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111f25a8f7 | ||
|
|
019db10e8e | ||
|
|
7423f99719 | ||
|
|
f2f38c43ce | ||
|
|
71f43ace1d | ||
|
|
347614c841 | ||
|
|
097fd6138d | ||
|
|
01e5a22759 | ||
|
|
b60b7d2afe | ||
|
|
dfe4e95fde | ||
|
|
60cc2644d6 | ||
|
|
10bccac61b | ||
|
|
1cfb9ce59a | ||
|
|
539ff08a79 | ||
|
|
dab93df94e | ||
|
|
3120147a76 | ||
|
|
cbcafae04c |
15
.github/workflows/coverage.yml
vendored
15
.github/workflows/coverage.yml
vendored
@@ -2,21 +2,24 @@ name: Coverage
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ main ]
|
||||
pull_request:
|
||||
branches: [ main ]
|
||||
branches: [main]
|
||||
|
||||
# Ensures that we cancel running jobs for the same PR / same workflow.
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
coverage:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- uses: actions/checkout@v4
|
||||
- name: Install Rust
|
||||
run: rustup toolchain install nightly --profile minimal --component llvm-tools-preview
|
||||
run: rustup toolchain install nightly-2024-07-01 --profile minimal --component llvm-tools-preview
|
||||
- uses: Swatinem/rust-cache@v2
|
||||
- uses: taiki-e/install-action@cargo-llvm-cov
|
||||
- name: Generate code coverage
|
||||
run: cargo +nightly llvm-cov --all-features --workspace --lcov --output-path lcov.info
|
||||
run: cargo +nightly-2024-07-01 llvm-cov --all-features --workspace --doctests --lcov --output-path lcov.info
|
||||
- name: Upload coverage to Codecov
|
||||
uses: codecov/codecov-action@v3
|
||||
continue-on-error: true
|
||||
|
||||
7
.github/workflows/long_running.yml
vendored
7
.github/workflows/long_running.yml
vendored
@@ -8,13 +8,18 @@ env:
|
||||
CARGO_TERM_COLOR: always
|
||||
NUM_FUNCTIONAL_TEST_ITERATIONS: 20000
|
||||
|
||||
# Ensures that we cancel running jobs for the same PR / same workflow.
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
test:
|
||||
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- uses: actions/checkout@v4
|
||||
- name: Install stable
|
||||
uses: actions-rs/toolchain@v1
|
||||
with:
|
||||
|
||||
18
.github/workflows/test.yml
vendored
18
.github/workflows/test.yml
vendored
@@ -9,13 +9,18 @@ on:
|
||||
env:
|
||||
CARGO_TERM_COLOR: always
|
||||
|
||||
# Ensures that we cancel running jobs for the same PR / same workflow.
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
check:
|
||||
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Install nightly
|
||||
uses: actions-rs/toolchain@v1
|
||||
@@ -34,6 +39,13 @@ jobs:
|
||||
|
||||
- name: Check Formatting
|
||||
run: cargo +nightly fmt --all -- --check
|
||||
|
||||
- name: Check Stable Compilation
|
||||
run: cargo build --all-features
|
||||
|
||||
|
||||
- name: Check Bench Compilation
|
||||
run: cargo +nightly bench --no-run --profile=dev --all-features
|
||||
|
||||
- uses: actions-rs/clippy-check@v1
|
||||
with:
|
||||
@@ -48,14 +60,14 @@ jobs:
|
||||
strategy:
|
||||
matrix:
|
||||
features: [
|
||||
{ label: "all", flags: "mmap,stopwords,brotli-compression,lz4-compression,snappy-compression,zstd-compression,failpoints" },
|
||||
{ label: "all", flags: "mmap,stopwords,lz4-compression,zstd-compression,failpoints" },
|
||||
{ label: "quickwit", flags: "mmap,quickwit,failpoints" }
|
||||
]
|
||||
|
||||
name: test-${{ matrix.features.label}}
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Install stable
|
||||
uses: actions-rs/toolchain@v1
|
||||
|
||||
2
.gitignore
vendored
2
.gitignore
vendored
@@ -13,3 +13,5 @@ benchmark
|
||||
.idea
|
||||
trace.dat
|
||||
cargo-timing*
|
||||
control
|
||||
variable
|
||||
|
||||
@@ -46,7 +46,7 @@ The file of a segment has the format
|
||||
|
||||
```segment-id . ext```
|
||||
|
||||
The extension signals which data structure (or [`SegmentComponent`](src/core/segment_component.rs)) is stored in the file.
|
||||
The extension signals which data structure (or [`SegmentComponent`](src/index/segment_component.rs)) is stored in the file.
|
||||
|
||||
A small `meta.json` file is in charge of keeping track of the list of segments, as well as the schema.
|
||||
|
||||
@@ -102,7 +102,7 @@ but users can extend tantivy with their own implementation.
|
||||
|
||||
Tantivy's document follows a very strict schema, decided before building any index.
|
||||
|
||||
The schema defines all of the fields that the indexes [`Document`](src/schema/document.rs) may and should contain, their types (`text`, `i64`, `u64`, `Date`, ...) as well as how it should be indexed / represented in tantivy.
|
||||
The schema defines all of the fields that the indexes [`Document`](src/schema/document/mod.rs) may and should contain, their types (`text`, `i64`, `u64`, `Date`, ...) as well as how it should be indexed / represented in tantivy.
|
||||
|
||||
Depending on the type of the field, you can decide to
|
||||
|
||||
@@ -254,7 +254,7 @@ The token positions of all of the terms are then stored in a separate file with
|
||||
The [TermInfo](src/postings/term_info.rs) gives an offset (expressed in position this time) in this file. As we iterate through the docset,
|
||||
we advance the position reader by the number of term frequencies of the current document.
|
||||
|
||||
## [fieldnorms/](src/fieldnorms): Here is my doc, how many tokens in this field?
|
||||
## [fieldnorm/](src/fieldnorm): Here is my doc, how many tokens in this field?
|
||||
|
||||
The [BM25](https://en.wikipedia.org/wiki/Okapi_BM25) formula also requires to know the number of tokens stored in a specific field for a given document. We store this information on one byte per document in the fieldnorm.
|
||||
The fieldnorm is therefore compressed. Values up to 40 are encoded unchanged.
|
||||
|
||||
286
CHANGELOG.md
286
CHANGELOG.md
@@ -1,3 +1,285 @@
|
||||
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
|
||||
================================
|
||||
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)
|
||||
|
||||
- **Performace/Memory**
|
||||
- lift clauses in LogicalAst for optimized ast during execution [#2449](https://github.com/quickwit-oss/tantivy/pull/2449)(@PSeitz)
|
||||
- Use Vec instead of BTreeMap to back OwnedValue object [#2364](https://github.com/quickwit-oss/tantivy/pull/2364)(@fulmicoton)
|
||||
- Replace TantivyDocument with CompactDoc. CompactDoc is much smaller and provides similar performance. [#2402](https://github.com/quickwit-oss/tantivy/pull/2402)(@PSeitz)
|
||||
- Recycling buffer in PrefixPhraseScorer [#2443](https://github.com/quickwit-oss/tantivy/pull/2443)(@fulmicoton)
|
||||
|
||||
- **Json Type**
|
||||
- JSON supports now all values on the root level. Previously an object was required. This enables support for flat mixed types. allow more JSON values, fix i64 special case [#2383](https://github.com/quickwit-oss/tantivy/pull/2383)(@PSeitz)
|
||||
- add json path constructor to term [#2367](https://github.com/quickwit-oss/tantivy/pull/2367)(@PSeitz)
|
||||
|
||||
- **QueryParser**
|
||||
- fix de-escaping too much in query parser [#2427](https://github.com/quickwit-oss/tantivy/pull/2427)(@trinity-1686a)
|
||||
- improve query parser [#2416](https://github.com/quickwit-oss/tantivy/pull/2416)(@trinity-1686a)
|
||||
- Support field grouping `title:(return AND "pink panther")` [#2333](https://github.com/quickwit-oss/tantivy/pull/2333)(@trinity-1686a)
|
||||
- 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
|
||||
================================
|
||||
|
||||
Tantivy 0.22 will be able to read indices created with Tantivy 0.21.
|
||||
|
||||
#### Bugfixes
|
||||
- Fix null byte handling in JSON paths (null bytes in json keys caused panic during indexing) [#2345](https://github.com/quickwit-oss/tantivy/pull/2345)(@PSeitz)
|
||||
- Fix bug that can cause `get_docids_for_value_range` to panic. [#2295](https://github.com/quickwit-oss/tantivy/pull/2295)(@fulmicoton)
|
||||
- Avoid 1 document indices by increase min memory to 15MB for indexing [#2176](https://github.com/quickwit-oss/tantivy/pull/2176)(@PSeitz)
|
||||
- Fix merge panic for JSON fields [#2284](https://github.com/quickwit-oss/tantivy/pull/2284)(@PSeitz)
|
||||
- Fix bug occurring when merging JSON object indexed with positions. [#2253](https://github.com/quickwit-oss/tantivy/pull/2253)(@fulmicoton)
|
||||
- Fix empty DateHistogram gap bug [#2183](https://github.com/quickwit-oss/tantivy/pull/2183)(@PSeitz)
|
||||
- Fix range query end check (fields with less than 1 value per doc are affected) [#2226](https://github.com/quickwit-oss/tantivy/pull/2226)(@PSeitz)
|
||||
- Handle exclusive out of bounds ranges on fastfield range queries [#2174](https://github.com/quickwit-oss/tantivy/pull/2174)(@PSeitz)
|
||||
|
||||
#### Breaking API Changes
|
||||
- rename ReloadPolicy onCommit to onCommitWithDelay [#2235](https://github.com/quickwit-oss/tantivy/pull/2235)(@giovannicuccu)
|
||||
- Move exports from the root into modules [#2220](https://github.com/quickwit-oss/tantivy/pull/2220)(@PSeitz)
|
||||
- Accept field name instead of `Field` in FilterCollector [#2196](https://github.com/quickwit-oss/tantivy/pull/2196)(@PSeitz)
|
||||
- remove deprecated IntOptions and DateTime [#2353](https://github.com/quickwit-oss/tantivy/pull/2353)(@PSeitz)
|
||||
|
||||
#### Features/Improvements
|
||||
- Tantivy documents as a trait: Index data directly without converting to tantivy types first [#2071](https://github.com/quickwit-oss/tantivy/pull/2071)(@ChillFish8)
|
||||
- encode some part of posting list as -1 instead of direct values (smaller inverted indices) [#2185](https://github.com/quickwit-oss/tantivy/pull/2185)(@trinity-1686a)
|
||||
- **Aggregation**
|
||||
- Support to deserialize f64 from string [#2311](https://github.com/quickwit-oss/tantivy/pull/2311)(@PSeitz)
|
||||
- Add a top_hits aggregator [#2198](https://github.com/quickwit-oss/tantivy/pull/2198)(@ditsuke)
|
||||
- Support bool type in term aggregation [#2318](https://github.com/quickwit-oss/tantivy/pull/2318)(@PSeitz)
|
||||
- Support ip addresses in term aggregation [#2319](https://github.com/quickwit-oss/tantivy/pull/2319)(@PSeitz)
|
||||
- Support date type in term aggregation [#2172](https://github.com/quickwit-oss/tantivy/pull/2172)(@PSeitz)
|
||||
- Support escaped dot when addressing field [#2250](https://github.com/quickwit-oss/tantivy/pull/2250)(@PSeitz)
|
||||
|
||||
- Add ExistsQuery to check documents that have a value [#2160](https://github.com/quickwit-oss/tantivy/pull/2160)(@imotov)
|
||||
- Expose TopDocs::order_by_u64_field again [#2282](https://github.com/quickwit-oss/tantivy/pull/2282)(@ditsuke)
|
||||
|
||||
- **Memory/Performance**
|
||||
- Faster TopN: replace BinaryHeap with TopNComputer [#2186](https://github.com/quickwit-oss/tantivy/pull/2186)(@PSeitz)
|
||||
- reduce number of allocations during indexing [#2257](https://github.com/quickwit-oss/tantivy/pull/2257)(@PSeitz)
|
||||
- Less Memory while indexing: docid deltas while indexing [#2249](https://github.com/quickwit-oss/tantivy/pull/2249)(@PSeitz)
|
||||
- Faster indexing: use term hashmap in fastfield [#2243](https://github.com/quickwit-oss/tantivy/pull/2243)(@PSeitz)
|
||||
- term hashmap remove copy in is_empty, unused unordered_id [#2229](https://github.com/quickwit-oss/tantivy/pull/2229)(@PSeitz)
|
||||
- add method to fetch block of first values in columnar [#2330](https://github.com/quickwit-oss/tantivy/pull/2330)(@PSeitz)
|
||||
- Faster aggregations: add fast path for full columns in fetch_block [#2328](https://github.com/quickwit-oss/tantivy/pull/2328)(@PSeitz)
|
||||
- Faster sstable loading: use fst for sstable index [#2268](https://github.com/quickwit-oss/tantivy/pull/2268)(@trinity-1686a)
|
||||
|
||||
- **QueryParser**
|
||||
- allow newline where we allow space in query parser [#2302](https://github.com/quickwit-oss/tantivy/pull/2302)(@trinity-1686a)
|
||||
- allow some mixing of occur and bool in strict query parser [#2323](https://github.com/quickwit-oss/tantivy/pull/2323)(@trinity-1686a)
|
||||
- handle * inside term in lenient query parser [#2228](https://github.com/quickwit-oss/tantivy/pull/2228)(@trinity-1686a)
|
||||
- add support for exists query syntax in query parser [#2170](https://github.com/quickwit-oss/tantivy/pull/2170)(@trinity-1686a)
|
||||
- Add shared search executor [#2312](https://github.com/quickwit-oss/tantivy/pull/2312)(@MochiXu)
|
||||
- Truncate keys to u16::MAX in term hashmap [#2299](https://github.com/quickwit-oss/tantivy/pull/2299)(@PSeitz)
|
||||
- report if a term matched when warming up posting list [#2309](https://github.com/quickwit-oss/tantivy/pull/2309)(@trinity-1686a)
|
||||
- Support json fields in FuzzyTermQuery [#2173](https://github.com/quickwit-oss/tantivy/pull/2173)(@PingXia-at)
|
||||
- Read list of fields encoded in term dictionary for JSON fields [#2184](https://github.com/quickwit-oss/tantivy/pull/2184)(@PSeitz)
|
||||
- add collect_block to BoxableSegmentCollector [#2331](https://github.com/quickwit-oss/tantivy/pull/2331)(@PSeitz)
|
||||
- expose collect_block buffer size [#2326](https://github.com/quickwit-oss/tantivy/pull/2326)(@PSeitz)
|
||||
- Forward regex parser errors [#2288](https://github.com/quickwit-oss/tantivy/pull/2288)(@adamreichold)
|
||||
- Make FacetCounts defaultable and cloneable. [#2322](https://github.com/quickwit-oss/tantivy/pull/2322)(@adamreichold)
|
||||
- Derive Debug for SchemaBuilder [#2254](https://github.com/quickwit-oss/tantivy/pull/2254)(@GodTamIt)
|
||||
- add missing inlines to tantivy options [#2245](https://github.com/quickwit-oss/tantivy/pull/2245)(@PSeitz)
|
||||
|
||||
Tantivy 0.21.1
|
||||
================================
|
||||
#### Bugfixes
|
||||
- Range queries on fast fields with less values on that field than documents had an invalid end condition, leading to missing results. [#2226](https://github.com/quickwit-oss/tantivy/issues/2226)(@appaquet @PSeitz)
|
||||
- Increase the minimum memory budget from 3MB to 15MB to avoid single doc segments (API fix). [#2176](https://github.com/quickwit-oss/tantivy/issues/2176)(@PSeitz)
|
||||
|
||||
Tantivy 0.21
|
||||
================================
|
||||
#### Bugfixes
|
||||
- Fix track fast field memory consumption, which led to higher memory consumption than the budget allowed during indexing [#2148](https://github.com/quickwit-oss/tantivy/issues/2148)[#2147](https://github.com/quickwit-oss/tantivy/issues/2147)(@PSeitz)
|
||||
- Fix a regression from 0.20 where sort index by date wasn't working anymore [#2124](https://github.com/quickwit-oss/tantivy/issues/2124)(@PSeitz)
|
||||
- Fix getting the root facet on the `FacetCollector`. [#2086](https://github.com/quickwit-oss/tantivy/issues/2086)(@adamreichold)
|
||||
- Align numerical type priority order of columnar and query. [#2088](https://github.com/quickwit-oss/tantivy/issues/2088)(@fmassot)
|
||||
#### Breaking Changes
|
||||
- Remove support for Brotli and Snappy compression [#2123](https://github.com/quickwit-oss/tantivy/issues/2123)(@adamreichold)
|
||||
#### Features/Improvements
|
||||
- Implement lenient query parser [#2129](https://github.com/quickwit-oss/tantivy/pull/2129)(@trinity-1686a)
|
||||
- order_by_u64_field and order_by_fast_field allow sorting in ascending and descending order [#2111](https://github.com/quickwit-oss/tantivy/issues/2111)(@naveenann)
|
||||
- Allow dynamic filters in text analyzer builder [#2110](https://github.com/quickwit-oss/tantivy/issues/2110)(@fulmicoton @fmassot)
|
||||
- **Aggregation**
|
||||
- Add missing parameter for term aggregation [#2149](https://github.com/quickwit-oss/tantivy/issues/2149)[#2103](https://github.com/quickwit-oss/tantivy/issues/2103)(@PSeitz)
|
||||
- Add missing parameter for percentiles [#2157](https://github.com/quickwit-oss/tantivy/issues/2157)(@PSeitz)
|
||||
- Add missing parameter for stats,min,max,count,sum,avg [#2151](https://github.com/quickwit-oss/tantivy/issues/2151)(@PSeitz)
|
||||
- Improve aggregation deserialization error message [#2150](https://github.com/quickwit-oss/tantivy/issues/2150)(@PSeitz)
|
||||
- Add validation for type Bytes to term_agg [#2077](https://github.com/quickwit-oss/tantivy/issues/2077)(@PSeitz)
|
||||
- Alternative mixed field collection [#2135](https://github.com/quickwit-oss/tantivy/issues/2135)(@PSeitz)
|
||||
- Add missing query_terms impl for TermSetQuery. [#2120](https://github.com/quickwit-oss/tantivy/issues/2120)(@adamreichold)
|
||||
- Minor improvements to OwnedBytes [#2134](https://github.com/quickwit-oss/tantivy/issues/2134)(@adamreichold)
|
||||
- Remove allocations in split compound words [#2080](https://github.com/quickwit-oss/tantivy/issues/2080)(@PSeitz)
|
||||
- Ngram tokenizer now returns an error with invalid arguments [#2102](https://github.com/quickwit-oss/tantivy/issues/2102)(@fmassot)
|
||||
- Make TextAnalyzerBuilder public [#2097](https://github.com/quickwit-oss/tantivy/issues/2097)(@adamreichold)
|
||||
- Return an error when tokenizer is not found while indexing [#2093](https://github.com/quickwit-oss/tantivy/issues/2093)(@naveenann)
|
||||
- Delayed column opening during merge [#2132](https://github.com/quickwit-oss/tantivy/issues/2132)(@PSeitz)
|
||||
|
||||
Tantivy 0.20.2
|
||||
================================
|
||||
- Align numerical type priority order on the search side. [#2088](https://github.com/quickwit-oss/tantivy/issues/2088) (@fmassot)
|
||||
- Fix is_child_of function not considering the root facet. [#2086](https://github.com/quickwit-oss/tantivy/issues/2086) (@adamreichhold)
|
||||
|
||||
Tantivy 0.20.1
|
||||
================================
|
||||
- Fix building on windows with mmap [#2070](https://github.com/quickwit-oss/tantivy/issues/2070) (@ChillFish8)
|
||||
|
||||
Tantivy 0.20
|
||||
================================
|
||||
#### Bugfixes
|
||||
- Fix phrase queries with slop (slop supports now transpositions, algorithm that carries slop so far for num terms > 2) [#2031](https://github.com/quickwit-oss/tantivy/issues/2031)[#2020](https://github.com/quickwit-oss/tantivy/issues/2020)(@PSeitz)
|
||||
- Handle error for exists on MMapDirectory [#1988](https://github.com/quickwit-oss/tantivy/issues/1988) (@PSeitz)
|
||||
- Aggregation
|
||||
- Fix min doc_count empty merge bug [#2057](https://github.com/quickwit-oss/tantivy/issues/2057) (@PSeitz)
|
||||
- Fix: Sort order for term aggregations (sort order on key was inverted) [#1858](https://github.com/quickwit-oss/tantivy/issues/1858) (@PSeitz)
|
||||
|
||||
#### Features/Improvements
|
||||
- Add PhrasePrefixQuery [#1842](https://github.com/quickwit-oss/tantivy/issues/1842) (@trinity-1686a)
|
||||
- Add `coerce` option for text and numbers types (convert the value instead of returning an error during indexing) [#1904](https://github.com/quickwit-oss/tantivy/issues/1904) (@PSeitz)
|
||||
- Add regex tokenizer [#1759](https://github.com/quickwit-oss/tantivy/issues/1759)(@mkleen)
|
||||
- Move tokenizer API to separate crate. Having a separate crate with a stable API will allow us to use tokenizers with different tantivy versions. [#1767](https://github.com/quickwit-oss/tantivy/issues/1767) (@PSeitz)
|
||||
- **Columnar crate**: New fast field handling (@fulmicoton @PSeitz) [#1806](https://github.com/quickwit-oss/tantivy/issues/1806)[#1809](https://github.com/quickwit-oss/tantivy/issues/1809)
|
||||
- Support for fast fields with optional values. Previously tantivy supported only single-valued and multi-value fast fields. The encoding of optional fast fields is now very compact.
|
||||
- Fast field Support for JSON (schemaless fast fields). Support multiple types on the same column. [#1876](https://github.com/quickwit-oss/tantivy/issues/1876) (@fulmicoton)
|
||||
- Unified access for fast fields over different cardinalities.
|
||||
- Unified storage for typed and untyped fields.
|
||||
- Move fastfield codecs into columnar. [#1782](https://github.com/quickwit-oss/tantivy/issues/1782) (@fulmicoton)
|
||||
- Sparse dense index for optional values [#1716](https://github.com/quickwit-oss/tantivy/issues/1716) (@PSeitz)
|
||||
- Switch to nanosecond precision in DateTime fastfield [#2016](https://github.com/quickwit-oss/tantivy/issues/2016) (@PSeitz)
|
||||
- **Aggregation**
|
||||
- Add `date_histogram` aggregation (only `fixed_interval` for now) [#1900](https://github.com/quickwit-oss/tantivy/issues/1900) (@PSeitz)
|
||||
- Add `percentiles` aggregations [#1984](https://github.com/quickwit-oss/tantivy/issues/1984) (@PSeitz)
|
||||
- [**breaking**] Drop JSON support on intermediate agg result (we use postcard as format in `quickwit` to send intermediate results) [#1992](https://github.com/quickwit-oss/tantivy/issues/1992) (@PSeitz)
|
||||
- Set memory limit in bytes for aggregations after which they abort (Previously there was only the bucket limit) [#1942](https://github.com/quickwit-oss/tantivy/issues/1942)[#1957](https://github.com/quickwit-oss/tantivy/issues/1957)(@PSeitz)
|
||||
- Add support for u64,i64,f64 fields in term aggregation [#1883](https://github.com/quickwit-oss/tantivy/issues/1883) (@PSeitz)
|
||||
- Allow histogram bounds to be passed as Rfc3339 [#2076](https://github.com/quickwit-oss/tantivy/issues/2076) (@PSeitz)
|
||||
- Add count, min, max, and sum aggregations [#1794](https://github.com/quickwit-oss/tantivy/issues/1794) (@guilload)
|
||||
- Switch to Aggregation without serde_untagged => better deserialization errors. [#2003](https://github.com/quickwit-oss/tantivy/issues/2003) (@PSeitz)
|
||||
- Switch to ms in histogram for date type (ES compatibility) [#2045](https://github.com/quickwit-oss/tantivy/issues/2045) (@PSeitz)
|
||||
- Reduce term aggregation memory consumption [#2013](https://github.com/quickwit-oss/tantivy/issues/2013) (@PSeitz)
|
||||
- Reduce agg memory consumption: Replace generic aggregation collector (which has a high memory requirement per instance) in aggregation tree with optimized versions behind a trait.
|
||||
- Split term collection count and sub_agg (Faster term agg with less memory consumption for cases without sub-aggs) [#1921](https://github.com/quickwit-oss/tantivy/issues/1921) (@PSeitz)
|
||||
- Schemaless aggregations: In combination with stacker tantivy supports now schemaless aggregations via the JSON type.
|
||||
- Add aggregation support for JSON type [#1888](https://github.com/quickwit-oss/tantivy/issues/1888) (@PSeitz)
|
||||
- Mixed types support on JSON fields in aggs [#1971](https://github.com/quickwit-oss/tantivy/issues/1971) (@PSeitz)
|
||||
- Perf: Fetch blocks of vals in aggregation for all cardinality [#1950](https://github.com/quickwit-oss/tantivy/issues/1950) (@PSeitz)
|
||||
- Allow histogram bounds to be passed as Rfc3339 [#2076](https://github.com/quickwit-oss/tantivy/issues/2076) (@PSeitz)
|
||||
- `Searcher` with disabled scoring via `EnableScoring::Disabled` [#1780](https://github.com/quickwit-oss/tantivy/issues/1780) (@shikhar)
|
||||
- Enable tokenizer on json fields [#2053](https://github.com/quickwit-oss/tantivy/issues/2053) (@PSeitz)
|
||||
- Enforcing "NOT" and "-" queries consistency in UserInputAst [#1609](https://github.com/quickwit-oss/tantivy/issues/1609) (@bazhenov)
|
||||
- Faster indexing
|
||||
- Refactor tokenization pipeline to use GATs [#1924](https://github.com/quickwit-oss/tantivy/issues/1924) (@trinity-1686a)
|
||||
- Faster term hash map [#2058](https://github.com/quickwit-oss/tantivy/issues/2058)[#1940](https://github.com/quickwit-oss/tantivy/issues/1940) (@PSeitz)
|
||||
- tokenizer-api: reduce Tokenizer allocation overhead [#2062](https://github.com/quickwit-oss/tantivy/issues/2062) (@PSeitz)
|
||||
- Refactor vint [#2010](https://github.com/quickwit-oss/tantivy/issues/2010) (@PSeitz)
|
||||
- Faster search
|
||||
- Work in batches of docs on the SegmentCollector (Only for cases without score for now) [#1937](https://github.com/quickwit-oss/tantivy/issues/1937) (@PSeitz)
|
||||
- Faster fast field range queries using SIMD [#1954](https://github.com/quickwit-oss/tantivy/issues/1954) (@fulmicoton)
|
||||
- Improve fast field range query performance [#1864](https://github.com/quickwit-oss/tantivy/issues/1864) (@PSeitz)
|
||||
- Make BM25 scoring more flexible [#1855](https://github.com/quickwit-oss/tantivy/issues/1855) (@alexcole)
|
||||
- Switch fs2 to fs4 as it is now unmaintained and does not support illumos [#1944](https://github.com/quickwit-oss/tantivy/issues/1944) (@Toasterson)
|
||||
- Made BooleanWeight and BoostWeight public [#1991](https://github.com/quickwit-oss/tantivy/issues/1991) (@fulmicoton)
|
||||
- Make index compatible with virtual drives on Windows [#1843](https://github.com/quickwit-oss/tantivy/issues/1843) (@gyk)
|
||||
- Add stop words for Hungarian language [#2069](https://github.com/quickwit-oss/tantivy/issues/2069) (@tnxbutno)
|
||||
- Auto downgrade index record option, instead of vint error [#1857](https://github.com/quickwit-oss/tantivy/issues/1857) (@PSeitz)
|
||||
- Enable range query on fast field for u64 compatible types [#1762](https://github.com/quickwit-oss/tantivy/issues/1762) (@PSeitz) [#1876]
|
||||
- sstable
|
||||
- Isolating sstable and stacker in independent crates. [#1718](https://github.com/quickwit-oss/tantivy/issues/1718) (@fulmicoton)
|
||||
- New sstable format [#1943](https://github.com/quickwit-oss/tantivy/issues/1943)[#1953](https://github.com/quickwit-oss/tantivy/issues/1953) (@trinity-1686a)
|
||||
- Use DeltaReader directly to implement Dictionary::ord_to_term [#1928](https://github.com/quickwit-oss/tantivy/issues/1928) (@trinity-1686a)
|
||||
- Use DeltaReader directly to implement Dictionary::term_ord [#1925](https://github.com/quickwit-oss/tantivy/issues/1925) (@trinity-1686a)
|
||||
- Add separate tokenizer manager for fast fields [#2019](https://github.com/quickwit-oss/tantivy/issues/2019) (@PSeitz)
|
||||
- Make construction of LevenshteinAutomatonBuilder for FuzzyTermQuery instances lazy. [#1756](https://github.com/quickwit-oss/tantivy/issues/1756) (@adamreichold)
|
||||
- Added support for madvise when opening an mmapped Index [#2036](https://github.com/quickwit-oss/tantivy/issues/2036) (@fulmicoton)
|
||||
- Rename `DatePrecision` to `DateTimePrecision` [#2051](https://github.com/quickwit-oss/tantivy/issues/2051) (@guilload)
|
||||
- Query Parser
|
||||
- Quotation mark can now be used for phrase queries. [#2050](https://github.com/quickwit-oss/tantivy/issues/2050) (@fulmicoton)
|
||||
- PhrasePrefixQuery is supported in the query parser via: `field:"phrase ter"*` [#2044](https://github.com/quickwit-oss/tantivy/issues/2044) (@adamreichold)
|
||||
- Docs
|
||||
- Update examples for literate docs [#1880](https://github.com/quickwit-oss/tantivy/issues/1880) (@PSeitz)
|
||||
- Add ip field example [#1775](https://github.com/quickwit-oss/tantivy/issues/1775) (@PSeitz)
|
||||
- Fix doc store cache documentation [#1821](https://github.com/quickwit-oss/tantivy/issues/1821) (@PSeitz)
|
||||
- Fix BooleanQuery document [#1999](https://github.com/quickwit-oss/tantivy/issues/1999) (@RT_Enzyme)
|
||||
- Update comments in the faceted search example [#1737](https://github.com/quickwit-oss/tantivy/issues/1737) (@DawChihLiou)
|
||||
|
||||
|
||||
Tantivy 0.19
|
||||
================================
|
||||
#### Bugfixes
|
||||
@@ -24,7 +306,7 @@ Tantivy 0.19
|
||||
- Add support for phrase slop in query language [#1393](https://github.com/quickwit-oss/tantivy/pull/1393) (@saroh)
|
||||
- Aggregation
|
||||
- Add aggregation support for date type [#1693](https://github.com/quickwit-oss/tantivy/pull/1693)(@PSeitz)
|
||||
- Add support for keyed parameter in range and histgram aggregations [#1424](https://github.com/quickwit-oss/tantivy/pull/1424) (@k-yomo)
|
||||
- Add support for keyed parameter in range and histogram aggregations [#1424](https://github.com/quickwit-oss/tantivy/pull/1424) (@k-yomo)
|
||||
- Add aggregation bucket limit [#1363](https://github.com/quickwit-oss/tantivy/pull/1363) (@PSeitz)
|
||||
- Faster indexing
|
||||
- [#1610](https://github.com/quickwit-oss/tantivy/pull/1610) (@PSeitz)
|
||||
@@ -467,7 +749,7 @@ 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"
|
||||
134
Cargo.toml
134
Cargo.toml
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "tantivy"
|
||||
version = "0.19.0"
|
||||
version = "0.25.0"
|
||||
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
|
||||
license = "MIT"
|
||||
categories = ["database-implementations", "data-structures"]
|
||||
@@ -11,72 +11,87 @@ repository = "https://github.com/quickwit-oss/tantivy"
|
||||
readme = "README.md"
|
||||
keywords = ["search", "information", "retrieval"]
|
||||
edition = "2021"
|
||||
rust-version = "1.62"
|
||||
rust-version = "1.85"
|
||||
exclude = ["benches/*.json", "benches/*.txt"]
|
||||
|
||||
[dependencies]
|
||||
oneshot = "0.1.5"
|
||||
base64 = "0.21.0"
|
||||
oneshot = "0.1.7"
|
||||
base64 = "0.22.0"
|
||||
byteorder = "1.4.3"
|
||||
crc32fast = "1.3.2"
|
||||
once_cell = "1.10.0"
|
||||
regex = { version = "1.5.5", default-features = false, features = ["std", "unicode"] }
|
||||
aho-corasick = "0.7"
|
||||
tantivy-fst = "0.4.0"
|
||||
memmap2 = { version = "0.5.3", optional = true }
|
||||
lz4_flex = { version = "0.10", default-features = false, features = ["checked-decode"], optional = true }
|
||||
brotli = { version = "3.3.4", optional = true }
|
||||
zstd = { version = "0.12", optional = true, default-features = false }
|
||||
snap = { version = "1.0.5", optional = true }
|
||||
tempfile = { version = "3.3.0", optional = true }
|
||||
regex = { version = "1.5.5", default-features = false, features = [
|
||||
"std",
|
||||
"unicode",
|
||||
] }
|
||||
aho-corasick = "1.0"
|
||||
tantivy-fst = "0.5"
|
||||
memmap2 = { version = "0.9.0", optional = true }
|
||||
lz4_flex = { version = "0.11", default-features = false, optional = true }
|
||||
zstd = { version = "0.13", optional = true, default-features = false }
|
||||
tempfile = { version = "3.12.0", optional = true }
|
||||
log = "0.4.16"
|
||||
serde = { version = "1.0.136", features = ["derive"] }
|
||||
serde_json = "1.0.79"
|
||||
num_cpus = "1.13.1"
|
||||
fs2 = { version = "0.4.3", optional = true }
|
||||
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 = "1.2.0"
|
||||
bitpacking = { version = "0.8.4", default-features = false, features = ["bitpacker4x"] }
|
||||
census = "0.4.0"
|
||||
rustc-hash = "1.1.0"
|
||||
thiserror = "1.0.30"
|
||||
downcast-rs = "2.0.1"
|
||||
bitpacking = { version = "0.9.2", default-features = false, features = [
|
||||
"bitpacker4x",
|
||||
] }
|
||||
census = "0.4.2"
|
||||
rustc-hash = "2.0.0"
|
||||
thiserror = "2.0.1"
|
||||
htmlescape = "0.3.1"
|
||||
fail = "0.5.0"
|
||||
murmurhash32 = "0.2.0"
|
||||
time = { version = "0.3.10", features = ["serde-well-known"] }
|
||||
fail = { version = "0.5.0", optional = true }
|
||||
time = { version = "0.3.35", features = ["serde-well-known"] }
|
||||
smallvec = "1.8.0"
|
||||
rayon = "1.5.2"
|
||||
lru = "0.9.0"
|
||||
lru = "0.12.0"
|
||||
fastdivide = "0.4.0"
|
||||
itertools = "0.10.3"
|
||||
measure_time = "0.8.2"
|
||||
async-trait = "0.1.53"
|
||||
itertools = "0.14.0"
|
||||
measure_time = "0.9.0"
|
||||
arc-swap = "1.5.0"
|
||||
bon = "3.3.1"
|
||||
|
||||
columnar = { version="0.1", path="./columnar", package ="tantivy-columnar" }
|
||||
sstable = { version="0.1", path="./sstable", package ="tantivy-sstable", optional = true }
|
||||
stacker = { version="0.1", path="./stacker", package ="tantivy-stacker" }
|
||||
tantivy-query-grammar = { version= "0.19.0", path="./query-grammar" }
|
||||
tantivy-bitpacker = { version= "0.3", path="./bitpacker" }
|
||||
common = { version= "0.5", path = "./common/", package = "tantivy-common" }
|
||||
tokenizer-api = { version="0.1", 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"
|
||||
|
||||
[target.'cfg(windows)'.dependencies]
|
||||
winapi = "0.3.9"
|
||||
|
||||
[dev-dependencies]
|
||||
binggan = "0.14.0"
|
||||
rand = "0.8.5"
|
||||
maplit = "1.0.2"
|
||||
matches = "0.1.9"
|
||||
pretty_assertions = "1.2.1"
|
||||
proptest = "1.0.0"
|
||||
criterion = "0.4"
|
||||
test-log = "0.2.10"
|
||||
env_logger = "0.10.0"
|
||||
pprof = { version = "0.11.0", features = ["flamegraph", "criterion"] }
|
||||
futures = "0.3.21"
|
||||
paste = "1.0.11"
|
||||
more-asserts = "0.3.1"
|
||||
rand_distr = "0.4.3"
|
||||
time = { version = "0.3.10", features = ["serde-well-known", "macros"] }
|
||||
postcard = { version = "1.0.4", features = [
|
||||
"use-std",
|
||||
], default-features = false }
|
||||
|
||||
[target.'cfg(not(windows))'.dev-dependencies]
|
||||
criterion = { version = "0.5", default-features = false }
|
||||
|
||||
[dev-dependencies.fail]
|
||||
version = "0.5.0"
|
||||
@@ -87,27 +102,47 @@ opt-level = 3
|
||||
debug = false
|
||||
debug-assertions = false
|
||||
|
||||
[profile.bench]
|
||||
opt-level = 3
|
||||
debug = true
|
||||
debug-assertions = false
|
||||
|
||||
[profile.test]
|
||||
debug-assertions = true
|
||||
overflow-checks = true
|
||||
|
||||
[features]
|
||||
default = ["mmap", "stopwords", "lz4-compression"]
|
||||
mmap = ["fs2", "tempfile", "memmap2"]
|
||||
default = ["mmap", "stopwords", "lz4-compression", "columnar-zstd-compression"]
|
||||
mmap = ["fs4", "tempfile", "memmap2"]
|
||||
stopwords = []
|
||||
|
||||
brotli-compression = ["brotli"]
|
||||
lz4-compression = ["lz4_flex"]
|
||||
snappy-compression = ["snap"]
|
||||
zstd-compression = ["zstd"]
|
||||
|
||||
failpoints = ["fail/failpoints"]
|
||||
unstable = [] # useful for benches.
|
||||
# enable zstd-compression in columnar (and sstable)
|
||||
columnar-zstd-compression = ["columnar/zstd-compression"]
|
||||
|
||||
quickwit = ["sstable"]
|
||||
failpoints = ["fail", "fail/failpoints"]
|
||||
unstable = [] # useful for benches.
|
||||
|
||||
quickwit = ["sstable", "futures-util", "futures-channel"]
|
||||
|
||||
# Compares only the hash of a string when indexing data.
|
||||
# Increases indexing speed, but may lead to extremely rare missing terms, when there's a hash collision.
|
||||
# Uses 64bit ahash.
|
||||
compare_hash_only = ["stacker/compare_hash_only"]
|
||||
|
||||
[workspace]
|
||||
members = ["query-grammar", "bitpacker", "common", "ownedbytes", "stacker", "sstable", "tokenizer-api", "columnar"]
|
||||
members = [
|
||||
"query-grammar",
|
||||
"bitpacker",
|
||||
"common",
|
||||
"ownedbytes",
|
||||
"stacker",
|
||||
"sstable",
|
||||
"tokenizer-api",
|
||||
"columnar",
|
||||
]
|
||||
|
||||
# Following the "fail" crate best practises, we isolate
|
||||
# tests that define specific behavior in fail check points
|
||||
@@ -119,7 +154,7 @@ members = ["query-grammar", "bitpacker", "common", "ownedbytes", "stacker", "sst
|
||||
[[test]]
|
||||
name = "failpoints"
|
||||
path = "tests/failpoints/mod.rs"
|
||||
required-features = ["fail/failpoints"]
|
||||
required-features = ["failpoints"]
|
||||
|
||||
[[bench]]
|
||||
name = "analyzer"
|
||||
@@ -129,3 +164,6 @@ harness = false
|
||||
name = "index-bench"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "agg_bench"
|
||||
harness = false
|
||||
|
||||
2
Makefile
2
Makefile
@@ -1,5 +1,5 @@
|
||||
test:
|
||||
echo "Run test only... No examples."
|
||||
@echo "Run test only... No examples."
|
||||
cargo test --tests --lib
|
||||
|
||||
fmt:
|
||||
|
||||
42
README.md
42
README.md
@@ -5,28 +5,29 @@
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://crates.io/crates/tantivy)
|
||||
|
||||

|
||||
<img src="https://tantivy-search.github.io/logo/tantivy-logo.png" alt="Tantivy, the fastest full-text search engine library written in Rust" height="250">
|
||||
|
||||
**Tantivy** is a **full-text search engine library** written in Rust.
|
||||
## Fast full-text search engine library written in Rust
|
||||
|
||||
It is closer to [Apache Lucene](https://lucene.apache.org/) than to [Elasticsearch](https://www.elastic.co/products/elasticsearch) or [Apache Solr](https://lucene.apache.org/solr/) in the sense it is not
|
||||
an off-the-shelf search engine server, but rather a crate that can be used
|
||||
to build such a search engine.
|
||||
**If you are looking for an alternative to Elasticsearch or Apache Solr, check out [Quickwit](https://github.com/quickwit-oss/quickwit), our distributed search engine built on top of Tantivy.**
|
||||
|
||||
Tantivy is closer to [Apache Lucene](https://lucene.apache.org/) than to [Elasticsearch](https://www.elastic.co/products/elasticsearch) or [Apache Solr](https://lucene.apache.org/solr/) in the sense it is not
|
||||
an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.
|
||||
|
||||
Tantivy is, in fact, strongly inspired by Lucene's design.
|
||||
|
||||
If you are looking for an alternative to Elasticsearch or Apache Solr, check out [Quickwit](https://github.com/quickwit-oss/quickwit), our search engine built on top of Tantivy.
|
||||
## Benchmark
|
||||
|
||||
# Benchmark
|
||||
|
||||
The following [benchmark](https://tantivy-search.github.io/bench/) breakdowns
|
||||
The following [benchmark](https://tantivy-search.github.io/bench/) breaks down the
|
||||
performance for different types of queries/collections.
|
||||
|
||||
Your mileage WILL vary depending on the nature of queries and their load.
|
||||
|
||||
<img src="doc/assets/images/searchbenchmark.png">
|
||||
|
||||
# Features
|
||||
Details about the benchmark can be found at this [repository](https://github.com/quickwit-oss/search-benchmark-game).
|
||||
|
||||
## Features
|
||||
|
||||
- Full-text search
|
||||
- Configurable tokenizer (stemming available for 17 Latin languages) with third party support for Chinese ([tantivy-jieba](https://crates.io/crates/tantivy-jieba) and [cang-jie](https://crates.io/crates/cang-jie)), Japanese ([lindera](https://github.com/lindera-morphology/lindera-tantivy), [Vaporetto](https://crates.io/crates/vaporetto_tantivy), and [tantivy-tokenizer-tiny-segmenter](https://crates.io/crates/tantivy-tokenizer-tiny-segmenter)) and Korean ([lindera](https://github.com/lindera-morphology/lindera-tantivy) + [lindera-ko-dic-builder](https://github.com/lindera-morphology/lindera-ko-dic-builder))
|
||||
@@ -42,7 +43,7 @@ Your mileage WILL vary depending on the nature of queries and their load.
|
||||
- Single valued and multivalued u64, i64, and f64 fast fields (equivalent of doc values in Lucene)
|
||||
- `&[u8]` fast fields
|
||||
- Text, i64, u64, f64, dates, ip, bool, and hierarchical facet fields
|
||||
- Compressed document store (LZ4, Zstd, None, Brotli, Snap)
|
||||
- Compressed document store (LZ4, Zstd, None)
|
||||
- Range queries
|
||||
- Faceted search
|
||||
- Configurable indexing (optional term frequency and position indexing)
|
||||
@@ -52,11 +53,11 @@ Your mileage WILL vary depending on the nature of queries and their load.
|
||||
- Searcher Warmer API
|
||||
- Cheesy logo with a horse
|
||||
|
||||
## Non-features
|
||||
### Non-features
|
||||
|
||||
Distributed search is out of the scope of Tantivy, but if you are looking for this feature, check out [Quickwit](https://github.com/quickwit-oss/quickwit/).
|
||||
|
||||
# Getting started
|
||||
## Getting started
|
||||
|
||||
Tantivy works on stable Rust and supports Linux, macOS, and Windows.
|
||||
|
||||
@@ -66,7 +67,7 @@ index documents, and search via the CLI or a small server with a REST API.
|
||||
It walks you through getting a Wikipedia search engine up and running in a few minutes.
|
||||
- [Reference doc for the last released version](https://docs.rs/tantivy/)
|
||||
|
||||
# How can I support this project?
|
||||
## How can I support this project?
|
||||
|
||||
There are many ways to support this project.
|
||||
|
||||
@@ -77,16 +78,16 @@ There are many ways to support this project.
|
||||
- Contribute code (you can join [our Discord server](https://discord.gg/MT27AG5EVE))
|
||||
- Talk about Tantivy around you
|
||||
|
||||
# Contributing code
|
||||
## Contributing code
|
||||
|
||||
We use the GitHub Pull Request workflow: reference a GitHub ticket and/or include a comprehensive commit message when opening a PR.
|
||||
Feel free to update CHANGELOG.md with your contribution.
|
||||
|
||||
## Tokenizer
|
||||
### Tokenizer
|
||||
|
||||
When implementing a tokenizer for tantivy depend on the `tantivy-tokenizer-api` crate.
|
||||
|
||||
## Clone and build locally
|
||||
### Clone and build locally
|
||||
|
||||
Tantivy compiles on stable Rust.
|
||||
To check out and run tests, you can simply run:
|
||||
@@ -97,10 +98,11 @@ cd tantivy
|
||||
cargo test
|
||||
```
|
||||
|
||||
# Companies Using Tantivy
|
||||
## Companies Using Tantivy
|
||||
|
||||
<p align="left">
|
||||
<img align="center" src="doc/assets/images/etsy.png" alt="Etsy" height="25" width="auto" />
|
||||
<img align="center" src="doc/assets/images/etsy.png" alt="Etsy" height="25" width="auto" />
|
||||
<img align="center" src="doc/assets/images/paradedb.png" alt="ParadeDB" height="25" width="auto" />
|
||||
<img align="center" src="doc/assets/images/Nuclia.png#gh-light-mode-only" alt="Nuclia" height="25" width="auto" />
|
||||
<img align="center" src="doc/assets/images/humanfirst.png#gh-light-mode-only" alt="Humanfirst.ai" height="30" width="auto" />
|
||||
<img align="center" src="doc/assets/images/element.io.svg#gh-light-mode-only" alt="Element.io" height="25" width="auto" />
|
||||
@@ -109,7 +111,7 @@ cargo test
|
||||
<img align="center" src="doc/assets/images/element-dark-theme.png#gh-dark-mode-only" alt="Element.io" height="25" width="auto" />
|
||||
</p>
|
||||
|
||||
# FAQ
|
||||
## FAQ
|
||||
|
||||
### Can I use Tantivy in other languages?
|
||||
|
||||
|
||||
38
RELEASE.md
Normal file
38
RELEASE.md
Normal file
@@ -0,0 +1,38 @@
|
||||
# Releasing a new Tantivy Version
|
||||
|
||||
## Steps
|
||||
|
||||
1. Identify new packages in workspace since last release
|
||||
2. Identify changed packages in workspace since last release
|
||||
3. Bump version in `Cargo.toml` and their dependents for all changed packages
|
||||
4. Update version of root `Cargo.toml`
|
||||
5. Publish version starting with leaf nodes
|
||||
6. Set git tag with new version
|
||||
|
||||
|
||||
[`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 0.24 --push-remote origin minor --no-tag
|
||||
```
|
||||
|
||||
`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
|
||||
```
|
||||
|
||||
|
||||
2
TODO.txt
2
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
|
||||
|
||||
23
appveyor.yml
23
appveyor.yml
@@ -1,23 +0,0 @@
|
||||
# Appveyor configuration template for Rust using rustup for Rust installation
|
||||
# https://github.com/starkat99/appveyor-rust
|
||||
|
||||
os: Visual Studio 2015
|
||||
environment:
|
||||
matrix:
|
||||
- channel: stable
|
||||
target: x86_64-pc-windows-msvc
|
||||
|
||||
install:
|
||||
- appveyor DownloadFile https://win.rustup.rs/ -FileName rustup-init.exe
|
||||
- rustup-init -yv --default-toolchain %channel% --default-host %target%
|
||||
- set PATH=%PATH%;%USERPROFILE%\.cargo\bin
|
||||
- if defined msys_bits set PATH=%PATH%;C:\msys64\mingw%msys_bits%\bin
|
||||
- rustc -vV
|
||||
- cargo -vV
|
||||
|
||||
build: false
|
||||
|
||||
test_script:
|
||||
- REM SET RUST_LOG=tantivy,test & cargo test --all --verbose --no-default-features --features lz4-compression --features mmap
|
||||
- REM SET RUST_LOG=tantivy,test & cargo test test_store --verbose --no-default-features --features lz4-compression --features snappy-compression --features brotli-compression --features mmap
|
||||
- REM SET RUST_BACKTRACE=1 & cargo build --examples
|
||||
462
benches/agg_bench.rs
Normal file
462
benches/agg_bench.rs
Normal file
@@ -0,0 +1,462 @@
|
||||
use binggan::plugins::PeakMemAllocPlugin;
|
||||
use binggan::{black_box, InputGroup, PeakMemAlloc, INSTRUMENTED_SYSTEM};
|
||||
use rand::prelude::SliceRandom;
|
||||
use rand::rngs::StdRng;
|
||||
use rand::{Rng, SeedableRng};
|
||||
use rand_distr::Distribution;
|
||||
use serde_json::json;
|
||||
use tantivy::aggregation::agg_req::Aggregations;
|
||||
use tantivy::aggregation::AggregationCollector;
|
||||
use tantivy::query::{AllQuery, TermQuery};
|
||||
use tantivy::schema::{IndexRecordOption, Schema, TextFieldIndexing, FAST, STRING};
|
||||
use tantivy::{doc, Index, Term};
|
||||
|
||||
#[global_allocator]
|
||||
pub static GLOBAL: &PeakMemAlloc<std::alloc::System> = &INSTRUMENTED_SYSTEM;
|
||||
|
||||
/// Mini macro to register a function via its name
|
||||
/// runner.register("average_u64", move |index| average_u64(index));
|
||||
macro_rules! register {
|
||||
($runner:expr, $func:ident) => {
|
||||
$runner.register(stringify!($func), move |index| {
|
||||
$func(index);
|
||||
})
|
||||
};
|
||||
}
|
||||
|
||||
fn main() {
|
||||
let inputs = vec![
|
||||
("full", get_test_index_bench(Cardinality::Full).unwrap()),
|
||||
(
|
||||
"dense",
|
||||
get_test_index_bench(Cardinality::OptionalDense).unwrap(),
|
||||
),
|
||||
(
|
||||
"sparse",
|
||||
get_test_index_bench(Cardinality::OptionalSparse).unwrap(),
|
||||
),
|
||||
(
|
||||
"multivalue",
|
||||
get_test_index_bench(Cardinality::Multivalued).unwrap(),
|
||||
),
|
||||
];
|
||||
|
||||
bench_agg(InputGroup::new_with_inputs(inputs));
|
||||
}
|
||||
|
||||
fn bench_agg(mut group: InputGroup<Index>) {
|
||||
group.add_plugin(PeakMemAllocPlugin::new(GLOBAL));
|
||||
|
||||
register!(group, average_u64);
|
||||
register!(group, average_f64);
|
||||
register!(group, average_f64_u64);
|
||||
register!(group, stats_f64);
|
||||
register!(group, extendedstats_f64);
|
||||
register!(group, percentiles_f64);
|
||||
register!(group, terms_few);
|
||||
register!(group, terms_many);
|
||||
register!(group, terms_many_top_1000);
|
||||
register!(group, terms_many_order_by_term);
|
||||
register!(group, terms_many_with_top_hits);
|
||||
register!(group, terms_many_with_avg_sub_agg);
|
||||
register!(group, terms_many_json_mixed_type_with_avg_sub_agg);
|
||||
|
||||
register!(group, cardinality_agg);
|
||||
register!(group, terms_few_with_cardinality_agg);
|
||||
|
||||
register!(group, range_agg);
|
||||
register!(group, range_agg_with_avg_sub_agg);
|
||||
register!(group, range_agg_with_term_agg_few);
|
||||
register!(group, range_agg_with_term_agg_many);
|
||||
register!(group, histogram);
|
||||
register!(group, histogram_hard_bounds);
|
||||
register!(group, histogram_with_avg_sub_agg);
|
||||
register!(group, avg_and_range_with_avg_sub_agg);
|
||||
|
||||
group.run();
|
||||
}
|
||||
|
||||
fn exec_term_with_agg(index: &Index, agg_req: serde_json::Value) {
|
||||
let agg_req: Aggregations = serde_json::from_value(agg_req).unwrap();
|
||||
|
||||
let reader = index.reader().unwrap();
|
||||
let text_field = reader.searcher().schema().get_field("text").unwrap();
|
||||
let term_query = TermQuery::new(
|
||||
Term::from_field_text(text_field, "cool"),
|
||||
IndexRecordOption::Basic,
|
||||
);
|
||||
let collector = get_collector(agg_req);
|
||||
let searcher = reader.searcher();
|
||||
black_box(searcher.search(&term_query, &collector).unwrap());
|
||||
}
|
||||
|
||||
fn average_u64(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"average": { "avg": { "field": "score", } }
|
||||
});
|
||||
exec_term_with_agg(index, agg_req)
|
||||
}
|
||||
fn average_f64(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"average": { "avg": { "field": "score_f64", } }
|
||||
});
|
||||
exec_term_with_agg(index, agg_req)
|
||||
}
|
||||
fn average_f64_u64(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"average_f64": { "avg": { "field": "score_f64" } },
|
||||
"average": { "avg": { "field": "score" } },
|
||||
});
|
||||
exec_term_with_agg(index, agg_req)
|
||||
}
|
||||
fn stats_f64(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"average_f64": { "stats": { "field": "score_f64", } }
|
||||
});
|
||||
exec_term_with_agg(index, agg_req)
|
||||
}
|
||||
fn extendedstats_f64(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"extendedstats_f64": { "extended_stats": { "field": "score_f64", } }
|
||||
});
|
||||
exec_term_with_agg(index, agg_req)
|
||||
}
|
||||
fn percentiles_f64(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"mypercentiles": {
|
||||
"percentiles": {
|
||||
"field": "score_f64",
|
||||
"percents": [ 95, 99, 99.9 ]
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn cardinality_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"cardinality": {
|
||||
"cardinality": {
|
||||
"field": "text_many_terms"
|
||||
},
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_few_with_cardinality_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
"terms": { "field": "text_few_terms" },
|
||||
"aggs": {
|
||||
"cardinality": {
|
||||
"cardinality": {
|
||||
"field": "text_many_terms"
|
||||
},
|
||||
}
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn terms_few(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": { "terms": { "field": "text_few_terms" } },
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_many(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": { "terms": { "field": "text_many_terms" } },
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_many_top_1000(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": { "terms": { "field": "text_many_terms", "size": 1000 } },
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_many_order_by_term(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": { "terms": { "field": "text_many_terms", "order": { "_key": "desc" } } },
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_many_with_top_hits(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
"terms": { "field": "text_many_terms" },
|
||||
"aggs": {
|
||||
"top_hits": { "top_hits":
|
||||
{
|
||||
"sort": [
|
||||
{ "score": "desc" }
|
||||
],
|
||||
"size": 2,
|
||||
"doc_value_fields": ["score_f64"]
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_many_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
"terms": { "field": "text_many_terms" },
|
||||
"aggs": {
|
||||
"average_f64": { "avg": { "field": "score_f64" } }
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn terms_many_json_mixed_type_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"my_texts": {
|
||||
"terms": { "field": "json.mixed_type" },
|
||||
"aggs": {
|
||||
"average_f64": { "avg": { "field": "score_f64" } }
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn execute_agg(index: &Index, agg_req: serde_json::Value) {
|
||||
let agg_req: Aggregations = serde_json::from_value(agg_req).unwrap();
|
||||
let collector = get_collector(agg_req);
|
||||
|
||||
let reader = index.reader().unwrap();
|
||||
let searcher = reader.searcher();
|
||||
black_box(searcher.search(&AllQuery, &collector).unwrap());
|
||||
}
|
||||
fn range_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"range_f64": { "range": { "field": "score_f64", "ranges": [
|
||||
{ "from": 3, "to": 7000 },
|
||||
{ "from": 7000, "to": 20000 },
|
||||
{ "from": 20000, "to": 30000 },
|
||||
{ "from": 30000, "to": 40000 },
|
||||
{ "from": 40000, "to": 50000 },
|
||||
{ "from": 50000, "to": 60000 }
|
||||
] } },
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn range_agg_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"rangef64": {
|
||||
"range": {
|
||||
"field": "score_f64",
|
||||
"ranges": [
|
||||
{ "from": 3, "to": 7000 },
|
||||
{ "from": 7000, "to": 20000 },
|
||||
{ "from": 20000, "to": 30000 },
|
||||
{ "from": 30000, "to": 40000 },
|
||||
{ "from": 40000, "to": 50000 },
|
||||
{ "from": 50000, "to": 60000 }
|
||||
]
|
||||
},
|
||||
"aggs": {
|
||||
"average_f64": { "avg": { "field": "score_f64" } }
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn range_agg_with_term_agg_few(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"rangef64": {
|
||||
"range": {
|
||||
"field": "score_f64",
|
||||
"ranges": [
|
||||
{ "from": 3, "to": 7000 },
|
||||
{ "from": 7000, "to": 20000 },
|
||||
{ "from": 20000, "to": 30000 },
|
||||
{ "from": 30000, "to": 40000 },
|
||||
{ "from": 40000, "to": 50000 },
|
||||
{ "from": 50000, "to": 60000 }
|
||||
]
|
||||
},
|
||||
"aggs": {
|
||||
"my_texts": { "terms": { "field": "text_few_terms" } },
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn range_agg_with_term_agg_many(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"rangef64": {
|
||||
"range": {
|
||||
"field": "score_f64",
|
||||
"ranges": [
|
||||
{ "from": 3, "to": 7000 },
|
||||
{ "from": 7000, "to": 20000 },
|
||||
{ "from": 20000, "to": 30000 },
|
||||
{ "from": 30000, "to": 40000 },
|
||||
{ "from": 40000, "to": 50000 },
|
||||
{ "from": 50000, "to": 60000 }
|
||||
]
|
||||
},
|
||||
"aggs": {
|
||||
"my_texts": { "terms": { "field": "text_many_terms" } },
|
||||
}
|
||||
},
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
fn histogram(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"rangef64": {
|
||||
"histogram": {
|
||||
"field": "score_f64",
|
||||
"interval": 100 // 1000 buckets
|
||||
},
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn histogram_hard_bounds(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"rangef64": { "histogram": { "field": "score_f64", "interval": 100, "hard_bounds": { "min": 1000, "max": 300000 } } },
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn histogram_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"rangef64": {
|
||||
"histogram": { "field": "score_f64", "interval": 100 },
|
||||
"aggs": {
|
||||
"average_f64": { "avg": { "field": "score_f64" } }
|
||||
}
|
||||
}
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
fn avg_and_range_with_avg_sub_agg(index: &Index) {
|
||||
let agg_req = json!({
|
||||
"rangef64": {
|
||||
"range": {
|
||||
"field": "score_f64",
|
||||
"ranges": [
|
||||
{ "from": 3, "to": 7000 },
|
||||
{ "from": 7000, "to": 20000 },
|
||||
{ "from": 20000, "to": 60000 }
|
||||
]
|
||||
},
|
||||
"aggs": {
|
||||
"average_in_range": { "avg": { "field": "score" } }
|
||||
}
|
||||
},
|
||||
"average": { "avg": { "field": "score" } }
|
||||
});
|
||||
execute_agg(index, agg_req);
|
||||
}
|
||||
|
||||
#[derive(Clone, Copy, Hash, Default, Debug, PartialEq, Eq, PartialOrd, Ord)]
|
||||
enum Cardinality {
|
||||
/// All documents contain exactly one value.
