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4
.github/workflows/coverage.yml
vendored
4
.github/workflows/coverage.yml
vendored
@@ -15,11 +15,11 @@ jobs:
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Install Rust
|
||||
run: rustup toolchain install nightly-2024-07-01 --profile minimal --component llvm-tools-preview
|
||||
run: rustup toolchain install nightly-2025-12-01 --profile minimal --component llvm-tools-preview
|
||||
- uses: Swatinem/rust-cache@v2
|
||||
- uses: taiki-e/install-action@cargo-llvm-cov
|
||||
- name: Generate code coverage
|
||||
run: cargo +nightly-2024-07-01 llvm-cov --all-features --workspace --doctests --lcov --output-path lcov.info
|
||||
run: cargo +nightly-2025-12-01 llvm-cov --all-features --workspace --doctests --lcov --output-path lcov.info
|
||||
- name: Upload coverage to Codecov
|
||||
uses: codecov/codecov-action@v3
|
||||
continue-on-error: true
|
||||
|
||||
@@ -46,7 +46,7 @@ The file of a segment has the format
|
||||
|
||||
```segment-id . ext```
|
||||
|
||||
The extension signals which data structure (or [`SegmentComponent`](src/core/segment_component.rs)) is stored in the file.
|
||||
The extension signals which data structure (or [`SegmentComponent`](src/index/segment_component.rs)) is stored in the file.
|
||||
|
||||
A small `meta.json` file is in charge of keeping track of the list of segments, as well as the schema.
|
||||
|
||||
@@ -102,7 +102,7 @@ but users can extend tantivy with their own implementation.
|
||||
|
||||
Tantivy's document follows a very strict schema, decided before building any index.
|
||||
|
||||
The schema defines all of the fields that the indexes [`Document`](src/schema/document.rs) may and should contain, their types (`text`, `i64`, `u64`, `Date`, ...) as well as how it should be indexed / represented in tantivy.
|
||||
The schema defines all of the fields that the indexes [`Document`](src/schema/document/mod.rs) may and should contain, their types (`text`, `i64`, `u64`, `Date`, ...) as well as how it should be indexed / represented in tantivy.
|
||||
|
||||
Depending on the type of the field, you can decide to
|
||||
|
||||
|
||||
138
CHANGELOG.md
138
CHANGELOG.md
@@ -1,3 +1,121 @@
|
||||
Tantivy 0.25
|
||||
================================
|
||||
|
||||
## Bugfixes
|
||||
- fix union performance regression in tantivy 0.24 [#2663](https://github.com/quickwit-oss/tantivy/pull/2663)(@PSeitz)
|
||||
- make zstd optional in sstable [#2633](https://github.com/quickwit-oss/tantivy/pull/2633)(@Parth)
|
||||
- Fix TopDocs::order_by_string_fast_field for asc order [#2672](https://github.com/quickwit-oss/tantivy/pull/2672)(@stuhood @PSeitz)
|
||||
|
||||
## Features/Improvements
|
||||
- add docs/example and Vec<u32> values to sstable [#2660](https://github.com/quickwit-oss/tantivy/pull/2660)(@PSeitz)
|
||||
- Add string fast field support to `TopDocs`. [#2642](https://github.com/quickwit-oss/tantivy/pull/2642)(@stuhood)
|
||||
- update edition to 2024 [#2620](https://github.com/quickwit-oss/tantivy/pull/2620)(@PSeitz)
|
||||
- Allow optional spaces between the field name and the value in the query parser [#2678](https://github.com/quickwit-oss/tantivy/pull/2678)(@Darkheir)
|
||||
- Support mixed field types in query parser [#2676](https://github.com/quickwit-oss/tantivy/pull/2676)(@trinity-1686a)
|
||||
- Add per-field size details [#2679](https://github.com/quickwit-oss/tantivy/pull/2679)(@fulmicoton)
|
||||
|
||||
Tantivy 0.24.2
|
||||
================================
|
||||
- Fix TopNComputer for reverse order. [#2672](https://github.com/quickwit-oss/tantivy/pull/2672)(@stuhood @PSeitz)
|
||||
|
||||
Affected queries are [order_by_fast_field](https://docs.rs/tantivy/latest/tantivy/collector/struct.TopDocs.html#method.order_by_fast_field) and
|
||||
[order_by_u64_field](https://docs.rs/tantivy/latest/tantivy/collector/struct.TopDocs.html#method.order_by_u64_field)
|
||||
for `Order::Asc`
|
||||
|
||||
Tantivy 0.24.1
|
||||
================================
|
||||
- Fix: bump required rust version to 1.81
|
||||
|
||||
Tantivy 0.24
|
||||
================================
|
||||
Tantivy 0.24 will be backwards compatible with indices created with v0.22 and v0.21. The new minimum rust version will be 1.75. Tantivy 0.23 will be skipped.
|
||||
|
||||
#### Bugfixes
|
||||
- fix potential endless loop in merge [#2457](https://github.com/quickwit-oss/tantivy/pull/2457)(@PSeitz)
|
||||
- fix bug that causes out-of-order sstable key. [#2445](https://github.com/quickwit-oss/tantivy/pull/2445)(@fulmicoton)
|
||||
- fix ReferenceValue API flaw [#2372](https://github.com/quickwit-oss/tantivy/pull/2372)(@PSeitz)
|
||||
- fix `OwnedBytes` debug panic [#2512](https://github.com/quickwit-oss/tantivy/pull/2512)(@b41sh)
|
||||
- catch panics during merges [#2582](https://github.com/quickwit-oss/tantivy/pull/2582)(@rdettai)
|
||||
- switch from u32 to usize in bitpacker. This enables multivalued columns larger than 4GB, which crashed during merge before. [#2581](https://github.com/quickwit-oss/tantivy/pull/2581) [#2586](https://github.com/quickwit-oss/tantivy/pull/2586)(@fulmicoton-dd @PSeitz)
|
||||
|
||||
#### Breaking API Changes
|
||||
- remove index sorting [#2434](https://github.com/quickwit-oss/tantivy/pull/2434)(@PSeitz)
|
||||
|
||||
#### Features/Improvements
|
||||
- **Aggregation**
|
||||
- Support for cardinality aggregation [#2337](https://github.com/quickwit-oss/tantivy/pull/2337) [#2446](https://github.com/quickwit-oss/tantivy/pull/2446) (@raphaelcoeffic @PSeitz)
|
||||
- Support for extended stats aggregation [#2247](https://github.com/quickwit-oss/tantivy/pull/2247)(@giovannicuccu)
|
||||
- Add Key::I64 and Key::U64 variants in aggregation to avoid f64 precision issues [#2468](https://github.com/quickwit-oss/tantivy/pull/2468)(@PSeitz)
|
||||
- Faster term aggregation fetch terms [#2447](https://github.com/quickwit-oss/tantivy/pull/2447)(@PSeitz)
|
||||
- Improve custom order deserialization [#2451](https://github.com/quickwit-oss/tantivy/pull/2451)(@PSeitz)
|
||||
- Change AggregationLimits behavior [#2495](https://github.com/quickwit-oss/tantivy/pull/2495)(@PSeitz)
|
||||
- lower contention on AggregationLimits [#2394](https://github.com/quickwit-oss/tantivy/pull/2394)(@PSeitz)
|
||||
- fix postcard compatibility for top_hits, add postcard test [#2346](https://github.com/quickwit-oss/tantivy/pull/2346)(@PSeitz)
|
||||
- reduce top hits memory consumption [#2426](https://github.com/quickwit-oss/tantivy/pull/2426)(@PSeitz)
|
||||
- check unsupported parameters top_hits [#2351](https://github.com/quickwit-oss/tantivy/pull/2351)(@PSeitz)
|
||||
- Change AggregationLimits to AggregationLimitsGuard [#2495](https://github.com/quickwit-oss/tantivy/pull/2495)(@PSeitz)
|
||||
- add support for counting non integer in aggregation [#2547](https://github.com/quickwit-oss/tantivy/pull/2547)(@trinity-1686a)
|
||||
- **Range Queries**
|
||||
- Support fast field range queries on json fields [#2456](https://github.com/quickwit-oss/tantivy/pull/2456)(@PSeitz)
|
||||
- Add support for str fast field range query [#2460](https://github.com/quickwit-oss/tantivy/pull/2460) [#2452](https://github.com/quickwit-oss/tantivy/pull/2452) [#2453](https://github.com/quickwit-oss/tantivy/pull/2453)(@PSeitz)
|
||||
- modify fastfield range query heuristic [#2375](https://github.com/quickwit-oss/tantivy/pull/2375)(@trinity-1686a)
|
||||
- add FastFieldRangeQuery for explicit range queries on fast field (for `RangeQuery` it is autodetected) [#2477](https://github.com/quickwit-oss/tantivy/pull/2477)(@PSeitz)
|
||||
|
||||
- add format backwards-compatibility tests [#2485](https://github.com/quickwit-oss/tantivy/pull/2485)(@PSeitz)
|
||||
- add columnar format compatibility tests [#2433](https://github.com/quickwit-oss/tantivy/pull/2433)(@PSeitz)
|
||||
- Improved snippet ranges algorithm [#2474](https://github.com/quickwit-oss/tantivy/pull/2474)(@gezihuzi)
|
||||
- make find_field_with_default return json fields without path [#2476](https://github.com/quickwit-oss/tantivy/pull/2476)(@trinity-1686a)
|
||||
- Make `BooleanQuery` support `minimum_number_should_match` [#2405](https://github.com/quickwit-oss/tantivy/pull/2405)(@LebranceBW)
|
||||
- Make `NUM_MERGE_THREADS` configurable [#2535](https://github.com/quickwit-oss/tantivy/pull/2535)(@Barre)
|
||||
|
||||
- **RegexPhraseQuery**
|
||||
`RegexPhraseQuery` supports phrase queries with regex. E.g. query "b.* b.* wolf" matches "big bad wolf". Slop is supported as well: "b.* wolf"~2 matches "big bad wolf" [#2516](https://github.com/quickwit-oss/tantivy/pull/2516)(@PSeitz)
|
||||
|
||||
- **Optional Index in Multivalue Columnar Index**
|
||||
For mostly empty multivalued indices there was a large overhead during creation when iterating all docids (merge case).
|
||||
This is alleviated by placing an optional index in the multivalued index to mark documents that have values.
|
||||
This will slightly increase space and access time. [#2439](https://github.com/quickwit-oss/tantivy/pull/2439)(@PSeitz)
|
||||
|
||||
- **Store DateTime as nanoseconds in doc store** DateTime in the doc store was truncated to microseconds previously. This removes this truncation, while still keeping backwards compatibility. [#2486](https://github.com/quickwit-oss/tantivy/pull/2486)(@PSeitz)
|
||||
|
||||
- **Performance/Memory**
|
||||
- lift clauses in LogicalAst for optimized ast during execution [#2449](https://github.com/quickwit-oss/tantivy/pull/2449)(@PSeitz)
|
||||
- Use Vec instead of BTreeMap to back OwnedValue object [#2364](https://github.com/quickwit-oss/tantivy/pull/2364)(@fulmicoton)
|
||||
- Replace TantivyDocument with CompactDoc. CompactDoc is much smaller and provides similar performance. [#2402](https://github.com/quickwit-oss/tantivy/pull/2402)(@PSeitz)
|
||||
- Recycling buffer in PrefixPhraseScorer [#2443](https://github.com/quickwit-oss/tantivy/pull/2443)(@fulmicoton)
|
||||
|
||||
- **Json Type**
|
||||
- JSON supports now all values on the root level. Previously an object was required. This enables support for flat mixed types. allow more JSON values, fix i64 special case [#2383](https://github.com/quickwit-oss/tantivy/pull/2383)(@PSeitz)
|
||||
- add json path constructor to term [#2367](https://github.com/quickwit-oss/tantivy/pull/2367)(@PSeitz)
|
||||
|
||||
- **QueryParser**
|
||||
- fix de-escaping too much in query parser [#2427](https://github.com/quickwit-oss/tantivy/pull/2427)(@trinity-1686a)
|
||||
- improve query parser [#2416](https://github.com/quickwit-oss/tantivy/pull/2416)(@trinity-1686a)
|
||||
- Support field grouping `title:(return AND "pink panther")` [#2333](https://github.com/quickwit-oss/tantivy/pull/2333)(@trinity-1686a)
|
||||
- allow term starting with wildcard [#2568](https://github.com/quickwit-oss/tantivy/pull/2568)(@trinity-1686a)
|
||||
|
||||
- Exist queries match subpath fields [#2558](https://github.com/quickwit-oss/tantivy/pull/2558)(@rdettai)
|
||||
- add access benchmark for columnar [#2432](https://github.com/quickwit-oss/tantivy/pull/2432)(@PSeitz)
|
||||
- extend indexwriter proptests [#2342](https://github.com/quickwit-oss/tantivy/pull/2342)(@PSeitz)
|
||||
- add bench & test for columnar merging [#2428](https://github.com/quickwit-oss/tantivy/pull/2428)(@PSeitz)
|
||||
- Change in Executor API [#2391](https://github.com/quickwit-oss/tantivy/pull/2391)(@fulmicoton)
|
||||
- Removed usage of num_cpus [#2387](https://github.com/quickwit-oss/tantivy/pull/2387)(@fulmicoton)
|
||||
- use bingang for agg and stacker benchmark [#2378](https://github.com/quickwit-oss/tantivy/pull/2378)[#2492](https://github.com/quickwit-oss/tantivy/pull/2492)(@PSeitz)
|
||||
- cleanup top level exports [#2382](https://github.com/quickwit-oss/tantivy/pull/2382)(@PSeitz)
|
||||
- make convert_to_fast_value_and_append_to_json_term pub [#2370](https://github.com/quickwit-oss/tantivy/pull/2370)(@PSeitz)
|
||||
- remove JsonTermWriter [#2238](https://github.com/quickwit-oss/tantivy/pull/2238)(@PSeitz)
|
||||
- validate sort by field type [#2336](https://github.com/quickwit-oss/tantivy/pull/2336)(@PSeitz)
|
||||
- Fix trait bound of StoreReader::iter [#2360](https://github.com/quickwit-oss/tantivy/pull/2360)(@adamreichold)
|
||||
- remove read_postings_no_deletes [#2526](https://github.com/quickwit-oss/tantivy/pull/2526)(@PSeitz)
|
||||
|
||||
Tantivy 0.22.1
|
||||
================================
|
||||
- Fix TopNComputer for reverse order. [#2672](https://github.com/quickwit-oss/tantivy/pull/2672)(@stuhood @PSeitz)
|
||||
|
||||
Affected queries are [order_by_fast_field](https://docs.rs/tantivy/latest/tantivy/collector/struct.TopDocs.html#method.order_by_fast_field) and
|
||||
[order_by_u64_field](https://docs.rs/tantivy/latest/tantivy/collector/struct.TopDocs.html#method.order_by_u64_field)
|
||||
for `Order::Asc`
|
||||
|
||||
Tantivy 0.22
|
||||
================================
|
||||
|
||||
@@ -8,7 +126,7 @@ Tantivy 0.22 will be able to read indices created with Tantivy 0.21.
|
||||
- Fix bug that can cause `get_docids_for_value_range` to panic. [#2295](https://github.com/quickwit-oss/tantivy/pull/2295)(@fulmicoton)
|
||||
- Avoid 1 document indices by increase min memory to 15MB for indexing [#2176](https://github.com/quickwit-oss/tantivy/pull/2176)(@PSeitz)
|
||||
- Fix merge panic for JSON fields [#2284](https://github.com/quickwit-oss/tantivy/pull/2284)(@PSeitz)
|
||||
- Fix bug occuring when merging JSON object indexed with positions. [#2253](https://github.com/quickwit-oss/tantivy/pull/2253)(@fulmicoton)
|
||||
- Fix bug occurring when merging JSON object indexed with positions. [#2253](https://github.com/quickwit-oss/tantivy/pull/2253)(@fulmicoton)
|
||||
- Fix empty DateHistogram gap bug [#2183](https://github.com/quickwit-oss/tantivy/pull/2183)(@PSeitz)
|
||||
- Fix range query end check (fields with less than 1 value per doc are affected) [#2226](https://github.com/quickwit-oss/tantivy/pull/2226)(@PSeitz)
|
||||
- Handle exclusive out of bounds ranges on fastfield range queries [#2174](https://github.com/quickwit-oss/tantivy/pull/2174)(@PSeitz)
|
||||
@@ -26,7 +144,7 @@ Tantivy 0.22 will be able to read indices created with Tantivy 0.21.
|
||||
- Support to deserialize f64 from string [#2311](https://github.com/quickwit-oss/tantivy/pull/2311)(@PSeitz)
|
||||
- Add a top_hits aggregator [#2198](https://github.com/quickwit-oss/tantivy/pull/2198)(@ditsuke)
|
||||
- Support bool type in term aggregation [#2318](https://github.com/quickwit-oss/tantivy/pull/2318)(@PSeitz)
|
||||
- Support ip adresses in term aggregation [#2319](https://github.com/quickwit-oss/tantivy/pull/2319)(@PSeitz)
|
||||
- Support ip addresses in term aggregation [#2319](https://github.com/quickwit-oss/tantivy/pull/2319)(@PSeitz)
|
||||
- Support date type in term aggregation [#2172](https://github.com/quickwit-oss/tantivy/pull/2172)(@PSeitz)
|
||||
- Support escaped dot when addressing field [#2250](https://github.com/quickwit-oss/tantivy/pull/2250)(@PSeitz)
|
||||
|
||||
@@ -116,7 +234,7 @@ Tantivy 0.20
|
||||
- Add PhrasePrefixQuery [#1842](https://github.com/quickwit-oss/tantivy/issues/1842) (@trinity-1686a)
|
||||
- Add `coerce` option for text and numbers types (convert the value instead of returning an error during indexing) [#1904](https://github.com/quickwit-oss/tantivy/issues/1904) (@PSeitz)
|
||||
- Add regex tokenizer [#1759](https://github.com/quickwit-oss/tantivy/issues/1759)(@mkleen)
|
||||
- Move tokenizer API to seperate crate. Having a seperate crate with a stable API will allow us to use tokenizers with different tantivy versions. [#1767](https://github.com/quickwit-oss/tantivy/issues/1767) (@PSeitz)
|
||||
- Move tokenizer API to separate crate. Having a separate crate with a stable API will allow us to use tokenizers with different tantivy versions. [#1767](https://github.com/quickwit-oss/tantivy/issues/1767) (@PSeitz)
|
||||
- **Columnar crate**: New fast field handling (@fulmicoton @PSeitz) [#1806](https://github.com/quickwit-oss/tantivy/issues/1806)[#1809](https://github.com/quickwit-oss/tantivy/issues/1809)
|
||||
- Support for fast fields with optional values. Previously tantivy supported only single-valued and multi-value fast fields. The encoding of optional fast fields is now very compact.
|
||||
- Fast field Support for JSON (schemaless fast fields). Support multiple types on the same column. [#1876](https://github.com/quickwit-oss/tantivy/issues/1876) (@fulmicoton)
|
||||
@@ -163,13 +281,13 @@ Tantivy 0.20
|
||||
- Auto downgrade index record option, instead of vint error [#1857](https://github.com/quickwit-oss/tantivy/issues/1857) (@PSeitz)
|
||||
- Enable range query on fast field for u64 compatible types [#1762](https://github.com/quickwit-oss/tantivy/issues/1762) (@PSeitz) [#1876]
|
||||
- sstable
|
||||
- Isolating sstable and stacker in independant crates. [#1718](https://github.com/quickwit-oss/tantivy/issues/1718) (@fulmicoton)
|
||||
- Isolating sstable and stacker in independent crates. [#1718](https://github.com/quickwit-oss/tantivy/issues/1718) (@fulmicoton)
|
||||
- New sstable format [#1943](https://github.com/quickwit-oss/tantivy/issues/1943)[#1953](https://github.com/quickwit-oss/tantivy/issues/1953) (@trinity-1686a)
|
||||
- Use DeltaReader directly to implement Dictionnary::ord_to_term [#1928](https://github.com/quickwit-oss/tantivy/issues/1928) (@trinity-1686a)
|
||||
- Use DeltaReader directly to implement Dictionnary::term_ord [#1925](https://github.com/quickwit-oss/tantivy/issues/1925) (@trinity-1686a)
|
||||
- Add seperate tokenizer manager for fast fields [#2019](https://github.com/quickwit-oss/tantivy/issues/2019) (@PSeitz)
|
||||
- Use DeltaReader directly to implement Dictionary::ord_to_term [#1928](https://github.com/quickwit-oss/tantivy/issues/1928) (@trinity-1686a)
|
||||
- Use DeltaReader directly to implement Dictionary::term_ord [#1925](https://github.com/quickwit-oss/tantivy/issues/1925) (@trinity-1686a)
|
||||
- Add separate tokenizer manager for fast fields [#2019](https://github.com/quickwit-oss/tantivy/issues/2019) (@PSeitz)
|
||||
- Make construction of LevenshteinAutomatonBuilder for FuzzyTermQuery instances lazy. [#1756](https://github.com/quickwit-oss/tantivy/issues/1756) (@adamreichold)
|
||||
- Added support for madvise when opening an mmaped Index [#2036](https://github.com/quickwit-oss/tantivy/issues/2036) (@fulmicoton)
|
||||
- Added support for madvise when opening an mmapped Index [#2036](https://github.com/quickwit-oss/tantivy/issues/2036) (@fulmicoton)
|
||||
- Rename `DatePrecision` to `DateTimePrecision` [#2051](https://github.com/quickwit-oss/tantivy/issues/2051) (@guilload)
|
||||
- Query Parser
|
||||
- Quotation mark can now be used for phrase queries. [#2050](https://github.com/quickwit-oss/tantivy/issues/2050) (@fulmicoton)
|
||||
@@ -208,7 +326,7 @@ Tantivy 0.19
|
||||
- Add support for phrase slop in query language [#1393](https://github.com/quickwit-oss/tantivy/pull/1393) (@saroh)
|
||||
- Aggregation
|
||||
- Add aggregation support for date type [#1693](https://github.com/quickwit-oss/tantivy/pull/1693)(@PSeitz)
|
||||
- Add support for keyed parameter in range and histgram aggregations [#1424](https://github.com/quickwit-oss/tantivy/pull/1424) (@k-yomo)
|
||||
- Add support for keyed parameter in range and histogram aggregations [#1424](https://github.com/quickwit-oss/tantivy/pull/1424) (@k-yomo)
|
||||
- Add aggregation bucket limit [#1363](https://github.com/quickwit-oss/tantivy/pull/1363) (@PSeitz)
|
||||
- Faster indexing
|
||||
- [#1610](https://github.com/quickwit-oss/tantivy/pull/1610) (@PSeitz)
|
||||
@@ -651,7 +769,7 @@ Tantivy 0.4.0
|
||||
- Raise the limit of number of fields (previously 256 fields) (@fulmicoton)
|
||||
- Removed u32 fields. They are replaced by u64 and i64 fields (#65) (@fulmicoton)
|
||||
- Optimized skip in SegmentPostings (#130) (@lnicola)
|
||||
- Replacing rustc_serialize by serde. Kudos to @KodrAus and @lnicola
|
||||
- Replacing rustc_serialize by serde. Kudos to benchmark@KodrAus and @lnicola
|
||||
- Using error-chain (@KodrAus)
|
||||
- QueryParser: (@fulmicoton)
|
||||
- Explicit error returned when searched for a term that is not indexed
|
||||
|
||||
10
CITATION.cff
Normal file
10
CITATION.cff
Normal file
@@ -0,0 +1,10 @@
|
||||
cff-version: 1.2.0
|
||||
message: "If you use this software, please cite it as below."
|
||||
authors:
|
||||
- alias: Quickwit Inc.
|
||||
website: "https://quickwit.io"
|
||||
title: "tantivy"
|
||||
version: 0.22.0
|
||||
doi: 10.5281/zenodo.13942948
|
||||
date-released: 2024-10-17
|
||||
url: "https://github.com/quickwit-oss/tantivy"
|
||||
60
Cargo.toml
60
Cargo.toml
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "tantivy"
|
||||
version = "0.23.0"
|
||||
version = "0.26.0"
|
||||
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
|
||||
license = "MIT"
|
||||
categories = ["database-implementations", "data-structures"]
|
||||
@@ -11,7 +11,7 @@ repository = "https://github.com/quickwit-oss/tantivy"
|
||||
readme = "README.md"
|
||||
keywords = ["search", "information", "retrieval"]
|
||||
edition = "2021"
|
||||
rust-version = "1.66"
|
||||
rust-version = "1.85"
|
||||
exclude = ["benches/*.json", "benches/*.txt"]
|
||||
|
||||
[dependencies]
|
||||
@@ -29,50 +29,53 @@ tantivy-fst = "0.5"
|
||||
memmap2 = { version = "0.9.0", optional = true }
|
||||
lz4_flex = { version = "0.11", default-features = false, optional = true }
|
||||
zstd = { version = "0.13", optional = true, default-features = false }
|
||||
tempfile = { version = "3.3.0", optional = true }
|
||||
tempfile = { version = "3.12.0", optional = true }
|
||||
log = "0.4.16"
|
||||
serde = { version = "1.0.136", features = ["derive"] }
|
||||
serde_json = "1.0.79"
|
||||
fs4 = { version = "0.8.0", optional = true }
|
||||
serde = { version = "1.0.219", features = ["derive"] }
|
||||
serde_json = "1.0.140"
|
||||
fs4 = { version = "0.13.1", optional = true }
|
||||
levenshtein_automata = "0.2.1"
|
||||
uuid = { version = "1.0.0", features = ["v4", "serde"] }
|
||||
crossbeam-channel = "0.5.4"
|
||||
rust-stemmers = "1.2.0"
|
||||
downcast-rs = "1.2.1"
|
||||
downcast-rs = "2.0.1"
|
||||
bitpacking = { version = "0.9.2", default-features = false, features = [
|
||||
"bitpacker4x",
|
||||
] }
|
||||
census = "0.4.2"
|
||||
rustc-hash = "1.1.0"
|
||||
thiserror = "1.0.30"
|
||||
rustc-hash = "2.0.0"
|
||||
thiserror = "2.0.1"
|
||||
htmlescape = "0.3.1"
|
||||
fail = { version = "0.5.0", optional = true }
|
||||
time = { version = "0.3.10", features = ["serde-well-known"] }
|
||||
time = { version = "0.3.35", features = ["serde-well-known"] }
|
||||
smallvec = "1.8.0"
|
||||
rayon = "1.5.2"
|
||||
lru = "0.12.0"
|
||||
fastdivide = "0.4.0"
|
||||
itertools = "0.13.0"
|
||||
measure_time = "0.8.2"
|
||||
itertools = "0.14.0"
|
||||
measure_time = "0.9.0"
|
||||
arc-swap = "1.5.0"
|
||||
bon = "3.3.1"
|
||||
|
||||
columnar = { version = "0.3", path = "./columnar", package = "tantivy-columnar" }
|
||||
sstable = { version = "0.3", path = "./sstable", package = "tantivy-sstable", optional = true }
|
||||
stacker = { version = "0.3", path = "./stacker", package = "tantivy-stacker" }
|
||||
query-grammar = { version = "0.22.0", path = "./query-grammar", package = "tantivy-query-grammar" }
|
||||
tantivy-bitpacker = { version = "0.6", path = "./bitpacker" }
|
||||
common = { version = "0.7", path = "./common/", package = "tantivy-common" }
|
||||
tokenizer-api = { version = "0.3", path = "./tokenizer-api", package = "tantivy-tokenizer-api" }
|
||||
columnar = { version = "0.6", path = "./columnar", package = "tantivy-columnar" }
|
||||
sstable = { version = "0.6", path = "./sstable", package = "tantivy-sstable", optional = true }
|
||||
stacker = { version = "0.6", path = "./stacker", package = "tantivy-stacker" }
|
||||
query-grammar = { version = "0.25.0", path = "./query-grammar", package = "tantivy-query-grammar" }
|
||||
tantivy-bitpacker = { version = "0.9", path = "./bitpacker" }
|
||||
common = { version = "0.10", path = "./common/", package = "tantivy-common" }
|
||||
tokenizer-api = { version = "0.6", path = "./tokenizer-api", package = "tantivy-tokenizer-api" }
|
||||
sketches-ddsketch = { version = "0.3.0", features = ["use_serde"] }
|
||||
hyperloglogplus = { version = "0.4.1", features = ["const-loop"] }
|
||||
futures-util = { version = "0.3.28", optional = true }
|
||||
futures-channel = { version = "0.3.28", optional = true }
|
||||
fnv = "1.0.7"
|
||||
typetag = "0.2.21"
|
||||
|
||||
[target.'cfg(windows)'.dependencies]
|
||||
winapi = "0.3.9"
|
||||
|
||||
[dev-dependencies]
|
||||
binggan = "0.8.0"
|
||||
binggan = "0.14.0"
|
||||
rand = "0.8.5"
|
||||
maplit = "1.0.2"
|
||||
matches = "0.1.9"
|
||||
@@ -85,7 +88,7 @@ more-asserts = "0.3.1"
|
||||
rand_distr = "0.4.3"
|
||||
time = { version = "0.3.10", features = ["serde-well-known", "macros"] }
|
||||
postcard = { version = "1.0.4", features = [
|
||||
"use-std",
|
||||
"use-std",
|
||||
], default-features = false }
|
||||
|
||||
[target.'cfg(not(windows))'.dev-dependencies]
|
||||
@@ -110,17 +113,20 @@ debug-assertions = true
|
||||
overflow-checks = true
|
||||
|
||||
[features]
|
||||
default = ["mmap", "stopwords", "lz4-compression"]
|
||||
default = ["mmap", "stopwords", "lz4-compression", "columnar-zstd-compression"]
|
||||
mmap = ["fs4", "tempfile", "memmap2"]
|
||||
stopwords = []
|
||||
|
||||
lz4-compression = ["lz4_flex"]
|
||||
zstd-compression = ["zstd"]
|
||||
|
||||
# enable zstd-compression in columnar (and sstable)
|
||||
columnar-zstd-compression = ["columnar/zstd-compression"]
|
||||
|
||||
failpoints = ["fail", "fail/failpoints"]
|
||||
unstable = [] # useful for benches.
