) -> impl 'b + Iterator<Item = (usize, TermOrdinal)> {
self.current_streamers
.iter()
.map(|heap_item| (heap_item.segment_ord, heap_item.streamer.term_ord()))
}
fn advance_segments(&mut self) {
let streamers = &mut self.current_streamers;
let heap = &mut self.heap;
for mut heap_item in streamers.drain(..) {
if heap_item.streamer.advance() {
heap.push(heap_item);
}
}
}
/// Advance the term iterator to the next term.
/// Returns true if there is indeed another term
/// False if there is none.
pub fn advance(&mut self) -> bool {
self.advance_segments();
if let Some(head) = self.heap.pop() {
self.current_streamers.push(head);
while let Some(next_streamer) = self.heap.peek() {
if self.current_streamers[0].streamer.key() != next_streamer.streamer.key() {
break;
}
let next_heap_it = self.heap.pop().unwrap(); // safe : we peeked beforehand
self.current_streamers.push(next_heap_it);
}
true
} else {
false
}
}
/// Returns the current term.
///
/// This method may be called
/// if and only if advance() has been called before
/// and "true" was returned.
pub fn key(&self) -> &[u8] {
self.current_streamers[0].streamer.key()
}
}

318
columnar/src/columnar/merge/tests.rs
View File

@@ -1,318 +0,0 @@
use super::*;
use crate::{Cardinality, ColumnarWriter, HasAssociatedColumnType, RowId};
fn make_columnar<T: Into<NumericalValue> + HasAssociatedColumnType + Copy>(
column_name: &str,
vals: &[T],
) -> ColumnarReader {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_column_type(column_name, T::column_type(), false);
for (row_id, val) in vals.iter().copied().enumerate() {
dataframe_writer.record_numerical(row_id as RowId, column_name, val.into());
}
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer
.serialize(vals.len() as RowId, None, &mut buffer)
.unwrap();
ColumnarReader::open(buffer).unwrap()
}
#[test]
fn test_column_coercion_to_u64() {
// i64 type
let columnar1 = make_columnar("numbers", &[1i64]);
// u64 type
let columnar2 = make_columnar("numbers", &[u64::MAX]);
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
group_columns_for_merge(&[&columnar1, &columnar2], &[]).unwrap();
assert_eq!(column_map.len(), 1);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::U64)));
}
#[test]
fn test_column_no_coercion_if_all_the_same() {
let columnar1 = make_columnar("numbers", &[1u64]);
let columnar2 = make_columnar("numbers", &[2u64]);
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
group_columns_for_merge(&[&columnar1, &columnar2], &[]).unwrap();
assert_eq!(column_map.len(), 1);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::U64)));
}
#[test]
fn test_column_coercion_to_i64() {
let columnar1 = make_columnar("numbers", &[-1i64]);
let columnar2 = make_columnar("numbers", &[2u64]);
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
group_columns_for_merge(&[&columnar1, &columnar2], &[]).unwrap();
assert_eq!(column_map.len(), 1);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::I64)));
}
#[test]
fn test_impossible_coercion_returns_an_error() {
let columnar1 = make_columnar("numbers", &[u64::MAX]);
let group_error =
group_columns_for_merge(&[&columnar1], &[("numbers".to_string(), ColumnType::I64)])
.map(|_| ())
.unwrap_err();
assert_eq!(group_error.kind(), io::ErrorKind::InvalidInput);
}
#[test]
fn test_group_columns_with_required_column() {
let columnar1 = make_columnar("numbers", &[1i64]);
let columnar2 = make_columnar("numbers", &[2u64]);
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
group_columns_for_merge(
&[&columnar1, &columnar2],
&[("numbers".to_string(), ColumnType::U64)],
)
.unwrap();
assert_eq!(column_map.len(), 1);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::U64)));
}
#[test]
fn test_group_columns_required_column_with_no_existing_columns() {
let columnar1 = make_columnar("numbers", &[2u64]);
let columnar2 = make_columnar("numbers", &[2u64]);
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
group_columns_for_merge(
&[&columnar1, &columnar2],
&[("required_col".to_string(), ColumnType::Str)],
)
.unwrap();
assert_eq!(column_map.len(), 2);
let columns = column_map
.get(&("required_col".to_string(), ColumnType::Str))
.unwrap();
assert_eq!(columns.len(), 2);
assert!(columns[0].is_none());
assert!(columns[1].is_none());
}
#[test]
fn test_group_columns_required_column_is_above_all_columns_have_the_same_type_rule() {
let columnar1 = make_columnar("numbers", &[2i64]);
let columnar2 = make_columnar("numbers", &[2i64]);
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
group_columns_for_merge(
&[&columnar1, &columnar2],
&[("numbers".to_string(), ColumnType::U64)],
)
.unwrap();
assert_eq!(column_map.len(), 1);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::U64)));
}
#[test]
fn test_missing_column() {
let columnar1 = make_columnar("numbers", &[-1i64]);
let columnar2 = make_columnar("numbers2", &[2u64]);
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
group_columns_for_merge(&[&columnar1, &columnar2], &[]).unwrap();
assert_eq!(column_map.len(), 2);
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::I64)));
{
let columns = column_map
.get(&("numbers".to_string(), ColumnType::I64))
.unwrap();
assert!(columns[0].is_some());
assert!(columns[1].is_none());
}
{
let columns = column_map
.get(&("numbers2".to_string(), ColumnType::U64))
.unwrap();
assert!(columns[0].is_none());
assert!(columns[1].is_some());
}
}
fn make_numerical_columnar_multiple_columns(
columns: &[(&str, &[&[NumericalValue]])],
) -> ColumnarReader {
let mut dataframe_writer = ColumnarWriter::default();
for (column_name, column_values) in columns {
for (row_id, vals) in column_values.iter().enumerate() {
for val in vals.iter() {
dataframe_writer.record_numerical(row_id as u32, column_name, *val);
}
}
}
let num_rows = columns
.iter()
.map(|(_, val_rows)| val_rows.len() as RowId)
.max()
.unwrap_or(0u32);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer
.serialize(num_rows, None, &mut buffer)
.unwrap();
ColumnarReader::open(buffer).unwrap()
}
fn make_byte_columnar_multiple_columns(columns: &[(&str, &[&[&[u8]]])]) -> ColumnarReader {
let mut dataframe_writer = ColumnarWriter::default();
for (column_name, column_values) in columns {
for (row_id, vals) in column_values.iter().enumerate() {
for val in vals.iter() {
dataframe_writer.record_bytes(row_id as u32, column_name, val);
}
}
}
let num_rows = columns
.iter()
.map(|(_, val_rows)| val_rows.len() as RowId)
.max()
.unwrap_or(0u32);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer
.serialize(num_rows, None, &mut buffer)
.unwrap();
ColumnarReader::open(buffer).unwrap()
}
fn make_text_columnar_multiple_columns(columns: &[(&str, &[&[&str]])]) -> ColumnarReader {
let mut dataframe_writer = ColumnarWriter::default();
for (column_name, column_values) in columns {
for (row_id, vals) in column_values.iter().enumerate() {
for val in vals.iter() {
dataframe_writer.record_str(row_id as u32, column_name, val);
}
}
}
let num_rows = columns
.iter()
.map(|(_, val_rows)| val_rows.len() as RowId)
.max()
.unwrap_or(0u32);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer
.serialize(num_rows, None, &mut buffer)
.unwrap();
ColumnarReader::open(buffer).unwrap()
}
#[test]
fn test_merge_columnar_numbers() {
let columnar1 =
make_numerical_columnar_multiple_columns(&[("numbers", &[&[NumericalValue::from(-1f64)]])]);
let columnar2 = make_numerical_columnar_multiple_columns(&[(
"numbers",
&[&[], &[NumericalValue::from(-3f64)]],
)]);
let mut buffer = Vec::new();
let columnars = &[&columnar1, &columnar2];
let stack_merge_order = StackMergeOrder::stack(columnars);
crate::columnar::merge_columnar(
columnars,
&[],
MergeRowOrder::Stack(stack_merge_order),
&mut buffer,
)
.unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_rows(), 3);
assert_eq!(columnar_reader.num_columns(), 1);
let cols = columnar_reader.read_columns("numbers").unwrap();
let dynamic_column = cols[0].open().unwrap();
let DynamicColumn::F64(vals) = dynamic_column else { panic!() };
assert_eq!(vals.get_cardinality(), Cardinality::Optional);
assert_eq!(vals.first(0u32), Some(-1f64));
assert_eq!(vals.first(1u32), None);
assert_eq!(vals.first(2u32), Some(-3f64));
}
#[test]
fn test_merge_columnar_texts() {
let columnar1 = make_text_columnar_multiple_columns(&[("texts", &[&["a"]])]);
let columnar2 = make_text_columnar_multiple_columns(&[("texts", &[&[], &["b"]])]);
let mut buffer = Vec::new();
let columnars = &[&columnar1, &columnar2];
let stack_merge_order = StackMergeOrder::stack(columnars);
crate::columnar::merge_columnar(
columnars,
&[],
MergeRowOrder::Stack(stack_merge_order),
&mut buffer,
)
.unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_rows(), 3);
assert_eq!(columnar_reader.num_columns(), 1);
let cols = columnar_reader.read_columns("texts").unwrap();
let dynamic_column = cols[0].open().unwrap();
let DynamicColumn::Str(vals) = dynamic_column else { panic!() };
let get_str_for_ord = |ord| {
let mut out = String::new();
vals.ord_to_str(ord, &mut out).unwrap();
out
};
assert_eq!(vals.dictionary.num_terms(), 2);
assert_eq!(get_str_for_ord(0), "a");
assert_eq!(get_str_for_ord(1), "b");
let get_str_for_row = |row_id| {
let term_ords: Vec<u64> = vals.term_ords(row_id).collect();
assert!(term_ords.len() <= 1);
let mut out = String::new();
if term_ords.len() == 1 {
vals.ord_to_str(term_ords[0], &mut out).unwrap();
}
out
};
assert_eq!(get_str_for_row(0), "a");
assert_eq!(get_str_for_row(1), "");
assert_eq!(get_str_for_row(2), "b");
}
#[test]
fn test_merge_columnar_byte() {
let columnar1 = make_byte_columnar_multiple_columns(&[("bytes", &[&[b"bbbb"], &[b"baaa"]])]);
let columnar2 = make_byte_columnar_multiple_columns(&[("bytes", &[&[], &[b"a"]])]);
let mut buffer = Vec::new();
let columnars = &[&columnar1, &columnar2];
let stack_merge_order = StackMergeOrder::stack(columnars);
crate::columnar::merge_columnar(
columnars,
&[],
MergeRowOrder::Stack(stack_merge_order),
&mut buffer,
)
.unwrap();
let columnar_reader = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar_reader.num_rows(), 4);
assert_eq!(columnar_reader.num_columns(), 1);
let cols = columnar_reader.read_columns("bytes").unwrap();
let dynamic_column = cols[0].open().unwrap();
let DynamicColumn::Bytes(vals) = dynamic_column else { panic!() };
let get_bytes_for_ord = |ord| {
let mut out = Vec::new();
vals.ord_to_bytes(ord, &mut out).unwrap();
out
};
assert_eq!(vals.dictionary.num_terms(), 3);
assert_eq!(get_bytes_for_ord(0), b"a");
assert_eq!(get_bytes_for_ord(1), b"baaa");
assert_eq!(get_bytes_for_ord(2), b"bbbb");
let get_bytes_for_row = |row_id| {
let term_ords: Vec<u64> = vals.term_ords(row_id).collect();
assert!(term_ords.len() <= 1);
let mut out = Vec::new();
if term_ords.len() == 1 {
vals.ord_to_bytes(term_ords[0], &mut out).unwrap();
}
out
};
assert_eq!(get_bytes_for_row(0), b"bbbb");
assert_eq!(get_bytes_for_row(1), b"baaa");
assert_eq!(get_bytes_for_row(2), b"");
assert_eq!(get_bytes_for_row(3), b"a");
}

10
columnar/src/columnar/mod.rs
View File

@@ -1,10 +0,0 @@
mod column_type;
mod format_version;
mod merge;
mod reader;
mod writer;
pub use column_type::{ColumnType, HasAssociatedColumnType};
pub use merge::{merge_columnar, MergeRowOrder, ShuffleMergeOrder, StackMergeOrder};
pub use reader::ColumnarReader;
pub use writer::ColumnarWriter;