|
||||
/// `Full` is the default for auto-detecting the Cardinality, since it is the most strict.
|
||||
#[default]
|
||||
Full = 0,
|
||||
/// All documents contain at most one value.
|
||||
OptionalDense = 1,
|
||||
/// All documents may contain any number of values.
|
||||
Multivalued = 2,
|
||||
/// 1 / 20 documents has a value
|
||||
OptionalSparse = 3,
|
||||
}
|
||||
|
||||
fn get_collector(agg_req: Aggregations) -> AggregationCollector {
|
||||
AggregationCollector::from_aggs(agg_req, Default::default())
|
||||
}
|
||||
|
||||
fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
|
||||
let mut schema_builder = Schema::builder();
|
||||
let text_fieldtype = tantivy::schema::TextOptions::default()
|
||||
.set_indexing_options(
|
||||
TextFieldIndexing::default().set_index_option(IndexRecordOption::WithFreqs),
|
||||
)
|
||||
.set_stored();
|
||||
let text_field = schema_builder.add_text_field("text", text_fieldtype);
|
||||
let json_field = schema_builder.add_json_field("json", FAST);
|
||||
let text_field_many_terms = schema_builder.add_text_field("text_many_terms", STRING | FAST);
|
||||
let text_field_few_terms = schema_builder.add_text_field("text_few_terms", STRING | FAST);
|
||||
let score_fieldtype = tantivy::schema::NumericOptions::default().set_fast();
|
||||
let score_field = schema_builder.add_u64_field("score", score_fieldtype.clone());
|
||||
let score_field_f64 = schema_builder.add_f64_field("score_f64", score_fieldtype.clone());
|
||||
let score_field_i64 = schema_builder.add_i64_field("score_i64", score_fieldtype);
|
||||
let index = Index::create_from_tempdir(schema_builder.build())?;
|
||||
let few_terms_data = ["INFO", "ERROR", "WARN", "DEBUG"];
|
||||
|
||||
let lg_norm = rand_distr::LogNormal::new(2.996f64, 0.979f64).unwrap();
|
||||
|
||||
let many_terms_data = (0..150_000)
|
||||
.map(|num| format!("author{num}"))
|
||||
.collect::<Vec<_>>();
|
||||
{
|
||||
let mut rng = StdRng::from_seed([1u8; 32]);
|
||||
let mut index_writer = index.writer_with_num_threads(1, 200_000_000)?;
|
||||
// To make the different test cases comparable we just change one doc to force the
|
||||
// cardinality
|
||||
if cardinality == Cardinality::OptionalDense {
|
||||
index_writer.add_document(doc!())?;
|
||||
}
|
||||
if cardinality == Cardinality::Multivalued {
|
||||
index_writer.add_document(doc!(
|
||||
json_field => json!({"mixed_type": 10.0}),
|
||||
json_field => json!({"mixed_type": 10.0}),
|
||||
text_field => "cool",
|
||||
text_field => "cool",
|
||||
text_field_many_terms => "cool",
|
||||
text_field_many_terms => "cool",
|
||||
text_field_few_terms => "cool",
|
||||
text_field_few_terms => "cool",
|
||||
score_field => 1u64,
|
||||
score_field => 1u64,
|
||||
score_field_f64 => lg_norm.sample(&mut rng),
|
||||
score_field_f64 => lg_norm.sample(&mut rng),
|
||||
score_field_i64 => 1i64,
|
||||
score_field_i64 => 1i64,
|
||||
))?;
|
||||
}
|
||||
let mut doc_with_value = 1_000_000;
|
||||
if cardinality == Cardinality::OptionalSparse {
|
||||
doc_with_value /= 20;
|
||||
}
|
||||
let _val_max = 1_000_000.0;
|
||||
for _ in 0..doc_with_value {
|
||||
let val: f64 = rng.gen_range(0.0..1_000_000.0);
|
||||
let json = if rng.gen_bool(0.1) {
|
||||
// 10% are numeric values
|
||||
json!({ "mixed_type": val })
|
||||
} else {
|
||||
json!({"mixed_type": many_terms_data.choose(&mut rng).unwrap().to_string()})
|
||||
};
|
||||
index_writer.add_document(doc!(
|
||||
text_field => "cool",
|
||||
json_field => json,
|
||||
text_field_many_terms => many_terms_data.choose(&mut rng).unwrap().to_string(),
|
||||
text_field_few_terms => few_terms_data.choose(&mut rng).unwrap().to_string(),
|
||||
score_field => val as u64,
|
||||
score_field_f64 => lg_norm.sample(&mut rng),
|
||||
score_field_i64 => val as i64,
|
||||
))?;
|
||||
if cardinality == Cardinality::OptionalSparse {
|
||||
for _ in 0..20 {
|
||||
index_writer.add_document(doc!(text_field => "cool"))?;
|
||||
}
|
||||
}
|
||||
}
|
||||
// writing the segment
|
||||
index_writer.commit()?;
|
||||
}
|
||||
|
||||
Ok(index)
|
||||
}
|
||||
@@ -1,11 +1,13 @@
|
||||
use criterion::{criterion_group, criterion_main, Criterion};
|
||||
use tantivy::tokenizer::TokenizerManager;
|
||||
use tantivy::tokenizer::{
|
||||
LowerCaser, RemoveLongFilter, SimpleTokenizer, TextAnalyzer, TokenizerManager,
|
||||
};
|
||||
|
||||
const ALICE_TXT: &str = include_str!("alice.txt");
|
||||
|
||||
pub fn criterion_benchmark(c: &mut Criterion) {
|
||||
let tokenizer_manager = TokenizerManager::default();
|
||||
let tokenizer = tokenizer_manager.get("default").unwrap();
|
||||
let mut tokenizer = tokenizer_manager.get("default").unwrap();
|
||||
c.bench_function("default-tokenize-alice", |b| {
|
||||
b.iter(|| {
|
||||
let mut word_count = 0;
|
||||
@@ -16,7 +18,26 @@ pub fn criterion_benchmark(c: &mut Criterion) {
|
||||
assert_eq!(word_count, 30_731);
|
||||
})
|
||||
});
|
||||
let mut dynamic_analyzer = TextAnalyzer::builder(SimpleTokenizer::default())
|
||||
.dynamic()
|
||||
.filter_dynamic(RemoveLongFilter::limit(40))
|
||||
.filter_dynamic(LowerCaser)
|
||||
.build();
|
||||
c.bench_function("dynamic-tokenize-alice", |b| {
|
||||
b.iter(|| {
|
||||
let mut word_count = 0;
|
||||
let mut token_stream = dynamic_analyzer.token_stream(ALICE_TXT);
|
||||
while token_stream.advance() {
|
||||
word_count += 1;
|
||||
}
|
||||
assert_eq!(word_count, 30_731);
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
criterion_group!(benches, criterion_benchmark);
|
||||
criterion_group! {
|
||||
name = benches;
|
||||
config = Criterion::default().sample_size(200);
|
||||
targets = criterion_benchmark
|
||||
}
|
||||
criterion_main!(benches);
|
||||
|
||||
1000
benches/gh.json
Normal file
1000
benches/gh.json
Normal file
File diff suppressed because one or more lines are too long
@@ -1,10 +1,99 @@
|
||||
use criterion::{criterion_group, criterion_main, Criterion};
|
||||
use pprof::criterion::{Output, PProfProfiler};
|
||||
use tantivy::schema::{INDEXED, STORED, STRING, TEXT};
|
||||
use tantivy::Index;
|
||||
use criterion::{criterion_group, criterion_main, BatchSize, Bencher, Criterion, Throughput};
|
||||
use tantivy::schema::{TantivyDocument, FAST, INDEXED, STORED, STRING, TEXT};
|
||||
use tantivy::{tokenizer, Index, IndexWriter};
|
||||
|
||||
const HDFS_LOGS: &str = include_str!("hdfs.json");
|
||||
const NUM_REPEATS: usize = 2;
|
||||
const GH_LOGS: &str = include_str!("gh.json");
|
||||
const WIKI: &str = include_str!("wiki.json");
|
||||
|
||||
fn benchmark(
|
||||
b: &mut Bencher,
|
||||
input: &str,
|
||||
schema: tantivy::schema::Schema,
|
||||
commit: bool,
|
||||
parse_json: bool,
|
||||
is_dynamic: bool,
|
||||
) {
|
||||
if is_dynamic {
|
||||
benchmark_dynamic_json(b, input, schema, commit, parse_json)
|
||||
} else {
|
||||
_benchmark(b, input, schema, commit, parse_json, |schema, doc_json| {
|
||||
TantivyDocument::parse_json(schema, doc_json).unwrap()
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
fn get_index(schema: tantivy::schema::Schema) -> Index {
|
||||
let mut index = Index::create_in_ram(schema.clone());
|
||||
let ff_tokenizer_manager = tokenizer::TokenizerManager::default();
|
||||
ff_tokenizer_manager.register(
|
||||
"raw",
|
||||
tokenizer::TextAnalyzer::builder(tokenizer::RawTokenizer::default())
|
||||
.filter(tokenizer::RemoveLongFilter::limit(255))
|
||||
.build(),
|
||||
);
|
||||
index.set_fast_field_tokenizers(ff_tokenizer_manager.clone());
|
||||
index
|
||||
}
|
||||
|
||||
fn _benchmark(
|
||||
b: &mut Bencher,
|
||||
input: &str,
|
||||
schema: tantivy::schema::Schema,
|
||||
commit: bool,
|
||||
include_json_parsing: bool,
|
||||
create_doc: impl Fn(&tantivy::schema::Schema, &str) -> TantivyDocument,
|
||||
) {
|
||||
if include_json_parsing {
|
||||
let lines: Vec<&str> = input.trim().split('\n').collect();
|
||||
b.iter(|| {
|
||||
let index = get_index(schema.clone());
|
||||
let mut index_writer: IndexWriter =
|
||||
index.writer_with_num_threads(1, 100_000_000).unwrap();
|
||||
for doc_json in &lines {
|
||||
let doc = create_doc(&schema, doc_json);
|
||||
index_writer.add_document(doc).unwrap();
|
||||
}
|
||||
if commit {
|
||||
index_writer.commit().unwrap();
|
||||
}
|
||||
})
|
||||
} else {
|
||||
let docs: Vec<_> = input
|
||||
.trim()
|
||||
.split('\n')
|
||||
.map(|doc_json| create_doc(&schema, doc_json))
|
||||
.collect();
|
||||
b.iter_batched(
|
||||
|| docs.clone(),
|
||||
|docs| {
|
||||
let index = get_index(schema.clone());
|
||||
let mut index_writer: IndexWriter =
|
||||
index.writer_with_num_threads(1, 100_000_000).unwrap();
|
||||
for doc in docs {
|
||||
index_writer.add_document(doc).unwrap();
|
||||
}
|
||||
if commit {
|
||||
index_writer.commit().unwrap();
|
||||
}
|
||||
},
|
||||
BatchSize::SmallInput,
|
||||
)
|
||||
}
|
||||
}
|
||||
fn benchmark_dynamic_json(
|
||||
b: &mut Bencher,
|
||||
input: &str,
|
||||
schema: tantivy::schema::Schema,
|
||||
commit: bool,
|
||||
parse_json: bool,
|
||||
) {
|
||||
let json_field = schema.get_field("json").unwrap();
|
||||
_benchmark(b, input, schema, commit, parse_json, |_schema, doc_json| {
|
||||
let json_val: serde_json::Value = serde_json::from_str(doc_json).unwrap();
|
||||
tantivy::doc!(json_field=>json_val)
|
||||
})
|
||||
}
|
||||
|
||||
pub fn hdfs_index_benchmark(c: &mut Criterion) {
|
||||
let schema = {
|
||||
@@ -14,7 +103,14 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
|
||||
schema_builder.add_text_field("severity", STRING);
|
||||
schema_builder.build()
|
||||
};
|
||||
let schema_with_store = {
|
||||
let schema_only_fast = {
|
||||
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
|
||||
schema_builder.add_u64_field("timestamp", FAST);
|
||||
schema_builder.add_text_field("body", FAST);
|
||||
schema_builder.add_text_field("severity", FAST);
|
||||
schema_builder.build()
|
||||
};
|
||||
let _schema_with_store = {
|
||||
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
|
||||
schema_builder.add_u64_field("timestamp", INDEXED | STORED);
|
||||
schema_builder.add_text_field("body", TEXT | STORED);
|
||||
@@ -23,99 +119,100 @@ pub fn hdfs_index_benchmark(c: &mut Criterion) {
|
||||
};
|
||||
let dynamic_schema = {
|
||||
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
|
||||
schema_builder.add_json_field("json", TEXT);
|
||||
schema_builder.add_json_field("json", TEXT | FAST);
|
||||
schema_builder.build()
|
||||
};
|
||||
|
||||
let mut group = c.benchmark_group("index-hdfs");
|
||||
group.throughput(Throughput::Bytes(HDFS_LOGS.len() as u64));
|
||||
group.sample_size(20);
|
||||
group.bench_function("index-hdfs-no-commit", |b| {
|
||||
b.iter(|| {
|
||||
let index = Index::create_in_ram(schema.clone());
|
||||
let index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
|
||||
for _ in 0..NUM_REPEATS {
|
||||
for doc_json in HDFS_LOGS.trim().split('\n') {
|
||||
let doc = schema.parse_document(doc_json).unwrap();
|
||||
index_writer.add_document(doc).unwrap();
|
||||
}
|
||||
|
||||
let benches = [
|
||||
("only-indexed-".to_string(), schema, false),
|
||||
//("stored-".to_string(), _schema_with_store, false),
|
||||
("only-fast-".to_string(), schema_only_fast, false),
|
||||
("dynamic-".to_string(), dynamic_schema, true),
|
||||
];
|
||||
|
||||
for (prefix, schema, is_dynamic) in benches {
|
||||
for commit in [false, true] {
|
||||
let suffix = if commit { "with-commit" } else { "no-commit" };
|
||||
{
|
||||
let parse_json = false;
|
||||
// for parse_json in [false, true] {
|
||||
let suffix = if parse_json {
|
||||
format!("{suffix}-with-json-parsing")
|
||||
} else {
|
||||
suffix.to_string()
|
||||
};
|
||||
|
||||
let bench_name = format!("{prefix}{suffix}");
|
||||
group.bench_function(bench_name, |b| {
|
||||
benchmark(b, HDFS_LOGS, schema.clone(), commit, parse_json, is_dynamic)
|
||||
});
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn gh_index_benchmark(c: &mut Criterion) {
|
||||
let dynamic_schema = {
|
||||
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
|
||||
schema_builder.add_json_field("json", TEXT | FAST);
|
||||
schema_builder.build()
|
||||
};
|
||||
let dynamic_schema_fast = {
|
||||
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
|
||||
schema_builder.add_json_field("json", FAST);
|
||||
schema_builder.build()
|
||||
};
|
||||
|
||||
let mut group = c.benchmark_group("index-gh");
|
||||
group.throughput(Throughput::Bytes(GH_LOGS.len() as u64));
|
||||
|
||||
group.bench_function("index-gh-no-commit", |b| {
|
||||
benchmark_dynamic_json(b, GH_LOGS, dynamic_schema.clone(), false, false)
|
||||
});
|
||||
group.bench_function("index-hdfs-with-commit", |b| {
|
||||
b.iter(|| {
|
||||
let index = Index::create_in_ram(schema.clone());
|
||||
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
|
||||
for _ in 0..NUM_REPEATS {
|
||||
for doc_json in HDFS_LOGS.trim().split('\n') {
|
||||
let doc = schema.parse_document(doc_json).unwrap();
|
||||
index_writer.add_document(doc).unwrap();
|
||||
}
|
||||
}
|
||||
index_writer.commit().unwrap();
|
||||
})
|
||||
group.bench_function("index-gh-fast", |b| {
|
||||
benchmark_dynamic_json(b, GH_LOGS, dynamic_schema_fast.clone(), false, false)
|
||||
});
|
||||
group.bench_function("index-hdfs-no-commit-with-docstore", |b| {
|
||||
b.iter(|| {
|
||||
let index = Index::create_in_ram(schema_with_store.clone());
|
||||
let index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
|
||||
for _ in 0..NUM_REPEATS {
|
||||
for doc_json in HDFS_LOGS.trim().split('\n') {
|
||||
let doc = schema.parse_document(doc_json).unwrap();
|
||||
index_writer.add_document(doc).unwrap();
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
group.bench_function("index-gh-fast-with-commit", |b| {
|
||||
benchmark_dynamic_json(b, GH_LOGS, dynamic_schema_fast.clone(), true, false)
|
||||
});
|
||||
group.bench_function("index-hdfs-with-commit-with-docstore", |b| {
|
||||
b.iter(|| {
|
||||
let index = Index::create_in_ram(schema_with_store.clone());
|
||||
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
|
||||
for _ in 0..NUM_REPEATS {
|
||||
for doc_json in HDFS_LOGS.trim().split('\n') {
|
||||
let doc = schema.parse_document(doc_json).unwrap();
|
||||
index_writer.add_document(doc).unwrap();
|
||||
}
|
||||
}
|
||||
index_writer.commit().unwrap();
|
||||
})
|
||||
}
|
||||
|
||||
pub fn wiki_index_benchmark(c: &mut Criterion) {
|
||||
let dynamic_schema = {
|
||||
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
|
||||
schema_builder.add_json_field("json", TEXT | FAST);
|
||||
schema_builder.build()
|
||||
};
|
||||
|
||||
let mut group = c.benchmark_group("index-wiki");
|
||||
group.throughput(Throughput::Bytes(WIKI.len() as u64));
|
||||
|
||||
group.bench_function("index-wiki-no-commit", |b| {
|
||||
benchmark_dynamic_json(b, WIKI, dynamic_schema.clone(), false, false)
|
||||
});
|
||||
group.bench_function("index-hdfs-no-commit-json-without-docstore", |b| {
|
||||
b.iter(|| {
|
||||
let index = Index::create_in_ram(dynamic_schema.clone());
|
||||
let json_field = dynamic_schema.get_field("json").unwrap();
|
||||
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
|
||||
for _ in 0..NUM_REPEATS {
|
||||
for doc_json in HDFS_LOGS.trim().split('\n') {
|
||||
let json_val: serde_json::Map<String, serde_json::Value> =
|
||||
serde_json::from_str(doc_json).unwrap();
|
||||
let doc = tantivy::doc!(json_field=>json_val);
|
||||
index_writer.add_document(doc).unwrap();
|
||||
}
|
||||
}
|
||||
index_writer.commit().unwrap();
|
||||
})
|
||||
});
|
||||
group.bench_function("index-hdfs-with-commit-json-without-docstore", |b| {
|
||||
b.iter(|| {
|
||||
let index = Index::create_in_ram(dynamic_schema.clone());
|
||||
let json_field = dynamic_schema.get_field("json").unwrap();
|
||||
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
|
||||
for _ in 0..NUM_REPEATS {
|
||||
for doc_json in HDFS_LOGS.trim().split('\n') {
|
||||
let json_val: serde_json::Map<String, serde_json::Value> =
|
||||
serde_json::from_str(doc_json).unwrap();
|
||||
let doc = tantivy::doc!(json_field=>json_val);
|
||||
index_writer.add_document(doc).unwrap();
|
||||
}
|
||||
}
|
||||
index_writer.commit().unwrap();
|
||||
})
|
||||
group.bench_function("index-wiki-with-commit", |b| {
|
||||
benchmark_dynamic_json(b, WIKI, dynamic_schema.clone(), true, false)
|
||||
});
|
||||
}
|
||||
|
||||
criterion_group! {
|
||||
name = benches;
|
||||
config = Criterion::default().with_profiler(PProfProfiler::new(100, Output::Flamegraph(None)));
|
||||
config = Criterion::default();
|
||||
targets = hdfs_index_benchmark
|
||||
}
|
||||
criterion_main!(benches);
|
||||
criterion_group! {
|
||||
name = gh_benches;
|
||||
config = Criterion::default();
|
||||
targets = gh_index_benchmark
|
||||
}
|
||||
criterion_group! {
|
||||
name = wiki_benches;
|
||||
config = Criterion::default();
|
||||
targets = wiki_index_benchmark
|
||||
}
|
||||
criterion_main!(benches, gh_benches, wiki_benches);
|
||||
|
||||
1000
benches/wiki.json
Normal file
1000
benches/wiki.json
Normal file
File diff suppressed because one or more lines are too long
@@ -1,7 +1,7 @@
|
||||
[package]
|
||||
name = "tantivy-bitpacker"
|
||||
version = "0.3.0"
|
||||
edition = "2021"
|
||||
version = "0.9.0"
|
||||
edition = "2024"
|
||||
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
|
||||
license = "MIT"
|
||||
categories = []
|
||||
@@ -15,6 +15,7 @@ homepage = "https://github.com/quickwit-oss/tantivy"
|
||||
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
|
||||
|
||||
[dependencies]
|
||||
bitpacking = { version = "0.9.2", default-features = false, features = ["bitpacker1x"] }
|
||||
|
||||
[dev-dependencies]
|
||||
rand = "0.8"
|
||||
|
||||
@@ -1,10 +1,13 @@
|
||||
use std::convert::TryInto;
|
||||
use std::io;
|
||||
use std::ops::{Range, RangeInclusive};
|
||||
|
||||
use bitpacking::{BitPacker as ExternalBitPackerTrait, BitPacker1x};
|
||||
|
||||
pub struct BitPacker {
|
||||
mini_buffer: u64,
|
||||
mini_buffer_written: usize,
|
||||
}
|
||||
|
||||
impl Default for BitPacker {
|
||||
fn default() -> Self {
|
||||
BitPacker::new()
|
||||
@@ -62,7 +65,7 @@ impl BitPacker {
|
||||
|
||||
#[derive(Clone, Debug, Default, Copy)]
|
||||
pub struct BitUnpacker {
|
||||
num_bits: u32,
|
||||
num_bits: usize,
|
||||
mask: u64,
|
||||
}
|
||||
|
||||
@@ -80,7 +83,7 @@ impl BitUnpacker {
|
||||
(1u64 << num_bits) - 1u64
|
||||
};
|
||||
BitUnpacker {
|
||||
num_bits: u32::from(num_bits),
|
||||
num_bits: usize::from(num_bits),
|
||||
mask,
|
||||
}
|
||||
}
|
||||
@@ -91,14 +94,14 @@ impl BitUnpacker {
|
||||
|
||||
#[inline]
|
||||
pub fn get(&self, idx: u32, data: &[u8]) -> u64 {
|
||||
let addr_in_bits = idx * self.num_bits;
|
||||
let addr = (addr_in_bits >> 3) as usize;
|
||||
let addr_in_bits = idx as usize * self.num_bits;
|
||||
let addr = addr_in_bits >> 3;
|
||||
if addr + 8 > data.len() {
|
||||
if self.num_bits == 0 {
|
||||
return 0;
|
||||
}
|
||||
let bit_shift = addr_in_bits & 7;
|
||||
return self.get_slow_path(addr, bit_shift, data);
|
||||
return self.get_slow_path(addr, bit_shift as u32, data);
|
||||
}
|
||||
let bit_shift = addr_in_bits & 7;
|
||||
let bytes: [u8; 8] = (&data[addr..addr + 8]).try_into().unwrap();
|
||||
@@ -118,6 +121,126 @@ impl BitUnpacker {
|
||||
let val_shifted = val_unshifted_unmasked >> bit_shift;
|
||||
val_shifted & self.mask
|
||||
}
|
||||
|
||||
// Decodes the range of bitpacked `u32` values with idx
|
||||
// in [start_idx, start_idx + output.len()).
|
||||
//
|
||||
// #Panics
|
||||
//
|
||||
// This methods panics if `num_bits` is > 32.
|
||||
fn get_batch_u32s(&self, start_idx: u32, data: &[u8], output: &mut [u32]) {
|
||||
assert!(
|
||||
self.bit_width() <= 32,
|
||||
"Bitwidth must be <= 32 to use this method."
|
||||
);
|
||||
|
||||
let end_idx: u32 = start_idx + output.len() as u32;
|
||||
|
||||
// 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 + 7) / 8;
|
||||
assert!(
|
||||
end_byte_read <= data.len(),
|
||||
"Requested index is out of bounds."
|
||||
);
|
||||
|
||||
// Simple slow implementation of get_batch_u32s, to deal with our ramps.
|
||||
let get_batch_ramp = |start_idx: u32, output: &mut [u32]| {
|
||||
for (out, idx) in output.iter_mut().zip(start_idx..) {
|
||||
*out = self.get(idx, data) as u32;
|
||||
}
|
||||
};
|
||||
|
||||
// We use an unrolled routine to decode 32 values at once.
|
||||
// We therefore decompose our range of values to decode into three ranges:
|
||||
// - Entrance ramp: [start_idx, fast_track_start) (up to 31 values)
|
||||
// - Highway: [fast_track_start, fast_track_end) (a length multiple of 32s)
|
||||
// - Exit ramp: [fast_track_end, start_idx + output.len()) (up to 31 values)
|
||||
|
||||
// We want the start of the fast track to start align with bytes.
|
||||
// A sufficient condition is to start with an idx that is a multiple of 8,
|
||||
// so highway start is the closest multiple of 8 that is >= start_idx.
|
||||
let entrance_ramp_len: 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 {
|
||||
// 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: 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 as usize * self.num_bits) / 8;
|
||||
let mut output_cursor = (highway_start - start_idx) as usize;
|
||||
for _ in 0..num_blocks {
|
||||
offset += BitPacker1x.decompress(
|
||||
&data[offset..],
|
||||
&mut output[output_cursor..],
|
||||
self.num_bits as u8,
|
||||
);
|
||||
output_cursor += 32;
|
||||
}
|
||||
|
||||
// Exit ramp
|
||||
let highway_end: u32 = highway_start + (num_blocks * BitPacker1x::BLOCK_LEN) as u32;
|
||||
get_batch_ramp(highway_end, &mut output[output_cursor..]);
|
||||
}
|
||||
|
||||
pub fn get_ids_for_value_range(
|
||||
&self,
|
||||
range: RangeInclusive<u64>,
|
||||
id_range: Range<u32>,
|
||||
data: &[u8],
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
if self.bit_width() > 32 {
|
||||
self.get_ids_for_value_range_slow(range, id_range, data, positions)
|
||||
} else {
|
||||
if *range.start() > u32::MAX as u64 {
|
||||
positions.clear();
|
||||
return;
|
||||
}
|
||||
let range_u32 = (*range.start() as u32)..=(*range.end()).min(u32::MAX as u64) as u32;
|
||||
self.get_ids_for_value_range_fast(range_u32, id_range, data, positions)
|
||||
}
|
||||
}
|
||||
|
||||
fn get_ids_for_value_range_slow(
|
||||
&self,
|
||||
range: RangeInclusive<u64>,
|
||||
id_range: Range<u32>,
|
||||
data: &[u8],
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
positions.clear();
|
||||
for i in id_range {
|
||||
// If we cared we could make this branchless, but the slow implementation should rarely
|
||||
// kick in.
|
||||
let val = self.get(i, data);
|
||||
if range.contains(&val) {
|
||||
positions.push(i);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn get_ids_for_value_range_fast(
|
||||
&self,
|
||||
value_range: RangeInclusive<u32>,
|
||||
id_range: Range<u32>,
|
||||
data: &[u8],
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
positions.resize(id_range.len(), 0u32);
|
||||
self.get_batch_u32s(id_range.start, data, positions);
|
||||
crate::filter_vec::filter_vec_in_place(value_range, id_range.start, positions)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
@@ -200,4 +323,58 @@ mod test {
|
||||
test_bitpacker_aux(num_bits, &vals);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic]
|
||||
fn test_get_batch_panics_over_32_bits() {
|
||||
let bitunpacker = BitUnpacker::new(33);
|
||||
let mut output: [u32; 1] = [0u32];
|
||||
bitunpacker.get_batch_u32s(0, &[0, 0, 0, 0, 0, 0, 0, 0], &mut output[..]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_get_batch_limit() {
|
||||
let bitunpacker = BitUnpacker::new(1);
|
||||
let mut output: [u32; 3] = [0u32, 0u32, 0u32];
|
||||
bitunpacker.get_batch_u32s(8 * 4 - 3, &[0u8, 0u8, 0u8, 0u8], &mut output[..]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic]
|
||||
fn test_get_batch_panics_when_off_scope() {
|
||||
let bitunpacker = BitUnpacker::new(1);
|
||||
let mut output: [u32; 3] = [0u32, 0u32, 0u32];
|
||||
// We are missing exactly one bit.
|
||||
bitunpacker.get_batch_u32s(8 * 4 - 2, &[0u8, 0u8, 0u8, 0u8], &mut output[..]);
|
||||
}
|
||||
|
||||
proptest::proptest! {
|
||||
#[test]
|
||||
fn test_get_batch_u32s_proptest(num_bits in 0u8..=32u8) {
|
||||
let mask =
|
||||
if num_bits == 32u8 {
|
||||
u32::MAX
|
||||
} else {
|
||||
(1u32 << num_bits) - 1
|
||||
};
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
let mut bitpacker = BitPacker::new();
|
||||
for val in 0..100 {
|
||||
bitpacker.write(val & mask as u64, num_bits, &mut buffer).unwrap();
|
||||
}
|
||||
bitpacker.flush(&mut buffer).unwrap();
|
||||
let bitunpacker = BitUnpacker::new(num_bits);
|
||||
let mut output: Vec<u32> = Vec::new();
|
||||
for len in [0, 1, 2, 32, 33, 34, 64] {
|
||||
for start_idx in 0u32..32u32 {
|
||||
output.resize(len, 0);
|
||||
bitunpacker.get_batch_u32s(start_idx, &buffer, &mut output);
|
||||
for (i, output_byte) in output.iter().enumerate() {
|
||||
let expected = (start_idx + i as u32) & mask;
|
||||
assert_eq!(*output_byte, expected);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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,
|
||||
@@ -64,10 +64,8 @@ fn mem_usage<T>(items: &Vec<T>) -> usize {
|
||||
|
||||
impl BlockedBitpacker {
|
||||
pub fn new() -> Self {
|
||||
let mut compressed_blocks = vec![];
|
||||
compressed_blocks.resize(8, 0);
|
||||
Self {
|
||||
compressed_blocks,
|
||||
compressed_blocks: vec![0; 8],
|
||||
buffer: vec![],
|
||||
offset_and_bits: vec![],
|
||||
}
|
||||
|
||||
365
bitpacker/src/filter_vec/avx2.rs
Normal file
365
bitpacker/src/filter_vec/avx2.rs
Normal file
@@ -0,0 +1,365 @@
|
||||
//! SIMD filtering of a vector as described in the following blog post.
|
||||
//! <https://quickwit.io/blog/filtering%20a%20vector%20with%20simd%20instructions%20avx-2%20and%20avx-512>
|
||||
use std::arch::x86_64::{
|
||||
__m256i as DataType, _mm256_add_epi32 as op_add, _mm256_cmpgt_epi32 as op_greater,
|
||||
_mm256_lddqu_si256 as load_unaligned, _mm256_or_si256 as op_or, _mm256_set1_epi32 as set1,
|
||||
_mm256_storeu_si256 as store_unaligned, _mm256_xor_si256 as op_xor, *,
|
||||
};
|
||||
use std::ops::RangeInclusive;
|
||||
|
||||
const NUM_LANES: usize = 8;
|
||||
|
||||
const HIGHEST_BIT: u32 = 1 << 31;
|
||||
|
||||
#[inline]
|
||||
fn u32_to_i32(val: u32) -> i32 {
|
||||
(val ^ HIGHEST_BIT) as i32
|
||||
}
|
||||
|
||||
#[inline]
|
||||
unsafe fn u32_to_i32_avx2(vals_u32x8s: DataType) -> DataType {
|
||||
const HIGHEST_BIT_MASK: DataType = from_u32x8([HIGHEST_BIT; NUM_LANES]);
|
||||
op_xor(vals_u32x8s, HIGHEST_BIT_MASK)
|
||||
}
|
||||
|
||||
pub fn filter_vec_in_place(range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
|
||||
// We use a monotonic mapping from u32 to i32 to make the comparison possible in AVX2.
|
||||
let range_i32: RangeInclusive<i32> = u32_to_i32(*range.start())..=u32_to_i32(*range.end());
|
||||
let num_words = output.len() / NUM_LANES;
|
||||
let mut output_len = unsafe {
|
||||
filter_vec_avx2_aux(
|
||||
output.as_ptr() as *const __m256i,
|
||||
range_i32,
|
||||
output.as_mut_ptr(),
|
||||
offset,
|
||||
num_words,
|
||||
)
|
||||
};
|
||||
let reminder_start = num_words * NUM_LANES;
|
||||
for i in reminder_start..output.len() {
|
||||
let val = output[i];
|
||||
output[output_len] = offset + i as u32;
|
||||
output_len += if range.contains(&val) { 1 } else { 0 };
|
||||
}
|
||||
output.truncate(output_len);
|
||||
}
|
||||
|
||||
#[target_feature(enable = "avx2")]
|
||||
unsafe fn filter_vec_avx2_aux(
|
||||
mut input: *const __m256i,
|
||||
range: RangeInclusive<i32>,
|
||||
output: *mut u32,
|
||||
offset: u32,
|
||||
num_words: usize,
|
||||
) -> usize {
|
||||
let mut output_tail = output;
|
||||
let range_simd = set1(*range.start())..=set1(*range.end());
|
||||
let mut ids = from_u32x8([
|
||||
offset,
|
||||
offset + 1,
|
||||
offset + 2,
|
||||
offset + 3,
|
||||
offset + 4,
|
||||
offset + 5,
|
||||
offset + 6,
|
||||
offset + 7,
|
||||
]);
|
||||
const SHIFT: __m256i = from_u32x8([NUM_LANES as u32; NUM_LANES]);
|
||||
for _ in 0..num_words {
|
||||
let word = load_unaligned(input);
|
||||
let word = u32_to_i32_avx2(word);
|
||||
let keeper_bitset = compute_filter_bitset(word, range_simd.clone());
|
||||
let added_len = keeper_bitset.count_ones();
|
||||
let filtered_doc_ids = compact(ids, keeper_bitset);
|
||||
store_unaligned(output_tail as *mut __m256i, filtered_doc_ids);
|
||||
output_tail = output_tail.offset(added_len as isize);
|
||||
ids = op_add(ids, SHIFT);
|
||||
input = input.offset(1);
|
||||
}
|
||||
output_tail.offset_from(output) as usize
|
||||
}
|
||||
|
||||
#[inline]
|
||||
#[target_feature(enable = "avx2")]
|
||||
unsafe fn compact(data: DataType, mask: u8) -> DataType {
|
||||
let vperm_mask = MASK_TO_PERMUTATION[mask as usize];
|
||||
_mm256_permutevar8x32_epi32(data, vperm_mask)
|
||||
}
|
||||
|
||||
#[inline]
|
||||
#[target_feature(enable = "avx2")]
|
||||
unsafe fn compute_filter_bitset(val: __m256i, range: std::ops::RangeInclusive<__m256i>) -> u8 {
|
||||
let too_low = op_greater(*range.start(), val);
|
||||
let too_high = op_greater(val, *range.end());
|
||||
let inside = op_or(too_low, too_high);
|
||||
255 - std::arch::x86_64::_mm256_movemask_ps(std::mem::transmute::<DataType, __m256>(inside))
|
||||
as u8
|
||||
}
|
||||
|
||||
union U8x32 {
|
||||
vector: DataType,
|
||||
vals: [u32; NUM_LANES],
|
||||
}
|
||||
|
||||
const fn from_u32x8(vals: [u32; NUM_LANES]) -> DataType {
|
||||
unsafe { U8x32 { vals }.vector }
|
||||
}
|
||||
|
||||
const MASK_TO_PERMUTATION: [DataType; 256] = [
|
||||
from_u32x8([0, 0, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 0, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 0, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 0, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([3, 0, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 0, 0, 0, 0]),
|
||||
from_u32x8([4, 0, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 4, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 4, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 4, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 4, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 4, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 4, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 4, 0, 0, 0, 0]),
|
||||
from_u32x8([3, 4, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 4, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 4, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 4, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 4, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 4, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 4, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 4, 0, 0, 0]),
|
||||
from_u32x8([5, 0, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 5, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 5, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 5, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([3, 5, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 5, 0, 0, 0]),
|
||||
from_u32x8([4, 5, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 4, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 4, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 4, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 4, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 4, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 4, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 4, 5, 0, 0, 0]),
|
||||
from_u32x8([3, 4, 5, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 4, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 4, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 4, 5, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 4, 5, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 4, 5, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 4, 5, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 4, 5, 0, 0]),
|
||||
from_u32x8([6, 0, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 6, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 6, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 6, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([3, 6, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 6, 0, 0, 0]),
|
||||
from_u32x8([4, 6, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 4, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 4, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 4, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 4, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 4, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 4, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 4, 6, 0, 0, 0]),
|
||||
from_u32x8([3, 4, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 4, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 4, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 4, 6, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 4, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 4, 6, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 4, 6, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 4, 6, 0, 0]),
|
||||
from_u32x8([5, 6, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 5, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 5, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 5, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([3, 5, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 5, 6, 0, 0]),
|
||||
from_u32x8([4, 5, 6, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 4, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 4, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 4, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([2, 4, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 4, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 4, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 4, 5, 6, 0, 0]),
|
||||
from_u32x8([3, 4, 5, 6, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 4, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 4, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 4, 5, 6, 0, 0]),
|
||||
from_u32x8([2, 3, 4, 5, 6, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 4, 5, 6, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 4, 5, 6, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 4, 5, 6, 0]),
|
||||
from_u32x8([7, 0, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 7, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 7, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 7, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([3, 7, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 7, 0, 0, 0]),
|
||||
from_u32x8([4, 7, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 4, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 4, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 4, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 4, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 4, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 4, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 4, 7, 0, 0, 0]),
|
||||
from_u32x8([3, 4, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 4, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 4, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 4, 7, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 4, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 4, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 4, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 4, 7, 0, 0]),
|
||||
from_u32x8([5, 7, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 5, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 5, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 5, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([3, 5, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 5, 7, 0, 0]),
|
||||
from_u32x8([4, 5, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 4, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 4, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 4, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([2, 4, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 4, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 4, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 4, 5, 7, 0, 0]),
|
||||
from_u32x8([3, 4, 5, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 4, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 4, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 4, 5, 7, 0, 0]),
|
||||
from_u32x8([2, 3, 4, 5, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 4, 5, 7, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 4, 5, 7, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 4, 5, 7, 0]),
|
||||
from_u32x8([6, 7, 0, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 6, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 6, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([2, 6, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([3, 6, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([2, 3, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 6, 7, 0, 0]),
|
||||
from_u32x8([4, 6, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 4, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 4, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 4, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([2, 4, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 4, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 4, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 4, 6, 7, 0, 0]),
|
||||
from_u32x8([3, 4, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 4, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 4, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 4, 6, 7, 0, 0]),
|
||||
from_u32x8([2, 3, 4, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 4, 6, 7, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 4, 6, 7, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 4, 6, 7, 0]),
|
||||
from_u32x8([5, 6, 7, 0, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 5, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([1, 5, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([2, 5, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 2, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([3, 5, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 3, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([2, 3, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([1, 2, 3, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 5, 6, 7, 0]),
|
||||
from_u32x8([4, 5, 6, 7, 0, 0, 0, 0]),
|
||||
from_u32x8([0, 4, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([1, 4, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 1, 4, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([2, 4, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 2, 4, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([1, 2, 4, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([0, 1, 2, 4, 5, 6, 7, 0]),
|
||||
from_u32x8([3, 4, 5, 6, 7, 0, 0, 0]),
|
||||
from_u32x8([0, 3, 4, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([1, 3, 4, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([0, 1, 3, 4, 5, 6, 7, 0]),
|
||||
from_u32x8([2, 3, 4, 5, 6, 7, 0, 0]),
|
||||
from_u32x8([0, 2, 3, 4, 5, 6, 7, 0]),
|
||||
from_u32x8([1, 2, 3, 4, 5, 6, 7, 0]),
|
||||
from_u32x8([0, 1, 2, 3, 4, 5, 6, 7]),
|
||||
];
|
||||
165
bitpacker/src/filter_vec/mod.rs
Normal file
165
bitpacker/src/filter_vec/mod.rs
Normal file
@@ -0,0 +1,165 @@
|
||||
use std::ops::RangeInclusive;
|
||||
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
mod avx2;
|
||||
|
||||
mod scalar;
|
||||
|
||||
#[derive(Clone, Copy, Eq, PartialEq, Debug)]
|
||||
#[repr(u8)]
|
||||
enum FilterImplPerInstructionSet {
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
AVX2 = 0u8,
|
||||
Scalar = 1u8,
|
||||
}
|
||||
|
||||
impl FilterImplPerInstructionSet {
|
||||
#[inline]
|
||||
pub fn is_available(&self) -> bool {
|
||||
match *self {
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
FilterImplPerInstructionSet::AVX2 => is_x86_feature_detected!("avx2"),
|
||||
FilterImplPerInstructionSet::Scalar => true,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// List of available implementation in preferred order.
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
const IMPLS: [FilterImplPerInstructionSet; 2] = [
|
||||
FilterImplPerInstructionSet::AVX2,
|
||||
FilterImplPerInstructionSet::Scalar,
|
||||
];
|
||||
|
||||
#[cfg(not(target_arch = "x86_64"))]
|
||||
const IMPLS: [FilterImplPerInstructionSet; 1] = [FilterImplPerInstructionSet::Scalar];
|
||||
|
||||
impl FilterImplPerInstructionSet {
|
||||
#[inline]
|
||||
#[allow(unused_variables)] // on non-x86_64, code is unused.
|
||||
fn from(code: u8) -> FilterImplPerInstructionSet {
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
if code == FilterImplPerInstructionSet::AVX2 as u8 {
|
||||
return FilterImplPerInstructionSet::AVX2;
|
||||
}
|
||||
FilterImplPerInstructionSet::Scalar
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn filter_vec_in_place(self, range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
|
||||
match self {
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
FilterImplPerInstructionSet::AVX2 => avx2::filter_vec_in_place(range, offset, output),
|
||||
FilterImplPerInstructionSet::Scalar => {
|
||||
scalar::filter_vec_in_place(range, offset, output)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn get_best_available_instruction_set() -> FilterImplPerInstructionSet {
|
||||
use std::sync::atomic::{AtomicU8, Ordering};
|
||||
static INSTRUCTION_SET_BYTE: AtomicU8 = AtomicU8::new(u8::MAX);
|
||||
let instruction_set_byte: u8 = INSTRUCTION_SET_BYTE.load(Ordering::Relaxed);
|
||||
if instruction_set_byte == u8::MAX {
|
||||
// Let's initialize the instruction set and cache it.
|
||||
let instruction_set = IMPLS
|
||||
.into_iter()
|
||||
.find(FilterImplPerInstructionSet::is_available)
|
||||
.unwrap();
|
||||
INSTRUCTION_SET_BYTE.store(instruction_set as u8, Ordering::Relaxed);
|
||||
return instruction_set;
|
||||
}
|
||||
FilterImplPerInstructionSet::from(instruction_set_byte)
|
||||
}
|
||||
|
||||
pub fn filter_vec_in_place(range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
|
||||
get_best_available_instruction_set().filter_vec_in_place(range, offset, output)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_get_best_available_instruction_set() {
|
||||
// This does not test much unfortunately.
|
||||
// We just make sure the function returns without crashing and returns the same result.
|
||||
let instruction_set = get_best_available_instruction_set();
|
||||
assert_eq!(get_best_available_instruction_set(), instruction_set);
|
||||
}
|
||||
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
#[test]
|
||||
fn test_instruction_set_to_code_from_code() {
|
||||
for instruction_set in [
|
||||
FilterImplPerInstructionSet::AVX2,
|
||||
FilterImplPerInstructionSet::Scalar,
|
||||
] {
|
||||
let code = instruction_set as u8;
|
||||
assert_eq!(instruction_set, FilterImplPerInstructionSet::from(code));
|
||||
}
|
||||
}
|
||||
|
||||
fn test_filter_impl_empty_aux(filter_impl: FilterImplPerInstructionSet) {
|
||||
let mut output = vec![];
|
||||
filter_impl.filter_vec_in_place(0..=u32::MAX, 0, &mut output);
|
||||
assert_eq!(&output, &[]);
|
||||
}
|
||||
|
||||
fn test_filter_impl_simple_aux(filter_impl: FilterImplPerInstructionSet) {
|
||||
let mut output = vec![3, 2, 1, 5, 11, 2, 5, 10, 2];
|
||||
filter_impl.filter_vec_in_place(3..=10, 0, &mut output);
|
||||
assert_eq!(&output, &[0, 3, 6, 7]);
|
||||
}
|
||||
|
||||
fn test_filter_impl_simple_aux_shifted(filter_impl: FilterImplPerInstructionSet) {
|
||||
let mut output = vec![3, 2, 1, 5, 11, 2, 5, 10, 2];
|
||||
filter_impl.filter_vec_in_place(3..=10, 10, &mut output);
|
||||
assert_eq!(&output, &[10, 13, 16, 17]);
|
||||
}
|
||||
|
||||
fn test_filter_impl_simple_outside_i32_range(filter_impl: FilterImplPerInstructionSet) {
|
||||
let mut output = vec![u32::MAX, i32::MAX as u32 + 1, 0, 1, 3, 1, 1, 1, 1];
|
||||
filter_impl.filter_vec_in_place(1..=i32::MAX as u32 + 1u32, 0, &mut output);
|
||||
assert_eq!(&output, &[1, 3, 4, 5, 6, 7, 8]);
|
||||
}
|
||||
|
||||
fn test_filter_impl_test_suite(filter_impl: FilterImplPerInstructionSet) {
|
||||
test_filter_impl_empty_aux(filter_impl);
|
||||
test_filter_impl_simple_aux(filter_impl);
|
||||
test_filter_impl_simple_aux_shifted(filter_impl);
|
||||
test_filter_impl_simple_outside_i32_range(filter_impl);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
fn test_filter_implementation_avx2() {
|
||||
if FilterImplPerInstructionSet::AVX2.is_available() {
|
||||
test_filter_impl_test_suite(FilterImplPerInstructionSet::AVX2);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_filter_implementation_scalar() {
|
||||
test_filter_impl_test_suite(FilterImplPerInstructionSet::Scalar);
|
||||
}
|
||||
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
proptest::proptest! {
|
||||
#[test]
|
||||
fn test_filter_compare_scalar_and_avx2_impl_proptest(
|
||||
start in proptest::prelude::any::<u32>(),
|
||||
end in proptest::prelude::any::<u32>(),
|
||||
offset in 0u32..2u32,
|
||||
mut vals in proptest::collection::vec(0..u32::MAX, 0..30)) {
|
||||
if FilterImplPerInstructionSet::AVX2.is_available() {
|
||||
let mut vals_clone = vals.clone();
|
||||
FilterImplPerInstructionSet::AVX2.filter_vec_in_place(start..=end, offset, &mut vals);
|
||||
FilterImplPerInstructionSet::Scalar.filter_vec_in_place(start..=end, offset, &mut vals_clone);
|
||||
assert_eq!(&vals, &vals_clone);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
13
bitpacker/src/filter_vec/scalar.rs
Normal file
13
bitpacker/src/filter_vec/scalar.rs
Normal file
@@ -0,0 +1,13 @@
|
||||
use std::ops::RangeInclusive;
|
||||
|
||||
pub fn filter_vec_in_place(range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
|
||||
// We restrict the accepted boundary, because unsigned integers & SIMD don't
|
||||
// play well.
|
||||
let mut output_cursor = 0;
|
||||
for i in 0..output.len() {
|
||||
let val = output[i];
|
||||
output[output_cursor] = offset + i as u32;
|
||||
output_cursor += if range.contains(&val) { 1 } else { 0 };
|
||||
}
|
||||
output.truncate(output_cursor);
|
||||
}
|
||||
@@ -1,5 +1,6 @@
|
||||
mod bitpacker;
|
||||
mod blocked_bitpacker;
|
||||
mod filter_vec;
|
||||
|
||||
use std::cmp::Ordering;
|
||||
|
||||
@@ -32,11 +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.
|
||||
|
||||
@@ -1,23 +0,0 @@
|
||||
# This script takes care of packaging the build artifacts that will go in the
|
||||
# release zipfile
|
||||
|
||||
$SRC_DIR = $PWD.Path
|
||||
$STAGE = [System.Guid]::NewGuid().ToString()
|
||||
|
||||
Set-Location $ENV:Temp
|
||||
New-Item -Type Directory -Name $STAGE
|
||||
Set-Location $STAGE
|
||||
|
||||
$ZIP = "$SRC_DIR\$($Env:CRATE_NAME)-$($Env:APPVEYOR_REPO_TAG_NAME)-$($Env:TARGET).zip"
|
||||
|
||||
# TODO Update this to package the right artifacts
|
||||
Copy-Item "$SRC_DIR\target\$($Env:TARGET)\release\hello.exe" '.\'
|
||||
|
||||
7z a "$ZIP" *
|
||||
|
||||
Push-AppveyorArtifact "$ZIP"
|
||||
|
||||
Remove-Item *.* -Force
|
||||
Set-Location ..
|
||||
Remove-Item $STAGE
|
||||
Set-Location $SRC_DIR
|
||||
@@ -1,33 +0,0 @@
|
||||
# This script takes care of building your crate and packaging it for release
|
||||
|
||||
set -ex
|
||||
|
||||
main() {
|
||||
local src=$(pwd) \
|
||||
stage=
|
||||
|
||||
case $TRAVIS_OS_NAME in
|
||||
linux)
|
||||
stage=$(mktemp -d)
|
||||
;;
|
||||
osx)
|
||||
stage=$(mktemp -d -t tmp)
|
||||
;;
|
||||
esac
|
||||
|
||||
test -f Cargo.lock || cargo generate-lockfile
|
||||
|
||||
# TODO Update this to build the artifacts that matter to you
|
||||
cross rustc --bin hello --target $TARGET --release -- -C lto
|
||||
|
||||
# TODO Update this to package the right artifacts
|
||||
cp target/$TARGET/release/hello $stage/
|
||||
|
||||
cd $stage
|
||||
tar czf $src/$CRATE_NAME-$TRAVIS_TAG-$TARGET.tar.gz *
|
||||
cd $src
|
||||
|
||||
rm -rf $stage
|
||||
}
|
||||
|
||||
main
|
||||
@@ -1,47 +0,0 @@
|
||||
set -ex
|
||||
|
||||
main() {
|
||||
local target=
|
||||
if [ $TRAVIS_OS_NAME = linux ]; then
|
||||
target=x86_64-unknown-linux-musl
|
||||
sort=sort
|
||||
else
|
||||
target=x86_64-apple-darwin
|
||||
sort=gsort # for `sort --sort-version`, from brew's coreutils.