|
||||
|
||||
quickwit = ["sstable", "futures-util"]
|
||||
quickwit = ["sstable", "futures-util", "futures-channel"]
|
||||
|
||||
# Compares only the hash of a string when indexing data.
|
||||
# Increases indexing speed, but may lead to extremely rare missing terms, when there's a hash collision.
|
||||
@@ -162,3 +168,11 @@ harness = false
|
||||
[[bench]]
|
||||
name = "agg_bench"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "exists_json"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "and_or_queries"
|
||||
harness = false
|
||||
|
||||
@@ -23,8 +23,6 @@ performance for different types of queries/collections.
|
||||
|
||||
Your mileage WILL vary depending on the nature of queries and their load.
|
||||
|
||||
<img src="doc/assets/images/searchbenchmark.png">
|
||||
|
||||
Details about the benchmark can be found at this [repository](https://github.com/quickwit-oss/search-benchmark-game).
|
||||
|
||||
## Features
|
||||
@@ -125,6 +123,7 @@ You can also find other bindings on [GitHub](https://github.com/search?q=tantivy
|
||||
- [seshat](https://github.com/matrix-org/seshat/): A matrix message database/indexer
|
||||
- [tantiny](https://github.com/baygeldin/tantiny): Tiny full-text search for Ruby
|
||||
- [lnx](https://github.com/lnx-search/lnx): adaptable, typo tolerant search engine with a REST API
|
||||
- [Bichon](https://github.com/rustmailer/bichon): A lightweight, high-performance Rust email archiver with WebUI
|
||||
- and [more](https://github.com/search?q=tantivy)!
|
||||
|
||||
### On average, how much faster is Tantivy compared to Lucene?
|
||||
|
||||
27
RELEASE.md
27
RELEASE.md
@@ -1,4 +1,4 @@
|
||||
# Release a new Tantivy Version
|
||||
# Releasing a new Tantivy Version
|
||||
|
||||
## Steps
|
||||
|
||||
@@ -10,12 +10,29 @@
|
||||
6. Set git tag with new version
|
||||
|
||||
|
||||
In conjucation with `cargo-release` Steps 1-4 (I'm not sure if the change detection works):
|
||||
Set new packages to version 0.0.0
|
||||
[`cargo-release`](https://github.com/crate-ci/cargo-release) will help us with steps 1-5:
|
||||
|
||||
Replace prev-tag-name
|
||||
```bash
|
||||
cargo release --workspace --no-publish -v --prev-tag-name 0.19 --push-remote origin minor --no-tag --execute
|
||||
cargo release --workspace --no-publish -v --prev-tag-name 0.24 --push-remote origin minor --no-tag
|
||||
```
|
||||
|
||||
no-tag or it will create tags for all the subpackages
|
||||
`no-tag` or it will create tags for all the subpackages
|
||||
|
||||
cargo release will _not_ ignore unchanged packages, but it will print warnings for them.
|
||||
e.g. "warning: updating ownedbytes to 0.10.0 despite no changes made since tag 0.24"
|
||||
|
||||
We need to manually ignore these unchanged packages
|
||||
```bash
|
||||
cargo release --workspace --no-publish -v --prev-tag-name 0.24 --push-remote origin minor --no-tag --exclude tokenizer-api
|
||||
```
|
||||
|
||||
Add `--execute` to actually publish the packages, otherwise it will only print the commands that would be run.
|
||||
|
||||
### Tag Version
|
||||
```bash
|
||||
git tag 0.25.0
|
||||
git push upstream tag 0.25.0
|
||||
```
|
||||
|
||||
|
||||
|
||||
4
TODO.txt
4
TODO.txt
@@ -1,7 +1,7 @@
|
||||
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
|
||||
u64 , etc. should return Result<Option> now that we support optional missing a column is really not an error
|
||||
remove fastfield codecs
|
||||
ditch the first_or_default trick. if it is still useful, improve its implementation.
|
||||
rename FastFieldReaders::open to load
|
||||
@@ -10,7 +10,7 @@ rename FastFieldReaders::open to load
|
||||
remove fast field reader
|
||||
|
||||
find a way to unify the two DateTime.
|
||||
readd type check in the filter wrapper
|
||||
re-add type check in the filter wrapper
|
||||
|
||||
add unit test on columnar list columns.
|
||||
|
||||
|
||||
@@ -1,4 +1,6 @@
|
||||
use binggan::plugins::PeakMemAllocPlugin;
|
||||
use binggan::{black_box, InputGroup, PeakMemAlloc, INSTRUMENTED_SYSTEM};
|
||||
use rand::distributions::WeightedIndex;
|
||||
use rand::prelude::SliceRandom;
|
||||
use rand::rngs::StdRng;
|
||||
use rand::{Rng, SeedableRng};
|
||||
@@ -17,7 +19,9 @@ pub static GLOBAL: &PeakMemAlloc<std::alloc::System> = &INSTRUMENTED_SYSTEM;
|
||||
/// runner.register("average_u64", move |index| average_u64(index));
|
||||
macro_rules! register {
|
||||
($runner:expr, $func:ident) => {
|
||||
$runner.register(stringify!($func), move |index| $func(index))
|
||||
$runner.register(stringify!($func), move |index| {
|
||||
$func(index);
|
||||
})
|
||||
};
|
||||
}
|
||||
|
||||
@@ -42,7 +46,8 @@ fn main() {
|
||||
}
|
||||
|
||||
fn bench_agg(mut group: InputGroup<Index>) {
|
||||
group.set_alloc(GLOBAL); // Set the peak mem allocator. This will enable peak memory reporting.
|
||||
group.add_plugin(PeakMemAllocPlugin::new(GLOBAL));
|
||||
|
||||
register!(group, average_u64);
|
||||
register!(group, average_f64);
|
||||
register!(group, average_f64_u64);
|
||||
@@ -50,11 +55,19 @@ fn bench_agg(mut group: InputGroup<Index>) {
|
||||
register!(group, extendedstats_f64);
|
||||
register!(group, percentiles_f64);
|
||||
register!(group, terms_few);
|
||||
register!(group, terms_all_unique);
|
||||
register!(group, terms_many);
|
||||
register!(group, terms_many_top_1000);
|
||||
register!(group, terms_many_order_by_term);
|
||||
register!(group, terms_many_with_top_hits);
|
||||
register!(group, terms_all_unique_with_avg_sub_agg);
|
||||
register!(group, terms_many_with_avg_sub_agg);
|
||||
register!(group, terms_few_with_avg_sub_agg);
|
||||
register!(group, terms_status_with_avg_sub_agg);
|
||||
register!(group, terms_status);
|
||||
register!(group, terms_few_with_histogram);
|
||||
register!(group, terms_status_with_histogram);
|
||||
|
||||
register!(group, terms_many_json_mixed_type_with_avg_sub_agg);
|
||||
|
||||
register!(group, cardinality_agg);
|
||||
@@ -67,8 +80,15 @@ fn bench_agg(mut group: InputGroup<Index>) {
|
||||
register!(group, histogram);
|
||||
register!(group, histogram_hard_bounds);
|
||||
register!(group, histogram_with_avg_sub_agg);
|
||||
register!(group, histogram_with_term_agg_few);
|
||||
register!(group, avg_and_range_with_avg_sub_agg);
|
||||
|
||||
// Filter aggregation benchmarks
|
||||
register!(group, filter_agg_all_query_count_agg);
|
||||
register!(group, filter_agg_term_query_count_agg);
|
||||
register!(group, filter_agg_all_query_with_sub_aggs);
|
||||
register!(group, filter_agg_term_query_with_sub_aggs);
|
||||
|
||||
group.run();
|
||||
}
|
||||
|
||||
@@ -119,12 +139,12 @@ fn extendedstats_f64(index: &Index) {
|
||||
}
|
||||
fn percentiles_f64(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"mypercentiles": {
|
||||
"percentiles": {
|
||||
"field": "score_f64",
|
||||
"percents": [ 95, 99, 99.9 ]
|
||||
"mypercentiles": {
|
||||
"percentiles": {
|
||||
"field": "score_f64",
|
||||
"percents": [ 95, 99, 99.9 ]
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
@@ -161,6 +181,19 @@ fn terms_few(index: &Index) {
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_status(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": { "terms": { "field": "text_few_terms_status" } },
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_all_unique(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": { "terms": { "field": "text_all_unique_terms" } },
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn terms_many(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": { "terms": { "field": "text_many_terms" } },
|
||||
@@ -209,6 +242,63 @@ fn terms_many_with_avg_sub_agg(index: &Index) {
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_all_unique_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
"terms": { "field": "text_all_unique_terms" },
|
||||
"aggs": {
|
||||
"average_f64": { "avg": { "field": "score_f64" } }
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_few_with_histogram(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
"terms": { "field": "text_few_terms" },
|
||||
"aggs": {
|
||||
"histo": {"histogram": { "field": "score_f64", "interval": 10 }}
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_status_with_histogram(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
"terms": { "field": "text_few_terms_status" },
|
||||
"aggs": {
|
||||
"histo": {"histogram": { "field": "score_f64", "interval": 10 }}
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn terms_few_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
"terms": { "field": "text_few_terms" },
|
||||
"aggs": {
|
||||
"average_f64": { "avg": { "field": "score_f64" } }
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_status_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
"terms": { "field": "text_few_terms_status" },
|
||||
"aggs": {
|
||||
"average_f64": { "avg": { "field": "score_f64" } }
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn terms_many_json_mixed_type_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
@@ -335,6 +425,17 @@ fn histogram_with_avg_sub_agg(index: &Index) {
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn histogram_with_term_agg_few(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"rangef64": {
|
||||
"histogram": { "field": "score_f64", "interval": 10 },
|
||||
"aggs": {
|
||||
"my_texts": { "terms": { "field": "text_few_terms" } }
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn avg_and_range_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"rangef64": {
|
||||
@@ -382,14 +483,21 @@ fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
|
||||
.set_stored();
|
||||
let text_field = schema_builder.add_text_field("text", text_fieldtype);
|
||||
let json_field = schema_builder.add_json_field("json", FAST);
|
||||
let text_field_all_unique_terms =
|
||||
schema_builder.add_text_field("text_all_unique_terms", STRING | FAST);
|
||||
let text_field_many_terms = schema_builder.add_text_field("text_many_terms", STRING | FAST);
|
||||
let text_field_many_terms = schema_builder.add_text_field("text_many_terms", STRING | FAST);
|
||||
let text_field_few_terms = schema_builder.add_text_field("text_few_terms", STRING | FAST);
|
||||
let text_field_few_terms_status =
|
||||
schema_builder.add_text_field("text_few_terms_status", STRING | FAST);
|
||||
let score_fieldtype = tantivy::schema::NumericOptions::default().set_fast();
|
||||
let score_field = schema_builder.add_u64_field("score", score_fieldtype.clone());
|
||||
let score_field_f64 = schema_builder.add_f64_field("score_f64", score_fieldtype.clone());
|
||||
let score_field_i64 = schema_builder.add_i64_field("score_i64", score_fieldtype);
|
||||
let index = Index::create_from_tempdir(schema_builder.build())?;
|
||||
let few_terms_data = ["INFO", "ERROR", "WARN", "DEBUG"];
|
||||
// Approximate production log proportions: INFO dominant, WARN and DEBUG occasional, ERROR rare.
|
||||
let log_level_distribution = WeightedIndex::new([80u32, 3, 12, 5]).unwrap();
|
||||
|
||||
let lg_norm = rand_distr::LogNormal::new(2.996f64, 0.979f64).unwrap();
|
||||
|
||||
@@ -405,15 +513,21 @@ fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
|
||||
index_writer.add_document(doc!())?;
|
||||
}
|
||||
if cardinality == Cardinality::Multivalued {
|
||||
let log_level_sample_a = few_terms_data[log_level_distribution.sample(&mut rng)];
|
||||
let log_level_sample_b = few_terms_data[log_level_distribution.sample(&mut rng)];
|
||||
index_writer.add_document(doc!(
|
||||
json_field => json!({"mixed_type": 10.0}),
|
||||
json_field => json!({"mixed_type": 10.0}),
|
||||
text_field => "cool",
|
||||
text_field => "cool",
|
||||
text_field_all_unique_terms => "cool",
|
||||
text_field_all_unique_terms => "coolo",
|
||||
text_field_many_terms => "cool",
|
||||
text_field_many_terms => "cool",
|
||||
text_field_few_terms => "cool",
|
||||
text_field_few_terms => "cool",
|
||||
text_field_few_terms_status => log_level_sample_a,
|
||||
text_field_few_terms_status => log_level_sample_b,
|
||||
score_field => 1u64,
|
||||
score_field => 1u64,
|
||||
score_field_f64 => lg_norm.sample(&mut rng),
|
||||
@@ -438,8 +552,10 @@ fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
|
||||
index_writer.add_document(doc!(
|
||||
text_field => "cool",
|
||||
json_field => json,
|
||||
text_field_all_unique_terms => format!("unique_term_{}", rng.gen::<u64>()),
|
||||
text_field_many_terms => many_terms_data.choose(&mut rng).unwrap().to_string(),
|
||||
text_field_few_terms => few_terms_data.choose(&mut rng).unwrap().to_string(),
|
||||
text_field_few_terms_status => few_terms_data[log_level_distribution.sample(&mut rng)],
|
||||
score_field => val as u64,
|
||||
score_field_f64 => lg_norm.sample(&mut rng),
|
||||
score_field_i64 => val as i64,
|
||||
@@ -456,3 +572,61 @@ fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
|
||||
|
||||
Ok(index)
|
||||
}
|
||||
|
||||
// Filter aggregation benchmarks
|
||||
|
||||
fn filter_agg_all_query_count_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"filtered": {
|
||||
"filter": "*",
|
||||
"aggs": {
|
||||
"count": { "value_count": { "field": "score" } }
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn filter_agg_term_query_count_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"filtered": {
|
||||
"filter": "text:cool",
|
||||
"aggs": {
|
||||
"count": { "value_count": { "field": "score" } }
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn filter_agg_all_query_with_sub_aggs(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"filtered": {
|
||||
"filter": "*",
|
||||
"aggs": {
|
||||
"avg_score": { "avg": { "field": "score" } },
|
||||
"stats_score": { "stats": { "field": "score_f64" } },
|
||||
"terms_text": {
|
||||
"terms": { "field": "text_few_terms" }
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn filter_agg_term_query_with_sub_aggs(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"filtered": {
|
||||
"filter": "text:cool",
|
||||
"aggs": {
|
||||
"avg_score": { "avg": { "field": "score" } },
|
||||
"stats_score": { "stats": { "field": "score_f64" } },
|
||||
"terms_text": {
|
||||
"terms": { "field": "text_few_terms" }
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
218
benches/and_or_queries.rs
Normal file
218
benches/and_or_queries.rs
Normal file
@@ -0,0 +1,218 @@
|
||||
// Benchmarks boolean conjunction queries using binggan.
|
||||
//
|
||||
// What’s measured:
|
||||
// - Or and And queries with varying selectivity (only `Term` queries for now on leafs)
|
||||
// - Nested AND/OR combinations (on multiple fields)
|
||||
// - No-scoring path using the Count collector (focus on iterator/skip performance)
|
||||
// - Top-K retrieval (k=10) using the TopDocs collector
|
||||
//
|
||||
// Corpus model:
|
||||
// - Synthetic docs; each token a/b/c is independently included per doc
|
||||
// - If none of a/b/c are included, emit a neutral filler token to keep doc length similar
|
||||
//
|
||||
// Notes:
|
||||
// - After optimization, when scoring is disabled Tantivy reads doc-only postings
|
||||
// (IndexRecordOption::Basic), avoiding frequency decoding overhead.
|
||||
// - This bench isolates boolean iteration speed and intersection/union cost.
|
||||
// - Use `cargo bench --bench boolean_conjunction` to run.
|
||||
|
||||
use binggan::{black_box, BenchGroup, BenchRunner};
|
||||
use rand::prelude::*;
|
||||
use rand::rngs::StdRng;
|
||||
use rand::SeedableRng;
|
||||
use tantivy::collector::sort_key::SortByStaticFastValue;
|
||||
use tantivy::collector::{Collector, Count, TopDocs};
|
||||
use tantivy::query::{Query, QueryParser};
|
||||
use tantivy::schema::{Schema, FAST, TEXT};
|
||||
use tantivy::{doc, Index, Order, ReloadPolicy, Searcher};
|
||||
|
||||
#[derive(Clone)]
|
||||
struct BenchIndex {
|
||||
#[allow(dead_code)]
|
||||
index: Index,
|
||||
searcher: Searcher,
|
||||
query_parser: QueryParser,
|
||||
}
|
||||
|
||||
/// Build a single index containing both fields (title, body) and
|
||||
/// return two BenchIndex views:
|
||||
/// - single_field: QueryParser defaults to only "body"
|
||||
/// - multi_field: QueryParser defaults to ["title", "body"]
|
||||
fn build_shared_indices(num_docs: usize, p_a: f32, p_b: f32, p_c: f32) -> (BenchIndex, BenchIndex) {
|
||||
// Unified schema (two text fields)
|
||||
let mut schema_builder = Schema::builder();
|
||||
let f_title = schema_builder.add_text_field("title", TEXT);
|
||||
let f_body = schema_builder.add_text_field("body", TEXT);
|
||||
let f_score = schema_builder.add_u64_field("score", FAST);
|
||||
let f_score2 = schema_builder.add_u64_field("score2", FAST);
|
||||
let schema = schema_builder.build();
|
||||
let index = Index::create_in_ram(schema.clone());
|
||||
|
||||
// Populate index with stable RNG for reproducibility.
|
||||
let mut rng = StdRng::from_seed([7u8; 32]);
|
||||
|
||||
// Populate: spread each present token 90/10 to body/title
|
||||
{
|
||||
let mut writer = index.writer_with_num_threads(1, 500_000_000).unwrap();
|
||||
for _ in 0..num_docs {
|
||||
let has_a = rng.gen_bool(p_a as f64);
|
||||
let has_b = rng.gen_bool(p_b as f64);
|
||||
let has_c = rng.gen_bool(p_c as f64);
|
||||
let score = rng.gen_range(0u64..100u64);
|
||||
let score2 = rng.gen_range(0u64..100_000u64);
|
||||
let mut title_tokens: Vec<&str> = Vec::new();
|
||||
let mut body_tokens: Vec<&str> = Vec::new();
|
||||
if has_a {
|
||||
if rng.gen_bool(0.1) {
|
||||
title_tokens.push("a");
|
||||
} else {
|
||||
body_tokens.push("a");
|
||||
}
|
||||
}
|
||||
if has_b {
|
||||
if rng.gen_bool(0.1) {
|
||||
title_tokens.push("b");
|
||||
} else {
|
||||
body_tokens.push("b");
|
||||
}
|
||||
}
|
||||
if has_c {
|
||||
if rng.gen_bool(0.1) {
|
||||
title_tokens.push("c");
|
||||
} else {
|
||||
body_tokens.push("c");
|
||||
}
|
||||
}
|
||||
if title_tokens.is_empty() && body_tokens.is_empty() {
|
||||
body_tokens.push("z");
|
||||
}
|
||||
writer
|
||||
.add_document(doc!(
|
||||
f_title=>title_tokens.join(" "),
|
||||
f_body=>body_tokens.join(" "),
|
||||
f_score=>score,
|
||||
f_score2=>score2,
|
||||
))
|
||||
.unwrap();
|
||||
}
|
||||
writer.commit().unwrap();
|
||||
}
|
||||
|
||||
// Prepare reader/searcher once.
|
||||
let reader = index
|
||||
.reader_builder()
|
||||
.reload_policy(ReloadPolicy::Manual)
|
||||
.try_into()
|
||||
.unwrap();
|
||||
let searcher = reader.searcher();
|
||||
|
||||
// Build two query parsers with different default fields.
|
||||
let qp_single = QueryParser::for_index(&index, vec![f_body]);
|
||||
let qp_multi = QueryParser::for_index(&index, vec![f_title, f_body]);
|
||||
|
||||
let single_view = BenchIndex {
|
||||
index: index.clone(),
|
||||
searcher: searcher.clone(),
|
||||
query_parser: qp_single,
|
||||
};
|
||||
let multi_view = BenchIndex {
|
||||
index,
|
||||
searcher,
|
||||
query_parser: qp_multi,
|
||||
};
|
||||
(single_view, multi_view)
|
||||
}
|
||||
|
||||
fn main() {
|
||||
// Prepare corpora with varying selectivity. Build one index per corpus
|
||||
// and derive two views (single-field vs multi-field) from it.
|
||||
let scenarios = vec![
|
||||
(
|
||||
"N=1M, p(a)=5%, p(b)=1%, p(c)=15%".to_string(),
|
||||
1_000_000,
|
||||
0.05,
|
||||
0.01,
|
||||
0.15,
|
||||
),
|
||||
(
|
||||
"N=1M, p(a)=1%, p(b)=1%, p(c)=15%".to_string(),
|
||||
1_000_000,
|
||||
0.01,
|
||||
0.01,
|
||||
0.15,
|
||||
),
|
||||
];
|
||||
|
||||
let queries = &["a", "+a +b", "+a +b +c", "a OR b", "a OR b OR c"];
|
||||
|
||||
let mut runner = BenchRunner::new();
|
||||
for (label, n, pa, pb, pc) in scenarios {
|
||||
let (single_view, multi_view) = build_shared_indices(n, pa, pb, pc);
|
||||
|
||||
for (view_name, bench_index) in [("single_field", single_view), ("multi_field", multi_view)]
|
||||
{
|
||||
// Single-field group: default field is body only
|
||||
let mut group = runner.new_group();
|
||||
group.set_name(format!("{} — {}", view_name, label));
|
||||
for query_str in queries {
|
||||
add_bench_task(&mut group, &bench_index, query_str, Count, "count");
|
||||
add_bench_task(
|
||||
&mut group,
|
||||
&bench_index,
|
||||
query_str,
|
||||
TopDocs::with_limit(10).order_by_score(),
|
||||
"top10",
|
||||
);
|
||||
add_bench_task(
|
||||
&mut group,
|
||||
&bench_index,
|
||||
query_str,
|
||||
TopDocs::with_limit(10).order_by_fast_field::<u64>("score", Order::Asc),
|
||||
"top10_by_ff",
|
||||
);
|
||||
add_bench_task(
|
||||
&mut group,
|
||||
&bench_index,
|
||||
query_str,
|
||||
TopDocs::with_limit(10).order_by((
|
||||
SortByStaticFastValue::<u64>::for_field("score"),
|
||||
SortByStaticFastValue::<u64>::for_field("score2"),
|
||||
)),
|
||||
"top10_by_2ff",
|
||||
);
|
||||
}
|
||||
group.run();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn add_bench_task<C: Collector + 'static>(
|
||||
bench_group: &mut BenchGroup,
|
||||
bench_index: &BenchIndex,
|
||||
query_str: &str,
|
||||
collector: C,
|
||||
collector_name: &str,
|
||||
) {
|
||||
let task_name = format!("{}_{}", query_str.replace(" ", "_"), collector_name);
|
||||
let query = bench_index.query_parser.parse_query(query_str).unwrap();
|
||||
let search_task = SearchTask {
|
||||
searcher: bench_index.searcher.clone(),
|
||||
collector,
|
||||
query,
|
||||
};
|
||||
bench_group.register(task_name, move |_| black_box(search_task.run()));
|
||||
}
|
||||
|
||||
struct SearchTask<C: Collector> {
|
||||
searcher: Searcher,
|
||||
collector: C,
|
||||
query: Box<dyn Query>,
|
||||
}
|
||||
|
||||
impl<C: Collector> SearchTask<C> {
|
||||
#[inline(never)]
|
||||
pub fn run(&self) -> usize {
|
||||
self.searcher.search(&self.query, &self.collector).unwrap();
|
||||
1
|
||||
}
|
||||
}
|
||||
69
benches/exists_json.rs
Normal file
69
benches/exists_json.rs
Normal file
@@ -0,0 +1,69 @@
|
||||
use binggan::plugins::PeakMemAllocPlugin;
|
||||
use binggan::{black_box, InputGroup, PeakMemAlloc, INSTRUMENTED_SYSTEM};
|
||||
use serde_json::json;
|
||||
use tantivy::collector::Count;
|
||||
use tantivy::query::ExistsQuery;
|
||||
use tantivy::schema::{Schema, FAST, TEXT};
|
||||
use tantivy::{doc, Index};
|
||||
|
||||
#[global_allocator]
|
||||
pub static GLOBAL: &PeakMemAlloc<std::alloc::System> = &INSTRUMENTED_SYSTEM;
|
||||
|
||||
fn main() {
|
||||
let doc_count: usize = 500_000;
|
||||
let subfield_counts: &[usize] = &[1, 2, 3, 4, 5, 6, 7, 8, 16, 256, 4096, 65536, 262144];
|
||||
|
||||
let indices: Vec<(String, Index)> = subfield_counts
|
||||
.iter()
|
||||
.map(|&sub_fields| {
|
||||
(
|
||||
format!("subfields={sub_fields}"),
|
||||
build_index_with_json_subfields(doc_count, sub_fields),
|
||||
)
|
||||
})
|
||||
.collect();
|
||||
|
||||
let mut group = InputGroup::new_with_inputs(indices);
|
||||
group.add_plugin(PeakMemAllocPlugin::new(GLOBAL));
|
||||
|
||||
group.config().num_iter_group = Some(1);
|
||||
group.config().num_iter_bench = Some(1);
|
||||
group.register("exists_json", exists_json_union);
|
||||
|
||||
group.run();
|
||||
}
|
||||
|
||||
fn exists_json_union(index: &Index) {
|
||||
let reader = index.reader().expect("reader");
|
||||
let searcher = reader.searcher();
|
||||
let query = ExistsQuery::new("json".to_string(), true);
|
||||
let count = searcher.search(&query, &Count).expect("exists search");
|
||||
// Prevents optimizer from eliding the search
|
||||
black_box(count);
|
||||
}
|
||||
|
||||
fn build_index_with_json_subfields(num_docs: usize, num_subfields: usize) -> Index {
|
||||
// Schema: single JSON field stored as FAST to support ExistsQuery.
|
||||
let mut schema_builder = Schema::builder();
|
||||
let json_field = schema_builder.add_json_field("json", TEXT | FAST);
|
||||
let schema = schema_builder.build();
|
||||
|
||||
let index = Index::create_from_tempdir(schema).expect("create index");
|
||||
{
|
||||
let mut index_writer = index
|
||||
.writer_with_num_threads(1, 200_000_000)
|
||||
.expect("writer");
|
||||
for i in 0..num_docs {
|
||||
let sub = i % num_subfields;
|
||||
// Only one subpath set per document; rotate subpaths so that
|
||||
// no single subpath is full, but the union covers all docs.
|
||||
let v = json!({ format!("field_{sub}"): i as u64 });
|
||||
index_writer
|
||||
.add_document(doc!(json_field => v))
|
||||
.expect("add_document");
|
||||
}
|
||||
index_writer.commit().expect("commit");
|
||||
}
|
||||
|
||||
index
|
||||
}
|
||||
@@ -1,7 +1,7 @@
|
||||
[package]
|
||||
name = "tantivy-bitpacker"
|
||||
version = "0.6.0"
|
||||
edition = "2021"
|
||||
version = "0.9.0"
|
||||
edition = "2024"
|
||||
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
|
||||
license = "MIT"
|
||||
categories = []
|
||||
|
||||
@@ -48,7 +48,7 @@ impl BitPacker {
|
||||
|
||||
pub fn flush<TWrite: io::Write + ?Sized>(&mut self, output: &mut TWrite) -> io::Result<()> {
|
||||
if self.mini_buffer_written > 0 {
|
||||
let num_bytes = (self.mini_buffer_written + 7) / 8;
|
||||
let num_bytes = self.mini_buffer_written.div_ceil(8);
|
||||
let bytes = self.mini_buffer.to_le_bytes();
|
||||
output.write_all(&bytes[..num_bytes])?;
|
||||
self.mini_buffer_written = 0;
|
||||
@@ -65,7 +65,7 @@ impl BitPacker {
|
||||
|
||||
#[derive(Clone, Debug, Default, Copy)]
|
||||
pub struct BitUnpacker {
|
||||
num_bits: u32,
|
||||
num_bits: usize,
|
||||
mask: u64,
|
||||
}
|
||||
|
||||
@@ -83,7 +83,7 @@ impl BitUnpacker {
|
||||
(1u64 << num_bits) - 1u64
|
||||
};
|
||||
BitUnpacker {
|
||||
num_bits: u32::from(num_bits),
|
||||
num_bits: usize::from(num_bits),
|
||||
mask,
|
||||
}
|
||||
}
|
||||
@@ -94,14 +94,14 @@ impl BitUnpacker {
|
||||
|
||||
#[inline]
|
||||
pub fn get(&self, idx: u32, data: &[u8]) -> u64 {
|
||||
let addr_in_bits = idx * self.num_bits;
|
||||
let addr = (addr_in_bits >> 3) as usize;
|
||||
let addr_in_bits = idx as usize * self.num_bits;
|
||||
let addr = addr_in_bits >> 3;
|
||||
if addr + 8 > data.len() {
|
||||
if self.num_bits == 0 {
|
||||
return 0;
|
||||
}
|
||||
let bit_shift = addr_in_bits & 7;
|
||||
return self.get_slow_path(addr, bit_shift, data);
|
||||
return self.get_slow_path(addr, bit_shift as u32, data);
|
||||
}
|
||||
let bit_shift = addr_in_bits & 7;
|
||||
let bytes: [u8; 8] = (&data[addr..addr + 8]).try_into().unwrap();
|
||||
@@ -134,12 +134,13 @@ impl BitUnpacker {
|
||||
"Bitwidth must be <= 32 to use this method."
|
||||
);
|
||||
|
||||
let end_idx = start_idx + output.len() as u32;
|
||||
let end_idx: u32 = start_idx + output.len() as u32;
|
||||
|
||||
let end_bit_read = end_idx * self.num_bits;
|
||||
let end_byte_read = (end_bit_read + 7) / 8;
|
||||
// We use `usize` here to avoid overflow issues.