192
columnar/src/columnar/reader/mod.rs
View File

@@ -1,192 +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;
use crate::RowId;
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.
#[derive(Clone)]
pub struct ColumnarReader {
column_dictionary: Dictionary<RangeSSTable>,
column_data: FileSlice,
num_rows: RowId,
}
/// Functions by both the async/sync code listing columns.
/// It takes a stream from the column sstable and return the list of
/// `DynamicColumn` available in it.
fn read_all_columns_in_stream(
mut stream: sstable::Streamer<'_, RangeSSTable>,
column_data: &FileSlice,
) -> io::Result<Vec<DynamicColumnHandle>> {
let mut results = Vec::new();
while stream.advance() {
let key_bytes: &[u8] = stream.key();
let Some(column_code) = key_bytes.last().copied() else {
return Err(io_invalid_data("Empty column name.".to_string()));
};
let column_type = ColumnType::try_from_code(column_code)
.map_err(|_| io_invalid_data(format!("Unknown column code `{column_code}`")))?;
let range = stream.value();
let file_slice = 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)
}
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>() + 4 + format_version::VERSION_FOOTER_NUM_BYTES);
let footer_bytes = footer_slice.read_bytes()?;
let sstable_len = u64::deserialize(&mut &footer_bytes[0..8])?;
let num_rows = u32::deserialize(&mut &footer_bytes[8..12])?;
let version_footer_bytes: [u8; format_version::VERSION_FOOTER_NUM_BYTES] =
footer_bytes[12..].try_into().unwrap();
let _version = format_version::parse_footer(version_footer_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,
num_rows,
})
}
pub fn num_rows(&self) -> RowId {
self.num_rows
}
// TODO Add unit tests
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 =
// The last two bytes are respectively the 0u8 separator and the column_type.
String::from_utf8_lossy(&key_bytes[..key_bytes.len() - 2]).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)
}
fn stream_for_column_range(&self, column_name: &str) -> sstable::StreamerBuilder<RangeSSTable> {
// 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 dictionary.
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);
self.column_dictionary
.range()
.ge(start_key.as_bytes())
.lt(end_key.as_bytes())
}
pub async fn read_columns_async(
&self,
column_name: &str,
) -> io::Result<Vec<DynamicColumnHandle>> {
let stream = self
.stream_for_column_range(column_name)
.into_stream_async()
.await?;
read_all_columns_in_stream(stream, &self.column_data)
}
/// 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.
pub fn read_columns(&self, column_name: &str) -> io::Result<Vec<DynamicColumnHandle>> {
let stream = self.stream_for_column_range(column_name).into_stream()?;
read_all_columns_in_stream(stream, &self.column_data)
}
/// Return the number of columns in the columnar.
pub fn num_columns(&self) -> usize {
self.column_dictionary.num_terms()
}
}
#[cfg(test)]
mod tests {
use crate::{ColumnType, ColumnarReader, ColumnarWriter};
#[test]
fn test_list_columns() {
let mut columnar_writer = ColumnarWriter::default();
columnar_writer.record_column_type("col1", ColumnType::Str, false);
columnar_writer.record_column_type("col2", ColumnType::U64, false);
let mut buffer = Vec::new();
columnar_writer.serialize(1, None, &mut buffer).unwrap();
let columnar = ColumnarReader::open(buffer).unwrap();
let columns = columnar.list_columns().unwrap();
assert_eq!(columns.len(), 2);
assert_eq!(&columns[0].0, "col1");
assert_eq!(columns[0].1.column_type(), ColumnType::Str);
assert_eq!(&columns[1].0, "col2");
assert_eq!(columns[1].1.column_type(), ColumnType::U64);
}
#[test]
fn test_list_columns_strict_typing_prevents_coercion() {
let mut columnar_writer = ColumnarWriter::default();
columnar_writer.record_column_type("count", ColumnType::U64, false);
columnar_writer.record_numerical(1, "count", 1u64);
let mut buffer = Vec::new();
columnar_writer.serialize(2, None, &mut buffer).unwrap();
let columnar = ColumnarReader::open(buffer).unwrap();
let columns = columnar.list_columns().unwrap();
assert_eq!(columns.len(), 1);
assert_eq!(&columns[0].0, "count");
assert_eq!(columns[0].1.column_type(), ColumnType::U64);
}
#[test]
#[should_panic(expected = "Input type forbidden")]
fn test_list_columns_strict_typing_panics_on_wrong_types() {
let mut columnar_writer = ColumnarWriter::default();
columnar_writer.record_column_type("count", ColumnType::U64, false);
columnar_writer.record_numerical(1, "count", 1i64);
}
}

360
columnar/src/columnar/writer/column_operation.rs
View File

@@ -1,360 +0,0 @@
use std::net::Ipv6Addr;
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, 6>(self.len) | place_bits::<6, 8>(self.op_type.to_code())
}
fn try_from_code(code: u8) -> Result<Self, InvalidData> {
let len = select_bits::<0, 6>(code);
let typ_code = select_bits::<6, 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
}
}
impl SymbolValue for Ipv6Addr {
fn serialize(self, buffer: &mut [u8]) -> u8 {
buffer[0..16].copy_from_slice(&self.octets());
16
}
fn deserialize(bytes: &[u8]) -> Self {
let octets: [u8; 16] = bytes[0..16].try_into().unwrap();
Ipv6Addr::from(octets)
}
}
#[derive(Default)]
struct MiniBuffer {
pub bytes: [u8; 17],
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(), 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() }, 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);
}
}

363
columnar/src/columnar/writer/column_writers.rs
View File

@@ -1,363 +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,
old_to_new_ids_opt: Option<&[RowId]>,
buffer: &'a mut Vec<u8>,
) -> impl Iterator<Item = ColumnOperation<V>> + 'a {
buffer.clear();
self.values.read_to_end(arena, buffer);
if let Some(old_to_new_ids) = old_to_new_ids_opt {
// TODO avoid the extra deserialization / serialization.
let mut sorted_ops: Vec<(RowId, ColumnOperation<V>)> = Vec::new();
let mut new_doc = 0u32;
let mut cursor = &buffer[..];
for op in std::iter::from_fn(|| ColumnOperation::<V>::deserialize(&mut cursor)) {
if let ColumnOperation::NewDoc(doc) = &op {
new_doc = old_to_new_ids[*doc as usize];
sorted_ops.push((new_doc, ColumnOperation::NewDoc(new_doc)));
} else {
sorted_ops.push((new_doc, op));
}
}
// stable sort is crucial here.
sorted_ops.sort_by_key(|(new_doc_id, _)| *new_doc_id);
buffer.clear();
for (_, op) in sorted_ops {
buffer.extend_from_slice(op.serialize().as_ref());
}
}
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,
}
impl NumericalColumnWriter {
pub fn force_numerical_type(&mut self, numerical_type: NumericalType) {
assert!(self
.compatible_numerical_types
.is_type_accepted(numerical_type));
self.compatible_numerical_types = CompatibleNumericalTypes::StaticType(numerical_type);
}
}
/// State used to store what types are still acceptable
/// after having seen a set of numerical values.
#[derive(Clone, Copy)]
pub(crate) enum CompatibleNumericalTypes {
Dynamic {
all_values_within_i64_range: bool,
all_values_within_u64_range: bool,
},
StaticType(NumericalType),
}
impl Default for CompatibleNumericalTypes {
fn default() -> CompatibleNumericalTypes {
CompatibleNumericalTypes::Dynamic {
all_values_within_i64_range: true,
all_values_within_u64_range: true,
}
}
}
impl CompatibleNumericalTypes {
pub fn is_type_accepted(&self, numerical_type: NumericalType) -> bool {
match self {
CompatibleNumericalTypes::Dynamic {
all_values_within_i64_range,
all_values_within_u64_range,
} => match numerical_type {
NumericalType::I64 => *all_values_within_i64_range,
NumericalType::U64 => *all_values_within_u64_range,
NumericalType::F64 => true,
},
CompatibleNumericalTypes::StaticType(static_numerical_type) => {
*static_numerical_type == numerical_type
}
}
}
pub fn accept_value(&mut self, numerical_value: NumericalValue) {
match self {
CompatibleNumericalTypes::Dynamic {
all_values_within_i64_range,
all_values_within_u64_range,
} => match numerical_value {
NumericalValue::I64(val_i64) => {
let value_within_u64_range = val_i64 >= 0i64;
*all_values_within_u64_range &= value_within_u64_range;
}
NumericalValue::U64(val_u64) => {
let value_within_i64_range = val_u64 < i64::MAX as u64;
*all_values_within_i64_range &= value_within_i64_range;
}
NumericalValue::F64(_) => {
*all_values_within_i64_range = false;
*all_values_within_u64_range = false;
}
},
CompatibleNumericalTypes::StaticType(typ) => {
assert_eq!(
numerical_value.numerical_type(),
*typ,
"Input type forbidden. This column has been forced to type {typ:?}, received \
{numerical_value:?}"
);
}
}
}
pub fn to_numerical_type(self) -> NumericalType {
for numerical_type in [NumericalType::I64, NumericalType::U64] {
if self.is_type_accepted(numerical_type) {
return numerical_type;
}
}
NumericalType::F64
}
}
impl NumericalColumnWriter {
pub fn numerical_type(&self) -> NumericalType {
self.compatible_numerical_types.to_numerical_type()
}
pub fn cardinality(&self, num_docs: RowId) -> Cardinality {
self.column_writer.get_cardinality(num_docs)
}
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,
old_to_new_ids: Option<&[RowId]>,
buffer: &'a mut Vec<u8>,
) -> impl Iterator<Item = ColumnOperation<NumericalValue>> + 'a {
self.column_writer
.operation_iterator(arena, old_to_new_ids, buffer)
}
}
#[derive(Copy, Clone)]
pub(crate) struct StrOrBytesColumnWriter {
pub(crate) dictionary_id: u32,
pub(crate) column_writer: ColumnWriter,
// If true, when facing a multivalued cardinality,
// values associated to a given document will be sorted.
//
// This is useful for facets.
//
// If false, the order of appearance in the document will be
// observed.
pub(crate) sort_values_within_row: bool,
}
impl StrOrBytesColumnWriter {
pub(crate) fn with_dictionary_id(dictionary_id: u32) -> StrOrBytesColumnWriter {
StrOrBytesColumnWriter {
dictionary_id,
column_writer: Default::default(),
sort_values_within_row: false,
}
}
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,
old_to_new_ids: Option<&[RowId]>,
byte_buffer: &'a mut Vec<u8>,
) -> impl Iterator<Item = ColumnOperation<UnorderedId>> + 'a {
self.column_writer
.operation_iterator(arena, old_to_new_ids, 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);
}
#[test]
#[should_panic]
fn test_compatible_numerical_types_static_incompatible_type() {
let mut compatible_numerical_types =
CompatibleNumericalTypes::StaticType(NumericalType::U64);
compatible_numerical_types.accept_value(NumericalValue::I64(1i64));
}
#[test]
fn test_compatible_numerical_types_static_different_type_forbidden() {
let mut compatible_numerical_types =
CompatibleNumericalTypes::StaticType(NumericalType::U64);
compatible_numerical_types.accept_value(NumericalValue::U64(u64::MAX));
}
#[test]
fn test_compatible_numerical_types_static() {
for typ in [NumericalType::I64, NumericalType::I64, NumericalType::F64] {
let compatible_numerical_types = CompatibleNumericalTypes::StaticType(typ);
assert_eq!(compatible_numerical_types.to_numerical_type(), typ);
}
}
}