|
||||
fi
|
||||
|
||||
# Builds for iOS are done on OSX, but require the specific target to be
|
||||
# installed.
|
||||
case $TARGET in
|
||||
aarch64-apple-ios)
|
||||
rustup target install aarch64-apple-ios
|
||||
;;
|
||||
armv7-apple-ios)
|
||||
rustup target install armv7-apple-ios
|
||||
;;
|
||||
armv7s-apple-ios)
|
||||
rustup target install armv7s-apple-ios
|
||||
;;
|
||||
i386-apple-ios)
|
||||
rustup target install i386-apple-ios
|
||||
;;
|
||||
x86_64-apple-ios)
|
||||
rustup target install x86_64-apple-ios
|
||||
;;
|
||||
esac
|
||||
|
||||
# This fetches latest stable release
|
||||
local tag=$(git ls-remote --tags --refs --exit-code https://github.com/japaric/cross \
|
||||
| cut -d/ -f3 \
|
||||
| grep -E '^v[0.1.0-9.]+$' \
|
||||
| $sort --version-sort \
|
||||
| tail -n1)
|
||||
curl -LSfs https://japaric.github.io/trust/install.sh | \
|
||||
sh -s -- \
|
||||
--force \
|
||||
--git japaric/cross \
|
||||
--tag $tag \
|
||||
--target $target
|
||||
}
|
||||
|
||||
main
|
||||
30
ci/script.sh
30
ci/script.sh
@@ -1,30 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# This script takes care of testing your crate
|
||||
|
||||
set -ex
|
||||
|
||||
main() {
|
||||
if [ ! -z $CODECOV ]; then
|
||||
echo "Codecov"
|
||||
cargo build --verbose && cargo coverage --verbose --all && bash <(curl -s https://codecov.io/bash) -s target/kcov
|
||||
else
|
||||
echo "Build"
|
||||
cross build --target $TARGET
|
||||
if [ ! -z $DISABLE_TESTS ]; then
|
||||
return
|
||||
fi
|
||||
echo "Test"
|
||||
cross test --target $TARGET --no-default-features --features mmap
|
||||
cross test --target $TARGET --no-default-features --features mmap query-grammar
|
||||
fi
|
||||
for example in $(ls examples/*.rs)
|
||||
do
|
||||
cargo run --example $(basename $example .rs)
|
||||
done
|
||||
}
|
||||
|
||||
# we don't run the "test phase" when doing deploys
|
||||
if [ -z $TRAVIS_TAG ]; then
|
||||
main
|
||||
fi
|
||||
93
cliff.toml
Normal file
93
cliff.toml
Normal file
@@ -0,0 +1,93 @@
|
||||
# configuration file for git-cliff{ pattern = "foo", replace = "bar"}
|
||||
# see https://github.com/orhun/git-cliff#configuration-file
|
||||
|
||||
[remote.github]
|
||||
owner = "quickwit-oss"
|
||||
repo = "tantivy"
|
||||
|
||||
[changelog]
|
||||
# changelog header
|
||||
header = """
|
||||
"""
|
||||
# template for the changelog body
|
||||
# https://tera.netlify.app/docs/#introduction
|
||||
body = """
|
||||
## What's Changed
|
||||
|
||||
{%- if version %} in {{ version }}{%- endif -%}
|
||||
{% for commit in commits %}
|
||||
{% if commit.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.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 -%}
|
||||
|
||||
{% if github.contributors | filter(attribute="is_first_time", value=true) | length != 0 %}
|
||||
{% raw %}\n{% endraw -%}
|
||||
## New Contributors
|
||||
{%- endif %}\
|
||||
{% for contributor in github.contributors | filter(attribute="is_first_time", value=true) %}
|
||||
* @{{ contributor.username }} made their first contribution
|
||||
{%- if contributor.pr_number %} in \
|
||||
[#{{ contributor.pr_number }}]({{ self::remote_url() }}/pull/{{ contributor.pr_number }}) \
|
||||
{%- endif %}
|
||||
{%- endfor -%}
|
||||
|
||||
{% if version %}
|
||||
{% if previous.version %}
|
||||
**Full Changelog**: {{ self::remote_url() }}/compare/{{ previous.version }}...{{ version }}
|
||||
{% endif %}
|
||||
{% else -%}
|
||||
{% raw %}\n{% endraw %}
|
||||
{% endif %}
|
||||
|
||||
{%- macro remote_url() -%}
|
||||
https://github.com/{{ remote.github.owner }}/{{ remote.github.repo }}
|
||||
{%- endmacro -%}
|
||||
"""
|
||||
# remove the leading and trailing whitespace from the template
|
||||
trim = true
|
||||
# changelog footer
|
||||
footer = """
|
||||
"""
|
||||
|
||||
postprocessors = [
|
||||
]
|
||||
|
||||
[git]
|
||||
# parse the commits based on https://www.conventionalcommits.org
|
||||
# This is required or commit.message contains the whole commit message and not just the title
|
||||
conventional_commits = false
|
||||
# filter out the commits that are not conventional
|
||||
filter_unconventional = true
|
||||
# process each line of a commit as an individual commit
|
||||
split_commits = false
|
||||
# regex for preprocessing the commit messages
|
||||
commit_preprocessors = [
|
||||
{ pattern = '\((\w+\s)?#([0-9]+)\)', replace = ""},
|
||||
]
|
||||
#link_parsers = [
|
||||
#{ pattern = "#(\\d+)", href = "https://github.com/quickwit-oss/tantivy/pulls/$1"},
|
||||
#]
|
||||
# regex for parsing and grouping commits
|
||||
# protect breaking changes from being skipped due to matching a skipping commit_parser
|
||||
protect_breaking_commits = false
|
||||
# filter out the commits that are not matched by commit parsers
|
||||
filter_commits = false
|
||||
# glob pattern for matching git tags
|
||||
tag_pattern = "v[0-9]*"
|
||||
# regex for skipping tags
|
||||
skip_tags = "v0.1.0-beta.1"
|
||||
# regex for ignoring tags
|
||||
ignore_tags = ""
|
||||
# sort the tags topologically
|
||||
topo_order = false
|
||||
# sort the commits inside sections by oldest/newest order
|
||||
sort_commits = "newest"
|
||||
# limit the number of commits included in the changelog.
|
||||
# limit_commits = 42
|
||||
@@ -1,27 +1,61 @@
|
||||
[package]
|
||||
name = "tantivy-columnar"
|
||||
version = "0.1.0"
|
||||
edition = "2021"
|
||||
version = "0.6.0"
|
||||
edition = "2024"
|
||||
license = "MIT"
|
||||
homepage = "https://github.com/quickwit-oss/tantivy"
|
||||
repository = "https://github.com/quickwit-oss/tantivy"
|
||||
description = "column oriented storage for tantivy"
|
||||
categories = ["database-implementations", "data-structures", "compression"]
|
||||
|
||||
[dependencies]
|
||||
itertools = "0.10.5"
|
||||
log = "0.4.17"
|
||||
fnv = "1.0.7"
|
||||
itertools = "0.14.0"
|
||||
fastdivide = "0.4.0"
|
||||
rand = { version = "0.8.5", optional = true }
|
||||
measure_time = { version = "0.8.2", optional = true }
|
||||
prettytable-rs = { version = "0.10.0", optional = true }
|
||||
|
||||
stacker = { path = "../stacker", package="tantivy-stacker"}
|
||||
sstable = { path = "../sstable", package = "tantivy-sstable" }
|
||||
common = { path = "../common", package = "tantivy-common" }
|
||||
tantivy-bitpacker = { version= "0.3", path = "../bitpacker/" }
|
||||
stacker = { version= "0.6", 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 = "2.0.1"
|
||||
|
||||
[dev-dependencies]
|
||||
proptest = "1"
|
||||
more-asserts = "0.3.1"
|
||||
rand = "0.8.5"
|
||||
rand = "0.8"
|
||||
binggan = "0.14.0"
|
||||
|
||||
[[bench]]
|
||||
name = "bench_merge"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "bench_access"
|
||||
harness = false
|
||||
|
||||
[[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]`
|
||||
|
||||
|
||||
68
columnar/benches/bench_access.rs
Normal file
68
columnar/benches/bench_access.rs
Normal file
@@ -0,0 +1,68 @@
|
||||
use binggan::{InputGroup, black_box};
|
||||
use common::*;
|
||||
use tantivy_columnar::Column;
|
||||
|
||||
pub mod common;
|
||||
|
||||
const NUM_DOCS: u32 = 2_000_000;
|
||||
|
||||
pub fn generate_columnar_and_open(card: Card, num_docs: u32) -> Column {
|
||||
let reader = generate_columnar_with_name(card, num_docs, "price");
|
||||
reader.read_columns("price").unwrap()[0]
|
||||
.open_u64_lenient()
|
||||
.unwrap()
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn main() {
|
||||
let mut inputs = Vec::new();
|
||||
|
||||
let mut add_card = |card1: Card| {
|
||||
inputs.push((
|
||||
card1.to_string(),
|
||||
generate_columnar_and_open(card1, NUM_DOCS),
|
||||
));
|
||||
};
|
||||
|
||||
add_card(Card::MultiSparse);
|
||||
add_card(Card::Multi);
|
||||
add_card(Card::Sparse);
|
||||
add_card(Card::Dense);
|
||||
add_card(Card::Full);
|
||||
|
||||
bench_group(InputGroup::new_with_inputs(inputs));
|
||||
}
|
||||
|
||||
fn bench_group(mut runner: InputGroup<Column>) {
|
||||
runner.register("access_values_for_doc", |column| {
|
||||
let mut sum = 0;
|
||||
for i in 0..NUM_DOCS {
|
||||
for value in column.values_for_doc(i) {
|
||||
sum += value;
|
||||
}
|
||||
}
|
||||
black_box(sum);
|
||||
});
|
||||
runner.register("access_first_vals", |column| {
|
||||
let mut sum = 0;
|
||||
const BLOCK_SIZE: usize = 32;
|
||||
let mut docs = vec![0; BLOCK_SIZE];
|
||||
let mut buffer = vec![None; BLOCK_SIZE];
|
||||
for i in (0..NUM_DOCS).step_by(BLOCK_SIZE) {
|
||||
// fill docs
|
||||
#[allow(clippy::needless_range_loop)]
|
||||
for idx in 0..BLOCK_SIZE {
|
||||
docs[idx] = idx as u32 + i;
|
||||
}
|
||||
|
||||
column.first_vals(&docs, &mut buffer);
|
||||
for val in buffer.iter() {
|
||||
let Some(val) = val else { continue };
|
||||
sum += *val;
|
||||
}
|
||||
}
|
||||
|
||||
black_box(sum);
|
||||
});
|
||||
runner.run();
|
||||
}
|
||||
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();
|
||||
}
|
||||
109
columnar/benches/bench_first_vals.rs
Normal file
109
columnar/benches/bench_first_vals.rs
Normal file
@@ -0,0 +1,109 @@
|
||||
use std::sync::Arc;
|
||||
|
||||
use binggan::{InputGroup, black_box};
|
||||
use rand::prelude::*;
|
||||
use tantivy_columnar::column_values::{CodecType, serialize_and_load_u64_based_column_values};
|
||||
use tantivy_columnar::*;
|
||||
|
||||
struct Columns {
|
||||
pub optional: Column,
|
||||
pub full: Column,
|
||||
pub multi: Column,
|
||||
}
|
||||
|
||||
fn get_test_columns() -> Columns {
|
||||
let data = generate_permutation();
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
for (idx, val) in data.iter().enumerate() {
|
||||
dataframe_writer.record_numerical(idx as u32, "full_values", NumericalValue::U64(*val));
|
||||
if idx % 2 == 0 {
|
||||
dataframe_writer.record_numerical(
|
||||
idx as u32,
|
||||
"optional_values",
|
||||
NumericalValue::U64(*val),
|
||||
);
|
||||
}
|
||||
dataframe_writer.record_numerical(idx as u32, "multi_values", NumericalValue::U64(*val));
|
||||
dataframe_writer.record_numerical(idx as u32, "multi_values", NumericalValue::U64(*val));
|
||||
}
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer
|
||||
.serialize(data.len() as u32, &mut buffer)
|
||||
.unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("optional_values").unwrap();
|
||||
assert_eq!(cols.len(), 1);
|
||||
let optional = cols[0].open_u64_lenient().unwrap().unwrap();
|
||||
assert_eq!(optional.index.get_cardinality(), Cardinality::Optional);
|
||||
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("full_values").unwrap();
|
||||
assert_eq!(cols.len(), 1);
|
||||
let column_full = cols[0].open_u64_lenient().unwrap().unwrap();
|
||||
assert_eq!(column_full.index.get_cardinality(), Cardinality::Full);
|
||||
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("multi_values").unwrap();
|
||||
assert_eq!(cols.len(), 1);
|
||||
let multi = cols[0].open_u64_lenient().unwrap().unwrap();
|
||||
assert_eq!(multi.index.get_cardinality(), Cardinality::Multivalued);
|
||||
|
||||
Columns {
|
||||
optional,
|
||||
full: column_full,
|
||||
multi,
|
||||
}
|
||||
}
|
||||
|
||||
const NUM_VALUES: u64 = 100_000;
|
||||
fn generate_permutation() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (0u64..NUM_VALUES).collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
pub fn serialize_and_load(column: &[u64], codec_type: CodecType) -> Arc<dyn ColumnValues<u64>> {
|
||||
serialize_and_load_u64_based_column_values(&column, &[codec_type])
|
||||
}
|
||||
|
||||
fn 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_VALUES as u32 {
|
||||
let val = column.first(i);
|
||||
sum += val.unwrap_or(0);
|
||||
}
|
||||
black_box(sum);
|
||||
});
|
||||
|
||||
group.register("first_block_fetch", |column| {
|
||||
let mut block: Vec<Option<u64>> = vec![None; 64];
|
||||
let fetch_docids = (0..64).collect::<Vec<_>>();
|
||||
column.first_vals(&fetch_docids, &mut block);
|
||||
black_box(block[0]);
|
||||
});
|
||||
|
||||
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]);
|
||||
}
|
||||
black_box(block[0]);
|
||||
});
|
||||
|
||||
group.run();
|
||||
}
|
||||
49
columnar/benches/bench_merge.rs
Normal file
49
columnar/benches/bench_merge.rs
Normal file
@@ -0,0 +1,49 @@
|
||||
pub mod common;
|
||||
|
||||
use binggan::BenchRunner;
|
||||
use common::{Card, generate_columnar_with_name};
|
||||
use tantivy_columnar::*;
|
||||
|
||||
const NUM_DOCS: u32 = 100_000;
|
||||
|
||||
fn main() {
|
||||
let mut inputs = Vec::new();
|
||||
|
||||
let mut add_combo = |card1: Card, card2: Card| {
|
||||
inputs.push((
|
||||
format!("merge_{card1}_and_{card2}"),
|
||||
vec![
|
||||
generate_columnar_with_name(card1, NUM_DOCS, "price"),
|
||||
generate_columnar_with_name(card2, NUM_DOCS, "price"),
|
||||
],
|
||||
));
|
||||
};
|
||||
|
||||
add_combo(Card::Multi, Card::Multi);
|
||||
add_combo(Card::MultiSparse, Card::MultiSparse);
|
||||
add_combo(Card::Dense, Card::Dense);
|
||||
add_combo(Card::Sparse, Card::Sparse);
|
||||
add_combo(Card::Sparse, Card::Dense);
|
||||
add_combo(Card::MultiSparse, Card::Dense);
|
||||
add_combo(Card::MultiSparse, Card::Sparse);
|
||||
add_combo(Card::Multi, Card::Dense);
|
||||
add_combo(Card::Multi, Card::Sparse);
|
||||
|
||||
let mut runner: BenchRunner = BenchRunner::new();
|
||||
let mut group = runner.new_group();
|
||||
for (input_name, columnar_readers) in inputs.iter() {
|
||||
group.register_with_input(
|
||||
input_name,
|
||||
columnar_readers,
|
||||
move |columnar_readers: &Vec<ColumnarReader>| {
|
||||
let mut out = Vec::new();
|
||||
let columnar_readers = columnar_readers.iter().collect::<Vec<_>>();
|
||||
let merge_row_order = StackMergeOrder::stack(&columnar_readers[..]);
|
||||
|
||||
merge_columnar(&columnar_readers, &[], merge_row_order.into(), &mut out).unwrap();
|
||||
Some(out.len() as u64)
|
||||
},
|
||||
);
|
||||
}
|
||||
group.run();
|
||||
}
|
||||
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();
|
||||
}
|
||||
@@ -1,124 +0,0 @@
|
||||
#![feature(test)]
|
||||
|
||||
use std::ops::RangeInclusive;
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::OwnedBytes;
|
||||
use rand::rngs::StdRng;
|
||||
use rand::seq::SliceRandom;
|
||||
use rand::{random, Rng, SeedableRng};
|
||||
use tantivy_columnar::ColumnValues;
|
||||
use test::Bencher;
|
||||
extern crate test;
|
||||
|
||||
// TODO does this make sense for IPv6 ?
|
||||
fn generate_random() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (0u64..100_000u64)
|
||||
.map(|el| el + random::<u16>() as u64)
|
||||
.collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
fn get_u128_column_random() -> Arc<dyn ColumnValues<u128>> {
|
||||
let permutation = generate_random();
|
||||
let permutation = permutation.iter().map(|el| *el as u128).collect::<Vec<_>>();
|
||||
get_u128_column_from_data(&permutation)
|
||||
}
|
||||
|
||||
fn get_u128_column_from_data(data: &[u128]) -> Arc<dyn ColumnValues<u128>> {
|
||||
let mut out = vec![];
|
||||
tantivy_columnar::column_values::serialize_column_values_u128(&data, &mut out).unwrap();
|
||||
let out = OwnedBytes::new(out);
|
||||
tantivy_columnar::column_values::open_u128_mapped::<u128>(out).unwrap()
|
||||
}
|
||||
|
||||
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
|
||||
const SINGLE_ITEM: u64 = 90;
|
||||
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
|
||||
|
||||
fn get_data_50percent_item() -> Vec<u128> {
|
||||
let mut rng = StdRng::from_seed([1u8; 32]);
|
||||
|
||||
let mut data = vec![];
|
||||
for _ in 0..300_000 {
|
||||
let val = rng.gen_range(1..=100);
|
||||
data.push(val);
|
||||
}
|
||||
data.push(SINGLE_ITEM);
|
||||
data.shuffle(&mut rng);
|
||||
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
|
||||
data
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u128_50percent_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let column = get_u128_column_from_data(&data);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_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_docids_for_value_range(
|
||||
*SINGLE_ITEM_RANGE.start() as u128..=*SINGLE_ITEM_RANGE.end() as u128,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u128_hit_all(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let column = get_u128_column_from_data(&data);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(0..=u128::MAX, 0..data.len() as u32, &mut positions);
|
||||
positions
|
||||
});
|
||||
}
|
||||
// U128 RANGE END
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_fflookup_u128(b: &mut Bencher) {
|
||||
let column = get_u128_column_random();
|
||||
|
||||
b.iter(|| {
|
||||
let mut a = 0u128;
|
||||
for i in 0u64..column.num_vals() as u64 {
|
||||
a += column.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_jumpy_stride5_u128(b: &mut Bencher) {
|
||||
let column = get_u128_column_random();
|
||||
|
||||
b.iter(|| {
|
||||
let n = column.num_vals();
|
||||
let mut a = 0u128;
|
||||
for i in (0..n / 5).map(|val| val * 5) {
|
||||
a += column.get_val(i);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
@@ -1,211 +0,0 @@
|
||||
#![feature(test)]
|
||||
extern crate test;
|
||||
|
||||
use std::ops::RangeInclusive;
|
||||
use std::sync::Arc;
|
||||
|
||||
use rand::prelude::*;
|
||||
use tantivy_columnar::column_values::{serialize_and_load_u64_based_column_values, CodecType};
|
||||
use tantivy_columnar::*;
|
||||
use test::Bencher;
|
||||
|
||||
// Warning: this generates the same permutation at each call
|
||||
fn generate_permutation() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (0u64..100_000u64).collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
fn generate_random() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (0u64..100_000u64)
|
||||
.map(|el| el + random::<u16>() as u64)
|
||||
.collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
// Warning: this generates the same permutation at each call
|
||||
fn generate_permutation_gcd() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (1u64..100_000u64).map(|el| el * 1000).collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
pub fn serialize_and_load(column: &[u64], codec_type: CodecType) -> Arc<dyn ColumnValues<u64>> {
|
||||
serialize_and_load_u64_based_column_values(&column, &[codec_type])
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_jumpy_veclookup(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for _ in 0..n {
|
||||
a = permutation[a as usize];
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_jumpy_fflookup_bitpacked(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for _ in 0..n {
|
||||
a = column.get_val(a as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
|
||||
const SINGLE_ITEM: u64 = 90;
|
||||
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
|
||||
const ONE_PERCENT_ITEM_RANGE: RangeInclusive<u64> = 49..=49;
|
||||
fn get_data_50percent_item() -> Vec<u128> {
|
||||
let mut rng = StdRng::from_seed([1u8; 32]);
|
||||
|
||||
let mut data = vec![];
|
||||
for _ in 0..300_000 {
|
||||
let val = rng.gen_range(1..=100);
|
||||
data.push(val);
|
||||
}
|
||||
data.push(SINGLE_ITEM);
|
||||
|
||||
data.shuffle(&mut rng);
|
||||
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
|
||||
data
|
||||
}
|
||||
|
||||
// U64 RANGE START
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_50percent_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(
|
||||
FIFTY_PERCENT_RANGE,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_1percent_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(
|
||||
ONE_PERCENT_ITEM_RANGE,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_single_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(SINGLE_ITEM_RANGE, 0..data.len() as u32, &mut positions);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_hit_all(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(0..=u64::MAX, 0..data.len() as u32, &mut positions);
|
||||
positions
|
||||
});
|
||||
}
|
||||
// U64 RANGE END
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_stride7_vec(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in (0..n / 7).map(|val| val * 7) {
|
||||
a += permutation[i as usize];
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_stride7_fflookup(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
|
||||
b.iter(|| {
|
||||
let mut a = 0;
|
||||
for i in (0..n / 7).map(|val| val * 7) {
|
||||
a += column.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_fflookup(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
|
||||
let column_ref = column.as_ref();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in 0u32..n as u32 {
|
||||
a += column_ref.get_val(i);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_fflookup_gcd(b: &mut Bencher) {
|
||||
let permutation = generate_permutation_gcd();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in 0..n {
|
||||
a += column.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_vec(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in 0..permutation.len() {
|
||||
a += permutation[i as usize] as u64;
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
120
columnar/benches/bench_values_u128.rs
Normal file
120
columnar/benches/bench_values_u128.rs
Normal file
@@ -0,0 +1,120 @@
|
||||
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::{Rng, SeedableRng, random};
|
||||
use tantivy_columnar::ColumnValues;
|
||||
|
||||
// TODO does this make sense for IPv6 ?
|
||||
fn generate_random() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (0u64..100_000u64)
|
||||
.map(|el| el + random::<u16>() as u64)
|
||||
.collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
fn get_u128_column_random() -> Arc<dyn ColumnValues<u128>> {
|
||||
let permutation = generate_random();
|
||||
let permutation = permutation.iter().map(|el| *el as u128).collect::<Vec<_>>();
|
||||
get_u128_column_from_data(&permutation)
|
||||
}
|
||||
|
||||
fn get_u128_column_from_data(data: &[u128]) -> Arc<dyn ColumnValues<u128>> {
|
||||
let mut out = vec![];
|
||||
tantivy_columnar::column_values::serialize_column_values_u128(&data, &mut out).unwrap();
|
||||
let out = OwnedBytes::new(out);
|
||||
tantivy_columnar::column_values::open_u128_mapped::<u128>(out).unwrap()
|
||||
}
|
||||
|
||||
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
|
||||
const SINGLE_ITEM: u64 = 90;
|
||||
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
|
||||
|
||||
fn get_data_50percent_item() -> Vec<u128> {
|
||||
let mut rng = StdRng::from_seed([1u8; 32]);
|
||||
|
||||
let mut data = vec![];
|
||||
for _ in 0..300_000 {
|
||||
let val = rng.gen_range(1..=100);
|
||||
data.push(val);
|
||||
}
|
||||
data.push(SINGLE_ITEM);
|
||||
data.shuffle(&mut rng);
|
||||
data.iter().map(|el| *el as u128).collect::<Vec<_>>()
|
||||
}
|
||||
|
||||
fn main() {
|
||||
let data = get_data_50percent_item();
|
||||
let column_range = get_u128_column_from_data(&data);
|
||||
let column_random = get_u128_column_random();
|
||||
|
||||
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();
|
||||
inp.column_range.get_row_ids_for_value_range(
|
||||
*SINGLE_ITEM_RANGE.start() as u128..=*SINGLE_ITEM_RANGE.end() as u128,
|
||||
0..inp.data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
black_box(positions.len());
|
||||
});
|
||||
|
||||
group.register("intfastfield_getrange_u128_hit_all", |inp: &Inputs| {
|
||||
let mut positions = Vec::new();
|
||||
inp.column_range.get_row_ids_for_value_range(
|
||||
0..=u128::MAX,
|
||||
0..inp.data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
black_box(positions.len());
|
||||
});
|
||||
|
||||
group.register("intfastfield_scan_all_fflookup_u128", |inp: &Inputs| {
|
||||
let mut a = 0u128;
|
||||
for i in 0u64..inp.column_random.num_vals() as u64 {
|
||||
a += inp.column_random.get_val(i as u32);
|
||||
}
|
||||
black_box(a);
|
||||
});
|
||||
|
||||
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 += inp.column_random.get_val(i);
|
||||
}
|
||||
black_box(a);
|
||||
});
|
||||
|
||||
group.run();
|
||||
}
|
||||
161
columnar/benches/bench_values_u64.rs
Normal file
161
columnar/benches/bench_values_u64.rs
Normal file
@@ -0,0 +1,161 @@
|
||||
use std::ops::RangeInclusive;
|
||||
use std::sync::Arc;
|
||||
|
||||
use binggan::{InputGroup, black_box};
|
||||
use rand::prelude::*;
|
||||
use tantivy_columnar::column_values::{CodecType, serialize_and_load_u64_based_column_values};
|
||||
use tantivy_columnar::*;
|
||||
|
||||
// Warning: this generates the same permutation at each call
|
||||
fn generate_permutation() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (0u64..100_000u64).collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
// Warning: this generates the same permutation at each call
|
||||
fn generate_permutation_gcd() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (1u64..100_000u64).map(|el| el * 1000).collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
pub fn serialize_and_load(column: &[u64], codec_type: CodecType) -> Arc<dyn ColumnValues<u64>> {
|
||||
serialize_and_load_u64_based_column_values(&column, &[codec_type])
|
||||
}
|
||||
|
||||
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
|
||||
const SINGLE_ITEM: u64 = 90;
|
||||
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
|
||||
const ONE_PERCENT_ITEM_RANGE: RangeInclusive<u64> = 49..=49;
|
||||
|
||||
fn get_data_50percent_item() -> Vec<u128> {
|
||||
let mut rng = StdRng::from_seed([1u8; 32]);
|
||||
|
||||
let mut data = vec![];
|
||||
for _ in 0..300_000 {
|
||||
let val = rng.gen_range(1..=100);
|
||||
data.push(val);
|
||||
}
|
||||
data.push(SINGLE_ITEM);
|
||||
|
||||
data.shuffle(&mut rng);
|
||||
data.iter().map(|el| *el as u128).collect::<Vec<_>>()
|
||||
}
|
||||
|
||||
type VecCol = (Vec<u64>, Arc<dyn ColumnValues<u64>>);
|
||||
|
||||
fn bench_access() {
|
||||
let permutation = generate_permutation();
|
||||
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 += inp.0[i];
|
||||
}
|
||||
black_box(a);
|
||||
});
|
||||
|
||||
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 += inp.1.get_val(i as u32);
|
||||
}
|
||||
black_box(a);
|
||||
});
|
||||
|
||||
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 += inp.1.get_val(i as u32);
|
||||
}
|
||||
black_box(a);
|
||||
});
|
||||
|
||||
group.run();
|
||||
}
|
||||
|
||||
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();
|
||||
}
|
||||
59
columnar/benches/common.rs
Normal file
59
columnar/benches/common.rs
Normal file
@@ -0,0 +1,59 @@
|
||||
extern crate tantivy_columnar;
|
||||
|
||||
use core::fmt;
|
||||
use std::fmt::{Display, Formatter};
|
||||
|
||||
use tantivy_columnar::{ColumnarReader, ColumnarWriter};
|
||||
|
||||
pub enum Card {
|
||||
MultiSparse,
|
||||
Multi,
|
||||
Sparse,
|
||||
Dense,
|
||||
Full,
|
||||
}
|
||||
impl Display for Card {
|
||||
fn fmt(&self, f: &mut Formatter) -> fmt::Result {
|
||||
match self {
|
||||
Card::MultiSparse => write!(f, "multi sparse 1/13"),
|
||||
Card::Multi => write!(f, "multi 2x"),
|
||||
Card::Sparse => write!(f, "sparse 1/13"),
|
||||
Card::Dense => write!(f, "dense 1/12"),
|
||||
Card::Full => write!(f, "full"),
|
||||
}
|
||||
}
|
||||
}
|
||||
pub fn generate_columnar_with_name(card: Card, num_docs: u32, column_name: &str) -> ColumnarReader {
|
||||
let mut columnar_writer = ColumnarWriter::default();
|
||||
|
||||
if let Card::MultiSparse = card {
|
||||
columnar_writer.record_numerical(0, column_name, 10u64);
|
||||
columnar_writer.record_numerical(0, column_name, 10u64);
|
||||
}
|
||||
|
||||
for i in 0..num_docs {
|
||||
match card {
|
||||
Card::MultiSparse | Card::Sparse => {
|
||||
if i % 13 == 0 {
|
||||
columnar_writer.record_numerical(i, column_name, i as u64);
|
||||
}
|
||||
}
|
||||
Card::Dense => {
|
||||
if i % 12 == 0 {
|
||||
columnar_writer.record_numerical(i, column_name, i as u64);
|
||||
}
|
||||
}
|
||||
Card::Full => {
|
||||
columnar_writer.record_numerical(i, column_name, i as u64);
|
||||
}
|
||||
Card::Multi => {
|
||||
columnar_writer.record_numerical(i, column_name, i as u64);
|
||||
columnar_writer.record_numerical(i, column_name, i as u64);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let mut wrt: Vec<u8> = Vec::new();
|
||||
columnar_writer.serialize(num_docs, &mut wrt).unwrap();
|
||||
ColumnarReader::open(wrt).unwrap()
|
||||
}
|
||||
18
columnar/columnar-cli-inspect/Cargo.toml
Normal file
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)
|
||||
}
|
||||
@@ -8,7 +8,6 @@ license = "MIT"
|
||||
columnar = {path="../", package="tantivy-columnar"}
|
||||
serde_json = "1"
|
||||
serde_json_borrow = {git="https://github.com/PSeitz/serde_json_borrow/"}
|
||||
serde = "1"
|
||||
|
||||
[workspace]
|
||||
members = []
|
||||
|
||||
BIN
columnar/compat_tests_data/v1.columnar
Normal file
BIN
columnar/compat_tests_data/v1.columnar
Normal file
Binary file not shown.
BIN
columnar/compat_tests_data/v2.columnar
Normal file
BIN
columnar/compat_tests_data/v2.columnar
Normal file
Binary file not shown.
@@ -1,7 +1,6 @@
|
||||
# zero to one
|
||||
|
||||
* revisit line codec
|
||||
* removal of all rows of a column in the schema due to deletes
|
||||
* add columns from schema on merge
|
||||
* Plugging JSON
|
||||
* replug examples
|
||||
@@ -11,7 +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
|
||||
@@ -31,7 +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
|
||||
@@ -44,5 +43,5 @@ isolate u128_based and uniform naming
|
||||
# Other
|
||||
fix enhance column-cli
|
||||
|
||||
# Santa claus
|
||||
# Santa Claus
|
||||
autodetect datetime ipaddr, plug customizable tokenizer.
|
||||
|
||||
158
columnar/src/block_accessor.rs
Normal file
158
columnar/src/block_accessor.rs
Normal file
@@ -0,0 +1,158 @@
|
||||
use std::cmp::Ordering;
|
||||
|
||||
use crate::{Column, DocId, RowId};
|
||||
|
||||
#[derive(Debug, Default, Clone)]
|
||||
pub struct ColumnBlockAccessor<T> {
|
||||
val_cache: Vec<T>,
|
||||
docid_cache: Vec<DocId>,
|
||||
missing_docids_cache: Vec<DocId>,
|
||||
row_id_cache: Vec<RowId>,
|
||||
}
|
||||
|
||||
impl<T: PartialOrd + Copy + std::fmt::Debug + Send + Sync + 'static + Default>
|
||||
ColumnBlockAccessor<T>
|
||||
{
|
||||
#[inline]
|
||||
pub fn fetch_block<'a>(&'a mut self, docs: &'a [u32], accessor: &Column<T>) {
|
||||
if accessor.index.get_cardinality().is_full() {
|
||||
self.val_cache.resize(docs.len(), T::default());
|
||||
accessor.values.get_vals(docs, &mut self.val_cache);
|
||||
} else {
|
||||
self.docid_cache.clear();
|
||||
self.row_id_cache.clear();
|
||||
accessor.row_ids_for_docs(docs, &mut self.docid_cache, &mut self.row_id_cache);
|
||||
self.val_cache.resize(self.row_id_cache.len(), T::default());
|
||||
accessor
|
||||
.values
|
||||
.get_vals(&self.row_id_cache, &mut self.val_cache);
|
||||
}
|
||||
}
|
||||
#[inline]
|
||||
pub fn fetch_block_with_missing(&mut self, docs: &[u32], accessor: &Column<T>, missing: T) {
|
||||
self.fetch_block(docs, accessor);
|
||||
// no missing values
|
||||
if accessor.index.get_cardinality().is_full() {
|
||||
return;
|
||||
}
|
||||
|
||||
// We can compare docid_cache length with docs to find missing docs
|
||||
// For multi value columns we can't rely on the length and always need to scan
|
||||
if accessor.index.get_cardinality().is_multivalue() || docs.len() != self.docid_cache.len()
|
||||
{
|
||||
self.missing_docids_cache.clear();
|
||||
find_missing_docs(docs, &self.docid_cache, |doc| {
|
||||
self.missing_docids_cache.push(doc);
|
||||
self.val_cache.push(missing);
|
||||
});
|
||||
self.docid_cache
|
||||
.extend_from_slice(&self.missing_docids_cache);
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
pub fn iter_vals(&self) -> impl Iterator<Item = T> + '_ {
|
||||
self.val_cache.iter().cloned()
|
||||
}
|
||||
|
||||
#[inline]
|
||||
/// Returns an iterator over the docids and values
|
||||
/// The passed in `docs` slice needs to be the same slice that was passed to `fetch_block` or
|
||||
/// `fetch_block_with_missing`.
|
||||
///
|
||||
/// The docs is used if the column is full (each docs has exactly one value), otherwise the
|
||||
/// internal docid vec is used for the iterator, which e.g. may contain duplicate docs.
|
||||
pub fn iter_docid_vals<'a>(
|
||||
&'a self,
|
||||
docs: &'a [u32],
|
||||
accessor: &Column<T>,
|
||||
) -> impl Iterator<Item = (DocId, T)> + 'a + use<'a, T> {
|
||||
if accessor.index.get_cardinality().is_full() {
|
||||
docs.iter().cloned().zip(self.val_cache.iter().cloned())
|
||||
} else {
|
||||
self.docid_cache
|
||||
.iter()
|
||||
.cloned()
|
||||
.zip(self.val_cache.iter().cloned())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Given two sorted lists of docids `docs` and `hits`, hits is a subset of `docs`.
|
||||
/// Return all docs that are not in `hits`.
|
||||
fn find_missing_docs<F>(docs: &[u32], hits: &[u32], mut callback: F)
|
||||
where F: FnMut(u32) {
|
||||
let mut docs_iter = docs.iter();
|
||||
let mut hits_iter = hits.iter();
|
||||
|
||||
let mut doc = docs_iter.next();
|
||||
let mut hit = hits_iter.next();
|
||||
|
||||
while let (Some(¤t_doc), Some(¤t_hit)) = (doc, hit) {
|
||||
match current_doc.cmp(¤t_hit) {
|
||||
Ordering::Less => {
|
||||
callback(current_doc);
|
||||
doc = docs_iter.next();
|
||||
}
|
||||
Ordering::Equal => {
|
||||
doc = docs_iter.next();
|
||||
hit = hits_iter.next();
|
||||
}
|
||||
Ordering::Greater => {
|
||||
hit = hits_iter.next();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
while let Some(¤t_doc) = doc {
|
||||
callback(current_doc);
|
||||
doc = docs_iter.next();
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_find_missing_docs() {
|
||||
let docs: Vec<u32> = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
|
||||
let hits: Vec<u32> = vec![2, 4, 6, 8, 10];
|
||||
|
||||
let mut missing_docs: Vec<u32> = Vec::new();
|
||||
|
||||
find_missing_docs(&docs, &hits, |missing_doc| {
|
||||
missing_docs.push(missing_doc);
|
||||
});
|
||||
|
||||
assert_eq!(missing_docs, vec![1, 3, 5, 7, 9]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_find_missing_docs_empty() {
|
||||
let docs: Vec<u32> = Vec::new();
|
||||
let hits: Vec<u32> = vec![2, 4, 6, 8, 10];
|
||||
|
||||
let mut missing_docs: Vec<u32> = Vec::new();
|
||||
|
||||
find_missing_docs(&docs, &hits, |missing_doc| {
|
||||
missing_docs.push(missing_doc);
|
||||
});
|
||||
|
||||
assert_eq!(missing_docs, Vec::<u32>::new());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_find_missing_docs_all_missing() {
|
||||
let docs: Vec<u32> = vec![1, 2, 3, 4, 5];
|
||||
let hits: Vec<u32> = Vec::new();
|
||||
|
||||
let mut missing_docs: Vec<u32> = Vec::new();
|
||||
|
||||
find_missing_docs(&docs, &hits, |missing_doc| {
|
||||
missing_docs.push(missing_doc);
|
||||
});
|
||||
|
||||
assert_eq!(missing_docs, vec![1, 2, 3, 4, 5]);
|
||||
}
|
||||
}
|
||||
@@ -1,11 +1,11 @@
|
||||
use std::io;
|
||||
use std::ops::Deref;
|
||||
use std::sync::Arc;
|
||||
use std::{fmt, io};
|
||||
|
||||
use sstable::{Dictionary, VoidSSTable};
|
||||
|
||||
use crate::column::Column;
|
||||
use crate::RowId;
|
||||
use crate::column::Column;
|
||||
|
||||
/// Dictionary encoded column.
|
||||
///
|
||||
@@ -21,7 +21,22 @@ pub struct BytesColumn {
|
||||
pub(crate) term_ord_column: Column<u64>,
|
||||
}
|
||||
|
||||
impl fmt::Debug for BytesColumn {
|
||||
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
|
||||
f.debug_struct("BytesColumn")
|
||||
.field("term_ord_column", &self.term_ord_column)
|
||||
.finish()
|
||||
}
|
||||
}
|
||||
|
||||
impl BytesColumn {
|
||||
pub fn empty(num_docs: u32) -> BytesColumn {
|
||||
BytesColumn {
|
||||
dictionary: Arc::new(Dictionary::empty()),
|
||||
term_ord_column: Column::build_empty_column(num_docs),
|
||||
}
|
||||
}
|
||||
|
||||
/// Fills the given `output` buffer with the term associated to the ordinal `ord`.
|
||||
///
|
||||
/// Returns `false` if the term does not exist (e.g. `term_ord` is greater or equal to the
|
||||
@@ -32,11 +47,11 @@ impl BytesColumn {
|
||||
|
||||
/// Returns the number of rows in the column.
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
self.term_ord_column.num_rows()
|
||||
self.term_ord_column.num_docs()
|
||||
}
|
||||
|
||||
pub fn term_ords(&self, row_id: RowId) -> impl Iterator<Item = u64> + '_ {
|
||||
self.term_ord_column.values(row_id)
|
||||
self.term_ord_column.values_for_doc(row_id)
|
||||
}
|
||||
|
||||
/// Returns the column of ordinals
|
||||
@@ -56,6 +71,12 @@ impl BytesColumn {
|
||||
#[derive(Clone)]
|
||||
pub struct StrColumn(BytesColumn);
|
||||
|
||||
impl fmt::Debug for StrColumn {
|
||||
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
|
||||
write!(f, "{:?}", self.term_ord_column)
|
||||
}
|
||||
}
|
||||
|
||||
impl From<StrColumn> for BytesColumn {
|
||||
fn from(str_column: StrColumn) -> BytesColumn {
|
||||
str_column.0
|
||||
@@ -63,7 +84,7 @@ impl From<StrColumn> for BytesColumn {
|
||||
}
|
||||
|
||||
impl StrColumn {
|
||||
pub(crate) fn wrap(bytes_column: BytesColumn) -> StrColumn {
|
||||
pub fn wrap(bytes_column: BytesColumn) -> StrColumn {
|
||||
StrColumn(bytes_column)
|
||||
}
|
||||
|
||||
|
||||
@@ -1,29 +1,49 @@
|
||||
mod dictionary_encoded;
|
||||
mod serialize;
|
||||
|
||||
use std::fmt::Debug;
|
||||
use std::fmt::{self, Debug};
|
||||
use std::io::Write;
|
||||
use std::ops::Deref;
|
||||
use std::ops::{Range, RangeInclusive};
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::BinarySerializable;
|
||||
pub use dictionary_encoded::{BytesColumn, StrColumn};
|
||||
pub use serialize::{
|
||||
open_column_bytes, open_column_str, open_column_u128, 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;
|
||||
use crate::column_index::{ColumnIndex, Set};
|
||||
use crate::column_values::monotonic_mapping::StrictlyMonotonicMappingToInternal;
|
||||
use crate::column_values::{monotonic_map_column, ColumnValues};
|
||||
use crate::{Cardinality, MonotonicallyMappableToU64, RowId};
|
||||
use crate::column_values::{ColumnValues, monotonic_map_column};
|
||||
use crate::{Cardinality, DocId, EmptyColumnValues, MonotonicallyMappableToU64, RowId};
|
||||
|
||||
#[derive(Clone)]
|
||||
pub struct Column<T = u64> {
|
||||
pub idx: ColumnIndex,
|
||||
pub index: ColumnIndex,
|
||||
pub values: Arc<dyn ColumnValues<T>>,
|
||||
}
|
||||
|
||||
impl<T: Debug + PartialOrd + Send + Sync + Copy + 'static> Debug for Column<T> {
|
||||
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
|
||||
let num_docs = self.num_docs();
|
||||
let entries = (0..num_docs)
|
||||
.map(|i| (i, self.values_for_doc(i).collect::<Vec<_>>()))
|
||||
.filter(|(_, vals)| !vals.is_empty());
|
||||
f.debug_map().entries(entries).finish()
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: PartialOrd + Default> Column<T> {
|
||||
pub fn build_empty_column(num_docs: u32) -> Column<T> {
|
||||
Column {
|
||||
index: ColumnIndex::Empty { num_docs },
|
||||
values: Arc::new(EmptyColumnValues),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: MonotonicallyMappableToU64> Column<T> {
|
||||
pub fn to_u64_monotonic(self) -> Column<u64> {
|
||||
let values = Arc::new(monotonic_map_column(
|
||||
@@ -31,25 +51,27 @@ impl<T: MonotonicallyMappableToU64> Column<T> {
|
||||
StrictlyMonotonicMappingToInternal::<T>::new(),
|
||||
));
|
||||
Column {
|
||||
idx: self.idx,
|
||||
index: self.index,
|
||||
values,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
|
||||
#[inline]
|
||||
pub fn get_cardinality(&self) -> Cardinality {
|
||||
self.idx.get_cardinality()
|
||||
self.index.get_cardinality()
|
||||
}
|
||||
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
match &self.idx {
|
||||
ColumnIndex::Full => self.values.num_vals() as u32,
|
||||
ColumnIndex::Optional(optional_index) => optional_index.num_rows(),
|
||||
pub fn num_docs(&self) -> RowId {
|
||||
match &self.index {
|
||||
ColumnIndex::Empty { num_docs } => *num_docs,
|
||||
ColumnIndex::Full => self.values.num_vals(),
|
||||
ColumnIndex::Optional(optional_index) => optional_index.num_docs(),
|
||||
ColumnIndex::Multivalued(col_index) => {
|
||||
// The multivalued index contains all value start row_id,
|
||||
// and one extra value at the end with the overall number of rows.
|
||||
col_index.num_rows()
|
||||
col_index.num_docs()
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -62,22 +84,87 @@ impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
|
||||
self.values.max_value()
|
||||
}
|
||||
|
||||
#[inline]
|
||||
pub fn first(&self, row_id: RowId) -> Option<T> {
|
||||
self.values(row_id).next()
|
||||
self.values_for_doc(row_id).next()
|
||||
}
|
||||
|
||||
pub fn values(&self, row_id: RowId) -> impl Iterator<Item = T> + '_ {
|
||||
self.value_row_ids(row_id)
|
||||
/// Load the first value for each docid in the provided slice.
|
||||
#[inline]
|
||||
pub fn first_vals(&self, docids: &[DocId], output: &mut [Option<T>]) {
|
||||
match &self.index {
|
||||
ColumnIndex::Empty { .. } => {}
|
||||
ColumnIndex::Full => self.values.get_vals_opt(docids, output),
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
for (i, docid) in docids.iter().enumerate() {
|
||||
output[i] = optional_index
|
||||
.rank_if_exists(*docid)
|
||||
.map(|rowid| self.values.get_val(rowid));
|
||||
}
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => {
|
||||
for (i, docid) in docids.iter().enumerate() {
|
||||
let range = multivalued_index.range(*docid);
|
||||
let is_empty = range.start == range.end;
|
||||
if !is_empty {
|
||||
output[i] = Some(self.values.get_val(range.start));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Translates a block of docids to row_ids.
|
||||
///
|
||||
/// returns the row_ids and the matching docids on the same index
|
||||
/// e.g.
|
||||
/// DocId In: [0, 5, 6]
|
||||
/// DocId Out: [0, 0, 6, 6]
|
||||
/// RowId Out: [0, 1, 2, 3]
|
||||
#[inline]
|
||||
pub fn row_ids_for_docs(
|
||||
&self,
|
||||
doc_ids: &[DocId],
|
||||
doc_ids_out: &mut Vec<DocId>,
|
||||
row_ids: &mut Vec<RowId>,
|
||||
) {
|
||||
self.index.docids_to_rowids(doc_ids, doc_ids_out, row_ids)
|
||||
}
|
||||
|
||||
pub fn values_for_doc(&self, doc_id: DocId) -> impl Iterator<Item = T> + '_ {
|
||||
self.index
|
||||
.value_row_ids(doc_id)
|
||||
.map(|value_row_id: RowId| self.values.get_val(value_row_id))
|
||||
}
|
||||
|
||||
/// Fils the output vector with the (possibly multiple values that are associated_with
|
||||
/// Get the docids of values which are in the provided value and docid range.
|
||||
#[inline]
|
||||
pub fn get_docids_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<T>,
|
||||
selected_docid_range: Range<u32>,
|
||||
doc_ids: &mut Vec<u32>,
|
||||
) {
|
||||
// convert passed docid range to row id range
|
||||
let rowid_range = self
|
||||
.index
|
||||
.docid_range_to_rowids(selected_docid_range.clone());
|
||||
|
||||
// Load rows
|
||||
self.values
|
||||
.get_row_ids_for_value_range(value_range, rowid_range, doc_ids);
|
||||
// Convert rows to docids
|
||||
self.index
|
||||
.select_batch_in_place(selected_docid_range.start, doc_ids);
|
||||
}
|
||||
|
||||
/// Fills the output vector with the (possibly multiple values that are associated_with
|
||||
/// `row_id`.
|
||||
///
|
||||
/// This method clears the `output` vector.
|
||||
pub fn fill_vals(&self, row_id: RowId, output: &mut Vec<T>) {
|
||||
output.clear();
|
||||
output.extend(self.values(row_id));
|
||||
output.extend(self.values_for_doc(row_id));
|
||||
}
|
||||
|
||||
pub fn first_or_default_col(self, default_value: T) -> Arc<dyn ColumnValues<T>> {
|
||||
@@ -88,14 +175,6 @@ impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
|
||||
}
|
||||
}
|
||||
|
||||
impl<T> Deref for Column<T> {
|
||||
type Target = ColumnIndex;
|
||||
|
||||
fn deref(&self) -> &Self::Target {
|
||||
&self.idx
|
||||
}
|
||||
}
|
||||
|
||||
impl BinarySerializable for Cardinality {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> std::io::Result<()> {
|
||||
self.to_code().serialize(writer)
|
||||
@@ -117,6 +196,7 @@ struct FirstValueWithDefault<T: Copy> {
|
||||
impl<T: PartialOrd + Debug + Send + Sync + Copy + 'static> ColumnValues<T>
|
||||
for FirstValueWithDefault<T>
|
||||
{
|
||||
#[inline(always)]
|
||||
fn get_val(&self, idx: u32) -> T {
|
||||
self.column.first(idx).unwrap_or(self.default_value)
|
||||
}
|
||||
@@ -130,10 +210,11 @@ impl<T: PartialOrd + Debug + Send + Sync + Copy + 'static> ColumnValues<T>
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
match &self.column.idx {
|
||||
match &self.column.index {
|
||||
ColumnIndex::Empty { .. } => 0u32,
|
||||
ColumnIndex::Full => self.column.values.num_vals(),
|
||||
ColumnIndex::Optional(optional_idx) => optional_idx.num_rows(),
|
||||
ColumnIndex::Multivalued(multivalue_idx) => multivalue_idx.num_rows(),
|
||||
ColumnIndex::Optional(optional_idx) => optional_idx.num_docs(),
|
||||
ColumnIndex::Multivalued(multivalue_idx) => multivalue_idx.num_docs(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -6,12 +6,13 @@ use common::OwnedBytes;
|
||||
use sstable::Dictionary;
|
||||
|
||||
use crate::column::{BytesColumn, Column};
|
||||
use crate::column_index::{serialize_column_index, SerializableColumnIndex};
|
||||
use crate::column_values::serialize::serialize_column_values_u128;
|
||||
use crate::column_values::u64_based::{serialize_u64_based_column_values, CodecType};
|
||||
use crate::column_values::{MonotonicallyMappableToU128, MonotonicallyMappableToU64};
|
||||
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,
|
||||
};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::StrColumn;
|
||||
use crate::{StrColumn, Version};
|
||||
|
||||
pub fn serialize_column_mappable_to_u128<T: MonotonicallyMappableToU128>(
|
||||
column_index: SerializableColumnIndex<'_>,
|
||||
@@ -39,26 +40,9 @@ pub fn serialize_column_mappable_to_u64<T: MonotonicallyMappableToU64>(
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn open_column_u64<T: MonotonicallyMappableToU64>(bytes: OwnedBytes) -> io::Result<Column<T>> {
|
||||
let (body, column_index_num_bytes_payload) = bytes.rsplit(4);
|
||||
let column_index_num_bytes = u32::from_le_bytes(
|
||||
column_index_num_bytes_payload
|
||||
.as_slice()
|
||||
.try_into()
|
||||
.unwrap(),
|
||||
);
|
||||
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
|
||||
let column_index = crate::column_index::open_column_index(column_index_data)?;
|
||||
let column_values =
|
||||
crate::column_values::u64_based::load_u64_based_column_values(column_values_data)?;
|
||||
Ok(Column {
|
||||
idx: column_index,
|
||||
values: column_values,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn open_column_u128<T: MonotonicallyMappableToU128>(
|
||||
pub fn open_column_u64<T: MonotonicallyMappableToU64>(
|
||||
bytes: OwnedBytes,
|
||||
format_version: Version,
|
||||
) -> io::Result<Column<T>> {
|
||||
let (body, column_index_num_bytes_payload) = bytes.rsplit(4);
|
||||
let column_index_num_bytes = u32::from_le_bytes(
|
||||
@@ -68,27 +52,70 @@ pub fn open_column_u128<T: MonotonicallyMappableToU128>(
|
||||
.unwrap(),
|
||||
);
|
||||
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
|
||||
let column_index = crate::column_index::open_column_index(column_index_data)?;
|
||||
let column_values = crate::column_values::open_u128_mapped(column_values_data)?;
|
||||
let column_index = crate::column_index::open_column_index(column_index_data, format_version)?;
|
||||
let column_values = load_u64_based_column_values(column_values_data)?;
|
||||
Ok(Column {
|
||||
idx: column_index,
|
||||
index: column_index,
|
||||
values: column_values,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn open_column_bytes(data: OwnedBytes) -> io::Result<BytesColumn> {
|
||||
pub fn open_column_u128<T: MonotonicallyMappableToU128>(
|
||||
bytes: OwnedBytes,
|
||||
format_version: Version,
|
||||
) -> io::Result<Column<T>> {
|
||||
let (body, column_index_num_bytes_payload) = bytes.rsplit(4);
|
||||
let column_index_num_bytes = u32::from_le_bytes(
|
||||
column_index_num_bytes_payload
|
||||
.as_slice()
|
||||
.try_into()
|
||||
.unwrap(),
|
||||
);
|
||||
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
|
||||
let column_index = crate::column_index::open_column_index(column_index_data, format_version)?;
|
||||
let column_values = crate::column_values::open_u128_mapped(column_values_data)?;
|
||||
Ok(Column {
|
||||
index: column_index,
|
||||
values: column_values,
|
||||
})
|
||||
}
|
||||
|
||||
/// Open the column as u64.
|
||||
///
|
||||
/// See [`open_u128_as_compact_u64`] for more details.
|
||||
pub fn open_column_u128_as_compact_u64(
|
||||
bytes: OwnedBytes,
|
||||
format_version: Version,
|
||||
) -> io::Result<Column<u64>> {
|
||||
let (body, column_index_num_bytes_payload) = bytes.rsplit(4);
|
||||
let column_index_num_bytes = u32::from_le_bytes(
|
||||
column_index_num_bytes_payload
|
||||
.as_slice()
|
||||
.try_into()
|
||||
.unwrap(),
|
||||
);
|
||||
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
|
||||
let column_index = crate::column_index::open_column_index(column_index_data, format_version)?;
|
||||
let column_values = crate::column_values::open_u128_as_compact_u64(column_values_data)?;
|
||||
Ok(Column {
|
||||
index: column_index,
|
||||
values: column_values,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn open_column_bytes(data: OwnedBytes, format_version: Version) -> io::Result<BytesColumn> {
|
||||
let (body, dictionary_len_bytes) = data.rsplit(4);
|
||||
let dictionary_len = u32::from_le_bytes(dictionary_len_bytes.as_slice().try_into().unwrap());
|
||||
let (dictionary_bytes, column_bytes) = body.split(dictionary_len as usize);
|
||||
let dictionary = Arc::new(Dictionary::from_bytes(dictionary_bytes)?);
|
||||
let term_ord_column = crate::column::open_column_u64::<u64>(column_bytes)?;
|
||||
let term_ord_column = crate::column::open_column_u64::<u64>(column_bytes, format_version)?;
|
||||
Ok(BytesColumn {
|
||||
dictionary,
|
||||
term_ord_column,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn open_column_str(data: OwnedBytes) -> io::Result<StrColumn> {
|
||||
let bytes_column = open_column_bytes(data)?;
|
||||
pub fn open_column_str(data: OwnedBytes, format_version: Version) -> io::Result<StrColumn> {
|
||||
let bytes_column = open_column_bytes(data, format_version)?;
|
||||
Ok(StrColumn::wrap(bytes_column))
|
||||
}
|
||||
|
||||
@@ -1,29 +1,82 @@
|
||||
mod shuffled;
|
||||
mod stacked;
|
||||
|
||||
use common::ReadOnlyBitSet;
|
||||
use shuffled::merge_column_index_shuffled;
|
||||
use stacked::merge_column_index_stacked;
|
||||
|
||||
use crate::column_index::SerializableColumnIndex;
|
||||
use crate::{Cardinality, ColumnIndex, MergeRowOrder};
|
||||
|
||||
// For simplification, we never have cardinality go down due to deletes.
|
||||
fn detect_cardinality(columns: &[Option<ColumnIndex>]) -> Cardinality {
|
||||
columns
|
||||
.iter()
|
||||
.flatten()
|
||||
.map(ColumnIndex::get_cardinality)
|
||||
.max()
|
||||
.unwrap_or(Cardinality::Full)
|
||||
fn detect_cardinality_single_column_index(
|
||||
column_index: &ColumnIndex,
|
||||
alive_bitset_opt: &Option<ReadOnlyBitSet>,
|
||||
) -> Cardinality {
|
||||
let Some(alive_bitset) = alive_bitset_opt else {
|
||||
return column_index.get_cardinality();
|
||||
};
|
||||
let cardinality_before_deletes = column_index.get_cardinality();
|
||||
if cardinality_before_deletes == Cardinality::Full {
|
||||
// The columnar cardinality can only become more restrictive in the presence of deletes
|
||||
// (where cardinality sorted from the more restrictive to the least restrictive are Full,
|
||||
// Optional, Multivalued)
|
||||
//
|
||||
// If we are already "Full", we are guaranteed to stay "Full" after deletes.
|
||||
return Cardinality::Full;
|
||||
}
|
||||
let mut cardinality_so_far = Cardinality::Full;
|
||||
for doc_id in alive_bitset.iter() {
|
||||
let num_values = column_index.value_row_ids(doc_id).len();
|
||||
let row_cardinality = match num_values {
|
||||
0 => Cardinality::Optional,
|
||||
1 => Cardinality::Full,
|
||||
_ => Cardinality::Multivalued,
|
||||
};
|
||||
cardinality_so_far = cardinality_so_far.max(row_cardinality);
|
||||
if cardinality_so_far >= cardinality_before_deletes {
|
||||
// There won't be any improvement in the cardinality.
|
||||
// We can early exit.
|
||||
return cardinality_before_deletes;
|
||||
}
|
||||
}
|
||||
cardinality_so_far
|
||||
}
|
||||
|
||||
fn detect_cardinality(
|
||||
column_indexes: &[ColumnIndex],
|
||||
merge_row_order: &MergeRowOrder,
|
||||
) -> Cardinality {
|
||||
match merge_row_order {
|
||||
MergeRowOrder::Stack(_) => column_indexes
|
||||
.iter()
|
||||
.map(ColumnIndex::get_cardinality)
|
||||
.max()
|
||||
.unwrap_or(Cardinality::Full),
|
||||
MergeRowOrder::Shuffled(shuffle_merge_order) => {
|
||||
let mut merged_cardinality = Cardinality::Full;
|
||||
for (column_index, alive_bitset_opt) in column_indexes
|
||||
.iter()
|
||||
.zip(shuffle_merge_order.alive_bitsets.iter())
|
||||
{
|
||||
let cardinality: Cardinality =
|
||||
detect_cardinality_single_column_index(column_index, alive_bitset_opt);
|
||||
if cardinality == Cardinality::Multivalued {
|
||||
return cardinality;
|
||||
}
|
||||
merged_cardinality = merged_cardinality.max(cardinality);
|
||||
}
|
||||
merged_cardinality
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn merge_column_index<'a>(
|
||||
columns: &'a [Option<ColumnIndex>],
|
||||
columns: &'a [ColumnIndex],
|
||||
merge_row_order: &'a MergeRowOrder,
|
||||
) -> SerializableColumnIndex<'a> {
|
||||
// For simplification, we do not try to detect whether the cardinality could be
|
||||
// downgraded thanks to deletes.