|
||||
let end_bit_read = (end_idx as usize) * self.num_bits;
|
||||
let end_byte_read = end_bit_read.div_ceil(8);
|
||||
assert!(
|
||||
end_byte_read as usize <= data.len(),
|
||||
end_byte_read <= data.len(),
|
||||
"Requested index is out of bounds."
|
||||
);
|
||||
|
||||
@@ -159,24 +160,24 @@ impl BitUnpacker {
|
||||
// We want the start of the fast track to start align with bytes.
|
||||
// A sufficient condition is to start with an idx that is a multiple of 8,
|
||||
// so highway start is the closest multiple of 8 that is >= start_idx.
|
||||
let entrance_ramp_len = 8 - (start_idx % 8) % 8;
|
||||
let entrance_ramp_len: u32 = 8 - (start_idx % 8) % 8;
|
||||
|
||||
let highway_start: u32 = start_idx + entrance_ramp_len;
|
||||
|
||||
if highway_start + BitPacker1x::BLOCK_LEN as u32 > end_idx {
|
||||
if highway_start + (BitPacker1x::BLOCK_LEN as u32) > end_idx {
|
||||
// We don't have enough values to have even a single block of highway.
|
||||
// Let's just supply the values the simple way.
|
||||
get_batch_ramp(start_idx, output);
|
||||
return;
|
||||
}
|
||||
|
||||
let num_blocks: u32 = (end_idx - highway_start) / BitPacker1x::BLOCK_LEN as u32;
|
||||
let num_blocks: usize = (end_idx - highway_start) as usize / BitPacker1x::BLOCK_LEN;
|
||||
|
||||
// Entrance ramp
|
||||
get_batch_ramp(start_idx, &mut output[..entrance_ramp_len as usize]);
|
||||
|
||||
// Highway
|
||||
let mut offset = (highway_start * self.num_bits) as usize / 8;
|
||||
let mut offset = (highway_start as usize * self.num_bits) / 8;
|
||||
let mut output_cursor = (highway_start - start_idx) as usize;
|
||||
for _ in 0..num_blocks {
|
||||
offset += BitPacker1x.decompress(
|
||||
@@ -188,7 +189,7 @@ impl BitUnpacker {
|
||||
}
|
||||
|
||||
// Exit ramp
|
||||
let highway_end = highway_start + num_blocks * BitPacker1x::BLOCK_LEN as u32;
|
||||
let highway_end: u32 = highway_start + (num_blocks * BitPacker1x::BLOCK_LEN) as u32;
|
||||
get_batch_ramp(highway_end, &mut output[output_cursor..]);
|
||||
}
|
||||
|
||||
@@ -257,7 +258,7 @@ 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).div_ceil(8));
|
||||
let bitunpacker = BitUnpacker::new(num_bits);
|
||||
(bitunpacker, vals, data)
|
||||
}
|
||||
@@ -303,7 +304,7 @@ mod test {
|
||||
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);
|
||||
assert_eq!(buffer.len(), (vals.len() * num_bits as usize).div_ceil(8));
|
||||
let bitunpacker = BitUnpacker::new(num_bits);
|
||||
let max_val = if num_bits == 64 {
|
||||
u64::MAX
|
||||
@@ -368,9 +369,9 @@ mod test {
|
||||
for start_idx in 0u32..32u32 {
|
||||
output.resize(len, 0);
|
||||
bitunpacker.get_batch_u32s(start_idx, &buffer, &mut output);
|
||||
for i in 0..len {
|
||||
for (i, output_byte) in output.iter().enumerate() {
|
||||
let expected = (start_idx + i as u32) & mask;
|
||||
assert_eq!(output[i], expected);
|
||||
assert_eq!(*output_byte, expected);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
use super::bitpacker::BitPacker;
|
||||
use super::compute_num_bits;
|
||||
use crate::{minmax, BitUnpacker};
|
||||
use crate::{BitUnpacker, minmax};
|
||||
|
||||
const BLOCK_SIZE: usize = 128;
|
||||
|
||||
@@ -34,7 +34,7 @@ struct BlockedBitpackerEntryMetaData {
|
||||
|
||||
impl BlockedBitpackerEntryMetaData {
|
||||
fn new(offset: u64, num_bits: u8, base_value: u64) -> Self {
|
||||
let encoded = offset | (num_bits as u64) << (64 - 8);
|
||||
let encoded = offset | (u64::from(num_bits) << (64 - 8));
|
||||
Self {
|
||||
encoded,
|
||||
base_value,
|
||||
@@ -140,10 +140,10 @@ impl BlockedBitpacker {
|
||||
pub fn iter(&self) -> impl Iterator<Item = u64> + '_ {
|
||||
// todo performance: we could decompress a whole block and cache it instead
|
||||
let bitpacked_elems = self.offset_and_bits.len() * BLOCK_SIZE;
|
||||
let iter = (0..bitpacked_elems)
|
||||
|
||||
(0..bitpacked_elems)
|
||||
.map(move |idx| self.get(idx))
|
||||
.chain(self.buffer.iter().cloned());
|
||||
iter
|
||||
.chain(self.buffer.iter().cloned())
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -19,7 +19,7 @@ fn u32_to_i32(val: u32) -> i32 {
|
||||
#[inline]
|
||||
unsafe fn u32_to_i32_avx2(vals_u32x8s: DataType) -> DataType {
|
||||
const HIGHEST_BIT_MASK: DataType = from_u32x8([HIGHEST_BIT; NUM_LANES]);
|
||||
op_xor(vals_u32x8s, HIGHEST_BIT_MASK)
|
||||
unsafe { op_xor(vals_u32x8s, HIGHEST_BIT_MASK) }
|
||||
}
|
||||
|
||||
pub fn filter_vec_in_place(range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
|
||||
@@ -66,17 +66,19 @@ unsafe fn filter_vec_avx2_aux(
|
||||
]);
|
||||
const SHIFT: __m256i = from_u32x8([NUM_LANES as u32; NUM_LANES]);
|
||||
for _ in 0..num_words {
|
||||
let word = load_unaligned(input);
|
||||
let word = u32_to_i32_avx2(word);
|
||||
let keeper_bitset = compute_filter_bitset(word, range_simd.clone());
|
||||
let added_len = keeper_bitset.count_ones();
|
||||
let filtered_doc_ids = compact(ids, keeper_bitset);
|
||||
store_unaligned(output_tail as *mut __m256i, filtered_doc_ids);
|
||||
output_tail = output_tail.offset(added_len as isize);
|
||||
ids = op_add(ids, SHIFT);
|
||||
input = input.offset(1);
|
||||
unsafe {
|
||||
let word = load_unaligned(input);
|
||||
let word = u32_to_i32_avx2(word);
|
||||
let keeper_bitset = compute_filter_bitset(word, range_simd.clone());
|
||||
let added_len = keeper_bitset.count_ones();
|
||||
let filtered_doc_ids = compact(ids, keeper_bitset);
|
||||
store_unaligned(output_tail as *mut __m256i, filtered_doc_ids);
|
||||
output_tail = output_tail.offset(added_len as isize);
|
||||
ids = op_add(ids, SHIFT);
|
||||
input = input.offset(1);
|
||||
}
|
||||
}
|
||||
output_tail.offset_from(output) as usize
|
||||
unsafe { output_tail.offset_from(output) as usize }
|
||||
}
|
||||
|
||||
#[inline]
|
||||
@@ -92,8 +94,7 @@ unsafe fn compute_filter_bitset(val: __m256i, range: std::ops::RangeInclusive<__
|
||||
let too_low = op_greater(*range.start(), val);
|
||||
let too_high = op_greater(val, *range.end());
|
||||
let inside = op_or(too_low, too_high);
|
||||
255 - std::arch::x86_64::_mm256_movemask_ps(std::mem::transmute::<DataType, __m256>(inside))
|
||||
as u8
|
||||
255 - std::arch::x86_64::_mm256_movemask_ps(_mm256_castsi256_ps(inside)) as u8
|
||||
}
|
||||
|
||||
union U8x32 {
|
||||
|
||||
@@ -35,8 +35,8 @@ const IMPLS: [FilterImplPerInstructionSet; 2] = [
|
||||
const IMPLS: [FilterImplPerInstructionSet; 1] = [FilterImplPerInstructionSet::Scalar];
|
||||
|
||||
impl FilterImplPerInstructionSet {
|
||||
#[allow(unused_variables)]
|
||||
#[inline]
|
||||
#[allow(unused_variables)] // on non-x86_64, code is unused.
|
||||
fn from(code: u8) -> FilterImplPerInstructionSet {
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
if code == FilterImplPerInstructionSet::AVX2 as u8 {
|
||||
|
||||
@@ -33,11 +33,7 @@ pub use crate::blocked_bitpacker::BlockedBitpacker;
|
||||
/// number of bits.
|
||||
pub fn compute_num_bits(n: u64) -> u8 {
|
||||
let amplitude = (64u32 - n.leading_zeros()) as u8;
|
||||
if amplitude <= 64 - 8 {
|
||||
amplitude
|
||||
} else {
|
||||
64
|
||||
}
|
||||
if amplitude <= 64 - 8 { amplitude } else { 64 }
|
||||
}
|
||||
|
||||
/// Computes the (min, max) of an iterator of `PartialOrd` values.
|
||||
|
||||
@@ -16,14 +16,14 @@ body = """
|
||||
|
||||
{%- if version %} in {{ version }}{%- endif -%}
|
||||
{% for commit in commits %}
|
||||
{% if commit.github.pr_title -%}
|
||||
{%- set commit_message = commit.github.pr_title -%}
|
||||
{% if commit.remote.pr_title -%}
|
||||
{%- set commit_message = commit.remote.pr_title -%}
|
||||
{%- else -%}
|
||||
{%- set commit_message = commit.message -%}
|
||||
{%- endif -%}
|
||||
- {{ commit_message | split(pat="\n") | first | trim }}\
|
||||
{% if commit.github.pr_number %} \
|
||||
[#{{ commit.github.pr_number }}]({{ self::remote_url() }}/pull/{{ commit.github.pr_number }}){% if commit.github.username %}(@{{ commit.github.username }}){%- endif -%} \
|
||||
{% if commit.remote.pr_number %} \
|
||||
[#{{ commit.remote.pr_number }}]({{ self::remote_url() }}/pull/{{ commit.remote.pr_number }}){% if commit.remote.username %}(@{{ commit.remote.username }}){%- endif -%} \
|
||||
{%- endif %}
|
||||
{%- endfor -%}
|
||||
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
[package]
|
||||
name = "tantivy-columnar"
|
||||
version = "0.3.0"
|
||||
edition = "2021"
|
||||
version = "0.6.0"
|
||||
edition = "2024"
|
||||
license = "MIT"
|
||||
homepage = "https://github.com/quickwit-oss/tantivy"
|
||||
repository = "https://github.com/quickwit-oss/tantivy"
|
||||
@@ -9,21 +9,21 @@ description = "column oriented storage for tantivy"
|
||||
categories = ["database-implementations", "data-structures", "compression"]
|
||||
|
||||
[dependencies]
|
||||
itertools = "0.13.0"
|
||||
itertools = "0.14.0"
|
||||
fastdivide = "0.4.0"
|
||||
|
||||
stacker = { version= "0.3", path = "../stacker", package="tantivy-stacker"}
|
||||
sstable = { version= "0.3", path = "../sstable", package = "tantivy-sstable" }
|
||||
common = { version= "0.7", path = "../common", package = "tantivy-common" }
|
||||
tantivy-bitpacker = { version= "0.6", path = "../bitpacker/" }
|
||||
stacker = { version= "0.6", path = "../stacker", package="tantivy-stacker"}
|
||||
sstable = { version= "0.6", path = "../sstable", package = "tantivy-sstable" }
|
||||
common = { version= "0.10", path = "../common", package = "tantivy-common" }
|
||||
tantivy-bitpacker = { version= "0.9", path = "../bitpacker/" }
|
||||
serde = "1.0.152"
|
||||
downcast-rs = "1.2.0"
|
||||
downcast-rs = "2.0.1"
|
||||
|
||||
[dev-dependencies]
|
||||
proptest = "1"
|
||||
more-asserts = "0.3.1"
|
||||
rand = "0.8"
|
||||
binggan = "0.8.1"
|
||||
binggan = "0.14.0"
|
||||
|
||||
[[bench]]
|
||||
name = "bench_merge"
|
||||
@@ -33,6 +33,29 @@ harness = false
|
||||
name = "bench_access"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "bench_first_vals"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "bench_values_u64"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "bench_values_u128"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "bench_create_column_values"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "bench_column_values_get"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "bench_optional_index"
|
||||
harness = false
|
||||
|
||||
[features]
|
||||
unstable = []
|
||||
zstd-compression = ["sstable/zstd-compression"]
|
||||
|
||||
@@ -31,7 +31,7 @@ restriction on 50% of the values (e.g. a 64-bit hash). On the other hand, a lot
|
||||
# Columnar format
|
||||
|
||||
This columnar format may have more than one column (with different types) associated to the same `column_name` (see [Coercion rules](#coercion-rules) above).
|
||||
The `(column_name, columne_type)` couple however uniquely identifies a column.
|
||||
The `(column_name, column_type)` couple however uniquely identifies a column.
|
||||
That couple is serialized as a column `column_key`. The format of that key is:
|
||||
`[column_name][ZERO_BYTE][column_type_header: u8]`
|
||||
|
||||
@@ -73,7 +73,7 @@ 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
|
||||
A `Column<T>` associates a `RowId` (u32) to any
|
||||
number of values.
|
||||
|
||||
This is made possible by wrapping a `ColumnIndex` and a `ColumnValue` object.
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
use binggan::{black_box, InputGroup};
|
||||
use binggan::{InputGroup, black_box};
|
||||
use common::*;
|
||||
use tantivy_columnar::Column;
|
||||
|
||||
@@ -19,7 +19,7 @@ fn main() {
|
||||
|
||||
let mut add_card = |card1: Card| {
|
||||
inputs.push((
|
||||
format!("{card1}"),
|
||||
card1.to_string(),
|
||||
generate_columnar_and_open(card1, NUM_DOCS),
|
||||
));
|
||||
};
|
||||
@@ -50,6 +50,7 @@ fn bench_group(mut runner: InputGroup<Column>) {
|
||||
let mut buffer = vec![None; BLOCK_SIZE];
|
||||
for i in (0..NUM_DOCS).step_by(BLOCK_SIZE) {
|
||||
// fill docs
|
||||
#[allow(clippy::needless_range_loop)]
|
||||
for idx in 0..BLOCK_SIZE {
|
||||
docs[idx] = idx as u32 + i;
|
||||
}
|
||||
|
||||
61
columnar/benches/bench_column_values_get.rs
Normal file
61
columnar/benches/bench_column_values_get.rs
Normal file
@@ -0,0 +1,61 @@
|
||||
use std::sync::Arc;
|
||||
|
||||
use binggan::{InputGroup, black_box};
|
||||
use rand::rngs::StdRng;
|
||||
use rand::{Rng, SeedableRng};
|
||||
use tantivy_columnar::ColumnValues;
|
||||
use tantivy_columnar::column_values::{CodecType, serialize_and_load_u64_based_column_values};
|
||||
|
||||
fn get_data() -> Vec<u64> {
|
||||
let mut rng = StdRng::seed_from_u64(2u64);
|
||||
let mut data: Vec<_> = (100..55_000_u64)
|
||||
.map(|num| num + rng.r#gen::<u8>() as u64)
|
||||
.collect();
|
||||
data.push(99_000);
|
||||
data.insert(1000, 2000);
|
||||
data.insert(2000, 100);
|
||||
data.insert(3000, 4100);
|
||||
data.insert(4000, 100);
|
||||
data.insert(5000, 800);
|
||||
data
|
||||
}
|
||||
|
||||
#[inline(never)]
|
||||
fn value_iter() -> impl Iterator<Item = u64> {
|
||||
0..20_000
|
||||
}
|
||||
|
||||
type Col = Arc<dyn ColumnValues<u64>>;
|
||||
|
||||
fn main() {
|
||||
let data = get_data();
|
||||
let inputs: Vec<(String, Col)> = vec![
|
||||
(
|
||||
"bitpacked".to_string(),
|
||||
serialize_and_load_u64_based_column_values(&data.as_slice(), &[CodecType::Bitpacked]),
|
||||
),
|
||||
(
|
||||
"linear".to_string(),
|
||||
serialize_and_load_u64_based_column_values(&data.as_slice(), &[CodecType::Linear]),
|
||||
),
|
||||
(
|
||||
"blockwise_linear".to_string(),
|
||||
serialize_and_load_u64_based_column_values(
|
||||
&data.as_slice(),
|
||||
&[CodecType::BlockwiseLinear],
|
||||
),
|
||||
),
|
||||
];
|
||||
|
||||
let mut group: InputGroup<Col> = InputGroup::new_with_inputs(inputs);
|
||||
|
||||
group.register("fastfield_get", |col: &Col| {
|
||||
let mut sum = 0u64;
|
||||
for pos in value_iter() {
|
||||
sum = sum.wrapping_add(col.get_val(pos as u32));
|
||||
}
|
||||
black_box(sum);
|
||||
});
|
||||
|
||||
group.run();
|
||||
}
|
||||
44
columnar/benches/bench_create_column_values.rs
Normal file
44
columnar/benches/bench_create_column_values.rs
Normal file
@@ -0,0 +1,44 @@
|
||||
use binggan::{InputGroup, black_box};
|
||||
use rand::rngs::StdRng;
|
||||
use rand::{Rng, SeedableRng};
|
||||
use tantivy_columnar::column_values::{CodecType, serialize_u64_based_column_values};
|
||||
|
||||
fn get_data() -> Vec<u64> {
|
||||
let mut rng = StdRng::seed_from_u64(2u64);
|
||||
let mut data: Vec<_> = (100..55_000_u64)
|
||||
.map(|num| num + rng.r#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 main() {
|
||||
let data = get_data();
|
||||
let mut group: InputGroup<(CodecType, Vec<u64>)> = InputGroup::new_with_inputs(vec![
|
||||
(
|
||||
"bitpacked codec".to_string(),
|
||||
(CodecType::Bitpacked, data.clone()),
|
||||
),
|
||||
(
|
||||
"linear codec".to_string(),
|
||||
(CodecType::Linear, data.clone()),
|
||||
),
|
||||
(
|
||||
"blockwise linear codec".to_string(),
|
||||
(CodecType::BlockwiseLinear, data.clone()),
|
||||
),
|
||||
]);
|
||||
|
||||
group.register("serialize column_values", |data| {
|
||||
let mut buffer = Vec::new();
|
||||
serialize_u64_based_column_values(&data.1.as_slice(), &[data.0], &mut buffer).unwrap();
|
||||
black_box(buffer.len());
|
||||
});
|
||||
|
||||
group.run();
|
||||
}
|
||||
@@ -1,12 +1,9 @@
|
||||
#![feature(test)]
|
||||
extern crate test;
|
||||
|
||||
use std::sync::Arc;
|
||||
|
||||
use binggan::{InputGroup, black_box};
|
||||
use rand::prelude::*;
|
||||
use tantivy_columnar::column_values::{serialize_and_load_u64_based_column_values, CodecType};
|
||||
use tantivy_columnar::column_values::{CodecType, serialize_and_load_u64_based_column_values};
|
||||
use tantivy_columnar::*;
|
||||
use test::{black_box, Bencher};
|
||||
|
||||
struct Columns {
|
||||
pub optional: Column,
|
||||
@@ -68,88 +65,38 @@ pub fn serialize_and_load(column: &[u64], codec_type: CodecType) -> Arc<dyn Colu
|
||||
serialize_and_load_u64_based_column_values(&column, &[codec_type])
|
||||
}
|
||||
|
||||
fn run_bench_on_column_full_scan(b: &mut Bencher, column: Column) {
|
||||
let num_iter = black_box(NUM_VALUES);
|
||||
b.iter(|| {
|
||||
fn main() {
|
||||
let Columns {
|
||||
optional,
|
||||
full,
|
||||
multi,
|
||||
} = get_test_columns();
|
||||
|
||||
let inputs = vec![
|
||||
("full".to_string(), full),
|
||||
("optional".to_string(), optional),
|
||||
("multi".to_string(), multi),
|
||||
];
|
||||
|
||||
let mut group = InputGroup::new_with_inputs(inputs);
|
||||
|
||||
group.register("first_full_scan", |column| {
|
||||
let mut sum = 0u64;
|
||||
for i in 0..num_iter as u32 {
|
||||
for i in 0..NUM_VALUES as u32 {
|
||||
let val = column.first(i);
|
||||
sum += val.unwrap_or(0);
|
||||
}
|
||||
sum
|
||||
black_box(sum);
|
||||
});
|
||||
}
|
||||
fn run_bench_on_column_block_fetch(b: &mut Bencher, column: Column) {
|
||||
let mut block: Vec<Option<u64>> = vec![None; 64];
|
||||
let fetch_docids = (0..64).collect::<Vec<_>>();
|
||||
b.iter(move || {
|
||||
column.first_vals(&fetch_docids, &mut block);
|
||||
block[0]
|
||||
});
|
||||
}
|
||||
fn run_bench_on_column_block_single_calls(b: &mut Bencher, column: Column) {
|
||||
let mut block: Vec<Option<u64>> = vec![None; 64];
|
||||
let fetch_docids = (0..64).collect::<Vec<_>>();
|
||||
b.iter(move || {
|
||||
|
||||
group.register("first_block_single_calls", |column| {
|
||||
let mut block: Vec<Option<u64>> = vec![None; 64];
|
||||
let fetch_docids = (0..64).collect::<Vec<_>>();
|
||||
for i in 0..fetch_docids.len() {
|
||||
block[i] = column.first(fetch_docids[i]);
|
||||
}
|
||||
block[0]
|
||||
black_box(block[0]);
|
||||
});
|
||||
}
|
||||
|
||||
/// Column first method
|
||||
#[bench]
|
||||
fn bench_get_first_on_full_column_full_scan(b: &mut Bencher) {
|
||||
let column = get_test_columns().full;
|
||||
run_bench_on_column_full_scan(b, column);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_get_first_on_optional_column_full_scan(b: &mut Bencher) {
|
||||
let column = get_test_columns().optional;
|
||||
run_bench_on_column_full_scan(b, column);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_get_first_on_multi_column_full_scan(b: &mut Bencher) {
|
||||
let column = get_test_columns().multi;
|
||||
run_bench_on_column_full_scan(b, column);
|
||||
}
|
||||
|
||||
/// Block fetch column accessor
|
||||
#[bench]
|
||||
fn bench_get_block_first_on_optional_column(b: &mut Bencher) {
|
||||
let column = get_test_columns().optional;
|
||||
run_bench_on_column_block_fetch(b, column);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_get_block_first_on_multi_column(b: &mut Bencher) {
|
||||
let column = get_test_columns().multi;
|
||||
run_bench_on_column_block_fetch(b, column);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_get_block_first_on_full_column(b: &mut Bencher) {
|
||||
let column = get_test_columns().full;
|
||||
run_bench_on_column_block_fetch(b, column);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_get_block_first_on_optional_column_single_calls(b: &mut Bencher) {
|
||||
let column = get_test_columns().optional;
|
||||
run_bench_on_column_block_single_calls(b, column);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_get_block_first_on_multi_column_single_calls(b: &mut Bencher) {
|
||||
let column = get_test_columns().multi;
|
||||
run_bench_on_column_block_single_calls(b, column);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_get_block_first_on_full_column_single_calls(b: &mut Bencher) {
|
||||
let column = get_test_columns().full;
|
||||
run_bench_on_column_block_single_calls(b, column);
|
||||
group.run();
|
||||
}
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
pub mod common;
|
||||
|
||||
use binggan::{black_box, BenchRunner};
|
||||
use common::{generate_columnar_with_name, Card};
|
||||
use binggan::BenchRunner;
|
||||
use common::{Card, generate_columnar_with_name};
|
||||
use tantivy_columnar::*;
|
||||
|
||||
const NUM_DOCS: u32 = 100_000;
|
||||
@@ -29,7 +29,7 @@ fn main() {
|
||||
add_combo(Card::Multi, Card::Dense);
|
||||
add_combo(Card::Multi, Card::Sparse);
|
||||
|
||||
let runner: BenchRunner = BenchRunner::new();
|
||||
let mut runner: BenchRunner = BenchRunner::new();
|
||||
let mut group = runner.new_group();
|
||||
for (input_name, columnar_readers) in inputs.iter() {
|
||||
group.register_with_input(
|
||||
@@ -41,7 +41,7 @@ fn main() {
|
||||
let merge_row_order = StackMergeOrder::stack(&columnar_readers[..]);
|
||||
|
||||
merge_columnar(&columnar_readers, &[], merge_row_order.into(), &mut out).unwrap();
|
||||
black_box(out);
|
||||
Some(out.len() as u64)
|
||||
},
|
||||
);
|
||||
}
|
||||
|
||||
106
columnar/benches/bench_optional_index.rs
Normal file
106
columnar/benches/bench_optional_index.rs
Normal file
@@ -0,0 +1,106 @@
|
||||
use binggan::{InputGroup, black_box};
|
||||
use rand::rngs::StdRng;
|
||||
use rand::{Rng, SeedableRng};
|
||||
use tantivy_columnar::column_index::{OptionalIndex, Set};
|
||||
|
||||
const TOTAL_NUM_VALUES: u32 = 1_000_000;
|
||||
|
||||
fn gen_optional_index(fill_ratio: f64) -> OptionalIndex {
|
||||
let mut rng: StdRng = StdRng::from_seed([1u8; 32]);
|
||||
let vals: Vec<u32> = (0..TOTAL_NUM_VALUES)
|
||||
.map(|_| rng.gen_bool(fill_ratio))
|
||||
.enumerate()
|
||||
.filter(|(_pos, val)| *val)
|
||||
.map(|(pos, _)| pos as u32)
|
||||
.collect();
|
||||
OptionalIndex::for_test(TOTAL_NUM_VALUES, &vals)
|
||||
}
|
||||
|
||||
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 / 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
|
||||
}
|
||||
|
||||
fn main() {
|
||||
// Build separate inputs for each fill ratio.
|
||||
let inputs: Vec<(String, OptionalIndex)> = vec![
|
||||
("fill=1%".to_string(), gen_optional_index(0.01)),
|
||||
("fill=5%".to_string(), gen_optional_index(0.05)),
|
||||
("fill=10%".to_string(), gen_optional_index(0.10)),
|
||||
("fill=50%".to_string(), gen_optional_index(0.50)),
|
||||
("fill=90%".to_string(), gen_optional_index(0.90)),
|
||||
];
|
||||
|
||||
let mut group: InputGroup<OptionalIndex> = InputGroup::new_with_inputs(inputs);
|
||||
|
||||
// Translate orig->codec (rank_if_exists) with sampling
|
||||
group.register("orig_to_codec_10pct_hit", |codec: &OptionalIndex| {
|
||||
black_box(walk_over_data(codec, 100));
|
||||
});
|
||||
group.register("orig_to_codec_1pct_hit", |codec: &OptionalIndex| {
|
||||
black_box(walk_over_data(codec, 1000));
|
||||
});
|
||||
group.register("orig_to_codec_full_scan", |codec: &OptionalIndex| {
|
||||
black_box(walk_over_data_from_positions(codec, 0..TOTAL_NUM_VALUES));
|
||||
});
|
||||
|
||||
// Translate codec->orig (select/select_batch) on sampled ranks
|
||||
fn bench_translate_codec_to_orig_util(codec: &OptionalIndex, percent_hit: f32) {
|
||||
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()];
|
||||
output.copy_from_slice(&idxs[..]);
|
||||
codec.select_batch(&mut output);
|
||||
black_box(output);
|
||||
}
|
||||
|
||||
group.register("codec_to_orig_0.005pct_hit", |codec: &OptionalIndex| {
|
||||
bench_translate_codec_to_orig_util(codec, 0.005);
|
||||
});
|
||||
group.register("codec_to_orig_10pct_hit", |codec: &OptionalIndex| {
|
||||
bench_translate_codec_to_orig_util(codec, 10.0);
|
||||
});
|
||||
group.register("codec_to_orig_full_scan", |codec: &OptionalIndex| {
|
||||
bench_translate_codec_to_orig_util(codec, 100.0);
|
||||
});
|
||||
|
||||
group.run();
|
||||
}
|
||||
@@ -1,15 +1,12 @@
|
||||
#![feature(test)]
|
||||
|
||||
use std::ops::RangeInclusive;
|
||||
use std::sync::Arc;
|
||||
|
||||
use binggan::{InputGroup, black_box};
|
||||
use common::OwnedBytes;
|
||||
use rand::rngs::StdRng;
|
||||
use rand::seq::SliceRandom;
|
||||
use rand::{random, Rng, SeedableRng};
|
||||
use rand::{Rng, SeedableRng, random};
|
||||
use tantivy_columnar::ColumnValues;
|
||||
use test::Bencher;
|
||||
extern crate test;
|
||||
|
||||
// TODO does this make sense for IPv6 ?