848
columnar/src/columnar/writer/mod.rs
View File

@@ -1,848 +0,0 @@
mod column_operation;
mod column_writers;
mod serializer;
mod value_index;
use std::io;
use std::net::Ipv6Addr;
use column_operation::ColumnOperation;
pub(crate) use column_writers::CompatibleNumericalTypes;
use common::CountingWriter;
pub(crate) use serializer::ColumnarSerializer;
use stacker::{Addr, ArenaHashMap, MemoryArena};
use crate::column_index::SerializableColumnIndex;
use crate::column_values::{
ColumnValues, MonotonicallyMappableToU128, MonotonicallyMappableToU64, VecColumn,
};
use crate::columnar::column_type::ColumnType;
use crate::columnar::writer::column_writers::{
ColumnWriter, NumericalColumnWriter, StrOrBytesColumnWriter,
};
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,
u64_values: Vec<u64>,
ip_addr_values: Vec<Ipv6Addr>,
}
/// 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, None, &mut wrt).unwrap();
/// ```
#[derive(Default)]
pub struct ColumnarWriter {
numerical_field_hash_map: ArenaHashMap,
datetime_field_hash_map: ArenaHashMap,
bool_field_hash_map: ArenaHashMap,
ip_addr_field_hash_map: ArenaHashMap,
bytes_field_hash_map: ArenaHashMap,
str_field_hash_map: ArenaHashMap,
arena: MemoryArena,
// Dictionaries used to store dictionary-encoded values.
dictionaries: Vec<DictionaryBuilder>,
buffers: SpareBuffers,
}
#[inline]
fn mutate_or_create_column<V, TMutator>(
arena_hash_map: &mut ArenaHashMap,
column_name: &str,
updater: TMutator,
) where
V: Copy + 'static,
TMutator: FnMut(Option<V>) -> V,
{
assert!(
!column_name.as_bytes().contains(&0u8),
"key may not contain the 0 byte"
);
arena_hash_map.mutate_or_create(column_name.as_bytes(), updater);
}
impl ColumnarWriter {
pub fn mem_usage(&self) -> usize {
// TODO add dictionary builders.
self.arena.mem_usage()
+ self.numerical_field_hash_map.mem_usage()
+ self.bool_field_hash_map.mem_usage()
+ self.bytes_field_hash_map.mem_usage()
+ self.str_field_hash_map.mem_usage()
+ self.ip_addr_field_hash_map.mem_usage()
+ self.datetime_field_hash_map.mem_usage()
}
/// Returns the list of doc ids from 0..num_docs sorted by the `sort_field`
/// column.
///
/// If the column is multivalued, use the first value for scoring.
/// If no value is associated to a specific row, the document is assigned
/// the lowest possible score.
///
/// The sort applied is stable.
pub fn sort_order(&self, sort_field: &str, num_docs: RowId, reversed: bool) -> Vec<u32> {
let Some(numerical_col_writer) =
self.numerical_field_hash_map.get::<NumericalColumnWriter>(sort_field.as_bytes()) else {
return Vec::new();
};
let mut symbols_buffer = Vec::new();
let mut values = Vec::new();
let mut last_doc_opt: Option<RowId> = None;
for op in numerical_col_writer.operation_iterator(&self.arena, None, &mut symbols_buffer) {
match op {
ColumnOperation::NewDoc(doc) => {
last_doc_opt = Some(doc);
}
ColumnOperation::Value(numerical_value) => {
if let Some(last_doc) = last_doc_opt {
let score: f32 = f64::coerce(numerical_value) as f32;
values.push((score, last_doc));
}
}
}
}
for doc in values.len() as u32..num_docs {
values.push((0.0f32, doc));
}
values.sort_by(|(left_score, _), (right_score, _)| {
if reversed {
right_score.partial_cmp(left_score).unwrap()
} else {
left_score.partial_cmp(right_score).unwrap()
}
});
values.into_iter().map(|(_score, doc)| doc).collect()
}
/// Records a column type. This is useful to bypass the coercion process,
/// makes sure the empty is present in the resulting columnar, or set
/// the `sort_values_within_row`.
///
/// `sort_values_within_row` is only allowed for `Bytes` or `Str` columns.
pub fn record_column_type(
&mut self,
column_name: &str,
column_type: ColumnType,
sort_values_within_row: bool,
) {
if sort_values_within_row {
assert!(
column_type == ColumnType::Bytes || column_type == ColumnType::Str,
"sort_values_within_row is only allowed for Bytes and Str columns",
);
}
match column_type {
ColumnType::Str | ColumnType::Bytes => {
let (hash_map, dictionaries) = (
if column_type == ColumnType::Str {
&mut self.str_field_hash_map
} else {
&mut self.bytes_field_hash_map
},
&mut self.dictionaries,
);
mutate_or_create_column(
hash_map,
column_name,
|column_opt: Option<StrOrBytesColumnWriter>| {
let mut column_writer = if let Some(column_writer) = column_opt {
column_writer
} else {
let dictionary_id = dictionaries.len() as u32;
dictionaries.push(DictionaryBuilder::default());
StrOrBytesColumnWriter::with_dictionary_id(dictionary_id)
};
column_writer.sort_values_within_row = sort_values_within_row;
column_writer
},
);
}
ColumnType::Bool => {
mutate_or_create_column(
&mut self.bool_field_hash_map,
column_name,
|column_opt: Option<ColumnWriter>| column_opt.unwrap_or_default(),
);
}
ColumnType::DateTime => {
mutate_or_create_column(
&mut self.datetime_field_hash_map,
column_name,
|column_opt: Option<ColumnWriter>| column_opt.unwrap_or_default(),
);
}
ColumnType::I64 | ColumnType::F64 | ColumnType::U64 => {
let numerical_type = column_type.numerical_type().unwrap();
mutate_or_create_column(
&mut self.numerical_field_hash_map,
column_name,
|column_opt: Option<NumericalColumnWriter>| {
let mut column: NumericalColumnWriter = column_opt.unwrap_or_default();
column.force_numerical_type(numerical_type);
column
},
);
}
ColumnType::IpAddr => mutate_or_create_column(
&mut self.ip_addr_field_hash_map,
column_name,
|column_opt: Option<ColumnWriter>| column_opt.unwrap_or_default(),
),
}
}
pub fn record_numerical<T: Into<NumericalValue> + Copy>(
&mut self,
doc: RowId,
column_name: &str,
numerical_value: T,
) {
let (hash_map, arena) = (&mut self.numerical_field_hash_map, &mut self.arena);
mutate_or_create_column(
hash_map,
column_name,
|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_ip_addr(&mut self, doc: RowId, column_name: &str, ip_addr: Ipv6Addr) {
assert!(
!column_name.as_bytes().contains(&0u8),
"key may not contain the 0 byte"
);
let (hash_map, arena) = (&mut self.ip_addr_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, ip_addr, arena);
column
},
);
}
pub fn record_bool(&mut self, doc: RowId, column_name: &str, val: bool) {
let (hash_map, arena) = (&mut self.bool_field_hash_map, &mut self.arena);
mutate_or_create_column(hash_map, column_name, |column_opt: Option<ColumnWriter>| {
let mut column: ColumnWriter = column_opt.unwrap_or_default();
column.record(doc, val, arena);
column
});
}
pub fn record_datetime(&mut self, doc: RowId, column_name: &str, datetime: common::DateTime) {
let (hash_map, arena) = (&mut self.datetime_field_hash_map, &mut self.arena);
mutate_or_create_column(hash_map, column_name, |column_opt: Option<ColumnWriter>| {
let mut column: ColumnWriter = column_opt.unwrap_or_default();
column.record(
doc,
NumericalValue::I64(datetime.into_timestamp_micros()),
arena,
);
column
});
}
pub fn record_str(&mut self, doc: RowId, column_name: &str, value: &str) {
let (hash_map, arena, dictionaries) = (
&mut self.str_field_hash_map,
&mut self.arena,
&mut self.dictionaries,
);
hash_map.mutate_or_create(
column_name.as_bytes(),
|column_opt: Option<StrOrBytesColumnWriter>| {
let mut column: StrOrBytesColumnWriter = column_opt.unwrap_or_else(|| {
// Each column has its own dictionary
let dictionary_id = dictionaries.len() as u32;
dictionaries.push(DictionaryBuilder::default());
StrOrBytesColumnWriter::with_dictionary_id(dictionary_id)
});
column.record_bytes(doc, value.as_bytes(), dictionaries, arena);
column
},
);
}
pub fn record_bytes(&mut self, doc: RowId, column_name: &str, value: &[u8]) {
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<StrOrBytesColumnWriter>| {
let mut column: StrOrBytesColumnWriter = column_opt.unwrap_or_else(|| {
// Each column has its own dictionary
let dictionary_id = dictionaries.len() as u32;
dictionaries.push(DictionaryBuilder::default());
StrOrBytesColumnWriter::with_dictionary_id(dictionary_id)
});
column.record_bytes(doc, value, dictionaries, arena);
column
},
);
}
pub fn serialize(
&mut self,
num_docs: RowId,
old_to_new_row_ids: Option<&[RowId]>,
wrt: &mut dyn io::Write,
) -> io::Result<()> {
let mut serializer = ColumnarSerializer::new(wrt);
let mut columns: Vec<(&[u8], ColumnType, Addr)> = self
.numerical_field_hash_map
.iter()
.map(|(column_name, addr, _)| {
let numerical_column_writer: NumericalColumnWriter =
self.numerical_field_hash_map.read(addr);
let column_type = numerical_column_writer.numerical_type().into();
(column_name, column_type, addr)
})
.collect();
columns.extend(
self.bytes_field_hash_map
.iter()
.map(|(term, addr, _)| (term, ColumnType::Bytes, addr)),
);
columns.extend(
self.str_field_hash_map
.iter()
.map(|(column_name, addr, _)| (column_name, ColumnType::Str, addr)),
);
columns.extend(
self.bool_field_hash_map
.iter()
.map(|(column_name, addr, _)| (column_name, ColumnType::Bool, addr)),
);
columns.extend(
self.ip_addr_field_hash_map
.iter()
.map(|(column_name, addr, _)| (column_name, ColumnType::IpAddr, addr)),
);
columns.extend(
self.datetime_field_hash_map
.iter()
.map(|(column_name, addr, _)| (column_name, ColumnType::DateTime, addr)),
);
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, column_type, addr) in columns {
match column_type {
ColumnType::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, column_type);
serialize_bool_column(
cardinality,
num_docs,
column_writer.operation_iterator(
arena,
old_to_new_row_ids,
&mut symbol_byte_buffer,
),
buffers,
&mut column_serializer,
)?;
}
ColumnType::IpAddr => {
let column_writer: ColumnWriter = self.ip_addr_field_hash_map.read(addr);
let cardinality = column_writer.get_cardinality(num_docs);
let mut column_serializer =
serializer.serialize_column(column_name, ColumnType::IpAddr);
serialize_ip_addr_column(
cardinality,
num_docs,
column_writer.operation_iterator(
arena,
old_to_new_row_ids,
&mut symbol_byte_buffer,
),
buffers,
&mut column_serializer,
)?;
}
ColumnType::Bytes | ColumnType::Str => {
let str_or_bytes_column_writer: StrOrBytesColumnWriter =
if column_type == ColumnType::Bytes {
self.bytes_field_hash_map.read(addr)
} else {
self.str_field_hash_map.read(addr)
};
let dictionary_builder =
&dictionaries[str_or_bytes_column_writer.dictionary_id as usize];
let cardinality = str_or_bytes_column_writer
.column_writer
.get_cardinality(num_docs);
let mut column_serializer =
serializer.serialize_column(column_name, column_type);
serialize_bytes_or_str_column(
cardinality,
num_docs,
str_or_bytes_column_writer.sort_values_within_row,
dictionary_builder,
str_or_bytes_column_writer.operation_iterator(
arena,
old_to_new_row_ids,
&mut symbol_byte_buffer,
),
buffers,
&mut column_serializer,
)?;
}
ColumnType::F64 | ColumnType::I64 | ColumnType::U64 => {
let numerical_column_writer: NumericalColumnWriter =
self.numerical_field_hash_map.read(addr);
let cardinality = numerical_column_writer.cardinality(num_docs);
let mut column_serializer =
serializer.serialize_column(column_name, column_type);
let numerical_type = column_type.numerical_type().unwrap();
serialize_numerical_column(
cardinality,
num_docs,
numerical_type,
numerical_column_writer.operation_iterator(
arena,
old_to_new_row_ids,
&mut symbol_byte_buffer,
),
buffers,
&mut column_serializer,
)?;
}
ColumnType::DateTime => {
let column_writer: ColumnWriter = self.datetime_field_hash_map.read(addr);
let cardinality = column_writer.get_cardinality(num_docs);
let mut column_serializer =
serializer.serialize_column(column_name, ColumnType::DateTime);
serialize_numerical_column(
cardinality,
num_docs,
NumericalType::I64,
column_writer.operation_iterator(
arena,
old_to_new_row_ids,
&mut symbol_byte_buffer,
),
buffers,
&mut column_serializer,
)?;
}
};
}
serializer.finalize(num_docs)?;
Ok(())
}
}
// Serialize [Dictionary, Column, dictionary num bytes U32::LE]
// Column: [Column Index, Column Values, column index num bytes U32::LE]
fn serialize_bytes_or_str_column(
cardinality: Cardinality,
num_docs: RowId,
sort_values_within_row: bool,
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),
}
});
send_to_serialize_column_mappable_to_u64(
operation_iterator,
cardinality,
num_docs,
sort_values_within_row,
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,
..
} = buffers;
match numerical_type {
NumericalType::I64 => {
send_to_serialize_column_mappable_to_u64(
coerce_numerical_symbol::<i64>(op_iterator),
cardinality,
num_docs,
false,
value_index_builders,
u64_values,
wrt,
)?;
}
NumericalType::U64 => {
send_to_serialize_column_mappable_to_u64(
coerce_numerical_symbol::<u64>(op_iterator),
cardinality,
num_docs,
false,
value_index_builders,
u64_values,
wrt,
)?;
}
NumericalType::F64 => {
send_to_serialize_column_mappable_to_u64(
coerce_numerical_symbol::<f64>(op_iterator),
cardinality,
num_docs,
false,
value_index_builders,
u64_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,
u64_values,
..
} = buffers;
send_to_serialize_column_mappable_to_u64(
column_operations_it.map(|bool_column_operation| match bool_column_operation {
ColumnOperation::NewDoc(doc) => ColumnOperation::NewDoc(doc),
ColumnOperation::Value(bool_val) => ColumnOperation::Value(bool_val.to_u64()),
}),
cardinality,
num_docs,
false,
value_index_builders,
u64_values,
wrt,
)?;
Ok(())
}
fn serialize_ip_addr_column(
cardinality: Cardinality,
num_docs: RowId,
column_operations_it: impl Iterator<Item = ColumnOperation<Ipv6Addr>>,
buffers: &mut SpareBuffers,
wrt: &mut impl io::Write,
) -> io::Result<()> {
let SpareBuffers {
value_index_builders,
ip_addr_values,
..
} = buffers;
send_to_serialize_column_mappable_to_u128(
column_operations_it,
cardinality,
num_docs,
value_index_builders,
ip_addr_values,
wrt,
)?;
Ok(())
}
fn send_to_serialize_column_mappable_to_u128<
T: Copy + Ord + std::fmt::Debug + Send + Sync + MonotonicallyMappableToU128 + PartialOrd,
>(
op_iterator: impl Iterator<Item = ColumnOperation<T>>,
cardinality: Cardinality,
num_rows: 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();
// TODO: split index and values
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_rows);
SerializableColumnIndex::Optional {
num_rows,
non_null_row_ids: 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_rows);
SerializableColumnIndex::Multivalued(Box::new(multivalued_index))
}
};
crate::column::serialize_column_mappable_to_u128(
serializable_column_index,
&&values[..],
&mut wrt,
)?;
Ok(())
}
fn sort_values_within_row_in_place(multivalued_index: &[RowId], values: &mut [u64]) {
let mut start_index: usize = 0;
for end_index in multivalued_index.iter().copied() {
let end_index = end_index as usize;
values[start_index..end_index].sort_unstable();
start_index = end_index;
}
}
fn send_to_serialize_column_mappable_to_u64(
op_iterator: impl Iterator<Item = ColumnOperation<u64>>,
cardinality: Cardinality,
num_rows: RowId,
sort_values_within_row: bool,
value_index_builders: &mut PreallocatedIndexBuilders,
values: &mut Vec<u64>,
mut wrt: impl io::Write,
) -> io::Result<()>
where
for<'a> VecColumn<'a, u64>: ColumnValues<u64>,
{
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_rows);
SerializableColumnIndex::Optional {
non_null_row_ids: Box::new(optional_index),
num_rows,
}
}
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_rows);
if sort_values_within_row {
sort_values_within_row_in_place(multivalued_index, values);
}
SerializableColumnIndex::Multivalued(Box::new(multivalued_index))
}
};
crate::column::serialize_column_mappable_to_u64(
serializable_column_index,
&&values[..],
&mut wrt,
)?;
Ok(())
}
fn coerce_numerical_symbol<T>(
operation_iterator: impl Iterator<Item = ColumnOperation<NumericalValue>>,
) -> impl Iterator<Item = ColumnOperation<u64>>
where T: Coerce + MonotonicallyMappableToU64 {
operation_iterator.map(|symbol| match symbol {
ColumnOperation::NewDoc(doc) => ColumnOperation::NewDoc(doc),
ColumnOperation::Value(numerical_value) => {
ColumnOperation::Value(T::coerce(numerical_value).to_u64())
}
})
}
fn consume_operation_iterator<T: Ord, 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);
}
}
}
}
#[cfg(test)]
mod tests {
use stacker::MemoryArena;
use crate::columnar::writer::column_operation::ColumnOperation;
use crate::{Cardinality, 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(&arena, None, &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(&arena, None, &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(&arena, None, &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(&arena, None, &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))
));
}
}