|
||||
let cardinality_after_merge = detect_cardinality(columns);
|
||||
let cardinality_after_merge = detect_cardinality(columns, merge_row_order);
|
||||
match merge_row_order {
|
||||
MergeRowOrder::Stack(stack_merge_order) => {
|
||||
merge_column_index_stacked(columns, cardinality_after_merge, stack_merge_order)
|
||||
@@ -42,45 +95,68 @@ pub fn merge_column_index<'a>(
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use common::OwnedBytes;
|
||||
|
||||
use crate::column_index::merge::detect_cardinality;
|
||||
use crate::column_index::multivalued_index::MultiValueIndex;
|
||||
use crate::column_index::{merge_column_index, OptionalIndex, SerializableColumnIndex};
|
||||
use crate::{Cardinality, ColumnIndex, MergeRowOrder, RowAddr, RowId, ShuffleMergeOrder};
|
||||
use crate::column_index::multivalued_index::{
|
||||
MultiValueIndex, open_multivalued_index, serialize_multivalued_index,
|
||||
};
|
||||
use crate::column_index::{OptionalIndex, SerializableColumnIndex, merge_column_index};
|
||||
use crate::{
|
||||
Cardinality, ColumnIndex, MergeRowOrder, RowAddr, RowId, ShuffleMergeOrder, StackMergeOrder,
|
||||
};
|
||||
|
||||
#[test]
|
||||
fn test_detect_cardinality() {
|
||||
assert_eq!(detect_cardinality(&[]), Cardinality::Full);
|
||||
assert_eq!(
|
||||
detect_cardinality(&[], &StackMergeOrder::stack_for_test(&[]).into()),
|
||||
Cardinality::Full
|
||||
);
|
||||
let optional_index: ColumnIndex = OptionalIndex::for_test(1, &[]).into();
|
||||
let multivalued_index: ColumnIndex = MultiValueIndex::for_test(&[0, 1]).into();
|
||||
assert_eq!(
|
||||
detect_cardinality(&[Some(optional_index.clone()), None]),
|
||||
detect_cardinality(
|
||||
&[optional_index.clone(), ColumnIndex::Empty { num_docs: 0 }],
|
||||
&StackMergeOrder::stack_for_test(&[1, 0]).into()
|
||||
),
|
||||
Cardinality::Optional
|
||||
);
|
||||
assert_eq!(
|
||||
detect_cardinality(&[Some(optional_index.clone()), Some(ColumnIndex::Full)]),
|
||||
detect_cardinality(
|
||||
&[optional_index.clone(), ColumnIndex::Full],
|
||||
&StackMergeOrder::stack_for_test(&[1, 1]).into()
|
||||
),
|
||||
Cardinality::Optional
|
||||
);
|
||||
assert_eq!(
|
||||
detect_cardinality(&[Some(multivalued_index.clone()), None]),
|
||||
detect_cardinality(
|
||||
&[
|
||||
multivalued_index.clone(),
|
||||
ColumnIndex::Empty { num_docs: 0 }
|
||||
],
|
||||
&StackMergeOrder::stack_for_test(&[1, 0]).into()
|
||||
),
|
||||
Cardinality::Multivalued
|
||||
);
|
||||
assert_eq!(
|
||||
detect_cardinality(&[
|
||||
Some(multivalued_index.clone()),
|
||||
Some(optional_index.clone())
|
||||
]),
|
||||
detect_cardinality(
|
||||
&[multivalued_index.clone(), optional_index.clone()],
|
||||
&StackMergeOrder::stack_for_test(&[1, 1]).into()
|
||||
),
|
||||
Cardinality::Multivalued
|
||||
);
|
||||
assert_eq!(
|
||||
detect_cardinality(&[Some(optional_index), Some(multivalued_index)]),
|
||||
detect_cardinality(
|
||||
&[optional_index, multivalued_index],
|
||||
&StackMergeOrder::stack_for_test(&[1, 1]).into()
|
||||
),
|
||||
Cardinality::Multivalued
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_index_multivalued_sorted() {
|
||||
let column_indexes: Vec<Option<ColumnIndex>> =
|
||||
vec![Some(MultiValueIndex::for_test(&[0, 2, 5]).into())];
|
||||
let column_indexes: Vec<ColumnIndex> = vec![MultiValueIndex::for_test(&[0, 2, 5]).into()];
|
||||
let merge_row_order: MergeRowOrder = ShuffleMergeOrder::for_test(
|
||||
&[2],
|
||||
vec![
|
||||
@@ -96,18 +172,23 @@ 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") };
|
||||
let start_indexes: Vec<RowId> = start_index_iterable.boxed_iter().collect();
|
||||
let SerializableColumnIndex::Multivalued(start_index_iterable) = merged_column_index else {
|
||||
panic!("Expected a multivalued index")
|
||||
};
|
||||
let mut output = Vec::new();
|
||||
serialize_multivalued_index(&start_index_iterable, &mut output).unwrap();
|
||||
let multivalue =
|
||||
open_multivalued_index(OwnedBytes::new(output), crate::Version::V2).unwrap();
|
||||
let start_indexes: Vec<RowId> = multivalue.get_start_index_column().iter().collect();
|
||||
assert_eq!(&start_indexes, &[0, 3, 5]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_index_multivalued_sorted_several_segment() {
|
||||
let column_indexes: Vec<Option<ColumnIndex>> = vec![
|
||||
Some(MultiValueIndex::for_test(&[0, 2, 5]).into()),
|
||||
None,
|
||||
Some(MultiValueIndex::for_test(&[0, 1, 4]).into()),
|
||||
let column_indexes: Vec<ColumnIndex> = vec![
|
||||
MultiValueIndex::for_test(&[0, 2, 5]).into(),
|
||||
ColumnIndex::Empty { num_docs: 0 },
|
||||
MultiValueIndex::for_test(&[0, 1, 4]).into(),
|
||||
];
|
||||
let merge_row_order: MergeRowOrder = ShuffleMergeOrder::for_test(
|
||||
&[2, 0, 2],
|
||||
@@ -127,10 +208,16 @@ 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") };
|
||||
let start_indexes: Vec<RowId> = start_index_iterable.boxed_iter().collect();
|
||||
let SerializableColumnIndex::Multivalued(start_index_iterable) = merged_column_index else {
|
||||
panic!("Expected a multivalued index")
|
||||
};
|
||||
let mut output = Vec::new();
|
||||
serialize_multivalued_index(&start_index_iterable, &mut output).unwrap();
|
||||
let multivalue =
|
||||
open_multivalued_index(OwnedBytes::new(output), crate::Version::V2).unwrap();
|
||||
let start_indexes: Vec<RowId> = multivalue.get_start_index_column().iter().collect();
|
||||
assert_eq!(&start_indexes, &[0, 3, 5, 6]);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,11 +1,13 @@
|
||||
use std::iter;
|
||||
|
||||
use crate::column_index::{SerializableColumnIndex, Set};
|
||||
use crate::column_index::{
|
||||
SerializableColumnIndex, SerializableMultivalueIndex, SerializableOptionalIndex, Set,
|
||||
};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{Cardinality, ColumnIndex, RowId, ShuffleMergeOrder};
|
||||
|
||||
pub fn merge_column_index_shuffled<'a>(
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
cardinality_after_merge: Cardinality,
|
||||
shuffle_merge_order: &'a ShuffleMergeOrder,
|
||||
) -> SerializableColumnIndex<'a> {
|
||||
@@ -14,15 +16,24 @@ pub fn merge_column_index_shuffled<'a>(
|
||||
Cardinality::Optional => {
|
||||
let non_null_row_ids =
|
||||
merge_column_index_shuffled_optional(column_indexes, shuffle_merge_order);
|
||||
SerializableColumnIndex::Optional {
|
||||
SerializableColumnIndex::Optional(SerializableOptionalIndex {
|
||||
non_null_row_ids,
|
||||
num_rows: shuffle_merge_order.num_rows(),
|
||||
}
|
||||
})
|
||||
}
|
||||
Cardinality::Multivalued => {
|
||||
let multivalue_start_index =
|
||||
merge_column_index_shuffled_multivalued(column_indexes, shuffle_merge_order);
|
||||
SerializableColumnIndex::Multivalued(multivalue_start_index)
|
||||
let non_null_row_ids =
|
||||
merge_column_index_shuffled_optional(column_indexes, shuffle_merge_order);
|
||||
SerializableColumnIndex::Multivalued(SerializableMultivalueIndex {
|
||||
doc_ids_with_values: SerializableOptionalIndex {
|
||||
non_null_row_ids,
|
||||
num_rows: shuffle_merge_order.num_rows(),
|
||||
},
|
||||
start_offsets: merge_column_index_shuffled_multivalued(
|
||||
column_indexes,
|
||||
shuffle_merge_order,
|
||||
),
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -33,41 +44,41 @@ pub fn merge_column_index_shuffled<'a>(
|
||||
///
|
||||
/// In other words the column_indexes passed as argument may NOT be multivalued.
|
||||
fn merge_column_index_shuffled_optional<'a>(
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
) -> Box<dyn Iterable<RowId> + 'a> {
|
||||
Box::new(ShuffledOptionalIndex {
|
||||
Box::new(ShuffledIndex {
|
||||
column_indexes,
|
||||
merge_order,
|
||||
})
|
||||
}
|
||||
|
||||
struct ShuffledOptionalIndex<'a> {
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
struct ShuffledIndex<'a> {
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
}
|
||||
|
||||
impl<'a> Iterable<u32> for ShuffledOptionalIndex<'a> {
|
||||
impl Iterable<u32> for ShuffledIndex<'_> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
Box::new(self.merge_order
|
||||
.iter_new_to_old_row_addrs()
|
||||
.enumerate()
|
||||
.filter_map(|(new_row_id, old_row_addr)| {
|
||||
let Some(column_index) = &self.column_indexes[old_row_addr.segment_ord as usize] else {
|
||||
return None;
|
||||
};
|
||||
let row_id = new_row_id as u32;
|
||||
if column_index.has_value(old_row_addr.row_id) {
|
||||
Some(row_id)
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}))
|
||||
Box::new(
|
||||
self.merge_order
|
||||
.iter_new_to_old_row_addrs()
|
||||
.enumerate()
|
||||
.filter_map(|(new_row_id, old_row_addr)| {
|
||||
let column_index = &self.column_indexes[old_row_addr.segment_ord as usize];
|
||||
let row_id = new_row_id as u32;
|
||||
if column_index.has_value(old_row_addr.row_id) {
|
||||
Some(row_id)
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
fn merge_column_index_shuffled_multivalued<'a>(
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
) -> Box<dyn Iterable<RowId> + 'a> {
|
||||
Box::new(ShuffledMultivaluedIndex {
|
||||
@@ -77,27 +88,21 @@ fn merge_column_index_shuffled_multivalued<'a>(
|
||||
}
|
||||
|
||||
struct ShuffledMultivaluedIndex<'a> {
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
}
|
||||
|
||||
fn iter_num_values<'a>(
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
) -> impl Iterator<Item = u32> + 'a {
|
||||
merge_order.iter_new_to_old_row_addrs().map(|row_addr| {
|
||||
let Some(column_index) = &column_indexes[row_addr.segment_ord as usize] else {
|
||||
// No values in the entire column. It surely means there are 0 values associated to this row.
|
||||
return 0u32;
|
||||
};
|
||||
let column_index = &column_indexes[row_addr.segment_ord as usize];
|
||||
match column_index {
|
||||
ColumnIndex::Empty { .. } => 0u32,
|
||||
ColumnIndex::Full => 1,
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
if optional_index.contains(row_addr.row_id) {
|
||||
1u32
|
||||
} else {
|
||||
0u32
|
||||
}
|
||||
u32::from(optional_index.contains(row_addr.row_id))
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => {
|
||||
multivalued_index.range(row_addr.row_id).len() as u32
|
||||
@@ -108,14 +113,21 @@ fn iter_num_values<'a>(
|
||||
|
||||
/// Transforms an iterator containing the number of vals per row (with `num_rows` elements)
|
||||
/// into a `start_offset` iterator starting at 0 and (with `num_rows + 1` element)
|
||||
///
|
||||
/// This will filter values with 0 values as these are covered by the optional index in the
|
||||
/// multivalue index.
|
||||
fn integrate_num_vals(num_vals: impl Iterator<Item = u32>) -> impl Iterator<Item = RowId> {
|
||||
iter::once(0u32).chain(num_vals.scan(0, |state, num_vals| {
|
||||
*state += num_vals;
|
||||
Some(*state)
|
||||
}))
|
||||
iter::once(0u32).chain(
|
||||
num_vals
|
||||
.filter(|num_vals| *num_vals != 0)
|
||||
.scan(0, |state, num_vals| {
|
||||
*state += num_vals;
|
||||
Some(*state)
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
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))
|
||||
@@ -125,8 +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() {
|
||||
@@ -140,13 +152,13 @@ mod tests {
|
||||
|
||||
#[test]
|
||||
fn test_integrate_num_vals_several() {
|
||||
assert!(integrate_num_vals([3, 0, 10, 20].into_iter()).eq([0, 3, 3, 13, 33].into_iter()));
|
||||
assert!(integrate_num_vals([3, 0, 10, 20].into_iter()).eq([0, 3, 13, 33].into_iter()));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_column_index_optional_shuffle() {
|
||||
let optional_index: ColumnIndex = OptionalIndex::for_test(2, &[0]).into();
|
||||
let column_indexes = vec![Some(optional_index), Some(ColumnIndex::Full)];
|
||||
let column_indexes = [optional_index, ColumnIndex::Full];
|
||||
let row_addrs = vec![
|
||||
RowAddr {
|
||||
segment_ord: 0u32,
|
||||
@@ -163,7 +175,13 @@ mod tests {
|
||||
Cardinality::Optional,
|
||||
&shuffle_merge_order,
|
||||
);
|
||||
let SerializableColumnIndex::Optional { non_null_row_ids, num_rows } = serializable_index else { panic!() };
|
||||
let SerializableColumnIndex::Optional(SerializableOptionalIndex {
|
||||
non_null_row_ids,
|
||||
num_rows,
|
||||
}) = serializable_index
|
||||
else {
|
||||
panic!()
|
||||
};
|
||||
assert_eq!(num_rows, 2);
|
||||
let non_null_rows: Vec<RowId> = non_null_row_ids.boxed_iter().collect();
|
||||
assert_eq!(&non_null_rows, &[1]);
|
||||
|
||||
@@ -1,6 +1,8 @@
|
||||
use std::iter;
|
||||
use std::ops::Range;
|
||||
|
||||
use crate::column_index::{SerializableColumnIndex, Set};
|
||||
use crate::column_index::SerializableColumnIndex;
|
||||
use crate::column_index::multivalued_index::{MultiValueIndex, SerializableMultivalueIndex};
|
||||
use crate::column_index::serialize::SerializableOptionalIndex;
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{Cardinality, ColumnIndex, RowId, StackMergeOrder};
|
||||
|
||||
@@ -9,31 +11,157 @@ use crate::{Cardinality, ColumnIndex, RowId, StackMergeOrder};
|
||||
///
|
||||
/// There are no sort nor deletes involved.
|
||||
pub fn merge_column_index_stacked<'a>(
|
||||
columns: &'a [Option<ColumnIndex>],
|
||||
columns: &'a [ColumnIndex],
|
||||
cardinality_after_merge: Cardinality,
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
) -> SerializableColumnIndex<'a> {
|
||||
match cardinality_after_merge {
|
||||
Cardinality::Full => SerializableColumnIndex::Full,
|
||||
Cardinality::Optional => SerializableColumnIndex::Optional {
|
||||
Cardinality::Optional => SerializableColumnIndex::Optional(SerializableOptionalIndex {
|
||||
non_null_row_ids: Box::new(StackedOptionalIndex {
|
||||
columns,
|
||||
stack_merge_order,
|
||||
}),
|
||||
num_rows: stack_merge_order.num_rows(),
|
||||
},
|
||||
}),
|
||||
Cardinality::Multivalued => {
|
||||
let stacked_multivalued_index = StackedMultivaluedIndex {
|
||||
columns,
|
||||
stack_merge_order,
|
||||
};
|
||||
SerializableColumnIndex::Multivalued(Box::new(stacked_multivalued_index))
|
||||
let serializable_multivalue_index =
|
||||
make_serializable_multivalued_index(columns, stack_merge_order);
|
||||
SerializableColumnIndex::Multivalued(serializable_multivalue_index)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct StackedDocIdsWithValues<'a> {
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
}
|
||||
|
||||
impl Iterable<u32> for StackedDocIdsWithValues<'_> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
Box::new((0..self.column_indexes.len()).flat_map(|i| {
|
||||
let column_index = &self.column_indexes[i];
|
||||
let doc_range = self.stack_merge_order.columnar_range(i);
|
||||
get_doc_ids_with_values(column_index, doc_range)
|
||||
}))
|
||||
}
|
||||
}
|
||||
|
||||
fn get_doc_ids_with_values<'a>(
|
||||
column_index: &'a ColumnIndex,
|
||||
doc_range: Range<u32>,
|
||||
) -> Box<dyn Iterator<Item = u32> + 'a> {
|
||||
match column_index {
|
||||
ColumnIndex::Empty { .. } => Box::new(0..0),
|
||||
ColumnIndex::Full => Box::new(doc_range),
|
||||
ColumnIndex::Optional(optional_index) => Box::new(
|
||||
optional_index
|
||||
.iter_docs()
|
||||
.map(move |row| row + doc_range.start),
|
||||
),
|
||||
ColumnIndex::Multivalued(multivalued_index) => match multivalued_index {
|
||||
MultiValueIndex::MultiValueIndexV1(multivalued_index) => {
|
||||
Box::new((0..multivalued_index.num_docs()).filter_map(move |docid| {
|
||||
let range = multivalued_index.range(docid);
|
||||
if range.is_empty() {
|
||||
None
|
||||
} else {
|
||||
Some(docid + doc_range.start)
|
||||
}
|
||||
}))
|
||||
}
|
||||
MultiValueIndex::MultiValueIndexV2(multivalued_index) => Box::new(
|
||||
multivalued_index
|
||||
.optional_index
|
||||
.iter_docs()
|
||||
.map(move |row| row + doc_range.start),
|
||||
),
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
fn stack_doc_ids_with_values<'a>(
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
) -> SerializableOptionalIndex<'a> {
|
||||
let num_rows = stack_merge_order.num_rows();
|
||||
SerializableOptionalIndex {
|
||||
non_null_row_ids: Box::new(StackedDocIdsWithValues {
|
||||
column_indexes,
|
||||
stack_merge_order,
|
||||
}),
|
||||
num_rows,
|
||||
}
|
||||
}
|
||||
|
||||
struct StackedStartOffsets<'a> {
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
}
|
||||
|
||||
fn get_num_values_iterator<'a>(
|
||||
column_index: &'a ColumnIndex,
|
||||
num_docs: u32,
|
||||
) -> Box<dyn Iterator<Item = u32> + 'a> {
|
||||
match column_index {
|
||||
ColumnIndex::Empty { .. } => Box::new(std::iter::empty()),
|
||||
ColumnIndex::Full => Box::new(std::iter::repeat(1u32).take(num_docs as usize)),
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
Box::new(std::iter::repeat(1u32).take(optional_index.num_non_nulls() as usize))
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => Box::new(
|
||||
multivalued_index
|
||||
.get_start_index_column()
|
||||
.iter()
|
||||
.scan(0u32, |previous_start_offset, current_start_offset| {
|
||||
let num_vals = current_start_offset - *previous_start_offset;
|
||||
*previous_start_offset = current_start_offset;
|
||||
Some(num_vals)
|
||||
})
|
||||
.skip(1),
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
impl Iterable<u32> for StackedStartOffsets<'_> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
let num_values_it = (0..self.column_indexes.len()).flat_map(|columnar_id| {
|
||||
let num_docs = self.stack_merge_order.columnar_range(columnar_id).len() as u32;
|
||||
let column_index = &self.column_indexes[columnar_id];
|
||||
get_num_values_iterator(column_index, num_docs)
|
||||
});
|
||||
Box::new(std::iter::once(0u32).chain(num_values_it.into_iter().scan(
|
||||
0u32,
|
||||
|cumulated, el| {
|
||||
*cumulated += el;
|
||||
Some(*cumulated)
|
||||
},
|
||||
)))
|
||||
}
|
||||
}
|
||||
|
||||
fn stack_start_offsets<'a>(
|
||||
column_indexes: &'a [ColumnIndex],
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
) -> Box<dyn Iterable<u32> + 'a> {
|
||||
Box::new(StackedStartOffsets {
|
||||
column_indexes,
|
||||
stack_merge_order,
|
||||
})
|
||||
}
|
||||
|
||||
fn make_serializable_multivalued_index<'a>(
|
||||
columns: &'a [ColumnIndex],
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
) -> SerializableMultivalueIndex<'a> {
|
||||
SerializableMultivalueIndex {
|
||||
doc_ids_with_values: stack_doc_ids_with_values(columns, stack_merge_order),
|
||||
start_offsets: stack_start_offsets(columns, stack_merge_order),
|
||||
}
|
||||
}
|
||||
|
||||
struct StackedOptionalIndex<'a> {
|
||||
columns: &'a [Option<ColumnIndex>],
|
||||
columns: &'a [ColumnIndex],
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
}
|
||||
|
||||
@@ -46,109 +174,19 @@ impl<'a> Iterable<RowId> for StackedOptionalIndex<'a> {
|
||||
.flat_map(|(columnar_id, column_index_opt)| {
|
||||
let columnar_row_range = self.stack_merge_order.columnar_range(columnar_id);
|
||||
let rows_it: Box<dyn Iterator<Item = RowId>> = match column_index_opt {
|
||||
Some(ColumnIndex::Full) => Box::new(columnar_row_range),
|
||||
Some(ColumnIndex::Optional(optional_index)) => Box::new(
|
||||
ColumnIndex::Full => Box::new(columnar_row_range),
|
||||
ColumnIndex::Optional(optional_index) => Box::new(
|
||||
optional_index
|
||||
.iter_rows()
|
||||
.iter_docs()
|
||||
.map(move |row_id: RowId| columnar_row_range.start + row_id),
|
||||
),
|
||||
Some(ColumnIndex::Multivalued(_)) => {
|
||||
ColumnIndex::Multivalued(_) => {
|
||||
panic!("No multivalued index is allowed when stacking column index");
|
||||
}
|
||||
None => Box::new(std::iter::empty()),
|
||||
ColumnIndex::Empty { .. } => Box::new(std::iter::empty()),
|
||||
};
|
||||
rows_it
|
||||
}),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone, Copy)]
|
||||
struct StackedMultivaluedIndex<'a> {
|
||||
columns: &'a [Option<ColumnIndex>],
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
}
|
||||
|
||||
fn convert_column_opt_to_multivalued_index<'a>(
|
||||
column_index_opt: Option<&'a ColumnIndex>,
|
||||
num_rows: RowId,
|
||||
) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
match column_index_opt {
|
||||
None => Box::new(iter::repeat(0u32).take(num_rows as usize + 1)),
|
||||
Some(ColumnIndex::Full) => Box::new(0..num_rows + 1),
|
||||
Some(ColumnIndex::Optional(optional_index)) => {
|
||||
Box::new(
|
||||
(0..num_rows)
|
||||
// TODO optimize
|
||||
.map(|row_id| optional_index.rank(row_id))
|
||||
.chain(std::iter::once(optional_index.num_non_nulls())),
|
||||
)
|
||||
}
|
||||
Some(ColumnIndex::Multivalued(multivalued_index)) => {
|
||||
multivalued_index.start_index_column.iter()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Iterable<RowId> for StackedMultivaluedIndex<'a> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = RowId> + '_> {
|
||||
let multivalued_indexes =
|
||||
self.columns
|
||||
.iter()
|
||||
.map(Option::as_ref)
|
||||
.enumerate()
|
||||
.map(|(columnar_id, column_opt)| {
|
||||
let num_rows =
|
||||
self.stack_merge_order.columnar_range(columnar_id).len() as RowId;
|
||||
convert_column_opt_to_multivalued_index(column_opt, num_rows)
|
||||
});
|
||||
stack_multivalued_indexes(multivalued_indexes)
|
||||
}
|
||||
}
|
||||
|
||||
// Refactor me
|
||||
fn stack_multivalued_indexes<'a>(
|
||||
mut multivalued_indexes: impl Iterator<Item = Box<dyn Iterator<Item = RowId> + 'a>> + 'a,
|
||||
) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
let mut offset = 0;
|
||||
let mut last_row_id = 0;
|
||||
let mut current_it = multivalued_indexes.next();
|
||||
Box::new(std::iter::from_fn(move || loop {
|
||||
let Some(multivalued_index) = current_it.as_mut() else {
|
||||
return None;
|
||||
};
|
||||
if let Some(row_id) = multivalued_index.next() {
|
||||
last_row_id = offset + row_id;
|
||||
return Some(last_row_id);
|
||||
}
|
||||
offset = last_row_id;
|
||||
loop {
|
||||
current_it = multivalued_indexes.next();
|
||||
if current_it.as_mut()?.next().is_some() {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}))
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use crate::RowId;
|
||||
|
||||
fn it<'a>(row_ids: &'a [RowId]) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
Box::new(row_ids.iter().copied())
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_stack() {
|
||||
let columns = [
|
||||
it(&[0u32, 0u32]),
|
||||
it(&[0u32, 1u32, 1u32, 4u32]),
|
||||
it(&[0u32, 3u32, 5u32]),
|
||||
it(&[0u32, 4u32]),
|
||||
]
|
||||
.into_iter();
|
||||
let start_offsets: Vec<RowId> = super::stack_multivalued_indexes(columns).collect();
|
||||
assert_eq!(start_offsets, &[0, 0, 1, 1, 4, 7, 9, 13]);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,3 +1,8 @@
|
||||
//! # `column_index`
|
||||
//!
|
||||
//! `column_index` provides rank and select operations to associate positions when not all
|
||||
//! documents have exactly one element.
|
||||
|
||||
mod merge;
|
||||
mod multivalued_index;
|
||||
mod optional_index;
|
||||
@@ -6,18 +11,24 @@ mod serialize;
|
||||
use std::ops::Range;
|
||||
|
||||
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};
|
||||
pub use serialize::{
|
||||
SerializableColumnIndex, SerializableOptionalIndex, open_column_index, serialize_column_index,
|
||||
};
|
||||
|
||||
use crate::column_index::multivalued_index::MultiValueIndex;
|
||||
use crate::{Cardinality, RowId};
|
||||
use crate::{Cardinality, DocId, RowId};
|
||||
|
||||
#[derive(Clone)]
|
||||
#[derive(Clone, Debug)]
|
||||
pub enum ColumnIndex {
|
||||
Empty {
|
||||
num_docs: u32,
|
||||
},
|
||||
Full,
|
||||
Optional(OptionalIndex),
|
||||
/// In addition, at index num_rows, an extra value is added
|
||||
/// containing the overal number of values.
|
||||
/// containing the overall number of values.
|
||||
Multivalued(MultiValueIndex),
|
||||
}
|
||||
|
||||
@@ -34,41 +45,138 @@ impl From<MultiValueIndex> for ColumnIndex {
|
||||
}
|
||||
|
||||
impl ColumnIndex {
|
||||
/// Returns the cardinality of the column index.
|
||||
///
|
||||
/// By convention, if the column contains no docs, we consider that it is
|
||||
/// full.
|
||||
#[inline]
|
||||
pub fn get_cardinality(&self) -> Cardinality {
|
||||
match self {
|
||||
ColumnIndex::Full => Cardinality::Full,
|
||||
ColumnIndex::Empty { num_docs: 0 } | ColumnIndex::Full => Cardinality::Full,
|
||||
ColumnIndex::Empty { .. } => Cardinality::Optional,
|
||||
ColumnIndex::Optional(_) => Cardinality::Optional,
|
||||
ColumnIndex::Multivalued(_) => Cardinality::Multivalued,
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns true if and only if there are at least one value associated to the row.
|
||||
pub fn has_value(&self, row_id: RowId) -> bool {
|
||||
pub fn has_value(&self, doc_id: DocId) -> bool {
|
||||
match self {
|
||||
ColumnIndex::Empty { .. } => false,
|
||||
ColumnIndex::Full => true,
|
||||
ColumnIndex::Optional(optional_index) => optional_index.contains(row_id),
|
||||
ColumnIndex::Optional(optional_index) => optional_index.contains(doc_id),
|
||||
ColumnIndex::Multivalued(multivalued_index) => {
|
||||
multivalued_index.range(row_id).len() > 0
|
||||
!multivalued_index.range(doc_id).is_empty()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn value_row_ids(&self, row_id: RowId) -> Range<RowId> {
|
||||
pub fn value_row_ids(&self, doc_id: DocId) -> Range<RowId> {
|
||||
match self {
|
||||
ColumnIndex::Full => row_id..row_id + 1,
|
||||
ColumnIndex::Empty { .. } => 0..0,
|
||||
ColumnIndex::Full => doc_id..doc_id + 1,
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
if let Some(val) = optional_index.rank_if_exists(row_id) {
|
||||
if let Some(val) = optional_index.rank_if_exists(doc_id) {
|
||||
val..val + 1
|
||||
} else {
|
||||
0..0
|
||||
}
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => multivalued_index.range(row_id),
|
||||
ColumnIndex::Multivalued(multivalued_index) => multivalued_index.range(doc_id),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn select_batch_in_place(&self, rank_ids: &mut Vec<RowId>) {
|
||||
/// Translates a block of docis to row_ids.
|
||||
///
|
||||
/// returns the row_ids and the matching docids on the same index
|
||||
/// e.g.
|
||||
/// DocId In: [0, 5, 6]
|
||||
/// DocId Out: [0, 0, 6, 6]
|
||||
/// RowId Out: [0, 1, 2, 3]
|
||||
#[inline]
|
||||
pub fn docids_to_rowids(
|
||||
&self,
|
||||
doc_ids: &[DocId],
|
||||
doc_ids_out: &mut Vec<DocId>,
|
||||
row_ids: &mut Vec<RowId>,
|
||||
) {
|
||||
match self {
|
||||
ColumnIndex::Empty { .. } => {}
|
||||
ColumnIndex::Full => {
|
||||
doc_ids_out.extend_from_slice(doc_ids);
|
||||
row_ids.extend_from_slice(doc_ids);
|
||||
}
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
for doc_id in doc_ids {
|
||||
if let Some(row_id) = optional_index.rank_if_exists(*doc_id) {
|
||||
doc_ids_out.push(*doc_id);
|
||||
row_ids.push(row_id);
|
||||
}
|
||||
}
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => {
|
||||
for doc_id in doc_ids {
|
||||
for row_id in multivalued_index.range(*doc_id) {
|
||||
doc_ids_out.push(*doc_id);
|
||||
row_ids.push(row_id);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn docid_range_to_rowids(&self, doc_id_range: Range<DocId>) -> Range<RowId> {
|
||||
match self {
|
||||
ColumnIndex::Empty { .. } => 0..0,
|
||||
ColumnIndex::Full => doc_id_range,
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
let row_start = optional_index.rank(doc_id_range.start);
|
||||
let row_end = optional_index.rank(doc_id_range.end);
|
||||
row_start..row_end
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => match multivalued_index {
|
||||
MultiValueIndex::MultiValueIndexV1(index) => {
|
||||
let row_start = index.start_index_column.get_val(doc_id_range.start);
|
||||
let row_end = index.start_index_column.get_val(doc_id_range.end);
|
||||
row_start..row_end
|
||||
}
|
||||
MultiValueIndex::MultiValueIndexV2(index) => {
|
||||
// In this case we will use the optional_index select the next values
|
||||
// that are valid. There are different cases to consider:
|
||||
// Not exists below means does not exist in the optional
|
||||
// index, because it has no values.
|
||||
// * doc_id_range may cover a range of docids which are non existent
|
||||
// => rank
|
||||
// will give us the next document outside the range with a value. They both
|
||||
// get the same rank and therefore return a zero range
|
||||
//
|
||||
// * doc_id_range.start and doc_id_range.end may not exist, but docids in
|
||||
// between may have values
|
||||
// => rank will give us the next document outside the range with a value.
|
||||
//
|
||||
// * doc_id_range.start may be not existent but doc_id_range.end may exist
|
||||
// * doc_id_range.start may exist but doc_id_range.end may not exist
|
||||
// * doc_id_range.start and doc_id_range.end may exist
|
||||
// => rank on doc_id_range.end will give use the next value, which matches
|
||||
// how the `start_index_column` works, so we get the value start of the next
|
||||
// docid which we use to create the exclusive range.
|
||||
//
|
||||
let rank_start = index.optional_index.rank(doc_id_range.start);
|
||||
let row_start = index.start_index_column.get_val(rank_start);
|
||||
let rank_end = index.optional_index.rank(doc_id_range.end);
|
||||
let row_end = index.start_index_column.get_val(rank_end);
|
||||
|
||||
row_start..row_end
|
||||
}
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
pub fn select_batch_in_place(&self, doc_id_start: DocId, rank_ids: &mut Vec<RowId>) {
|
||||
match self {
|
||||
ColumnIndex::Empty { .. } => {
|
||||
rank_ids.clear();
|
||||
}
|
||||
ColumnIndex::Full => {
|
||||
// No need to do anything:
|
||||
// value_idx and row_idx are the same.
|
||||
@@ -77,9 +185,26 @@ impl ColumnIndex {
|
||||
optional_index.select_batch(&mut rank_ids[..]);
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => {
|
||||
// TODO important: avoid using 0u32, and restart from the beginning all of the time.
|
||||
multivalued_index.select_batch_in_place(0u32, rank_ids)
|
||||
multivalued_index.select_batch_in_place(doc_id_start, rank_ids)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use crate::{Cardinality, ColumnIndex};
|
||||
|
||||
#[test]
|
||||
fn test_column_index_get_cardinality() {
|
||||
assert_eq!(
|
||||
ColumnIndex::Empty { num_docs: 0 }.get_cardinality(),
|
||||
Cardinality::Full
|
||||
);
|
||||
assert_eq!(ColumnIndex::Full.get_cardinality(), Cardinality::Full);
|
||||
assert_eq!(
|
||||
ColumnIndex::Empty { num_docs: 1 }.get_cardinality(),
|
||||
Cardinality::Optional
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -3,69 +3,111 @@ use std::io::Write;
|
||||
use std::ops::Range;
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::OwnedBytes;
|
||||
use common::{CountingWriter, OwnedBytes};
|
||||
|
||||
use crate::column_values::u64_based::CodecType;
|
||||
use crate::column_values::ColumnValues;
|
||||
use super::optional_index::{open_optional_index, serialize_optional_index};
|
||||
use super::{OptionalIndex, SerializableOptionalIndex, Set};
|
||||
use crate::column_values::{
|
||||
CodecType, ColumnValues, load_u64_based_column_values, serialize_u64_based_column_values,
|
||||
};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::RowId;
|
||||
use crate::{DocId, RowId, Version};
|
||||
|
||||
pub struct SerializableMultivalueIndex<'a> {
|
||||
pub doc_ids_with_values: SerializableOptionalIndex<'a>,
|
||||
pub start_offsets: Box<dyn Iterable<u32> + 'a>,
|
||||
}
|
||||
|
||||
pub fn serialize_multivalued_index(
|
||||
multivalued_index: &dyn Iterable<RowId>,
|
||||
multivalued_index: &SerializableMultivalueIndex,
|
||||
output: &mut impl Write,
|
||||
) -> io::Result<()> {
|
||||
crate::column_values::u64_based::serialize_u64_based_column_values(
|
||||
multivalued_index,
|
||||
let SerializableMultivalueIndex {
|
||||
doc_ids_with_values,
|
||||
start_offsets,
|
||||
} = multivalued_index;
|
||||
let mut count_writer = CountingWriter::wrap(output);
|
||||
let SerializableOptionalIndex {
|
||||
non_null_row_ids,
|
||||
num_rows,
|
||||
} = doc_ids_with_values;
|
||||
serialize_optional_index(&**non_null_row_ids, *num_rows, &mut count_writer)?;
|
||||
let optional_len = count_writer.written_bytes() as u32;
|
||||
let output = count_writer.finish();
|
||||
serialize_u64_based_column_values(
|
||||
&**start_offsets,
|
||||
&[CodecType::Bitpacked, CodecType::Linear],
|
||||
output,
|
||||
)?;
|
||||
output.write_all(&optional_len.to_le_bytes())?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn open_multivalued_index(bytes: OwnedBytes) -> io::Result<MultiValueIndex> {
|
||||
let start_index_column: Arc<dyn ColumnValues<RowId>> =
|
||||
crate::column_values::u64_based::load_u64_based_column_values(bytes)?;
|
||||
Ok(MultiValueIndex { start_index_column })
|
||||
pub fn open_multivalued_index(
|
||||
bytes: OwnedBytes,
|
||||
format_version: Version,
|
||||
) -> io::Result<MultiValueIndex> {
|
||||
match format_version {
|
||||
Version::V1 => {
|
||||
let start_index_column: Arc<dyn ColumnValues<RowId>> =
|
||||
load_u64_based_column_values(bytes)?;
|
||||
Ok(MultiValueIndex::MultiValueIndexV1(MultiValueIndexV1 {
|
||||
start_index_column,
|
||||
}))
|
||||
}
|
||||
Version::V2 => {
|
||||
let (body_bytes, optional_index_len) = bytes.rsplit(4);
|
||||
let optional_index_len =
|
||||
u32::from_le_bytes(optional_index_len.as_slice().try_into().unwrap());
|
||||
let (optional_index_bytes, start_index_bytes) =
|
||||
body_bytes.split(optional_index_len as usize);
|
||||
let optional_index = open_optional_index(optional_index_bytes)?;
|
||||
let start_index_column: Arc<dyn ColumnValues<RowId>> =
|
||||
load_u64_based_column_values(start_index_bytes)?;
|
||||
Ok(MultiValueIndex::MultiValueIndexV2(MultiValueIndexV2 {
|
||||
optional_index,
|
||||
start_index_column,
|
||||
}))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
/// Index to resolve value range for given doc_id.
|
||||
/// Starts at 0.
|
||||
pub struct MultiValueIndex {
|
||||
pub enum MultiValueIndex {
|
||||
MultiValueIndexV1(MultiValueIndexV1),
|
||||
MultiValueIndexV2(MultiValueIndexV2),
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
/// Index to resolve value range for given doc_id.
|
||||
/// Starts at 0.
|
||||
pub struct MultiValueIndexV1 {
|
||||
pub start_index_column: Arc<dyn crate::ColumnValues<RowId>>,
|
||||
}
|
||||
|
||||
impl From<Arc<dyn ColumnValues<RowId>>> for MultiValueIndex {
|
||||
fn from(start_index_column: Arc<dyn ColumnValues<RowId>>) -> Self {
|
||||
MultiValueIndex { start_index_column }
|
||||
}
|
||||
}
|
||||
|
||||
impl MultiValueIndex {
|
||||
pub fn for_test(start_offsets: &[RowId]) -> MultiValueIndex {
|
||||
let mut buffer = Vec::new();
|
||||
serialize_multivalued_index(&start_offsets, &mut buffer).unwrap();
|
||||
let bytes = OwnedBytes::new(buffer);
|
||||
open_multivalued_index(bytes).unwrap()
|
||||
}
|
||||
|
||||
impl MultiValueIndexV1 {
|
||||
/// Returns `[start, end)`, such that the values associated with
|
||||
/// the given document are `start..end`.
|
||||
#[inline]
|
||||
pub(crate) fn range(&self, row_id: RowId) -> Range<RowId> {
|
||||
let start = self.start_index_column.get_val(row_id);
|
||||
let end = self.start_index_column.get_val(row_id + 1);
|
||||
pub(crate) fn range(&self, doc_id: DocId) -> Range<RowId> {
|
||||
if doc_id >= self.num_docs() {
|
||||
return 0..0;
|
||||
}
|
||||
let start = self.start_index_column.get_val(doc_id);
|
||||
let end = self.start_index_column.get_val(doc_id + 1);
|
||||
start..end
|
||||
}
|
||||
|
||||
/// Returns the number of documents in the index.
|
||||
#[inline]
|
||||
pub fn num_rows(&self) -> u32 {
|
||||
pub fn num_docs(&self) -> u32 {
|
||||
self.start_index_column.num_vals() - 1
|
||||
}
|
||||
|
||||
/// Converts a list of ranks (row ids of values) in a 1:n index to the corresponding list of
|
||||
/// row_ids. Positions are converted inplace to docids.
|
||||
/// docids. Positions are converted inplace to docids.
|
||||
///
|
||||
/// Since there is no index for value pos -> docid, but docid -> value pos range, we scan the
|
||||
/// index.
|
||||
@@ -75,21 +117,20 @@ impl MultiValueIndex {
|
||||
///
|
||||
/// TODO: Instead of a linear scan we can employ a exponential search into binary search to
|
||||
/// match a docid to its value position.
|
||||
#[allow(clippy::bool_to_int_with_if)]
|
||||
pub(crate) fn select_batch_in_place(&self, row_start: RowId, ranks: &mut Vec<u32>) {
|
||||
pub(crate) fn select_batch_in_place(&self, docid_start: DocId, ranks: &mut Vec<u32>) {
|
||||
if ranks.is_empty() {
|
||||
return;
|
||||
}
|
||||
let mut cur_doc = row_start;
|
||||
let mut cur_doc = docid_start;
|
||||
let mut last_doc = None;
|
||||
|
||||
assert!(self.start_index_column.get_val(row_start) as u32 <= ranks[0]);
|
||||
assert!(self.start_index_column.get_val(docid_start) <= ranks[0]);
|
||||
|
||||
let mut write_doc_pos = 0;
|
||||
for i in 0..ranks.len() {
|
||||
let pos = ranks[i];
|
||||
loop {
|
||||
let end = self.start_index_column.get_val(cur_doc + 1) as u32;
|
||||
let end = self.start_index_column.get_val(cur_doc + 1);
|
||||
if end > pos {
|
||||
ranks[write_doc_pos] = cur_doc;
|
||||
write_doc_pos += if last_doc == Some(cur_doc) { 0 } else { 1 };
|
||||
@@ -103,14 +144,170 @@ impl MultiValueIndex {
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
/// Index to resolve value range for given doc_id.
|
||||
/// Starts at 0.
|
||||
pub struct MultiValueIndexV2 {
|
||||
pub optional_index: OptionalIndex,
|
||||
pub start_index_column: Arc<dyn crate::ColumnValues<RowId>>,
|
||||
}
|
||||
|
||||
impl std::fmt::Debug for MultiValueIndex {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
|
||||
let index = match self {
|
||||
MultiValueIndex::MultiValueIndexV1(idx) => &idx.start_index_column,
|
||||
MultiValueIndex::MultiValueIndexV2(idx) => &idx.start_index_column,
|
||||
};
|
||||
f.debug_struct("MultiValuedIndex")
|
||||
.field("num_rows", &index.num_vals())
|
||||
.finish_non_exhaustive()
|
||||
}
|
||||
}
|
||||
|
||||
impl MultiValueIndex {
|
||||
pub fn for_test(start_offsets: &[RowId]) -> MultiValueIndex {
|
||||
assert!(!start_offsets.is_empty());
|
||||
assert_eq!(start_offsets[0], 0);
|
||||
let mut doc_with_values = Vec::new();
|
||||
let mut compact_start_offsets: Vec<u32> = vec![0];
|
||||
for doc in 0..start_offsets.len() - 1 {
|
||||
if start_offsets[doc] < start_offsets[doc + 1] {
|
||||
doc_with_values.push(doc as RowId);
|
||||
compact_start_offsets.push(start_offsets[doc + 1]);
|
||||
}
|
||||
}
|
||||
let serializable_multivalued_index = SerializableMultivalueIndex {
|
||||
doc_ids_with_values: SerializableOptionalIndex {
|
||||
non_null_row_ids: Box::new(&doc_with_values[..]),
|
||||
num_rows: start_offsets.len() as u32 - 1,
|
||||
},
|
||||
start_offsets: Box::new(&compact_start_offsets[..]),
|
||||
};
|
||||
let mut buffer = Vec::new();
|
||||
serialize_multivalued_index(&serializable_multivalued_index, &mut buffer).unwrap();
|
||||
let bytes = OwnedBytes::new(buffer);
|
||||
open_multivalued_index(bytes, Version::V2).unwrap()
|
||||
}
|
||||
|
||||
pub fn get_start_index_column(&self) -> &Arc<dyn crate::ColumnValues<RowId>> {
|
||||
match self {
|
||||
MultiValueIndex::MultiValueIndexV1(idx) => &idx.start_index_column,
|
||||
MultiValueIndex::MultiValueIndexV2(idx) => &idx.start_index_column,
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns `[start, end)` values range, such that the values associated with
|
||||
/// the given document are `start..end`.
|
||||
#[inline]
|
||||
pub(crate) fn range(&self, doc_id: DocId) -> Range<RowId> {
|
||||
match self {
|
||||
MultiValueIndex::MultiValueIndexV1(idx) => idx.range(doc_id),
|
||||
MultiValueIndex::MultiValueIndexV2(idx) => idx.range(doc_id),
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the number of documents in the index.
|
||||
#[inline]
|
||||
pub fn num_docs(&self) -> u32 {
|
||||
match self {
|
||||
MultiValueIndex::MultiValueIndexV1(idx) => idx.start_index_column.num_vals() - 1,
|
||||
MultiValueIndex::MultiValueIndexV2(idx) => idx.optional_index.num_docs(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Converts a list of ranks (row ids of values) in a 1:n index to the corresponding list of
|
||||
/// docids. Positions are converted inplace to docids.
|
||||
///
|
||||
/// Since there is no index for value pos -> docid, but docid -> value pos range, we scan the
|
||||
/// index.
|
||||
///
|
||||
/// Correctness: positions needs to be sorted. idx_reader needs to contain monotonically
|
||||
/// increasing positions.
|
||||
///
|
||||
/// TODO: Instead of a linear scan we can employ a exponential search into binary search to
|
||||
/// match a docid to its value position.
|
||||
pub(crate) fn select_batch_in_place(&self, docid_start: DocId, ranks: &mut Vec<u32>) {
|
||||
match self {
|
||||
MultiValueIndex::MultiValueIndexV1(idx) => {
|
||||
idx.select_batch_in_place(docid_start, ranks)
|
||||
}
|
||||
MultiValueIndex::MultiValueIndexV2(idx) => {
|
||||
idx.select_batch_in_place(docid_start, ranks)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
impl MultiValueIndexV2 {
|
||||
/// Returns `[start, end)`, such that the values associated with
|
||||
/// the given document are `start..end`.
|
||||
#[inline]
|
||||
pub(crate) fn range(&self, doc_id: DocId) -> Range<RowId> {
|
||||
let Some(rank) = self.optional_index.rank_if_exists(doc_id) else {
|
||||
return 0..0;
|
||||
};
|
||||
let start = self.start_index_column.get_val(rank);
|
||||
let end = self.start_index_column.get_val(rank + 1);
|
||||
start..end
|
||||
}
|
||||
|
||||
/// Returns the number of documents in the index.
|
||||
#[inline]
|
||||
pub fn num_docs(&self) -> u32 {
|
||||
self.optional_index.num_docs()
|
||||
}
|
||||
|
||||
/// Converts a list of ranks (row ids of values) in a 1:n index to the corresponding list of
|
||||
/// docids. Positions are converted inplace to docids.
|
||||
///
|
||||
/// Since there is no index for value pos -> docid, but docid -> value pos range, we scan the
|
||||
/// index.
|
||||
///
|
||||
/// Correctness: positions needs to be sorted. idx_reader needs to contain monotonically
|
||||
/// increasing positions.
|
||||
///
|
||||
/// TODO: Instead of a linear scan we can employ a exponential search into binary search to
|
||||
/// match a docid to its value position.
|
||||
pub(crate) fn select_batch_in_place(&self, docid_start: DocId, ranks: &mut Vec<u32>) {
|
||||
if ranks.is_empty() {
|
||||
return;
|
||||
}
|
||||
let mut cur_pos_in_idx = self.optional_index.rank(docid_start);
|
||||
let mut last_doc = None;
|
||||
|
||||
assert!(cur_pos_in_idx <= ranks[0]);
|
||||
|
||||
let mut write_doc_pos = 0;
|
||||
for i in 0..ranks.len() {
|
||||
let pos = ranks[i];
|
||||
loop {
|
||||
let end = self.start_index_column.get_val(cur_pos_in_idx + 1);
|
||||
if end > pos {
|
||||
ranks[write_doc_pos] = cur_pos_in_idx;
|
||||
write_doc_pos += if last_doc == Some(cur_pos_in_idx) {
|
||||
0
|
||||
} else {
|
||||
1
|
||||
};
|
||||
last_doc = Some(cur_pos_in_idx);
|
||||
break;
|
||||
}
|
||||
cur_pos_in_idx += 1;
|
||||
}
|
||||
}
|
||||
ranks.truncate(write_doc_pos);
|
||||
|
||||
for rank in ranks.iter_mut() {
|
||||
*rank = self.optional_index.select(*rank);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::ops::Range;
|
||||
use std::sync::Arc;
|
||||
|
||||
use super::MultiValueIndex;
|
||||
use crate::column_values::IterColumn;
|
||||
use crate::{ColumnValues, RowId};
|
||||
use crate::{ColumnarReader, DynamicColumn};
|
||||
|
||||
fn index_to_pos_helper(
|
||||
index: &MultiValueIndex,
|
||||
@@ -124,13 +321,12 @@ mod tests {
|
||||
|
||||
#[test]
|
||||
fn test_positions_to_docid() {
|
||||
let offsets: Vec<RowId> = vec![0, 10, 12, 15, 22, 23]; // docid values are [0..10, 10..12, 12..15, etc.]
|
||||
let column: Arc<dyn ColumnValues<RowId>> = Arc::new(IterColumn::from(offsets.into_iter()));
|
||||
let index = MultiValueIndex::from(column);
|
||||
assert_eq!(index.num_rows(), 5);
|
||||
let index = MultiValueIndex::for_test(&[0, 10, 12, 15, 22, 23]);
|
||||
assert_eq!(index.num_docs(), 5);
|
||||
let positions = &[10u32, 11, 15, 20, 21, 22];
|
||||
assert_eq!(index_to_pos_helper(&index, 0..5, positions), vec![1, 3, 4]);
|
||||
assert_eq!(index_to_pos_helper(&index, 1..5, positions), vec![1, 3, 4]);
|
||||
|
||||
assert_eq!(index_to_pos_helper(&index, 0..5, &[9]), vec![0]);
|
||||
assert_eq!(index_to_pos_helper(&index, 1..5, &[10]), vec![1]);
|
||||
assert_eq!(index_to_pos_helper(&index, 1..5, &[11]), vec![1]);
|
||||
@@ -138,4 +334,67 @@ mod tests {
|
||||
assert_eq!(index_to_pos_helper(&index, 2..5, &[12, 14]), vec![2]);
|
||||
assert_eq!(index_to_pos_helper(&index, 2..5, &[12, 14, 15]), vec![2, 3]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_range_to_rowids() {
|
||||
use crate::ColumnarWriter;
|
||||
|
||||
let mut columnar_writer = ColumnarWriter::default();
|
||||
|
||||
// This column gets coerced to u64
|
||||
columnar_writer.record_numerical(1, "full", u64::MAX);
|
||||
columnar_writer.record_numerical(1, "full", u64::MAX);
|
||||
|
||||
columnar_writer.record_numerical(5, "full", u64::MAX);
|
||||
columnar_writer.record_numerical(5, "full", u64::MAX);
|
||||
|
||||
let mut wrt: Vec<u8> = Vec::new();
|
||||
columnar_writer.serialize(7, &mut wrt).unwrap();
|
||||
|
||||
let reader = ColumnarReader::open(wrt).unwrap();
|
||||
// Open the column as u64
|
||||
let column = reader.read_columns("full").unwrap()[0]
|
||||
.open()
|
||||
.unwrap()
|
||||
.coerce_numerical(crate::NumericalType::U64)
|
||||
.unwrap();
|
||||
let DynamicColumn::U64(column) = column else {
|
||||
panic!();
|
||||
};
|
||||
|
||||
let row_id_range = column.index.docid_range_to_rowids(1..2);
|
||||
assert_eq!(row_id_range, 0..2);
|
||||
|
||||
let row_id_range = column.index.docid_range_to_rowids(0..2);
|
||||
assert_eq!(row_id_range, 0..2);
|
||||
|
||||
let row_id_range = column.index.docid_range_to_rowids(0..4);
|
||||
assert_eq!(row_id_range, 0..2);
|
||||
|
||||
let row_id_range = column.index.docid_range_to_rowids(3..4);
|
||||
assert_eq!(row_id_range, 2..2);
|
||||
|
||||
let row_id_range = column.index.docid_range_to_rowids(1..6);
|
||||
assert_eq!(row_id_range, 0..4);
|
||||
|
||||
let row_id_range = column.index.docid_range_to_rowids(3..6);
|
||||
assert_eq!(row_id_range, 2..4);
|
||||
|
||||
let row_id_range = column.index.docid_range_to_rowids(0..6);
|
||||
assert_eq!(row_id_range, 0..4);
|
||||
|
||||
let row_id_range = column.index.docid_range_to_rowids(0..6);
|
||||
assert_eq!(row_id_range, 0..4);
|
||||
|
||||
let check = |range, expected| {
|
||||
let full_range = 0..=u64::MAX;
|
||||
let mut docids = Vec::new();
|
||||
column.get_docids_for_value_range(full_range, range, &mut docids);
|
||||
assert_eq!(docids, expected);
|
||||
};
|
||||
|
||||
// check(0..1, vec![]);
|
||||
// check(0..2, vec![1]);
|
||||
check(1..2, vec![1]);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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::{InvalidData, RowId};
|
||||
use crate::{DocId, InvalidData, RowId};
|
||||
|
||||
/// The threshold for for number of elements after which we switch to dense block encoding.
|
||||
///
|
||||
@@ -21,8 +21,6 @@ const DENSE_BLOCK_THRESHOLD: u32 =
|
||||
|
||||
const ELEMENTS_PER_BLOCK: u32 = u16::MAX as u32 + 1;
|
||||
|
||||
const BLOCK_SIZE: RowId = 1 << 16;
|
||||
|
||||
#[derive(Copy, Clone, Debug)]
|
||||
struct BlockMeta {
|
||||
non_null_rows_before_block: u32,
|
||||
@@ -82,12 +80,27 @@ 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 Iterable<u32> for &OptionalIndex {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
Box::new(self.iter_docs())
|
||||
}
|
||||
}
|
||||
|
||||
impl std::fmt::Debug for OptionalIndex {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
|
||||
f.debug_struct("OptionalIndex")
|
||||
.field("num_docs", &self.num_docs)
|
||||
.field("num_non_null_docs", &self.num_non_null_docs)
|
||||
.finish_non_exhaustive()
|
||||
}
|
||||
}
|
||||
|
||||
/// Splits a value address into lower and upper 16bits.
|
||||
/// The lower 16 bits are the value in the block
|
||||
/// The upper 16 bits are the block index
|
||||
@@ -100,8 +113,8 @@ struct RowAddr {
|
||||
#[inline(always)]
|
||||
fn row_addr_from_row_id(row_id: RowId) -> RowAddr {
|
||||
RowAddr {
|
||||
block_id: (row_id / BLOCK_SIZE) as u16,
|
||||
in_block_row_id: (row_id % BLOCK_SIZE) as u16,
|
||||
block_id: (row_id / ELEMENTS_PER_BLOCK) as u16,
|
||||
in_block_row_id: (row_id % ELEMENTS_PER_BLOCK) as u16,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -110,7 +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),
|
||||
@@ -128,7 +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
|
||||
@@ -152,7 +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;
|
||||
@@ -161,7 +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 {
|
||||
@@ -176,14 +191,20 @@ impl Set<RowId> for OptionalIndex {
|
||||
}
|
||||
}
|
||||
|
||||
/// Any value doc_id is allowed.
|
||||
/// In particular, doc_id = num_rows.
|
||||
#[inline]
|
||||
fn rank(&self, row_id: RowId) -> RowId {
|
||||
fn rank(&self, doc_id: DocId) -> RowId {
|
||||
if doc_id >= self.num_docs() {
|
||||
return self.num_non_nulls();
|
||||
}
|
||||
let RowAddr {
|
||||
block_id,
|
||||
in_block_row_id,
|
||||
} = row_addr_from_row_id(row_id);
|
||||
} = row_addr_from_row_id(doc_id);
|
||||
let block_meta = self.block_metas[block_id as usize];
|
||||
let block = self.block(block_meta);
|
||||
|
||||
let block_offset_row_id = match block {
|
||||
Block::Dense(dense_block) => dense_block.rank(in_block_row_id),
|
||||
Block::Sparse(sparse_block) => sparse_block.rank(in_block_row_id),
|
||||
@@ -191,13 +212,15 @@ impl Set<RowId> for OptionalIndex {
|
||||
block_meta.non_null_rows_before_block + block_offset_row_id
|
||||
}
|
||||
|
||||
/// Any value doc_id is allowed.
|
||||
/// In particular, doc_id = num_rows.