|
||||
fn generate_random() -> Vec<u64> {
|
||||
@@ -47,78 +44,77 @@ fn get_data_50percent_item() -> Vec<u128> {
|
||||
}
|
||||
data.push(SINGLE_ITEM);
|
||||
data.shuffle(&mut rng);
|
||||
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
|
||||
data
|
||||
data.iter().map(|el| *el as u128).collect::<Vec<_>>()
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u128_50percent_hit(b: &mut Bencher) {
|
||||
fn main() {
|
||||
let data = get_data_50percent_item();
|
||||
let column = get_u128_column_from_data(&data);
|
||||
let column_range = get_u128_column_from_data(&data);
|
||||
let column_random = get_u128_column_random();
|
||||
|
||||
b.iter(|| {
|
||||
struct Inputs {
|
||||
data: Vec<u128>,
|
||||
column_range: Arc<dyn ColumnValues<u128>>,
|
||||
column_random: Arc<dyn ColumnValues<u128>>,
|
||||
}
|
||||
|
||||
let inputs = Inputs {
|
||||
data,
|
||||
column_range,
|
||||
column_random,
|
||||
};
|
||||
let mut group: InputGroup<Inputs> =
|
||||
InputGroup::new_with_inputs(vec![("u128 benches".to_string(), inputs)]);
|
||||
|
||||
group.register(
|
||||
"intfastfield_getrange_u128_50percent_hit",
|
||||
|inp: &Inputs| {
|
||||
let mut positions = Vec::new();
|
||||
inp.column_range.get_row_ids_for_value_range(
|
||||
*FIFTY_PERCENT_RANGE.start() as u128..=*FIFTY_PERCENT_RANGE.end() as u128,
|
||||
0..inp.data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
black_box(positions.len());
|
||||
},
|
||||
);
|
||||
|
||||
group.register("intfastfield_getrange_u128_single_hit", |inp: &Inputs| {
|
||||
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(
|
||||
inp.column_range.get_row_ids_for_value_range(
|
||||
*SINGLE_ITEM_RANGE.start() as u128..=*SINGLE_ITEM_RANGE.end() as u128,
|
||||
0..data.len() as u32,
|
||||
0..inp.data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
black_box(positions.len());
|
||||
});
|
||||
}
|
||||
|
||||
#[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(|| {
|
||||
group.register("intfastfield_getrange_u128_hit_all", |inp: &Inputs| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_row_ids_for_value_range(0..=u128::MAX, 0..data.len() as u32, &mut positions);
|
||||
positions
|
||||
inp.column_range.get_row_ids_for_value_range(
|
||||
0..=u128::MAX,
|
||||
0..inp.data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
black_box(positions.len());
|
||||
});
|
||||
}
|
||||
// U128 RANGE END
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_fflookup_u128(b: &mut Bencher) {
|
||||
let column = get_u128_column_random();
|
||||
|
||||
b.iter(|| {
|
||||
group.register("intfastfield_scan_all_fflookup_u128", |inp: &Inputs| {
|
||||
let mut a = 0u128;
|
||||
for i in 0u64..column.num_vals() as u64 {
|
||||
a += column.get_val(i as u32);
|
||||
for i in 0u64..inp.column_random.num_vals() as u64 {
|
||||
a += inp.column_random.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
black_box(a);
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_jumpy_stride5_u128(b: &mut Bencher) {
|
||||
let column = get_u128_column_random();
|
||||
|
||||
b.iter(|| {
|
||||
let n = column.num_vals();
|
||||
group.register("intfastfield_jumpy_stride5_u128", |inp: &Inputs| {
|
||||
let n = inp.column_random.num_vals();
|
||||
let mut a = 0u128;
|
||||
for i in (0..n / 5).map(|val| val * 5) {
|
||||
a += column.get_val(i);
|
||||
a += inp.column_random.get_val(i);
|
||||
}
|
||||
a
|
||||
black_box(a);
|
||||
});
|
||||
|
||||
group.run();
|
||||
}
|
||||
|
||||
@@ -1,13 +1,10 @@
|
||||
#![feature(test)]
|
||||
extern crate test;
|
||||
|
||||
use std::ops::RangeInclusive;
|
||||
use std::sync::Arc;
|
||||
|
||||
use binggan::{InputGroup, black_box};
|
||||
use rand::prelude::*;
|
||||
use tantivy_columnar::column_values::{serialize_and_load_u64_based_column_values, CodecType};
|
||||
use tantivy_columnar::column_values::{CodecType, serialize_and_load_u64_based_column_values};
|
||||
use tantivy_columnar::*;
|
||||
use test::Bencher;
|
||||
|
||||
// Warning: this generates the same permutation at each call
|
||||
fn generate_permutation() -> Vec<u64> {
|
||||
@@ -27,37 +24,11 @@ pub fn serialize_and_load(column: &[u64], codec_type: CodecType) -> Arc<dyn Colu
|
||||
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]);
|
||||
|
||||
@@ -69,135 +40,122 @@ fn get_data_50percent_item() -> Vec<u128> {
|
||||
data.push(SINGLE_ITEM);
|
||||
|
||||
data.shuffle(&mut rng);
|
||||
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
|
||||
data
|
||||
data.iter().map(|el| *el as u128).collect::<Vec<_>>()
|
||||
}
|
||||
|
||||
// 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
|
||||
});
|
||||
}
|
||||
type VecCol = (Vec<u64>, Arc<dyn ColumnValues<u64>>);
|
||||
|
||||
#[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) {
|
||||
fn bench_access() {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
b.iter(|| {
|
||||
let column_perm: Arc<dyn ColumnValues<u64>> =
|
||||
serialize_and_load(&permutation, CodecType::Bitpacked);
|
||||
|
||||
let permutation_gcd = generate_permutation_gcd();
|
||||
let column_perm_gcd: Arc<dyn ColumnValues<u64>> =
|
||||
serialize_and_load(&permutation_gcd, CodecType::Bitpacked);
|
||||
|
||||
let mut group: InputGroup<VecCol> = InputGroup::new_with_inputs(vec![
|
||||
(
|
||||
"access".to_string(),
|
||||
(permutation.clone(), column_perm.clone()),
|
||||
),
|
||||
(
|
||||
"access_gcd".to_string(),
|
||||
(permutation_gcd.clone(), column_perm_gcd.clone()),
|
||||
),
|
||||
]);
|
||||
|
||||
group.register("stride7_vec", |inp: &VecCol| {
|
||||
let n = inp.0.len();
|
||||
let mut a = 0u64;
|
||||
for i in (0..n / 7).map(|val| val * 7) {
|
||||
a += permutation[i as usize];
|
||||
a += inp.0[i];
|
||||
}
|
||||
a
|
||||
black_box(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;
|
||||
group.register("fullscan_vec", |inp: &VecCol| {
|
||||
let mut a = 0u64;
|
||||
for i in 0..inp.0.len() {
|
||||
a += inp.0[i];
|
||||
}
|
||||
black_box(a);
|
||||
});
|
||||
|
||||
group.register("stride7_column_values", |inp: &VecCol| {
|
||||
let n = inp.1.num_vals() as usize;
|
||||
let mut a = 0u64;
|
||||
for i in (0..n / 7).map(|val| val * 7) {
|
||||
a += column.get_val(i as u32);
|
||||
a += inp.1.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
black_box(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(|| {
|
||||
group.register("fullscan_column_values", |inp: &VecCol| {
|
||||
let mut a = 0u64;
|
||||
let n = inp.1.num_vals() as usize;
|
||||
for i in 0..n {
|
||||
a += column.get_val(i as u32);
|
||||
a += inp.1.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
black_box(a);
|
||||
});
|
||||
|
||||
group.run();
|
||||
}
|
||||
|
||||
#[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
|
||||
});
|
||||
fn bench_range() {
|
||||
let data_50 = get_data_50percent_item();
|
||||
let data_u64 = data_50.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column_data: Arc<dyn ColumnValues<u64>> =
|
||||
serialize_and_load(&data_u64, CodecType::Bitpacked);
|
||||
|
||||
let mut group: InputGroup<Arc<dyn ColumnValues<u64>>> =
|
||||
InputGroup::new_with_inputs(vec![("dist_50pct_item".to_string(), column_data.clone())]);
|
||||
|
||||
group.register(
|
||||
"fastfield_getrange_u64_50percent_hit",
|
||||
|col: &Arc<dyn ColumnValues<u64>>| {
|
||||
let mut positions = Vec::new();
|
||||
col.get_row_ids_for_value_range(FIFTY_PERCENT_RANGE, 0..col.num_vals(), &mut positions);
|
||||
black_box(positions.len());
|
||||
},
|
||||
);
|
||||
|
||||
group.register(
|
||||
"fastfield_getrange_u64_1percent_hit",
|
||||
|col: &Arc<dyn ColumnValues<u64>>| {
|
||||
let mut positions = Vec::new();
|
||||
col.get_row_ids_for_value_range(
|
||||
ONE_PERCENT_ITEM_RANGE,
|
||||
0..col.num_vals(),
|
||||
&mut positions,
|
||||
);
|
||||
black_box(positions.len());
|
||||
},
|
||||
);
|
||||
|
||||
group.register(
|
||||
"fastfield_getrange_u64_single_hit",
|
||||
|col: &Arc<dyn ColumnValues<u64>>| {
|
||||
let mut positions = Vec::new();
|
||||
col.get_row_ids_for_value_range(SINGLE_ITEM_RANGE, 0..col.num_vals(), &mut positions);
|
||||
black_box(positions.len());
|
||||
},
|
||||
);
|
||||
|
||||
group.register(
|
||||
"fastfield_getrange_u64_hit_all",
|
||||
|col: &Arc<dyn ColumnValues<u64>>| {
|
||||
let mut positions = Vec::new();
|
||||
col.get_row_ids_for_value_range(0..=u64::MAX, 0..col.num_vals(), &mut positions);
|
||||
black_box(positions.len());
|
||||
},
|
||||
);
|
||||
|
||||
group.run();
|
||||
}
|
||||
|
||||
fn main() {
|
||||
bench_access();
|
||||
bench_range();
|
||||
}
|
||||
|
||||
18
columnar/columnar-cli-inspect/Cargo.toml
Normal file
18
columnar/columnar-cli-inspect/Cargo.toml
Normal file
@@ -0,0 +1,18 @@
|
||||
[package]
|
||||
name = "tantivy-columnar-inspect"
|
||||
version = "0.1.0"
|
||||
edition = "2021"
|
||||
license = "MIT"
|
||||
|
||||
[dependencies]
|
||||
tantivy = {path="../..", package="tantivy"}
|
||||
columnar = {path="../", package="tantivy-columnar"}
|
||||
common = {path="../../common", package="tantivy-common"}
|
||||
|
||||
[workspace]
|
||||
members = []
|
||||
|
||||
[profile.release]
|
||||
debug = true
|
||||
#debug-assertions = true
|
||||
#overflow-checks = true
|
||||
54
columnar/columnar-cli-inspect/src/main.rs
Normal file
54
columnar/columnar-cli-inspect/src/main.rs
Normal file
@@ -0,0 +1,54 @@
|
||||
use columnar::ColumnarReader;
|
||||
use common::file_slice::{FileSlice, WrapFile};
|
||||
use std::io;
|
||||
use std::path::Path;
|
||||
use tantivy::directory::footer::Footer;
|
||||
|
||||
fn main() -> io::Result<()> {
|
||||
println!("Opens a columnar file written by tantivy and validates it.");
|
||||
let path = std::env::args().nth(1).unwrap();
|
||||
|
||||
let path = Path::new(&path);
|
||||
println!("Reading {:?}", path);
|
||||
let _reader = open_and_validate_columnar(path.to_str().unwrap())?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn validate_columnar_reader(reader: &ColumnarReader) {
|
||||
let num_rows = reader.num_rows();
|
||||
println!("num_rows: {}", num_rows);
|
||||
let columns = reader.list_columns().unwrap();
|
||||
println!("num columns: {:?}", columns.len());
|
||||
for (col_name, dynamic_column_handle) in columns {
|
||||
let col = dynamic_column_handle.open().unwrap();
|
||||
match col {
|
||||
columnar::DynamicColumn::Bool(_)
|
||||
| columnar::DynamicColumn::I64(_)
|
||||
| columnar::DynamicColumn::U64(_)
|
||||
| columnar::DynamicColumn::F64(_)
|
||||
| columnar::DynamicColumn::IpAddr(_)
|
||||
| columnar::DynamicColumn::DateTime(_)
|
||||
| columnar::DynamicColumn::Bytes(_) => {}
|
||||
columnar::DynamicColumn::Str(str_column) => {
|
||||
let num_vals = str_column.ords().values.num_vals();
|
||||
let num_terms_dict = str_column.num_terms() as u64;
|
||||
let max_ord = str_column.ords().values.iter().max().unwrap_or_default();
|
||||
println!("{col_name:35} num_vals {num_vals:10} \t num_terms_dict {num_terms_dict:8} max_ord: {max_ord:8}",);
|
||||
for ord in str_column.ords().values.iter() {
|
||||
assert!(ord < num_terms_dict);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Opens a columnar file that was written by tantivy and validates it.
|
||||
pub fn open_and_validate_columnar(path: &str) -> io::Result<ColumnarReader> {
|
||||
let wrap_file = WrapFile::new(std::fs::File::open(path)?)?;
|
||||
let slice = FileSlice::new(std::sync::Arc::new(wrap_file));
|
||||
let (_footer, slice) = Footer::extract_footer(slice.clone()).unwrap();
|
||||
let reader = ColumnarReader::open(slice).unwrap();
|
||||
validate_columnar_reader(&reader);
|
||||
Ok(reader)
|
||||
}
|
||||
@@ -10,7 +10,7 @@
|
||||
|
||||
# Perf and Size
|
||||
* remove alloc in `ord_to_term`
|
||||
+ multivaued range queries restrat frm the beginning all of the time.
|
||||
+ multivaued range queries restart from the beginning all of the time.
|
||||
* re-add ZSTD compression for dictionaries
|
||||
no systematic monotonic mapping
|
||||
consider removing multilinear
|
||||
@@ -30,7 +30,7 @@ investigate if should have better errors? io::Error is overused at the moment.
|
||||
rename rank/select in unit tests
|
||||
Review the public API via cargo doc
|
||||
go through TODOs
|
||||
remove all doc_id occurences -> row_id
|
||||
remove all doc_id occurrences -> row_id
|
||||
use the rank & select naming in unit tests branch.
|
||||
multi-linear -> blockwise
|
||||
linear codec -> simply a multiplication for the index column
|
||||
@@ -43,5 +43,5 @@ isolate u128_based and uniform naming
|
||||
# Other
|
||||
fix enhance column-cli
|
||||
|
||||
# Santa claus
|
||||
# Santa Claus
|
||||
autodetect datetime ipaddr, plug customizable tokenizer.
|
||||
|
||||
@@ -66,7 +66,7 @@ impl<T: PartialOrd + Copy + std::fmt::Debug + Send + Sync + 'static + Default>
|
||||
&'a self,
|
||||
docs: &'a [u32],
|
||||
accessor: &Column<T>,
|
||||
) -> impl Iterator<Item = (DocId, T)> + '_ {
|
||||
) -> impl Iterator<Item = (DocId, T)> + 'a + use<'a, T> {
|
||||
if accessor.index.get_cardinality().is_full() {
|
||||
docs.iter().cloned().zip(self.val_cache.iter().cloned())
|
||||
} else {
|
||||
@@ -139,7 +139,7 @@ mod tests {
|
||||
missing_docs.push(missing_doc);
|
||||
});
|
||||
|
||||
assert_eq!(missing_docs, vec![]);
|
||||
assert_eq!(missing_docs, Vec::<u32>::new());
|
||||
}
|
||||
|
||||
#[test]
|
||||
|
||||
@@ -4,8 +4,8 @@ use std::{fmt, io};
|
||||
|
||||
use sstable::{Dictionary, VoidSSTable};
|
||||
|
||||
use crate::column::Column;
|
||||
use crate::RowId;
|
||||
use crate::column::Column;
|
||||
|
||||
/// Dictionary encoded column.
|
||||
///
|
||||
|
||||
@@ -9,13 +9,14 @@ 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_u128_as_compact_u64,
|
||||
open_column_u64, serialize_column_mappable_to_u128, serialize_column_mappable_to_u64,
|
||||
open_column_bytes, open_column_str, open_column_u64, open_column_u128,
|
||||
open_column_u128_as_compact_u64, serialize_column_mappable_to_u64,
|
||||
serialize_column_mappable_to_u128,
|
||||
};
|
||||
|
||||
use crate::column_index::{ColumnIndex, Set};
|
||||
use crate::column_values::monotonic_mapping::StrictlyMonotonicMappingToInternal;
|
||||
use crate::column_values::{monotonic_map_column, ColumnValues};
|
||||
use crate::column_values::{ColumnValues, monotonic_map_column};
|
||||
use crate::{Cardinality, DocId, EmptyColumnValues, MonotonicallyMappableToU64, RowId};
|
||||
|
||||
#[derive(Clone)]
|
||||
@@ -113,7 +114,7 @@ impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
|
||||
}
|
||||
}
|
||||
|
||||
/// Translates a block of docis to row_ids.
|
||||
/// Translates a block of docids to row_ids.
|
||||
///
|
||||
/// returns the row_ids and the matching docids on the same index
|
||||
/// e.g.
|
||||
@@ -130,6 +131,8 @@ impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
|
||||
self.index.docids_to_rowids(doc_ids, doc_ids_out, row_ids)
|
||||
}
|
||||
|
||||
/// Get an iterator over the values for the provided docid.
|
||||
#[inline]
|
||||
pub fn values_for_doc(&self, doc_id: DocId) -> impl Iterator<Item = T> + '_ {
|
||||
self.index
|
||||
.value_row_ids(doc_id)
|
||||
@@ -157,15 +160,6 @@ impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
|
||||
.select_batch_in_place(selected_docid_range.start, doc_ids);
|
||||
}
|
||||
|
||||
/// Fills the output vector with the (possibly multiple values that are associated_with
|
||||
/// `row_id`.
|
||||
///
|
||||
/// This method clears the `output` vector.
|
||||
pub fn fill_vals(&self, row_id: RowId, output: &mut Vec<T>) {
|
||||
output.clear();
|
||||
output.extend(self.values_for_doc(row_id));
|
||||
}
|
||||
|
||||
pub fn first_or_default_col(self, default_value: T) -> Arc<dyn ColumnValues<T>> {
|
||||
Arc::new(FirstValueWithDefault {
|
||||
column: self,
|
||||
|
||||
@@ -6,10 +6,10 @@ use common::OwnedBytes;
|
||||
use sstable::Dictionary;
|
||||
|
||||
use crate::column::{BytesColumn, Column};
|
||||
use crate::column_index::{serialize_column_index, SerializableColumnIndex};
|
||||
use crate::column_index::{SerializableColumnIndex, serialize_column_index};
|
||||
use crate::column_values::{
|
||||
CodecType, MonotonicallyMappableToU64, MonotonicallyMappableToU128,
|
||||
load_u64_based_column_values, serialize_column_values_u128, serialize_u64_based_column_values,
|
||||
CodecType, MonotonicallyMappableToU128, MonotonicallyMappableToU64,
|
||||
};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{StrColumn, Version};
|
||||
|
||||
@@ -99,9 +99,9 @@ mod tests {
|
||||
|
||||
use crate::column_index::merge::detect_cardinality;
|
||||
use crate::column_index::multivalued_index::{
|
||||
open_multivalued_index, serialize_multivalued_index, MultiValueIndex,
|
||||
MultiValueIndex, open_multivalued_index, serialize_multivalued_index,
|
||||
};
|
||||
use crate::column_index::{merge_column_index, OptionalIndex, SerializableColumnIndex};
|
||||
use crate::column_index::{OptionalIndex, SerializableColumnIndex, merge_column_index};
|
||||
use crate::{
|
||||
Cardinality, ColumnIndex, MergeRowOrder, RowAddr, RowId, ShuffleMergeOrder, StackMergeOrder,
|
||||
};
|
||||
@@ -173,7 +173,7 @@ mod tests {
|
||||
.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")
|
||||
panic!("Expected a multivalued index")
|
||||
};
|
||||
let mut output = Vec::new();
|
||||
serialize_multivalued_index(&start_index_iterable, &mut output).unwrap();
|
||||
@@ -211,7 +211,7 @@ mod tests {
|
||||
|
||||
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")
|
||||
panic!("Expected a multivalued index")
|
||||
};
|
||||
let mut output = Vec::new();
|
||||
serialize_multivalued_index(&start_index_iterable, &mut output).unwrap();
|
||||
|
||||
@@ -58,7 +58,7 @@ struct ShuffledIndex<'a> {
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
}
|
||||
|
||||
impl<'a> Iterable<u32> for ShuffledIndex<'a> {
|
||||
impl Iterable<u32> for ShuffledIndex<'_> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
Box::new(
|
||||
self.merge_order
|
||||
@@ -127,7 +127,7 @@ fn integrate_num_vals(num_vals: impl Iterator<Item = u32>) -> impl Iterator<Item
|
||||
)
|
||||
}
|
||||
|
||||
impl<'a> Iterable<u32> for ShuffledMultivaluedIndex<'a> {
|
||||
impl Iterable<u32> for ShuffledMultivaluedIndex<'_> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
let num_vals_per_row = iter_num_values(self.column_indexes, self.merge_order);
|
||||
Box::new(integrate_num_vals(num_vals_per_row))
|
||||
@@ -137,8 +137,8 @@ impl<'a> Iterable<u32> for ShuffledMultivaluedIndex<'a> {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::column_index::OptionalIndex;
|
||||
use crate::RowAddr;
|
||||
use crate::column_index::OptionalIndex;
|
||||
|
||||
#[test]
|
||||
fn test_integrate_num_vals_empty() {
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
use std::ops::Range;
|
||||
|
||||
use crate::column_index::SerializableColumnIndex;
|
||||
use crate::column_index::multivalued_index::{MultiValueIndex, SerializableMultivalueIndex};
|
||||
use crate::column_index::serialize::SerializableOptionalIndex;
|
||||
use crate::column_index::SerializableColumnIndex;
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{Cardinality, ColumnIndex, RowId, StackMergeOrder};
|
||||
|
||||
@@ -56,7 +56,7 @@ fn get_doc_ids_with_values<'a>(
|
||||
ColumnIndex::Full => Box::new(doc_range),
|
||||
ColumnIndex::Optional(optional_index) => Box::new(
|
||||
optional_index
|
||||
.iter_rows()
|
||||
.iter_non_null_docs()
|
||||
.map(move |row| row + doc_range.start),
|
||||
),
|
||||
ColumnIndex::Multivalued(multivalued_index) => match multivalued_index {
|
||||
@@ -73,7 +73,7 @@ fn get_doc_ids_with_values<'a>(
|
||||
MultiValueIndex::MultiValueIndexV2(multivalued_index) => Box::new(
|
||||
multivalued_index
|
||||
.optional_index
|
||||
.iter_rows()
|
||||
.iter_non_null_docs()
|
||||
.map(move |row| row + doc_range.start),
|
||||
),
|
||||
},
|
||||
@@ -105,10 +105,11 @@ fn get_num_values_iterator<'a>(
|
||||
) -> Box<dyn Iterator<Item = u32> + 'a> {
|
||||
match column_index {
|
||||
ColumnIndex::Empty { .. } => Box::new(std::iter::empty()),
|
||||
ColumnIndex::Full => Box::new(std::iter::repeat(1u32).take(num_docs as usize)),
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
Box::new(std::iter::repeat(1u32).take(optional_index.num_non_nulls() as usize))
|
||||
}
|
||||
ColumnIndex::Full => Box::new(std::iter::repeat_n(1u32, num_docs as usize)),
|
||||
ColumnIndex::Optional(optional_index) => Box::new(std::iter::repeat_n(
|
||||
1u32,
|
||||
optional_index.num_non_nulls() as usize,
|
||||
)),
|
||||
ColumnIndex::Multivalued(multivalued_index) => Box::new(
|
||||
multivalued_index
|
||||
.get_start_index_column()
|
||||
@@ -123,7 +124,7 @@ fn get_num_values_iterator<'a>(
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Iterable<u32> for StackedStartOffsets<'a> {
|
||||
impl Iterable<u32> for StackedStartOffsets<'_> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
let num_values_it = (0..self.column_indexes.len()).flat_map(|columnar_id| {
|
||||
let num_docs = self.stack_merge_order.columnar_range(columnar_id).len() as u32;
|
||||
@@ -177,7 +178,7 @@ impl<'a> Iterable<RowId> for StackedOptionalIndex<'a> {
|
||||
ColumnIndex::Full => Box::new(columnar_row_range),
|
||||
ColumnIndex::Optional(optional_index) => Box::new(
|
||||
optional_index
|
||||
.iter_rows()
|
||||
.iter_non_null_docs()
|
||||
.map(move |row_id: RowId| columnar_row_range.start + row_id),
|
||||
),
|
||||
ColumnIndex::Multivalued(_) => {
|
||||
|
||||
@@ -14,7 +14,7 @@ pub use merge::merge_column_index;
|
||||
pub(crate) use multivalued_index::SerializableMultivalueIndex;
|
||||
pub use optional_index::{OptionalIndex, Set};
|
||||
pub use serialize::{
|
||||
open_column_index, serialize_column_index, SerializableColumnIndex, SerializableOptionalIndex,
|
||||
SerializableColumnIndex, SerializableOptionalIndex, open_column_index, serialize_column_index,
|
||||
};
|
||||
|
||||
use crate::column_index::multivalued_index::MultiValueIndex;
|
||||
@@ -28,7 +28,7 @@ pub enum ColumnIndex {
|
||||
Full,
|
||||
Optional(OptionalIndex),
|
||||
/// In addition, at index num_rows, an extra value is added
|
||||
/// containing the overal number of values.
|
||||
/// containing the overall number of values.
|
||||
Multivalued(MultiValueIndex),
|
||||
}
|
||||
|
||||
|
||||
@@ -8,7 +8,7 @@ use common::{CountingWriter, OwnedBytes};
|
||||
use super::optional_index::{open_optional_index, serialize_optional_index};
|
||||
use super::{OptionalIndex, SerializableOptionalIndex, Set};
|
||||
use crate::column_values::{
|
||||
load_u64_based_column_values, serialize_u64_based_column_values, CodecType, ColumnValues,
|
||||
CodecType, ColumnValues, load_u64_based_column_values, serialize_u64_based_column_values,
|
||||
};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{DocId, RowId, Version};
|
||||
@@ -215,6 +215,32 @@ impl MultiValueIndex {
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns an iterator over document ids that have at least one value.
|
||||
pub fn iter_non_null_docs(&self) -> Box<dyn Iterator<Item = DocId> + '_> {
|
||||
match self {
|
||||
MultiValueIndex::MultiValueIndexV1(idx) => {
|
||||
let mut doc: DocId = 0u32;
|
||||
let num_docs = idx.num_docs();
|
||||
Box::new(std::iter::from_fn(move || {
|
||||
// This is not the most efficient way to do this, but it's legacy code.
|
||||
while doc < num_docs {
|
||||
let cur = doc;
|
||||
doc += 1;
|
||||
let start = idx.start_index_column.get_val(cur);
|
||||
let end = idx.start_index_column.get_val(cur + 1);
|
||||
if end > start {
|
||||
return Some(cur);
|
||||
}
|
||||
}
|
||||
None
|
||||
}))
|
||||
}
|
||||
MultiValueIndex::MultiValueIndexV2(idx) => {
|
||||
Box::new(idx.optional_index.iter_non_null_docs())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Converts a list of ranks (row ids of values) in a 1:n index to the corresponding list of
|
||||
/// docids. Positions are converted inplace to docids.
|
||||
///
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
use std::io::{self, Write};
|
||||
use std::io;
|
||||
use std::sync::Arc;
|
||||
|
||||
mod set;
|
||||
@@ -7,11 +7,11 @@ 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,
|
||||
DENSE_BLOCK_NUM_BYTES, DenseBlock, DenseBlockCodec, SparseBlock, SparseBlockCodec,
|
||||
};
|
||||
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{DocId, InvalidData, RowId};
|
||||
use crate::{DocId, RowId};
|
||||
|
||||
/// The threshold for for number of elements after which we switch to dense block encoding.
|
||||
///
|
||||
@@ -80,23 +80,23 @@ impl BlockVariant {
|
||||
/// 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,
|
||||
num_docs: RowId,
|
||||
num_non_null_docs: RowId,
|
||||
block_data: OwnedBytes,
|
||||
block_metas: Arc<[BlockMeta]>,
|
||||
}
|
||||
|
||||
impl<'a> Iterable<u32> for &'a OptionalIndex {
|
||||
impl Iterable<u32> for &OptionalIndex {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
Box::new(self.iter_rows())
|
||||
Box::new(self.iter_non_null_docs())
|
||||
}
|
||||
}
|
||||
|
||||
impl std::fmt::Debug for OptionalIndex {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
|
||||
f.debug_struct("OptionalIndex")
|
||||
.field("num_rows", &self.num_rows)
|
||||
.field("num_non_null_rows", &self.num_non_null_rows)
|
||||
.field("num_docs", &self.num_docs)
|
||||
.field("num_non_null_docs", &self.num_non_null_docs)
|
||||
.finish_non_exhaustive()
|
||||
}
|
||||
}
|
||||
@@ -123,7 +123,7 @@ enum BlockSelectCursor<'a> {
|
||||
Sparse(<SparseBlock<'a> as Set<u16>>::SelectCursor<'a>),
|
||||
}
|
||||
|
||||
impl<'a> BlockSelectCursor<'a> {
|
||||
impl BlockSelectCursor<'_> {
|
||||
fn select(&mut self, rank: u16) -> u16 {
|
||||
match self {
|
||||
BlockSelectCursor::Dense(dense_select_cursor) => dense_select_cursor.select(rank),
|
||||
@@ -141,7 +141,7 @@ pub struct OptionalIndexSelectCursor<'a> {
|
||||
num_null_rows_before_block: RowId,
|
||||
}
|
||||
|
||||
impl<'a> OptionalIndexSelectCursor<'a> {
|
||||
impl OptionalIndexSelectCursor<'_> {
|
||||
fn search_and_load_block(&mut self, rank: RowId) {
|
||||
if rank < self.current_block_end_rank {
|
||||
// we are already in the right block
|
||||
@@ -165,7 +165,7 @@ impl<'a> OptionalIndexSelectCursor<'a> {
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> SelectCursor<RowId> for OptionalIndexSelectCursor<'a> {
|
||||
impl SelectCursor<RowId> for OptionalIndexSelectCursor<'_> {
|
||||
fn select(&mut self, rank: RowId) -> RowId {
|
||||
self.search_and_load_block(rank);
|
||||
let index_in_block = (rank - self.num_null_rows_before_block) as u16;
|
||||
@@ -174,7 +174,9 @@ impl<'a> SelectCursor<RowId> for OptionalIndexSelectCursor<'a> {
|
||||
}
|
||||
|
||||
impl Set<RowId> for OptionalIndex {
|
||||
type SelectCursor<'b> = OptionalIndexSelectCursor<'b> where Self: 'b;
|
||||
type SelectCursor<'b>
|
||||
= OptionalIndexSelectCursor<'b>
|
||||
where Self: 'b;
|
||||
// Check if value at position is not null.