108
columnar/src/columnar/writer/serializer.rs
View File

@@ -1,108 +0,0 @@
use std::io;
use std::io::Write;
use common::{BinarySerializable, CountingWriter};
use sstable::value::RangeValueWriter;
use sstable::RangeSSTable;
use crate::columnar::ColumnType;
use crate::RowId;
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, num_rows: RowId) -> 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()[..])?;
num_rows.serialize(&mut self.wrt)?;
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::Str, &mut buffer);
assert_eq!(buffer.len(), 12);
assert_eq!(&buffer[..10], b"root\0child");
assert_eq!(buffer[10], 0u8);
assert_eq!(buffer[11], ColumnType::Str.to_code());
}
}

165
columnar/src/columnar/writer/value_index.rs
View File

@@ -1,165 +0,0 @@
use crate::iterable::Iterable;
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>,
}
impl OptionalIndexBuilder {
pub fn finish(&mut self, num_rows: RowId) -> impl Iterable<RowId> + '_ {
debug_assert!(self
.docs
.last()
.copied()
.map(|last_doc| last_doc < num_rows)
.unwrap_or(true));
&self.docs[..]
}
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) -> &[u32] {
self.start_offsets
.resize(num_docs as usize + 1, self.total_num_vals_seen);
&self.start_offsets[..]
}
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)
.boxed_iter()
.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)
.boxed_iter()
.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).to_vec(),
vec![0, 0, 2, 3, 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).to_vec(),
vec![0, 0, 0, 2, 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));
}
}

258
columnar/src/dynamic_column.rs
View File

@@ -1,258 +0,0 @@
use std::io;
use std::net::Ipv6Addr;
use std::sync::Arc;
use common::file_slice::FileSlice;
use common::{DateTime, HasLen, OwnedBytes};
use crate::column::{BytesColumn, Column, StrColumn};
use crate::column_values::{monotonic_map_column, StrictlyMonotonicFn};
use crate::columnar::ColumnType;
use crate::{Cardinality, NumericalType};
#[derive(Clone)]
pub enum DynamicColumn {
Bool(Column<bool>),
I64(Column<i64>),
U64(Column<u64>),
F64(Column<f64>),
IpAddr(Column<Ipv6Addr>),
DateTime(Column<DateTime>),
Bytes(BytesColumn),
Str(StrColumn),
}
impl DynamicColumn {
pub fn get_cardinality(&self) -> Cardinality {
match self {
DynamicColumn::Bool(c) => c.get_cardinality(),
DynamicColumn::I64(c) => c.get_cardinality(),
DynamicColumn::U64(c) => c.get_cardinality(),
DynamicColumn::F64(c) => c.get_cardinality(),
DynamicColumn::IpAddr(c) => c.get_cardinality(),
DynamicColumn::DateTime(c) => c.get_cardinality(),
DynamicColumn::Bytes(c) => c.ords().get_cardinality(),
DynamicColumn::Str(c) => c.ords().get_cardinality(),
}
}
pub fn column_type(&self) -> ColumnType {
match self {
DynamicColumn::Bool(_) => ColumnType::Bool,
DynamicColumn::I64(_) => ColumnType::I64,
DynamicColumn::U64(_) => ColumnType::U64,
DynamicColumn::F64(_) => ColumnType::F64,
DynamicColumn::IpAddr(_) => ColumnType::IpAddr,
DynamicColumn::DateTime(_) => ColumnType::DateTime,
DynamicColumn::Bytes(_) => ColumnType::Bytes,
DynamicColumn::Str(_) => ColumnType::Str,
}
}
pub fn coerce_numerical(self, target_numerical_type: NumericalType) -> Option<Self> {
match target_numerical_type {
NumericalType::I64 => self.coerce_to_i64(),
NumericalType::U64 => self.coerce_to_u64(),
NumericalType::F64 => self.coerce_to_f64(),
}
}
pub fn is_numerical(&self) -> bool {
self.column_type().numerical_type().is_some()
}
pub fn is_f64(&self) -> bool {
self.column_type().numerical_type() == Some(NumericalType::F64)
}
pub fn is_i64(&self) -> bool {
self.column_type().numerical_type() == Some(NumericalType::I64)
}
pub fn is_u64(&self) -> bool {
self.column_type().numerical_type() == Some(NumericalType::U64)
}
fn coerce_to_f64(self) -> Option<DynamicColumn> {
match self {
DynamicColumn::I64(column) => Some(DynamicColumn::F64(Column {
idx: column.idx,
values: Arc::new(monotonic_map_column(column.values, MapI64ToF64)),
})),
DynamicColumn::U64(column) => Some(DynamicColumn::F64(Column {
idx: column.idx,
values: Arc::new(monotonic_map_column(column.values, MapU64ToF64)),
})),
DynamicColumn::F64(_) => Some(self),
_ => None,
}
}
fn coerce_to_i64(self) -> Option<DynamicColumn> {
match self {
DynamicColumn::U64(column) => {
if column.max_value() > i64::MAX as u64 {
return None;
}
Some(DynamicColumn::I64(Column {
idx: column.idx,
values: Arc::new(monotonic_map_column(column.values, MapU64ToI64)),
}))
}
DynamicColumn::I64(_) => Some(self),
_ => None,
}
}
fn coerce_to_u64(self) -> Option<DynamicColumn> {
match self {
DynamicColumn::I64(column) => {
if column.min_value() < 0 {
return None;
}
Some(DynamicColumn::U64(Column {
idx: column.idx,
values: Arc::new(monotonic_map_column(column.values, MapI64ToU64)),
}))
}
DynamicColumn::U64(_) => Some(self),
_ => None,
}
}
}
struct MapI64ToF64;
impl StrictlyMonotonicFn<i64, f64> for MapI64ToF64 {
#[inline(always)]
fn mapping(&self, inp: i64) -> f64 {
inp as f64
}
#[inline(always)]
fn inverse(&self, out: f64) -> i64 {
out as i64
}
}
struct MapU64ToF64;
impl StrictlyMonotonicFn<u64, f64> for MapU64ToF64 {
#[inline(always)]
fn mapping(&self, inp: u64) -> f64 {
inp as f64
}
#[inline(always)]
fn inverse(&self, out: f64) -> u64 {
out as u64
}
}
struct MapU64ToI64;
impl StrictlyMonotonicFn<u64, i64> for MapU64ToI64 {
#[inline(always)]
fn mapping(&self, inp: u64) -> i64 {
inp as i64
}
#[inline(always)]
fn inverse(&self, out: i64) -> u64 {
out as u64
}
}
struct MapI64ToU64;
impl StrictlyMonotonicFn<i64, u64> for MapI64ToU64 {
#[inline(always)]
fn mapping(&self, inp: i64) -> u64 {
inp as u64
}
#[inline(always)]
fn inverse(&self, out: u64) -> i64 {
out as i64
}
}
macro_rules! static_dynamic_conversions {
($typ:ty, $enum_name:ident) => {
impl From<DynamicColumn> for Option<$typ> {
fn from(dynamic_column: DynamicColumn) -> Option<$typ> {
if let DynamicColumn::$enum_name(col) = dynamic_column {
Some(col)
} else {
None
}
}
}
impl From<$typ> for DynamicColumn {
fn from(typed_column: $typ) -> Self {
DynamicColumn::$enum_name(typed_column)
}
}
};
}
static_dynamic_conversions!(Column<bool>, Bool);
static_dynamic_conversions!(Column<u64>, U64);
static_dynamic_conversions!(Column<i64>, I64);
static_dynamic_conversions!(Column<f64>, F64);
static_dynamic_conversions!(Column<DateTime>, DateTime);
static_dynamic_conversions!(StrColumn, Str);
static_dynamic_conversions!(BytesColumn, Bytes);
static_dynamic_conversions!(Column<Ipv6Addr>, IpAddr);
#[derive(Clone)]
pub struct DynamicColumnHandle {
pub(crate) file_slice: FileSlice,
pub(crate) column_type: ColumnType,
}
impl DynamicColumnHandle {
// TODO rename load
pub fn open(&self) -> io::Result<DynamicColumn> {
let column_bytes: OwnedBytes = self.file_slice.read_bytes()?;
self.open_internal(column_bytes)
}
#[doc(hidden)]
pub fn file_slice(&self) -> &FileSlice {
&self.file_slice
}
/// Returns the `u64` fast field reader reader associated with `fields` of types
/// Str, u64, i64, f64, or datetime.
///
/// If not, the fastfield reader will returns the u64-value associated with the original
/// FastValue.
pub fn open_u64_lenient(&self) -> io::Result<Option<Column<u64>>> {
let column_bytes = self.file_slice.read_bytes()?;
match self.column_type {
ColumnType::Str | ColumnType::Bytes => {
let column: BytesColumn = crate::column::open_column_bytes(column_bytes)?;
Ok(Some(column.term_ord_column))
}
ColumnType::Bool => Ok(None),
ColumnType::IpAddr => Ok(None),
ColumnType::I64 | ColumnType::U64 | ColumnType::F64 | ColumnType::DateTime => {
let column = crate::column::open_column_u64::<u64>(column_bytes)?;
Ok(Some(column))
}
}
}
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::Str => crate::column::open_column_str(column_bytes)?.into(),
ColumnType::I64 => crate::column::open_column_u64::<i64>(column_bytes)?.into(),
ColumnType::U64 => crate::column::open_column_u64::<u64>(column_bytes)?.into(),
ColumnType::F64 => crate::column::open_column_u64::<f64>(column_bytes)?.into(),
ColumnType::Bool => crate::column::open_column_u64::<bool>(column_bytes)?.into(),
ColumnType::IpAddr => crate::column::open_column_u128::<Ipv6Addr>(column_bytes)?.into(),
ColumnType::DateTime => {
crate::column::open_column_u64::<DateTime>(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
}
}