|
||||
#[inline]
|
||||
fn rank_if_exists(&self, row_id: RowId) -> Option<RowId> {
|
||||
fn rank_if_exists(&self, doc_id: DocId) -> Option<RowId> {
|
||||
let RowAddr {
|
||||
block_id,
|
||||
in_block_row_id,
|
||||
} = row_addr_from_row_id(row_id);
|
||||
let block_meta = self.block_metas[block_id as usize];
|
||||
} = row_addr_from_row_id(doc_id);
|
||||
let block_meta = *self.block_metas.get(block_id as usize)?;
|
||||
let block = self.block(block_meta);
|
||||
let block_offset_row_id = match block {
|
||||
Block::Dense(dense_block) => dense_block.rank_if_exists(in_block_row_id),
|
||||
@@ -220,7 +243,7 @@ impl Set<RowId> for OptionalIndex {
|
||||
block_doc_idx_start + in_block_rank as u32
|
||||
}
|
||||
|
||||
fn select_cursor<'b>(&'b self) -> OptionalIndexSelectCursor<'b> {
|
||||
fn select_cursor(&self) -> OptionalIndexSelectCursor<'_> {
|
||||
OptionalIndexSelectCursor {
|
||||
current_block_cursor: BlockSelectCursor::Sparse(
|
||||
SparseBlockCodec::open(b"").select_cursor(),
|
||||
@@ -236,29 +259,31 @@ 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);
|
||||
open_optional_index(bytes).unwrap()
|
||||
}
|
||||
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
self.num_rows
|
||||
pub fn num_docs(&self) -> RowId {
|
||||
self.num_docs
|
||||
}
|
||||
|
||||
pub fn num_non_nulls(&self) -> RowId {
|
||||
self.num_non_null_rows
|
||||
self.num_non_null_docs
|
||||
}
|
||||
|
||||
pub fn iter_rows<'a>(&'a self) -> impl Iterator<Item = RowId> + 'a {
|
||||
pub fn iter_docs(&self) -> impl Iterator<Item = RowId> + '_ {
|
||||
// TODO optimize
|
||||
let mut select_batch = self.select_cursor();
|
||||
(0..self.num_non_null_rows).map(move |rank| select_batch.select(rank))
|
||||
(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();
|
||||
@@ -268,7 +293,7 @@ impl OptionalIndex {
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn block<'a>(&'a self, block_meta: BlockMeta) -> Block<'a> {
|
||||
fn block(&self, block_meta: BlockMeta) -> Block<'_> {
|
||||
let BlockMeta {
|
||||
start_byte_offset,
|
||||
block_variant,
|
||||
@@ -351,7 +376,7 @@ fn serialize_optional_index_block(block_els: &[u16], out: &mut impl io::Write) -
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn serialize_optional_index<'a, W: io::Write>(
|
||||
pub fn serialize_optional_index<W: io::Write>(
|
||||
non_null_rows: &dyn Iterable<RowId>,
|
||||
num_rows: RowId,
|
||||
output: &mut W,
|
||||
@@ -427,7 +452,7 @@ impl SerializedBlockMeta {
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn to_bytes(&self) -> [u8; SERIALIZED_BLOCK_META_NUM_BYTES] {
|
||||
fn to_bytes(self) -> [u8; SERIALIZED_BLOCK_META_NUM_BYTES] {
|
||||
assert!(self.num_non_null_rows > 0);
|
||||
let mut bytes = [0u8; SERIALIZED_BLOCK_META_NUM_BYTES];
|
||||
bytes[0..2].copy_from_slice(&self.block_id.to_le_bytes());
|
||||
@@ -440,7 +465,7 @@ impl SerializedBlockMeta {
|
||||
|
||||
#[inline]
|
||||
fn is_sparse(num_rows_in_block: u32) -> bool {
|
||||
num_rows_in_block < DENSE_BLOCK_THRESHOLD as u32
|
||||
num_rows_in_block < DENSE_BLOCK_THRESHOLD
|
||||
}
|
||||
|
||||
fn deserialize_optional_index_block_metadatas(
|
||||
@@ -448,7 +473,7 @@ fn deserialize_optional_index_block_metadatas(
|
||||
num_rows: u32,
|
||||
) -> (Box<[BlockMeta]>, u32) {
|
||||
let num_blocks = data.len() / SERIALIZED_BLOCK_META_NUM_BYTES;
|
||||
let mut block_metas = Vec::with_capacity(num_blocks as usize + 1);
|
||||
let mut block_metas = Vec::with_capacity(num_blocks + 1);
|
||||
let mut start_byte_offset = 0;
|
||||
let mut non_null_rows_before_block = 0;
|
||||
for block_meta_bytes in data.chunks_exact(SERIALIZED_BLOCK_META_NUM_BYTES) {
|
||||
@@ -479,10 +504,10 @@ fn deserialize_optional_index_block_metadatas(
|
||||
block_variant,
|
||||
});
|
||||
start_byte_offset += block_variant.num_bytes_in_block();
|
||||
non_null_rows_before_block += num_non_null_rows as u32;
|
||||
non_null_rows_before_block += num_non_null_rows;
|
||||
}
|
||||
block_metas.resize(
|
||||
((num_rows + BLOCK_SIZE - 1) / BLOCK_SIZE) as usize,
|
||||
num_rows.div_ceil(ELEMENTS_PER_BLOCK) as usize,
|
||||
BlockMeta {
|
||||
non_null_rows_before_block,
|
||||
start_byte_offset,
|
||||
@@ -496,15 +521,15 @@ 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).into();
|
||||
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(),
|
||||
};
|
||||
|
||||
@@ -10,7 +10,7 @@ pub trait SetCodec {
|
||||
///
|
||||
/// May panic if the elements are not sorted.
|
||||
fn serialize(els: impl Iterator<Item = Self::Item>, wrt: impl io::Write) -> io::Result<()>;
|
||||
fn open<'a>(data: &'a [u8]) -> Self::Reader<'a>;
|
||||
fn open(data: &[u8]) -> Self::Reader<'_>;
|
||||
}
|
||||
|
||||
/// Stateful object that makes it possible to compute several select in a row,
|
||||
@@ -28,10 +28,11 @@ pub trait Set<T> {
|
||||
/// Returns true if the elements is contained in the Set
|
||||
fn contains(&self, el: T) -> bool;
|
||||
|
||||
/// Returns the number of rows in the set that are < `el`
|
||||
/// Returns the element's rank (its position in the set).
|
||||
/// If the set does not contain the element, it will return the next existing elements rank.
|
||||
fn rank(&self, el: T) -> T;
|
||||
|
||||
/// If the set contains `el` returns the element rank.
|
||||
/// If the set contains `el`, returns the element's rank (its position in the set).
|
||||
/// If the set does not contain the element, it returns `None`.
|
||||
fn rank_if_exists(&self, el: T) -> Option<T>;
|
||||
|
||||
@@ -39,9 +40,10 @@ pub trait Set<T> {
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// May panic if rank is greater than the number of elements in the Set.
|
||||
/// May panic if rank is greater or equal to the number of
|
||||
/// elements in the Set.
|
||||
fn select(&self, rank: T) -> T;
|
||||
|
||||
/// Creates a brand new select cursor.
|
||||
fn select_cursor<'b>(&'b self) -> Self::SelectCursor<'b>;
|
||||
fn select_cursor(&self) -> Self::SelectCursor<'_>;
|
||||
}
|
||||
|
||||
@@ -1,9 +1,8 @@
|
||||
use std::convert::TryInto;
|
||||
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 {
|
||||
@@ -24,7 +23,6 @@ fn set_bit_at(input: &mut u64, n: u16) {
|
||||
///
|
||||
/// When translating a dense index to the original index, we can use the offset to find the correct
|
||||
/// block. Direct computation is not possible, but we can employ a linear or binary search.
|
||||
|
||||
const ELEMENTS_PER_MINI_BLOCK: u16 = 64;
|
||||
const MINI_BLOCK_BITVEC_NUM_BYTES: usize = 8;
|
||||
const MINI_BLOCK_OFFSET_NUM_BYTES: usize = 2;
|
||||
@@ -32,7 +30,7 @@ pub const MINI_BLOCK_NUM_BYTES: usize = MINI_BLOCK_BITVEC_NUM_BYTES + MINI_BLOCK
|
||||
|
||||
/// Number of bytes in a dense block.
|
||||
pub const DENSE_BLOCK_NUM_BYTES: u32 =
|
||||
(ELEMENTS_PER_BLOCK as u32 / ELEMENTS_PER_MINI_BLOCK as u32) * MINI_BLOCK_NUM_BYTES as u32;
|
||||
(ELEMENTS_PER_BLOCK / ELEMENTS_PER_MINI_BLOCK as u32) * MINI_BLOCK_NUM_BYTES as u32;
|
||||
|
||||
pub struct DenseBlockCodec;
|
||||
|
||||
@@ -45,7 +43,7 @@ impl SetCodec for DenseBlockCodec {
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn open<'a>(data: &'a [u8]) -> Self::Reader<'a> {
|
||||
fn open(data: &[u8]) -> Self::Reader<'_> {
|
||||
assert_eq!(data.len(), DENSE_BLOCK_NUM_BYTES as usize);
|
||||
DenseBlock(data)
|
||||
}
|
||||
@@ -94,7 +92,7 @@ impl DenseMiniBlock {
|
||||
Self { bitvec, rank }
|
||||
}
|
||||
|
||||
fn to_bytes(&self) -> [u8; MINI_BLOCK_NUM_BYTES] {
|
||||
fn to_bytes(self) -> [u8; MINI_BLOCK_NUM_BYTES] {
|
||||
let mut bytes = [0u8; MINI_BLOCK_NUM_BYTES];
|
||||
bytes[..MINI_BLOCK_BITVEC_NUM_BYTES].copy_from_slice(&self.bitvec.to_le_bytes());
|
||||
bytes[MINI_BLOCK_BITVEC_NUM_BYTES..].copy_from_slice(&self.rank.to_le_bytes());
|
||||
@@ -110,7 +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
|
||||
@@ -124,7 +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 {
|
||||
@@ -166,7 +166,7 @@ impl<'a> Set<u16> for DenseBlock<'a> {
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn select_cursor<'b>(&'b self) -> Self::SelectCursor<'b> {
|
||||
fn select_cursor(&self) -> Self::SelectCursor<'_> {
|
||||
DenseBlockSelectCursor {
|
||||
block_id: 0,
|
||||
dense_block: *self,
|
||||
@@ -174,7 +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;
|
||||
@@ -229,7 +229,7 @@ pub fn serialize_dense_codec(
|
||||
while block_id > current_block_id {
|
||||
let dense_mini_block = DenseMiniBlock {
|
||||
bitvec: block,
|
||||
rank: non_null_rows_before as u16,
|
||||
rank: non_null_rows_before,
|
||||
};
|
||||
output.write_all(&dense_mini_block.to_bytes())?;
|
||||
non_null_rows_before += block.count_ones() as u16;
|
||||
|
||||
@@ -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)]
|
||||
|
||||
@@ -16,7 +16,7 @@ impl SetCodec for SparseBlockCodec {
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn open<'a>(data: &'a [u8]) -> Self::Reader<'a> {
|
||||
fn open(data: &[u8]) -> Self::Reader<'_> {
|
||||
SparseBlock(data)
|
||||
}
|
||||
}
|
||||
@@ -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 {
|
||||
@@ -56,7 +58,7 @@ impl<'a> Set<u16> for SparseBlock<'a> {
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn select_cursor<'b>(&'b self) -> Self::SelectCursor<'b> {
|
||||
fn select_cursor(&self) -> Self::SelectCursor<'_> {
|
||||
*self
|
||||
}
|
||||
}
|
||||
@@ -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;
|
||||
|
||||
@@ -22,8 +22,8 @@ fn test_set_helper<C: SetCodec<Item = u16>>(vals: &[u16]) -> usize {
|
||||
vals.iter().cloned().take_while(|v| *v < val).count() as u16
|
||||
);
|
||||
}
|
||||
for rank in 0..vals.len() {
|
||||
assert_eq!(tested_set.select(rank as u16), vals[rank]);
|
||||
for (rank, val) in vals.iter().enumerate() {
|
||||
assert_eq!(tested_set.select(rank as u16), *val);
|
||||
}
|
||||
buffer.len()
|
||||
}
|
||||
@@ -107,3 +107,41 @@ fn test_simple_translate_codec_idx_to_original_idx_dense() {
|
||||
assert_eq!(i, select_cursor.select(i));
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_simple_translate_idx_to_value_idx_dense() {
|
||||
let mut buffer = Vec::new();
|
||||
DenseBlockCodec::serialize([1, 10].iter().copied(), &mut buffer).unwrap();
|
||||
let tested_set = DenseBlockCodec::open(buffer.as_slice());
|
||||
assert!(tested_set.contains(1));
|
||||
assert!(!tested_set.contains(2));
|
||||
assert_eq!(tested_set.rank(0), 0);
|
||||
assert_eq!(tested_set.rank(1), 0);
|
||||
for rank in 2..10 {
|
||||
// ranks that don't exist select the next highest one
|
||||
assert_eq!(tested_set.rank_if_exists(rank), None);
|
||||
assert_eq!(tested_set.rank(rank), 1);
|
||||
}
|
||||
assert_eq!(tested_set.rank(10), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_simple_translate_idx_to_value_idx_sparse() {
|
||||
let mut buffer = Vec::new();
|
||||
SparseBlockCodec::serialize([1, 10].iter().copied(), &mut buffer).unwrap();
|
||||
let tested_set = SparseBlockCodec::open(buffer.as_slice());
|
||||
assert!(tested_set.contains(1));
|
||||
assert!(!tested_set.contains(2));
|
||||
assert_eq!(tested_set.rank(0), 0);
|
||||
assert_eq!(tested_set.select(tested_set.rank(0)), 1);
|
||||
assert_eq!(tested_set.rank(1), 0);
|
||||
assert_eq!(tested_set.select(tested_set.rank(1)), 1);
|
||||
for rank in 2..10 {
|
||||
// ranks that don't exist select the next highest one
|
||||
assert_eq!(tested_set.rank_if_exists(rank), None);
|
||||
assert_eq!(tested_set.rank(rank), 1);
|
||||
assert_eq!(tested_set.select(tested_set.rank(rank)), 10);
|
||||
}
|
||||
assert_eq!(tested_set.rank(10), 1);
|
||||
assert_eq!(tested_set.select(tested_set.rank(10)), 10);
|
||||
}
|
||||
|
||||
@@ -1,8 +1,29 @@
|
||||
use proptest::prelude::{any, prop, *};
|
||||
use proptest::strategy::Strategy;
|
||||
use proptest::prelude::*;
|
||||
use proptest::{prop_oneof, proptest};
|
||||
|
||||
use super::*;
|
||||
use crate::{ColumnarReader, ColumnarWriter, DynamicColumnHandle};
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_bug_2293() {
|
||||
// tests for panic in docid_range_to_rowids for docid == num_docs
|
||||
test_optional_index_with_num_docs(ELEMENTS_PER_BLOCK - 1);
|
||||
test_optional_index_with_num_docs(ELEMENTS_PER_BLOCK);
|
||||
test_optional_index_with_num_docs(ELEMENTS_PER_BLOCK + 1);
|
||||
}
|
||||
fn test_optional_index_with_num_docs(num_docs: u32) {
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
dataframe_writer.record_numerical(100, "score", 80i64);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(num_docs, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar.num_columns(), 1);
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("score").unwrap();
|
||||
assert_eq!(cols.len(), 1);
|
||||
|
||||
let col = cols[0].open().unwrap();
|
||||
col.column_index().docid_range_to_rowids(0..num_docs);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_dense_block_threshold() {
|
||||
@@ -35,9 +56,9 @@ proptest! {
|
||||
|
||||
#[test]
|
||||
fn test_with_random_sets_simple() {
|
||||
let vals = 10..BLOCK_SIZE * 2;
|
||||
let vals = 10..ELEMENTS_PER_BLOCK * 2;
|
||||
let mut out: Vec<u8> = Vec::new();
|
||||
serialize_optional_index(&vals.clone(), 100, &mut out).unwrap();
|
||||
serialize_optional_index(&vals, 100, &mut out).unwrap();
|
||||
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
|
||||
let ranks: Vec<u32> = (65_472u32..65_473u32).collect();
|
||||
let els: Vec<u32> = ranks.iter().copied().map(|rank| rank + 10).collect();
|
||||
@@ -89,8 +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);
|
||||
@@ -142,8 +163,8 @@ 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_rows(), num_rows);
|
||||
assert!(optional_index.iter_rows().eq(row_ids.iter().copied()));
|
||||
assert_eq!(optional_index.num_docs(), num_rows);
|
||||
assert!(optional_index.iter_docs().eq(row_ids.iter().copied()));
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -154,7 +175,7 @@ fn test_optional_index_iter_empty() {
|
||||
fn test_optional_index_rank_aux(row_ids: &[RowId]) {
|
||||
let num_rows = row_ids.last().copied().unwrap_or(0u32) + 1;
|
||||
let null_index = OptionalIndex::for_test(num_rows, row_ids);
|
||||
assert_eq!(null_index.num_rows(), num_rows);
|
||||
assert_eq!(null_index.num_docs(), num_rows);
|
||||
for (row_id, row_val) in row_ids.iter().copied().enumerate() {
|
||||
assert_eq!(null_index.rank(row_val), row_id as u32);
|
||||
assert_eq!(null_index.rank_if_exists(row_val), Some(row_id as u32));
|
||||
@@ -171,7 +192,7 @@ fn test_optional_index_rank() {
|
||||
test_optional_index_rank_aux(&[0u32, 1u32]);
|
||||
let mut block = Vec::new();
|
||||
block.push(3u32);
|
||||
block.extend((0..BLOCK_SIZE).map(|i| i + BLOCK_SIZE + 1));
|
||||
block.extend((0..ELEMENTS_PER_BLOCK).map(|i| i + ELEMENTS_PER_BLOCK + 1));
|
||||
test_optional_index_rank_aux(&block);
|
||||
}
|
||||
|
||||
@@ -185,8 +206,8 @@ fn test_optional_index_iter_empty_one() {
|
||||
fn test_optional_index_iter_dense_block() {
|
||||
let mut block = Vec::new();
|
||||
block.push(3u32);
|
||||
block.extend((0..BLOCK_SIZE).map(|i| i + BLOCK_SIZE + 1));
|
||||
test_optional_index_iter_aux(&block, 3 * BLOCK_SIZE);
|
||||
block.extend((0..ELEMENTS_PER_BLOCK).map(|i| i + ELEMENTS_PER_BLOCK + 1));
|
||||
test_optional_index_iter_aux(&block, 3 * ELEMENTS_PER_BLOCK);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -196,173 +217,5 @@ fn test_optional_index_for_tests() {
|
||||
assert!(optional_index.contains(1));
|
||||
assert!(optional_index.contains(2));
|
||||
assert!(!optional_index.contains(3));
|
||||
assert_eq!(optional_index.num_rows(), 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<bool> = (0..TOTAL_NUM_VALUES)
|
||||
.map(|_| rng.gen_bool(fill_ratio))
|
||||
.collect();
|
||||
serialize_optional_index(&&vals[..], &mut out).unwrap();
|
||||
let codec = open_optional_index(OwnedBytes::new(out)).unwrap();
|
||||
codec
|
||||
}
|
||||
|
||||
fn random_range_iterator(
|
||||
start: u32,
|
||||
end: u32,
|
||||
avg_step_size: u32,
|
||||
avg_deviation: u32,
|
||||
) -> impl Iterator<Item = u32> {
|
||||
let mut rng: StdRng = StdRng::from_seed([1u8; 32]);
|
||||
let mut current = start;
|
||||
std::iter::from_fn(move || {
|
||||
current += rng.gen_range(avg_step_size - avg_deviation..=avg_step_size + avg_deviation);
|
||||
if current >= end {
|
||||
None
|
||||
} else {
|
||||
Some(current)
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
fn n_percent_step_iterator(percent: f32, num_values: u32) -> impl Iterator<Item = u32> {
|
||||
let ratio = percent as f32 / 100.0;
|
||||
let step_size = (1f32 / ratio) as u32;
|
||||
let deviation = step_size - 1;
|
||||
random_range_iterator(0, num_values, step_size, deviation)
|
||||
}
|
||||
|
||||
fn walk_over_data(codec: &OptionalIndex, avg_step_size: u32) -> Option<u32> {
|
||||
walk_over_data_from_positions(
|
||||
codec,
|
||||
random_range_iterator(0, TOTAL_NUM_VALUES, avg_step_size, 0),
|
||||
)
|
||||
}
|
||||
|
||||
fn walk_over_data_from_positions(
|
||||
codec: &OptionalIndex,
|
||||
positions: impl Iterator<Item = u32>,
|
||||
) -> Option<u32> {
|
||||
let mut dense_idx: Option<u32> = None;
|
||||
for idx in positions {
|
||||
dense_idx = dense_idx.or(codec.rank_if_exists(idx));
|
||||
}
|
||||
dense_idx
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_orig_to_codec_1percent_filled_10percent_hit(bench: &mut Bencher) {
|
||||
let codec = gen_bools(0.01f64);
|
||||
bench.iter(|| walk_over_data(&codec, 100));
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_orig_to_codec_5percent_filled_10percent_hit(bench: &mut Bencher) {
|
||||
let codec = gen_bools(0.05f64);
|
||||
bench.iter(|| walk_over_data(&codec, 100));
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_orig_to_codec_5percent_filled_1percent_hit(bench: &mut Bencher) {
|
||||
let codec = gen_bools(0.05f64);
|
||||
bench.iter(|| walk_over_data(&codec, 1000));
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_orig_to_codec_full_scan_1percent_filled(bench: &mut Bencher) {
|
||||
let codec = gen_bools(0.01f64);
|
||||
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_orig_to_codec_full_scan_10percent_filled(bench: &mut Bencher) {
|
||||
let codec = gen_bools(0.1f64);
|
||||
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_orig_to_codec_full_scan_90percent_filled(bench: &mut Bencher) {
|
||||
let codec = gen_bools(0.9f64);
|
||||
bench.iter(|| walk_over_data_from_positions(&codec, 0..TOTAL_NUM_VALUES));
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_orig_to_codec_10percent_filled_1percent_hit(bench: &mut Bencher) {
|
||||
let codec = gen_bools(0.1f64);
|
||||
bench.iter(|| walk_over_data(&codec, 100));
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_orig_to_codec_50percent_filled_1percent_hit(bench: &mut Bencher) {
|
||||
let codec = gen_bools(0.5f64);
|
||||
bench.iter(|| walk_over_data(&codec, 100));
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_orig_to_codec_90percent_filled_1percent_hit(bench: &mut Bencher) {
|
||||
let codec = gen_bools(0.9f64);
|
||||
bench.iter(|| walk_over_data(&codec, 100));
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_codec_to_orig_1percent_filled_0comma005percent_hit(bench: &mut Bencher) {
|
||||
bench_translate_codec_to_orig_util(0.01f64, 0.005f32, bench);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_codec_to_orig_10percent_filled_0comma005percent_hit(bench: &mut Bencher) {
|
||||
bench_translate_codec_to_orig_util(0.1f64, 0.005f32, bench);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_codec_to_orig_1percent_filled_10percent_hit(bench: &mut Bencher) {
|
||||
bench_translate_codec_to_orig_util(0.01f64, 10f32, bench);
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_translate_codec_to_orig_1percent_filled_full_scan(bench: &mut Bencher) {
|
||||
bench_translate_codec_to_orig_util(0.01f64, 100f32, bench);
|
||||
}
|
||||
|
||||
fn bench_translate_codec_to_orig_util(
|
||||
percent_filled: f64,
|
||||
percent_hit: f32,
|
||||
bench: &mut Bencher,
|
||||
) {
|
||||
let codec = gen_bools(percent_filled);
|
||||
let num_non_nulls = codec.num_non_nulls();
|
||||
let idxs: Vec<u32> = if percent_hit == 100.0f32 {
|
||||
(0..num_non_nulls).collect()
|
||||
} else {
|
||||
n_percent_step_iterator(percent_hit, num_non_nulls).collect()
|
||||
};
|
||||
let mut output = vec![0u32; idxs.len()];
|
||||
bench.iter(|| {
|
||||
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);
|
||||
}
|
||||
assert_eq!(optional_index.num_docs(), 4);
|
||||
}
|
||||
|
||||
@@ -3,33 +3,45 @@ use std::io::Write;
|
||||
|
||||
use common::{CountingWriter, OwnedBytes};
|
||||
|
||||
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};
|
||||
use crate::{Cardinality, RowId, Version};
|
||||
|
||||
pub struct SerializableOptionalIndex<'a> {
|
||||
pub non_null_row_ids: Box<dyn Iterable<RowId> + 'a>,
|
||||
pub num_rows: RowId,
|
||||
}
|
||||
|
||||
impl<'a> From<&'a OptionalIndex> for SerializableOptionalIndex<'a> {
|
||||
fn from(optional_index: &'a OptionalIndex) -> Self {
|
||||
SerializableOptionalIndex {
|
||||
non_null_row_ids: Box::new(optional_index),
|
||||
num_rows: optional_index.num_docs(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub enum SerializableColumnIndex<'a> {
|
||||
Full,
|
||||
Optional {
|
||||
non_null_row_ids: Box<dyn Iterable<RowId> + 'a>,
|
||||
num_rows: RowId,
|
||||
},
|
||||
// TODO remove the Arc<dyn> apart from serialization this is not
|
||||
// dynamic at all.
|
||||
Multivalued(Box<dyn Iterable<RowId> + 'a>),
|
||||
Optional(SerializableOptionalIndex<'a>),
|
||||
Multivalued(SerializableMultivalueIndex<'a>),
|
||||
}
|
||||
|
||||
impl<'a> SerializableColumnIndex<'a> {
|
||||
impl SerializableColumnIndex<'_> {
|
||||
pub fn get_cardinality(&self) -> Cardinality {
|
||||
match self {
|
||||
SerializableColumnIndex::Full => Cardinality::Full,
|
||||
SerializableColumnIndex::Optional { .. } => Cardinality::Optional,
|
||||
SerializableColumnIndex::Optional(_) => Cardinality::Optional,
|
||||
SerializableColumnIndex::Multivalued(_) => Cardinality::Multivalued,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Serialize a column index.
|
||||
pub fn serialize_column_index(
|
||||
column_index: SerializableColumnIndex,
|
||||
output: &mut impl Write,
|
||||
@@ -39,19 +51,23 @@ pub fn serialize_column_index(
|
||||
output.write_all(&[cardinality])?;
|
||||
match column_index {
|
||||
SerializableColumnIndex::Full => {}
|
||||
SerializableColumnIndex::Optional {
|
||||
SerializableColumnIndex::Optional(SerializableOptionalIndex {
|
||||
non_null_row_ids,
|
||||
num_rows,
|
||||
} => serialize_optional_index(non_null_row_ids.as_ref(), num_rows, &mut output)?,
|
||||
}) => serialize_optional_index(non_null_row_ids.as_ref(), num_rows, &mut output)?,
|
||||
SerializableColumnIndex::Multivalued(multivalued_index) => {
|
||||
serialize_multivalued_index(&*multivalued_index, &mut output)?
|
||||
serialize_multivalued_index(&multivalued_index, &mut output)?
|
||||
}
|
||||
}
|
||||
let column_index_num_bytes = output.written_bytes() as u32;
|
||||
Ok(column_index_num_bytes)
|
||||
}
|
||||
|
||||
pub fn open_column_index(mut bytes: OwnedBytes) -> io::Result<ColumnIndex> {
|
||||
/// Open a serialized column index.
|
||||
pub fn open_column_index(
|
||||
mut bytes: OwnedBytes,
|
||||
format_version: Version,
|
||||
) -> io::Result<ColumnIndex> {
|
||||
if bytes.is_empty() {
|
||||
return Err(io::Error::new(
|
||||
io::ErrorKind::UnexpectedEof,
|
||||
@@ -68,7 +84,8 @@ pub fn open_column_index(mut bytes: OwnedBytes) -> io::Result<ColumnIndex> {
|
||||
Ok(ColumnIndex::Optional(optional_index))
|
||||
}
|
||||
Cardinality::Multivalued => {
|
||||
let multivalue_index = super::multivalued_index::open_multivalued_index(bytes)?;
|
||||
let multivalue_index =
|
||||
super::multivalued_index::open_multivalued_index(bytes, format_version)?;
|
||||
Ok(ColumnIndex::Multivalued(multivalue_index))
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,383 +0,0 @@
|
||||
use std::fmt::Debug;
|
||||
use std::marker::PhantomData;
|
||||
use std::ops::{Range, RangeInclusive};
|
||||
use std::sync::Arc;
|
||||
|
||||
use tantivy_bitpacker::minmax;
|
||||
|
||||
use crate::column_values::monotonic_mapping::StrictlyMonotonicFn;
|
||||
|
||||
/// `ColumnValues` provides access to a dense field column.
|
||||
///
|
||||
/// `Column` are just a wrapper over `ColumnValues` and a `ColumnIndex`.
|
||||
pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync {
|
||||
/// Return the value associated with the given idx.
|
||||
///
|
||||
/// This accessor should return as fast as possible.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// May panic if `idx` is greater than the column length.
|
||||
fn get_val(&self, idx: u32) -> T;
|
||||
|
||||
/// Fills an output buffer with the fast field values
|
||||
/// associated with the `DocId` going from
|
||||
/// `start` to `start + output.len()`.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// Must panic if `start + output.len()` is greater than
|
||||
/// the segment's `maxdoc`.
|
||||
#[inline(always)]
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
for (out, idx) in output.iter_mut().zip(start..) {
|
||||
*out = self.get_val(idx as u32);
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the positions of values which are in the provided value range.
|
||||
///
|
||||
/// Note that position == docid for single value fast fields
|
||||
#[inline(always)]
|
||||
fn get_docids_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<T>,
|
||||
doc_id_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
let doc_id_range = doc_id_range.start..doc_id_range.end.min(self.num_vals());
|
||||
for idx in doc_id_range.start..doc_id_range.end {
|
||||
let val = self.get_val(idx);
|
||||
if value_range.contains(&val) {
|
||||
positions.push(idx);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the minimum value for this fast field.
|
||||
///
|
||||
/// This min_value may not be exact.
|
||||
/// For instance, the min value does not take in account of possible
|
||||
/// deleted document. All values are however guaranteed to be higher than
|
||||
/// `.min_value()`.
|
||||
fn min_value(&self) -> T;
|
||||
|
||||
/// Returns the maximum value for this fast field.
|
||||
///
|
||||
/// This max_value may not be exact.
|
||||
/// For instance, the max value does not take in account of possible
|
||||
/// deleted document. All values are however guaranteed to be higher than
|
||||
/// `.max_value()`.
|
||||
fn max_value(&self) -> T;
|
||||
|
||||
/// The number of values in the column.
|
||||
fn num_vals(&self) -> u32;
|
||||
|
||||
/// Returns a iterator over the data
|
||||
fn iter<'a>(&'a self) -> Box<dyn Iterator<Item = T> + 'a> {
|
||||
Box::new((0..self.num_vals()).map(|idx| self.get_val(idx)))
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: Copy + PartialOrd + Debug> ColumnValues<T> for Arc<dyn ColumnValues<T>> {
|
||||
#[inline(always)]
|
||||
fn get_val(&self, idx: u32) -> T {
|
||||
self.as_ref().get_val(idx)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn min_value(&self) -> T {
|
||||
self.as_ref().min_value()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn max_value(&self) -> T {
|
||||
self.as_ref().max_value()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.as_ref().num_vals()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
|
||||
self.as_ref().iter()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
self.as_ref().get_range(start, output)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a, C: ColumnValues<T> + ?Sized, T: Copy + PartialOrd + Debug> ColumnValues<T> for &'a C {
|
||||
fn get_val(&self, idx: u32) -> T {
|
||||
(*self).get_val(idx)
|
||||
}
|
||||
|
||||
fn min_value(&self) -> T {
|
||||
(*self).min_value()
|
||||
}
|
||||
|
||||
fn max_value(&self) -> T {
|
||||
(*self).max_value()
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
(*self).num_vals()
|
||||
}
|
||||
|
||||
fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
|
||||
(*self).iter()
|
||||
}
|
||||
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
(*self).get_range(start, output)
|
||||
}
|
||||
}
|
||||
|
||||
/// VecColumn provides `Column` over a slice.
|
||||
pub struct VecColumn<'a, T = u64> {
|
||||
pub(crate) values: &'a [T],
|
||||
pub(crate) min_value: T,
|
||||
pub(crate) max_value: T,
|
||||
}
|
||||
|
||||
impl<'a, T: Copy + PartialOrd + Send + Sync + Debug> ColumnValues<T> for VecColumn<'a, T> {
|
||||
fn get_val(&self, position: u32) -> T {
|
||||
self.values[position as usize]
|
||||
}
|
||||
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
Box::new(self.values.iter().copied())
|
||||
}
|
||||
|
||||
fn min_value(&self) -> T {
|
||||
self.min_value
|
||||
}
|
||||
|
||||
fn max_value(&self) -> T {
|
||||
self.max_value
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.values.len() as u32
|
||||
}
|
||||
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
output.copy_from_slice(&self.values[start as usize..][..output.len()])
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a, T: Copy + PartialOrd + Default, V> From<&'a V> for VecColumn<'a, T>
|
||||
where V: AsRef<[T]> + ?Sized
|
||||
{
|
||||
fn from(values: &'a V) -> Self {
|
||||
let values = values.as_ref();
|
||||
let (min_value, max_value) = minmax(values.iter().copied()).unwrap_or_default();
|
||||
Self {
|
||||
values,
|
||||
min_value,
|
||||
max_value,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct MonotonicMappingColumn<C, T, Input> {
|
||||
from_column: C,
|
||||
monotonic_mapping: T,
|
||||
_phantom: PhantomData<Input>,
|
||||
}
|
||||
|
||||
/// Creates a view of a column transformed by a strictly monotonic mapping. See
|
||||
/// [`StrictlyMonotonicFn`].
|
||||
///
|
||||
/// E.g. apply a gcd monotonic_mapping([100, 200, 300]) == [1, 2, 3]
|
||||
/// monotonic_mapping.mapping() is expected to be injective, and we should always have
|
||||
/// monotonic_mapping.inverse(monotonic_mapping.mapping(el)) == el
|
||||
///
|
||||
/// The inverse of the mapping is required for:
|
||||
/// `fn get_positions_for_value_range(&self, range: RangeInclusive<T>) -> Vec<u64> `
|
||||
/// The user provides the original value range and we need to monotonic map them in the same way the
|
||||
/// serialization does before calling the underlying column.
|
||||
///
|
||||
/// Note that when opening a codec, the monotonic_mapping should be the inverse of the mapping
|
||||
/// during serialization. And therefore the monotonic_mapping_inv when opening is the same as
|
||||
/// monotonic_mapping during serialization.
|
||||
pub fn monotonic_map_column<C, T, Input, Output>(
|
||||
from_column: C,
|
||||
monotonic_mapping: T,
|
||||
) -> impl ColumnValues<Output>
|
||||
where
|
||||
C: ColumnValues<Input>,
|
||||
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
|
||||
Input: PartialOrd + Debug + Send + Sync + Clone,
|
||||
Output: PartialOrd + Debug + Send + Sync + Clone,
|
||||
{
|
||||
MonotonicMappingColumn {
|
||||
from_column,
|
||||
monotonic_mapping,
|
||||
_phantom: PhantomData,
|
||||
}
|
||||
}
|
||||
|
||||
impl<C, T, Input, Output> ColumnValues<Output> for MonotonicMappingColumn<C, T, Input>
|
||||
where
|
||||
C: ColumnValues<Input>,
|
||||
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
|
||||
Input: PartialOrd + Send + Debug + Sync + Clone,
|
||||
Output: PartialOrd + Send + Debug + Sync + Clone,
|
||||
{
|
||||
#[inline]
|
||||
fn get_val(&self, idx: u32) -> Output {
|
||||
let from_val = self.from_column.get_val(idx);
|
||||
self.monotonic_mapping.mapping(from_val)
|
||||
}
|
||||
|
||||
fn min_value(&self) -> Output {
|
||||
let from_min_value = self.from_column.min_value();
|
||||
self.monotonic_mapping.mapping(from_min_value)
|
||||
}
|
||||
|
||||
fn max_value(&self) -> Output {
|
||||
let from_max_value = self.from_column.max_value();
|
||||
self.monotonic_mapping.mapping(from_max_value)
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.from_column.num_vals()
|
||||
}
|
||||
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = Output> + '_> {
|
||||
Box::new(
|
||||
self.from_column
|
||||
.iter()
|
||||
.map(|el| self.monotonic_mapping.mapping(el)),
|
||||
)
|
||||
}
|
||||
|
||||
fn get_docids_for_value_range(
|
||||
&self,
|
||||
range: RangeInclusive<Output>,
|
||||
doc_id_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
self.from_column.get_docids_for_value_range(
|
||||
self.monotonic_mapping.inverse(range.start().clone())
|
||||
..=self.monotonic_mapping.inverse(range.end().clone()),
|
||||
doc_id_range,
|
||||
positions,
|
||||
)
|
||||
}
|
||||
|
||||
// We voluntarily do not implement get_range as it yields a regression,
|
||||
// and we do not have any specialized implementation anyway.
|
||||
}
|
||||
|
||||
/// Wraps an iterator into a `Column`.
|
||||
pub struct IterColumn<T>(T);
|
||||
|
||||
impl<T> From<T> for IterColumn<T>
|
||||
where T: Iterator + Clone + ExactSizeIterator
|
||||
{
|
||||
fn from(iter: T) -> Self {
|
||||
IterColumn(iter)
|
||||
}
|
||||
}
|
||||
|
||||
impl<T> ColumnValues<T::Item> for IterColumn<T>
|
||||
where
|
||||
T: Iterator + Clone + ExactSizeIterator + Send + Sync,
|
||||
T::Item: PartialOrd + Debug,
|
||||
{
|
||||
fn get_val(&self, idx: u32) -> T::Item {
|
||||
self.0.clone().nth(idx as usize).unwrap()
|
||||
}
|
||||
|
||||
fn min_value(&self) -> T::Item {
|
||||
self.0.clone().next().unwrap()
|
||||
}
|
||||
|
||||
fn max_value(&self) -> T::Item {
|
||||
self.0.clone().last().unwrap()
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.0.len() as u32
|
||||
}
|
||||
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = T::Item> + '_> {
|
||||
Box::new(self.0.clone())
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::column_values::monotonic_mapping::{
|
||||
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternalBaseval,
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval,
|
||||
};
|
||||
|
||||
#[test]
|
||||
fn test_monotonic_mapping() {
|
||||
let vals = &[3u64, 5u64][..];
|
||||
let col = VecColumn::from(vals);
|
||||
let mapped = monotonic_map_column(col, StrictlyMonotonicMappingToInternalBaseval::new(2));
|
||||
assert_eq!(mapped.min_value(), 1u64);
|
||||
assert_eq!(mapped.max_value(), 3u64);
|
||||
assert_eq!(mapped.num_vals(), 2);
|
||||
assert_eq!(mapped.num_vals(), 2);
|
||||
assert_eq!(mapped.get_val(0), 1);
|
||||
assert_eq!(mapped.get_val(1), 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_range_as_col() {
|
||||
let col = IterColumn::from(10..100);
|
||||
assert_eq!(col.num_vals(), 90);
|
||||
assert_eq!(col.max_value(), 99);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_monotonic_mapping_iter() {
|
||||
let vals: Vec<u64> = (10..110u64).map(|el| el * 10).collect();
|
||||
let col = VecColumn::from(&vals);
|
||||
let mapped = monotonic_map_column(
|
||||
col,
|
||||
StrictlyMonotonicMappingInverter::from(
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval::new(10, 100),
|
||||
),
|
||||
);
|
||||
let val_i64s: Vec<u64> = mapped.iter().collect();
|
||||
for i in 0..100 {
|
||||
assert_eq!(val_i64s[i as usize], mapped.get_val(i));
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_monotonic_mapping_get_range() {
|
||||
let vals: Vec<u64> = (0..100u64).map(|el| el * 10).collect();
|
||||
let col = VecColumn::from(&vals);
|
||||
let mapped = monotonic_map_column(
|
||||
col,
|
||||
StrictlyMonotonicMappingInverter::from(
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval::new(10, 0),
|
||||
),
|
||||
);
|
||||
|
||||
assert_eq!(mapped.min_value(), 0u64);
|
||||
assert_eq!(mapped.max_value(), 9900u64);
|
||||
assert_eq!(mapped.num_vals(), 100);
|
||||
let val_u64s: Vec<u64> = mapped.iter().collect();
|
||||
assert_eq!(val_u64s.len(), 100);
|
||||
for i in 0..100 {
|
||||
assert_eq!(val_u64s[i as usize], mapped.get_val(i));
|
||||
assert_eq!(val_u64s[i as usize], vals[i as usize] * 10);
|
||||
}
|
||||
let mut buf = [0u64; 20];
|
||||
mapped.get_range(7, &mut buf[..]);
|
||||
assert_eq!(&val_u64s[7..][..20], &buf);
|
||||
}
|
||||
}
|
||||
40
columnar/src/column_values/merge.rs
Normal file
40
columnar/src/column_values/merge.rs
Normal file
@@ -0,0 +1,40 @@
|
||||
use std::fmt::Debug;
|
||||
use std::sync::Arc;
|
||||
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{ColumnIndex, ColumnValues, MergeRowOrder};
|
||||
|
||||
pub(crate) struct MergedColumnValues<'a, T> {
|
||||
pub(crate) column_indexes: &'a [ColumnIndex],
|
||||
pub(crate) column_values: &'a [Option<Arc<dyn ColumnValues<T>>>],
|
||||
pub(crate) merge_row_order: &'a MergeRowOrder,
|
||||
}
|
||||
|
||||
impl<T: Copy + PartialOrd + Debug + 'static> Iterable<T> for MergedColumnValues<'_, T> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
match self.merge_row_order {
|
||||
MergeRowOrder::Stack(_) => Box::new(
|
||||
self.column_values
|
||||
.iter()
|
||||
.flatten()
|
||||
.flat_map(|column_value| column_value.iter()),
|
||||
),
|
||||
MergeRowOrder::Shuffled(shuffle_merge_order) => Box::new(
|
||||
shuffle_merge_order
|
||||
.iter_new_to_old_row_addrs()
|
||||
.flat_map(|row_addr| {
|
||||
let column_index = &self.column_indexes[row_addr.segment_ord as usize];
|
||||
let column_values =
|
||||
self.column_values[row_addr.segment_ord as usize].as_ref()?;
|
||||
let value_range = column_index.value_row_ids(row_addr.row_id);
|
||||
Some((value_range, column_values))
|
||||
})
|
||||
.flat_map(|(value_range, column_values)| {
|
||||
value_range
|
||||
.into_iter()
|
||||
.map(|val| column_values.get_val(val))
|
||||
}),
|
||||
),
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,255 +1,244 @@
|
||||
#![warn(missing_docs)]
|
||||
#![cfg_attr(all(feature = "unstable", test), feature(test))]
|
||||
|
||||
//! # `fastfield_codecs`
|
||||
//!
|
||||
//! - Columnar storage of data for tantivy [`Column`].
|
||||
//! - Columnar storage of data for tantivy [`crate::Column`].
|
||||
//! - Encode data in different codecs.
|
||||
//! - Monotonically map values to u64/u128
|
||||
|
||||
use std::fmt::Debug;
|
||||
use std::io;
|
||||
use std::io::Write;
|
||||
use std::ops::{Range, RangeInclusive};
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use compact_space::CompactSpaceDecompressor;
|
||||
use downcast_rs::DowncastSync;
|
||||
pub use monotonic_mapping::{MonotonicallyMappableToU64, StrictlyMonotonicFn};
|
||||
use monotonic_mapping::{StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal};
|
||||
pub use monotonic_mapping_u128::MonotonicallyMappableToU128;
|
||||
use serialize::U128Header;
|
||||
|
||||
mod compact_space;
|
||||
mod merge;
|
||||
pub(crate) mod monotonic_mapping;
|
||||
pub(crate) mod monotonic_mapping_u128;
|
||||
mod stats;
|
||||
pub(crate) mod u64_based;
|
||||
mod u128_based;
|
||||
mod u64_based;
|
||||
mod vec_column;
|
||||
|
||||
mod column;
|
||||
pub mod serialize;
|
||||
mod monotonic_column;
|
||||
|
||||
pub use serialize::serialize_column_values_u128;
|
||||
pub use stats::Stats;
|
||||
pub(crate) use merge::MergedColumnValues;
|
||||
pub use stats::ColumnStats;
|
||||
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;
|
||||
|
||||
pub use self::column::{monotonic_map_column, ColumnValues, IterColumn, VecColumn};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{ColumnIndex, MergeRowOrder};
|
||||
pub use self::monotonic_column::monotonic_map_column;
|
||||
use crate::RowId;
|
||||
|
||||
pub(crate) struct MergedColumnValues<'a, T> {
|
||||
pub(crate) column_indexes: &'a [Option<ColumnIndex>],
|
||||
pub(crate) column_values: &'a [Option<Arc<dyn ColumnValues<T>>>],
|
||||
pub(crate) merge_row_order: &'a MergeRowOrder,
|
||||
}
|
||||
/// `ColumnValues` provides access to a dense field column.
|
||||
///
|
||||
/// `Column` are just a wrapper over `ColumnValues` and a `ColumnIndex`.
|
||||
///
|
||||
/// Any methods with a default and specialized implementation need to be called in the
|
||||
/// wrappers that implement the trait: Arc and MonotonicMappingColumn
|
||||
pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync + DowncastSync {
|
||||
/// Return the value associated with the given idx.
|
||||
///
|
||||
/// This accessor should return as fast as possible.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// May panic if `idx` is greater than the column length.
|
||||
fn get_val(&self, idx: u32) -> T;
|
||||
|
||||
impl<'a, T: Copy + PartialOrd + Debug> Iterable<T> for MergedColumnValues<'a, T> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
match self.merge_row_order {
|
||||
MergeRowOrder::Stack(_) => {
|
||||
Box::new(self
|
||||
.column_values
|
||||
.iter()
|
||||
.flatten()
|
||||
.flat_map(|column_value| column_value.iter()))
|
||||
},
|
||||
MergeRowOrder::Shuffled(shuffle_merge_order) => {
|
||||
Box::new(shuffle_merge_order
|
||||
.iter_new_to_old_row_addrs()
|
||||
.flat_map(|row_addr| {
|
||||
let Some(column_index) = self.column_indexes[row_addr.segment_ord as usize].as_ref() else {
|
||||
return None;
|
||||
};
|
||||
let Some(column_values) = self.column_values[row_addr.segment_ord as usize].as_ref() else {
|
||||
return None;
|
||||
};
|
||||
let value_range = column_index.value_row_ids(row_addr.row_id);
|
||||
Some((value_range, column_values))
|
||||
})
|
||||
.flat_map(|(value_range, column_values)| {
|
||||
value_range
|
||||
.into_iter()
|
||||
.map(|val| column_values.get_val(val))
|
||||
})
|
||||
)
|
||||
},
|
||||
/// Allows to push down multiple fetch calls, to avoid dynamic dispatch overhead.
|
||||
///
|
||||
/// idx and output should have the same length
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// May panic if `idx` is greater than the column length.
|
||||
fn get_vals(&self, indexes: &[u32], output: &mut [T]) {
|
||||
assert!(indexes.len() == output.len());
|
||||
let out_and_idx_chunks = output.chunks_exact_mut(4).zip(indexes.chunks_exact(4));
|
||||
for (out_x4, idx_x4) in out_and_idx_chunks {
|
||||
out_x4[0] = self.get_val(idx_x4[0]);
|
||||
out_x4[1] = self.get_val(idx_x4[1]);
|
||||
out_x4[2] = self.get_val(idx_x4[2]);
|
||||
out_x4[3] = self.get_val(idx_x4[3]);
|
||||
}
|
||||
|
||||
let out_and_idx_chunks = output
|
||||
.chunks_exact_mut(4)
|
||||
.into_remainder()
|
||||
.iter_mut()
|
||||
.zip(indexes.chunks_exact(4).remainder());
|
||||
for (out, idx) in out_and_idx_chunks {
|
||||
*out = self.get_val(*idx);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
|
||||
#[repr(u8)]
|
||||
/// Available codecs to use to encode the u128 (via [`MonotonicallyMappableToU128`]) converted data.
|
||||
pub enum U128FastFieldCodecType {
|
||||
/// This codec takes a large number space (u128) and reduces it to a compact number space, by
|
||||
/// removing the holes.
|
||||
CompactSpace = 1,
|
||||
}
|
||||
|
||||
impl BinarySerializable for U128FastFieldCodecType {
|
||||
fn serialize<W: Write + ?Sized>(&self, wrt: &mut W) -> io::Result<()> {
|
||||
self.to_code().serialize(wrt)
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let code = u8::deserialize(reader)?;
|
||||
let codec_type: Self = Self::from_code(code)
|
||||
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Unknown code `{code}.`"))?;
|
||||
Ok(codec_type)
|
||||
}
|
||||
}
|
||||
|
||||
impl U128FastFieldCodecType {
|
||||
pub(crate) fn to_code(self) -> u8 {
|
||||
self as u8
|
||||
}
|
||||
|
||||
pub(crate) fn from_code(code: u8) -> Option<Self> {
|
||||
match code {
|
||||
1 => Some(Self::CompactSpace),
|
||||
_ => None,
|
||||
/// Allows to push down multiple fetch calls, to avoid dynamic dispatch overhead.
|
||||
/// The slightly weird `Option<T>` in output allows pushdown to full columns.
|
||||
///
|
||||
/// idx and output should have the same length
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// May panic if `idx` is greater than the column length.
|
||||
fn get_vals_opt(&self, indexes: &[u32], output: &mut [Option<T>]) {
|
||||
assert!(indexes.len() == output.len());
|
||||
let out_and_idx_chunks = output.chunks_exact_mut(4).zip(indexes.chunks_exact(4));
|
||||
for (out_x4, idx_x4) in out_and_idx_chunks {
|
||||
out_x4[0] = Some(self.get_val(idx_x4[0]));
|
||||
out_x4[1] = Some(self.get_val(idx_x4[1]));
|
||||
out_x4[2] = Some(self.get_val(idx_x4[2]));
|
||||
out_x4[3] = Some(self.get_val(idx_x4[3]));
|
||||
}
|
||||
let out_and_idx_chunks = output
|
||||
.chunks_exact_mut(4)
|
||||
.into_remainder()
|
||||
.iter_mut()
|
||||
.zip(indexes.chunks_exact(4).remainder());
|
||||
for (out, idx) in out_and_idx_chunks {
|
||||
*out = Some(self.get_val(*idx));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the correct codec reader wrapped in the `Arc` for the data.
|
||||
pub fn open_u128_mapped<T: MonotonicallyMappableToU128 + Debug>(
|
||||
mut bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn ColumnValues<T>>> {
|
||||
let header = U128Header::deserialize(&mut bytes)?;
|
||||
assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
|
||||
let reader = CompactSpaceDecompressor::open(bytes)?;
|
||||
|
||||
let inverted: StrictlyMonotonicMappingInverter<StrictlyMonotonicMappingToInternal<T>> =
|
||||
StrictlyMonotonicMappingToInternal::<T>::new().into();
|
||||
Ok(Arc::new(monotonic_map_column(reader, inverted)))
|
||||
}
|
||||
|
||||
#[cfg(all(test, feature = "unstable"))]
|
||||
mod bench {
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::OwnedBytes;
|
||||
use rand::rngs::StdRng;
|
||||
use rand::{Rng, SeedableRng};
|
||||
use test::{self, Bencher};
|
||||
|
||||
use super::*;
|
||||
|
||||
fn get_data() -> Vec<u64> {
|
||||
let mut rng = StdRng::seed_from_u64(2u64);
|
||||
let mut data: Vec<_> = (100..55000_u64)
|
||||
.map(|num| num + rng.gen::<u8>() as u64)
|
||||
.collect();
|
||||
data.push(99_000);
|
||||
data.insert(1000, 2000);
|
||||
data.insert(2000, 100);
|
||||
data.insert(3000, 4100);
|
||||
data.insert(4000, 100);
|
||||
data.insert(5000, 800);
|
||||
data
|
||||
/// Fills an output buffer with the fast field values
|
||||
/// associated with the `DocId` going from
|
||||
/// `start` to `start + output.len()`.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// Must panic if `start + output.len()` is greater than
|
||||
/// the segment's `maxdoc`.
|
||||
#[inline(always)]
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
for (out, idx) in output.iter_mut().zip(start..) {
|
||||
*out = self.get_val(idx as u32);
|
||||
}
|
||||
}
|
||||
|
||||
#[inline(never)]
|
||||
fn value_iter() -> impl Iterator<Item = u64> {
|
||||
0..20_000
|
||||
}
|
||||
fn get_reader_for_bench<Codec: FastFieldCodec>(data: &[u64]) -> Codec::Reader {
|
||||
let mut bytes = Vec::new();
|
||||
let min_value = *data.iter().min().unwrap();
|
||||
let data = data.iter().map(|el| *el - min_value).collect::<Vec<_>>();
|
||||
let col = VecColumn::from(&data);
|
||||
let normalized_header = NormalizedHeader {
|
||||
num_vals: col.num_vals(),
|
||||
max_value: col.max_value(),
|
||||
};
|
||||
Codec::serialize(&VecColumn::from(&data), &mut bytes).unwrap();
|
||||
Codec::open_from_bytes(OwnedBytes::new(bytes), normalized_header).unwrap()
|
||||
}
|
||||
fn bench_get<Codec: FastFieldCodec>(b: &mut Bencher, data: &[u64]) {
|
||||
let col = get_reader_for_bench::<Codec>(data);
|
||||
b.iter(|| {
|
||||
let mut sum = 0u64;
|
||||
for pos in value_iter() {
|
||||
let val = col.get_val(pos as u32);
|
||||
sum = sum.wrapping_add(val);
|
||||
/// Get the row ids of values which are in the provided value range.
|
||||
///
|
||||
/// Note that position == docid for single value fast fields
|
||||
fn get_row_ids_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<T>,
|
||||
row_id_range: Range<RowId>,
|
||||
row_id_hits: &mut Vec<RowId>,
|
||||
) {
|
||||
let row_id_range = row_id_range.start..row_id_range.end.min(self.num_vals());
|
||||
for idx in row_id_range {
|
||||
let val = self.get_val(idx);
|
||||
if value_range.contains(&val) {
|
||||
row_id_hits.push(idx);
|
||||
}
|
||||
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
|
||||
});
|
||||
}
|
||||
/// Returns a lower bound for this column of values.
|
||||
///
|
||||
/// All values are guaranteed to be higher than `.min_value()`
|
||||
/// but this value is not necessary the best boundary value.
|
||||
///
|
||||
/// We have
|
||||
/// ∀i < self.num_vals(), self.get_val(i) >= self.min_value()
|
||||
/// But we don't have necessarily
|
||||
/// ∃i < self.num_vals(), self.get_val(i) == self.min_value()
|
||||
fn min_value(&self) -> T;
|
||||
|
||||
fn bench_get_dynamic<Codec: FastFieldCodec>(b: &mut Bencher, data: &[u64]) {
|
||||
let col = Arc::new(get_reader_for_bench::<Codec>(data));
|
||||
bench_get_dynamic_helper(b, col);
|
||||
}
|
||||
fn bench_create<Codec: FastFieldCodec>(b: &mut Bencher, data: &[u64]) {
|
||||
let min_value = *data.iter().min().unwrap();
|
||||
let data = data.iter().map(|el| *el - min_value).collect::<Vec<_>>();
|
||||
/// Returns an upper bound for this column of values.
|
||||
///
|
||||
/// All values are guaranteed to be lower than `.max_value()`
|
||||
/// but this value is not necessary the best boundary value.
|
||||
///
|
||||
/// We have
|
||||
/// ∀i < self.num_vals(), self.get_val(i) <= self.max_value()
|
||||
/// But we don't have necessarily
|
||||
/// ∃i < self.num_vals(), self.get_val(i) == self.max_value()
|
||||
fn max_value(&self) -> T;
|
||||
|
||||
let mut bytes = Vec::new();
|
||||
b.iter(|| {
|
||||
bytes.clear();
|
||||
Codec::serialize(&VecColumn::from(&data), &mut bytes).unwrap();
|
||||
});
|
||||
}
|
||||
/// The number of values in the column.
|
||||
fn num_vals(&self) -> u32;
|
||||
|
||||
#[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);
|
||||
/// Returns a iterator over the data
|
||||
fn iter<'a>(&'a self) -> Box<dyn Iterator<Item = T> + 'a> {
|
||||
Box::new((0..self.num_vals()).map(|idx| self.get_val(idx)))
|
||||
}
|
||||
}
|
||||
downcast_rs::impl_downcast!(sync ColumnValues<T> where T: PartialOrd);
|
||||
|
||||
/// Empty column of values.
|
||||
pub struct EmptyColumnValues;
|
||||
|
||||
impl<T: PartialOrd + Default> ColumnValues<T> for EmptyColumnValues {
|
||||
fn get_val(&self, _idx: u32) -> T {
|
||||
panic!("Internal Error: Called get_val of empty column.")
|
||||
}
|
||||
|
||||
fn min_value(&self) -> T {
|
||||
T::default()
|
||||
}
|
||||
|
||||
fn max_value(&self) -> T {
|
||||
T::default()
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
0
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: Copy + PartialOrd + Debug + 'static> ColumnValues<T> for Arc<dyn ColumnValues<T>> {
|
||||
#[inline(always)]
|
||||
fn get_val(&self, idx: u32) -> T {
|
||||
self.as_ref().get_val(idx)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn get_vals_opt(&self, indexes: &[u32], output: &mut [Option<T>]) {
|
||||
self.as_ref().get_vals_opt(indexes, output)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn min_value(&self) -> T {
|
||||
self.as_ref().min_value()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn max_value(&self) -> T {
|
||||
self.as_ref().max_value()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.as_ref().num_vals()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
|
||||
self.as_ref().iter()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
self.as_ref().get_range(start, output)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn get_row_ids_for_value_range(
|
||||
&self,
|
||||
range: RangeInclusive<T>,
|
||||
doc_id_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
self.as_ref()
|
||||
.get_row_ids_for_value_range(range, doc_id_range, positions)
|
||||
}
|
||||
}
|
||||
|
||||
120
columnar/src/column_values/monotonic_column.rs
Normal file
120
columnar/src/column_values/monotonic_column.rs
Normal file
@@ -0,0 +1,120 @@
|
||||
use std::fmt::Debug;
|
||||
use std::marker::PhantomData;
|
||||
use std::ops::{Range, RangeInclusive};
|
||||
|
||||
use crate::ColumnValues;
|
||||
use crate::column_values::monotonic_mapping::StrictlyMonotonicFn;
|
||||
|
||||
struct MonotonicMappingColumn<C, T, Input> {
|
||||
from_column: C,
|
||||
monotonic_mapping: T,
|
||||
_phantom: PhantomData<Input>,
|
||||
}
|
||||
|
||||
/// Creates a view of a column transformed by a strictly monotonic mapping. See
|
||||
/// [`StrictlyMonotonicFn`].
|
||||
///
|
||||
/// E.g. apply a gcd monotonic_mapping([100, 200, 300]) == [1, 2, 3]
|
||||
/// monotonic_mapping.mapping() is expected to be injective, and we should always have
|
||||
/// monotonic_mapping.inverse(monotonic_mapping.mapping(el)) == el
|
||||
///
|
||||
/// The inverse of the mapping is required for:
|
||||
/// `fn get_positions_for_value_range(&self, range: RangeInclusive<T>) -> Vec<u64> `
|
||||
/// The user provides the original value range and we need to monotonic map them in the same way the
|
||||
/// serialization does before calling the underlying column.
|
||||
///
|
||||
/// Note that when opening a codec, the monotonic_mapping should be the inverse of the mapping
|
||||
/// during serialization. And therefore the monotonic_mapping_inv when opening is the same as
|
||||
/// monotonic_mapping during serialization.
|
||||
pub fn monotonic_map_column<C, T, Input, Output>(
|
||||
from_column: C,
|
||||
monotonic_mapping: T,
|
||||
) -> impl ColumnValues<Output>
|
||||
where
|
||||
C: ColumnValues<Input> + 'static,
|
||||
T: StrictlyMonotonicFn<Input, Output> + Send + Sync + 'static,
|
||||
Input: PartialOrd + Debug + Send + Sync + Clone + 'static,
|
||||
Output: PartialOrd + Debug + Send + Sync + Clone + 'static,
|
||||
{
|
||||
MonotonicMappingColumn {
|
||||
from_column,
|
||||
monotonic_mapping,
|
||||
_phantom: PhantomData,
|
||||
}
|
||||
}
|
||||
|
||||
impl<C, T, Input, Output> ColumnValues<Output> for MonotonicMappingColumn<C, T, Input>
|
||||
where
|
||||
C: ColumnValues<Input> + 'static,
|
||||
T: StrictlyMonotonicFn<Input, Output> + Send + Sync + 'static,
|
||||
Input: PartialOrd + Send + Debug + Sync + Clone + 'static,
|
||||
Output: PartialOrd + Send + Debug + Sync + Clone + 'static,
|
||||
{
|
||||
#[inline(always)]
|
||||
fn get_val(&self, idx: u32) -> Output {
|
||||
let from_val = self.from_column.get_val(idx);
|
||||
self.monotonic_mapping.mapping(from_val)
|
||||
}
|
||||
|
||||
fn min_value(&self) -> Output {
|
||||
let from_min_value = self.from_column.min_value();
|
||||
self.monotonic_mapping.mapping(from_min_value)
|
||||
}
|
||||
|
||||
fn max_value(&self) -> Output {
|
||||
let from_max_value = self.from_column.max_value();
|
||||
self.monotonic_mapping.mapping(from_max_value)
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.from_column.num_vals()
|
||||
}
|
||||
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = Output> + '_> {
|
||||
Box::new(
|
||||
self.from_column
|
||||
.iter()
|
||||
.map(|el| self.monotonic_mapping.mapping(el)),
|
||||
)
|
||||
}
|
||||
|
||||
fn get_row_ids_for_value_range(
|
||||
&self,
|
||||
range: RangeInclusive<Output>,
|
||||
doc_id_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
self.from_column.get_row_ids_for_value_range(
|
||||
self.monotonic_mapping.inverse(range.start().clone())
|
||||
..=self.monotonic_mapping.inverse(range.end().clone()),
|
||||
doc_id_range,
|
||||
positions,
|
||||
)
|
||||
}
|
||||
|
||||
// We voluntarily do not implement get_range as it yields a regression,
|
||||
// and we do not have any specialized implementation anyway.