|
||||
#[inline]
|
||||
fn contains(&self, row_id: RowId) -> bool {
|
||||
@@ -257,11 +259,13 @@ impl Set<RowId> for OptionalIndex {
|
||||
|
||||
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));
|
||||
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);
|
||||
@@ -269,17 +273,18 @@ impl OptionalIndex {
|
||||
}
|
||||
|
||||
pub fn num_docs(&self) -> RowId {
|
||||
self.num_rows
|
||||
self.num_docs
|
||||
}
|
||||
|
||||
pub fn num_non_nulls(&self) -> RowId {
|
||||
self.num_non_null_rows
|
||||
self.num_non_null_docs
|
||||
}
|
||||
|
||||
pub fn iter_rows(&self) -> impl Iterator<Item = RowId> + '_ {
|
||||
// TODO optimize
|
||||
pub fn iter_non_null_docs(&self) -> impl Iterator<Item = RowId> + '_ {
|
||||
// TODO optimize. We could iterate over the blocks directly.
|
||||
// We use the dense value ids and retrieve the doc ids via select.
|
||||
let mut select_batch = self.select_cursor();
|
||||
(0..self.num_non_null_rows).map(move |rank| select_batch.select(rank))
|
||||
(0..self.num_non_null_docs).map(move |rank| select_batch.select(rank))
|
||||
}
|
||||
pub fn select_batch(&self, ranks: &mut [RowId]) {
|
||||
let mut select_cursor = self.select_cursor();
|
||||
@@ -330,38 +335,6 @@ enum Block<'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 {
|
||||
@@ -503,7 +476,7 @@ fn deserialize_optional_index_block_metadatas(
|
||||
non_null_rows_before_block += num_non_null_rows;
|
||||
}
|
||||
block_metas.resize(
|
||||
((num_rows + ELEMENTS_PER_BLOCK - 1) / ELEMENTS_PER_BLOCK) as usize,
|
||||
num_rows.div_ceil(ELEMENTS_PER_BLOCK) as usize,
|
||||
BlockMeta {
|
||||
non_null_rows_before_block,
|
||||
start_byte_offset,
|
||||
@@ -517,15 +490,15 @@ 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 num_docs = 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 (block_metas, num_non_null_docs) =
|
||||
deserialize_optional_index_block_metadatas(block_metas.as_slice(), num_docs);
|
||||
let optional_index = OptionalIndex {
|
||||
num_rows,
|
||||
num_non_null_rows,
|
||||
num_docs,
|
||||
num_non_null_docs,
|
||||
block_data,
|
||||
block_metas: block_metas.into(),
|
||||
};
|
||||
|
||||
@@ -2,7 +2,7 @@ use std::io::{self, Write};
|
||||
|
||||
use common::BinarySerializable;
|
||||
|
||||
use crate::column_index::optional_index::{SelectCursor, Set, SetCodec, ELEMENTS_PER_BLOCK};
|
||||
use crate::column_index::optional_index::{ELEMENTS_PER_BLOCK, SelectCursor, Set, SetCodec};
|
||||
|
||||
#[inline(always)]
|
||||
fn get_bit_at(input: u64, n: u16) -> bool {
|
||||
@@ -23,7 +23,6 @@ fn set_bit_at(input: &mut u64, n: u16) {
|
||||
///
|
||||
/// When translating a dense index to the original index, we can use the offset to find the correct
|
||||
/// block. Direct computation is not possible, but we can employ a linear or binary search.
|
||||
|
||||
const ELEMENTS_PER_MINI_BLOCK: u16 = 64;
|
||||
const MINI_BLOCK_BITVEC_NUM_BYTES: usize = 8;
|
||||
const MINI_BLOCK_OFFSET_NUM_BYTES: usize = 2;
|
||||
@@ -109,7 +108,7 @@ pub struct DenseBlockSelectCursor<'a> {
|
||||
dense_block: DenseBlock<'a>,
|
||||
}
|
||||
|
||||
impl<'a> SelectCursor<u16> for DenseBlockSelectCursor<'a> {
|
||||
impl SelectCursor<u16> for DenseBlockSelectCursor<'_> {
|
||||
#[inline]
|
||||
fn select(&mut self, rank: u16) -> u16 {
|
||||
self.block_id = self
|
||||
@@ -123,7 +122,9 @@ impl<'a> SelectCursor<u16> for DenseBlockSelectCursor<'a> {
|
||||
}
|
||||
|
||||
impl<'a> Set<u16> for DenseBlock<'a> {
|
||||
type SelectCursor<'b> = DenseBlockSelectCursor<'a> where Self: 'b;
|
||||
type SelectCursor<'b>
|
||||
= DenseBlockSelectCursor<'a>
|
||||
where Self: 'b;
|
||||
|
||||
#[inline(always)]
|
||||
fn contains(&self, el: u16) -> bool {
|
||||
@@ -173,7 +174,7 @@ impl<'a> Set<u16> for DenseBlock<'a> {
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> DenseBlock<'a> {
|
||||
impl DenseBlock<'_> {
|
||||
#[inline]
|
||||
fn mini_block(&self, mini_block_id: u16) -> DenseMiniBlock {
|
||||
let data_start_pos = mini_block_id as usize * MINI_BLOCK_NUM_BYTES;
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
mod dense;
|
||||
mod sparse;
|
||||
|
||||
pub use dense::{DenseBlock, DenseBlockCodec, DENSE_BLOCK_NUM_BYTES};
|
||||
pub use dense::{DENSE_BLOCK_NUM_BYTES, DenseBlock, DenseBlockCodec};
|
||||
pub use sparse::{SparseBlock, SparseBlockCodec};
|
||||
|
||||
#[cfg(test)]
|
||||
|
||||
@@ -31,8 +31,10 @@ impl<'a> SelectCursor<u16> for SparseBlock<'a> {
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Set<u16> for SparseBlock<'a> {
|
||||
type SelectCursor<'b> = Self where Self: 'b;
|
||||
impl Set<u16> for SparseBlock<'_> {
|
||||
type SelectCursor<'b>
|
||||
= Self
|
||||
where Self: 'b;
|
||||
|
||||
#[inline(always)]
|
||||
fn contains(&self, el: u16) -> bool {
|
||||
@@ -67,7 +69,7 @@ fn get_u16(data: &[u8], byte_position: usize) -> u16 {
|
||||
u16::from_le_bytes(bytes)
|
||||
}
|
||||
|
||||
impl<'a> SparseBlock<'a> {
|
||||
impl SparseBlock<'_> {
|
||||
#[inline(always)]
|
||||
fn value_at_idx(&self, data: &[u8], idx: u16) -> u16 {
|
||||
let start_offset: usize = idx as usize * 2;
|
||||
@@ -80,7 +82,7 @@ impl<'a> SparseBlock<'a> {
|
||||
}
|
||||
|
||||
#[inline]
|
||||
#[allow(clippy::comparison_chain)]
|
||||
#[expect(clippy::comparison_chain)]
|
||||
// Looks for the element in the block. Returns the positions if found.
|
||||
fn binary_search(&self, target: u16) -> Result<u16, u16> {
|
||||
let data = &self.0;
|
||||
|
||||
@@ -110,8 +110,8 @@ fn test_null_index(data: &[bool]) {
|
||||
.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]);
|
||||
for (i, expected) in orig_idx_with_value.iter().enumerate() {
|
||||
assert_eq!(select_iter.select(i as u32), *expected);
|
||||
}
|
||||
|
||||
let step_size = (orig_idx_with_value.len() / 100).max(1);
|
||||
@@ -164,7 +164,11 @@ fn test_optional_index_large() {
|
||||
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()));
|
||||
assert!(
|
||||
optional_index
|
||||
.iter_non_null_docs()
|
||||
.eq(row_ids.iter().copied())
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -219,174 +223,3 @@ fn test_optional_index_for_tests() {
|
||||
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();
|
||||
|
||||
open_optional_index(OwnedBytes::new(out)).unwrap()
|
||||
}
|
||||
|
||||
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 / 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);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -3,11 +3,11 @@ use std::io::Write;
|
||||
|
||||
use common::{CountingWriter, OwnedBytes};
|
||||
|
||||
use super::multivalued_index::SerializableMultivalueIndex;
|
||||
use super::OptionalIndex;
|
||||
use super::multivalued_index::SerializableMultivalueIndex;
|
||||
use crate::column_index::ColumnIndex;
|
||||
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, Version};
|
||||
|
||||
@@ -31,7 +31,7 @@ pub enum SerializableColumnIndex<'a> {
|
||||
Multivalued(SerializableMultivalueIndex<'a>),
|
||||
}
|
||||
|
||||
impl<'a> SerializableColumnIndex<'a> {
|
||||
impl SerializableColumnIndex<'_> {
|
||||
pub fn get_cardinality(&self) -> Cardinality {
|
||||
match self {
|
||||
SerializableColumnIndex::Full => Cardinality::Full,
|
||||
|
||||
@@ -1,139 +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)
|
||||
.unwrap();
|
||||
|
||||
Codec::load(OwnedBytes::new(bytes)).unwrap()
|
||||
}
|
||||
|
||||
fn bench_get<Codec: ColumnCodec>(b: &mut Bencher, data: &[u64]) {
|
||||
let col = get_reader_for_bench::<Codec>(data);
|
||||
b.iter(|| {
|
||||
let mut sum = 0u64;
|
||||
for pos in value_iter() {
|
||||
let val = col.get_val(pos as u32);
|
||||
sum = sum.wrapping_add(val);
|
||||
}
|
||||
sum
|
||||
});
|
||||
}
|
||||
|
||||
#[inline(never)]
|
||||
fn bench_get_dynamic_helper(b: &mut Bencher, col: Arc<dyn ColumnValues>) {
|
||||
b.iter(|| {
|
||||
let mut sum = 0u64;
|
||||
for pos in value_iter() {
|
||||
let val = col.get_val(pos as u32);
|
||||
sum = sum.wrapping_add(val);
|
||||
}
|
||||
sum
|
||||
});
|
||||
}
|
||||
|
||||
fn bench_get_dynamic<Codec: ColumnCodec>(b: &mut Bencher, data: &[u64]) {
|
||||
let col = Arc::new(get_reader_for_bench::<Codec>(data));
|
||||
bench_get_dynamic_helper(b, col);
|
||||
}
|
||||
fn bench_create<Codec: ColumnCodec>(b: &mut Bencher, data: &[u64]) {
|
||||
let stats = compute_stats(data.iter().cloned());
|
||||
|
||||
let mut bytes = Vec::new();
|
||||
b.iter(|| {
|
||||
bytes.clear();
|
||||
let mut codec_serializer = Codec::estimator();
|
||||
for val in data.iter().take(1024) {
|
||||
codec_serializer.collect(*val);
|
||||
}
|
||||
|
||||
codec_serializer.serialize(&stats, Box::new(data.iter().copied()).as_mut(), &mut bytes)
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_fastfield_bitpack_create(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_create::<BitpackedCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_linearinterpol_create(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_create::<LinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_multilinearinterpol_create(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_create::<BlockwiseLinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_bitpack_get(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get::<BitpackedCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_bitpack_get_dynamic(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get_dynamic::<BitpackedCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_linearinterpol_get(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get::<LinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_linearinterpol_get_dynamic(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get_dynamic::<LinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_multilinearinterpol_get(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get::<BlockwiseLinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_multilinearinterpol_get_dynamic(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get_dynamic::<BlockwiseLinearCodec>(b, &data);
|
||||
}
|
||||
@@ -10,7 +10,7 @@ pub(crate) struct MergedColumnValues<'a, T> {
|
||||
pub(crate) merge_row_order: &'a MergeRowOrder,
|
||||
}
|
||||
|
||||
impl<'a, T: Copy + PartialOrd + Debug + 'static> Iterable<T> for MergedColumnValues<'a, T> {
|
||||
impl<T: Copy + PartialOrd + Debug + 'static> Iterable<T> for MergedColumnValues<'_, T> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
match self.merge_row_order {
|
||||
MergeRowOrder::Stack(_) => Box::new(
|
||||
|
||||
@@ -26,13 +26,13 @@ mod monotonic_column;
|
||||
|
||||
pub(crate) use merge::MergedColumnValues;
|
||||
pub use stats::ColumnStats;
|
||||
pub use u128_based::{
|
||||
open_u128_as_compact_u64, open_u128_mapped, serialize_column_values_u128,
|
||||
CompactSpaceU64Accessor,
|
||||
};
|
||||
pub use u64_based::{
|
||||
load_u64_based_column_values, serialize_and_load_u64_based_column_values,
|
||||
serialize_u64_based_column_values, CodecType, ALL_U64_CODEC_TYPES,
|
||||
ALL_U64_CODEC_TYPES, CodecType, load_u64_based_column_values,
|
||||
serialize_and_load_u64_based_column_values, serialize_u64_based_column_values,
|
||||
};
|
||||
pub use u128_based::{
|
||||
CompactSpaceU64Accessor, open_u128_as_compact_u64, open_u128_mapped,
|
||||
serialize_column_values_u128,
|
||||
};
|
||||
pub use vec_column::VecColumn;
|
||||
|
||||
@@ -242,6 +242,3 @@ impl<T: Copy + PartialOrd + Debug + 'static> ColumnValues<T> for Arc<dyn ColumnV
|
||||
.get_row_ids_for_value_range(range, doc_id_range, positions)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(all(test, feature = "unstable"))]
|
||||
mod bench;
|
||||
|
||||
@@ -2,8 +2,8 @@ use std::fmt::Debug;
|
||||
use std::marker::PhantomData;
|
||||
use std::ops::{Range, RangeInclusive};
|
||||
|
||||
use crate::column_values::monotonic_mapping::StrictlyMonotonicFn;
|
||||
use crate::ColumnValues;
|
||||
use crate::column_values::monotonic_mapping::StrictlyMonotonicFn;
|
||||
|
||||
struct MonotonicMappingColumn<C, T, Input> {
|
||||
from_column: C,
|
||||
@@ -99,10 +99,10 @@ where
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::column_values::VecColumn;
|
||||
use crate::column_values::monotonic_mapping::{
|
||||
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
|
||||
};
|
||||
use crate::column_values::VecColumn;
|
||||
|
||||
#[test]
|
||||
fn test_monotonic_mapping_iter() {
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
use std::fmt::Debug;
|
||||
use std::net::Ipv6Addr;
|
||||
|
||||
/// Montonic maps a value to u128 value space
|
||||
/// Monotonic 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.
|
||||
|
||||
@@ -184,11 +184,11 @@ impl CompactSpaceBuilder {
|
||||
|
||||
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);
|
||||
}
|
||||
// beginning of the blanks
|
||||
if let Some(first_blank_start) = self.blanks.first().map(RangeInclusive::start)
|
||||
&& *first_blank_start != 0
|
||||
{
|
||||
covered_space.push(0..=first_blank_start - 1);
|
||||
}
|
||||
|
||||
// Between the blanks
|
||||
@@ -202,10 +202,10 @@ impl CompactSpaceBuilder {
|
||||
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 let Some(last_blank_end) = self.blanks.last().map(RangeInclusive::end)
|
||||
&& *last_blank_end != u128::MAX
|
||||
{
|
||||
covered_space.push(last_blank_end + 1..=u128::MAX);
|
||||
}
|
||||
|
||||
if covered_space.is_empty() {
|
||||
|
||||
@@ -24,8 +24,8 @@ use build_compact_space::get_compact_space;
|
||||
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt, VIntU128};
|
||||
use tantivy_bitpacker::{BitPacker, BitUnpacker};
|
||||
|
||||
use crate::column_values::ColumnValues;
|
||||
use crate::RowId;
|
||||
use crate::column_values::ColumnValues;
|
||||
|
||||
/// The cost per blank is quite hard actually, since blanks are delta encoded, the actual cost of
|
||||
/// blanks depends on the number of blanks.
|
||||
@@ -653,12 +653,14 @@ mod tests {
|
||||
),
|
||||
&[3]
|
||||
);
|
||||
assert!(get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99998u128..=99998u128,
|
||||
complete_range.clone()
|
||||
)
|
||||
.is_empty());
|
||||
assert!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99998u128..=99998u128,
|
||||
complete_range.clone()
|
||||
)
|
||||
.is_empty()
|
||||
);
|
||||
assert_eq!(
|
||||
&get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
|
||||
@@ -128,13 +128,13 @@ pub fn open_u128_as_compact_u64(mut bytes: OwnedBytes) -> io::Result<Arc<dyn Col
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
pub mod tests {
|
||||
pub(crate) mod tests {
|
||||
use super::*;
|
||||
use crate::column_values::u64_based::{
|
||||
serialize_and_load_u64_based_column_values, serialize_u64_based_column_values,
|
||||
ALL_U64_CODEC_TYPES,
|
||||
};
|
||||
use crate::column_values::CodecType;
|
||||
use crate::column_values::u64_based::{
|
||||
ALL_U64_CODEC_TYPES, serialize_and_load_u64_based_column_values,
|
||||
serialize_u64_based_column_values,
|
||||
};
|
||||
|
||||
#[test]
|
||||
fn test_serialize_deserialize_u128_header() {
|
||||
|
||||
@@ -4,7 +4,7 @@ use std::ops::{Range, RangeInclusive};
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use fastdivide::DividerU64;
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
use tantivy_bitpacker::{BitPacker, BitUnpacker, compute_num_bits};
|
||||
|
||||
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
|
||||
use crate::{ColumnValues, RowId};
|
||||
@@ -23,11 +23,7 @@ const fn div_ceil(n: u64, q: NonZeroU64) -> u64 {
|
||||
// copied from unstable rust standard library.
|
||||
let d = n / q.get();
|
||||
let r = n % q.get();
|
||||
if r > 0 {
|
||||
d + 1
|
||||
} else {
|
||||
d
|
||||
}
|
||||
if r > 0 { d + 1 } else { d }
|
||||
}
|
||||
|
||||
// The bitpacked codec applies a linear transformation `f` over data that are bitpacked.
|
||||
@@ -109,7 +105,7 @@ impl ColumnCodecEstimator for BitpackedCodecEstimator {
|
||||
|
||||
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)
|
||||
Some(stats.num_bytes() + (stats.num_rows as u64 * (num_bits_per_value as u64)).div_ceil(8))
|
||||
}
|
||||
|
||||
fn serialize(
|
||||
|
||||
@@ -4,12 +4,12 @@ use std::{io, iter};
|
||||
|
||||
use common::{BinarySerializable, CountingWriter, DeserializeFrom, OwnedBytes};
|
||||
use fastdivide::DividerU64;
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
use tantivy_bitpacker::{BitPacker, BitUnpacker, compute_num_bits};
|
||||
|
||||
use crate::MonotonicallyMappableToU64;
|
||||
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;
|
||||
|
||||
@@ -39,7 +39,7 @@ impl BinarySerializable for Block {
|
||||
}
|
||||
|
||||
fn compute_num_blocks(num_vals: u32) -> u32 {
|
||||
(num_vals + BLOCK_SIZE - 1) / BLOCK_SIZE
|
||||
num_vals.div_ceil(BLOCK_SIZE)
|
||||
}
|
||||
|
||||
pub struct BlockwiseLinearEstimator {
|
||||
|
||||
@@ -8,7 +8,7 @@ use crate::column_values::ColumnValues;
|
||||
const MID_POINT: u64 = (1u64 << 32) - 1u64;
|
||||
|
||||
/// `Line` describes a line function `y: ax + b` using integer
|
||||
/// arithmetics.
|
||||
/// arithmetic.
|
||||
///
|
||||
/// The slope is in fact a decimal split into a 32 bit integer value,
|
||||
/// and a 32-bit decimal value.
|
||||
@@ -94,7 +94,7 @@ impl Line {
|
||||
// `(i, ys[])`.
|
||||
//
|
||||
// The best intercept therefore has the form
|
||||
// `y[i] - line.eval(i)` (using wrapping arithmetics).
|
||||
// `y[i] - line.eval(i)` (using wrapping arithmetic).
|
||||
// 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.
|
||||
//
|
||||
@@ -122,12 +122,11 @@ impl Line {
|
||||
line
|
||||
}
|
||||
|
||||
/// Returns a line that attemps to approximate a function
|
||||
/// Returns a line that attempts to approximate a function
|
||||
/// f: i in 0..[ys.num_vals()) -> ys[i].
|
||||
///
|
||||
/// - The approximation is always lower than the actual value.
|
||||
/// Or more rigorously, formally `f(i).wrapping_sub(ys[i])` is small
|
||||
/// for any i in [0..ys.len()).
|
||||
/// - The approximation is always lower than the actual value. Or more rigorously, formally
|
||||
/// `f(i).wrapping_sub(ys[i])` is small for any i in [0..ys.len()).
|
||||
/// - It computes without panicking for any value of it.
|
||||
///
|
||||
/// This function is only invariable by translation if all of the
|
||||
|
||||
@@ -1,13 +1,13 @@
|
||||
use std::io;
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
use tantivy_bitpacker::{BitPacker, BitUnpacker, compute_num_bits};
|
||||
|
||||
use super::line::Line;
|
||||
use super::ColumnValues;
|
||||
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
|
||||
use crate::column_values::VecColumn;
|
||||
use super::line::Line;
|
||||
use crate::RowId;
|
||||
use crate::column_values::VecColumn;
|
||||
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
|
||||
|
||||
const HALF_SPACE: u64 = u64::MAX / 2;
|
||||
const LINE_ESTIMATION_BLOCK_LEN: usize = 512;
|
||||
@@ -117,7 +117,7 @@ impl ColumnCodecEstimator for LinearCodecEstimator {
|
||||
Some(
|
||||
stats.num_bytes()
|
||||
+ linear_params.num_bytes()
|
||||
+ (num_bits as u64 * stats.num_rows as u64 + 7) / 8,
|
||||
+ (num_bits as u64 * stats.num_rows as u64).div_ceil(8),
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
@@ -17,7 +17,7 @@ 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::column_values::{ColumnStats, monotonic_map_column};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{ColumnValues, MonotonicallyMappableToU64};
|
||||
|
||||
@@ -52,7 +52,7 @@ pub trait ColumnCodecEstimator<T = u64>: 'static {
|
||||
) -> io::Result<()>;
|
||||
}
|
||||
|
||||
/// A column codec describes a colunm serialization format.
|
||||
/// A column codec describes a column serialization format.
|
||||
pub trait ColumnCodec<T: PartialOrd = u64> {
|
||||
/// Specialized `ColumnValues` type.
|
||||
type ColumnValues: ColumnValues<T> + 'static;
|
||||
|
||||
@@ -2,8 +2,8 @@ use std::num::NonZeroU64;
|
||||
|
||||
use fastdivide::DividerU64;
|
||||
|
||||
use crate::column_values::ColumnStats;
|
||||
use crate::RowId;
|
||||
use crate::column_values::ColumnStats;
|
||||
|
||||
/// Compute the gcd of two non null numbers.
|
||||
///
|
||||
@@ -96,8 +96,8 @@ impl StatsCollector {
|
||||
mod tests {
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use crate::column_values::u64_based::stats_collector::{compute_gcd, StatsCollector};
|
||||
use crate::column_values::u64_based::ColumnStats;
|
||||
use crate::column_values::u64_based::stats_collector::{StatsCollector, compute_gcd};
|
||||
|
||||
fn compute_stats(vals: impl Iterator<Item = u64>) -> ColumnStats {
|
||||
let mut stats_collector = StatsCollector::default();
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
use proptest::prelude::*;
|
||||
use proptest::{prop_oneof, proptest};
|
||||
use rand::Rng;
|
||||
|
||||
#[test]
|
||||
fn test_serialize_and_load_simple() {
|
||||
|
||||
@@ -4,8 +4,8 @@ use std::net::Ipv6Addr;
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use crate::value::NumericalType;
|
||||
use crate::InvalidData;
|
||||
use crate::value::NumericalType;
|
||||
|
||||
/// The column type represents the column type.
|
||||
/// Any changes need to be propagated to `COLUMN_TYPES`.
|
||||
|
||||
@@ -3,7 +3,7 @@ use std::io::{self, Write};
|
||||
use common::{BitSet, CountingWriter, ReadOnlyBitSet};
|
||||
use sstable::{SSTable, Streamer, TermOrdinal, VoidSSTable};
|
||||
|
||||
use super::term_merger::TermMerger;
|
||||
use super::term_merger::{TermMerger, TermsWithSegmentOrd};
|
||||
use crate::column::serialize_column_mappable_to_u64;
|
||||
use crate::column_index::SerializableColumnIndex;
|
||||
use crate::iterable::Iterable;
|
||||
@@ -39,7 +39,7 @@ struct RemappedTermOrdinalsValues<'a> {
|
||||
merge_row_order: &'a MergeRowOrder,
|
||||
}
|
||||
|
||||
impl<'a> Iterable for RemappedTermOrdinalsValues<'a> {
|
||||
impl Iterable for RemappedTermOrdinalsValues<'_> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u64> + '_> {
|
||||
match self.merge_row_order {
|
||||
MergeRowOrder::Stack(_) => self.boxed_iter_stacked(),
|
||||
@@ -50,7 +50,7 @@ impl<'a> Iterable for RemappedTermOrdinalsValues<'a> {
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> RemappedTermOrdinalsValues<'a> {
|
||||
impl RemappedTermOrdinalsValues<'_> {
|
||||
fn boxed_iter_stacked(&self) -> Box<dyn Iterator<Item = u64> + '_> {
|
||||
let iter = self
|
||||
.bytes_columns
|
||||
@@ -126,14 +126,17 @@ fn serialize_merged_dict(
|
||||
let mut term_ord_mapping = TermOrdinalMapping::default();
|
||||
|
||||
let mut field_term_streams = Vec::new();
|
||||
for column_opt in bytes_columns.iter() {
|
||||
for (segment_ord, column_opt) in bytes_columns.iter().enumerate() {
|
||||
if let Some(column) = column_opt {
|
||||
term_ord_mapping.add_segment(column.dictionary.num_terms());
|
||||
let terms: Streamer<VoidSSTable> = column.dictionary.stream()?;
|
||||
field_term_streams.push(terms);
|
||||
field_term_streams.push(TermsWithSegmentOrd { terms, segment_ord });
|
||||
} else {
|
||||
term_ord_mapping.add_segment(0);
|
||||
field_term_streams.push(Streamer::empty());
|
||||
field_term_streams.push(TermsWithSegmentOrd {
|
||||
terms: Streamer::empty(),
|
||||
segment_ord,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
@@ -191,6 +194,7 @@ fn serialize_merged_dict(
|
||||
|
||||
#[derive(Default, Debug)]
|
||||
struct TermOrdinalMapping {
|
||||
/// Contains the new term ordinals for each segment.
|
||||
per_segment_new_term_ordinals: Vec<Vec<TermOrdinal>>,
|
||||
}
|
||||
|
||||
@@ -205,6 +209,6 @@ impl TermOrdinalMapping {
|
||||
}
|
||||
|
||||
fn get_segment(&self, segment_ord: u32) -> &[TermOrdinal] {
|
||||
&(self.per_segment_new_term_ordinals[segment_ord as usize])[..]
|
||||
&self.per_segment_new_term_ordinals[segment_ord as usize]
|
||||
}
|
||||
}
|
||||
|
||||
@@ -26,7 +26,7 @@ impl 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_id += columnar.num_docs();
|
||||
cumulated_row_ids.push(cumulated_row_id);
|
||||
}
|
||||
StackMergeOrder { cumulated_row_ids }
|
||||
|
||||
@@ -10,11 +10,11 @@ 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::{serialize_column_mappable_to_u64, serialize_column_mappable_to_u128};
|
||||
use crate::column_values::MergedColumnValues;
|
||||
use crate::columnar::ColumnarReader;
|
||||
use crate::columnar::merge::merge_dict_column::merge_bytes_or_str_column;
|
||||
use crate::columnar::writer::CompatibleNumericalTypes;
|
||||
use crate::columnar::ColumnarReader;
|
||||
use crate::dynamic_column::DynamicColumn;
|
||||
use crate::{
|
||||
BytesColumn, Column, ColumnIndex, ColumnType, ColumnValues, DynamicColumnHandle, NumericalType,
|
||||
@@ -25,7 +25,7 @@ use crate::{
|
||||
/// 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.
|
||||
/// In practise, today, only Numerical columns are coerced into one type today.
|
||||
///
|
||||
/// See also [README.md].
|
||||
///
|
||||
@@ -63,11 +63,10 @@ impl From<ColumnType> for ColumnTypeCategory {
|
||||
/// `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.
|
||||
/// - If the required column type is compatible with all of the input columnar, the resulting merged
|
||||
/// columnar will simply coerce the input column and use the required column type.
|
||||
/// - If the required column type is incompatible with one of the input columnar, the merged will
|
||||
/// fail with an InvalidData error.
|
||||
///
|
||||
/// `merge_row_order` makes it possible to remove or reorder row in the resulting
|
||||
/// `Columnar` table.
|
||||
@@ -81,13 +80,12 @@ pub fn merge_columnar(
|
||||
output: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
let mut serializer = ColumnarSerializer::new(output);
|
||||
let num_rows_per_columnar = columnar_readers
|
||||
let num_docs_per_columnar = columnar_readers
|
||||
.iter()
|
||||
.map(|reader| reader.num_rows())
|
||||
.map(|reader| reader.num_docs())
|
||||
.collect::<Vec<u32>>();
|
||||
|
||||
let columns_to_merge =
|
||||
group_columns_for_merge(columnar_readers, required_columns, &merge_row_order)?;
|
||||
let columns_to_merge = group_columns_for_merge(columnar_readers, required_columns)?;
|
||||
for res in columns_to_merge {
|
||||
let ((column_name, _column_type_category), grouped_columns) = res;
|
||||
let grouped_columns = grouped_columns.open(&merge_row_order)?;
|
||||
@@ -95,15 +93,18 @@ pub fn merge_columnar(
|
||||
continue;
|
||||
}
|
||||
|
||||
let column_type = grouped_columns.column_type_after_merge();
|
||||
let column_type_after_merge = grouped_columns.column_type_after_merge();
|
||||
let mut columns = grouped_columns.columns;
|
||||
coerce_columns(column_type, &mut columns)?;
|
||||
// Make sure the number of columns is the same as the number of columnar readers.