19
columnar/src/iterable.rs
View File

@@ -1,19 +0,0 @@
use std::ops::Range;
pub trait Iterable<T = u64> {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_>;
}
impl<'a, T: Copy> Iterable<T> for &'a [T] {
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
Box::new(self.iter().copied())
}
}
impl<T: Copy> Iterable<T> for Range<T>
where Range<T>: Iterator<Item = T>
{
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
Box::new(self.clone())
}
}

96
columnar/src/lib.rs
View File

@@ -1,96 +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;
pub mod column_values;
mod columnar;
mod dictionary;
mod dynamic_column;
mod iterable;
pub(crate) mod utils;
mod value;
pub use column::{BytesColumn, Column, StrColumn};
pub use column_index::ColumnIndex;
pub use column_values::{ColumnValues, MonotonicallyMappableToU128, MonotonicallyMappableToU64};
pub use columnar::{
merge_columnar, ColumnType, ColumnarReader, ColumnarWriter, HasAssociatedColumnType,
MergeRowOrder, ShuffleMergeOrder, StackMergeOrder,
};
use sstable::VoidSSTable;
pub use value::{NumericalType, NumericalValue};
pub use self::dynamic_column::{DynamicColumn, DynamicColumnHandle};
pub type RowId = u32;
pub type DocId = u32;
#[derive(Clone, Copy)]
pub struct RowAddr {
pub segment_ord: u32,
pub row_id: RowId,
}
pub use sstable::Dictionary;
pub type Streamer<'a> = sstable::Streamer<'a, VoidSSTable>;
pub use common::DateTime;
#[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 fn is_optional(&self) -> bool {
matches!(self, Cardinality::Optional)
}
pub fn is_multivalue(&self) -> bool {
matches!(self, Cardinality::Multivalued)
}
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;

212
columnar/src/tests.rs
View File

@@ -1,212 +0,0 @@
use std::net::Ipv6Addr;
use crate::column_values::MonotonicallyMappableToU128;
use crate::columnar::ColumnType;
use crate::dynamic_column::{DynamicColumn, DynamicColumnHandle};
use crate::value::NumericalValue;
use crate::{Cardinality, ColumnarReader, ColumnarWriter};
#[test]
fn test_dataframe_writer_str() {
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, None, &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(), 158);
}
#[test]
fn test_dataframe_writer_bytes() {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_bytes(1u32, "my_string", b"hello");
dataframe_writer.record_bytes(3u32, "my_string", b"helloeee");
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(5, None, &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(), 158);
}
#[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, None, &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(), 22);
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_u64_multivalued() {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_numerical(2u32, "divisor", 2u64);
dataframe_writer.record_numerical(3u32, "divisor", 3u64);
dataframe_writer.record_numerical(4u32, "divisor", 2u64);
dataframe_writer.record_numerical(5u32, "divisor", 5u64);
dataframe_writer.record_numerical(6u32, "divisor", 2u64);
dataframe_writer.record_numerical(6u32, "divisor", 3u64);
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(7, None, &mut buffer).unwrap();
let columnar = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar.num_columns(), 1);
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("divisor").unwrap();
assert_eq!(cols.len(), 1);
assert_eq!(cols[0].num_bytes(), 29);
let dyn_i64_col = cols[0].open().unwrap();
let DynamicColumn::I64(divisor_col) = dyn_i64_col else { panic!(); };
assert_eq!(
divisor_col.get_cardinality(),
crate::Cardinality::Multivalued
);
assert_eq!(divisor_col.num_docs(), 7);
}
#[test]
fn test_dataframe_writer_ip_addr() {
let mut dataframe_writer = ColumnarWriter::default();
dataframe_writer.record_ip_addr(1, "ip_addr", Ipv6Addr::from_u128(1001));
dataframe_writer.record_ip_addr(3, "ip_addr", Ipv6Addr::from_u128(1050));
let mut buffer: Vec<u8> = Vec::new();
dataframe_writer.serialize(5, None, &mut buffer).unwrap();
let columnar = ColumnarReader::open(buffer).unwrap();
assert_eq!(columnar.num_columns(), 1);
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("ip_addr").unwrap();
assert_eq!(cols.len(), 1);
assert_eq!(cols[0].num_bytes(), 42);
assert_eq!(cols[0].column_type(), ColumnType::IpAddr);
let dyn_bool_col = cols[0].open().unwrap();
let DynamicColumn::IpAddr(ip_col) = dyn_bool_col else { panic!(); };
let vals: Vec<Option<Ipv6Addr>> = (0..5).map(|row_id| ip_col.first(row_id)).collect();
assert_eq!(
&vals,
&[
None,
Some(Ipv6Addr::from_u128(1001)),
None,
Some(Ipv6Addr::from_u128(1050)),
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, None, &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(), 33);
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_str() {
let mut buffer = Vec::new();
let mut columnar_writer = ColumnarWriter::default();
columnar_writer.record_str(1, "my.column", "a");
columnar_writer.record_str(3, "my.column", "c");
columnar_writer.record_str(3, "my.column2", "different_column!");
columnar_writer.record_str(4, "my.column", "b");
columnar_writer.serialize(5, None, &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!(); };
let index: Vec<Option<u64>> = (0..5).map(|row_id| str_col.ords().first(row_id)).collect();
assert_eq!(index, &[None, Some(0), None, Some(2), Some(1)]);
assert_eq!(str_col.num_rows(), 5);
let mut term_buffer = String::new();
let term_ords = str_col.ords();
assert_eq!(term_ords.first(0), None);
assert_eq!(term_ords.first(1), Some(0));
str_col.ord_to_str(0u64, &mut term_buffer).unwrap();
assert_eq!(term_buffer, "a");
assert_eq!(term_ords.first(2), None);
assert_eq!(term_ords.first(3), Some(2));
str_col.ord_to_str(2u64, &mut term_buffer).unwrap();
assert_eq!(term_buffer, "c");
assert_eq!(term_ords.first(4), Some(1));
str_col.ord_to_str(1u64, &mut term_buffer).unwrap();
assert_eq!(term_buffer, "b");
}
#[test]
fn test_dictionary_encoded_bytes() {
let mut buffer = Vec::new();
let mut columnar_writer = ColumnarWriter::default();
columnar_writer.record_bytes(1, "my.column", b"a");
columnar_writer.record_bytes(3, "my.column", b"c");
columnar_writer.record_bytes(3, "my.column2", b"different_column!");
columnar_writer.record_bytes(4, "my.column", b"b");
columnar_writer.serialize(5, None, &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::Bytes(bytes_col) = col_handles[0].open().unwrap() else { panic!(); };
let index: Vec<Option<u64>> = (0..5)
.map(|row_id| bytes_col.ords().first(row_id))
.collect();
assert_eq!(index, &[None, Some(0), None, Some(2), Some(1)]);
assert_eq!(bytes_col.num_rows(), 5);
let mut term_buffer = Vec::new();
let term_ords = bytes_col.ords();
assert_eq!(term_ords.first(0), None);
assert_eq!(term_ords.first(1), Some(0));
bytes_col
.dictionary
.ord_to_term(0u64, &mut term_buffer)
.unwrap();
assert_eq!(term_buffer, b"a");
assert_eq!(term_ords.first(2), None);
assert_eq!(term_ords.first(3), Some(2));
bytes_col
.dictionary
.ord_to_term(2u64, &mut term_buffer)
.unwrap();
assert_eq!(term_buffer, b"c");
assert_eq!(term_ords.first(4), Some(1));
bytes_col
.dictionary
.ord_to_term(1u64, &mut term_buffer)
.unwrap();
assert_eq!(term_buffer, b"b");
}

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);
}
}

131
columnar/src/value.rs
View File

@@ -1,131 +0,0 @@
use common::DateTime;
use crate::InvalidData;
#[derive(Copy, Clone, PartialEq, Debug)]
pub enum NumericalValue {
I64(i64),
U64(u64),
F64(f64),
}
impl NumericalValue {
pub fn numerical_type(&self) -> NumericalType {
match self {
NumericalValue::I64(_) => NumericalType::I64,
NumericalValue::U64(_) => NumericalType::U64,
NumericalValue::F64(_) => NumericalType::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)
}
}
#[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,
}
}
}
impl Coerce for DateTime {
fn coerce(value: NumericalValue) -> Self {
let timestamp_micros = i64::coerce(value);
DateTime::from_timestamp_micros(timestamp_micros)
}
}
#[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);
}
}

11
common/Cargo.toml
View File

@@ -1,23 +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"
time = { version = "0.3.10", features = ["serde-well-known"] }
serde = { version = "1.0.136", features = ["derive"] }
ownedbytes = { version="0.3", path="../ownedbytes" }
[dev-dependencies]
proptest = "1.0.0"

20
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)
}
@@ -259,7 +259,11 @@ impl BitSet {
// we do not check saturated els.
let higher = el / 64u32;
let lower = el % 64u32;
self.len += u64::from(self.tinysets[higher as usize].insert_mut(lower));
self.len += if self.tinysets[higher as usize].insert_mut(lower) {
1
} else {
0
};
}
/// Inserts an element in the `BitSet`
@@ -268,7 +272,11 @@ impl BitSet {
// we do not check saturated els.
let higher = el / 64u32;
let lower = el % 64u32;
self.len -= u64::from(self.tinysets[higher as usize].remove_mut(lower));
self.len -= if self.tinysets[higher as usize].remove_mut(lower) {
1
} else {
0
};
}
/// Returns true iff the elements is in the `BitSet`.
@@ -277,7 +285,7 @@ impl BitSet {
self.tinyset(el / 64u32).contains(el % 64)
}
/// Returns the first non-empty `TinySet` associated with a bucket lower
/// Returns the first non-empty `TinySet` associated to a bucket lower
/// or greater than bucket.
///
/// Reminder: the tiny set with the bucket `bucket`, represents the
@@ -421,7 +429,7 @@ 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 +440,7 @@ 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]

136
common/src/datetime.rs
View File

@@ -1,136 +0,0 @@
use std::fmt;
use serde::{Deserialize, Serialize};
use time::format_description::well_known::Rfc3339;
use time::{OffsetDateTime, PrimitiveDateTime, UtcOffset};
/// DateTime Precision
#[derive(
Clone, Copy, Debug, Hash, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize, Default,
)]
#[serde(rename_all = "lowercase")]
pub enum DatePrecision {
/// Seconds precision
#[default]
Seconds,
/// Milli-seconds precision.
Milliseconds,
/// Micro-seconds precision.
Microseconds,
}
/// A date/time value with microsecond precision.
///
/// This timestamp does not carry any explicit time zone information.
/// Users are responsible for applying the provided conversion
/// functions consistently. Internally the time zone is assumed
/// to be UTC, which is also used implicitly for JSON serialization.
///
/// All constructors and conversions are provided as explicit
/// functions and not by implementing any `From`/`Into` traits
/// to prevent unintended usage.
#[derive(Clone, Default, Copy, PartialEq, Eq, PartialOrd, Ord, Hash)]
pub struct DateTime {
// Timestamp in microseconds.
pub(crate) timestamp_micros: i64,
}
impl DateTime {
/// Create new from UNIX timestamp in seconds
pub const fn from_timestamp_secs(seconds: i64) -> Self {
Self {
timestamp_micros: seconds * 1_000_000,
}
}
/// Create new from UNIX timestamp in milliseconds
pub const fn from_timestamp_millis(milliseconds: i64) -> Self {
Self {
timestamp_micros: milliseconds * 1_000,
}
}
/// Create new from UNIX timestamp in microseconds.
pub const fn from_timestamp_micros(microseconds: i64) -> Self {
Self {
timestamp_micros: microseconds,
}
}
/// Create new from `OffsetDateTime`
///
/// The given date/time is converted to UTC and the actual
/// time zone is discarded.
pub const fn from_utc(dt: OffsetDateTime) -> Self {
let timestamp_micros = dt.unix_timestamp() * 1_000_000 + dt.microsecond() as i64;
Self { timestamp_micros }
}
/// Create new from `PrimitiveDateTime`
///
/// Implicitly assumes that the given date/time is in UTC!
/// Otherwise the original value must only be reobtained with
/// [`Self::into_primitive()`].
pub fn from_primitive(dt: PrimitiveDateTime) -> Self {
Self::from_utc(dt.assume_utc())
}
/// Convert to UNIX timestamp in seconds.
pub const fn into_timestamp_secs(self) -> i64 {
self.timestamp_micros / 1_000_000
}
/// Convert to UNIX timestamp in milliseconds.
pub const fn into_timestamp_millis(self) -> i64 {
self.timestamp_micros / 1_000
}
/// Convert to UNIX timestamp in microseconds.
pub const fn into_timestamp_micros(self) -> i64 {
self.timestamp_micros
}
/// Convert to UTC `OffsetDateTime`
pub fn into_utc(self) -> OffsetDateTime {
let timestamp_nanos = self.timestamp_micros as i128 * 1000;
let utc_datetime = OffsetDateTime::from_unix_timestamp_nanos(timestamp_nanos)
.expect("valid UNIX timestamp");
debug_assert_eq!(UtcOffset::UTC, utc_datetime.offset());
utc_datetime
}
/// Convert to `OffsetDateTime` with the given time zone
pub fn into_offset(self, offset: UtcOffset) -> OffsetDateTime {
self.into_utc().to_offset(offset)
}
/// Convert to `PrimitiveDateTime` without any time zone
///
/// The value should have been constructed with [`Self::from_primitive()`].
/// Otherwise the time zone is implicitly assumed to be UTC.
pub fn into_primitive(self) -> PrimitiveDateTime {
let utc_datetime = self.into_utc();
// Discard the UTC time zone offset
debug_assert_eq!(UtcOffset::UTC, utc_datetime.offset());
PrimitiveDateTime::new(utc_datetime.date(), utc_datetime.time())
}
/// Truncates the microseconds value to the corresponding precision.
pub fn truncate(self, precision: DatePrecision) -> Self {
let truncated_timestamp_micros = match precision {
DatePrecision::Seconds => (self.timestamp_micros / 1_000_000) * 1_000_000,
DatePrecision::Milliseconds => (self.timestamp_micros / 1_000) * 1_000,
DatePrecision::Microseconds => self.timestamp_micros,
};
Self {
timestamp_micros: truncated_timestamp_micros,
}
}
}
impl fmt::Debug for DateTime {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
let utc_rfc3339 = self.into_utc().format(&Rfc3339).map_err(|_| fmt::Error)?;
f.write_str(&utc_rfc3339)
}
}