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::column_values::VecColumn;
|
||||
use crate::column_values::monotonic_mapping::{
|
||||
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
|
||||
};
|
||||
|
||||
#[test]
|
||||
fn test_monotonic_mapping_iter() {
|
||||
let vals: Vec<u64> = (0..100u64).map(|el| el * 10).collect();
|
||||
let col = VecColumn::from(vals);
|
||||
let mapped = monotonic_map_column(
|
||||
col,
|
||||
StrictlyMonotonicMappingInverter::from(StrictlyMonotonicMappingToInternal::<i64>::new()),
|
||||
);
|
||||
let val_i64s: Vec<u64> = mapped.iter().collect();
|
||||
for i in 0..100 {
|
||||
assert_eq!(val_i64s[i as usize], mapped.get_val(i));
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,7 +1,7 @@
|
||||
use std::fmt::Debug;
|
||||
use std::marker::PhantomData;
|
||||
|
||||
use fastdivide::DividerU64;
|
||||
use common::DateTime;
|
||||
|
||||
use super::MonotonicallyMappableToU128;
|
||||
use crate::RowId;
|
||||
@@ -112,65 +112,6 @@ where T: MonotonicallyMappableToU64
|
||||
}
|
||||
}
|
||||
|
||||
/// Mapping dividing by gcd and a base value.
|
||||
///
|
||||
/// The function is assumed to be only called on values divided by passed
|
||||
/// gcd value. (It is necessary for the function to be monotonic.)
|
||||
pub(crate) struct StrictlyMonotonicMappingToInternalGCDBaseval {
|
||||
gcd_divider: DividerU64,
|
||||
gcd: u64,
|
||||
min_value: u64,
|
||||
}
|
||||
impl StrictlyMonotonicMappingToInternalGCDBaseval {
|
||||
pub(crate) fn new(gcd: u64, min_value: u64) -> Self {
|
||||
let gcd_divider = DividerU64::divide_by(gcd);
|
||||
Self {
|
||||
gcd_divider,
|
||||
gcd,
|
||||
min_value,
|
||||
}
|
||||
}
|
||||
}
|
||||
impl<External: MonotonicallyMappableToU64> StrictlyMonotonicFn<External, u64>
|
||||
for StrictlyMonotonicMappingToInternalGCDBaseval
|
||||
{
|
||||
#[inline(always)]
|
||||
fn mapping(&self, inp: External) -> u64 {
|
||||
self.gcd_divider
|
||||
.divide(External::to_u64(inp) - self.min_value)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn inverse(&self, out: u64) -> External {
|
||||
External::from_u64(self.min_value + out * self.gcd)
|
||||
}
|
||||
}
|
||||
|
||||
/// Strictly monotonic mapping with a base value.
|
||||
pub(crate) struct StrictlyMonotonicMappingToInternalBaseval {
|
||||
min_value: u64,
|
||||
}
|
||||
impl StrictlyMonotonicMappingToInternalBaseval {
|
||||
#[inline(always)]
|
||||
pub(crate) fn new(min_value: u64) -> Self {
|
||||
Self { min_value }
|
||||
}
|
||||
}
|
||||
|
||||
impl<External: MonotonicallyMappableToU64> StrictlyMonotonicFn<External, u64>
|
||||
for StrictlyMonotonicMappingToInternalBaseval
|
||||
{
|
||||
#[inline(always)]
|
||||
fn mapping(&self, val: External) -> u64 {
|
||||
External::to_u64(val) - self.min_value
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn inverse(&self, val: u64) -> External {
|
||||
External::from_u64(self.min_value + val)
|
||||
}
|
||||
}
|
||||
|
||||
impl MonotonicallyMappableToU64 for u64 {
|
||||
#[inline(always)]
|
||||
fn to_u64(self) -> u64 {
|
||||
@@ -195,17 +136,15 @@ impl MonotonicallyMappableToU64 for i64 {
|
||||
}
|
||||
}
|
||||
|
||||
impl MonotonicallyMappableToU64 for crate::DateTime {
|
||||
impl MonotonicallyMappableToU64 for DateTime {
|
||||
#[inline(always)]
|
||||
fn to_u64(self) -> u64 {
|
||||
common::i64_to_u64(self.timestamp_micros)
|
||||
common::i64_to_u64(self.into_timestamp_nanos())
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn from_u64(val: u64) -> Self {
|
||||
crate::DateTime {
|
||||
timestamp_micros: common::u64_to_i64(val),
|
||||
}
|
||||
DateTime::from_timestamp_nanos(common::u64_to_i64(val))
|
||||
}
|
||||
}
|
||||
|
||||
@@ -261,13 +200,6 @@ mod tests {
|
||||
// TODO
|
||||
// identity mapping
|
||||
// test_round_trip(&StrictlyMonotonicMappingToInternal::<u128>::new(), 100u128);
|
||||
|
||||
// base value to i64 round trip
|
||||
let mapping = StrictlyMonotonicMappingToInternalBaseval::new(100);
|
||||
test_round_trip::<_, _, u64>(&mapping, 100i64);
|
||||
// base value and gcd to u64 round trip
|
||||
let mapping = StrictlyMonotonicMappingToInternalGCDBaseval::new(10, 100);
|
||||
test_round_trip::<_, _, u64>(&mapping, 100u64);
|
||||
}
|
||||
|
||||
fn test_round_trip<T: StrictlyMonotonicFn<K, L>, K: std::fmt::Debug + Eq + Copy, L>(
|
||||
|
||||
@@ -6,21 +6,28 @@ use common::{BinarySerializable, VInt};
|
||||
|
||||
use crate::RowId;
|
||||
|
||||
/// Column statistics.
|
||||
#[derive(Debug, Clone, Eq, PartialEq)]
|
||||
pub struct Stats {
|
||||
pub struct ColumnStats {
|
||||
/// GCD of the elements `el - min(column)`.
|
||||
pub gcd: NonZeroU64,
|
||||
/// Minimum value of the column.
|
||||
pub min_value: u64,
|
||||
/// Maximum value of the column.
|
||||
pub max_value: u64,
|
||||
/// Number of rows in the column.
|
||||
pub num_rows: RowId,
|
||||
}
|
||||
|
||||
impl Stats {
|
||||
impl ColumnStats {
|
||||
/// Amplitude of value.
|
||||
/// Difference between the maximum and the minimum value.
|
||||
pub fn amplitude(&self) -> u64 {
|
||||
self.max_value - self.min_value
|
||||
}
|
||||
}
|
||||
|
||||
impl BinarySerializable for Stats {
|
||||
impl BinarySerializable for ColumnStats {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.min_value).serialize(writer)?;
|
||||
VInt(self.gcd.get()).serialize(writer)?;
|
||||
@@ -37,7 +44,7 @@ impl BinarySerializable for Stats {
|
||||
let amplitude = VInt::deserialize(reader)?.0 * gcd.get();
|
||||
let max_value = min_value + amplitude;
|
||||
let num_rows = VInt::deserialize(reader)?.0 as RowId;
|
||||
Ok(Stats {
|
||||
Ok(ColumnStats {
|
||||
min_value,
|
||||
max_value,
|
||||
num_rows,
|
||||
@@ -52,21 +59,21 @@ mod tests {
|
||||
|
||||
use common::BinarySerializable;
|
||||
|
||||
use crate::column_values::Stats;
|
||||
use crate::column_values::ColumnStats;
|
||||
|
||||
#[track_caller]
|
||||
fn test_stats_ser_deser_aux(stats: &Stats, num_bytes: usize) {
|
||||
fn test_stats_ser_deser_aux(stats: &ColumnStats, num_bytes: usize) {
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
stats.serialize(&mut buffer).unwrap();
|
||||
assert_eq!(buffer.len(), num_bytes);
|
||||
let deser_stats = Stats::deserialize(&mut &buffer[..]).unwrap();
|
||||
let deser_stats = ColumnStats::deserialize(&mut &buffer[..]).unwrap();
|
||||
assert_eq!(stats, &deser_stats);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_stats_serialization() {
|
||||
test_stats_ser_deser_aux(
|
||||
&(Stats {
|
||||
&(ColumnStats {
|
||||
gcd: NonZeroU64::new(3).unwrap(),
|
||||
min_value: 1,
|
||||
max_value: 3001,
|
||||
@@ -75,7 +82,7 @@ mod tests {
|
||||
5,
|
||||
);
|
||||
test_stats_ser_deser_aux(
|
||||
&(Stats {
|
||||
&(ColumnStats {
|
||||
gcd: NonZeroU64::new(1_000).unwrap(),
|
||||
min_value: 1,
|
||||
max_value: 3001,
|
||||
@@ -84,7 +91,7 @@ mod tests {
|
||||
5,
|
||||
);
|
||||
test_stats_ser_deser_aux(
|
||||
&(Stats {
|
||||
&(ColumnStats {
|
||||
gcd: NonZeroU64::new(1).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 0,
|
||||
|
||||
@@ -38,6 +38,6 @@ impl Ord for BlankRange {
|
||||
}
|
||||
impl PartialOrd for BlankRange {
|
||||
fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
|
||||
Some(self.blank_size().cmp(&other.blank_size()))
|
||||
Some(self.cmp(other))
|
||||
}
|
||||
}
|
||||
@@ -10,7 +10,7 @@ use super::{CompactSpace, RangeMapping};
|
||||
/// Put the blanks for the sorted values into a binary heap
|
||||
fn get_blanks(values_sorted: &BTreeSet<u128>) -> BinaryHeap<BlankRange> {
|
||||
let mut blanks: BinaryHeap<BlankRange> = BinaryHeap::new();
|
||||
for (first, second) in values_sorted.iter().tuple_windows() {
|
||||
for (first, second) in values_sorted.iter().copied().tuple_windows() {
|
||||
// Correctness Overflow: the values are deduped and sorted (BTreeSet property), that means
|
||||
// there's always space between two values.
|
||||
let blank_range = first + 1..=second - 1;
|
||||
@@ -65,12 +65,12 @@ pub fn get_compact_space(
|
||||
return compact_space_builder.finish();
|
||||
}
|
||||
|
||||
let mut blanks: BinaryHeap<BlankRange> = get_blanks(values_deduped_sorted);
|
||||
// Replace after stabilization of https://github.com/rust-lang/rust/issues/62924
|
||||
|
||||
// We start by space that's limited to min_value..=max_value
|
||||
let min_value = *values_deduped_sorted.iter().next().unwrap_or(&0);
|
||||
let max_value = *values_deduped_sorted.iter().last().unwrap_or(&0);
|
||||
// Replace after stabilization of https://github.com/rust-lang/rust/issues/62924
|
||||
let min_value = values_deduped_sorted.iter().next().copied().unwrap_or(0);
|
||||
let max_value = values_deduped_sorted.iter().last().copied().unwrap_or(0);
|
||||
|
||||
let mut blanks: BinaryHeap<BlankRange> = get_blanks(values_deduped_sorted);
|
||||
|
||||
// +1 for null, in case min and max covers the whole space, we are off by one.
|
||||
let mut amplitude_compact_space = (max_value - min_value).saturating_add(1);
|
||||
@@ -84,6 +84,7 @@ pub fn get_compact_space(
|
||||
let mut amplitude_bits: u8 = num_bits(amplitude_compact_space);
|
||||
|
||||
let mut blank_collector = BlankCollector::new();
|
||||
|
||||
// We will stage blanks until they reduce the compact space by at least 1 bit and then flush
|
||||
// them if the metadata cost is lower than the total number of saved bits.
|
||||
// Binary heap to process the gaps by their size
|
||||
@@ -93,6 +94,7 @@ pub fn get_compact_space(
|
||||
let staged_spaces_sum: u128 = blank_collector.staged_blanks_sum();
|
||||
let amplitude_new_compact_space = amplitude_compact_space - staged_spaces_sum;
|
||||
let amplitude_new_bits = num_bits(amplitude_new_compact_space);
|
||||
|
||||
if amplitude_bits == amplitude_new_bits {
|
||||
continue;
|
||||
}
|
||||
@@ -100,7 +102,16 @@ pub fn get_compact_space(
|
||||
// TODO: Maybe calculate exact cost of blanks and run this more expensive computation only,
|
||||
// when amplitude_new_bits changes
|
||||
let cost = blank_collector.num_staged_blanks() * cost_per_blank;
|
||||
if cost >= saved_bits {
|
||||
|
||||
// We want to end up with a compact space that fits into 32 bits.
|
||||
// In order to deal with pathological cases, we force the algorithm to keep
|
||||
// refining the compact space the amplitude bits is lower than 32.
|
||||
//
|
||||
// The worst case scenario happens for a large number of u128s regularly
|
||||
// spread over the full u128 space.
|
||||
//
|
||||
// This change will force the algorithm to degenerate into dictionary encoding.
|
||||
if amplitude_bits <= 32 && cost >= saved_bits {
|
||||
// Continue here, since although we walk over the blanks by size,
|
||||
// we can potentially save a lot at the last bits, which are smaller blanks
|
||||
//
|
||||
@@ -115,6 +126,8 @@ pub fn get_compact_space(
|
||||
compact_space_builder.add_blanks(blank_collector.drain().map(|blank| blank.blank_range()));
|
||||
}
|
||||
|
||||
assert!(amplitude_bits <= 32);
|
||||
|
||||
// special case, when we don't collected any blanks because:
|
||||
// * the data is empty (early exit)
|
||||
// * the algorithm did decide it's not worth the cost, which can be the case for single values
|
||||
@@ -171,7 +184,7 @@ impl CompactSpaceBuilder {
|
||||
|
||||
let mut covered_space = Vec::with_capacity(self.blanks.len());
|
||||
|
||||
// begining of the blanks
|
||||
// beginning of the blanks
|
||||
if let Some(first_blank_start) = self.blanks.first().map(RangeInclusive::start) {
|
||||
if *first_blank_start != 0 {
|
||||
covered_space.push(0..=first_blank_start - 1);
|
||||
@@ -199,7 +212,7 @@ impl CompactSpaceBuilder {
|
||||
covered_space.push(0..=0); // empty data case
|
||||
};
|
||||
|
||||
let mut compact_start: u64 = 1; // 0 is reserved for `null`
|
||||
let mut compact_start: u32 = 1; // 0 is reserved for `null`
|
||||
let mut ranges_mapping: Vec<RangeMapping> = Vec::with_capacity(covered_space.len());
|
||||
for cov in covered_space {
|
||||
let range_mapping = super::RangeMapping {
|
||||
@@ -218,6 +231,7 @@ impl CompactSpaceBuilder {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::column_values::u128_based::compact_space::COST_PER_BLANK_IN_BITS;
|
||||
|
||||
#[test]
|
||||
fn test_binary_heap_pop_order() {
|
||||
@@ -228,4 +242,11 @@ mod tests {
|
||||
assert_eq!(blanks.pop().unwrap().blank_size(), 101);
|
||||
assert_eq!(blanks.pop().unwrap().blank_size(), 11);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_worst_case_scenario() {
|
||||
let vals: BTreeSet<u128> = (0..8).map(|i| i * ((1u128 << 34) / 8)).collect();
|
||||
let compact_space = get_compact_space(&vals, vals.len() as u32, COST_PER_BLANK_IN_BITS);
|
||||
assert!(compact_space.amplitude_compact_space() < u32::MAX as u128);
|
||||
}
|
||||
}
|
||||
@@ -17,16 +17,16 @@ use std::{
|
||||
ops::{Range, RangeInclusive},
|
||||
};
|
||||
|
||||
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt, VIntU128};
|
||||
use tantivy_bitpacker::{self, BitPacker, BitUnpacker};
|
||||
|
||||
use crate::column_values::compact_space::build_compact_space::get_compact_space;
|
||||
use crate::column_values::ColumnValues;
|
||||
use crate::RowId;
|
||||
|
||||
mod blank_range;
|
||||
mod build_compact_space;
|
||||
|
||||
use build_compact_space::get_compact_space;
|
||||
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt, VIntU128};
|
||||
use tantivy_bitpacker::{BitPacker, BitUnpacker};
|
||||
|
||||
use crate::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.
|
||||
///
|
||||
@@ -42,15 +42,15 @@ pub struct CompactSpace {
|
||||
#[derive(Debug, Clone, Eq, PartialEq)]
|
||||
struct RangeMapping {
|
||||
value_range: RangeInclusive<u128>,
|
||||
compact_start: u64,
|
||||
compact_start: u32,
|
||||
}
|
||||
impl RangeMapping {
|
||||
fn range_length(&self) -> u64 {
|
||||
(self.value_range.end() - self.value_range.start()) as u64 + 1
|
||||
fn range_length(&self) -> u32 {
|
||||
(self.value_range.end() - self.value_range.start()) as u32 + 1
|
||||
}
|
||||
|
||||
// The last value of the compact space in this range
|
||||
fn compact_end(&self) -> u64 {
|
||||
fn compact_end(&self) -> u32 {
|
||||
self.compact_start + self.range_length() - 1
|
||||
}
|
||||
}
|
||||
@@ -81,7 +81,7 @@ impl BinarySerializable for CompactSpace {
|
||||
let num_ranges = VInt::deserialize(reader)?.0;
|
||||
let mut ranges_mapping: Vec<RangeMapping> = vec![];
|
||||
let mut value = 0u128;
|
||||
let mut compact_start = 1u64; // 0 is reserved for `null`
|
||||
let mut compact_start = 1u32; // 0 is reserved for `null`
|
||||
for _ in 0..num_ranges {
|
||||
let blank_delta_start = VIntU128::deserialize(reader)?.0;
|
||||
value += blank_delta_start;
|
||||
@@ -122,10 +122,10 @@ impl CompactSpace {
|
||||
|
||||
/// Returns either Ok(the value in the compact space) or if it is outside the compact space the
|
||||
/// Err(position where it would be inserted)
|
||||
fn u128_to_compact(&self, value: u128) -> Result<u64, usize> {
|
||||
fn u128_to_compact(&self, value: u128) -> Result<u32, usize> {
|
||||
self.ranges_mapping
|
||||
.binary_search_by(|probe| {
|
||||
let value_range = &probe.value_range;
|
||||
let value_range: &RangeInclusive<u128> = &probe.value_range;
|
||||
if value < *value_range.start() {
|
||||
Ordering::Greater
|
||||
} else if value > *value_range.end() {
|
||||
@@ -136,19 +136,19 @@ impl CompactSpace {
|
||||
})
|
||||
.map(|pos| {
|
||||
let range_mapping = &self.ranges_mapping[pos];
|
||||
let pos_in_range = (value - range_mapping.value_range.start()) as u64;
|
||||
let pos_in_range: u32 = (value - range_mapping.value_range.start()) as u32;
|
||||
range_mapping.compact_start + pos_in_range
|
||||
})
|
||||
}
|
||||
|
||||
/// Unpacks a value from compact space u64 to u128 space
|
||||
fn compact_to_u128(&self, compact: u64) -> u128 {
|
||||
/// Unpacks a value from compact space u32 to u128 space
|
||||
fn compact_to_u128(&self, compact: u32) -> u128 {
|
||||
let pos = self
|
||||
.ranges_mapping
|
||||
.binary_search_by_key(&compact, |range_mapping| range_mapping.compact_start)
|
||||
// Correctness: Overflow. The first range starts at compact space 0, the error from
|
||||
// binary search can never be 0
|
||||
.map_or_else(|e| e - 1, |v| v);
|
||||
.unwrap_or_else(|e| e - 1);
|
||||
|
||||
let range_mapping = &self.ranges_mapping[pos];
|
||||
let diff = compact - range_mapping.compact_start;
|
||||
@@ -178,11 +178,15 @@ impl CompactSpaceCompressor {
|
||||
/// Taking the vals as Vec may cost a lot of memory. It is used to sort the vals.
|
||||
pub fn train_from(iter: impl Iterator<Item = u128>) -> Self {
|
||||
let mut values_sorted = BTreeSet::new();
|
||||
// Total number of values, with their redundancy.
|
||||
let mut total_num_values = 0u32;
|
||||
for val in iter {
|
||||
total_num_values += 1u32;
|
||||
values_sorted.insert(val);
|
||||
}
|
||||
let min_value = *values_sorted.iter().next().unwrap_or(&0);
|
||||
let max_value = *values_sorted.iter().last().unwrap_or(&0);
|
||||
|
||||
let compact_space =
|
||||
get_compact_space(&values_sorted, total_num_values, COST_PER_BLANK_IN_BITS);
|
||||
let amplitude_compact_space = compact_space.amplitude_compact_space();
|
||||
@@ -193,13 +197,12 @@ impl CompactSpaceCompressor {
|
||||
);
|
||||
|
||||
let num_bits = tantivy_bitpacker::compute_num_bits(amplitude_compact_space as u64);
|
||||
let min_value = *values_sorted.iter().next().unwrap_or(&0);
|
||||
let max_value = *values_sorted.iter().last().unwrap_or(&0);
|
||||
|
||||
assert_eq!(
|
||||
compact_space
|
||||
.u128_to_compact(max_value)
|
||||
.expect("could not convert max value to compact space"),
|
||||
amplitude_compact_space as u64
|
||||
amplitude_compact_space as u32
|
||||
);
|
||||
CompactSpaceCompressor {
|
||||
params: IPCodecParams {
|
||||
@@ -240,7 +243,7 @@ impl CompactSpaceCompressor {
|
||||
"Could not convert value to compact_space. This is a bug.",
|
||||
)
|
||||
})?;
|
||||
bitpacker.write(compact, self.params.num_bits, write)?;
|
||||
bitpacker.write(compact as u64, self.params.num_bits, write)?;
|
||||
}
|
||||
bitpacker.close(write)?;
|
||||
self.write_footer(write)?;
|
||||
@@ -289,6 +292,63 @@ impl BinarySerializable for IPCodecParams {
|
||||
}
|
||||
}
|
||||
|
||||
/// Exposes the compact space compressed values as u64.
|
||||
///
|
||||
/// This allows faster access to the values, as u64 is faster to work with than u128.
|
||||
/// It also allows to handle u128 values like u64, via the `open_u64_lenient` as a uniform
|
||||
/// access interface.
|
||||
///
|
||||
/// When converting from the internal u64 to u128 `compact_to_u128` can be used.
|
||||
pub struct CompactSpaceU64Accessor(CompactSpaceDecompressor);
|
||||
impl CompactSpaceU64Accessor {
|
||||
pub(crate) fn open(data: OwnedBytes) -> io::Result<CompactSpaceU64Accessor> {
|
||||
let decompressor = CompactSpaceU64Accessor(CompactSpaceDecompressor::open(data)?);
|
||||
Ok(decompressor)
|
||||
}
|
||||
/// Convert a compact space value to u128
|
||||
pub fn compact_to_u128(&self, compact: u32) -> u128 {
|
||||
self.0.compact_to_u128(compact)
|
||||
}
|
||||
}
|
||||
|
||||
impl ColumnValues<u64> for CompactSpaceU64Accessor {
|
||||
#[inline]
|
||||
fn get_val(&self, doc: u32) -> u64 {
|
||||
let compact = self.0.get_compact(doc);
|
||||
compact as u64
|
||||
}
|
||||
|
||||
fn min_value(&self) -> u64 {
|
||||
self.0.u128_to_compact(self.0.min_value()).unwrap() as u64
|
||||
}
|
||||
|
||||
fn max_value(&self) -> u64 {
|
||||
self.0.u128_to_compact(self.0.max_value()).unwrap() as u64
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.0.params.num_vals
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = u64> + '_> {
|
||||
Box::new(self.0.iter_compact().map(|el| el as u64))
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn get_row_ids_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<u64>,
|
||||
position_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
let value_range = self.0.compact_to_u128(*value_range.start() as u32)
|
||||
..=self.0.compact_to_u128(*value_range.end() as u32);
|
||||
self.0
|
||||
.get_row_ids_for_value_range(value_range, position_range, positions)
|
||||
}
|
||||
}
|
||||
|
||||
impl ColumnValues<u128> for CompactSpaceDecompressor {
|
||||
#[inline]
|
||||
fn get_val(&self, doc: u32) -> u128 {
|
||||
@@ -313,49 +373,7 @@ impl ColumnValues<u128> for CompactSpaceDecompressor {
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn get_docids_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<u128>,
|
||||
positions_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
self.get_positions_for_value_range(value_range, positions_range, positions)
|
||||
}
|
||||
}
|
||||
|
||||
impl CompactSpaceDecompressor {
|
||||
pub fn open(data: OwnedBytes) -> io::Result<CompactSpaceDecompressor> {
|
||||
let (data_slice, footer_len_bytes) = data.split_at(data.len() - 4);
|
||||
let footer_len = u32::deserialize(&mut &footer_len_bytes[..])?;
|
||||
|
||||
let data_footer = &data_slice[data_slice.len() - footer_len as usize..];
|
||||
let params = IPCodecParams::deserialize(&mut &data_footer[..])?;
|
||||
let decompressor = CompactSpaceDecompressor { data, params };
|
||||
|
||||
Ok(decompressor)
|
||||
}
|
||||
|
||||
/// Converting to compact space for the decompressor is more complex, since we may get values
|
||||
/// which are outside the compact space. e.g. if we map
|
||||
/// 1000 => 5
|
||||
/// 2000 => 6
|
||||
///
|
||||
/// and we want a mapping for 1005, there is no equivalent compact space. We instead return an
|
||||
/// error with the index of the next range.
|
||||
fn u128_to_compact(&self, value: u128) -> Result<u64, usize> {
|
||||
self.params.compact_space.u128_to_compact(value)
|
||||
}
|
||||
|
||||
fn compact_to_u128(&self, compact: u64) -> u128 {
|
||||
self.params.compact_space.compact_to_u128(compact)
|
||||
}
|
||||
|
||||
/// Comparing on compact space: Random dataset 0,24 (50% random hit) - 1.05 GElements/s
|
||||
/// Comparing on compact space: Real dataset 1.08 GElements/s
|
||||
///
|
||||
/// Comparing on original space: Real dataset .06 GElements/s (not completely optimized)
|
||||
#[inline]
|
||||
pub fn get_positions_for_value_range(
|
||||
fn get_row_ids_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<u128>,
|
||||
position_range: Range<u32>,
|
||||
@@ -395,44 +413,42 @@ impl CompactSpaceDecompressor {
|
||||
range_mapping.compact_end()
|
||||
});
|
||||
|
||||
let range = compact_from..=compact_to;
|
||||
let value_range = compact_from..=compact_to;
|
||||
self.get_positions_for_compact_value_range(value_range, position_range, positions);
|
||||
}
|
||||
}
|
||||
|
||||
let scan_num_docs = position_range.end - position_range.start;
|
||||
impl CompactSpaceDecompressor {
|
||||
pub fn open(data: OwnedBytes) -> io::Result<CompactSpaceDecompressor> {
|
||||
let (data_slice, footer_len_bytes) = data.split_at(data.len() - 4);
|
||||
let footer_len = u32::deserialize(&mut &footer_len_bytes[..])?;
|
||||
|
||||
let step_size = 4;
|
||||
let cutoff = position_range.start + scan_num_docs - scan_num_docs % step_size;
|
||||
let data_footer = &data_slice[data_slice.len() - footer_len as usize..];
|
||||
let params = IPCodecParams::deserialize(&mut &data_footer[..])?;
|
||||
let decompressor = CompactSpaceDecompressor { data, params };
|
||||
|
||||
let mut push_if_in_range = |idx, val| {
|
||||
if range.contains(&val) {
|
||||
positions.push(idx);
|
||||
}
|
||||
};
|
||||
let get_val = |idx| self.params.bit_unpacker.get(idx, &self.data);
|
||||
// unrolled loop
|
||||
for idx in (position_range.start..cutoff).step_by(step_size as usize) {
|
||||
let idx1 = idx;
|
||||
let idx2 = idx + 1;
|
||||
let idx3 = idx + 2;
|
||||
let idx4 = idx + 3;
|
||||
let val1 = get_val(idx1);
|
||||
let val2 = get_val(idx2);
|
||||
let val3 = get_val(idx3);
|
||||
let val4 = get_val(idx4);
|
||||
push_if_in_range(idx1, val1);
|
||||
push_if_in_range(idx2, val2);
|
||||
push_if_in_range(idx3, val3);
|
||||
push_if_in_range(idx4, val4);
|
||||
}
|
||||
Ok(decompressor)
|
||||
}
|
||||
|
||||
// handle rest
|
||||
for idx in cutoff..position_range.end {
|
||||
push_if_in_range(idx, get_val(idx));
|
||||
}
|
||||
/// Converting to compact space for the decompressor is more complex, since we may get values
|
||||
/// which are outside the compact space. e.g. if we map
|
||||
/// 1000 => 5
|
||||
/// 2000 => 6
|
||||
///
|
||||
/// and we want a mapping for 1005, there is no equivalent compact space. We instead return an
|
||||
/// error with the index of the next range.
|
||||
fn u128_to_compact(&self, value: u128) -> Result<u32, usize> {
|
||||
self.params.compact_space.u128_to_compact(value)
|
||||
}
|
||||
|
||||
fn compact_to_u128(&self, compact: u32) -> u128 {
|
||||
self.params.compact_space.compact_to_u128(compact)
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn iter_compact(&self) -> impl Iterator<Item = u64> + '_ {
|
||||
(0..self.params.num_vals).map(move |idx| self.params.bit_unpacker.get(idx, &self.data))
|
||||
fn iter_compact(&self) -> impl Iterator<Item = u32> + '_ {
|
||||
(0..self.params.num_vals)
|
||||
.map(move |idx| self.params.bit_unpacker.get(idx, &self.data) as u32)
|
||||
}
|
||||
|
||||
#[inline]
|
||||
@@ -443,9 +459,14 @@ impl CompactSpaceDecompressor {
|
||||
.map(|compact| self.compact_to_u128(compact))
|
||||
}
|
||||
|
||||
#[inline]
|
||||
pub fn get_compact(&self, idx: u32) -> u32 {
|
||||
self.params.bit_unpacker.get(idx, &self.data) as u32
|
||||
}
|
||||
|
||||
#[inline]
|
||||
pub fn get(&self, idx: u32) -> u128 {
|
||||
let compact = self.params.bit_unpacker.get(idx, &self.data);
|
||||
let compact = self.get_compact(idx);
|
||||
self.compact_to_u128(compact)
|
||||
}
|
||||
|
||||
@@ -456,6 +477,20 @@ impl CompactSpaceDecompressor {
|
||||
pub fn max_value(&self) -> u128 {
|
||||
self.params.max_value
|
||||
}
|
||||
|
||||
fn get_positions_for_compact_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<u32>,
|
||||
position_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
self.params.bit_unpacker.get_ids_for_value_range(
|
||||
*value_range.start() as u64..=*value_range.end() as u64,
|
||||
position_range,
|
||||
&self.data,
|
||||
positions,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
@@ -464,17 +499,17 @@ mod tests {
|
||||
use itertools::Itertools;
|
||||
|
||||
use super::*;
|
||||
use crate::column_values::serialize::U128Header;
|
||||
use crate::column_values::u128_based::U128Header;
|
||||
use crate::column_values::{open_u128_mapped, serialize_column_values_u128};
|
||||
|
||||
#[test]
|
||||
fn compact_space_test() {
|
||||
let ips = &[
|
||||
let ips: BTreeSet<u128> = [
|
||||
2u128, 4u128, 1000, 1001, 1002, 1003, 1004, 1005, 1008, 1010, 1012, 1260,
|
||||
]
|
||||
.into_iter()
|
||||
.collect();
|
||||
let compact_space = get_compact_space(ips, ips.len() as u32, 11);
|
||||
let compact_space = get_compact_space(&ips, ips.len() as u32, 11);
|
||||
let amplitude = compact_space.amplitude_compact_space();
|
||||
assert_eq!(amplitude, 17);
|
||||
assert_eq!(1, compact_space.u128_to_compact(2).unwrap());
|
||||
@@ -497,8 +532,8 @@ mod tests {
|
||||
);
|
||||
|
||||
for ip in ips {
|
||||
let compact = compact_space.u128_to_compact(*ip).unwrap();
|
||||
assert_eq!(compact_space.compact_to_u128(compact), *ip);
|
||||
let compact = compact_space.u128_to_compact(ip).unwrap();
|
||||
assert_eq!(compact_space.compact_to_u128(compact), ip);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -524,7 +559,7 @@ mod tests {
|
||||
.map(|pos| pos as u32)
|
||||
.collect::<Vec<_>>();
|
||||
let mut positions = Vec::new();
|
||||
decompressor.get_positions_for_value_range(
|
||||
decompressor.get_row_ids_for_value_range(
|
||||
range,
|
||||
0..decompressor.num_vals(),
|
||||
&mut positions,
|
||||
@@ -569,7 +604,7 @@ mod tests {
|
||||
let val = *val;
|
||||
let pos = pos as u32;
|
||||
let mut positions = Vec::new();
|
||||
decomp.get_positions_for_value_range(val..=val, pos..pos + 1, &mut positions);
|
||||
decomp.get_row_ids_for_value_range(val..=val, pos..pos + 1, &mut positions);
|
||||
assert_eq!(positions, vec![pos]);
|
||||
}
|
||||
|
||||
@@ -618,12 +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,
|
||||
@@ -709,7 +746,7 @@ mod tests {
|
||||
doc_id_range: Range<u32>,
|
||||
) -> Vec<u32> {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(value_range, doc_id_range, &mut positions);
|
||||
column.get_row_ids_for_value_range(value_range, doc_id_range, &mut positions);
|
||||
positions
|
||||
}
|
||||
|
||||
@@ -1,25 +1,21 @@
|
||||
use std::fmt::Debug;
|
||||
use std::io;
|
||||
use std::io::Write;
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::{BinarySerializable, VInt};
|
||||
mod compact_space;
|
||||
|
||||
use crate::column_values::compact_space::CompactSpaceCompressor;
|
||||
use crate::column_values::U128FastFieldCodecType;
|
||||
use common::{BinarySerializable, OwnedBytes, VInt};
|
||||
pub use compact_space::{
|
||||
CompactSpaceCompressor, CompactSpaceDecompressor, CompactSpaceU64Accessor,
|
||||
};
|
||||
|
||||
use crate::column_values::monotonic_map_column;
|
||||
use crate::column_values::monotonic_mapping::{
|
||||
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
|
||||
};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::MonotonicallyMappableToU128;
|
||||
|
||||
/// The normalized header gives some parameters after applying the following
|
||||
/// normalization of the vector:
|
||||
/// `val -> (val - min_value) / gcd`
|
||||
///
|
||||
/// By design, after normalization, `min_value = 0` and `gcd = 1`.
|
||||
#[derive(Debug, Copy, Clone)]
|
||||
pub struct NormalizedHeader {
|
||||
/// The number of values in the underlying column.
|
||||
pub num_vals: u32,
|
||||
/// The max value of the underlying column.
|
||||
pub max_value: u64,
|
||||
}
|
||||
use crate::{ColumnValues, MonotonicallyMappableToU128};
|
||||
|
||||
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
|
||||
pub(crate) struct U128Header {
|
||||
@@ -68,14 +64,77 @@ pub fn serialize_column_values_u128<T: MonotonicallyMappableToU128>(
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
|
||||
#[repr(u8)]
|
||||
/// Available codecs to use to encode the u128 (via [`MonotonicallyMappableToU128`]) converted data.
|
||||
pub(crate) enum U128FastFieldCodecType {
|
||||
/// This codec takes a large number space (u128) and reduces it to a compact number space, by
|
||||
/// removing the holes.
|
||||
CompactSpace = 1,
|
||||
}
|
||||
|
||||
impl BinarySerializable for U128FastFieldCodecType {
|
||||
fn serialize<W: Write + ?Sized>(&self, wrt: &mut W) -> io::Result<()> {
|
||||
self.to_code().serialize(wrt)
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let code = u8::deserialize(reader)?;
|
||||
let codec_type: Self = Self::from_code(code)
|
||||
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Unknown code `{code}.`"))?;
|
||||
Ok(codec_type)
|
||||
}
|
||||
}
|
||||
|
||||
impl U128FastFieldCodecType {
|
||||
pub(crate) fn to_code(self) -> u8 {
|
||||
self as u8
|
||||
}
|
||||
|
||||
pub(crate) fn from_code(code: u8) -> Option<Self> {
|
||||
match code {
|
||||
1 => Some(Self::CompactSpace),
|
||||
_ => None,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the correct codec reader wrapped in the `Arc` for the data.
|
||||
pub fn open_u128_mapped<T: MonotonicallyMappableToU128 + Debug>(
|
||||
mut bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn ColumnValues<T>>> {
|
||||
let header = U128Header::deserialize(&mut bytes)?;
|
||||
assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
|
||||
let reader = CompactSpaceDecompressor::open(bytes)?;
|
||||
let inverted: StrictlyMonotonicMappingInverter<StrictlyMonotonicMappingToInternal<T>> =
|
||||
StrictlyMonotonicMappingToInternal::<T>::new().into();
|
||||
Ok(Arc::new(monotonic_map_column(reader, inverted)))
|
||||
}
|
||||
|
||||
/// Returns the u64 representation of the u128 data.
|
||||
/// The internal representation of the data as u64 is useful for faster processing.
|
||||
///
|
||||
/// In order to convert to u128 back cast to `CompactSpaceU64Accessor` and call
|
||||
/// `compact_to_u128`.
|
||||
///
|
||||
/// # Notice
|
||||
/// In case there are new codecs added, check for usages of `CompactSpaceDecompressorU64` and
|
||||
/// also handle the new codecs.
|
||||
pub fn open_u128_as_compact_u64(mut bytes: OwnedBytes) -> io::Result<Arc<dyn ColumnValues<u64>>> {
|
||||
let header = U128Header::deserialize(&mut bytes)?;
|
||||
assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
|
||||
let reader = CompactSpaceU64Accessor::open(bytes)?;
|
||||
Ok(Arc::new(reader))
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
pub 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() {
|
||||
@@ -1,10 +1,12 @@
|
||||
use std::io::{self, Write};
|
||||
use std::num::NonZeroU64;
|
||||
use std::ops::{Range, RangeInclusive};
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use fastdivide::DividerU64;
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
use tantivy_bitpacker::{BitPacker, BitUnpacker, compute_num_bits};
|
||||
|
||||
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, Stats};
|
||||
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, ColumnStats};
|
||||
use crate::{ColumnValues, RowId};
|
||||
|
||||
/// Depending on the field type, a different
|
||||
@@ -13,7 +15,43 @@ use crate::{ColumnValues, RowId};
|
||||
pub struct BitpackedReader {
|
||||
data: OwnedBytes,
|
||||
bit_unpacker: BitUnpacker,
|
||||
stats: Stats,
|
||||
stats: ColumnStats,
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
const fn div_ceil(n: u64, q: NonZeroU64) -> u64 {
|
||||
// copied from unstable rust standard library.
|
||||
let d = n / q.get();
|
||||
let r = n % q.get();
|
||||
if r > 0 { d + 1 } else { d }
|
||||
}
|
||||
|
||||
// The bitpacked codec applies a linear transformation `f` over data that are bitpacked.
|
||||
// f is defined by:
|
||||
// f: bitpacked -> stats.min_value + stats.gcd * bitpacked
|
||||
//
|
||||
// In order to run range queries, we invert the transformation.
|
||||
// `transform_range_before_linear_transformation` returns the range of values
|
||||
// [min_bipacked_value..max_bitpacked_value] such that
|
||||
// f(bitpacked) ∈ [min_value, max_value] <=> bitpacked ∈ [min_bitpacked_value, max_bitpacked_value]
|
||||
fn transform_range_before_linear_transformation(
|
||||
stats: &ColumnStats,
|
||||
range: RangeInclusive<u64>,
|
||||
) -> Option<RangeInclusive<u64>> {
|
||||
if range.is_empty() {
|
||||
return None;
|
||||
}
|
||||
if stats.min_value > *range.end() {
|
||||
return None;
|
||||
}
|
||||
if stats.max_value < *range.start() {
|
||||
return None;
|
||||
}
|
||||
let shifted_range =
|
||||
range.start().saturating_sub(stats.min_value)..=range.end().saturating_sub(stats.min_value);
|
||||
let start_before_gcd_multiplication: u64 = div_ceil(*shifted_range.start(), stats.gcd);
|
||||
let end_before_gcd_multiplication: u64 = *shifted_range.end() / stats.gcd;
|
||||
Some(start_before_gcd_multiplication..=end_before_gcd_multiplication)
|
||||
}
|
||||
|
||||
impl ColumnValues for BitpackedReader {
|
||||
@@ -21,7 +59,6 @@ impl ColumnValues for BitpackedReader {
|
||||
fn get_val(&self, doc: u32) -> u64 {
|
||||
self.stats.min_value + self.stats.gcd.get() * self.bit_unpacker.get(doc, &self.data)
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn min_value(&self) -> u64 {
|
||||
self.stats.min_value
|
||||
@@ -34,9 +71,29 @@ impl ColumnValues for BitpackedReader {
|
||||
fn num_vals(&self) -> RowId {
|
||||
self.stats.num_rows
|
||||
}
|
||||
|
||||
fn get_row_ids_for_value_range(
|
||||
&self,
|
||||
range: RangeInclusive<u64>,
|
||||
doc_id_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
let Some(transformed_range) =
|
||||
transform_range_before_linear_transformation(&self.stats, range)
|
||||
else {
|
||||
positions.clear();
|
||||
return;
|
||||
};
|
||||
self.bit_unpacker.get_ids_for_value_range(
|
||||
transformed_range,
|
||||
doc_id_range,
|
||||
&self.data,
|
||||
positions,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
fn num_bits(stats: &Stats) -> u8 {
|
||||
fn num_bits(stats: &ColumnStats) -> u8 {
|
||||
compute_num_bits(stats.amplitude() / stats.gcd)
|
||||
}
|
||||
|
||||
@@ -46,14 +103,14 @@ pub struct BitpackedCodecEstimator;
|
||||
impl ColumnCodecEstimator for BitpackedCodecEstimator {
|
||||
fn collect(&mut self, _value: u64) {}
|
||||
|
||||
fn estimate(&self, stats: &Stats) -> Option<u64> {
|
||||
fn estimate(&self, stats: &ColumnStats) -> Option<u64> {
|
||||
let num_bits_per_value = num_bits(stats);
|
||||
Some(stats.num_bytes() + (stats.num_rows as u64 * (num_bits_per_value as u64) + 7) / 8)
|
||||
}
|
||||
|
||||
fn serialize(
|
||||
&self,
|
||||
stats: &Stats,
|
||||
stats: &ColumnStats,
|
||||
vals: &mut dyn Iterator<Item = u64>,
|
||||
wrt: &mut dyn Write,
|
||||
) -> io::Result<()> {
|
||||
@@ -72,12 +129,12 @@ impl ColumnCodecEstimator for BitpackedCodecEstimator {
|
||||
pub struct BitpackedCodec;
|
||||
|
||||
impl ColumnCodec for BitpackedCodec {
|
||||
type Reader = BitpackedReader;
|
||||
type ColumnValues = BitpackedReader;
|
||||
type Estimator = BitpackedCodecEstimator;
|
||||
|
||||
/// Opens a fast field given a file.
|
||||
fn load(mut data: OwnedBytes) -> io::Result<Self::Reader> {
|
||||
let stats = Stats::deserialize(&mut data)?;
|
||||
fn load(mut data: OwnedBytes) -> io::Result<Self::ColumnValues> {
|
||||
let stats = ColumnStats::deserialize(&mut data)?;
|
||||
let num_bits = num_bits(&stats);
|
||||
let bit_unpacker = BitUnpacker::new(num_bits);
|
||||
Ok(BitpackedReader {
|
||||
|
||||
@@ -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::column_values::u64_based::line::Line;
|
||||
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, Stats};
|
||||
use crate::column_values::{ColumnValues, VecColumn};
|
||||
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};
|
||||
|
||||
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 {
|
||||
@@ -63,7 +63,10 @@ impl BlockwiseLinearEstimator {
|
||||
if self.block.is_empty() {
|
||||
return;
|
||||
}
|
||||
let line = Line::train(&VecColumn::from(&self.block));
|
||||
let column = VecColumn::from(std::mem::take(&mut self.block));
|
||||
let line = Line::train(&column);
|
||||
self.block = column.into();
|
||||
|
||||
let mut max_value = 0u64;
|
||||
for (i, buffer_val) in self.block.iter().enumerate() {
|
||||
let interpolated_val = line.eval(i as u32);
|
||||
@@ -84,7 +87,7 @@ impl ColumnCodecEstimator for BlockwiseLinearEstimator {
|
||||
self.block.clear();
|
||||
}
|
||||
}
|
||||
fn estimate(&self, stats: &Stats) -> Option<u64> {
|
||||
fn estimate(&self, stats: &ColumnStats) -> Option<u64> {
|
||||
let mut estimate = 4 + stats.num_bytes() + self.meta_num_bytes + self.values_num_bytes;
|
||||
if stats.gcd.get() > 1 {
|
||||
let estimate_gain_from_gcd =
|
||||
@@ -100,7 +103,7 @@ impl ColumnCodecEstimator for BlockwiseLinearEstimator {
|
||||
|
||||
fn serialize(
|
||||
&self,
|
||||
stats: &Stats,
|
||||
stats: &ColumnStats,
|
||||
mut vals: &mut dyn Iterator<Item = u64>,
|
||||
wrt: &mut dyn Write,
|
||||
) -> io::Result<()> {
|
||||
@@ -125,7 +128,7 @@ impl ColumnCodecEstimator for BlockwiseLinearEstimator {
|
||||
*buffer_val = gcd_divider.divide(*buffer_val - stats.min_value);
|
||||
}
|
||||
|
||||
let line = Line::train(&VecColumn::from(&buffer));
|
||||
let line = Line::train(&VecColumn::from(buffer.to_vec()));
|
||||
|
||||
assert!(!buffer.is_empty());
|
||||
|
||||
@@ -165,12 +168,12 @@ impl ColumnCodecEstimator for BlockwiseLinearEstimator {
|
||||
pub struct BlockwiseLinearCodec;
|
||||
|
||||
impl ColumnCodec<u64> for BlockwiseLinearCodec {
|
||||
type Reader = BlockwiseLinearReader;
|
||||
type ColumnValues = BlockwiseLinearReader;
|
||||
|
||||
type Estimator = BlockwiseLinearEstimator;
|
||||
|
||||
fn load(mut bytes: OwnedBytes) -> io::Result<Self::Reader> {
|
||||
let stats = Stats::deserialize(&mut bytes)?;
|
||||
fn load(mut bytes: OwnedBytes) -> io::Result<Self::ColumnValues> {
|
||||
let stats = ColumnStats::deserialize(&mut bytes)?;
|
||||
let footer_len: u32 = (&bytes[bytes.len() - 4..]).deserialize()?;
|
||||
let footer_offset = bytes.len() - 4 - footer_len as usize;
|
||||
let (data, mut footer) = bytes.split(footer_offset);
|
||||
@@ -195,14 +198,14 @@ impl ColumnCodec<u64> for BlockwiseLinearCodec {
|
||||
pub struct BlockwiseLinearReader {
|
||||
blocks: Arc<[Block]>,
|
||||
data: OwnedBytes,
|
||||
stats: Stats,
|
||||
stats: ColumnStats,
|
||||
}
|
||||
|
||||
impl ColumnValues for BlockwiseLinearReader {
|
||||
#[inline(always)]
|
||||
fn get_val(&self, idx: u32) -> u64 {
|
||||
let block_id = (idx / BLOCK_SIZE as u32) as usize;
|
||||
let idx_within_block = idx % (BLOCK_SIZE as u32);
|
||||
let block_id = (idx / BLOCK_SIZE) as usize;
|
||||
let idx_within_block = idx % BLOCK_SIZE;
|
||||
let block = &self.blocks[block_id];
|
||||
let interpoled_val: u64 = block.line.eval(idx_within_block);
|
||||
let block_bytes = &self.data[block.data_start_offset..];
|
||||
|
||||
@@ -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
|
||||
@@ -184,7 +183,7 @@ mod tests {
|
||||
}
|
||||
|
||||
fn test_eval_max_err(ys: &[u64]) -> Option<u64> {
|
||||
let line = Line::train(&VecColumn::from(&ys));
|
||||
let line = Line::train(&VecColumn::from(ys.to_vec()));
|
||||
ys.iter()
|
||||
.enumerate()
|
||||
.map(|(x, y)| y.wrapping_sub(line.eval(x as u32)))
|
||||
|
||||
@@ -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, Stats};
|
||||
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;
|
||||
@@ -18,7 +18,7 @@ const LINE_ESTIMATION_BLOCK_LEN: usize = 512;
|
||||
pub struct LinearReader {
|
||||
data: OwnedBytes,
|
||||
linear_params: LinearParams,
|
||||
stats: Stats,
|
||||
stats: ColumnStats,
|
||||
}
|
||||
|
||||
impl ColumnValues for LinearReader {
|
||||
@@ -106,7 +106,7 @@ impl ColumnCodecEstimator for LinearCodecEstimator {
|
||||
}
|
||||
}
|
||||
|
||||
fn estimate(&self, stats: &Stats) -> Option<u64> {
|
||||
fn estimate(&self, stats: &ColumnStats) -> Option<u64> {
|
||||
let line = self.line?;
|
||||
let amplitude = self.max_deviation - self.min_deviation;
|
||||
let num_bits = compute_num_bits(amplitude);
|
||||
@@ -123,7 +123,7 @@ impl ColumnCodecEstimator for LinearCodecEstimator {
|
||||
|
||||
fn serialize(
|
||||
&self,
|
||||
stats: &Stats,
|
||||
stats: &ColumnStats,
|
||||
vals: &mut dyn Iterator<Item = u64>,
|
||||
wrt: &mut dyn io::Write,
|
||||
) -> io::Result<()> {
|
||||
@@ -173,7 +173,9 @@ impl LinearCodecEstimator {
|
||||
fn collect_before_line_estimation(&mut self, value: u64) {
|
||||
self.block.push(value);
|
||||
if self.block.len() == LINE_ESTIMATION_BLOCK_LEN {
|
||||
let line = Line::train(&VecColumn::from(&self.block));
|
||||
let column = VecColumn::from(std::mem::take(&mut self.block));
|
||||
let line = Line::train(&column);
|
||||
self.block = column.into();
|
||||
let block = std::mem::take(&mut self.block);
|
||||
for val in block {
|
||||
self.collect_after_line_estimation(&line, val);
|
||||
@@ -184,12 +186,12 @@ impl LinearCodecEstimator {
|
||||
}
|
||||
|
||||
impl ColumnCodec for LinearCodec {
|
||||
type Reader = LinearReader;
|
||||
type ColumnValues = LinearReader;
|
||||
|
||||
type Estimator = LinearCodecEstimator;
|
||||
|
||||
fn load(mut data: OwnedBytes) -> io::Result<Self::Reader> {
|
||||
let stats = Stats::deserialize(&mut data)?;
|
||||
fn load(mut data: OwnedBytes) -> io::Result<Self::ColumnValues> {
|
||||
let stats = ColumnStats::deserialize(&mut data)?;
|
||||
let linear_params = LinearParams::deserialize(&mut data)?;
|
||||
Ok(LinearReader {
|
||||
stats,
|
||||
|
||||
@@ -13,35 +13,61 @@ use common::{BinarySerializable, OwnedBytes};
|
||||
use crate::column_values::monotonic_mapping::{
|
||||
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
|
||||
};
|
||||
use crate::column_values::u64_based::bitpacked::BitpackedCodec;
|
||||
use crate::column_values::u64_based::blockwise_linear::BlockwiseLinearCodec;
|
||||
use crate::column_values::u64_based::linear::LinearCodec;
|
||||
use crate::column_values::u64_based::stats_collector::StatsCollector;
|
||||
use crate::column_values::{monotonic_map_column, Stats};
|
||||
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::{ColumnStats, monotonic_map_column};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{ColumnValues, MonotonicallyMappableToU64};
|
||||
|
||||
/// A `ColumnCodecEstimator` is in charge of gathering all
|
||||
/// data required to serialize a column.
|
||||
///
|
||||
/// This happens during a first pass on data of the column elements.
|
||||
/// During that pass, all column estimators receive a call to their
|
||||
/// `.collect(el)`.
|
||||
///
|
||||
/// After this first pass, finalize is called.
|
||||
/// `.estimate(..)` then should return an accurate estimation of the
|
||||
/// size of the serialized column (were we to pick this codec.).
|
||||
/// `.serialize(..)` then serializes the column using this codec.
|
||||
pub trait ColumnCodecEstimator<T = u64>: 'static {
|
||||
/// Records a new value for estimation.
|
||||
/// This method will be called for each element of the column during
|
||||
/// `estimation`.
|
||||
fn collect(&mut self, value: u64);
|
||||
fn estimate(&self, stats: &Stats) -> Option<u64>;
|
||||
/// Finalizes the first pass phase.
|
||||
fn finalize(&mut self) {}
|
||||
/// Returns an accurate estimation of the number of bytes that will
|
||||
/// be used to represent this column.
|
||||
fn estimate(&self, stats: &ColumnStats) -> Option<u64>;
|
||||
/// Serializes the column using the given codec.
|
||||
/// This constitutes a second pass over the columns values.
|
||||
fn serialize(
|
||||
&self,
|
||||
stats: &Stats,
|
||||
stats: &ColumnStats,
|
||||
vals: &mut dyn Iterator<Item = T>,
|
||||
wrt: &mut dyn io::Write,
|
||||
) -> io::Result<()>;
|
||||
}
|
||||
|
||||
/// A column codec describes a colunm serialization format.
|
||||
pub trait ColumnCodec<T: PartialOrd = u64> {
|
||||
type Reader: ColumnValues<T> + 'static;
|
||||
/// Specialized `ColumnValues` type.
|
||||
type ColumnValues: ColumnValues<T> + 'static;
|
||||
/// `Estimator` for the given codec.
|
||||
type Estimator: ColumnCodecEstimator + Default;
|
||||
|
||||
fn load(bytes: OwnedBytes) -> io::Result<Self::Reader>;
|
||||
/// Loads a column that has been serialized using this codec.
|
||||
fn load(bytes: OwnedBytes) -> io::Result<Self::ColumnValues>;
|
||||
|
||||
/// Returns an estimator.
|
||||
fn estimator() -> Self::Estimator {
|
||||
Self::Estimator::default()
|
||||
}
|
||||
|
||||
/// Returns a boxed estimator.
|
||||
fn boxed_estimator() -> Box<dyn ColumnCodecEstimator> {
|
||||
Box::new(Self::estimator())
|
||||
}
|
||||
@@ -62,6 +88,7 @@ pub enum CodecType {
|
||||
BlockwiseLinear = 2u8,
|
||||
}
|
||||
|
||||
/// List of all available u64-base codecs.