|
||||
// Or num_docs_per_columnar would be incorrect.
|
||||
assert_eq!(columns.len(), columnar_readers.len());
|
||||
coerce_columns(column_type_after_merge, &mut columns)?;
|
||||
|
||||
let mut column_serializer =
|
||||
serializer.start_serialize_column(column_name.as_bytes(), column_type);
|
||||
serializer.start_serialize_column(column_name.as_bytes(), column_type_after_merge);
|
||||
merge_column(
|
||||
column_type,
|
||||
&num_rows_per_columnar,
|
||||
column_type_after_merge,
|
||||
&num_docs_per_columnar,
|
||||
columns,
|
||||
&merge_row_order,
|
||||
&mut column_serializer,
|
||||
@@ -129,7 +130,7 @@ fn dynamic_column_to_u64_monotonic(dynamic_column: DynamicColumn) -> Option<Colu
|
||||
fn merge_column(
|
||||
column_type: ColumnType,
|
||||
num_docs_per_column: &[u32],
|
||||
columns: Vec<Option<DynamicColumn>>,
|
||||
columns_to_merge: Vec<Option<DynamicColumn>>,
|
||||
merge_row_order: &MergeRowOrder,
|
||||
wrt: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
@@ -139,20 +140,21 @@ fn merge_column(
|
||||
| ColumnType::F64
|
||||
| ColumnType::DateTime
|
||||
| ColumnType::Bool => {
|
||||
let mut column_indexes: Vec<ColumnIndex> = Vec::with_capacity(columns.len());
|
||||
let mut column_indexes: Vec<ColumnIndex> = Vec::with_capacity(columns_to_merge.len());
|
||||
let mut column_values: Vec<Option<Arc<dyn ColumnValues>>> =
|
||||
Vec::with_capacity(columns.len());
|
||||
for (i, dynamic_column_opt) in columns.into_iter().enumerate() {
|
||||
if let Some(Column { index: idx, values }) =
|
||||
dynamic_column_opt.and_then(dynamic_column_to_u64_monotonic)
|
||||
{
|
||||
column_indexes.push(idx);
|
||||
column_values.push(Some(values));
|
||||
} else {
|
||||
column_indexes.push(ColumnIndex::Empty {
|
||||
num_docs: num_docs_per_column[i],
|
||||
});
|
||||
column_values.push(None);
|
||||
Vec::with_capacity(columns_to_merge.len());
|
||||
for (i, dynamic_column_opt) in columns_to_merge.into_iter().enumerate() {
|
||||
match dynamic_column_opt.and_then(dynamic_column_to_u64_monotonic) {
|
||||
Some(Column { index: idx, values }) => {
|
||||
column_indexes.push(idx);
|
||||
column_values.push(Some(values));
|
||||
}
|
||||
None => {
|
||||
column_indexes.push(ColumnIndex::Empty {
|
||||
num_docs: num_docs_per_column[i],
|
||||
});
|
||||
column_values.push(None);
|
||||
}
|
||||
}
|
||||
}
|
||||
let merged_column_index =
|
||||
@@ -165,10 +167,10 @@ fn merge_column(
|
||||
serialize_column_mappable_to_u64(merged_column_index, &merge_column_values, wrt)?;
|
||||
}
|
||||
ColumnType::IpAddr => {
|
||||
let mut column_indexes: Vec<ColumnIndex> = Vec::with_capacity(columns.len());
|
||||
let mut column_indexes: Vec<ColumnIndex> = Vec::with_capacity(columns_to_merge.len());
|
||||
let mut column_values: Vec<Option<Arc<dyn ColumnValues<Ipv6Addr>>>> =
|
||||
Vec::with_capacity(columns.len());
|
||||
for (i, dynamic_column_opt) in columns.into_iter().enumerate() {
|
||||
Vec::with_capacity(columns_to_merge.len());
|
||||
for (i, dynamic_column_opt) in columns_to_merge.into_iter().enumerate() {
|
||||
if let Some(DynamicColumn::IpAddr(Column { index: idx, values })) =
|
||||
dynamic_column_opt
|
||||
{
|
||||
@@ -193,9 +195,10 @@ fn merge_column(
|
||||
serialize_column_mappable_to_u128(merged_column_index, &merge_column_values, wrt)?;
|
||||
}
|
||||
ColumnType::Bytes | ColumnType::Str => {
|
||||
let mut column_indexes: Vec<ColumnIndex> = Vec::with_capacity(columns.len());
|
||||
let mut bytes_columns: Vec<Option<BytesColumn>> = Vec::with_capacity(columns.len());
|
||||
for (i, dynamic_column_opt) in columns.into_iter().enumerate() {
|
||||
let mut column_indexes: Vec<ColumnIndex> = Vec::with_capacity(columns_to_merge.len());
|
||||
let mut bytes_columns: Vec<Option<BytesColumn>> =
|
||||
Vec::with_capacity(columns_to_merge.len());
|
||||
for (i, dynamic_column_opt) in columns_to_merge.into_iter().enumerate() {
|
||||
match dynamic_column_opt {
|
||||
Some(DynamicColumn::Str(str_column)) => {
|
||||
column_indexes.push(str_column.term_ord_column.index.clone());
|
||||
@@ -249,13 +252,15 @@ impl GroupedColumns {
|
||||
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
|
||||
// At the moment, only the numerical column type category has more than one possible
|
||||
// column type.
|
||||
assert!(self
|
||||
.columns
|
||||
.iter()
|
||||
.flatten()
|
||||
.all(|el| ColumnTypeCategory::from(el.column_type()) == ColumnTypeCategory::Numerical));
|
||||
assert!(
|
||||
self.columns
|
||||
.iter()
|
||||
.flatten()
|
||||
.all(|el| ColumnTypeCategory::from(el.column_type())
|
||||
== ColumnTypeCategory::Numerical)
|
||||
);
|
||||
merged_numerical_columns_type(self.columns.iter().flatten()).into()
|
||||
}
|
||||
}
|
||||
@@ -362,7 +367,7 @@ fn is_empty_after_merge(
|
||||
ColumnIndex::Empty { .. } => true,
|
||||
ColumnIndex::Full => alive_bitset.len() == 0,
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
for doc in optional_index.iter_rows() {
|
||||
for doc in optional_index.iter_non_null_docs() {
|
||||
if alive_bitset.contains(doc) {
|
||||
return false;
|
||||
}
|
||||
@@ -392,7 +397,6 @@ fn is_empty_after_merge(
|
||||
fn group_columns_for_merge<'a>(
|
||||
columnar_readers: &'a [&'a ColumnarReader],
|
||||
required_columns: &'a [(String, ColumnType)],
|
||||
_merge_row_order: &'a MergeRowOrder,
|
||||
) -> io::Result<BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle>> {
|
||||
let mut columns: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> = BTreeMap::new();
|
||||
|
||||
|
||||
@@ -5,28 +5,29 @@ use sstable::TermOrdinal;
|
||||
|
||||
use crate::Streamer;
|
||||
|
||||
pub struct HeapItem<'a> {
|
||||
pub streamer: Streamer<'a>,
|
||||
/// The terms of a column with the ordinal of the segment.
|
||||
pub struct TermsWithSegmentOrd<'a> {
|
||||
pub terms: Streamer<'a>,
|
||||
pub segment_ord: usize,
|
||||
}
|
||||
|
||||
impl<'a> PartialEq for HeapItem<'a> {
|
||||
impl PartialEq for TermsWithSegmentOrd<'_> {
|
||||
fn eq(&self, other: &Self) -> bool {
|
||||
self.segment_ord == other.segment_ord
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Eq for HeapItem<'a> {}
|
||||
impl Eq for TermsWithSegmentOrd<'_> {}
|
||||
|
||||
impl<'a> PartialOrd for HeapItem<'a> {
|
||||
fn partial_cmp(&self, other: &HeapItem<'a>) -> Option<Ordering> {
|
||||
impl<'a> PartialOrd for TermsWithSegmentOrd<'a> {
|
||||
fn partial_cmp(&self, other: &TermsWithSegmentOrd<'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))
|
||||
impl<'a> Ord for TermsWithSegmentOrd<'a> {
|
||||
fn cmp(&self, other: &TermsWithSegmentOrd<'a>) -> Ordering {
|
||||
(&other.terms.key(), &other.segment_ord).cmp(&(&self.terms.key(), &self.segment_ord))
|
||||
}
|
||||
}
|
||||
|
||||
@@ -35,42 +36,34 @@ impl<'a> Ord for HeapItem<'a> {
|
||||
///
|
||||
/// The item yield is actually a pair with
|
||||
/// - the term
|
||||
/// - a slice with the ordinal of the segments containing
|
||||
/// the terms.
|
||||
/// - a slice with the ordinal of the segments containing the terms.
|
||||
pub struct TermMerger<'a> {
|
||||
heap: BinaryHeap<HeapItem<'a>>,
|
||||
current_streamers: Vec<HeapItem<'a>>,
|
||||
heap: BinaryHeap<TermsWithSegmentOrd<'a>>,
|
||||
term_streams_with_segment: Vec<TermsWithSegmentOrd<'a>>,
|
||||
}
|
||||
|
||||
impl<'a> TermMerger<'a> {
|
||||
/// Stream of merged term dictionary
|
||||
pub fn new(streams: Vec<Streamer<'a>>) -> TermMerger<'a> {
|
||||
pub fn new(term_streams_with_segment: Vec<TermsWithSegmentOrd<'a>>) -> TermMerger<'a> {
|
||||
TermMerger {
|
||||
heap: BinaryHeap::new(),
|
||||
current_streamers: streams
|
||||
.into_iter()
|
||||
.enumerate()
|
||||
.map(|(ord, streamer)| HeapItem {
|
||||
streamer,
|
||||
segment_ord: ord,
|
||||
})
|
||||
.collect(),
|
||||
term_streams_with_segment,
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) fn matching_segments<'b: 'a>(
|
||||
&'b self,
|
||||
) -> impl 'b + Iterator<Item = (usize, TermOrdinal)> {
|
||||
self.current_streamers
|
||||
self.term_streams_with_segment
|
||||
.iter()
|
||||
.map(|heap_item| (heap_item.segment_ord, heap_item.streamer.term_ord()))
|
||||
.map(|heap_item| (heap_item.segment_ord, heap_item.terms.term_ord()))
|
||||
}
|
||||
|
||||
fn advance_segments(&mut self) {
|
||||
let streamers = &mut self.current_streamers;
|
||||
let streamers = &mut self.term_streams_with_segment;
|
||||
let heap = &mut self.heap;
|
||||
for mut heap_item in streamers.drain(..) {
|
||||
if heap_item.streamer.advance() {
|
||||
if heap_item.terms.advance() {
|
||||
heap.push(heap_item);
|
||||
}
|
||||
}
|
||||
@@ -81,18 +74,19 @@ impl<'a> TermMerger<'a> {
|
||||
/// 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;
|
||||
match self.heap.pop() {
|
||||
Some(head) => {
|
||||
self.term_streams_with_segment.push(head);
|
||||
while let Some(next_streamer) = self.heap.peek() {
|
||||
if self.term_streams_with_segment[0].terms.key() != next_streamer.terms.key() {
|
||||
break;
|
||||
}
|
||||
let next_heap_it = self.heap.pop().unwrap(); // safe : we peeked beforehand
|
||||
self.term_streams_with_segment.push(next_heap_it);
|
||||
}
|
||||
let next_heap_it = self.heap.pop().unwrap(); // safe : we peeked beforehand
|
||||
self.current_streamers.push(next_heap_it);
|
||||
true
|
||||
}
|
||||
true
|
||||
} else {
|
||||
false
|
||||
_ => false,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -102,6 +96,6 @@ impl<'a> TermMerger<'a> {
|
||||
/// if and only if advance() has been called before
|
||||
/// and "true" was returned.
|
||||
pub fn key(&self) -> &[u8] {
|
||||
self.current_streamers[0].streamer.key()
|
||||
self.term_streams_with_segment[0].terms.key()
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,7 +1,10 @@
|
||||
use itertools::Itertools;
|
||||
use proptest::collection::vec;
|
||||
use proptest::prelude::*;
|
||||
|
||||
use super::*;
|
||||
use crate::{Cardinality, ColumnarWriter, HasAssociatedColumnType, RowId};
|
||||
use crate::columnar::{ColumnarReader, MergeRowOrder, StackMergeOrder, merge_columnar};
|
||||
use crate::{Cardinality, ColumnarWriter, DynamicColumn, HasAssociatedColumnType, RowId};
|
||||
|
||||
fn make_columnar<T: Into<NumericalValue> + HasAssociatedColumnType + Copy>(
|
||||
column_name: &str,
|
||||
@@ -26,9 +29,8 @@ fn test_column_coercion_to_u64() {
|
||||
// u64 type
|
||||
let columnar2 = make_columnar("numbers", &[u64::MAX]);
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let merge_order = StackMergeOrder::stack(columnars).into();
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
group_columns_for_merge(columnars, &[], &merge_order).unwrap();
|
||||
group_columns_for_merge(columnars, &[]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
|
||||
}
|
||||
@@ -38,9 +40,8 @@ fn test_column_coercion_to_i64() {
|
||||
let columnar1 = make_columnar("numbers", &[-1i64]);
|
||||
let columnar2 = make_columnar("numbers", &[2u64]);
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let merge_order = StackMergeOrder::stack(columnars).into();
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
group_columns_for_merge(columnars, &[], &merge_order).unwrap();
|
||||
group_columns_for_merge(columnars, &[]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
|
||||
}
|
||||
@@ -63,14 +64,8 @@ fn test_group_columns_with_required_column() {
|
||||
let columnar1 = make_columnar("numbers", &[1i64]);
|
||||
let columnar2 = make_columnar("numbers", &[2u64]);
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let merge_order = StackMergeOrder::stack(columnars).into();
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
group_columns_for_merge(
|
||||
&[&columnar1, &columnar2],
|
||||
&[("numbers".to_string(), ColumnType::U64)],
|
||||
&merge_order,
|
||||
)
|
||||
.unwrap();
|
||||
group_columns_for_merge(columnars, &[("numbers".to_string(), ColumnType::U64)]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
|
||||
}
|
||||
@@ -80,13 +75,9 @@ fn test_group_columns_required_column_with_no_existing_columns() {
|
||||
let columnar1 = make_columnar("numbers", &[2u64]);
|
||||
let columnar2 = make_columnar("numbers", &[2u64]);
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let merge_order = StackMergeOrder::stack(columnars).into();
|
||||
let column_map: BTreeMap<_, _> = group_columns_for_merge(
|
||||
columnars,
|
||||
&[("required_col".to_string(), ColumnType::Str)],
|
||||
&merge_order,
|
||||
)
|
||||
.unwrap();
|
||||
let column_map: BTreeMap<_, _> =
|
||||
group_columns_for_merge(columnars, &[("required_col".to_string(), ColumnType::Str)])
|
||||
.unwrap();
|
||||
assert_eq!(column_map.len(), 2);
|
||||
let columns = &column_map
|
||||
.get(&("required_col".to_string(), ColumnTypeCategory::Str))
|
||||
@@ -102,14 +93,8 @@ fn test_group_columns_required_column_is_above_all_columns_have_the_same_type_ru
|
||||
let columnar1 = make_columnar("numbers", &[2i64]);
|
||||
let columnar2 = make_columnar("numbers", &[2i64]);
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let merge_order = StackMergeOrder::stack(columnars).into();
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
group_columns_for_merge(
|
||||
columnars,
|
||||
&[("numbers".to_string(), ColumnType::U64)],
|
||||
&merge_order,
|
||||
)
|
||||
.unwrap();
|
||||
group_columns_for_merge(columnars, &[("numbers".to_string(), ColumnType::U64)]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
|
||||
}
|
||||
@@ -119,9 +104,8 @@ fn test_missing_column() {
|
||||
let columnar1 = make_columnar("numbers", &[-1i64]);
|
||||
let columnar2 = make_columnar("numbers2", &[2u64]);
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let merge_order = StackMergeOrder::stack(columnars).into();
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
group_columns_for_merge(columnars, &[], &merge_order).unwrap();
|
||||
group_columns_for_merge(columnars, &[]).unwrap();
|
||||
assert_eq!(column_map.len(), 2);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
|
||||
{
|
||||
@@ -224,7 +208,7 @@ fn test_merge_columnar_numbers() {
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 3);
|
||||
assert_eq!(columnar_reader.num_docs(), 3);
|
||||
assert_eq!(columnar_reader.num_columns(), 1);
|
||||
let cols = columnar_reader.read_columns("numbers").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
@@ -252,7 +236,7 @@ fn test_merge_columnar_texts() {
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 3);
|
||||
assert_eq!(columnar_reader.num_docs(), 3);
|
||||
assert_eq!(columnar_reader.num_columns(), 1);
|
||||
let cols = columnar_reader.read_columns("texts").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
@@ -301,7 +285,7 @@ fn test_merge_columnar_byte() {
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 4);
|
||||
assert_eq!(columnar_reader.num_docs(), 4);
|
||||
assert_eq!(columnar_reader.num_columns(), 1);
|
||||
let cols = columnar_reader.read_columns("bytes").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
@@ -357,7 +341,7 @@ fn test_merge_columnar_byte_with_missing() {
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 3 + 2 + 3);
|
||||
assert_eq!(columnar_reader.num_docs(), 3 + 2 + 3);
|
||||
assert_eq!(columnar_reader.num_columns(), 2);
|
||||
let cols = columnar_reader.read_columns("col").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
@@ -409,7 +393,7 @@ fn test_merge_columnar_different_types() {
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 4);
|
||||
assert_eq!(columnar_reader.num_docs(), 4);
|
||||
assert_eq!(columnar_reader.num_columns(), 2);
|
||||
let cols = columnar_reader.read_columns("mixed").unwrap();
|
||||
|
||||
@@ -419,11 +403,11 @@ fn test_merge_columnar_different_types() {
|
||||
panic!()
|
||||
};
|
||||
assert_eq!(vals.get_cardinality(), Cardinality::Optional);
|
||||
assert_eq!(vals.values_for_doc(0).collect_vec(), vec![]);
|
||||
assert_eq!(vals.values_for_doc(1).collect_vec(), vec![]);
|
||||
assert_eq!(vals.values_for_doc(2).collect_vec(), vec![]);
|
||||
assert_eq!(vals.values_for_doc(0).collect_vec(), Vec::<i64>::new());
|
||||
assert_eq!(vals.values_for_doc(1).collect_vec(), Vec::<i64>::new());
|
||||
assert_eq!(vals.values_for_doc(2).collect_vec(), Vec::<i64>::new());
|
||||
assert_eq!(vals.values_for_doc(3).collect_vec(), vec![1]);
|
||||
assert_eq!(vals.values_for_doc(4).collect_vec(), vec![]);
|
||||
assert_eq!(vals.values_for_doc(4).collect_vec(), Vec::<i64>::new());
|
||||
|
||||
// text column
|
||||
let dynamic_column = cols[1].open().unwrap();
|
||||
@@ -474,7 +458,7 @@ fn test_merge_columnar_different_empty_cardinality() {
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 2);
|
||||
assert_eq!(columnar_reader.num_docs(), 2);
|
||||
assert_eq!(columnar_reader.num_columns(), 2);
|
||||
let cols = columnar_reader.read_columns("mixed").unwrap();
|
||||
|
||||
@@ -486,3 +470,119 @@ fn test_merge_columnar_different_empty_cardinality() {
|
||||
let dynamic_column = cols[1].open().unwrap();
|
||||
assert_eq!(dynamic_column.get_cardinality(), Cardinality::Optional);
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
struct ColumnSpec {
|
||||
column_name: String,
|
||||
/// (row_id, term)
|
||||
terms: Vec<(RowId, Vec<u8>)>,
|
||||
}
|
||||
|
||||
#[derive(Clone, Debug)]
|
||||
struct ColumnarSpec {
|
||||
columns: Vec<ColumnSpec>,
|
||||
}
|
||||
|
||||
/// Generate a random (row_id, term) pair:
|
||||
/// - row_id in [0..10]
|
||||
/// - term is either from POSSIBLE_TERMS or random bytes
|
||||
fn rowid_and_term_strategy() -> impl Strategy<Value = (RowId, Vec<u8>)> {
|
||||
const POSSIBLE_TERMS: &[&[u8]] = &[b"a", b"b", b"allo"];
|
||||
|
||||
let term_strat = prop_oneof![
|
||||
// pick from the fixed list
|
||||
(0..POSSIBLE_TERMS.len()).prop_map(|i| POSSIBLE_TERMS[i].to_vec()),
|
||||
// or random bytes (length 0..10)
|
||||
prop::collection::vec(any::<u8>(), 0..10),
|
||||
];
|
||||
|
||||
(0u32..11, term_strat)
|
||||
}
|
||||
|
||||
/// Generate one ColumnSpec, with a random name and a random list of (row_id, term).
|
||||
/// We sort it by row_id so that data is in ascending order.
|
||||
fn column_spec_strategy() -> impl Strategy<Value = ColumnSpec> {
|
||||
let column_name = prop_oneof![
|
||||
Just("col".to_string()),
|
||||
Just("col2".to_string()),
|
||||
"col.*".prop_map(|s| s),
|
||||
];
|
||||
|
||||
// We'll produce 0..8 (rowid,term) entries for this column
|
||||
let data_strat = vec(rowid_and_term_strategy(), 0..8).prop_map(|mut pairs| {
|
||||
// Sort by row_id
|
||||
pairs.sort_by_key(|(row_id, _)| *row_id);
|
||||
pairs
|
||||
});
|
||||
|
||||
(column_name, data_strat).prop_map(|(name, data)| ColumnSpec {
|
||||
column_name: name,
|
||||
terms: data,
|
||||
})
|
||||
}
|
||||
|
||||
/// Strategy to generate an ColumnarSpec
|
||||
fn columnar_strategy() -> impl Strategy<Value = ColumnarSpec> {
|
||||
vec(column_spec_strategy(), 0..3).prop_map(|columns| ColumnarSpec { columns })
|
||||
}
|
||||
|
||||
/// Strategy to generate multiple ColumnarSpecs, each of which we will treat
|
||||
/// as one "columnar" to be merged together.
|
||||
fn columnars_strategy() -> impl Strategy<Value = Vec<ColumnarSpec>> {
|
||||
vec(columnar_strategy(), 1..4)
|
||||
}
|
||||
|
||||
/// Build a `ColumnarReader` from a `ColumnarSpec`
|
||||
fn build_columnar(spec: &ColumnarSpec) -> ColumnarReader {
|
||||
let mut writer = ColumnarWriter::default();
|
||||
let mut max_row_id = 0;
|
||||
for col in &spec.columns {
|
||||
for &(row_id, ref term) in &col.terms {
|
||||
writer.record_bytes(row_id, &col.column_name, term);
|
||||
max_row_id = max_row_id.max(row_id);
|
||||
}
|
||||
}
|
||||
|
||||
let mut buffer = Vec::new();
|
||||
writer.serialize(max_row_id + 1, &mut buffer).unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
}
|
||||
|
||||
proptest! {
|
||||
// We just test that the merge_columnar function doesn't crash.
|
||||
#![proptest_config(ProptestConfig::with_cases(256))]
|
||||
#[test]
|
||||
fn test_merge_columnar_bytes_no_crash(columnars in columnars_strategy(), second_merge_columnars in columnars_strategy()) {
|
||||
let columnars: Vec<ColumnarReader> = columnars.iter()
|
||||
.map(build_columnar)
|
||||
.collect();
|
||||
|
||||
let mut out = Vec::new();
|
||||
let columnar_refs: Vec<&ColumnarReader> = columnars.iter().collect();
|
||||
let stack_merge_order = StackMergeOrder::stack(&columnar_refs);
|
||||
merge_columnar(
|
||||
&columnar_refs,
|
||||
&[],
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut out,
|
||||
).unwrap();
|
||||
|
||||
let merged_reader = ColumnarReader::open(out).unwrap();
|
||||
|
||||
// Merge the second set of columnars with the result of the first merge
|
||||
let mut columnars: Vec<ColumnarReader> = second_merge_columnars.iter()
|
||||
.map(build_columnar)
|
||||
.collect();
|
||||
columnars.push(merged_reader);
|
||||
let mut out = Vec::new();
|
||||
let columnar_refs: Vec<&ColumnarReader> = columnars.iter().collect();
|
||||
let stack_merge_order = StackMergeOrder::stack(&columnar_refs);
|
||||
merge_columnar(
|
||||
&columnar_refs,
|
||||
&[],
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut out,
|
||||
).unwrap();
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
@@ -5,9 +5,9 @@ mod reader;
|
||||
mod writer;
|
||||
|
||||
pub use column_type::{ColumnType, HasAssociatedColumnType};
|
||||
pub use format_version::{Version, CURRENT_VERSION};
|
||||
pub use format_version::{CURRENT_VERSION, Version};
|
||||
#[cfg(test)]
|
||||
pub(crate) use merge::ColumnTypeCategory;
|
||||
pub use merge::{merge_columnar, MergeRowOrder, ShuffleMergeOrder, StackMergeOrder};
|
||||
pub use merge::{MergeRowOrder, ShuffleMergeOrder, StackMergeOrder, merge_columnar};
|
||||
pub use reader::ColumnarReader;
|
||||
pub use writer::ColumnarWriter;
|
||||
|
||||
@@ -1,10 +1,11 @@
|
||||
use std::{fmt, io, mem};
|
||||
|
||||
use common::file_slice::FileSlice;
|
||||
use common::BinarySerializable;
|
||||
use common::file_slice::FileSlice;
|
||||
use common::json_path_writer::JSON_PATH_SEGMENT_SEP;
|
||||
use sstable::{Dictionary, RangeSSTable};
|
||||
|
||||
use crate::columnar::{format_version, ColumnType};
|
||||
use crate::columnar::{ColumnType, format_version};
|
||||
use crate::dynamic_column::DynamicColumnHandle;
|
||||
use crate::{RowId, Version};
|
||||
|
||||
@@ -18,13 +19,13 @@ fn io_invalid_data(msg: String) -> io::Error {
|
||||
pub struct ColumnarReader {
|
||||
column_dictionary: Dictionary<RangeSSTable>,
|
||||
column_data: FileSlice,
|
||||
num_rows: RowId,
|
||||
num_docs: RowId,
|
||||
format_version: Version,
|
||||
}
|
||||
|
||||
impl fmt::Debug for ColumnarReader {
|
||||
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
|
||||
let num_rows = self.num_rows();
|
||||
let num_rows = self.num_docs();
|
||||
let columns = self.list_columns().unwrap();
|
||||
let num_cols = columns.len();
|
||||
let mut debug_struct = f.debug_struct("Columnar");
|
||||
@@ -76,6 +77,19 @@ fn read_all_columns_in_stream(
|
||||
Ok(results)
|
||||
}
|
||||
|
||||
fn column_dictionary_prefix_for_column_name(column_name: &str) -> String {
|
||||
// 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`.
|
||||
format!("{}{}", column_name, '\0')
|
||||
}
|
||||
|
||||
fn column_dictionary_prefix_for_subpath(root_path: &str) -> String {
|
||||
format!("{}{}", root_path, JSON_PATH_SEGMENT_SEP as char)
|
||||
}
|
||||
|
||||
impl ColumnarReader {
|
||||
/// Opens a new Columnar file.
|
||||
pub fn open<F>(file_slice: F) -> io::Result<ColumnarReader>
|
||||
@@ -98,13 +112,13 @@ impl ColumnarReader {
|
||||
Ok(ColumnarReader {
|
||||
column_dictionary,
|
||||
column_data,
|
||||
num_rows,
|
||||
num_docs: num_rows,
|
||||
format_version,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
self.num_rows
|
||||
pub fn num_docs(&self) -> RowId {
|
||||
self.num_docs
|
||||
}
|
||||
// Iterate over the columns in a sorted way
|
||||
pub fn iter_columns(
|
||||
@@ -144,32 +158,14 @@ impl ColumnarReader {
|
||||
Ok(self.iter_columns()?.collect())
|
||||
}
|
||||
|
||||
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 prefix = column_dictionary_prefix_for_column_name(column_name);
|
||||
let stream = self
|
||||
.stream_for_column_range(column_name)
|
||||
.column_dictionary
|
||||
.prefix_range(prefix)
|
||||
.into_stream_async()
|
||||
.await?;
|
||||
read_all_columns_in_stream(stream, &self.column_data, self.format_version)
|
||||
@@ -180,7 +176,35 @@ impl ColumnarReader {
|
||||
/// 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()?;
|
||||
let prefix = column_dictionary_prefix_for_column_name(column_name);
|
||||
let stream = self.column_dictionary.prefix_range(prefix).into_stream()?;
|
||||
read_all_columns_in_stream(stream, &self.column_data, self.format_version)
|
||||
}
|
||||
|
||||
pub async fn read_subpath_columns_async(
|
||||
&self,
|
||||
root_path: &str,
|
||||
) -> io::Result<Vec<DynamicColumnHandle>> {
|
||||
let prefix = column_dictionary_prefix_for_subpath(root_path);
|
||||
let stream = self
|
||||
.column_dictionary
|
||||
.prefix_range(prefix)
|
||||
.into_stream_async()
|
||||
.await?;
|
||||
read_all_columns_in_stream(stream, &self.column_data, self.format_version)
|
||||
}
|
||||
|
||||
/// Get all inner columns for a given JSON prefix, i.e columns for which the name starts
|
||||
/// with the prefix then contain the [`JSON_PATH_SEGMENT_SEP`].
|
||||
///
|
||||
/// There can be more than one column associated to each path within the JSON structure,
|
||||
/// provided they have different types.