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])]
);
}
}

51
common/src/lib.rs
View File

@@ -5,21 +5,13 @@ use std::ops::Deref;
pub use byteorder::LittleEndian as Endianness;
mod bitset;
mod datetime;
pub mod file_slice;
mod group_by;
mod serialize;
mod vint;
mod writer;
pub use bitset::*;
pub use datetime::{DatePrecision, DateTime};
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,
serialize_vint_u32, write_u32_vint, VInt, VIntU128,
};
pub use vint::{read_u32_vint, read_u32_vint_no_advance, serialize_vint_u32, write_u32_vint, VInt};
pub use writer::{AntiCallToken, CountingWriter, TerminatingWrite};
/// Has length trait
@@ -60,13 +52,13 @@ const HIGHEST_BIT: u64 = 1 << 63;
/// to values over 2^63, and all values end up requiring 64 bits.
///
/// # See also
/// The reverse mapping is [`u64_to_i64()`].
/// The [reverse mapping is `u64_to_i64`](./fn.u64_to_i64.html).
#[inline]
pub fn i64_to_u64(val: i64) -> u64 {
(val as u64) ^ HIGHEST_BIT
}
/// Reverse the mapping given by [`i64_to_u64()`].
/// Reverse the mapping given by [`i64_to_u64`](./fn.i64_to_u64.html).
#[inline]
pub fn u64_to_i64(val: u64) -> i64 {
(val ^ HIGHEST_BIT) as i64
@@ -88,7 +80,7 @@ pub fn u64_to_i64(val: u64) -> i64 {
/// explains the mapping in a clear manner.
///
/// # See also
/// The reverse mapping is [`u64_to_f64()`].
/// The [reverse mapping is `u64_to_f64`](./fn.u64_to_f64.html).
#[inline]
pub fn f64_to_u64(val: f64) -> u64 {
let bits = val.to_bits();
@@ -99,7 +91,7 @@ pub fn f64_to_u64(val: f64) -> u64 {
}
}
/// Reverse the mapping given by [`f64_to_u64()`].
/// Reverse the mapping given by [`i64_to_u64`](./fn.i64_to_u64.html).
#[inline]
pub fn u64_to_f64(val: u64) -> f64 {
f64::from_bits(if val & HIGHEST_BIT != 0 {
@@ -109,21 +101,6 @@ pub fn u64_to_f64(val: u64) -> f64 {
})
}
/// Replaces a given byte in the `bytes` slice of bytes.
///
/// This function assumes that the needle is rarely contained in the bytes string
/// and offers a fast path if the needle is not present.
pub fn replace_in_place(needle: u8, replacement: u8, bytes: &mut [u8]) {
if !bytes.contains(&needle) {
return;
}
for b in bytes {
if *b == needle {
*b = replacement;
}
}
}
#[cfg(test)]
pub mod test {
@@ -188,20 +165,4 @@ pub mod test {
assert!(f64_to_u64(-2.0) < f64_to_u64(1.0));
assert!(f64_to_u64(-2.0) < f64_to_u64(-1.5));
}
#[test]
fn test_replace_in_place() {
let test_aux = |before_replacement: &[u8], expected: &[u8]| {
let mut bytes: Vec<u8> = before_replacement.to_vec();
super::replace_in_place(b'b', b'c', &mut bytes);
assert_eq!(&bytes[..], expected);
};
test_aux(b"", b"");
test_aux(b"b", b"c");
test_aux(b"baaa", b"caaa");
test_aux(b"aaab", b"aaac");
test_aux(b"aaabaa", b"aaacaa");
test_aux(b"aaaaaa", b"aaaaaa");
test_aux(b"bbbb", b"cccc");
}
}

79
common/src/serialize.rs
View File

@@ -5,37 +5,12 @@ use byteorder::{ReadBytesExt, WriteBytesExt};
use crate::{Endianness, VInt};
#[derive(Default)]
struct Counter(u64);
impl io::Write for Counter {
fn write(&mut self, buf: &[u8]) -> io::Result<usize> {
self.0 += buf.len() as u64;
Ok(buf.len())
}
fn write_all(&mut self, buf: &[u8]) -> io::Result<()> {
self.0 += buf.len() as u64;
Ok(())
}
fn flush(&mut self) -> io::Result<()> {
Ok(())
}
}
/// Trait for a simple binary serialization.
pub trait BinarySerializable: fmt::Debug + Sized {
/// Serialize
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()>;
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()>;
/// Deserialize
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self>;
fn num_bytes(&self) -> u64 {
let mut counter = Counter::default();
self.serialize(&mut counter).unwrap();
counter.0
}
}
pub trait DeserializeFrom<T: BinarySerializable> {
@@ -59,7 +34,7 @@ pub trait FixedSize: BinarySerializable {
}
impl BinarySerializable for () {
fn serialize<W: Write + ?Sized>(&self, _: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, _: &mut W) -> io::Result<()> {
Ok(())
}
fn deserialize<R: Read>(_: &mut R) -> io::Result<Self> {
@@ -72,7 +47,7 @@ impl FixedSize for () {
}
impl<T: BinarySerializable> BinarySerializable for Vec<T> {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
VInt(self.len() as u64).serialize(writer)?;
for it in self {
it.serialize(writer)?;
@@ -91,7 +66,7 @@ impl<T: BinarySerializable> BinarySerializable for Vec<T> {
}
impl<Left: BinarySerializable, Right: BinarySerializable> BinarySerializable for (Left, Right) {
fn serialize<W: Write + ?Sized>(&self, write: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, write: &mut W) -> io::Result<()> {
self.0.serialize(write)?;
self.1.serialize(write)
}
@@ -106,7 +81,7 @@ impl<Left: BinarySerializable + FixedSize, Right: BinarySerializable + FixedSize
}
impl BinarySerializable for u32 {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_u32::<Endianness>(*self)
}
@@ -119,22 +94,8 @@ impl FixedSize for u32 {
const SIZE_IN_BYTES: usize = 4;
}
impl BinarySerializable for u16 {
fn serialize<W: Write + ?Sized>(&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 + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_u64::<Endianness>(*self)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
@@ -146,21 +107,8 @@ impl FixedSize for u64 {
const SIZE_IN_BYTES: usize = 8;
}
impl BinarySerializable for u128 {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
writer.write_u128::<Endianness>(*self)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
reader.read_u128::<Endianness>()
}
}
impl FixedSize for u128 {
const SIZE_IN_BYTES: usize = 16;
}
impl BinarySerializable for f32 {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_f32::<Endianness>(*self)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
@@ -173,7 +121,7 @@ impl FixedSize for f32 {
}
impl BinarySerializable for i64 {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_i64::<Endianness>(*self)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
@@ -186,7 +134,7 @@ impl FixedSize for i64 {
}
impl BinarySerializable for f64 {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_f64::<Endianness>(*self)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
@@ -199,7 +147,7 @@ impl FixedSize for f64 {
}
impl BinarySerializable for u8 {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_u8(*self)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<u8> {
@@ -212,8 +160,9 @@ impl FixedSize for u8 {
}
impl BinarySerializable for bool {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
writer.write_u8(u8::from(*self))
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
let val = if *self { 1 } else { 0 };
writer.write_u8(val)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<bool> {
let val = reader.read_u8()?;
@@ -233,7 +182,7 @@ impl FixedSize for bool {
}
impl BinarySerializable for String {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
let data: &[u8] = self.as_bytes();
VInt(data.len() as u64).serialize(writer)?;
writer.write_all(data)

94
common/src/vint.rs
View File

@@ -5,75 +5,6 @@ use byteorder::{ByteOrder, LittleEndian};
use super::BinarySerializable;
/// Variable int serializes a u128 number
pub fn serialize_vint_u128(mut val: u128, output: &mut Vec<u8>) {
loop {
let next_byte: u8 = (val % 128u128) as u8;
val /= 128u128;
if val == 0 {
output.push(next_byte | STOP_BIT);
return;
} else {
output.push(next_byte);
}
}
}
/// Deserializes a u128 number
///
/// Returns the number and the slice after the vint
pub fn deserialize_vint_u128(data: &[u8]) -> io::Result<(u128, &[u8])> {
let mut result = 0u128;
let mut shift = 0u64;
for i in 0..19 {
let b = data[i];
result |= u128::from(b % 128u8) << shift;
if b >= STOP_BIT {
return Ok((result, &data[i + 1..]));
}
shift += 7;
}
Err(io::Error::new(
io::ErrorKind::InvalidData,
"Failed to deserialize u128 vint",
))
}
/// Wrapper over a `u128` that serializes as a variable int.
#[derive(Clone, Copy, Debug, Eq, PartialEq)]
pub struct VIntU128(pub u128);
impl BinarySerializable for VIntU128 {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
let mut buffer = vec![];
serialize_vint_u128(self.0, &mut buffer);
writer.write_all(&buffer)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
let mut bytes = reader.bytes();
let mut result = 0u128;
let mut shift = 0u64;
loop {
match bytes.next() {
Some(Ok(b)) => {
result |= u128::from(b % 128u8) << shift;
if b >= STOP_BIT {
return Ok(VIntU128(result));
}
shift += 7;
}
_ => {
return Err(io::Error::new(
io::ErrorKind::InvalidData,
"Reach end of buffer while reading VInt",
));
}
}
}
}
}
/// Wrapper over a `u64` that serializes as a variable int.
#[derive(Clone, Copy, Debug, Eq, PartialEq)]
pub struct VInt(pub u64);
@@ -157,7 +88,7 @@ fn vint_len(data: &[u8]) -> usize {
/// If the buffer does not start by a valid
/// vint payload
pub fn read_u32_vint(data: &mut &[u8]) -> u32 {
let (result, vlen) = read_u32_vint_no_advance(data);
let (result, vlen) = read_u32_vint_no_advance(*data);
*data = &data[vlen..];
result
}
@@ -211,7 +142,7 @@ impl VInt {
}
impl BinarySerializable for VInt {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
let mut buffer = [0u8; 10];
let num_bytes = self.serialize_into(&mut buffer);
writer.write_all(&buffer[0..num_bytes])
@@ -245,7 +176,6 @@ impl BinarySerializable for VInt {
mod tests {
use super::{serialize_vint_u32, BinarySerializable, VInt};
use crate::vint::{deserialize_vint_u128, serialize_vint_u128, VIntU128};
fn aux_test_vint(val: u64) {
let mut v = [14u8; 10];
@@ -287,26 +217,6 @@ mod tests {
assert_eq!(&buffer[..len_vint], res2, "array wrong for {}", val);
}
fn aux_test_vint_u128(val: u128) {
let mut data = vec![];
serialize_vint_u128(val, &mut data);
let (deser_val, _data) = deserialize_vint_u128(&data).unwrap();
assert_eq!(val, deser_val);
let mut out = vec![];
VIntU128(val).serialize(&mut out).unwrap();
let deser_val = VIntU128::deserialize(&mut &out[..]).unwrap();
assert_eq!(val, deser_val.0);
}
#[test]
fn test_vint_u128() {
aux_test_vint_u128(0);
aux_test_vint_u128(1);
aux_test_vint_u128(u128::MAX / 3);
aux_test_vint_u128(u128::MAX);
}
#[test]
fn test_vint_u32() {
aux_test_serialize_vint_u32(0);

2
common/src/writer.rs
View File

@@ -55,7 +55,7 @@ impl<W: TerminatingWrite> TerminatingWrite for CountingWriter<W> {
}
/// Struct used to prevent from calling
/// [`terminate_ref`](TerminatingWrite::terminate_ref) directly
/// [`terminate_ref`](trait.TerminatingWrite.html#tymethod.terminate_ref) directly
///
/// The point is that while the type is public, it cannot be built by anyone
/// outside of this module.