|
||||
pub const ALL_U64_CODEC_TYPES: [CodecType; 3] = [
|
||||
CodecType::Bitpacked,
|
||||
CodecType::Linear,
|
||||
@@ -106,6 +133,7 @@ fn load_specific_codec<C: ColumnCodec, T: MonotonicallyMappableToU64>(
|
||||
}
|
||||
|
||||
impl CodecType {
|
||||
/// Returns a boxed codec estimator associated to a given `CodecType`.
|
||||
pub fn estimator(&self) -> Box<dyn ColumnCodecEstimator> {
|
||||
match self {
|
||||
CodecType::Bitpacked => BitpackedCodec::boxed_estimator(),
|
||||
@@ -115,7 +143,8 @@ impl CodecType {
|
||||
}
|
||||
}
|
||||
|
||||
pub fn serialize_u64_based_column_values<'a, T: MonotonicallyMappableToU64>(
|
||||
/// Serializes a given column of u64-mapped values.
|
||||
pub fn serialize_u64_based_column_values<T: MonotonicallyMappableToU64>(
|
||||
vals: &dyn Iterable<T>,
|
||||
codec_types: &[CodecType],
|
||||
wrt: &mut dyn Write,
|
||||
@@ -156,11 +185,14 @@ pub fn serialize_u64_based_column_values<'a, T: MonotonicallyMappableToU64>(
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Load u64-based column values.
|
||||
///
|
||||
/// This method first identifies the codec off the first byte.
|
||||
pub fn load_u64_based_column_values<T: MonotonicallyMappableToU64>(
|
||||
mut bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn ColumnValues<T>>> {
|
||||
let codec_type: CodecType = bytes
|
||||
.get(0)
|
||||
.first()
|
||||
.copied()
|
||||
.and_then(CodecType::try_from_code)
|
||||
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Failed to read codec type"))?;
|
||||
|
||||
@@ -2,8 +2,8 @@ use std::num::NonZeroU64;
|
||||
|
||||
use fastdivide::DividerU64;
|
||||
|
||||
use crate::column_values::Stats;
|
||||
use crate::RowId;
|
||||
use crate::column_values::ColumnStats;
|
||||
|
||||
/// Compute the gcd of two non null numbers.
|
||||
///
|
||||
@@ -27,20 +27,20 @@ pub struct StatsCollector {
|
||||
// This is the same as computing the difference between the values and the first value.
|
||||
//
|
||||
// This way, we can compress i64-converted-to-u64 (e.g. timestamp that were supplied in
|
||||
// seconds, only to be converted in microseconds).
|
||||
// seconds, only to be converted in nanoseconds).
|
||||
increment_gcd_opt: Option<(NonZeroU64, DividerU64)>,
|
||||
first_value_opt: Option<u64>,
|
||||
}
|
||||
|
||||
impl StatsCollector {
|
||||
pub fn stats(&self) -> Stats {
|
||||
pub fn stats(&self) -> ColumnStats {
|
||||
let (min_value, max_value) = self.min_max_opt.unwrap_or((0u64, 0u64));
|
||||
let increment_gcd = if let Some((increment_gcd, _)) = self.increment_gcd_opt {
|
||||
increment_gcd
|
||||
} else {
|
||||
NonZeroU64::new(1u64).unwrap()
|
||||
};
|
||||
Stats {
|
||||
ColumnStats {
|
||||
min_value,
|
||||
max_value,
|
||||
num_rows: self.num_rows,
|
||||
@@ -96,10 +96,10 @@ 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::Stats;
|
||||
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>) -> Stats {
|
||||
fn compute_stats(vals: impl Iterator<Item = u64>) -> ColumnStats {
|
||||
let mut stats_collector = StatsCollector::default();
|
||||
for val in vals {
|
||||
stats_collector.collect(val);
|
||||
@@ -144,7 +144,7 @@ mod tests {
|
||||
fn test_stats() {
|
||||
assert_eq!(
|
||||
compute_stats([].into_iter()),
|
||||
Stats {
|
||||
ColumnStats {
|
||||
gcd: NonZeroU64::new(1).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 0,
|
||||
@@ -153,7 +153,7 @@ mod tests {
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([0, 1].into_iter()),
|
||||
Stats {
|
||||
ColumnStats {
|
||||
gcd: NonZeroU64::new(1).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 1,
|
||||
@@ -162,7 +162,7 @@ mod tests {
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([0, 1].into_iter()),
|
||||
Stats {
|
||||
ColumnStats {
|
||||
gcd: NonZeroU64::new(1).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 1,
|
||||
@@ -171,7 +171,7 @@ mod tests {
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([10, 20, 30].into_iter()),
|
||||
Stats {
|
||||
ColumnStats {
|
||||
gcd: NonZeroU64::new(10).unwrap(),
|
||||
min_value: 10,
|
||||
max_value: 30,
|
||||
@@ -180,7 +180,7 @@ mod tests {
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([10, 50, 10, 30].into_iter()),
|
||||
Stats {
|
||||
ColumnStats {
|
||||
gcd: NonZeroU64::new(20).unwrap(),
|
||||
min_value: 10,
|
||||
max_value: 50,
|
||||
@@ -189,7 +189,7 @@ mod tests {
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([10, 0, 30].into_iter()),
|
||||
Stats {
|
||||
ColumnStats {
|
||||
gcd: NonZeroU64::new(10).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 30,
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
use proptest::prelude::*;
|
||||
use proptest::strategy::Strategy;
|
||||
use proptest::{prop_oneof, proptest};
|
||||
use rand::Rng;
|
||||
|
||||
#[test]
|
||||
fn test_serialize_and_load_simple() {
|
||||
@@ -19,6 +19,62 @@ fn test_serialize_and_load_simple() {
|
||||
assert_eq!(col.get_val(1), 2);
|
||||
assert_eq!(col.get_val(2), 5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_empty_column_i64() {
|
||||
let vals: [i64; 0] = [];
|
||||
let mut num_acceptable_codecs = 0;
|
||||
for codec in ALL_U64_CODEC_TYPES {
|
||||
let mut buffer = Vec::new();
|
||||
if serialize_u64_based_column_values(&&vals[..], &[codec], &mut buffer).is_err() {
|
||||
continue;
|
||||
}
|
||||
num_acceptable_codecs += 1;
|
||||
let col = load_u64_based_column_values::<i64>(OwnedBytes::new(buffer)).unwrap();
|
||||
assert_eq!(col.num_vals(), 0);
|
||||
assert_eq!(col.min_value(), i64::MIN);
|
||||
assert_eq!(col.max_value(), i64::MIN);
|
||||
}
|
||||
assert!(num_acceptable_codecs > 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_empty_column_u64() {
|
||||
let vals: [u64; 0] = [];
|
||||
let mut num_acceptable_codecs = 0;
|
||||
for codec in ALL_U64_CODEC_TYPES {
|
||||
let mut buffer = Vec::new();
|
||||
if serialize_u64_based_column_values(&&vals[..], &[codec], &mut buffer).is_err() {
|
||||
continue;
|
||||
}
|
||||
num_acceptable_codecs += 1;
|
||||
let col = load_u64_based_column_values::<u64>(OwnedBytes::new(buffer)).unwrap();
|
||||
assert_eq!(col.num_vals(), 0);
|
||||
assert_eq!(col.min_value(), u64::MIN);
|
||||
assert_eq!(col.max_value(), u64::MIN);
|
||||
}
|
||||
assert!(num_acceptable_codecs > 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_empty_column_f64() {
|
||||
let vals: [f64; 0] = [];
|
||||
let mut num_acceptable_codecs = 0;
|
||||
for codec in ALL_U64_CODEC_TYPES {
|
||||
let mut buffer = Vec::new();
|
||||
if serialize_u64_based_column_values(&&vals[..], &[codec], &mut buffer).is_err() {
|
||||
continue;
|
||||
}
|
||||
num_acceptable_codecs += 1;
|
||||
let col = load_u64_based_column_values::<f64>(OwnedBytes::new(buffer)).unwrap();
|
||||
assert_eq!(col.num_vals(), 0);
|
||||
// FIXME. f64::MIN would be better!
|
||||
assert!(col.min_value().is_nan());
|
||||
assert!(col.max_value().is_nan());
|
||||
}
|
||||
assert!(num_acceptable_codecs > 0);
|
||||
}
|
||||
|
||||
pub(crate) fn create_and_validate<TColumnCodec: ColumnCodec>(
|
||||
vals: &[u64],
|
||||
name: &str,
|
||||
@@ -43,14 +99,28 @@ pub(crate) fn create_and_validate<TColumnCodec: ColumnCodec>(
|
||||
|
||||
let reader = TColumnCodec::load(OwnedBytes::new(buffer)).unwrap();
|
||||
assert_eq!(reader.num_vals(), vals.len() as u32);
|
||||
let mut buffer = Vec::new();
|
||||
for (doc, orig_val) in vals.iter().copied().enumerate() {
|
||||
let val = reader.get_val(doc as u32);
|
||||
assert_eq!(
|
||||
val, orig_val,
|
||||
"val `{val}` does not match orig_val {orig_val:?}, in data set {name}, data `{vals:?}`",
|
||||
);
|
||||
|
||||
buffer.resize(1, 0);
|
||||
reader.get_vals(&[doc as u32], &mut buffer);
|
||||
let val = buffer[0];
|
||||
assert_eq!(
|
||||
val, orig_val,
|
||||
"val `{val}` does not match orig_val {orig_val:?}, in data set {name}, data `{vals:?}`",
|
||||
);
|
||||
}
|
||||
|
||||
let all_docs: Vec<u32> = (0..vals.len() as u32).collect();
|
||||
buffer.resize(all_docs.len(), 0);
|
||||
reader.get_vals(&all_docs, &mut buffer);
|
||||
assert_eq!(vals, buffer);
|
||||
|
||||
if !vals.is_empty() {
|
||||
let test_rand_idx = rand::thread_rng().gen_range(0..=vals.len() - 1);
|
||||
let expected_positions: Vec<u32> = vals
|
||||
@@ -60,7 +130,7 @@ pub(crate) fn create_and_validate<TColumnCodec: ColumnCodec>(
|
||||
.map(|(pos, _)| pos as u32)
|
||||
.collect();
|
||||
let mut positions = Vec::new();
|
||||
reader.get_docids_for_value_range(
|
||||
reader.get_row_ids_for_value_range(
|
||||
vals[test_rand_idx]..=vals[test_rand_idx],
|
||||
0..vals.len() as u32,
|
||||
&mut positions,
|
||||
|
||||
54
columnar/src/column_values/vec_column.rs
Normal file
54
columnar/src/column_values/vec_column.rs
Normal file
@@ -0,0 +1,54 @@
|
||||
use std::fmt::Debug;
|
||||
|
||||
use tantivy_bitpacker::minmax;
|
||||
|
||||
use crate::ColumnValues;
|
||||
|
||||
/// VecColumn provides `Column` over a `Vec<T>`.
|
||||
pub struct VecColumn<T = u64> {
|
||||
pub(crate) values: Vec<T>,
|
||||
pub(crate) min_value: T,
|
||||
pub(crate) max_value: T,
|
||||
}
|
||||
|
||||
impl<T: Copy + PartialOrd + Send + Sync + Debug + 'static> ColumnValues<T> for VecColumn<T> {
|
||||
fn get_val(&self, position: u32) -> T {
|
||||
self.values[position as usize]
|
||||
}
|
||||
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
Box::new(self.values.iter().copied())
|
||||
}
|
||||
|
||||
fn min_value(&self) -> T {
|
||||
self.min_value
|
||||
}
|
||||
|
||||
fn max_value(&self) -> T {
|
||||
self.max_value
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.values.len() as u32
|
||||
}
|
||||
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
output.copy_from_slice(&self.values[start as usize..][..output.len()])
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: Copy + PartialOrd + Default> From<Vec<T>> for VecColumn<T> {
|
||||
fn from(values: Vec<T>) -> Self {
|
||||
let (min_value, max_value) = minmax(values.iter().copied()).unwrap_or_default();
|
||||
Self {
|
||||
values,
|
||||
min_value,
|
||||
max_value,
|
||||
}
|
||||
}
|
||||
}
|
||||
impl From<VecColumn> for Vec<u64> {
|
||||
fn from(column: VecColumn) -> Self {
|
||||
column.values
|
||||
}
|
||||
}
|
||||
@@ -1,12 +1,15 @@
|
||||
use std::fmt;
|
||||
use std::fmt::Debug;
|
||||
use std::net::Ipv6Addr;
|
||||
|
||||
use crate::value::NumericalType;
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use crate::InvalidData;
|
||||
use crate::value::NumericalType;
|
||||
|
||||
/// The column type represents the column type.
|
||||
/// Any changes need to be propagated to `COLUMN_TYPES`.
|
||||
#[derive(Hash, Eq, PartialEq, Debug, Clone, Copy, Ord, PartialOrd)]
|
||||
#[derive(Hash, Eq, PartialEq, Debug, Clone, Copy, Ord, PartialOrd, Serialize, Deserialize)]
|
||||
#[repr(u8)]
|
||||
pub enum ColumnType {
|
||||
I64 = 0u8,
|
||||
@@ -19,6 +22,22 @@ pub enum ColumnType {
|
||||
DateTime = 7u8,
|
||||
}
|
||||
|
||||
impl fmt::Display for ColumnType {
|
||||
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
|
||||
let short_str = match self {
|
||||
ColumnType::I64 => "i64",
|
||||
ColumnType::U64 => "u64",
|
||||
ColumnType::F64 => "f64",
|
||||
ColumnType::Bytes => "bytes",
|
||||
ColumnType::Str => "str",
|
||||
ColumnType::Bool => "bool",
|
||||
ColumnType::IpAddr => "ip",
|
||||
ColumnType::DateTime => "datetime",
|
||||
};
|
||||
write!(f, "{short_str}")
|
||||
}
|
||||
}
|
||||
|
||||
// The order needs to match _exactly_ the order in the enum
|
||||
const COLUMN_TYPES: [ColumnType; 8] = [
|
||||
ColumnType::I64,
|
||||
@@ -35,6 +54,9 @@ impl ColumnType {
|
||||
pub fn to_code(self) -> u8 {
|
||||
self as u8
|
||||
}
|
||||
pub fn is_date_time(&self) -> bool {
|
||||
self == &ColumnType::DateTime
|
||||
}
|
||||
|
||||
pub(crate) fn try_from_code(code: u8) -> Result<ColumnType, InvalidData> {
|
||||
COLUMN_TYPES.get(code as usize).copied().ok_or(InvalidData)
|
||||
@@ -111,7 +133,7 @@ impl HasAssociatedColumnType for bool {
|
||||
}
|
||||
}
|
||||
|
||||
impl HasAssociatedColumnType for crate::DateTime {
|
||||
impl HasAssociatedColumnType for common::DateTime {
|
||||
fn column_type() -> ColumnType {
|
||||
ColumnType::DateTime
|
||||
}
|
||||
@@ -143,7 +165,7 @@ mod tests {
|
||||
}
|
||||
}
|
||||
for code in COLUMN_TYPES.len() as u8..=u8::MAX {
|
||||
assert!(ColumnType::try_from_code(code as u8).is_err());
|
||||
assert!(ColumnType::try_from_code(code).is_err());
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -1,14 +1,17 @@
|
||||
use core::fmt;
|
||||
use std::fmt::{Display, Formatter};
|
||||
|
||||
use crate::InvalidData;
|
||||
|
||||
pub const VERSION_FOOTER_NUM_BYTES: usize = MAGIC_BYTES.len() + std::mem::size_of::<u32>();
|
||||
|
||||
/// We end the file by these 4 bytes just to somewhat identify that
|
||||
/// this is indeed a columnar file.
|
||||
const MAGIC_BYTES: [u8; 4] = [2, 113, 119, 066];
|
||||
const MAGIC_BYTES: [u8; 4] = [2, 113, 119, 66];
|
||||
|
||||
pub fn footer() -> [u8; VERSION_FOOTER_NUM_BYTES] {
|
||||
let mut footer_bytes = [0u8; VERSION_FOOTER_NUM_BYTES];
|
||||
footer_bytes[0..4].copy_from_slice(&Version::V1.to_bytes());
|
||||
footer_bytes[0..4].copy_from_slice(&CURRENT_VERSION.to_bytes());
|
||||
footer_bytes[4..8].copy_from_slice(&MAGIC_BYTES[..]);
|
||||
footer_bytes
|
||||
}
|
||||
@@ -20,21 +23,34 @@ pub fn parse_footer(footer_bytes: [u8; VERSION_FOOTER_NUM_BYTES]) -> Result<Vers
|
||||
Version::try_from_bytes(footer_bytes[0..4].try_into().unwrap())
|
||||
}
|
||||
|
||||
pub const CURRENT_VERSION: Version = Version::V2;
|
||||
|
||||
#[derive(Debug, Copy, Clone, Eq, PartialEq)]
|
||||
#[repr(u32)]
|
||||
pub enum Version {
|
||||
V1 = 1u32,
|
||||
V2 = 2u32,
|
||||
}
|
||||
|
||||
impl Display for Version {
|
||||
fn fmt(&self, f: &mut Formatter) -> fmt::Result {
|
||||
match self {
|
||||
Version::V1 => write!(f, "v1"),
|
||||
Version::V2 => write!(f, "v2"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Version {
|
||||
fn to_bytes(&self) -> [u8; 4] {
|
||||
(*self as u32).to_le_bytes()
|
||||
fn to_bytes(self) -> [u8; 4] {
|
||||
(self as u32).to_le_bytes()
|
||||
}
|
||||
|
||||
fn try_from_bytes(bytes: [u8; 4]) -> Result<Version, InvalidData> {
|
||||
let code = u32::from_le_bytes(bytes);
|
||||
match code {
|
||||
1u32 => Ok(Version::V1),
|
||||
2u32 => Ok(Version::V2),
|
||||
_ => Err(InvalidData),
|
||||
}
|
||||
}
|
||||
@@ -47,9 +63,9 @@ mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_footer_dserialization() {
|
||||
fn test_footer_deserialization() {
|
||||
let parsed_version: Version = parse_footer(footer()).unwrap();
|
||||
assert_eq!(Version::V1, parsed_version);
|
||||
assert_eq!(Version::V2, parsed_version);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -63,11 +79,10 @@ mod tests {
|
||||
for &i in &version_to_tests {
|
||||
let version_res = Version::try_from_bytes(i.to_le_bytes());
|
||||
if let Ok(version) = version_res {
|
||||
assert_eq!(version, Version::V1);
|
||||
assert_eq!(version.to_bytes(), i.to_le_bytes());
|
||||
valid_versions.insert(i);
|
||||
}
|
||||
}
|
||||
assert_eq!(valid_versions.len(), 1);
|
||||
assert_eq!(valid_versions.len(), 2);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
use std::io::{self, Write};
|
||||
|
||||
use common::{BitSet, CountingWriter, ReadOnlyBitSet};
|
||||
use sstable::{SSTable, TermOrdinal};
|
||||
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(),
|
||||
@@ -50,23 +50,25 @@ 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
|
||||
.iter()
|
||||
.enumerate()
|
||||
.flat_map(|(segment_ord, byte_column)| {
|
||||
let segment_ord = self.term_ord_mapping.get_segment(segment_ord as u32);
|
||||
byte_column.into_iter().flat_map(move |bytes_column| {
|
||||
bytes_column
|
||||
.ords()
|
||||
.values
|
||||
.iter()
|
||||
.map(move |term_ord| segment_ord[term_ord as usize])
|
||||
})
|
||||
.flat_map(|(seg_ord, bytes_column_opt)| {
|
||||
let bytes_column = bytes_column_opt.as_ref()?;
|
||||
Some((seg_ord, bytes_column))
|
||||
})
|
||||
.flat_map(move |(seg_ord, bytes_column)| {
|
||||
let term_ord_after_merge_mapping =
|
||||
self.term_ord_mapping.get_segment(seg_ord as u32);
|
||||
bytes_column
|
||||
.ords()
|
||||
.values
|
||||
.iter()
|
||||
.map(move |term_ord| term_ord_after_merge_mapping[term_ord as usize])
|
||||
});
|
||||
// TODO see if we can better decompose the mapping / and the stacking
|
||||
Box::new(iter)
|
||||
}
|
||||
|
||||
@@ -96,7 +98,7 @@ fn compute_term_bitset(column: &BytesColumn, row_bitset: &ReadOnlyBitSet) -> Bit
|
||||
let num_terms = column.dictionary().num_terms();
|
||||
let mut term_bitset = BitSet::with_max_value(num_terms as u32);
|
||||
for row_id in row_bitset.iter() {
|
||||
for term_ord in column.term_ord_column.values(row_id) {
|
||||
for term_ord in column.term_ord_column.values_for_doc(row_id) {
|
||||
term_bitset.insert(term_ord as u32);
|
||||
}
|
||||
}
|
||||
@@ -124,16 +126,23 @@ fn serialize_merged_dict(
|
||||
let mut term_ord_mapping = TermOrdinalMapping::default();
|
||||
|
||||
let mut field_term_streams = Vec::new();
|
||||
for column in bytes_columns.iter().flatten() {
|
||||
term_ord_mapping.add_segment(column.dictionary.num_terms());
|
||||
let terms = column.dictionary.stream()?;
|
||||
field_term_streams.push(terms);
|
||||
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(TermsWithSegmentOrd { terms, segment_ord });
|
||||
} else {
|
||||
term_ord_mapping.add_segment(0);
|
||||
field_term_streams.push(TermsWithSegmentOrd {
|
||||
terms: Streamer::empty(),
|
||||
segment_ord,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
let mut merged_terms = TermMerger::new(field_term_streams);
|
||||
let mut sstable_builder = sstable::VoidSSTable::writer(output);
|
||||
|
||||
// TODO support complex `merge_row_order`.
|
||||
match merge_row_order {
|
||||
MergeRowOrder::Stack(_) => {
|
||||
let mut current_term_ord = 0;
|
||||
@@ -185,13 +194,14 @@ 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>>,
|
||||
}
|
||||
|
||||
impl TermOrdinalMapping {
|
||||
fn add_segment(&mut self, max_term_ord: usize) {
|
||||
self.per_segment_new_term_ordinals
|
||||
.push(vec![TermOrdinal::default(); max_term_ord as usize]);
|
||||
.push(vec![TermOrdinal::default(); max_term_ord]);
|
||||
}
|
||||
|
||||
fn register_from_to(&mut self, segment_ord: usize, from_ord: TermOrdinal, to_ord: TermOrdinal) {
|
||||
@@ -199,6 +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]
|
||||
}
|
||||
}
|
||||
|
||||
@@ -11,11 +11,22 @@ pub struct StackMergeOrder {
|
||||
}
|
||||
|
||||
impl StackMergeOrder {
|
||||
#[cfg(test)]
|
||||
pub fn stack_for_test(num_rows_per_columnar: &[u32]) -> StackMergeOrder {
|
||||
let mut cumulated_row_ids: Vec<RowId> = Vec::with_capacity(num_rows_per_columnar.len());
|
||||
let mut cumulated_row_id = 0;
|
||||
for &num_rows in num_rows_per_columnar {
|
||||
cumulated_row_id += num_rows;
|
||||
cumulated_row_ids.push(cumulated_row_id);
|
||||
}
|
||||
StackMergeOrder { cumulated_row_ids }
|
||||
}
|
||||
|
||||
pub fn stack(columnars: &[&ColumnarReader]) -> StackMergeOrder {
|
||||
let mut cumulated_row_ids: Vec<RowId> = Vec::with_capacity(columnars.len());
|
||||
let mut cumulated_row_id = 0;
|
||||
for columnar in columnars {
|
||||
cumulated_row_id += columnar.num_rows();
|
||||
cumulated_row_id += columnar.num_docs();
|
||||
cumulated_row_ids.push(cumulated_row_id);
|
||||
}
|
||||
StackMergeOrder { cumulated_row_ids }
|
||||
@@ -41,8 +52,8 @@ pub enum MergeRowOrder {
|
||||
/// Columnar tables are simply stacked one above the other.
|
||||
/// If the i-th columnar_readers has n_rows_i rows, then
|
||||
/// in the resulting columnar,
|
||||
/// rows [r0..n_row_0) contains the row of columnar_readers[0], in ordder
|
||||
/// rows [n_row_0..n_row_0 + n_row_1 contains the row of columnar_readers[1], in order.
|
||||
/// rows [r0..n_row_0) contains the row of `columnar_readers[0]`, in ordder
|
||||
/// rows [n_row_0..n_row_0 + n_row_1 contains the row of `columnar_readers[1]`, in order.
|
||||
/// ..
|
||||
/// No documents is deleted.
|
||||
Stack(StackMergeOrder),
|
||||
|
||||
@@ -2,9 +2,7 @@ mod merge_dict_column;
|
||||
mod merge_mapping;
|
||||
mod term_merger;
|
||||
|
||||
// mod sorted_doc_id_column;
|
||||
|
||||
use std::collections::{BTreeMap, HashMap, HashSet};
|
||||
use std::collections::{BTreeMap, HashSet};
|
||||
use std::io;
|
||||
use std::net::Ipv6Addr;
|
||||
use std::sync::Arc;
|
||||
@@ -12,31 +10,34 @@ 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, NumericalType, NumericalValue,
|
||||
BytesColumn, Column, ColumnIndex, ColumnType, ColumnValues, DynamicColumnHandle, NumericalType,
|
||||
NumericalValue,
|
||||
};
|
||||
|
||||
/// Column types are grouped into different categories.
|
||||
/// After merge, all columns belonging to the same category are coerced to
|
||||
/// the same column type.
|
||||
///
|
||||
/// In practise, today, only Numerical colummns are coerced into one type today.
|
||||
/// In practise, today, only Numerical columns are coerced into one type today.
|
||||
///
|
||||
/// See also [README.md].
|
||||
#[derive(Copy, Clone, Eq, PartialEq, Hash, Debug)]
|
||||
enum ColumnTypeCategory {
|
||||
Bool,
|
||||
Str,
|
||||
///
|
||||
/// The ordering has to match the ordering of the variants in [ColumnType].
|
||||
#[derive(Copy, Clone, Eq, PartialOrd, Ord, PartialEq, Hash, Debug)]
|
||||
pub(crate) enum ColumnTypeCategory {
|
||||
Numerical,
|
||||
DateTime,
|
||||
Bytes,
|
||||
Str,
|
||||
Bool,
|
||||
IpAddr,
|
||||
DateTime,
|
||||
}
|
||||
|
||||
impl From<ColumnType> for ColumnTypeCategory {
|
||||
@@ -54,26 +55,64 @@ impl From<ColumnType> for ColumnTypeCategory {
|
||||
}
|
||||
}
|
||||
|
||||
/// Merge several columnar table together.
|
||||
///
|
||||
/// If several columns with the same name are conflicting with the numerical types in the
|
||||
/// input columnars, the first type compatible out of i64, u64, f64 in that order will be used.
|
||||
///
|
||||
/// `require_columns` makes it possible to ensure that some columns will be present in the
|
||||
/// resulting columnar. When a required column is a numerical column type, one of two things can
|
||||
/// happen:
|
||||
/// - If the required column type is compatible with all of the input columnar, the resulting merged
|
||||
/// columnar will simply coerce the input column and use the required column type.
|
||||
/// - If the required column type is incompatible with one of the input columnar, the merged will
|
||||
/// fail with an InvalidData error.
|
||||
///
|
||||
/// `merge_row_order` makes it possible to remove or reorder row in the resulting
|
||||
/// `Columnar` table.
|
||||
///
|
||||
/// Reminder: a string and a numerical column may bare the same column name. This is not
|
||||
/// considered a conflict.
|
||||
pub fn merge_columnar(
|
||||
columnar_readers: &[&ColumnarReader],
|
||||
required_columns: &[(String, ColumnType)],
|
||||
merge_row_order: MergeRowOrder,
|
||||
output: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
let mut serializer = ColumnarSerializer::new(output);
|
||||
let num_docs_per_columnar = columnar_readers
|
||||
.iter()
|
||||
.map(|reader| reader.num_docs())
|
||||
.collect::<Vec<u32>>();
|
||||
|
||||
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)?;
|
||||
if grouped_columns.is_empty() {
|
||||
continue;
|
||||
}
|
||||
|
||||
let column_type_after_merge = grouped_columns.column_type_after_merge();
|
||||
let mut columns = grouped_columns.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 columns_to_merge = group_columns_for_merge(columnar_readers)?;
|
||||
for ((column_name, column_type), columns) in columns_to_merge {
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name.as_bytes(), column_type);
|
||||
serializer.start_serialize_column(column_name.as_bytes(), column_type_after_merge);
|
||||
merge_column(
|
||||
column_type,
|
||||
column_type_after_merge,
|
||||
&num_docs_per_columnar,
|
||||
columns,
|
||||
&merge_row_order,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
column_serializer.finalize()?;
|
||||
}
|
||||
serializer.finalize(merge_row_order.num_rows())?;
|
||||
|
||||
serializer.finalize(merge_row_order.num_rows())?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
@@ -90,7 +129,8 @@ fn dynamic_column_to_u64_monotonic(dynamic_column: DynamicColumn) -> Option<Colu
|
||||
|
||||
fn merge_column(
|
||||
column_type: ColumnType,
|
||||
columns: Vec<Option<DynamicColumn>>,
|
||||
num_docs_per_column: &[u32],
|
||||
columns_to_merge: Vec<Option<DynamicColumn>>,
|
||||
merge_row_order: &MergeRowOrder,
|
||||
wrt: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
@@ -100,18 +140,21 @@ fn merge_column(
|
||||
| ColumnType::F64
|
||||
| ColumnType::DateTime
|
||||
| ColumnType::Bool => {
|
||||
let mut column_indexes: Vec<Option<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 dynamic_column_opt in columns {
|
||||
if let Some(Column { idx, values }) =
|
||||
dynamic_column_opt.and_then(dynamic_column_to_u64_monotonic)
|
||||
{
|
||||
column_indexes.push(Some(idx));
|
||||
column_values.push(Some(values));
|
||||
} else {
|
||||
column_indexes.push(None);
|
||||
column_values.push(None);
|
||||
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 =
|
||||
@@ -124,15 +167,19 @@ fn merge_column(
|
||||
serialize_column_mappable_to_u64(merged_column_index, &merge_column_values, wrt)?;
|
||||
}
|
||||
ColumnType::IpAddr => {
|
||||
let mut column_indexes: Vec<Option<ColumnIndex>> = Vec::with_capacity(columns.len());
|
||||
let mut column_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 dynamic_column_opt in columns {
|
||||
if let Some(DynamicColumn::IpAddr(Column { idx, values })) = dynamic_column_opt {
|
||||
column_indexes.push(Some(idx));
|
||||
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
|
||||
{
|
||||
column_indexes.push(idx);
|
||||
column_values.push(Some(values));
|
||||
} else {
|
||||
column_indexes.push(None);
|
||||
column_indexes.push(ColumnIndex::Empty {
|
||||
num_docs: num_docs_per_column[i],
|
||||
});
|
||||
column_values.push(None);
|
||||
}
|
||||
}
|
||||
@@ -148,20 +195,23 @@ fn merge_column(
|
||||
serialize_column_mappable_to_u128(merged_column_index, &merge_column_values, wrt)?;
|
||||
}
|
||||
ColumnType::Bytes | ColumnType::Str => {
|
||||
let mut column_indexes: Vec<Option<ColumnIndex>> = Vec::with_capacity(columns.len());
|
||||
let mut bytes_columns: Vec<Option<BytesColumn>> = Vec::with_capacity(columns.len());
|
||||
for dynamic_column_opt in columns {
|
||||
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(Some(str_column.term_ord_column.idx.clone()));
|
||||
column_indexes.push(str_column.term_ord_column.index.clone());
|
||||
bytes_columns.push(Some(str_column.into()));
|
||||
}
|
||||
Some(DynamicColumn::Bytes(bytes_column)) => {
|
||||
column_indexes.push(Some(bytes_column.term_ord_column.idx.clone()));
|
||||
column_indexes.push(bytes_column.term_ord_column.index.clone());
|
||||
bytes_columns.push(Some(bytes_column));
|
||||
}
|
||||
_ => {
|
||||
column_indexes.push(None);
|
||||
column_indexes.push(ColumnIndex::Empty {
|
||||
num_docs: num_docs_per_column[i],
|
||||
});
|
||||
bytes_columns.push(None);
|
||||
}
|
||||
}
|
||||
@@ -174,97 +224,253 @@ fn merge_column(
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn group_columns_for_merge(
|
||||
columnar_readers: &[&ColumnarReader],
|
||||
) -> io::Result<BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>>> {
|
||||
// Each column name may have multiple types of column associated.
|
||||
// For merging we are interested in the same column type category since they can be merged.
|
||||
let mut columns_grouped: HashMap<(String, ColumnTypeCategory), Vec<Option<DynamicColumn>>> =
|
||||
HashMap::new();
|
||||
|
||||
let num_columnars = columnar_readers.len();
|
||||
|
||||
for (columnar_id, columnar_reader) in columnar_readers.iter().enumerate() {
|
||||
let column_name_and_handle = columnar_reader.list_columns()?;
|
||||
for (column_name, handle) in column_name_and_handle {
|
||||
let column_type_category: ColumnTypeCategory = handle.column_type().into();
|
||||
let columns = columns_grouped
|
||||
.entry((column_name, column_type_category))
|
||||
.or_insert_with(|| vec![None; num_columnars]);
|
||||
let column = handle.open()?;
|
||||
columns[columnar_id] = Some(column);
|
||||
}
|
||||
}
|
||||
|
||||
let mut merge_columns: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
BTreeMap::default();
|
||||
|
||||
for ((column_name, col_category), mut columns) in columns_grouped {
|
||||
if col_category == ColumnTypeCategory::Numerical {
|
||||
coerce_numerical_columns_to_same_type(&mut columns);
|
||||
}
|
||||
let column_type = columns
|
||||
.iter()
|
||||
.flatten()
|
||||
.map(|col| col.column_type())
|
||||
.next()
|
||||
.unwrap();
|
||||
merge_columns.insert((column_name, column_type), columns);
|
||||
}
|
||||
|
||||
Ok(merge_columns)
|
||||
struct GroupedColumns {
|
||||
required_column_type: Option<ColumnType>,
|
||||
columns: Vec<Option<DynamicColumn>>,
|
||||
}
|
||||
|
||||
/// Coerce a set of numerical columns to the same type.
|
||||
///
|
||||
/// If all columns are already from the same type, keep this type
|
||||
/// (even if they could all be coerced to i64).
|
||||
fn coerce_numerical_columns_to_same_type(columns: &mut [Option<DynamicColumn>]) {
|
||||
let mut column_types: HashSet<NumericalType> = HashSet::default();
|
||||
let mut compatible_numerical_types = CompatibleNumericalTypes::default();
|
||||
for column in columns.iter().flatten() {
|
||||
let min_value: NumericalValue;
|
||||
let max_value: NumericalValue;
|
||||
match column {
|
||||
DynamicColumn::I64(column) => {
|
||||
min_value = column.min_value().into();
|
||||
max_value = column.max_value().into();
|
||||
}
|
||||
DynamicColumn::U64(column) => {
|
||||
min_value = column.min_value().into();
|
||||
max_value = column.min_value().into();
|
||||
}
|
||||
DynamicColumn::F64(column) => {
|
||||
min_value = column.min_value().into();
|
||||
max_value = column.min_value().into();
|
||||
}
|
||||
DynamicColumn::Bool(_)
|
||||
| DynamicColumn::IpAddr(_)
|
||||
| DynamicColumn::DateTime(_)
|
||||
| DynamicColumn::Bytes(_)
|
||||
| DynamicColumn::Str(_) => {
|
||||
panic!("We expected only numerical columns.");
|
||||
impl GroupedColumns {
|
||||
/// Check is column group can be skipped during serialization.
|
||||
fn is_empty(&self) -> bool {
|
||||
self.required_column_type.is_none() && self.columns.iter().all(Option::is_none)
|
||||
}
|
||||
|
||||
/// Returns the column type after merge.
|
||||
///
|
||||
/// This method does not check if the column types can actually be coerced to
|
||||
/// this type.
|
||||
fn column_type_after_merge(&self) -> ColumnType {
|
||||
if let Some(required_type) = self.required_column_type {
|
||||
return required_type;
|
||||
}
|
||||
let column_type: HashSet<ColumnType> = self
|
||||
.columns
|
||||
.iter()
|
||||
.flatten()
|
||||
.map(|column| column.column_type())
|
||||
.collect();
|
||||
if column_type.len() == 1 {
|
||||
return column_type.into_iter().next().unwrap();
|
||||
}
|
||||
// At the moment, only the numerical column type category has more than one possible
|
||||
// column type.
|
||||
assert!(
|
||||
self.columns
|
||||
.iter()
|
||||
.flatten()
|
||||
.all(|el| ColumnTypeCategory::from(el.column_type())
|
||||
== ColumnTypeCategory::Numerical)
|
||||
);
|
||||
merged_numerical_columns_type(self.columns.iter().flatten()).into()
|
||||
}
|
||||
}
|
||||
|
||||
struct GroupedColumnsHandle {
|
||||
required_column_type: Option<ColumnType>,
|
||||
columns: Vec<Option<DynamicColumnHandle>>,
|
||||
}
|
||||
|
||||
impl GroupedColumnsHandle {
|
||||
fn new(num_columnars: usize) -> Self {
|
||||
GroupedColumnsHandle {
|
||||
required_column_type: None,
|
||||
columns: vec![None; num_columnars],
|
||||
}
|
||||
}
|
||||
fn open(self, merge_row_order: &MergeRowOrder) -> io::Result<GroupedColumns> {
|
||||
let mut columns: Vec<Option<DynamicColumn>> = Vec::new();
|
||||
for (columnar_id, column) in self.columns.iter().enumerate() {
|
||||
if let Some(column) = column {
|
||||
let column = column.open()?;
|
||||
// We skip columns that end up with 0 documents.
|
||||
// That way, we make sure they don't end up influencing the merge type or
|
||||
// creating empty columns.
|
||||
|
||||
if is_empty_after_merge(merge_row_order, &column, columnar_id) {
|
||||
columns.push(None);
|
||||
} else {
|
||||
columns.push(Some(column));
|
||||
}
|
||||
} else {
|
||||
columns.push(None);
|
||||
}
|
||||
}
|
||||
column_types.insert(column.column_type().numerical_type().unwrap());
|
||||
Ok(GroupedColumns {
|
||||
required_column_type: self.required_column_type,
|
||||
columns,
|
||||
})
|
||||
}
|
||||
|
||||
/// Set the dynamic column for a given columnar.
|
||||
fn set_column(&mut self, columnar_id: usize, column: DynamicColumnHandle) {
|
||||
self.columns[columnar_id] = Some(column);
|
||||
}
|
||||
|
||||
/// Force the existence of a column, as well as its type.
|
||||
fn require_type(&mut self, required_type: ColumnType) -> io::Result<()> {
|
||||
if let Some(existing_required_type) = self.required_column_type {
|
||||
if existing_required_type == required_type {
|
||||
// This was just a duplicate in the `required_columns`.
|
||||
// Nothing to do.
|
||||
return Ok(());
|
||||
} else {
|
||||
return Err(io::Error::new(
|
||||
io::ErrorKind::InvalidInput,
|
||||
"Required column conflicts with another required column of the same type \
|
||||
category.",
|
||||
));
|
||||
}
|
||||
}
|
||||
self.required_column_type = Some(required_type);
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the type of the merged numerical column.
|
||||
///
|
||||
/// This function picks the first numerical type out of i64, u64, f64 (order matters
|
||||
/// here), that is compatible with all the `columns`.
|
||||
///
|
||||
/// # Panics
|
||||
/// Panics if one of the column is not numerical.
|
||||
fn merged_numerical_columns_type<'a>(
|
||||
columns: impl Iterator<Item = &'a DynamicColumn>,
|
||||
) -> NumericalType {
|
||||
let mut compatible_numerical_types = CompatibleNumericalTypes::default();
|
||||
for column in columns {
|
||||
let (min_value, max_value) =
|
||||
min_max_if_numerical(column).expect("All columns re required to be numerical");
|
||||
compatible_numerical_types.accept_value(min_value);
|
||||
compatible_numerical_types.accept_value(max_value);
|
||||
}
|
||||
if column_types.len() <= 1 {
|
||||
// No need to do anything. The columns are already all from the same type.
|
||||
// This is necessary to let use force a given type.
|
||||
compatible_numerical_types.to_numerical_type()
|
||||
}
|
||||
|
||||
// TODO This works in a world where we do not allow a change of schema,
|
||||
// but in the future, we will have to pass some kind of schema to enforce
|
||||
// the logic.
|
||||
return;
|
||||
fn is_empty_after_merge(
|
||||
merge_row_order: &MergeRowOrder,
|
||||
column: &DynamicColumn,
|
||||
columnar_ord: usize,
|
||||
) -> bool {
|
||||
if column.num_values() == 0u32 {
|
||||
// It was empty before the merge.
|
||||
return true;
|
||||
}
|
||||
let coerce_type = compatible_numerical_types.to_numerical_type();
|
||||
match merge_row_order {
|
||||
MergeRowOrder::Stack(_) => {
|
||||
// If we are stacking the columnar, no rows are being deleted.
|
||||
false
|
||||
}
|
||||
MergeRowOrder::Shuffled(shuffled) => {
|
||||
if let Some(alive_bitset) = &shuffled.alive_bitsets[columnar_ord] {
|
||||
let column_index = column.column_index();
|
||||
match column_index {
|
||||
ColumnIndex::Empty { .. } => true,
|
||||
ColumnIndex::Full => alive_bitset.len() == 0,
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
for doc in optional_index.iter_docs() {
|
||||
if alive_bitset.contains(doc) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
true
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => {
|
||||
for alive_docid in alive_bitset.iter() {
|
||||
if !multivalued_index.range(alive_docid).is_empty() {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
true
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// No document is being deleted.
|
||||
// The shuffle is applying a permutation.
|
||||
false
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Iterates over the columns of the columnar readers, grouped by column name.
|
||||
/// Key functionality is that `open` of the Columns is done lazy per group.
|
||||
fn group_columns_for_merge<'a>(
|
||||
columnar_readers: &'a [&'a ColumnarReader],
|
||||
required_columns: &'a [(String, ColumnType)],
|
||||
) -> io::Result<BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle>> {
|
||||
let mut columns: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> = BTreeMap::new();
|
||||
|
||||
for &(ref column_name, column_type) in required_columns {
|
||||
columns
|
||||
.entry((column_name.clone(), column_type.into()))
|
||||
.or_insert_with(|| GroupedColumnsHandle::new(columnar_readers.len()))
|
||||
.require_type(column_type)?;
|
||||
}
|
||||
|
||||
for (columnar_id, columnar_reader) in columnar_readers.iter().enumerate() {
|
||||
let column_name_and_handle = columnar_reader.iter_columns()?;
|
||||
|
||||
for (column_name, handle) in column_name_and_handle {
|
||||
let column_category: ColumnTypeCategory = handle.column_type().into();
|
||||
columns
|
||||
.entry((column_name, column_category))
|
||||
.or_insert_with(|| GroupedColumnsHandle::new(columnar_readers.len()))
|
||||
.set_column(columnar_id, handle);
|
||||
}
|
||||
}
|
||||
Ok(columns)
|
||||
}
|
||||
|
||||
fn coerce_columns(
|
||||
column_type: ColumnType,
|
||||
columns: &mut [Option<DynamicColumn>],
|
||||
) -> io::Result<()> {
|
||||
for column_opt in columns.iter_mut() {
|
||||
if let Some(column) = column_opt.take() {
|
||||
*column_opt = column.coerce_numerical(coerce_type);
|
||||
*column_opt = Some(coerce_column(column_type, column)?);
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn coerce_column(column_type: ColumnType, column: DynamicColumn) -> io::Result<DynamicColumn> {
|
||||
if let Some(numerical_type) = column_type.numerical_type() {
|
||||
column
|
||||
.coerce_numerical(numerical_type)
|
||||
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidInput, ""))
|
||||
} else {
|
||||
if column.column_type() != column_type {
|
||||
return Err(io::Error::new(
|
||||
io::ErrorKind::InvalidInput,
|
||||
format!(
|
||||
"Cannot coerce column of type `{:?}` to `{column_type:?}`",
|
||||
column.column_type()
|
||||
),
|
||||
));
|
||||
}
|
||||
Ok(column)
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the (min, max) of a column provided it is numerical (i64, u64. f64).
|
||||
///
|
||||
/// The min and the max are simply the numerical value as defined by `ColumnValue::min_value()`,
|
||||
/// and `ColumnValue::max_value()`.
|
||||
///
|
||||
/// It is important to note that these values are only guaranteed to be lower/upper bound
|
||||
/// (as opposed to min/max value).
|
||||
/// If a column is empty, the min and max values are currently set to 0.
|
||||
fn min_max_if_numerical(column: &DynamicColumn) -> Option<(NumericalValue, NumericalValue)> {
|
||||
match column {
|
||||
DynamicColumn::I64(column) => Some((column.min_value().into(), column.max_value().into())),
|
||||
DynamicColumn::U64(column) => Some((column.min_value().into(), column.max_value().into())),
|
||||
DynamicColumn::F64(column) => Some((column.min_value().into(), column.max_value().into())),
|
||||
DynamicColumn::Bool(_)
|
||||
| DynamicColumn::IpAddr(_)
|
||||
| DynamicColumn::DateTime(_)
|
||||
| DynamicColumn::Bytes(_)
|
||||
| DynamicColumn::Str(_) => None,
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
|
||||
@@ -1,107 +0,0 @@
|
||||
use std::sync::Arc;
|
||||
|
||||
use fastfield_codecs::Column;
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::indexer::doc_id_mapping::SegmentDocIdMapping;
|
||||
use crate::SegmentReader;
|
||||
|
||||
pub(crate) struct RemappedDocIdColumn<'a> {
|
||||
doc_id_mapping: &'a SegmentDocIdMapping,
|
||||
fast_field_readers: Vec<Arc<dyn Column<u64>>>,
|
||||
min_value: u64,
|
||||
max_value: u64,
|
||||
num_vals: u32,
|
||||
}
|
||||
|
||||
fn compute_min_max_val(
|
||||
u64_reader: &dyn Column<u64>,
|
||||
segment_reader: &SegmentReader,
|
||||
) -> Option<(u64, u64)> {
|
||||
if segment_reader.max_doc() == 0 {
|
||||
return None;
|
||||
}
|
||||
|
||||
if segment_reader.alive_bitset().is_none() {
|
||||
// no deleted documents,
|
||||
// we can use the previous min_val, max_val.
|
||||
return Some((u64_reader.min_value(), u64_reader.max_value()));
|
||||
}
|
||||
// some deleted documents,
|
||||
// we need to recompute the max / min
|
||||
segment_reader
|
||||
.doc_ids_alive()
|
||||
.map(|doc_id| u64_reader.get_val(doc_id))
|
||||
.minmax()
|
||||
.into_option()
|
||||
}
|
||||
|
||||
impl<'a> RemappedDocIdColumn<'a> {
|
||||
pub(crate) fn new(
|
||||
readers: &'a [SegmentReader],
|
||||
doc_id_mapping: &'a SegmentDocIdMapping,
|
||||
field: &str,
|
||||
) -> Self {
|
||||
let (min_value, max_value) = readers
|
||||
.iter()
|
||||
.filter_map(|reader| {
|
||||
let u64_reader: Arc<dyn Column<u64>> =
|
||||
reader.fast_fields().typed_fast_field_reader(field).expect(
|
||||
"Failed to find a reader for single fast field. This is a tantivy bug and \
|
||||
it should never happen.",
|
||||
);
|
||||
compute_min_max_val(&*u64_reader, reader)
|
||||
})
|
||||
.reduce(|a, b| (a.0.min(b.0), a.1.max(b.1)))
|
||||
.expect("Unexpected error, empty readers in IndexMerger");
|
||||
|
||||
let fast_field_readers = readers
|
||||
.iter()
|
||||
.map(|reader| {
|
||||
let u64_reader: Arc<dyn Column<u64>> =
|
||||
reader.fast_fields().typed_fast_field_reader(field).expect(
|
||||
"Failed to find a reader for single fast field. This is a tantivy bug and \
|
||||
it should never happen.",
|
||||
);
|
||||
u64_reader
|
||||
})
|
||||
.collect::<Vec<_>>();
|
||||
|
||||
RemappedDocIdColumn {
|
||||
doc_id_mapping,
|
||||
fast_field_readers,
|
||||
min_value,
|
||||
max_value,
|
||||
num_vals: doc_id_mapping.len() as u32,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Column for RemappedDocIdColumn<'a> {
|
||||
fn get_val(&self, _doc: u32) -> u64 {
|
||||
unimplemented!()
|
||||
}
|
||||
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = u64> + '_> {
|
||||
Box::new(
|
||||
self.doc_id_mapping
|
||||
.iter_old_doc_addrs()
|
||||
.map(|old_doc_addr| {
|
||||
let fast_field_reader =
|
||||
&self.fast_field_readers[old_doc_addr.segment_ord as usize];
|
||||
fast_field_reader.get_val(old_doc_addr.doc_id)
|
||||
}),
|
||||
)
|
||||
}
|
||||
fn min_value(&self) -> u64 {
|
||||
self.min_value
|
||||
}
|
||||
|
||||
fn max_value(&self) -> u64 {
|
||||
self.max_value
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.num_vals
|
||||
}
|
||||
}
|
||||
@@ -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()
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,5 +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,
|
||||
@@ -12,7 +17,7 @@ fn make_columnar<T: Into<NumericalValue> + HasAssociatedColumnType + Copy>(
|
||||
}
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer
|
||||
.serialize(vals.len() as RowId, None, &mut buffer)
|
||||
.serialize(vals.len() as RowId, &mut buffer)
|
||||
.unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
}
|
||||
@@ -23,51 +28,99 @@ fn test_column_coercion_to_u64() {
|
||||
let columnar1 = make_columnar("numbers", &[1i64]);
|
||||
// u64 type
|
||||
let columnar2 = make_columnar("numbers", &[u64::MAX]);
|
||||
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
group_columns_for_merge(&[&columnar1, &columnar2]).unwrap();
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
group_columns_for_merge(columnars, &[]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::U64)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_column_no_coercion_if_all_the_same() {
|
||||
let columnar1 = make_columnar("numbers", &[1u64]);
|
||||
let columnar2 = make_columnar("numbers", &[2u64]);
|
||||
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
group_columns_for_merge(&[&columnar1, &columnar2]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::U64)));
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_column_coercion_to_i64() {
|
||||
let columnar1 = make_columnar("numbers", &[-1i64]);
|
||||
let columnar2 = make_columnar("numbers", &[2u64]);
|
||||
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
group_columns_for_merge(&[&columnar1, &columnar2]).unwrap();
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
group_columns_for_merge(columnars, &[]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::I64)));
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
|
||||
}
|
||||
|
||||
//#[test]
|
||||
// fn test_impossible_coercion_returns_an_error() {
|
||||
// let columnar1 = make_columnar("numbers", &[u64::MAX]);
|
||||
// let merge_order = StackMergeOrder::stack(&[&columnar1]).into();
|
||||
// let group_error = group_columns_for_merge_iter(
|
||||
//&[&columnar1],
|
||||
//&[("numbers".to_string(), ColumnType::I64)],
|
||||
//&merge_order,
|
||||
//)
|
||||
//.unwrap_err();
|
||||
// assert_eq!(group_error.kind(), io::ErrorKind::InvalidInput);
|
||||
//}
|
||||
|
||||
#[test]
|
||||
fn test_group_columns_with_required_column() {
|
||||
let columnar1 = make_columnar("numbers", &[1i64]);
|
||||
let columnar2 = make_columnar("numbers", &[2u64]);
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
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)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_group_columns_required_column_with_no_existing_columns() {
|
||||
let columnar1 = make_columnar("numbers", &[2u64]);
|
||||
let columnar2 = make_columnar("numbers", &[2u64]);
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let 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))
|
||||
.unwrap()
|
||||
.columns;
|
||||
assert_eq!(columns.len(), 2);
|
||||
assert!(columns[0].is_none());
|
||||
assert!(columns[1].is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_group_columns_required_column_is_above_all_columns_have_the_same_type_rule() {
|
||||
let columnar1 = make_columnar("numbers", &[2i64]);
|
||||
let columnar2 = make_columnar("numbers", &[2i64]);
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
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)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_missing_column() {
|
||||
let columnar1 = make_columnar("numbers", &[-1i64]);
|
||||
let columnar2 = make_columnar("numbers2", &[2u64]);
|
||||
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
group_columns_for_merge(&[&columnar1, &columnar2]).unwrap();
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let column_map: BTreeMap<(String, ColumnTypeCategory), GroupedColumnsHandle> =
|
||||
group_columns_for_merge(columnars, &[]).unwrap();
|
||||
assert_eq!(column_map.len(), 2);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::I64)));
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnTypeCategory::Numerical)));
|
||||
{
|
||||
let columns = column_map
|
||||
.get(&("numbers".to_string(), ColumnType::I64))
|
||||
.unwrap();
|
||||
let columns = &column_map
|
||||
.get(&("numbers".to_string(), ColumnTypeCategory::Numerical))
|
||||
.unwrap()
|
||||
.columns;
|
||||
assert!(columns[0].is_some());
|
||||
assert!(columns[1].is_none());
|
||||
}
|
||||
{
|
||||
let columns = column_map
|
||||
.get(&("numbers2".to_string(), ColumnType::U64))
|
||||
.unwrap();
|
||||
let columns = &column_map
|
||||
.get(&("numbers2".to_string(), ColumnTypeCategory::Numerical))
|
||||
.unwrap()
|
||||
.columns;
|
||||
assert!(columns[0].is_none());
|
||||
assert!(columns[1].is_some());
|
||||
}
|
||||
@@ -90,30 +143,30 @@ fn make_numerical_columnar_multiple_columns(
|
||||
.max()
|
||||
.unwrap_or(0u32);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer
|
||||
.serialize(num_rows, None, &mut buffer)
|
||||
.unwrap();
|
||||
dataframe_writer.serialize(num_rows, &mut buffer).unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
}
|
||||
|
||||
fn make_byte_columnar_multiple_columns(columns: &[(&str, &[&[&[u8]]])]) -> ColumnarReader {
|
||||
#[track_caller]
|
||||
fn make_byte_columnar_multiple_columns(
|
||||
columns: &[(&str, &[&[&[u8]]])],
|
||||
num_rows: u32,
|
||||
) -> ColumnarReader {
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
for (column_name, column_values) in columns {
|
||||
assert_eq!(
|
||||
column_values.len(),
|
||||
num_rows as usize,
|
||||
"All columns must have `{num_rows}` rows"
|
||||
);
|
||||
for (row_id, vals) in column_values.iter().enumerate() {
|
||||
for val in vals.iter() {
|
||||
dataframe_writer.record_bytes(row_id as u32, column_name, *val);
|
||||
dataframe_writer.record_bytes(row_id as u32, column_name, val);
|
||||
}
|
||||
}
|
||||
}
|
||||
let num_rows = columns
|
||||
.iter()
|
||||
.map(|(_, val_rows)| val_rows.len() as RowId)
|
||||
.max()
|
||||
.unwrap_or(0u32);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer
|
||||
.serialize(num_rows, None, &mut buffer)
|
||||
.unwrap();
|
||||
dataframe_writer.serialize(num_rows, &mut buffer).unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
}
|
||||
|
||||
@@ -122,7 +175,7 @@ fn make_text_columnar_multiple_columns(columns: &[(&str, &[&[&str]])]) -> Column
|
||||
for (column_name, column_values) in columns {
|
||||
for (row_id, vals) in column_values.iter().enumerate() {
|
||||
for val in vals.iter() {
|
||||
dataframe_writer.record_str(row_id as u32, column_name, *val);
|
||||
dataframe_writer.record_str(row_id as u32, column_name, val);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -132,9 +185,7 @@ fn make_text_columnar_multiple_columns(columns: &[(&str, &[&[&str]])]) -> Column
|
||||
.max()
|
||||
.unwrap_or(0u32);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer
|
||||
.serialize(num_rows, None, &mut buffer)
|
||||
.unwrap();
|
||||
dataframe_writer.serialize(num_rows, &mut buffer).unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
}
|
||||
|
||||
@@ -151,16 +202,19 @@ fn test_merge_columnar_numbers() {
|
||||
let stack_merge_order = StackMergeOrder::stack(columnars);
|
||||
crate::columnar::merge_columnar(
|
||||
columnars,
|
||||
&[],
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 3);
|
||||
assert_eq!(columnar_reader.num_docs(), 3);
|
||||
assert_eq!(columnar_reader.num_columns(), 1);
|
||||
let cols = columnar_reader.read_columns("numbers").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
let DynamicColumn::F64(vals) = dynamic_column else { panic!() };
|
||||
let DynamicColumn::F64(vals) = dynamic_column else {
|
||||
panic!()
|
||||
};
|
||||
assert_eq!(vals.get_cardinality(), Cardinality::Optional);
|
||||
assert_eq!(vals.first(0u32), Some(-1f64));
|
||||
assert_eq!(vals.first(1u32), None);
|
||||
@@ -176,16 +230,21 @@ fn test_merge_columnar_texts() {
|
||||
let stack_merge_order = StackMergeOrder::stack(columnars);
|
||||
crate::columnar::merge_columnar(
|
||||
columnars,
|
||||
&[],
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 3);
|
||||
assert_eq!(columnar_reader.num_docs(), 3);
|
||||
assert_eq!(columnar_reader.num_columns(), 1);
|
||||
let cols = columnar_reader.read_columns("texts").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
let DynamicColumn::Str(vals) = dynamic_column else { panic!() };
|
||||
let DynamicColumn::Str(vals) = dynamic_column else {
|
||||
panic!()
|
||||
};
|
||||
assert_eq!(vals.ords().get_cardinality(), Cardinality::Optional);
|
||||
|
||||
let get_str_for_ord = |ord| {
|
||||
let mut out = String::new();
|
||||
vals.ord_to_str(ord, &mut out).unwrap();
|
||||
@@ -213,23 +272,26 @@ fn test_merge_columnar_texts() {
|
||||
|
||||
#[test]
|
||||
fn test_merge_columnar_byte() {
|
||||
let columnar1 = make_byte_columnar_multiple_columns(&[("bytes", &[&[b"bbbb"], &[b"baaa"]])]);
|
||||
let columnar2 = make_byte_columnar_multiple_columns(&[("bytes", &[&[], &[b"a"]])]);
|
||||
let columnar1 = make_byte_columnar_multiple_columns(&[("bytes", &[&[b"bbbb"], &[b"baaa"]])], 2);
|
||||
let columnar2 = make_byte_columnar_multiple_columns(&[("bytes", &[&[], &[b"a"]])], 2);
|
||||
let mut buffer = Vec::new();
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let stack_merge_order = StackMergeOrder::stack(columnars);
|
||||
crate::columnar::merge_columnar(
|
||||
columnars,
|
||||
&[],
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 4);
|
||||
assert_eq!(columnar_reader.num_docs(), 4);
|
||||
assert_eq!(columnar_reader.num_columns(), 1);
|
||||
let cols = columnar_reader.read_columns("bytes").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
let DynamicColumn::Bytes(vals) = dynamic_column else { panic!() };
|
||||
let DynamicColumn::Bytes(vals) = dynamic_column else {
|
||||
panic!()
|
||||
};
|
||||
let get_bytes_for_ord = |ord| {
|
||||
let mut out = Vec::new();
|
||||
vals.ord_to_bytes(ord, &mut out).unwrap();
|
||||
@@ -256,3 +318,271 @@ fn test_merge_columnar_byte() {
|
||||
assert_eq!(get_bytes_for_row(2), b"");
|
||||
assert_eq!(get_bytes_for_row(3), b"a");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_columnar_byte_with_missing() {
|
||||
let columnar1 = make_byte_columnar_multiple_columns(&[], 3);
|
||||
let columnar2 = make_byte_columnar_multiple_columns(&[("col", &[&[b"b"], &[]])], 2);
|
||||
let columnar3 = make_byte_columnar_multiple_columns(
|
||||
&[
|
||||
("col", &[&[], &[b"b"], &[b"a", b"b"]]),
|
||||
("col2", &[&[b"hello"], &[], &[b"a", b"b"]]),
|
||||
],
|
||||
3,
|
||||
);
|
||||
let mut buffer = Vec::new();
|
||||
let columnars = &[&columnar1, &columnar2, &columnar3];
|
||||
let stack_merge_order = StackMergeOrder::stack(columnars);
|
||||
crate::columnar::merge_columnar(
|
||||
columnars,
|
||||
&[],
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_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();
|
||||
let DynamicColumn::Bytes(vals) = dynamic_column else {
|
||||
panic!()
|
||||
};
|
||||
let get_bytes_for_ord = |ord| {
|
||||
let mut out = Vec::new();
|
||||
vals.ord_to_bytes(ord, &mut out).unwrap();
|
||||
out
|
||||
};
|
||||
assert_eq!(vals.dictionary.num_terms(), 2);
|
||||
assert_eq!(get_bytes_for_ord(0), b"a");
|
||||
assert_eq!(get_bytes_for_ord(1), b"b");
|
||||
let get_bytes_for_row = |row_id| {
|
||||
let terms: Vec<Vec<u8>> = vals
|
||||
.term_ords(row_id)
|
||||
.map(|term_ord| {
|
||||
let mut out = Vec::new();
|
||||
vals.ord_to_bytes(term_ord, &mut out).unwrap();
|
||||
out
|
||||
})
|
||||
.collect();
|
||||
terms
|
||||
};
|
||||
assert!(get_bytes_for_row(0).is_empty());
|
||||
assert!(get_bytes_for_row(1).is_empty());
|
||||
assert!(get_bytes_for_row(2).is_empty());
|
||||
assert_eq!(get_bytes_for_row(3), vec![b"b".to_vec()]);
|
||||
assert!(get_bytes_for_row(4).is_empty());
|
||||
assert!(get_bytes_for_row(5).is_empty());
|
||||
assert_eq!(get_bytes_for_row(6), vec![b"b".to_vec()]);
|
||||
assert_eq!(get_bytes_for_row(7), vec![b"a".to_vec(), b"b".to_vec()]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_columnar_different_types() {
|
||||
let columnar1 = make_text_columnar_multiple_columns(&[("mixed", &[&["a"]])]);
|
||||
let columnar2 = make_text_columnar_multiple_columns(&[("mixed", &[&[], &["b"]])]);
|
||||
let columnar3 = make_columnar("mixed", &[1i64]);
|
||||
let mut buffer = Vec::new();
|
||||
let columnars = &[&columnar1, &columnar2, &columnar3];
|
||||
let stack_merge_order = StackMergeOrder::stack(columnars);
|
||||
crate::columnar::merge_columnar(
|
||||
columnars,
|
||||
&[],
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_docs(), 4);
|
||||
assert_eq!(columnar_reader.num_columns(), 2);
|
||||
let cols = columnar_reader.read_columns("mixed").unwrap();
|
||||
|
||||
// numeric column
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
let DynamicColumn::I64(vals) = dynamic_column else {
|
||||
panic!()
|
||||
};
|
||||
assert_eq!(vals.get_cardinality(), Cardinality::Optional);
|
||||
assert_eq!(vals.values_for_doc(0).collect_vec(), Vec::<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::<i64>::new());
|
||||
|
||||
// text column
|
||||
let dynamic_column = cols[1].open().unwrap();
|
||||
let DynamicColumn::Str(vals) = dynamic_column else {
|
||||
panic!()
|
||||
};
|
||||
assert_eq!(vals.ords().get_cardinality(), Cardinality::Optional);
|
||||
let get_str_for_ord = |ord| {
|
||||
let mut out = String::new();
|
||||
vals.ord_to_str(ord, &mut out).unwrap();
|
||||
out
|
||||
};
|
||||
|
||||
assert_eq!(vals.dictionary.num_terms(), 2);
|
||||
assert_eq!(get_str_for_ord(0), "a");
|
||||
assert_eq!(get_str_for_ord(1), "b");
|
||||
|
||||
let get_str_for_row = |row_id| {
|
||||
let term_ords: Vec<String> = vals
|
||||
.term_ords(row_id)
|
||||
.map(|el| {
|
||||
let mut out = String::new();
|
||||
vals.ord_to_str(el, &mut out).unwrap();
|
||||
out
|
||||
})
|
||||
.collect();
|
||||
term_ords
|
||||
};
|
||||
|
||||
assert_eq!(get_str_for_row(0), vec!["a".to_string()]);
|
||||
assert_eq!(get_str_for_row(1), Vec::<String>::new());
|
||||
assert_eq!(get_str_for_row(2), vec!["b".to_string()]);
|
||||
assert_eq!(get_str_for_row(3), Vec::<String>::new());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_columnar_different_empty_cardinality() {
|
||||
let columnar1 = make_text_columnar_multiple_columns(&[("mixed", &[&["a"]])]);
|
||||
let columnar2 = make_columnar("mixed", &[1i64]);
|
||||
let mut buffer = Vec::new();
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let stack_merge_order = StackMergeOrder::stack(columnars);
|
||||
crate::columnar::merge_columnar(
|
||||
columnars,
|
||||
&[],
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_docs(), 2);
|
||||
assert_eq!(columnar_reader.num_columns(), 2);
|
||||
let cols = columnar_reader.read_columns("mixed").unwrap();
|
||||
|
||||
// numeric column
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
assert_eq!(dynamic_column.get_cardinality(), Cardinality::Optional);
|
||||
|
||||
// text column
|
||||
let dynamic_column = cols[1].open().unwrap();
|
||||
assert_eq!(dynamic_column.get_cardinality(), Cardinality::Optional);
|
||||
}
|
||||
|
||||
#[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();
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
|
||||
@@ -1,11 +1,13 @@
|
||||
mod column_type;
|
||||
mod format_version;
|
||||
mod merge;
|
||||
mod merge_index;
|
||||
mod reader;
|
||||
mod writer;
|
||||
|
||||
pub use column_type::{ColumnType, HasAssociatedColumnType};
|
||||
pub use merge::{merge_columnar, MergeRowOrder, ShuffleMergeOrder, StackMergeOrder};
|
||||
pub use format_version::{CURRENT_VERSION, Version};
|
||||
#[cfg(test)]
|
||||
pub(crate) use merge::ColumnTypeCategory;
|
||||
pub use merge::{MergeRowOrder, ShuffleMergeOrder, StackMergeOrder, merge_columnar};
|
||||
pub use reader::ColumnarReader;
|
||||
pub use writer::ColumnarWriter;
|
||||
|
||||
@@ -1,12 +1,13 @@
|
||||
use std::{io, mem};
|
||||
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;
|
||||
use crate::{RowId, Version};
|
||||
|
||||
fn io_invalid_data(msg: String) -> io::Error {
|
||||
io::Error::new(io::ErrorKind::InvalidData, msg)
|
||||
@@ -18,7 +19,75 @@ 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_docs();
|
||||
let columns = self.list_columns().unwrap();
|
||||
let num_cols = columns.len();
|
||||
let mut debug_struct = f.debug_struct("Columnar");
|
||||
debug_struct
|
||||
.field("num_rows", &num_rows)
|
||||
.field("num_cols", &num_cols);
|
||||
for (col_name, dynamic_column_handle) in columns.into_iter().take(5) {
|
||||
let col = dynamic_column_handle.open().unwrap();
|
||||
if col.num_values() > 10 {
|
||||
debug_struct.field(&col_name, &"..");
|
||||
} else {
|
||||
debug_struct.field(&col_name, &col);
|
||||
}
|
||||
}
|
||||
if num_cols > 5 {
|
||||
debug_struct.finish_non_exhaustive()?;
|
||||
} else {
|
||||
debug_struct.finish()?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
/// Functions by both the async/sync code listing columns.