|
||||
pub fn read_subpath_columns(&self, root_path: &str) -> io::Result<Vec<DynamicColumnHandle>> {
|
||||
let prefix = column_dictionary_prefix_for_subpath(root_path);
|
||||
let stream = self
|
||||
.column_dictionary
|
||||
.prefix_range(prefix.as_bytes())
|
||||
.into_stream()?;
|
||||
read_all_columns_in_stream(stream, &self.column_data, self.format_version)
|
||||
}
|
||||
|
||||
@@ -192,6 +216,8 @@ impl ColumnarReader {
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use common::json_path_writer::JSON_PATH_SEGMENT_SEP;
|
||||
|
||||
use crate::{ColumnType, ColumnarReader, ColumnarWriter};
|
||||
|
||||
#[test]
|
||||
@@ -224,6 +250,64 @@ mod tests {
|
||||
assert_eq!(columns[0].1.column_type(), ColumnType::U64);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_read_columns() {
|
||||
let mut columnar_writer = ColumnarWriter::default();
|
||||
columnar_writer.record_column_type("col", ColumnType::U64, false);
|
||||
columnar_writer.record_numerical(1, "col", 1u64);
|
||||
let mut buffer = Vec::new();
|
||||
columnar_writer.serialize(2, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
{
|
||||
let columns = columnar.read_columns("col").unwrap();
|
||||
assert_eq!(columns.len(), 1);
|
||||
assert_eq!(columns[0].column_type(), ColumnType::U64);
|
||||
}
|
||||
{
|
||||
let columns = columnar.read_columns("other").unwrap();
|
||||
assert_eq!(columns.len(), 0);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_read_subpath_columns() {
|
||||
let mut columnar_writer = ColumnarWriter::default();
|
||||
columnar_writer.record_str(
|
||||
0,
|
||||
&format!("col1{}subcol1", JSON_PATH_SEGMENT_SEP as char),
|
||||
"hello",
|
||||
);
|
||||
columnar_writer.record_numerical(
|
||||
0,
|
||||
&format!("col1{}subcol2", JSON_PATH_SEGMENT_SEP as char),
|
||||
1i64,
|
||||
);
|
||||
columnar_writer.record_str(1, "col1", "hello");
|
||||
columnar_writer.record_str(0, "col2", "hello");
|
||||
let mut buffer = Vec::new();
|
||||
columnar_writer.serialize(2, &mut buffer).unwrap();
|
||||
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
{
|
||||
let columns = columnar.read_subpath_columns("col1").unwrap();
|
||||
assert_eq!(columns.len(), 2);
|
||||
assert_eq!(columns[0].column_type(), ColumnType::Str);
|
||||
assert_eq!(columns[1].column_type(), ColumnType::I64);
|
||||
}
|
||||
{
|
||||
let columns = columnar.read_subpath_columns("col1.subcol1").unwrap();
|
||||
assert_eq!(columns.len(), 0);
|
||||
}
|
||||
{
|
||||
let columns = columnar.read_subpath_columns("col2").unwrap();
|
||||
assert_eq!(columns.len(), 0);
|
||||
}
|
||||
{
|
||||
let columns = columnar.read_subpath_columns("other").unwrap();
|
||||
assert_eq!(columns.len(), 0);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "Input type forbidden")]
|
||||
fn test_list_columns_strict_typing_panics_on_wrong_types() {
|
||||
|
||||
@@ -87,7 +87,7 @@ impl<V: SymbolValue> ColumnOperation<V> {
|
||||
minibuf
|
||||
}
|
||||
|
||||
/// Deserialize a colummn operation.
|
||||
/// Deserialize a column operation.
|
||||
/// Returns None if the buffer is empty.
|
||||
///
|
||||
/// Panics if the payload is invalid:
|
||||
@@ -122,7 +122,6 @@ impl<T> From<T> for ColumnOperation<T> {
|
||||
// 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.
|
||||
@@ -245,7 +244,7 @@ impl SymbolValue for UnorderedId {
|
||||
|
||||
fn compute_num_bytes_for_u64(val: u64) -> usize {
|
||||
let msb = (64u32 - val.leading_zeros()) as usize;
|
||||
(msb + 7) / 8
|
||||
msb.div_ceil(8)
|
||||
}
|
||||
|
||||
fn encode_zig_zag(n: i64) -> u64 {
|
||||
|
||||
@@ -42,7 +42,7 @@ impl ColumnWriter {
|
||||
&self,
|
||||
arena: &MemoryArena,
|
||||
buffer: &'a mut Vec<u8>,
|
||||
) -> impl Iterator<Item = ColumnOperation<V>> + 'a {
|
||||
) -> impl Iterator<Item = ColumnOperation<V>> + 'a + use<'a, V> {
|
||||
buffer.clear();
|
||||
self.values.read_to_end(arena, buffer);
|
||||
let mut cursor: &[u8] = &buffer[..];
|
||||
@@ -104,9 +104,10 @@ pub(crate) struct NumericalColumnWriter {
|
||||
|
||||
impl NumericalColumnWriter {
|
||||
pub fn force_numerical_type(&mut self, numerical_type: NumericalType) {
|
||||
assert!(self
|
||||
.compatible_numerical_types
|
||||
.is_type_accepted(numerical_type));
|
||||
assert!(
|
||||
self.compatible_numerical_types
|
||||
.is_type_accepted(numerical_type)
|
||||
);
|
||||
self.compatible_numerical_types = CompatibleNumericalTypes::StaticType(numerical_type);
|
||||
}
|
||||
}
|
||||
@@ -211,7 +212,7 @@ impl NumericalColumnWriter {
|
||||
self,
|
||||
arena: &MemoryArena,
|
||||
buffer: &'a mut Vec<u8>,
|
||||
) -> impl Iterator<Item = ColumnOperation<NumericalValue>> + 'a {
|
||||
) -> impl Iterator<Item = ColumnOperation<NumericalValue>> + 'a + use<'a> {
|
||||
self.column_writer.operation_iterator(arena, buffer)
|
||||
}
|
||||
}
|
||||
@@ -255,7 +256,7 @@ impl StrOrBytesColumnWriter {
|
||||
&self,
|
||||
arena: &MemoryArena,
|
||||
byte_buffer: &'a mut Vec<u8>,
|
||||
) -> impl Iterator<Item = ColumnOperation<UnorderedId>> + 'a {
|
||||
) -> impl Iterator<Item = ColumnOperation<UnorderedId>> + 'a + use<'a> {
|
||||
self.column_writer.operation_iterator(arena, byte_buffer)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -8,13 +8,13 @@ use std::net::Ipv6Addr;
|
||||
|
||||
use column_operation::ColumnOperation;
|
||||
pub(crate) use column_writers::CompatibleNumericalTypes;
|
||||
use common::json_path_writer::JSON_END_OF_PATH;
|
||||
use common::CountingWriter;
|
||||
use common::json_path_writer::JSON_END_OF_PATH;
|
||||
pub(crate) use serializer::ColumnarSerializer;
|
||||
use stacker::{Addr, ArenaHashMap, MemoryArena};
|
||||
|
||||
use crate::column_index::{SerializableColumnIndex, SerializableOptionalIndex};
|
||||
use crate::column_values::{MonotonicallyMappableToU128, MonotonicallyMappableToU64};
|
||||
use crate::column_values::{MonotonicallyMappableToU64, MonotonicallyMappableToU128};
|
||||
use crate::columnar::column_type::ColumnType;
|
||||
use crate::columnar::writer::column_writers::{
|
||||
ColumnWriter, NumericalColumnWriter, StrOrBytesColumnWriter,
|
||||
@@ -285,7 +285,6 @@ impl ColumnarWriter {
|
||||
.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 {
|
||||
@@ -392,7 +391,7 @@ impl ColumnarWriter {
|
||||
|
||||
// Serialize [Dictionary, Column, dictionary num bytes U32::LE]
|
||||
// Column: [Column Index, Column Values, column index num bytes U32::LE]
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
#[expect(clippy::too_many_arguments)]
|
||||
fn serialize_bytes_or_str_column(
|
||||
cardinality: Cardinality,
|
||||
num_docs: RowId,
|
||||
|
||||
@@ -3,11 +3,11 @@ use std::io::Write;
|
||||
|
||||
use common::json_path_writer::JSON_END_OF_PATH;
|
||||
use common::{BinarySerializable, CountingWriter};
|
||||
use sstable::value::RangeValueWriter;
|
||||
use sstable::RangeSSTable;
|
||||
use sstable::value::RangeValueWriter;
|
||||
|
||||
use crate::columnar::ColumnType;
|
||||
use crate::RowId;
|
||||
use crate::columnar::ColumnType;
|
||||
|
||||
pub struct ColumnarSerializer<W: io::Write> {
|
||||
wrt: CountingWriter<W>,
|
||||
@@ -67,7 +67,7 @@ pub struct ColumnSerializer<'a, W: io::Write> {
|
||||
start_offset: u64,
|
||||
}
|
||||
|
||||
impl<'a, W: io::Write> ColumnSerializer<'a, W> {
|
||||
impl<W: io::Write> ColumnSerializer<'_, W> {
|
||||
pub fn finalize(self) -> io::Result<()> {
|
||||
let end_offset: u64 = self.columnar_serializer.wrt.written_bytes();
|
||||
let byte_range = self.start_offset..end_offset;
|
||||
@@ -80,7 +80,7 @@ impl<'a, W: io::Write> ColumnSerializer<'a, W> {
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a, W: io::Write> io::Write for ColumnSerializer<'a, W> {
|
||||
impl<W: io::Write> io::Write for ColumnSerializer<'_, W> {
|
||||
fn write(&mut self, buf: &[u8]) -> io::Result<usize> {
|
||||
self.columnar_serializer.wrt.write(buf)
|
||||
}
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
use crate::RowId;
|
||||
use crate::column_index::{SerializableMultivalueIndex, SerializableOptionalIndex};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::RowId;
|
||||
|
||||
/// The `IndexBuilder` interprets a sequence of
|
||||
/// calls of the form:
|
||||
@@ -31,12 +31,13 @@ pub struct OptionalIndexBuilder {
|
||||
|
||||
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));
|
||||
debug_assert!(
|
||||
self.docs
|
||||
.last()
|
||||
.copied()
|
||||
.map(|last_doc| last_doc < num_rows)
|
||||
.unwrap_or(true)
|
||||
);
|
||||
&self.docs[..]
|
||||
}
|
||||
|
||||
@@ -48,12 +49,13 @@ impl OptionalIndexBuilder {
|
||||
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));
|
||||
debug_assert!(
|
||||
self.docs
|
||||
.last()
|
||||
.copied()
|
||||
.map(|prev_doc| doc > prev_doc)
|
||||
.unwrap_or(true)
|
||||
);
|
||||
self.docs.push(doc);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -3,8 +3,8 @@ use std::path::PathBuf;
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
merge_columnar, Cardinality, Column, ColumnarReader, DynamicColumn, StackMergeOrder,
|
||||
CURRENT_VERSION,
|
||||
CURRENT_VERSION, Cardinality, Column, ColumnarReader, DynamicColumn, StackMergeOrder,
|
||||
merge_columnar,
|
||||
};
|
||||
|
||||
const NUM_DOCS: u32 = u16::MAX as u32;
|
||||
|
||||
@@ -3,10 +3,11 @@ use std::sync::Arc;
|
||||
use std::{fmt, io};
|
||||
|
||||
use common::file_slice::FileSlice;
|
||||
use common::{ByteCount, DateTime, HasLen, OwnedBytes};
|
||||
use common::{ByteCount, DateTime, OwnedBytes};
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use crate::column::{BytesColumn, Column, StrColumn};
|
||||
use crate::column_values::{monotonic_map_column, StrictlyMonotonicFn};
|
||||
use crate::column_values::{StrictlyMonotonicFn, monotonic_map_column};
|
||||
use crate::columnar::ColumnType;
|
||||
use crate::{Cardinality, ColumnIndex, ColumnValues, NumericalType, Version};
|
||||
|
||||
@@ -317,10 +318,89 @@ impl DynamicColumnHandle {
|
||||
}
|
||||
|
||||
pub fn num_bytes(&self) -> ByteCount {
|
||||
self.file_slice.len().into()
|
||||
self.file_slice.num_bytes()
|
||||
}
|
||||
|
||||
/// Legacy helper returning the column space usage.
|
||||
pub fn column_and_dictionary_num_bytes(&self) -> io::Result<ColumnSpaceUsage> {
|
||||
self.space_usage()
|
||||
}
|
||||
|
||||
/// Return the space usage of the column, optionally broken down by dictionary and column
|
||||
/// values.
|
||||
///
|
||||
/// For dictionary encoded columns (strings and bytes), this splits the total footprint into
|
||||
/// the dictionary and the remaining column data (including index and values).
|
||||
/// For all other column types, the dictionary size is `None` and the column size
|
||||
/// equals the total bytes.
|
||||
pub fn space_usage(&self) -> io::Result<ColumnSpaceUsage> {
|
||||
let total_num_bytes = self.num_bytes();
|
||||
let dynamic_column = self.open()?;
|
||||
let dictionary_num_bytes = match &dynamic_column {
|
||||
DynamicColumn::Bytes(bytes_column) => bytes_column.dictionary().num_bytes(),
|
||||
DynamicColumn::Str(str_column) => str_column.dictionary().num_bytes(),
|
||||
_ => {
|
||||
return Ok(ColumnSpaceUsage::new(self.num_bytes(), None));
|
||||
}
|
||||
};
|
||||
assert!(dictionary_num_bytes <= total_num_bytes);
|
||||
let column_num_bytes =
|
||||
ByteCount::from(total_num_bytes.get_bytes() - dictionary_num_bytes.get_bytes());
|
||||
Ok(ColumnSpaceUsage::new(
|
||||
column_num_bytes,
|
||||
Some(dictionary_num_bytes),
|
||||
))
|
||||
}
|
||||
|
||||
pub fn column_type(&self) -> ColumnType {
|
||||
self.column_type
|
||||
}
|
||||
}
|
||||
|
||||
/// Represents space usage of a column.
|
||||
///
|
||||
/// `column_num_bytes` tracks the column payload (index, values and footer).
|
||||
/// For dictionary encoded columns, `dictionary_num_bytes` captures the dictionary footprint.
|
||||
/// [`ColumnSpaceUsage::total_num_bytes`] returns the sum of both parts.
|
||||
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||
pub struct ColumnSpaceUsage {
|
||||
column_num_bytes: ByteCount,
|
||||
dictionary_num_bytes: Option<ByteCount>,
|
||||
}
|
||||
|
||||
impl ColumnSpaceUsage {
|
||||
pub(crate) fn new(
|
||||
column_num_bytes: ByteCount,
|
||||
dictionary_num_bytes: Option<ByteCount>,
|
||||
) -> Self {
|
||||
ColumnSpaceUsage {
|
||||
column_num_bytes,
|
||||
dictionary_num_bytes,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn column_num_bytes(&self) -> ByteCount {
|
||||
self.column_num_bytes
|
||||
}
|
||||
|
||||
pub fn dictionary_num_bytes(&self) -> Option<ByteCount> {
|
||||
self.dictionary_num_bytes
|
||||
}
|
||||
|
||||
pub fn total_num_bytes(&self) -> ByteCount {
|
||||
self.column_num_bytes + self.dictionary_num_bytes.unwrap_or_default()
|
||||
}
|
||||
|
||||
/// Merge two space usage values by summing their components.
|
||||
pub fn merge(&self, other: &ColumnSpaceUsage) -> ColumnSpaceUsage {
|
||||
let dictionary_num_bytes = match (self.dictionary_num_bytes, other.dictionary_num_bytes) {
|
||||
(Some(lhs), Some(rhs)) => Some(lhs + rhs),
|
||||
(Some(val), None) | (None, Some(val)) => Some(val),
|
||||
(None, None) => None,
|
||||
};
|
||||
ColumnSpaceUsage {
|
||||
column_num_bytes: self.column_num_bytes + other.column_num_bytes,
|
||||
dictionary_num_bytes,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -7,7 +7,7 @@ pub trait Iterable<T = u64> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_>;
|
||||
}
|
||||
|
||||
impl<'a, T: Copy> Iterable<T> for &'a [T] {
|
||||
impl<T: Copy> Iterable<T> for &[T] {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
Box::new(self.iter().copied())
|
||||
}
|
||||
|
||||
@@ -17,15 +17,10 @@
|
||||
//! column.
|
||||
//! - [column_values]: Stores the values of a column in a dense format.
|
||||
|
||||
#![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::fmt::Display;
|
||||
use std::io;
|
||||
|
||||
@@ -44,16 +39,16 @@ pub use block_accessor::ColumnBlockAccessor;
|
||||
pub use column::{BytesColumn, Column, StrColumn};
|
||||
pub use column_index::ColumnIndex;
|
||||
pub use column_values::{
|
||||
ColumnValues, EmptyColumnValues, MonotonicallyMappableToU128, MonotonicallyMappableToU64,
|
||||
ColumnValues, EmptyColumnValues, MonotonicallyMappableToU64, MonotonicallyMappableToU128,
|
||||
};
|
||||
pub use columnar::{
|
||||
merge_columnar, ColumnType, ColumnarReader, ColumnarWriter, HasAssociatedColumnType,
|
||||
MergeRowOrder, ShuffleMergeOrder, StackMergeOrder, Version, CURRENT_VERSION,
|
||||
CURRENT_VERSION, ColumnType, ColumnarReader, ColumnarWriter, HasAssociatedColumnType,
|
||||
MergeRowOrder, ShuffleMergeOrder, StackMergeOrder, Version, merge_columnar,
|
||||
};
|
||||
use sstable::VoidSSTable;
|
||||
pub use value::{NumericalType, NumericalValue};
|
||||
|
||||
pub use self::dynamic_column::{DynamicColumn, DynamicColumnHandle};
|
||||
pub use self::dynamic_column::{ColumnSpaceUsage, DynamicColumn, DynamicColumnHandle};
|
||||
|
||||
pub type RowId = u32;
|
||||
pub type DocId = u32;
|
||||
|
||||
@@ -380,7 +380,7 @@ fn assert_columnar_eq(
|
||||
right: &ColumnarReader,
|
||||
lenient_on_numerical_value: bool,
|
||||
) {
|
||||
assert_eq!(left.num_rows(), right.num_rows());
|
||||
assert_eq!(left.num_docs(), right.num_docs());
|
||||
let left_columns = left.list_columns().unwrap();
|
||||
let right_columns = right.list_columns().unwrap();
|
||||
assert_eq!(left_columns.len(), right_columns.len());
|
||||
@@ -588,7 +588,7 @@ proptest! {
|
||||
#[test]
|
||||
fn test_single_columnar_builder_proptest(docs in columnar_docs_strategy()) {
|
||||
let columnar = build_columnar(&docs[..]);
|
||||
assert_eq!(columnar.num_rows() as usize, docs.len());
|
||||
assert_eq!(columnar.num_docs() as usize, docs.len());
|
||||
let mut expected_columns: HashMap<(&str, ColumnTypeCategory), HashMap<u32, Vec<&ColumnValue>> > = Default::default();
|
||||
for (doc_id, doc_vals) in docs.iter().enumerate() {
|
||||
for (col_name, col_val) in doc_vals {
|
||||
@@ -715,8 +715,9 @@ fn test_columnar_merging_number_columns() {
|
||||
// TODO test required_columns
|
||||
// TODO document edge case: required_columns incompatible with values.
|
||||
|
||||
fn columnar_docs_and_remap(
|
||||
) -> impl Strategy<Value = (Vec<Vec<Vec<(&'static str, ColumnValue)>>>, Vec<RowAddr>)> {
|
||||
#[allow(clippy::type_complexity)]
|
||||
fn columnar_docs_and_remap()
|
||||
-> impl Strategy<Value = (Vec<Vec<Vec<(&'static str, ColumnValue)>>>, Vec<RowAddr>)> {
|
||||
proptest::collection::vec(columnar_docs_strategy(), 2..=3).prop_flat_map(
|
||||
|columnars_docs: Vec<Vec<Vec<(&str, ColumnValue)>>>| {
|
||||
let row_addrs: Vec<RowAddr> = columnars_docs
|
||||
@@ -819,7 +820,7 @@ fn test_columnar_merge_empty() {
|
||||
)
|
||||
.unwrap();
|
||||
let merged_columnar = ColumnarReader::open(output).unwrap();
|
||||
assert_eq!(merged_columnar.num_rows(), 0);
|
||||
assert_eq!(merged_columnar.num_docs(), 0);
|
||||
assert_eq!(merged_columnar.num_columns(), 0);
|
||||
}
|
||||
|
||||
@@ -845,7 +846,7 @@ fn test_columnar_merge_single_str_column() {
|
||||
)
|
||||
.unwrap();
|
||||
let merged_columnar = ColumnarReader::open(output).unwrap();
|
||||
assert_eq!(merged_columnar.num_rows(), 1);
|
||||
assert_eq!(merged_columnar.num_docs(), 1);
|
||||
assert_eq!(merged_columnar.num_columns(), 1);
|
||||
}
|
||||
|
||||
@@ -877,7 +878,7 @@ fn test_delete_decrease_cardinality() {
|
||||
)
|
||||
.unwrap();
|
||||
let merged_columnar = ColumnarReader::open(output).unwrap();
|
||||
assert_eq!(merged_columnar.num_rows(), 1);
|
||||
assert_eq!(merged_columnar.num_docs(), 1);
|
||||
assert_eq!(merged_columnar.num_columns(), 1);
|
||||
let cols = merged_columnar.read_columns("c").unwrap();
|
||||
assert_eq!(cols.len(), 1);
|
||||
|
||||
@@ -1,3 +1,5 @@
|
||||
use std::str::FromStr;
|
||||
|
||||
use common::DateTime;
|
||||
|
||||
use crate::InvalidData;
|
||||
@@ -9,6 +11,23 @@ pub enum NumericalValue {
|
||||
F64(f64),
|
||||
}
|
||||
|
||||
impl FromStr for NumericalValue {
|
||||
type Err = ();
|
||||
|
||||
fn from_str(s: &str) -> Result<Self, ()> {
|
||||
if let Ok(val_i64) = s.parse::<i64>() {
|
||||
return Ok(val_i64.into());
|
||||
}
|
||||
if let Ok(val_u64) = s.parse::<u64>() {
|
||||
return Ok(val_u64.into());
|
||||
}
|
||||
if let Ok(val_f64) = s.parse::<f64>() {
|
||||
return Ok(NumericalValue::from(val_f64).normalize());
|
||||
}
|
||||
Err(())
|
||||
}
|
||||
}
|
||||
|
||||
impl NumericalValue {
|
||||
pub fn numerical_type(&self) -> NumericalType {
|
||||
match self {
|
||||
@@ -17,6 +36,31 @@ impl NumericalValue {
|
||||
NumericalValue::F64(_) => NumericalType::F64,
|
||||
}
|
||||
}
|
||||
|
||||
/// Tries to normalize the numerical value in the following priorities:
|
||||
/// i64, i64, f64
|
||||
pub fn normalize(self) -> Self {
|
||||
match self {
|
||||
NumericalValue::U64(val) => {
|
||||
if val <= i64::MAX as u64 {
|
||||
NumericalValue::I64(val as i64)
|
||||
} else {
|
||||
NumericalValue::U64(val)
|
||||
}
|
||||
}
|
||||
NumericalValue::I64(val) => NumericalValue::I64(val),
|
||||
NumericalValue::F64(val) => {
|
||||
let fract = val.fract();
|
||||
if fract == 0.0 && val >= i64::MIN as f64 && val <= i64::MAX as f64 {
|
||||
NumericalValue::I64(val as i64)
|
||||
} else if fract == 0.0 && val >= u64::MIN as f64 && val <= u64::MAX as f64 {
|
||||
NumericalValue::U64(val as u64)
|
||||
} else {
|
||||
NumericalValue::F64(val)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl From<u64> for NumericalValue {
|
||||
@@ -116,6 +160,7 @@ impl Coerce for DateTime {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::NumericalType;
|
||||
use crate::NumericalValue;
|
||||
|
||||
#[test]
|
||||
fn test_numerical_type_code() {
|
||||
@@ -128,4 +173,58 @@ mod tests {
|
||||
}
|
||||
assert_eq!(num_numerical_type, 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_parse_numerical() {
|
||||
assert_eq!(
|
||||
"123".parse::<NumericalValue>().unwrap(),
|
||||
NumericalValue::I64(123)
|
||||
);
|
||||
assert_eq!(
|
||||
"18446744073709551615".parse::<NumericalValue>().unwrap(),
|
||||
NumericalValue::U64(18446744073709551615u64)
|
||||
);
|
||||
assert_eq!(
|
||||
"1.0".parse::<NumericalValue>().unwrap(),
|
||||
NumericalValue::I64(1i64)
|
||||
);
|
||||
assert_eq!(
|
||||
"1.1".parse::<NumericalValue>().unwrap(),
|
||||
NumericalValue::F64(1.1f64)
|
||||
);
|
||||
assert_eq!(
|
||||
"-1.0".parse::<NumericalValue>().unwrap(),
|
||||
NumericalValue::I64(-1i64)
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_normalize_numerical() {
|
||||
assert_eq!(
|
||||
NumericalValue::from(1u64).normalize(),
|
||||
NumericalValue::I64(1i64),
|
||||
);
|
||||
let limit_val = i64::MAX as u64 + 1u64;
|
||||
assert_eq!(
|
||||
NumericalValue::from(limit_val).normalize(),
|
||||
NumericalValue::U64(limit_val),
|
||||
);
|
||||
assert_eq!(
|
||||
NumericalValue::from(-1i64).normalize(),
|
||||
NumericalValue::I64(-1i64),
|
||||
);
|
||||
assert_eq!(
|
||||
NumericalValue::from(-2.0f64).normalize(),
|
||||
NumericalValue::I64(-2i64),
|
||||
);
|
||||
assert_eq!(
|
||||
NumericalValue::from(-2.1f64).normalize(),
|
||||
NumericalValue::F64(-2.1f64),
|
||||
);
|
||||
let large_float = 2.0f64.powf(70.0f64);
|
||||
assert_eq!(
|
||||
NumericalValue::from(large_float).normalize(),
|
||||
NumericalValue::F64(large_float),
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
[package]
|
||||
name = "tantivy-common"
|
||||
version = "0.7.0"
|
||||
version = "0.10.0"
|
||||
authors = ["Paul Masurel <paul@quickwit.io>", "Pascal Seitz <pascal@quickwit.io>"]
|
||||
license = "MIT"
|
||||
edition = "2021"
|
||||
edition = "2024"
|
||||
description = "common traits and utility functions used by multiple tantivy subcrates"
|
||||
documentation = "https://docs.rs/tantivy_common/"
|
||||
homepage = "https://github.com/quickwit-oss/tantivy"
|
||||
@@ -13,13 +13,13 @@ repository = "https://github.com/quickwit-oss/tantivy"
|
||||
|
||||
[dependencies]
|
||||
byteorder = "1.4.3"
|
||||
ownedbytes = { version= "0.7", path="../ownedbytes" }
|
||||
ownedbytes = { version= "0.9", path="../ownedbytes" }
|
||||
async-trait = "0.1"
|
||||
time = { version = "0.3.10", features = ["serde-well-known"] }
|
||||
serde = { version = "1.0.136", features = ["derive"] }
|
||||
|
||||
[dev-dependencies]
|
||||
binggan = "0.8.1"
|
||||
binggan = "0.14.0"
|
||||
proptest = "1.0.0"
|
||||
rand = "0.8.4"
|
||||
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
use binggan::{black_box, BenchRunner};
|
||||
use binggan::{BenchRunner, black_box};
|
||||
use rand::seq::IteratorRandom;
|
||||
use rand::thread_rng;
|
||||
use tantivy_common::{serialize_vint_u32, BitSet, TinySet};
|
||||
use tantivy_common::{BitSet, TinySet, serialize_vint_u32};
|
||||
|
||||
fn bench_vint() {
|
||||
let mut runner = BenchRunner::new();
|
||||
|
||||
@@ -183,7 +183,7 @@ pub struct BitSet {
|
||||
}
|
||||
|
||||
fn num_buckets(max_val: u32) -> u32 {
|
||||
(max_val + 63u32) / 64u32
|
||||
max_val.div_ceil(64u32)
|
||||
}
|
||||
|
||||
impl BitSet {
|
||||
|
||||
130
common/src/bounds.rs
Normal file
130
common/src/bounds.rs
Normal file
@@ -0,0 +1,130 @@
|
||||
use std::io;
|
||||
use std::ops::Bound;
|
||||
|
||||
#[derive(Clone, Debug)]
|
||||
pub struct BoundsRange<T> {
|
||||
pub lower_bound: Bound<T>,
|
||||
pub upper_bound: Bound<T>,
|
||||
}
|
||||
impl<T> BoundsRange<T> {
|
||||
pub fn new(lower_bound: Bound<T>, upper_bound: Bound<T>) -> Self {
|
||||
BoundsRange {
|
||||
lower_bound,
|
||||
upper_bound,
|
||||
}
|
||||
}
|
||||
pub fn is_unbounded(&self) -> bool {
|
||||
matches!(self.lower_bound, Bound::Unbounded) && matches!(self.upper_bound, Bound::Unbounded)
|
||||
}
|
||||
pub fn map_bound<TTo>(&self, transform: impl Fn(&T) -> TTo) -> BoundsRange<TTo> {
|
||||
BoundsRange {
|
||||
lower_bound: map_bound(&self.lower_bound, &transform),
|
||||
upper_bound: map_bound(&self.upper_bound, &transform),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn map_bound_res<TTo, Err>(
|
||||
&self,
|
||||
transform: impl Fn(&T) -> Result<TTo, Err>,
|
||||
) -> Result<BoundsRange<TTo>, Err> {
|
||||
Ok(BoundsRange {
|
||||
lower_bound: map_bound_res(&self.lower_bound, &transform)?,
|
||||
upper_bound: map_bound_res(&self.upper_bound, &transform)?,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn transform_inner<TTo>(
|
||||
&self,
|
||||
transform_lower: impl Fn(&T) -> TransformBound<TTo>,
|
||||
transform_upper: impl Fn(&T) -> TransformBound<TTo>,
|
||||
) -> BoundsRange<TTo> {
|
||||
BoundsRange {
|
||||
lower_bound: transform_bound_inner(&self.lower_bound, &transform_lower),
|
||||
upper_bound: transform_bound_inner(&self.upper_bound, &transform_upper),
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the first set inner value
|
||||
pub fn get_inner(&self) -> Option<&T> {
|
||||
inner_bound(&self.lower_bound).or(inner_bound(&self.upper_bound))
|
||||
}
|
||||
}
|
||||
|
||||
pub enum TransformBound<T> {
|
||||
/// Overwrite the bounds
|
||||
NewBound(Bound<T>),
|
||||
/// Use Existing bounds with new value
|
||||
Existing(T),
|
||||
}
|
||||
|
||||
/// Takes a bound and transforms the inner value into a new bound via a closure.