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2
doc/src/basis.md
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@@ -50,7 +50,7 @@ to get tantivy to fit your use case:
*Example 1* You could for instance use hadoop to build a very large search index in a timely manner, copy all of the resulting segment files in the same directory and edit the `meta.json` to get a functional index.[^2]
*Example 2* You could also disable your merge policy and enforce daily segments. Removing data after one week can then be done very efficiently by just editing the `meta.json` and deleting the files associated with segment `D-7`.
*Example 2* You could also disable your merge policy and enforce daily segments. Removing data after one week can then be done very efficiently by just editing the `meta.json` and deleting the files associated to segment `D-7`.
## Merging

351
examples/aggregation.rs
View File

@@ -1,319 +1,130 @@
// # Aggregation example
//
// This example shows how you can use built-in aggregations.
// We will use nested aggregations with buckets and metrics:
// - Range buckets and compute the average in each bucket.
// - Term aggregation and compute the min price in each bucket
// ---
// We will use range buckets and compute the average in each bucket.
//
use serde_json::{Deserializer, Value};
use serde_json::Value;
use tantivy::aggregation::agg_req::{
Aggregation, Aggregations, BucketAggregation, BucketAggregationType, MetricAggregation,
RangeAggregation,
};
use tantivy::aggregation::agg_result::AggregationResults;
use tantivy::aggregation::bucket::RangeAggregationRange;
use tantivy::aggregation::metric::AverageAggregation;
use tantivy::aggregation::AggregationCollector;
use tantivy::query::AllQuery;
use tantivy::schema::{self, IndexRecordOption, Schema, TextFieldIndexing, FAST};
use tantivy::Index;
use tantivy::query::TermQuery;
use tantivy::schema::{self, Cardinality, IndexRecordOption, Schema, TextFieldIndexing};
use tantivy::{doc, Index, Term};
fn main() -> tantivy::Result<()> {
// # Create Schema
//
// Lets create a schema for a footwear shop, with 4 fields: name, category, stock and price.
// category, stock and price will be fast fields as that's the requirement
// for aggregation queries.
//
let mut schema_builder = Schema::builder();
// In preparation of the `TermsAggregation`, the category field is configured with:
// - `set_fast`
// - `raw` tokenizer
//
// The tokenizer is set to "raw", because the fast field uses the same dictionary as the
// inverted index. (This behaviour will change in tantivy 0.20, where the fast field will
// always be raw tokenized independent from the regular tokenizing)
//
let text_fieldtype = schema::TextOptions::default()
.set_indexing_options(
TextFieldIndexing::default()
.set_index_option(IndexRecordOption::WithFreqs)
.set_tokenizer("raw"),
TextFieldIndexing::default().set_index_option(IndexRecordOption::WithFreqs),
)
.set_fast()
.set_stored();
schema_builder.add_text_field("category", text_fieldtype);
schema_builder.add_f64_field("stock", FAST);
schema_builder.add_f64_field("price", FAST);
let text_field = schema_builder.add_text_field("text", text_fieldtype);
let score_fieldtype =
crate::schema::NumericOptions::default().set_fast(Cardinality::SingleValue);
let highscore_field = schema_builder.add_f64_field("highscore", score_fieldtype.clone());
let price_field = schema_builder.add_f64_field("price", score_fieldtype.clone());
let schema = schema_builder.build();
// # Indexing documents
//
// Lets index a bunch of documents for this example.
let index = Index::create_in_ram(schema.clone());
let data = r#"{
"name": "Almond Toe Court Shoes, Patent Black",
"category": "Womens Footwear",
"price": 99.00,
"stock": 5
}
{
"name": "Suede Shoes, Blue",
"category": "Womens Footwear",
"price": 42.00,
"stock": 4
}
{
"name": "Leather Driver Saddle Loafers, Tan",
"category": "Mens Footwear",
"price": 34.00,
"stock": 12
}
{
"name": "Flip Flops, Red",
"category": "Mens Footwear",
"price": 19.00,
"stock": 6
}
{
"name": "Flip Flops, Blue",
"category": "Mens Footwear",
"price": 19.00,
"stock": 0
}
{
"name": "Gold Button Cardigan, Black",
"category": "Womens Casualwear",
"price": 167.00,
"stock": 6
}
{
"name": "Cotton Shorts, Medium Red",
"category": "Womens Casualwear",
"price": 30.00,
"stock": 5
}
{
"name": "Fine Stripe Short SleeveShirt, Grey",
"category": "Mens Casualwear",
"price": 49.99,
"stock": 9
}
{
"name": "Fine Stripe Short SleeveShirt, Green",
"category": "Mens Casualwear",
"price": 49.99,
"offer": 39.99,
"stock": 9
}
{
"name": "Sharkskin Waistcoat, Charcoal",
"category": "Mens Formalwear",
"price": 75.00,
"stock": 2
}
{
"name": "Lightweight Patch PocketBlazer, Deer",
"category": "Mens Formalwear",
"price": 175.50,
"stock": 1
}
{
"name": "Bird Print Dress, Black",
"category": "Womens Formalwear",
"price": 270.00,
"stock": 10
}
{
"name": "Mid Twist Cut-Out Dress, Pink",
"category": "Womens Formalwear",
"price": 540.00,
"stock": 5
}"#;
let stream = Deserializer::from_str(data).into_iter::<Value>();
let index = Index::create_in_ram(schema);
let mut index_writer = index.writer(50_000_000)?;
let mut num_indexed = 0;
for value in stream {
let doc = schema.parse_document(&serde_json::to_string(&value.unwrap())?)?;
index_writer.add_document(doc)?;
num_indexed += 1;
if num_indexed > 4 {
// Writing the first segment
index_writer.commit()?;
}
}
// writing the segment
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 1f64,
price_field => 0f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 3f64,
price_field => 1f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 5f64,
price_field => 1f64,
))?;
index_writer.add_document(doc!(
text_field => "nohit",
highscore_field => 6f64,
price_field => 2f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 7f64,
price_field => 2f64,
))?;
index_writer.commit()?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 11f64,
price_field => 10f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 14f64,
price_field => 15f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 15f64,
price_field => 20f64,
))?;
// Writing the second segment
index_writer.commit()?;
// We have two segments now. The `AggregationCollector` will run the aggregation on each
// segment and then merge the results into an `IntermediateAggregationResult`.
let reader = index.reader()?;
let searcher = reader.searcher();
// ---
// # Aggregation Query
//
//
// We can construct the query by building the request structure or by deserializing from JSON.
// The JSON API is more stable and therefore recommended.
//
// ## Request 1
let text_field = reader.searcher().schema().get_field("text").unwrap();
let agg_req_str = r#"
{
"group_by_stock": {
"aggs": {
"average_price": { "avg": { "field": "price" } }
},
"range": {
"field": "stock",
"ranges": [
{ "key": "few", "to": 1.0 },
{ "key": "some", "from": 1.0, "to": 10.0 },
{ "key": "many", "from": 10.0 }
]
}
}
} "#;
let term_query = TermQuery::new(
Term::from_field_text(text_field, "cool"),
IndexRecordOption::Basic,
);
// In this Aggregation we want to get the average price for different groups, depending on how
// many items are in stock. We define custom ranges `few`, `some`, `many` via the
// range aggregation.
// For every bucket we want the average price, so we create a nested metric aggregation on the
// range bucket aggregation. Only buckets support nested aggregations.
// ### Request JSON API
//
let sub_agg_req_1: Aggregations = vec![(
"average_price".to_string(),
Aggregation::Metric(MetricAggregation::Average(
AverageAggregation::from_field_name("price".to_string()),
)),
)]
.into_iter()
.collect();
let agg_req: Aggregations = serde_json::from_str(agg_req_str)?;
let collector = AggregationCollector::from_aggs(agg_req, None);
let agg_res: AggregationResults = searcher.search(&AllQuery, &collector).unwrap();
let res2: Value = serde_json::to_value(agg_res)?;
// ### Request Rust API
//
// This is exactly the same request as above, but via the rust structures.
//
let agg_req: Aggregations = vec![(
"group_by_stock".to_string(),
let agg_req_1: Aggregations = vec![(
"score_ranges".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Range(RangeAggregation {
field: "stock".to_string(),
field: "highscore".to_string(),
ranges: vec![
RangeAggregationRange {
key: Some("few".into()),
from: None,
to: Some(1f64),
},
RangeAggregationRange {
key: Some("some".into()),
from: Some(1f64),
to: Some(10f64),
},
RangeAggregationRange {
key: Some("many".into()),
from: Some(10f64),
to: None,
},
(-1f64..9f64).into(),
(9f64..14f64).into(),
(14f64..20f64).into(),
],
..Default::default()
}),
sub_aggregation: vec![(
"average_price".to_string(),
Aggregation::Metric(MetricAggregation::Average(
AverageAggregation::from_field_name("price".to_string()),
)),
)]
.into_iter()
.collect(),
sub_aggregation: sub_agg_req_1.clone(),
}),
)]
.into_iter()
.collect();
let collector = AggregationCollector::from_aggs(agg_req, None);
// We use the `AllQuery` which will pass all documents to the AggregationCollector.
let agg_res: AggregationResults = searcher.search(&AllQuery, &collector).unwrap();
let collector = AggregationCollector::from_aggs(agg_req_1, None);
let res1: Value = serde_json::to_value(agg_res)?;
let searcher = reader.searcher();
let agg_res: AggregationResults = searcher.search(&term_query, &collector).unwrap();
// ### Aggregation Result
//
// The resulting structure deserializes in the same JSON format as elastic search.
//
let expected_res = r#"
{
"group_by_stock":{
"buckets":[
{"average_price":{"value":19.0},"doc_count":1,"key":"few","to":1.0},
{"average_price":{"value":124.748},"doc_count":10,"from":1.0,"key":"some","to":10.0},
{"average_price":{"value":152.0},"doc_count":2,"from":10.0,"key":"many"}
]
}
}
"#;
let expected_json: Value = serde_json::from_str(expected_res)?;
assert_eq!(expected_json, res1);
assert_eq!(expected_json, res2);
// ### Request 2
//
// Now we are interested in the minimum price per category, so we create a bucket per
// category via `TermsAggregation`. We are interested in the highest minimum prices, and set the
// order of the buckets `"order": { "min_price": "desc" }` to be sorted by the the metric of
// the sub aggregation. (awesome)
//
let agg_req_str = r#"
{
"min_price_per_category": {
"aggs": {
"min_price": { "min": { "field": "price" } }
},
"terms": {
"field": "category",
"min_doc_count": 1,
"order": { "min_price": "desc" }
}
}
} "#;
let agg_req: Aggregations = serde_json::from_str(agg_req_str)?;
let collector = AggregationCollector::from_aggs(agg_req, None);
let agg_res: AggregationResults = searcher.search(&AllQuery, &collector).unwrap();
let res: Value = serde_json::to_value(agg_res)?;
// Minimum price per category, sorted by minimum price descending
//
// As you can see, the starting prices for `Formalwear` are higher than `Casualwear`.
//
let expected_res = r#"
{
"min_price_per_category": {
"buckets": [
{ "doc_count": 2, "key": "Womens Formalwear", "min_price": { "value": 270.0 } },
{ "doc_count": 2, "key": "Mens Formalwear", "min_price": { "value": 75.0 } },
{ "doc_count": 2, "key": "Mens Casualwear", "min_price": { "value": 49.99 } },
{ "doc_count": 2, "key": "Womens Footwear", "min_price": { "value": 42.0 } },
{ "doc_count": 2, "key": "Womens Casualwear", "min_price": { "value": 30.0 } },
{ "doc_count": 3, "key": "Mens Footwear", "min_price": { "value": 19.0 } }
],
"sum_other_doc_count": 0
}
}
"#;
let expected_json: Value = serde_json::from_str(expected_res)?;
assert_eq!(expected_json, res);
let res: Value = serde_json::to_value(&agg_res)?;
println!("{}", serde_json::to_string_pretty(&res)?);
Ok(())
}

31
examples/custom_collector.rs
View File

@@ -7,12 +7,13 @@
// Of course, you can have a look at the tantivy's built-in collectors
// such as the `CountCollector` for more examples.
use columnar::Column;
use fastfield_codecs::Column;
// ---
// Importing tantivy...
use tantivy::collector::{Collector, SegmentCollector};
use tantivy::fastfield::DynamicFastFieldReader;
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)]
@@ -50,11 +51,11 @@ impl Stats {
}
struct StatsCollector {
field: String,
field: Field,
}
impl StatsCollector {
fn with_field(field: String) -> StatsCollector {
fn with_field(field: Field) -> StatsCollector {
StatsCollector { field }
}
}
@@ -71,7 +72,7 @@ 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(),
@@ -95,7 +96,7 @@ impl Collector for StatsCollector {
}
struct StatsSegmentCollector {
fast_field_reader: Column,
fast_field_reader: DynamicFastFieldReader<u64>,
stats: Stats,
}
@@ -103,14 +104,10 @@ impl SegmentCollector for StatsSegmentCollector {
type Fruit = Option<Stats>;
fn collect(&mut self, doc: u32, _score: Score) {
// Since we know the values are single value, we could call `first_or_default_col` on the
// column and fetch single values.
for value in self.fast_field_reader.values_for_doc(doc) {
let value = value as f64;
self.stats.count += 1;
self.stats.sum += value;
self.stats.squared_sum += value * value;
}
let value = self.fast_field_reader.get_val(doc as u64) as f64;
self.stats.count += 1;
self.stats.sum += value;
self.stats.squared_sum += value * value;
}
fn harvest(self) -> <Self as SegmentCollector>::Fruit {
@@ -171,11 +168,9 @@ fn main() -> tantivy::Result<()> {
let searcher = reader.searcher();
let query_parser = QueryParser::for_index(&index, vec![product_name, product_description]);
// here we want to search for `broom` and use `StatsCollector` on the hits.
// 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());

7
examples/custom_tokenizer.rs
View File

@@ -1,7 +1,7 @@
// # Defining a tokenizer pipeline
//
// In this example, we'll see how to define a tokenizer
// by creating a custom `NgramTokenizer`.
// In this example, we'll see how to define a tokenizer pipeline
// by aligning a bunch of `TokenFilter`.
use tantivy::collector::TopDocs;
use tantivy::query::QueryParser;
use tantivy::schema::*;
@@ -36,7 +36,8 @@ fn main() -> tantivy::Result<()> {
// need to be able to be able to retrieve it
// for our application.
//
// We can make our index lighter by omitting the `STORED` flag.
// We can make our index lighter and
// by omitting `STORED` flag.
let body = schema_builder.add_text_field("body", TEXT);
let schema = schema_builder.build();

12
examples/date_time_field.rs
View File

@@ -4,7 +4,7 @@
use tantivy::collector::TopDocs;
use tantivy::query::QueryParser;
use tantivy::schema::{DateOptions, Schema, Value, INDEXED, STORED, STRING};
use tantivy::schema::{Cardinality, DateOptions, Schema, Value, INDEXED, STORED, STRING};
use tantivy::Index;
fn main() -> tantivy::Result<()> {
@@ -12,9 +12,8 @@ fn main() -> tantivy::Result<()> {
let mut schema_builder = Schema::builder();
let opts = DateOptions::from(INDEXED)
.set_stored()
.set_fast()
.set_fast(Cardinality::SingleValue)
.set_precision(tantivy::DatePrecision::Seconds);
// Add `occurred_at` date field type
let occurred_at = schema_builder.add_date_field("occurred_at", opts);
let event_type = schema_builder.add_text_field("event", STRING | STORED);
let schema = schema_builder.build();
@@ -23,7 +22,6 @@ fn main() -> tantivy::Result<()> {
let index = Index::create_in_ram(schema.clone());
let mut index_writer = index.writer(50_000_000)?;
// The dates are passed as string in the RFC3339 format
let doc = schema.parse_document(
r#"{
"occurred_at": "2022-06-22T12:53:50.53Z",
@@ -43,16 +41,14 @@ fn main() -> tantivy::Result<()> {
let reader = index.reader()?;
let searcher = reader.searcher();
// # Search
// # Default fields: event_type
let query_parser = QueryParser::for_index(&index, vec![event_type]);
{
// Simple exact search on the date
let query = query_parser.parse_query("occurred_at:\"2022-06-22T12:53:50.53Z\"")?;
let query = query_parser.parse_query("event:comment")?;
let count_docs = searcher.search(&*query, &TopDocs::with_limit(5))?;
assert_eq!(count_docs.len(), 1);
}
{
// Range query on the date field
let query = query_parser
.parse_query(r#"occurred_at:[2022-06-22T12:58:00Z TO 2022-06-23T00:00:00Z}"#)?;
let count_docs = searcher.search(&*query, &TopDocs::with_limit(4))?;

2
examples/deleting_updating_documents.rs
View File

@@ -113,7 +113,7 @@ fn main() -> tantivy::Result<()> {
// on its id.
//
// Note that `tantivy` does nothing to enforce the idea that
// there is only one document associated with this id.
// there is only one document associated to this id.
//
// Also you might have noticed that we apply the delete before
// having committed. This does not matter really...

24
examples/faceted_search.rs
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@@ -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());
@@ -71,7 +69,7 @@ fn main() -> tantivy::Result<()> {
let reader = index.reader()?;
let searcher = reader.searcher();
{
let mut facet_collector = FacetCollector::for_field("classification");
let mut facet_collector = FacetCollector::for_field(classification);
facet_collector.add_facet("/Felidae");
let facet_counts = searcher.search(&AllQuery, &facet_collector)?;
// This lists all of the facet counts, right below "/Felidae".
@@ -97,7 +95,7 @@ fn main() -> tantivy::Result<()> {
let facet = Facet::from("/Felidae/Pantherinae");
let facet_term = Term::from_facet(classification, &facet);
let facet_term_query = TermQuery::new(facet_term, IndexRecordOption::Basic);
let mut facet_collector = FacetCollector::for_field("classification");
let mut facet_collector = FacetCollector::for_field(classification);
facet_collector.add_facet("/Felidae/Pantherinae");
let facet_counts = searcher.search(&facet_term_query, &facet_collector)?;
let facets: Vec<(&Facet, u64)> = facet_counts.get("/Felidae/Pantherinae").collect();

20
examples/faceted_search_with_tweaked_score.rs
View File

@@ -1,12 +1,3 @@
// # Faceted Search With Tweak Score
//
// This example covers the faceted search functionalities of
// tantivy.
//
// We will :
// - define a text field "name" in our schema
// - define a facet field "classification" in our schema
use std::collections::HashSet;
use tantivy::collector::TopDocs;
@@ -64,9 +55,8 @@ fn main() -> tantivy::Result<()> {
.collect(),
);
let top_docs_by_custom_score =
// Call TopDocs with a custom tweak score
TopDocs::with_limit(2).tweak_score(move |segment_reader: &SegmentReader| {
let ingredient_reader = segment_reader.facet_reader("ingredient").unwrap();
let ingredient_reader = segment_reader.facet_reader(ingredient).unwrap();
let facet_dict = ingredient_reader.facet_dict();
let query_ords: HashSet<u64> = facets
@@ -74,10 +64,12 @@ fn main() -> tantivy::Result<()> {
.filter_map(|key| facet_dict.term_ord(key.encoded_str()).unwrap())
.collect();
let mut facet_ords_buffer: Vec<u64> = Vec::with_capacity(20);
move |doc: DocId, original_score: Score| {
// Update the original score with a tweaked score
let missing_ingredients = ingredient_reader
.facet_ords(doc)
ingredient_reader.facet_ords(doc, &mut facet_ords_buffer);
let missing_ingredients = facet_ords_buffer
.iter()
.filter(|ord| !query_ords.contains(ord))
.count();
let tweak = 1.0 / 4_f32.powi(missing_ingredients as i32);

167
examples/fuzzy_search.rs
View File

@@ -1,167 +0,0 @@
// # Basic Example
//
// This example covers the basic functionalities of
// tantivy.
//
// We will :
// - define our schema
// - create an index in a directory
// - index a few documents into our index
// - search for the best document matching a basic query
// - retrieve the best document's original content.
// ---
// Importing tantivy...
use tantivy::collector::{Count, TopDocs};
use tantivy::query::FuzzyTermQuery;
use tantivy::schema::*;
use tantivy::{doc, Index, ReloadPolicy};
use tempfile::TempDir;
fn main() -> tantivy::Result<()> {
// Let's create a temporary directory for the
// sake of this example
let index_path = TempDir::new()?;
// # Defining the schema
//
// The Tantivy index requires a very strict schema.
// The schema declares which fields are in the index,
// and for each field, its type and "the way it should
// be indexed".
// First we need to define a schema ...
let mut schema_builder = Schema::builder();
// Our first field is title.
// We want full-text search for it, and we also want
// to be able to retrieve the document after the search.
//
// `TEXT | STORED` is some syntactic sugar to describe
// that.
//
// `TEXT` means the field should be tokenized and indexed,
// along with its term frequency and term positions.
//
// `STORED` means that the field will also be saved
// in a compressed, row-oriented key-value store.
// This store is useful for reconstructing the
// documents that were selected during the search phase.
let title = schema_builder.add_text_field("title", TEXT | STORED);
let schema = schema_builder.build();
// # Indexing documents
//
// Let's create a brand new index.
//
// This will actually just save a meta.json
// with our schema in the directory.
let index = Index::create_in_dir(&index_path, schema.clone())?;
// To insert a document we will need an index writer.
// There must be only one writer at a time.
// This single `IndexWriter` is already
// multithreaded.
//
// Here we give tantivy a budget of `50MB`.
// Using a bigger memory_arena for the indexer may increase
// throughput, but 50 MB is already plenty.
let mut index_writer = index.writer(50_000_000)?;
// Let's index our documents!
// We first need a handle on the title and the body field.
// ### Adding documents
//
index_writer.add_document(doc!(
title => "The Name of the Wind",
))?;
index_writer.add_document(doc!(
title => "The Diary of Muadib",
))?;
index_writer.add_document(doc!(
title => "A Dairy Cow",
))?;
index_writer.add_document(doc!(
title => "The Diary of a Young Girl",
))?;
index_writer.commit()?;
// ### Committing
//
// At this point our documents are not searchable.
//
//
// We need to call `.commit()` explicitly to force the
// `index_writer` to finish processing the documents in the queue,
// flush the current index to the disk, and advertise
// the existence of new documents.
//
// This call is blocking.
index_writer.commit()?;
// If `.commit()` returns correctly, then all of the
// documents that have been added are guaranteed to be
// persistently indexed.
//
// In the scenario of a crash or a power failure,
// tantivy behaves as if it has rolled back to its last
// commit.
// # Searching
//
// ### Searcher
//
// A reader is required first in order to search an index.
// It acts as a `Searcher` pool that reloads itself,
// depending on a `ReloadPolicy`.
//
// For a search server you will typically create one reader for the entire lifetime of your
// program, and acquire a new searcher for every single request.
//
// In the code below, we rely on the 'ON_COMMIT' policy: the reader
// will reload the index automatically after each commit.
let reader = index
.reader_builder()
.reload_policy(ReloadPolicy::OnCommit)
.try_into()?;
// We now need to acquire a searcher.
//
// A searcher points to a snapshotted, immutable version of the index.
//
// Some search experience might require more than
// one query. Using the same searcher ensures that all of these queries will run on the
// same version of the index.
//
// Acquiring a `searcher` is very cheap.
//
// You should acquire a searcher every time you start processing a request and
// and release it right after your query is finished.
let searcher = reader.searcher();
// ### FuzzyTermQuery
{
let term = Term::from_field_text(title, "Diary");
let query = FuzzyTermQuery::new(term, 2, true);
let (top_docs, count) = searcher
.search(&query, &(TopDocs::with_limit(5), Count))
.unwrap();
assert_eq!(count, 3);
assert_eq!(top_docs.len(), 3);
for (score, doc_address) in top_docs {
let retrieved_doc = searcher.doc(doc_address)?;
// Note that the score is not lower for the fuzzy hit.
// There's an issue open for that: https://github.com/quickwit-oss/tantivy/issues/563
println!("score {score:?} doc {}", schema.to_json(&retrieved_doc));
// score 1.0 doc {"title":["The Diary of Muadib"]}
//
// score 1.0 doc {"title":["The Diary of a Young Girl"]}
//
// score 1.0 doc {"title":["A Dairy Cow"]}
}
}
Ok(())
}

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