|
||||
/// The bound variant may change by the value returned value from the closure.
|
||||
pub fn transform_bound_inner_res<TFrom, TTo>(
|
||||
bound: &Bound<TFrom>,
|
||||
transform: impl Fn(&TFrom) -> io::Result<TransformBound<TTo>>,
|
||||
) -> io::Result<Bound<TTo>> {
|
||||
use self::Bound::*;
|
||||
Ok(match bound {
|
||||
Excluded(from_val) => match transform(from_val)? {
|
||||
TransformBound::NewBound(new_val) => new_val,
|
||||
TransformBound::Existing(new_val) => Excluded(new_val),
|
||||
},
|
||||
Included(from_val) => match transform(from_val)? {
|
||||
TransformBound::NewBound(new_val) => new_val,
|
||||
TransformBound::Existing(new_val) => Included(new_val),
|
||||
},
|
||||
Unbounded => Unbounded,
|
||||
})
|
||||
}
|
||||
|
||||
/// Takes a bound and transforms the inner value into a new bound via a closure.
|
||||
/// The bound variant may change by the value returned value from the closure.
|
||||
pub fn transform_bound_inner<TFrom, TTo>(
|
||||
bound: &Bound<TFrom>,
|
||||
transform: impl Fn(&TFrom) -> TransformBound<TTo>,
|
||||
) -> Bound<TTo> {
|
||||
use self::Bound::*;
|
||||
match bound {
|
||||
Excluded(from_val) => match transform(from_val) {
|
||||
TransformBound::NewBound(new_val) => new_val,
|
||||
TransformBound::Existing(new_val) => Excluded(new_val),
|
||||
},
|
||||
Included(from_val) => match transform(from_val) {
|
||||
TransformBound::NewBound(new_val) => new_val,
|
||||
TransformBound::Existing(new_val) => Included(new_val),
|
||||
},
|
||||
Unbounded => Unbounded,
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the inner value of a `Bound`
|
||||
pub fn inner_bound<T>(val: &Bound<T>) -> Option<&T> {
|
||||
match val {
|
||||
Bound::Included(term) | Bound::Excluded(term) => Some(term),
|
||||
Bound::Unbounded => None,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn map_bound<TFrom, TTo>(
|
||||
bound: &Bound<TFrom>,
|
||||
transform: impl Fn(&TFrom) -> TTo,
|
||||
) -> Bound<TTo> {
|
||||
use self::Bound::*;
|
||||
match bound {
|
||||
Excluded(from_val) => Bound::Excluded(transform(from_val)),
|
||||
Included(from_val) => Bound::Included(transform(from_val)),
|
||||
Unbounded => Unbounded,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn map_bound_res<TFrom, TTo, Err>(
|
||||
bound: &Bound<TFrom>,
|
||||
transform: impl Fn(&TFrom) -> Result<TTo, Err>,
|
||||
) -> Result<Bound<TTo>, Err> {
|
||||
use self::Bound::*;
|
||||
Ok(match bound {
|
||||
Excluded(from_val) => Excluded(transform(from_val)?),
|
||||
Included(from_val) => Included(transform(from_val)?),
|
||||
Unbounded => Unbounded,
|
||||
})
|
||||
}
|
||||
@@ -1,5 +1,6 @@
|
||||
use std::fs::File;
|
||||
use std::ops::{Deref, Range, RangeBounds};
|
||||
use std::path::Path;
|
||||
use std::sync::Arc;
|
||||
use std::{fmt, io};
|
||||
|
||||
@@ -73,7 +74,7 @@ impl FileHandle for WrapFile {
|
||||
{
|
||||
use std::io::{Read, Seek};
|
||||
let mut file = self.file.try_clone()?; // Clone the file to read from it separately
|
||||
// Seek to the start position in the file
|
||||
// Seek to the start position in the file
|
||||
file.seek(io::SeekFrom::Start(start as u64))?;
|
||||
// Read the data into the buffer
|
||||
file.read_exact(&mut buffer)?;
|
||||
@@ -177,6 +178,12 @@ fn combine_ranges<R: RangeBounds<usize>>(orig_range: Range<usize>, rel_range: R)
|
||||
}
|
||||
|
||||
impl FileSlice {
|
||||
/// Creates a FileSlice from a path.
|
||||
pub fn open(path: &Path) -> io::Result<FileSlice> {
|
||||
let wrap_file = WrapFile::new(File::open(path)?)?;
|
||||
Ok(FileSlice::new(Arc::new(wrap_file)))
|
||||
}
|
||||
|
||||
/// Wraps a FileHandle.
|
||||
pub fn new(file_handle: Arc<dyn FileHandle>) -> Self {
|
||||
let num_bytes = file_handle.len();
|
||||
@@ -339,8 +346,8 @@ mod tests {
|
||||
use std::sync::Arc;
|
||||
|
||||
use super::{FileHandle, FileSlice};
|
||||
use crate::file_slice::combine_ranges;
|
||||
use crate::HasLen;
|
||||
use crate::file_slice::combine_ranges;
|
||||
|
||||
#[test]
|
||||
fn test_file_slice() -> io::Result<()> {
|
||||
|
||||
@@ -5,6 +5,7 @@ use std::ops::Deref;
|
||||
pub use byteorder::LittleEndian as Endianness;
|
||||
|
||||
mod bitset;
|
||||
pub mod bounds;
|
||||
mod byte_count;
|
||||
mod datetime;
|
||||
pub mod file_slice;
|
||||
@@ -21,7 +22,7 @@ pub use json_path_writer::JsonPathWriter;
|
||||
pub use ownedbytes::{OwnedBytes, StableDeref};
|
||||
pub use serialize::{BinarySerializable, DeserializeFrom, FixedSize};
|
||||
pub use vint::{
|
||||
read_u32_vint, read_u32_vint_no_advance, serialize_vint_u32, write_u32_vint, VInt, VIntU128,
|
||||
VInt, VIntU128, read_u32_vint, read_u32_vint_no_advance, serialize_vint_u32, write_u32_vint,
|
||||
};
|
||||
pub use writer::{AntiCallToken, CountingWriter, TerminatingWrite};
|
||||
|
||||
@@ -129,11 +130,11 @@ pub fn replace_in_place(needle: u8, replacement: u8, bytes: &mut [u8]) {
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
pub mod test {
|
||||
pub(crate) mod test {
|
||||
|
||||
use proptest::prelude::*;
|
||||
|
||||
use super::{f64_to_u64, i64_to_u64, u64_to_f64, u64_to_i64, BinarySerializable, FixedSize};
|
||||
use super::{f64_to_u64, i64_to_u64, u64_to_f64, u64_to_i64};
|
||||
|
||||
fn test_i64_converter_helper(val: i64) {
|
||||
assert_eq!(u64_to_i64(i64_to_u64(val)), val);
|
||||
@@ -143,12 +144,6 @@ pub mod test {
|
||||
assert_eq!(u64_to_f64(f64_to_u64(val)), val);
|
||||
}
|
||||
|
||||
pub fn fixed_size_test<O: BinarySerializable + FixedSize + Default>() {
|
||||
let mut buffer = Vec::new();
|
||||
O::default().serialize(&mut buffer).unwrap();
|
||||
assert_eq!(buffer.len(), O::SIZE_IN_BYTES);
|
||||
}
|
||||
|
||||
proptest! {
|
||||
#[test]
|
||||
fn test_f64_converter_monotonicity_proptest((left, right) in (proptest::num::f64::NORMAL, proptest::num::f64::NORMAL)) {
|
||||
@@ -182,8 +177,10 @@ pub mod test {
|
||||
|
||||
#[test]
|
||||
fn test_f64_order() {
|
||||
assert!(!(f64_to_u64(f64::NEG_INFINITY)..f64_to_u64(f64::INFINITY))
|
||||
.contains(&f64_to_u64(f64::NAN))); // nan is not a number
|
||||
assert!(
|
||||
!(f64_to_u64(f64::NEG_INFINITY)..f64_to_u64(f64::INFINITY))
|
||||
.contains(&f64_to_u64(f64::NAN))
|
||||
); // nan is not a number
|
||||
assert!(f64_to_u64(1.5) > f64_to_u64(1.0)); // same exponent, different mantissa
|
||||
assert!(f64_to_u64(2.0) > f64_to_u64(1.0)); // same mantissa, different exponent
|
||||
assert!(f64_to_u64(2.0) > f64_to_u64(1.5)); // different exponent and mantissa
|
||||
|
||||
@@ -74,14 +74,14 @@ impl FixedSize for () {
|
||||
|
||||
impl<T: BinarySerializable> BinarySerializable for Vec<T> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.len() as u64).serialize(writer)?;
|
||||
BinarySerializable::serialize(&VInt(self.len() as u64), writer)?;
|
||||
for it in self {
|
||||
it.serialize(writer)?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Vec<T>> {
|
||||
let num_items = VInt::deserialize(reader)?.val();
|
||||
let num_items = <VInt as BinarySerializable>::deserialize(reader)?.val();
|
||||
let mut items: Vec<T> = Vec::with_capacity(num_items as usize);
|
||||
for _ in 0..num_items {
|
||||
let item = T::deserialize(reader)?;
|
||||
@@ -236,12 +236,12 @@ impl FixedSize for bool {
|
||||
impl BinarySerializable for String {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
let data: &[u8] = self.as_bytes();
|
||||
VInt(data.len() as u64).serialize(writer)?;
|
||||
BinarySerializable::serialize(&VInt(data.len() as u64), writer)?;
|
||||
writer.write_all(data)
|
||||
}
|
||||
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<String> {
|
||||
let string_length = VInt::deserialize(reader)?.val() as usize;
|
||||
let string_length = <VInt as BinarySerializable>::deserialize(reader)?.val() as usize;
|
||||
let mut result = String::with_capacity(string_length);
|
||||
reader
|
||||
.take(string_length as u64)
|
||||
@@ -253,12 +253,12 @@ impl BinarySerializable for String {
|
||||
impl<'a> BinarySerializable for Cow<'a, str> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
let data: &[u8] = self.as_bytes();
|
||||
VInt(data.len() as u64).serialize(writer)?;
|
||||
BinarySerializable::serialize(&VInt(data.len() as u64), writer)?;
|
||||
writer.write_all(data)
|
||||
}
|
||||
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Cow<'a, str>> {
|
||||
let string_length = VInt::deserialize(reader)?.val() as usize;
|
||||
let string_length = <VInt as BinarySerializable>::deserialize(reader)?.val() as usize;
|
||||
let mut result = String::with_capacity(string_length);
|
||||
reader
|
||||
.take(string_length as u64)
|
||||
@@ -269,18 +269,18 @@ impl<'a> BinarySerializable for Cow<'a, str> {
|
||||
|
||||
impl<'a> BinarySerializable for Cow<'a, [u8]> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.len() as u64).serialize(writer)?;
|
||||
BinarySerializable::serialize(&VInt(self.len() as u64), writer)?;
|
||||
for it in self.iter() {
|
||||
it.serialize(writer)?;
|
||||
BinarySerializable::serialize(it, writer)?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Cow<'a, [u8]>> {
|
||||
let num_items = VInt::deserialize(reader)?.val();
|
||||
let num_items = <VInt as BinarySerializable>::deserialize(reader)?.val();
|
||||
let mut items: Vec<u8> = Vec::with_capacity(num_items as usize);
|
||||
for _ in 0..num_items {
|
||||
let item = u8::deserialize(reader)?;
|
||||
let item = <u8 as BinarySerializable>::deserialize(reader)?;
|
||||
items.push(item);
|
||||
}
|
||||
Ok(Cow::Owned(items))
|
||||
|
||||
@@ -28,7 +28,9 @@ impl BinarySerializable for VIntU128 {
|
||||
writer.write_all(&buffer)
|
||||
}
|
||||
|
||||
#[allow(clippy::unbuffered_bytes)]
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
|
||||
#[allow(clippy::unbuffered_bytes)]
|
||||
let mut bytes = reader.bytes();
|
||||
let mut result = 0u128;
|
||||
let mut shift = 0u64;
|
||||
@@ -195,7 +197,9 @@ impl BinarySerializable for VInt {
|
||||
writer.write_all(&buffer[0..num_bytes])
|
||||
}
|
||||
|
||||
#[allow(clippy::unbuffered_bytes)]
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
|
||||
#[allow(clippy::unbuffered_bytes)]
|
||||
let mut bytes = reader.bytes();
|
||||
let mut result = 0u64;
|
||||
let mut shift = 0u64;
|
||||
@@ -222,7 +226,7 @@ impl BinarySerializable for VInt {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
|
||||
use super::{serialize_vint_u32, BinarySerializable, VInt};
|
||||
use super::{BinarySerializable, VInt, serialize_vint_u32};
|
||||
|
||||
fn aux_test_vint(val: u64) {
|
||||
let mut v = [14u8; 10];
|
||||
|
||||
@@ -87,7 +87,7 @@ impl<W: TerminatingWrite> TerminatingWrite for BufWriter<W> {
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> TerminatingWrite for &'a mut Vec<u8> {
|
||||
impl TerminatingWrite for &mut Vec<u8> {
|
||||
fn terminate_ref(&mut self, _a: AntiCallToken) -> io::Result<()> {
|
||||
self.flush()
|
||||
}
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 30 KiB After Width: | Height: | Size: 7.4 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 653 KiB |
@@ -2,7 +2,7 @@
|
||||
|
||||
> Tantivy is a **search** engine **library** for Rust.
|
||||
|
||||
If you are familiar with Lucene, it's an excellent approximation to consider tantivy as Lucene for rust. tantivy is heavily inspired by Lucene's design and
|
||||
If you are familiar with Lucene, it's an excellent approximation to consider tantivy as Lucene for Rust. Tantivy is heavily inspired by Lucene's design and
|
||||
they both have the same scope and targeted use cases.
|
||||
|
||||
If you are not familiar with Lucene, let's break down our little tagline.
|
||||
@@ -17,7 +17,7 @@ relevancy, collapsing, highlighting, spatial search.
|
||||
experience. But keep in mind this is just a toolbox.
|
||||
Which bring us to the second keyword...
|
||||
|
||||
- **Library** means that you will have to write code. tantivy is not an *all-in-one* server solution like elastic search for instance.
|
||||
- **Library** means that you will have to write code. Tantivy is not an *all-in-one* server solution like Elasticsearch for instance.
|
||||
|
||||
Sometimes a functionality will not be available in tantivy because it is too
|
||||
specific to your use case. By design, tantivy should make it possible to extend
|
||||
@@ -31,4 +31,4 @@ relevancy, collapsing, highlighting, spatial search.
|
||||
index from a different format.
|
||||
|
||||
Tantivy exposes a lot of low level API to do all of these things.
|
||||
|
||||
|
||||
|
||||
@@ -11,7 +11,7 @@ directory shipped with tantivy is the `MmapDirectory`.
|
||||
While this design has some downsides, this greatly simplifies the source code of
|
||||
tantivy. Caching is also entirely delegated to the OS.
|
||||
|
||||
`tantivy` works entirely (or almost) by directly reading the datastructures as they are laid on disk. As a result, the act of opening an indexing does not involve loading different datastructures from the disk into random access memory : starting a process, opening an index, and performing your first query can typically be done in a matter of milliseconds.
|
||||
Tantivy works entirely (or almost) by directly reading the datastructures as they are laid on disk. As a result, the act of opening an indexing does not involve loading different datastructures from the disk into random access memory : starting a process, opening an index, and performing your first query can typically be done in a matter of milliseconds.
|
||||
|
||||
This is an interesting property for a command line search engine, or for some multi-tenant log search engine : spawning a new process for each new query can be a perfectly sensible solution in some use case.
|
||||
|
||||
|
||||
@@ -31,13 +31,13 @@ Compression ratio is mainly affected on the fast field of the sorted property, e
|
||||
When data is presorted by a field and search queries request sorting by the same field, we can leverage the natural order of the documents.
|
||||
E.g. if the data is sorted by timestamp and want the top n newest docs containing a term, we can simply leveraging the order of the docids.
|
||||
|
||||
Note: Tantivy 0.16 does not do this optimization yet.
|
||||
Note: tantivy 0.16 does not do this optimization yet.
|
||||
|
||||
### Pruning
|
||||
|
||||
Let's say we want all documents and want to apply the filter `>= 2010-08-11`. When the data is sorted, we could make a lookup in the fast field to find the docid range and use this as the filter.
|
||||
|
||||
Note: Tantivy 0.16 does not do this optimization yet.
|
||||
Note: tantivy 0.16 does not do this optimization yet.
|
||||
|
||||
### Other?
|
||||
|
||||
@@ -45,7 +45,7 @@ In principle there are many algorithms possible that exploit the monotonically i
|
||||
|
||||
## Usage
|
||||
|
||||
The index sorting can be configured setting [`sort_by_field`](https://github.com/quickwit-oss/tantivy/blob/000d76b11a139a84b16b9b95060a1c93e8b9851c/src/core/index_meta.rs#L238) on `IndexSettings` and passing it to a `IndexBuilder`. As of Tantivy 0.16 only fast fields are allowed to be used.
|
||||
The index sorting can be configured setting [`sort_by_field`](https://github.com/quickwit-oss/tantivy/blob/000d76b11a139a84b16b9b95060a1c93e8b9851c/src/core/index_meta.rs#L238) on `IndexSettings` and passing it to a `IndexBuilder`. As of tantivy 0.16 only fast fields are allowed to be used.
|
||||
|
||||
```rust
|
||||
let settings = IndexSettings {
|
||||
|
||||
@@ -39,7 +39,7 @@ Its representation is done by separating segments by a unicode char `\x01`, and
|
||||
- `value`: The value representation is just the regular Value representation.
|
||||
|
||||
This representation is designed to align the natural sort of Terms with the lexicographical sort
|
||||
of their binary representation (Tantivy's dictionary (whether fst or sstable) is sorted and does prefix encoding).
|
||||
of their binary representation (tantivy's dictionary (whether fst or sstable) is sorted and does prefix encoding).
|
||||
|
||||
In the example above, the terms will be sorted as
|
||||
|
||||
|
||||
@@ -51,7 +51,7 @@ fn main() -> tantivy::Result<()> {
|
||||
|
||||
// Our second field is body.
|
||||
// We want full-text search for it, but we do not
|
||||
// need to be able to be able to retrieve it
|
||||
// need to be able to retrieve it
|
||||
// for our application.
|
||||
//
|
||||
// We can make our index lighter by omitting the `STORED` flag.
|
||||
@@ -208,7 +208,7 @@ fn main() -> tantivy::Result<()> {
|
||||
// is the role of the `TopDocs` collector.
|
||||
|
||||
// We can now perform our query.
|
||||
let top_docs = searcher.search(&query, &TopDocs::with_limit(10))?;
|
||||
let top_docs = searcher.search(&query, &TopDocs::with_limit(10).order_by_score())?;
|
||||
|
||||
// The actual documents still need to be
|
||||
// retrieved from Tantivy's store.
|
||||
@@ -226,7 +226,7 @@ fn main() -> tantivy::Result<()> {
|
||||
let query = query_parser.parse_query("title:sea^20 body:whale^70")?;
|
||||
|
||||
let (_score, doc_address) = searcher
|
||||
.search(&query, &TopDocs::with_limit(1))?
|
||||
.search(&query, &TopDocs::with_limit(1).order_by_score())?
|
||||
.into_iter()
|
||||
.next()
|
||||
.unwrap();
|
||||
|
||||
@@ -100,7 +100,7 @@ fn main() -> tantivy::Result<()> {
|
||||
// here we want to get a hit on the 'ken' in Frankenstein
|
||||
let query = query_parser.parse_query("ken")?;
|
||||
|
||||
let top_docs = searcher.search(&query, &TopDocs::with_limit(10))?;
|
||||
let top_docs = searcher.search(&query, &TopDocs::with_limit(10).order_by_score())?;
|
||||
|
||||
for (_, doc_address) in top_docs {
|
||||
let retrieved_doc: TantivyDocument = searcher.doc(doc_address)?;
|
||||
|
||||
@@ -50,14 +50,14 @@ fn main() -> tantivy::Result<()> {
|
||||
{
|
||||
// Simple exact search on the date
|
||||
let query = query_parser.parse_query("occurred_at:\"2022-06-22T12:53:50.53Z\"")?;
|
||||
let count_docs = searcher.search(&*query, &TopDocs::with_limit(5))?;
|
||||
let count_docs = searcher.search(&*query, &TopDocs::with_limit(5).order_by_score())?;
|
||||
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))?;
|
||||
let count_docs = searcher.search(&*query, &TopDocs::with_limit(4).order_by_score())?;
|
||||
assert_eq!(count_docs.len(), 1);
|
||||
for (_score, doc_address) in count_docs {
|
||||
let retrieved_doc = searcher.doc::<TantivyDocument>(doc_address)?;
|
||||
|
||||
@@ -28,7 +28,7 @@ fn extract_doc_given_isbn(
|
||||
// The second argument is here to tell we don't care about decoding positions,
|
||||
// or term frequencies.
|
||||
let term_query = TermQuery::new(isbn_term.clone(), IndexRecordOption::Basic);
|
||||
let top_docs = searcher.search(&term_query, &TopDocs::with_limit(1))?;
|
||||
let top_docs = searcher.search(&term_query, &TopDocs::with_limit(1).order_by_score())?;
|
||||
|
||||
if let Some((_score, doc_address)) = top_docs.first() {
|
||||
let doc = searcher.doc(*doc_address)?;
|
||||
|
||||
212
examples/filter_aggregation.rs
Normal file
212
examples/filter_aggregation.rs
Normal file
@@ -0,0 +1,212 @@
|
||||
// # Filter Aggregation Example
|
||||
//
|
||||
// This example demonstrates filter aggregations - creating buckets of documents
|
||||
// matching specific queries, with nested aggregations computed on each bucket.
|
||||
//
|
||||
// Filter aggregations are useful for computing metrics on different subsets of
|
||||
// your data in a single query, like "average price overall + average price for
|
||||
// electronics + count of in-stock items".
|
||||
|
||||
use serde_json::json;
|
||||
use tantivy::aggregation::agg_req::Aggregations;
|
||||
use tantivy::aggregation::AggregationCollector;
|
||||
use tantivy::query::AllQuery;
|
||||
use tantivy::schema::{Schema, FAST, INDEXED, TEXT};
|
||||
use tantivy::{doc, Index};
|
||||
|
||||
fn main() -> tantivy::Result<()> {
|
||||
// Create a simple product schema
|
||||
let mut schema_builder = Schema::builder();
|
||||
schema_builder.add_text_field("category", TEXT | FAST);
|
||||
schema_builder.add_text_field("brand", TEXT | FAST);
|
||||
schema_builder.add_u64_field("price", FAST);
|
||||
schema_builder.add_f64_field("rating", FAST);
|
||||
schema_builder.add_bool_field("in_stock", FAST | INDEXED);
|
||||
let schema = schema_builder.build();
|
||||
|
||||
// Create index and add sample products
|
||||
let index = Index::create_in_ram(schema.clone());
|
||||
let mut writer = index.writer(50_000_000)?;
|
||||
|
||||
writer.add_document(doc!(
|
||||
schema.get_field("category")? => "electronics",
|
||||
schema.get_field("brand")? => "apple",
|
||||
schema.get_field("price")? => 999u64,
|
||||
schema.get_field("rating")? => 4.5f64,
|
||||
schema.get_field("in_stock")? => true
|
||||
))?;
|
||||
writer.add_document(doc!(
|
||||
schema.get_field("category")? => "electronics",
|
||||
schema.get_field("brand")? => "samsung",
|
||||
schema.get_field("price")? => 799u64,
|
||||
schema.get_field("rating")? => 4.2f64,
|
||||
schema.get_field("in_stock")? => true
|
||||
))?;
|
||||
writer.add_document(doc!(
|
||||
schema.get_field("category")? => "clothing",
|
||||
schema.get_field("brand")? => "nike",
|
||||
schema.get_field("price")? => 120u64,
|
||||
schema.get_field("rating")? => 4.1f64,
|
||||
schema.get_field("in_stock")? => false
|
||||
))?;
|
||||
writer.add_document(doc!(
|
||||
schema.get_field("category")? => "books",
|
||||
schema.get_field("brand")? => "penguin",
|
||||
schema.get_field("price")? => 25u64,
|
||||
schema.get_field("rating")? => 4.8f64,
|
||||
schema.get_field("in_stock")? => true
|
||||
))?;
|
||||
|
||||
writer.commit()?;
|
||||
|
||||
let reader = index.reader()?;
|
||||
let searcher = reader.searcher();
|
||||
|
||||
// Example 1: Basic filter with metric aggregation
|
||||
println!("=== Example 1: Electronics average price ===");
|
||||
let agg_req = json!({
|
||||
"electronics": {
|
||||
"filter": "category:electronics",
|
||||
"aggs": {
|
||||
"avg_price": { "avg": { "field": "price" } }
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
let agg: Aggregations = serde_json::from_value(agg_req)?;
|
||||
let collector = AggregationCollector::from_aggs(agg, Default::default());
|
||||
let result = searcher.search(&AllQuery, &collector)?;
|
||||
|
||||
let expected = json!({
|
||||
"electronics": {
|
||||
"doc_count": 2,
|
||||
"avg_price": { "value": 899.0 }
|
||||
}
|
||||
});
|
||||
assert_eq!(serde_json::to_value(&result)?, expected);
|
||||
println!("{}\n", serde_json::to_string_pretty(&result)?);
|
||||
|
||||
// Example 2: Multiple independent filters
|
||||
println!("=== Example 2: Multiple filters in one query ===");
|
||||
let agg_req = json!({
|
||||
"electronics": {
|
||||
"filter": "category:electronics",
|
||||
"aggs": { "avg_price": { "avg": { "field": "price" } } }
|
||||
},
|
||||
"in_stock": {
|
||||
"filter": "in_stock:true",
|
||||
"aggs": { "count": { "value_count": { "field": "brand" } } }
|
||||
},
|
||||
"high_rated": {
|
||||
"filter": "rating:[4.5 TO *]",
|
||||
"aggs": { "count": { "value_count": { "field": "brand" } } }
|
||||
}
|
||||
});
|
||||
|
||||
let agg: Aggregations = serde_json::from_value(agg_req)?;
|
||||
let collector = AggregationCollector::from_aggs(agg, Default::default());
|
||||
let result = searcher.search(&AllQuery, &collector)?;
|
||||
|
||||
let expected = json!({
|
||||
"electronics": {
|
||||
"doc_count": 2,
|
||||
"avg_price": { "value": 899.0 }
|
||||
},
|
||||
"in_stock": {
|
||||
"doc_count": 3,
|
||||
"count": { "value": 3.0 }
|
||||
},
|
||||
"high_rated": {
|
||||
"doc_count": 2,
|
||||
"count": { "value": 2.0 }
|
||||
}
|
||||
});
|
||||
assert_eq!(serde_json::to_value(&result)?, expected);
|
||||
println!("{}\n", serde_json::to_string_pretty(&result)?);
|
||||
|
||||
// Example 3: Nested filters - progressive refinement
|
||||
println!("=== Example 3: Nested filters ===");
|
||||
let agg_req = json!({
|
||||
"in_stock": {
|
||||
"filter": "in_stock:true",
|
||||
"aggs": {
|
||||
"electronics": {
|
||||
"filter": "category:electronics",
|
||||
"aggs": {
|
||||
"expensive": {
|
||||
"filter": "price:[800 TO *]",
|
||||
"aggs": {
|
||||
"avg_rating": { "avg": { "field": "rating" } }
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
let agg: Aggregations = serde_json::from_value(agg_req)?;
|
||||
let collector = AggregationCollector::from_aggs(agg, Default::default());
|
||||
let result = searcher.search(&AllQuery, &collector)?;
|
||||
|
||||
let expected = json!({
|
||||
"in_stock": {
|
||||
"doc_count": 3, // apple, samsung, penguin
|
||||
"electronics": {
|
||||
"doc_count": 2, // apple, samsung
|
||||
"expensive": {
|
||||
"doc_count": 1, // only apple (999)
|
||||
"avg_rating": { "value": 4.5 }
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
assert_eq!(serde_json::to_value(&result)?, expected);
|
||||
println!("{}\n", serde_json::to_string_pretty(&result)?);
|
||||
|
||||
// Example 4: Filter with sub-aggregation (terms)
|
||||
println!("=== Example 4: Filter with terms sub-aggregation ===");
|
||||
let agg_req = json!({
|
||||
"electronics": {
|
||||
"filter": "category:electronics",
|
||||
"aggs": {
|
||||
"by_brand": {
|
||||
"terms": { "field": "brand" },
|
||||
"aggs": {
|
||||
"avg_price": { "avg": { "field": "price" } }
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
let agg: Aggregations = serde_json::from_value(agg_req)?;
|
||||
let collector = AggregationCollector::from_aggs(agg, Default::default());
|
||||
let result = searcher.search(&AllQuery, &collector)?;
|
||||
|
||||
let expected = json!({
|
||||
"electronics": {
|
||||
"doc_count": 2,
|
||||
"by_brand": {
|
||||
"buckets": [
|
||||
{
|
||||
"key": "samsung",
|
||||
"doc_count": 1,
|
||||
"avg_price": { "value": 799.0 }
|
||||
},
|
||||
{
|
||||
"key": "apple",
|
||||
"doc_count": 1,
|
||||
"avg_price": { "value": 999.0 }
|
||||
}
|
||||
],
|
||||
"sum_other_doc_count": 0,
|
||||
"doc_count_error_upper_bound": 0
|
||||
}
|
||||
}
|
||||
});
|
||||
assert_eq!(serde_json::to_value(&result)?, expected);
|
||||
println!("{}", serde_json::to_string_pretty(&result)?);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user