|
||||
/// It takes a stream from the column sstable and return the list of
|
||||
/// `DynamicColumn` available in it.
|
||||
fn read_all_columns_in_stream(
|
||||
mut stream: sstable::Streamer<'_, RangeSSTable>,
|
||||
column_data: &FileSlice,
|
||||
format_version: Version,
|
||||
) -> io::Result<Vec<DynamicColumnHandle>> {
|
||||
let mut results = Vec::new();
|
||||
while stream.advance() {
|
||||
let key_bytes: &[u8] = stream.key();
|
||||
let Some(column_code) = key_bytes.last().copied() else {
|
||||
return Err(io_invalid_data("Empty column name.".to_string()));
|
||||
};
|
||||
let column_type = ColumnType::try_from_code(column_code)
|
||||
.map_err(|_| io_invalid_data(format!("Unknown column code `{column_code}`")))?;
|
||||
let range = stream.value();
|
||||
let file_slice = column_data.slice(range.start as usize..range.end as usize);
|
||||
let dynamic_column_handle = DynamicColumnHandle {
|
||||
file_slice,
|
||||
column_type,
|
||||
format_version,
|
||||
};
|
||||
results.push(dynamic_column_handle);
|
||||
}
|
||||
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 {
|
||||
@@ -36,44 +105,70 @@ impl ColumnarReader {
|
||||
let num_rows = u32::deserialize(&mut &footer_bytes[8..12])?;
|
||||
let version_footer_bytes: [u8; format_version::VERSION_FOOTER_NUM_BYTES] =
|
||||
footer_bytes[12..].try_into().unwrap();
|
||||
let _version = format_version::parse_footer(version_footer_bytes)?;
|
||||
let format_version = format_version::parse_footer(version_footer_bytes)?;
|
||||
let (column_data, sstable) =
|
||||
file_slice_without_sstable_len.split_from_end(sstable_len as usize);
|
||||
let column_dictionary = Dictionary::open(sstable)?;
|
||||
Ok(ColumnarReader {
|
||||
column_dictionary,
|
||||
column_data,
|
||||
num_rows,
|
||||
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(
|
||||
&self,
|
||||
) -> io::Result<impl Iterator<Item = (String, DynamicColumnHandle)> + '_> {
|
||||
let mut stream = self.column_dictionary.stream()?;
|
||||
Ok(std::iter::from_fn(move || {
|
||||
if stream.advance() {
|
||||
let key_bytes: &[u8] = stream.key();
|
||||
let column_code: u8 = key_bytes.last().cloned().unwrap();
|
||||
// TODO Error Handling. The API gets quite ugly when returning the error here, so
|
||||
// instead we could just check the first N columns upfront.
|
||||
let column_type: ColumnType = ColumnType::try_from_code(column_code)
|
||||
.map_err(|_| io_invalid_data(format!("Unknown column code `{column_code}`")))
|
||||
.unwrap();
|
||||
let range = stream.value().clone();
|
||||
let column_name =
|
||||
// The last two bytes are respectively the 0u8 separator and the column_type.
|
||||
String::from_utf8_lossy(&key_bytes[..key_bytes.len() - 2]).to_string();
|
||||
let file_slice = self
|
||||
.column_data
|
||||
.slice(range.start as usize..range.end as usize);
|
||||
let column_handle = DynamicColumnHandle {
|
||||
file_slice,
|
||||
column_type,
|
||||
format_version: self.format_version,
|
||||
};
|
||||
Some((column_name, column_handle))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}))
|
||||
}
|
||||
|
||||
// TODO Add unit tests
|
||||
pub fn list_columns(&self) -> io::Result<Vec<(String, DynamicColumnHandle)>> {
|
||||
let mut stream = self.column_dictionary.stream()?;
|
||||
let mut results = Vec::new();
|
||||
while stream.advance() {
|
||||
let key_bytes: &[u8] = stream.key();
|
||||
let column_code: u8 = key_bytes.last().cloned().unwrap();
|
||||
let column_type: ColumnType = ColumnType::try_from_code(column_code)
|
||||
.map_err(|_| io_invalid_data(format!("Unknown column code `{column_code}`")))?;
|
||||
let range = stream.value().clone();
|
||||
let column_name =
|
||||
// The last two bytes are respectively the 0u8 separator and the column_type.
|
||||
String::from_utf8_lossy(&key_bytes[..key_bytes.len() - 2]).to_string();
|
||||
let file_slice = self
|
||||
.column_data
|
||||
.slice(range.start as usize..range.end as usize);
|
||||
let column_handle = DynamicColumnHandle {
|
||||
file_slice,
|
||||
column_type,
|
||||
};
|
||||
results.push((column_name, column_handle));
|
||||
}
|
||||
Ok(results)
|
||||
Ok(self.iter_columns()?.collect())
|
||||
}
|
||||
|
||||
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
|
||||
.column_dictionary
|
||||
.prefix_range(prefix)
|
||||
.into_stream_async()
|
||||
.await?;
|
||||
read_all_columns_in_stream(stream, &self.column_data, self.format_version)
|
||||
}
|
||||
|
||||
/// Get all columns for the given column name.
|
||||
@@ -81,44 +176,36 @@ 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>> {
|
||||
// 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)`.
|
||||
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)
|
||||
}
|
||||
|
||||
// TODO can we get some more generic `prefix(..)` logic in the dictioanry.
|
||||
let mut start_key = column_name.to_string();
|
||||
start_key.push('\0');
|
||||
let mut end_key = column_name.to_string();
|
||||
end_key.push(1u8 as char);
|
||||
let mut stream = self
|
||||
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
|
||||
.range()
|
||||
.ge(start_key.as_bytes())
|
||||
.lt(end_key.as_bytes())
|
||||
.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()?;
|
||||
let mut results = Vec::new();
|
||||
while stream.advance() {
|
||||
let key_bytes: &[u8] = stream.key();
|
||||
assert!(key_bytes.starts_with(start_key.as_bytes()));
|
||||
let column_code: u8 = key_bytes.last().cloned().unwrap();
|
||||
let column_type = ColumnType::try_from_code(column_code)
|
||||
.map_err(|_| io_invalid_data(format!("Unknown column code `{column_code}`")))?;
|
||||
let range = stream.value().clone();
|
||||
let file_slice = self
|
||||
.column_data
|
||||
.slice(range.start as usize..range.end as usize);
|
||||
let dynamic_column_handle = DynamicColumnHandle {
|
||||
file_slice,
|
||||
column_type,
|
||||
};
|
||||
results.push(dynamic_column_handle);
|
||||
}
|
||||
Ok(results)
|
||||
read_all_columns_in_stream(stream, &self.column_data, self.format_version)
|
||||
}
|
||||
|
||||
/// Return the number of columns in the columnar.
|
||||
@@ -129,6 +216,8 @@ impl ColumnarReader {
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use common::json_path_writer::JSON_PATH_SEGMENT_SEP;
|
||||
|
||||
use crate::{ColumnType, ColumnarReader, ColumnarWriter};
|
||||
|
||||
#[test]
|
||||
@@ -137,7 +226,7 @@ mod tests {
|
||||
columnar_writer.record_column_type("col1", ColumnType::Str, false);
|
||||
columnar_writer.record_column_type("col2", ColumnType::U64, false);
|
||||
let mut buffer = Vec::new();
|
||||
columnar_writer.serialize(1, None, &mut buffer).unwrap();
|
||||
columnar_writer.serialize(1, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
let columns = columnar.list_columns().unwrap();
|
||||
assert_eq!(columns.len(), 2);
|
||||
@@ -153,7 +242,7 @@ mod tests {
|
||||
columnar_writer.record_column_type("count", ColumnType::U64, false);
|
||||
columnar_writer.record_numerical(1, "count", 1u64);
|
||||
let mut buffer = Vec::new();
|
||||
columnar_writer.serialize(2, None, &mut buffer).unwrap();
|
||||
columnar_writer.serialize(2, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
let columns = columnar.list_columns().unwrap();
|
||||
assert_eq!(columns.len(), 1);
|
||||
@@ -162,7 +251,65 @@ mod tests {
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expect = "Input type forbidden")]
|
||||
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() {
|
||||
let mut columnar_writer = ColumnarWriter::default();
|
||||
columnar_writer.record_column_type("count", ColumnType::U64, false);
|
||||
|
||||
@@ -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.
|
||||
@@ -310,7 +309,7 @@ mod tests {
|
||||
buffer.extend_from_slice(b"234234");
|
||||
let mut bytes = &buffer[..];
|
||||
let serdeser_symbol = ColumnOperation::deserialize(&mut bytes).unwrap();
|
||||
assert_eq!(bytes.len() + buf.as_ref().len() as usize, buffer.len());
|
||||
assert_eq!(bytes.len() + buf.as_ref().len(), buffer.len());
|
||||
assert_eq!(column_op, serdeser_symbol);
|
||||
}
|
||||
|
||||
@@ -341,7 +340,7 @@ mod tests {
|
||||
fn test_column_operation_unordered_aux(val: u32, expected_len: usize) {
|
||||
let column_op = ColumnOperation::Value(UnorderedId(val));
|
||||
let minibuf = column_op.serialize();
|
||||
assert_eq!(minibuf.as_ref().len() as usize, expected_len);
|
||||
assert_eq!({ minibuf.as_ref().len() }, expected_len);
|
||||
let mut buf = minibuf.as_ref().to_vec();
|
||||
buf.extend_from_slice(&[2, 2, 2, 2, 2, 2]);
|
||||
let mut cursor = &buf[..];
|
||||
|
||||
@@ -41,31 +41,10 @@ impl ColumnWriter {
|
||||
pub(super) fn operation_iterator<'a, V: SymbolValue>(
|
||||
&self,
|
||||
arena: &MemoryArena,
|
||||
old_to_new_ids_opt: Option<&[RowId]>,
|
||||
buffer: &'a mut Vec<u8>,
|
||||
) -> impl Iterator<Item = ColumnOperation<V>> + 'a {
|
||||
) -> impl Iterator<Item = ColumnOperation<V>> + 'a + use<'a, V> {
|
||||
buffer.clear();
|
||||
self.values.read_to_end(arena, buffer);
|
||||
if let Some(old_to_new_ids) = old_to_new_ids_opt {
|
||||
// TODO avoid the extra deserialization / serialization.
|
||||
let mut sorted_ops: Vec<(RowId, ColumnOperation<V>)> = Vec::new();
|
||||
let mut new_doc = 0u32;
|
||||
let mut cursor = &buffer[..];
|
||||
for op in std::iter::from_fn(|| ColumnOperation::<V>::deserialize(&mut cursor)) {
|
||||
if let ColumnOperation::NewDoc(doc) = &op {
|
||||
new_doc = old_to_new_ids[*doc as usize];
|
||||
sorted_ops.push((new_doc, ColumnOperation::NewDoc(new_doc)));
|
||||
} else {
|
||||
sorted_ops.push((new_doc, op));
|
||||
}
|
||||
}
|
||||
// stable sort is crucial here.
|
||||
sorted_ops.sort_by_key(|(new_doc_id, _)| *new_doc_id);
|
||||
buffer.clear();
|
||||
for (_, op) in sorted_ops {
|
||||
buffer.extend_from_slice(op.serialize().as_ref());
|
||||
}
|
||||
}
|
||||
let mut cursor: &[u8] = &buffer[..];
|
||||
std::iter::from_fn(move || ColumnOperation::deserialize(&mut cursor))
|
||||
}
|
||||
@@ -125,9 +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);
|
||||
}
|
||||
}
|
||||
@@ -231,11 +211,9 @@ impl NumericalColumnWriter {
|
||||
pub(super) fn operation_iterator<'a>(
|
||||
self,
|
||||
arena: &MemoryArena,
|
||||
old_to_new_ids: Option<&[RowId]>,
|
||||
buffer: &'a mut Vec<u8>,
|
||||
) -> impl Iterator<Item = ColumnOperation<NumericalValue>> + 'a {
|
||||
self.column_writer
|
||||
.operation_iterator(arena, old_to_new_ids, buffer)
|
||||
) -> impl Iterator<Item = ColumnOperation<NumericalValue>> + 'a + use<'a> {
|
||||
self.column_writer.operation_iterator(arena, buffer)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -269,18 +247,17 @@ impl StrOrBytesColumnWriter {
|
||||
dictionaries: &mut [DictionaryBuilder],
|
||||
arena: &mut MemoryArena,
|
||||
) {
|
||||
let unordered_id = dictionaries[self.dictionary_id as usize].get_or_allocate_id(bytes);
|
||||
let unordered_id =
|
||||
dictionaries[self.dictionary_id as usize].get_or_allocate_id(bytes, arena);
|
||||
self.column_writer.record(doc, unordered_id, arena);
|
||||
}
|
||||
|
||||
pub(super) fn operation_iterator<'a>(
|
||||
&self,
|
||||
arena: &MemoryArena,
|
||||
old_to_new_ids: Option<&[RowId]>,
|
||||
byte_buffer: &'a mut Vec<u8>,
|
||||
) -> impl Iterator<Item = ColumnOperation<UnorderedId>> + 'a {
|
||||
self.column_writer
|
||||
.operation_iterator(arena, old_to_new_ids, byte_buffer)
|
||||
) -> impl Iterator<Item = ColumnOperation<UnorderedId>> + 'a + use<'a> {
|
||||
self.column_writer.operation_iterator(arena, byte_buffer)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -9,13 +9,12 @@ use std::net::Ipv6Addr;
|
||||
use column_operation::ColumnOperation;
|
||||
pub(crate) use column_writers::CompatibleNumericalTypes;
|
||||
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;
|
||||
use crate::column_values::{
|
||||
ColumnValues, MonotonicallyMappableToU128, MonotonicallyMappableToU64, VecColumn,
|
||||
};
|
||||
use crate::column_index::{SerializableColumnIndex, SerializableOptionalIndex};
|
||||
use crate::column_values::{MonotonicallyMappableToU64, MonotonicallyMappableToU128};
|
||||
use crate::columnar::column_type::ColumnType;
|
||||
use crate::columnar::writer::column_writers::{
|
||||
ColumnWriter, NumericalColumnWriter, StrOrBytesColumnWriter,
|
||||
@@ -45,8 +44,9 @@ struct SpareBuffers {
|
||||
/// columnar_writer.record_str(1u32 /* doc id */, "product_name", "Apple");
|
||||
/// columnar_writer.record_numerical(0u32 /* doc id */, "price", 10.5f64); //< uh oh we ended up mixing integer and floats.
|
||||
/// let mut wrt: Vec<u8> = Vec::new();
|
||||
/// columnar_writer.serialize(2u32, None, &mut wrt).unwrap();
|
||||
/// columnar_writer.serialize(2u32, &mut wrt).unwrap();
|
||||
/// ```
|
||||
#[derive(Default)]
|
||||
pub struct ColumnarWriter {
|
||||
numerical_field_hash_map: ArenaHashMap,
|
||||
datetime_field_hash_map: ArenaHashMap,
|
||||
@@ -60,41 +60,8 @@ pub struct ColumnarWriter {
|
||||
buffers: SpareBuffers,
|
||||
}
|
||||
|
||||
impl Default for ColumnarWriter {
|
||||
fn default() -> Self {
|
||||
ColumnarWriter {
|
||||
numerical_field_hash_map: ArenaHashMap::new(10_000),
|
||||
bool_field_hash_map: ArenaHashMap::new(10_000),
|
||||
ip_addr_field_hash_map: ArenaHashMap::new(10_000),
|
||||
bytes_field_hash_map: ArenaHashMap::new(10_000),
|
||||
str_field_hash_map: ArenaHashMap::new(10_000),
|
||||
datetime_field_hash_map: ArenaHashMap::new(10_000),
|
||||
dictionaries: Vec::new(),
|
||||
arena: MemoryArena::default(),
|
||||
buffers: SpareBuffers::default(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn mutate_or_create_column<V, TMutator>(
|
||||
arena_hash_map: &mut ArenaHashMap,
|
||||
column_name: &str,
|
||||
updater: TMutator,
|
||||
) where
|
||||
V: Copy + 'static,
|
||||
TMutator: FnMut(Option<V>) -> V,
|
||||
{
|
||||
assert!(
|
||||
!column_name.as_bytes().contains(&0u8),
|
||||
"key may not contain the 0 byte"
|
||||
);
|
||||
arena_hash_map.mutate_or_create(column_name.as_bytes(), updater);
|
||||
}
|
||||
|
||||
impl ColumnarWriter {
|
||||
pub fn mem_usage(&self) -> usize {
|
||||
// TODO add dictionary builders.
|
||||
self.arena.mem_usage()
|
||||
+ self.numerical_field_hash_map.mem_usage()
|
||||
+ self.bool_field_hash_map.mem_usage()
|
||||
@@ -102,48 +69,11 @@ impl ColumnarWriter {
|
||||
+ self.str_field_hash_map.mem_usage()
|
||||
+ self.ip_addr_field_hash_map.mem_usage()
|
||||
+ self.datetime_field_hash_map.mem_usage()
|
||||
}
|
||||
|
||||
/// Returns the list of doc ids from 0..num_docs sorted by the `sort_field`
|
||||
/// column.
|
||||
///
|
||||
/// If the column is multivalued, use the first value for scoring.
|
||||
/// If no value is associated to a specific row, the document is assigned
|
||||
/// the lowest possible score.
|
||||
///
|
||||
/// The sort applied is stable.
|
||||
pub fn sort_order(&self, sort_field: &str, num_docs: RowId, reversed: bool) -> Vec<u32> {
|
||||
let Some(numerical_col_writer) =
|
||||
self.numerical_field_hash_map.get::<NumericalColumnWriter>(sort_field.as_bytes()) else {
|
||||
return Vec::new();
|
||||
};
|
||||
let mut symbols_buffer = Vec::new();
|
||||
let mut values = Vec::new();
|
||||
let mut last_doc_opt: Option<RowId> = None;
|
||||
for op in numerical_col_writer.operation_iterator(&self.arena, None, &mut symbols_buffer) {
|
||||
match op {
|
||||
ColumnOperation::NewDoc(doc) => {
|
||||
last_doc_opt = Some(doc);
|
||||
}
|
||||
ColumnOperation::Value(numerical_value) => {
|
||||
if let Some(last_doc) = last_doc_opt {
|
||||
let score: f32 = f64::coerce(numerical_value) as f32;
|
||||
values.push((score, last_doc));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
for doc in values.len() as u32..num_docs {
|
||||
values.push((0.0f32, doc));
|
||||
}
|
||||
values.sort_by(|(left_score, _), (right_score, _)| {
|
||||
if reversed {
|
||||
right_score.partial_cmp(left_score).unwrap()
|
||||
} else {
|
||||
left_score.partial_cmp(right_score).unwrap()
|
||||
}
|
||||
});
|
||||
values.into_iter().map(|(_score, doc)| doc).collect()
|
||||
+ self
|
||||
.dictionaries
|
||||
.iter()
|
||||
.map(|dict| dict.mem_usage())
|
||||
.sum::<usize>()
|
||||
}
|
||||
|
||||
/// Records a column type. This is useful to bypass the coercion process,
|
||||
@@ -173,9 +103,8 @@ impl ColumnarWriter {
|
||||
},
|
||||
&mut self.dictionaries,
|
||||
);
|
||||
mutate_or_create_column(
|
||||
hash_map,
|
||||
column_name,
|
||||
hash_map.mutate_or_create(
|
||||
column_name.as_bytes(),
|
||||
|column_opt: Option<StrOrBytesColumnWriter>| {
|
||||
let mut column_writer = if let Some(column_writer) = column_opt {
|
||||
column_writer
|
||||
@@ -190,24 +119,21 @@ impl ColumnarWriter {
|
||||
);
|
||||
}
|
||||
ColumnType::Bool => {
|
||||
mutate_or_create_column(
|
||||
&mut self.bool_field_hash_map,
|
||||
column_name,
|
||||
self.bool_field_hash_map.mutate_or_create(
|
||||
column_name.as_bytes(),
|
||||
|column_opt: Option<ColumnWriter>| column_opt.unwrap_or_default(),
|
||||
);
|
||||
}
|
||||
ColumnType::DateTime => {
|
||||
mutate_or_create_column(
|
||||
&mut self.datetime_field_hash_map,
|
||||
column_name,
|
||||
self.datetime_field_hash_map.mutate_or_create(
|
||||
column_name.as_bytes(),
|
||||
|column_opt: Option<ColumnWriter>| column_opt.unwrap_or_default(),
|
||||
);
|
||||
}
|
||||
ColumnType::I64 | ColumnType::F64 | ColumnType::U64 => {
|
||||
let numerical_type = column_type.numerical_type().unwrap();
|
||||
mutate_or_create_column(
|
||||
&mut self.numerical_field_hash_map,
|
||||
column_name,
|
||||
self.numerical_field_hash_map.mutate_or_create(
|
||||
column_name.as_bytes(),
|
||||
|column_opt: Option<NumericalColumnWriter>| {
|
||||
let mut column: NumericalColumnWriter = column_opt.unwrap_or_default();
|
||||
column.force_numerical_type(numerical_type);
|
||||
@@ -215,9 +141,8 @@ impl ColumnarWriter {
|
||||
},
|
||||
);
|
||||
}
|
||||
ColumnType::IpAddr => mutate_or_create_column(
|
||||
&mut self.ip_addr_field_hash_map,
|
||||
column_name,
|
||||
ColumnType::IpAddr => self.ip_addr_field_hash_map.mutate_or_create(
|
||||
column_name.as_bytes(),
|
||||
|column_opt: Option<ColumnWriter>| column_opt.unwrap_or_default(),
|
||||
),
|
||||
}
|
||||
@@ -230,9 +155,8 @@ impl ColumnarWriter {
|
||||
numerical_value: T,
|
||||
) {
|
||||
let (hash_map, arena) = (&mut self.numerical_field_hash_map, &mut self.arena);
|
||||
mutate_or_create_column(
|
||||
hash_map,
|
||||
column_name,
|
||||
hash_map.mutate_or_create(
|
||||
column_name.as_bytes(),
|
||||
|column_opt: Option<NumericalColumnWriter>| {
|
||||
let mut column: NumericalColumnWriter = column_opt.unwrap_or_default();
|
||||
column.record_numerical_value(doc, numerical_value.into(), arena);
|
||||
@@ -242,10 +166,6 @@ impl ColumnarWriter {
|
||||
}
|
||||
|
||||
pub fn record_ip_addr(&mut self, doc: RowId, column_name: &str, ip_addr: Ipv6Addr) {
|
||||
assert!(
|
||||
!column_name.as_bytes().contains(&0u8),
|
||||
"key may not contain the 0 byte"
|
||||
);
|
||||
let (hash_map, arena) = (&mut self.ip_addr_field_hash_map, &mut self.arena);
|
||||
hash_map.mutate_or_create(
|
||||
column_name.as_bytes(),
|
||||
@@ -259,20 +179,30 @@ impl ColumnarWriter {
|
||||
|
||||
pub fn record_bool(&mut self, doc: RowId, column_name: &str, val: bool) {
|
||||
let (hash_map, arena) = (&mut self.bool_field_hash_map, &mut self.arena);
|
||||
mutate_or_create_column(hash_map, column_name, |column_opt: Option<ColumnWriter>| {
|
||||
let mut column: ColumnWriter = column_opt.unwrap_or_default();
|
||||
column.record(doc, val, arena);
|
||||
column
|
||||
});
|
||||
hash_map.mutate_or_create(
|
||||
column_name.as_bytes(),
|
||||
|column_opt: Option<ColumnWriter>| {
|
||||
let mut column: ColumnWriter = column_opt.unwrap_or_default();
|
||||
column.record(doc, val, arena);
|
||||
column
|
||||
},
|
||||
);
|
||||
}
|
||||
|
||||
pub fn record_datetime(&mut self, doc: RowId, column_name: &str, datetime: crate::DateTime) {
|
||||
pub fn record_datetime(&mut self, doc: RowId, column_name: &str, datetime: common::DateTime) {
|
||||
let (hash_map, arena) = (&mut self.datetime_field_hash_map, &mut self.arena);
|
||||
mutate_or_create_column(hash_map, column_name, |column_opt: Option<ColumnWriter>| {
|
||||
let mut column: ColumnWriter = column_opt.unwrap_or_default();
|
||||
column.record(doc, NumericalValue::I64(datetime.timestamp_micros), arena);
|
||||
column
|
||||
});
|
||||
hash_map.mutate_or_create(
|
||||
column_name.as_bytes(),
|
||||
|column_opt: Option<ColumnWriter>| {
|
||||
let mut column: ColumnWriter = column_opt.unwrap_or_default();
|
||||
column.record(
|
||||
doc,
|
||||
NumericalValue::I64(datetime.into_timestamp_nanos()),
|
||||
arena,
|
||||
);
|
||||
column
|
||||
},
|
||||
);
|
||||
}
|
||||
|
||||
pub fn record_str(&mut self, doc: RowId, column_name: &str, value: &str) {
|
||||
@@ -297,10 +227,6 @@ impl ColumnarWriter {
|
||||
}
|
||||
|
||||
pub fn record_bytes(&mut self, doc: RowId, column_name: &str, value: &[u8]) {
|
||||
assert!(
|
||||
!column_name.as_bytes().contains(&0u8),
|
||||
"key may not contain the 0 byte"
|
||||
);
|
||||
let (hash_map, arena, dictionaries) = (
|
||||
&mut self.bytes_field_hash_map,
|
||||
&mut self.arena,
|
||||
@@ -320,17 +246,13 @@ impl ColumnarWriter {
|
||||
},
|
||||
);
|
||||
}
|
||||
pub fn serialize(
|
||||
&mut self,
|
||||
num_docs: RowId,
|
||||
old_to_new_row_ids: Option<&[RowId]>,
|
||||
wrt: &mut dyn io::Write,
|
||||
) -> io::Result<()> {
|
||||
pub fn serialize(&mut self, num_docs: RowId, wrt: &mut dyn io::Write) -> io::Result<()> {
|
||||
let mut serializer = ColumnarSerializer::new(wrt);
|
||||
|
||||
let mut columns: Vec<(&[u8], ColumnType, Addr)> = self
|
||||
.numerical_field_hash_map
|
||||
.iter()
|
||||
.map(|(column_name, addr, _)| {
|
||||
.map(|(column_name, addr)| {
|
||||
let numerical_column_writer: NumericalColumnWriter =
|
||||
self.numerical_field_hash_map.read(addr);
|
||||
let column_type = numerical_column_writer.numerical_type().into();
|
||||
@@ -340,67 +262,66 @@ impl ColumnarWriter {
|
||||
columns.extend(
|
||||
self.bytes_field_hash_map
|
||||
.iter()
|
||||
.map(|(term, addr, _)| (term, ColumnType::Bytes, addr)),
|
||||
.map(|(column_name, addr)| (column_name, ColumnType::Bytes, addr)),
|
||||
);
|
||||
columns.extend(
|
||||
self.str_field_hash_map
|
||||
.iter()
|
||||
.map(|(column_name, addr, _)| (column_name, ColumnType::Str, addr)),
|
||||
.map(|(column_name, addr)| (column_name, ColumnType::Str, addr)),
|
||||
);
|
||||
columns.extend(
|
||||
self.bool_field_hash_map
|
||||
.iter()
|
||||
.map(|(column_name, addr, _)| (column_name, ColumnType::Bool, addr)),
|
||||
.map(|(column_name, addr)| (column_name, ColumnType::Bool, addr)),
|
||||
);
|
||||
columns.extend(
|
||||
self.ip_addr_field_hash_map
|
||||
.iter()
|
||||
.map(|(column_name, addr, _)| (column_name, ColumnType::IpAddr, addr)),
|
||||
.map(|(column_name, addr)| (column_name, ColumnType::IpAddr, addr)),
|
||||
);
|
||||
columns.extend(
|
||||
self.datetime_field_hash_map
|
||||
.iter()
|
||||
.map(|(column_name, addr, _)| (column_name, ColumnType::DateTime, addr)),
|
||||
.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 {
|
||||
if column_name.contains(&JSON_END_OF_PATH) {
|
||||
// Tantivy uses b'0' as a separator for nested fields in JSON.
|
||||
// Column names with a b'0' are not simply ignored by the columnar (and the inverted
|
||||
// index).
|
||||
continue;
|
||||
}
|
||||
match column_type {
|
||||
ColumnType::Bool => {
|
||||
let column_writer: ColumnWriter = self.bool_field_hash_map.read(addr);
|
||||
let cardinality = column_writer.get_cardinality(num_docs);
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name, column_type);
|
||||
serializer.start_serialize_column(column_name, column_type);
|
||||
serialize_bool_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
|
||||
buffers,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
column_serializer.finalize()?;
|
||||
}
|
||||
ColumnType::IpAddr => {
|
||||
let column_writer: ColumnWriter = self.ip_addr_field_hash_map.read(addr);
|
||||
let cardinality = column_writer.get_cardinality(num_docs);
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name, ColumnType::IpAddr);
|
||||
serializer.start_serialize_column(column_name, ColumnType::IpAddr);
|
||||
serialize_ip_addr_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
|
||||
buffers,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
column_serializer.finalize()?;
|
||||
}
|
||||
ColumnType::Bytes | ColumnType::Str => {
|
||||
let str_or_bytes_column_writer: StrOrBytesColumnWriter =
|
||||
@@ -415,58 +336,51 @@ impl ColumnarWriter {
|
||||
.column_writer
|
||||
.get_cardinality(num_docs);
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name, column_type);
|
||||
serializer.start_serialize_column(column_name, column_type);
|
||||
serialize_bytes_or_str_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
str_or_bytes_column_writer.sort_values_within_row,
|
||||
dictionary_builder,
|
||||
str_or_bytes_column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
str_or_bytes_column_writer
|
||||
.operation_iterator(arena, &mut symbol_byte_buffer),
|
||||
buffers,
|
||||
&self.arena,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
column_serializer.finalize()?;
|
||||
}
|
||||
ColumnType::F64 | ColumnType::I64 | ColumnType::U64 => {
|
||||
let numerical_column_writer: NumericalColumnWriter =
|
||||
self.numerical_field_hash_map.read(addr);
|
||||
let cardinality = numerical_column_writer.cardinality(num_docs);
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name, column_type);
|
||||
serializer.start_serialize_column(column_name, column_type);
|
||||
let numerical_type = column_type.numerical_type().unwrap();
|
||||
serialize_numerical_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
numerical_type,
|
||||
numerical_column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
numerical_column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
|
||||
buffers,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
column_serializer.finalize()?;
|
||||
}
|
||||
ColumnType::DateTime => {
|
||||
let column_writer: ColumnWriter = self.datetime_field_hash_map.read(addr);
|
||||
let cardinality = column_writer.get_cardinality(num_docs);
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name, ColumnType::DateTime);
|
||||
serializer.start_serialize_column(column_name, ColumnType::DateTime);
|
||||
serialize_numerical_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
NumericalType::I64,
|
||||
column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
|
||||
buffers,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
column_serializer.finalize()?;
|
||||
}
|
||||
};
|
||||
}
|
||||
@@ -477,6 +391,7 @@ impl ColumnarWriter {
|
||||
|
||||
// Serialize [Dictionary, Column, dictionary num bytes U32::LE]
|
||||
// Column: [Column Index, Column Values, column index num bytes U32::LE]
|
||||
#[expect(clippy::too_many_arguments)]
|
||||
fn serialize_bytes_or_str_column(
|
||||
cardinality: Cardinality,
|
||||
num_docs: RowId,
|
||||
@@ -484,6 +399,7 @@ fn serialize_bytes_or_str_column(
|
||||
dictionary_builder: &DictionaryBuilder,
|
||||
operation_it: impl Iterator<Item = ColumnOperation<UnorderedId>>,
|
||||
buffers: &mut SpareBuffers,
|
||||
arena: &MemoryArena,
|
||||
wrt: impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
let SpareBuffers {
|
||||
@@ -492,7 +408,8 @@ fn serialize_bytes_or_str_column(
|
||||
..
|
||||
} = buffers;
|
||||
let mut counting_writer = CountingWriter::wrap(wrt);
|
||||
let term_id_mapping: TermIdMapping = dictionary_builder.serialize(&mut counting_writer)?;
|
||||
let term_id_mapping: TermIdMapping =
|
||||
dictionary_builder.serialize(arena, &mut counting_writer)?;
|
||||
let dictionary_num_bytes: u32 = counting_writer.written_bytes() as u32;
|
||||
let mut wrt = counting_writer.finish();
|
||||
let operation_iterator = operation_it.map(|symbol: ColumnOperation<UnorderedId>| {
|
||||
@@ -628,10 +545,7 @@ fn send_to_serialize_column_mappable_to_u128<
|
||||
value_index_builders: &mut PreallocatedIndexBuilders,
|
||||
values: &mut Vec<T>,
|
||||
mut wrt: impl io::Write,
|
||||
) -> io::Result<()>
|
||||
where
|
||||
for<'a> VecColumn<'a, T>: ColumnValues<T>,
|
||||
{
|
||||
) -> io::Result<()> {
|
||||
values.clear();
|
||||
// TODO: split index and values
|
||||
let serializable_column_index = match cardinality {
|
||||
@@ -647,16 +561,16 @@ where
|
||||
let optional_index_builder = value_index_builders.borrow_optional_index_builder();
|
||||
consume_operation_iterator(op_iterator, optional_index_builder, values);
|
||||
let optional_index = optional_index_builder.finish(num_rows);
|
||||
SerializableColumnIndex::Optional {
|
||||
SerializableColumnIndex::Optional(SerializableOptionalIndex {
|
||||
num_rows,
|
||||
non_null_row_ids: Box::new(optional_index),
|
||||
}
|
||||
})
|
||||
}
|
||||
Cardinality::Multivalued => {
|
||||
let multivalued_index_builder = value_index_builders.borrow_multivalued_index_builder();
|
||||
consume_operation_iterator(op_iterator, multivalued_index_builder, values);
|
||||
let multivalued_index = multivalued_index_builder.finish(num_rows);
|
||||
SerializableColumnIndex::Multivalued(Box::new(multivalued_index))
|
||||
let serializable_multivalued_index = multivalued_index_builder.finish(num_rows);
|
||||
SerializableColumnIndex::Multivalued(serializable_multivalued_index)
|
||||
}
|
||||
};
|
||||
crate::column::serialize_column_mappable_to_u128(
|
||||
@@ -667,15 +581,6 @@ where
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn sort_values_within_row_in_place(multivalued_index: &[RowId], values: &mut Vec<u64>) {
|
||||
let mut start_index: usize = 0;
|
||||
for end_index in multivalued_index.iter().copied() {
|
||||
let end_index = end_index as usize;
|
||||
values[start_index..end_index].sort_unstable();
|
||||
start_index = end_index;
|
||||
}
|
||||
}
|
||||
|
||||
fn send_to_serialize_column_mappable_to_u64(
|
||||
op_iterator: impl Iterator<Item = ColumnOperation<u64>>,
|
||||
cardinality: Cardinality,
|
||||
@@ -684,10 +589,7 @@ fn send_to_serialize_column_mappable_to_u64(
|
||||
value_index_builders: &mut PreallocatedIndexBuilders,
|
||||
values: &mut Vec<u64>,
|
||||
mut wrt: impl io::Write,
|
||||
) -> io::Result<()>
|
||||
where
|
||||
for<'a> VecColumn<'a, u64>: ColumnValues<u64>,
|
||||
{
|
||||
) -> io::Result<()> {
|
||||
values.clear();
|
||||
let serializable_column_index = match cardinality {
|
||||
Cardinality::Full => {
|
||||
@@ -702,19 +604,22 @@ where
|
||||
let optional_index_builder = value_index_builders.borrow_optional_index_builder();
|
||||
consume_operation_iterator(op_iterator, optional_index_builder, values);
|
||||
let optional_index = optional_index_builder.finish(num_rows);
|
||||
SerializableColumnIndex::Optional {
|
||||
SerializableColumnIndex::Optional(SerializableOptionalIndex {
|
||||
non_null_row_ids: Box::new(optional_index),
|
||||
num_rows,
|
||||
}
|
||||
})
|
||||
}
|
||||
Cardinality::Multivalued => {
|
||||
let multivalued_index_builder = value_index_builders.borrow_multivalued_index_builder();
|
||||
consume_operation_iterator(op_iterator, multivalued_index_builder, values);
|
||||
let multivalued_index = multivalued_index_builder.finish(num_rows);
|
||||
let serializable_multivalued_index = multivalued_index_builder.finish(num_rows);
|
||||
if sort_values_within_row {
|
||||
sort_values_within_row_in_place(multivalued_index, values);
|
||||
sort_values_within_row_in_place(
|
||||
serializable_multivalued_index.start_offsets.boxed_iter(),
|
||||
values,
|
||||
);
|
||||
}
|
||||
SerializableColumnIndex::Multivalued(Box::new(multivalued_index))
|
||||
SerializableColumnIndex::Multivalued(serializable_multivalued_index)
|
||||
}
|
||||
};
|
||||
crate::column::serialize_column_mappable_to_u64(
|
||||
@@ -725,6 +630,18 @@ where
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn sort_values_within_row_in_place(
|
||||
multivalued_index: impl Iterator<Item = RowId>,
|
||||
values: &mut [u64],
|
||||
) {
|
||||
let mut start_index: usize = 0;
|
||||
for end_index in multivalued_index {
|
||||
let end_index = end_index as usize;
|
||||
values[start_index..end_index].sort_unstable();
|
||||
start_index = end_index;
|
||||
}
|
||||
}
|
||||
|
||||
fn coerce_numerical_symbol<T>(
|
||||
operation_iterator: impl Iterator<Item = ColumnOperation<NumericalValue>>,
|
||||
) -> impl Iterator<Item = ColumnOperation<u64>>
|
||||
@@ -772,7 +689,7 @@ mod tests {
|
||||
assert_eq!(column_writer.get_cardinality(3), Cardinality::Full);
|
||||
let mut buffer = Vec::new();
|
||||
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
|
||||
.operation_iterator(&mut arena, None, &mut buffer)
|
||||
.operation_iterator(&arena, &mut buffer)
|
||||
.collect();
|
||||
assert_eq!(symbols.len(), 6);
|
||||
assert!(matches!(symbols[0], ColumnOperation::NewDoc(0u32)));
|
||||
@@ -801,7 +718,7 @@ mod tests {
|
||||
assert_eq!(column_writer.get_cardinality(3), Cardinality::Optional);
|
||||
let mut buffer = Vec::new();
|
||||
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
|
||||
.operation_iterator(&mut arena, None, &mut buffer)
|
||||
.operation_iterator(&arena, &mut buffer)
|
||||
.collect();
|
||||
assert_eq!(symbols.len(), 4);
|
||||
assert!(matches!(symbols[0], ColumnOperation::NewDoc(1u32)));
|
||||
@@ -824,7 +741,7 @@ mod tests {
|
||||
assert_eq!(column_writer.get_cardinality(2), Cardinality::Optional);
|
||||
let mut buffer = Vec::new();
|
||||
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
|
||||
.operation_iterator(&mut arena, None, &mut buffer)
|
||||
.operation_iterator(&arena, &mut buffer)
|
||||
.collect();
|
||||
assert_eq!(symbols.len(), 2);
|
||||
assert!(matches!(symbols[0], ColumnOperation::NewDoc(0u32)));
|
||||
@@ -843,7 +760,7 @@ mod tests {
|
||||
assert_eq!(column_writer.get_cardinality(1), Cardinality::Multivalued);
|
||||
let mut buffer = Vec::new();
|
||||
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
|
||||
.operation_iterator(&mut arena, None, &mut buffer)
|
||||
.operation_iterator(&arena, &mut buffer)
|
||||
.collect();
|
||||
assert_eq!(symbols.len(), 3);
|
||||
assert!(matches!(symbols[0], ColumnOperation::NewDoc(0u32)));
|
||||
|
||||
@@ -1,12 +1,13 @@
|
||||
use std::io;
|
||||
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>,
|
||||
@@ -19,7 +20,7 @@ pub struct ColumnarSerializer<W: io::Write> {
|
||||
fn prepare_key(key: &[u8], column_type: ColumnType, buffer: &mut Vec<u8>) {
|
||||
buffer.clear();
|
||||
buffer.extend_from_slice(key);
|
||||
buffer.push(0u8);
|
||||
buffer.push(JSON_END_OF_PATH);
|
||||
buffer.push(column_type.to_code());
|
||||
}
|
||||
|
||||
@@ -34,11 +35,12 @@ impl<W: io::Write> ColumnarSerializer<W> {
|
||||
}
|
||||
}
|
||||
|
||||
pub fn serialize_column<'a>(
|
||||
/// Creates a ColumnSerializer.
|
||||
pub fn start_serialize_column<'a>(
|
||||
&'a mut self,
|
||||
column_name: &[u8],
|
||||
column_type: ColumnType,
|
||||
) -> impl io::Write + 'a {
|
||||
) -> ColumnSerializer<'a, W> {
|
||||
let start_offset = self.wrt.written_bytes();
|
||||
prepare_key(column_name, column_type, &mut self.prepare_key_buffer);
|
||||
ColumnSerializer {
|
||||
@@ -60,24 +62,25 @@ impl<W: io::Write> ColumnarSerializer<W> {
|
||||
}
|
||||
}
|
||||
|
||||
struct ColumnSerializer<'a, W: io::Write> {
|
||||
pub struct ColumnSerializer<'a, W: io::Write> {
|
||||
columnar_serializer: &'a mut ColumnarSerializer<W>,
|
||||
start_offset: u64,
|
||||
}
|
||||
|
||||
impl<'a, W: io::Write> Drop for ColumnSerializer<'a, W> {
|
||||
fn drop(&mut self) {
|
||||
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;
|
||||
self.columnar_serializer.sstable_range.insert_cannot_fail(
|
||||
self.columnar_serializer.sstable_range.insert(
|
||||
&self.columnar_serializer.prepare_key_buffer[..],
|
||||
&byte_range,
|
||||
);
|
||||
)?;
|
||||
self.columnar_serializer.prepare_key_buffer.clear();
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
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)
|
||||
}
|
||||
@@ -90,19 +93,3 @@ impl<'a, W: io::Write> io::Write for ColumnSerializer<'a, W> {
|
||||
self.columnar_serializer.wrt.write_all(buf)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::columnar::column_type::ColumnType;
|
||||
|
||||
#[test]
|
||||
fn test_prepare_key_bytes() {
|
||||
let mut buffer: Vec<u8> = b"somegarbage".to_vec();
|
||||
prepare_key(b"root\0child", ColumnType::Str, &mut buffer);
|
||||
assert_eq!(buffer.len(), 12);
|
||||
assert_eq!(&buffer[..10], b"root\0child");
|
||||
assert_eq!(buffer[10], 0u8);
|
||||
assert_eq!(buffer[11], ColumnType::Str.to_code());
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
use crate::iterable::Iterable;
|
||||
use crate::RowId;
|
||||
use crate::column_index::{SerializableMultivalueIndex, SerializableOptionalIndex};
|
||||
use crate::iterable::Iterable;
|
||||
|
||||
/// The `IndexBuilder` interprets a sequence of
|
||||
/// calls of the form:
|
||||
@@ -29,13 +30,14 @@ pub struct OptionalIndexBuilder {
|
||||
}
|
||||
|
||||
impl OptionalIndexBuilder {
|
||||
pub fn finish<'a>(&'a mut self, num_rows: RowId) -> impl Iterable<RowId> + 'a {
|
||||
debug_assert!(self
|
||||
.docs
|
||||
.last()
|
||||
.copied()
|
||||
.map(|last_doc| last_doc < num_rows)
|
||||
.unwrap_or(true));
|
||||
pub fn finish(&mut self, num_rows: RowId) -> impl Iterable<RowId> + '_ {
|
||||
debug_assert!(
|
||||
self.docs
|
||||
.last()
|
||||
.copied()
|
||||
.map(|last_doc| last_doc < num_rows)
|
||||
.unwrap_or(true)
|
||||
);
|
||||
&self.docs[..]
|
||||
}
|
||||
|
||||
@@ -47,43 +49,60 @@ 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);
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Default)]
|
||||
pub struct MultivaluedIndexBuilder {
|
||||
start_offsets: Vec<RowId>,
|
||||
doc_with_values: Vec<RowId>,
|
||||
start_offsets: Vec<u32>,
|
||||
total_num_vals_seen: u32,
|
||||
current_row: RowId,
|
||||
current_row_has_value: bool,
|
||||
}
|
||||
|
||||
impl MultivaluedIndexBuilder {
|
||||
pub fn finish(&mut self, num_docs: RowId) -> &[u32] {
|
||||
self.start_offsets
|
||||
.resize(num_docs as usize + 1, self.total_num_vals_seen);
|
||||
&self.start_offsets[..]
|
||||
pub fn finish(&mut self, num_docs: RowId) -> SerializableMultivalueIndex<'_> {
|
||||
self.start_offsets.push(self.total_num_vals_seen);
|
||||
let non_null_row_ids: Box<dyn Iterable<RowId>> = Box::new(&self.doc_with_values[..]);
|
||||
SerializableMultivalueIndex {
|
||||
doc_ids_with_values: SerializableOptionalIndex {
|
||||
non_null_row_ids,
|
||||
num_rows: num_docs,
|
||||
},
|
||||
start_offsets: Box::new(&self.start_offsets[..]),
|
||||
}
|
||||
}
|
||||
|
||||
fn reset(&mut self) {
|
||||
self.doc_with_values.clear();
|
||||
self.start_offsets.clear();
|
||||
self.start_offsets.push(0u32);
|
||||
self.total_num_vals_seen = 0;
|
||||
self.current_row = 0;
|
||||
self.current_row_has_value = false;
|
||||
}
|
||||
}
|
||||
|
||||
impl IndexBuilder for MultivaluedIndexBuilder {
|
||||
fn record_row(&mut self, row_id: RowId) {
|
||||
self.start_offsets
|
||||
.resize(row_id as usize + 1, self.total_num_vals_seen);
|
||||
self.current_row = row_id;
|
||||
self.current_row_has_value = false;
|
||||
}
|
||||
|
||||
fn record_value(&mut self) {
|
||||
if !self.current_row_has_value {
|
||||
self.current_row_has_value = true;
|
||||
self.doc_with_values.push(self.current_row);
|
||||
self.start_offsets.push(self.total_num_vals_seen);
|
||||
}
|
||||
self.total_num_vals_seen += 1;
|
||||
}
|
||||
}
|
||||
@@ -141,6 +160,32 @@ mod tests {
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_multivalued_value_index_builder_simple() {
|
||||
let mut multivalued_value_index_builder = MultivaluedIndexBuilder::default();
|
||||
{
|
||||
multivalued_value_index_builder.record_row(0u32);
|
||||
multivalued_value_index_builder.record_value();
|
||||
multivalued_value_index_builder.record_value();
|
||||
let serialized_multivalue_index = multivalued_value_index_builder.finish(1u32);
|
||||
let start_offsets: Vec<u32> = serialized_multivalue_index
|
||||
.start_offsets
|
||||
.boxed_iter()
|
||||
.collect();
|
||||
assert_eq!(&start_offsets, &[0, 2]);
|
||||
}
|
||||
multivalued_value_index_builder.reset();
|
||||
multivalued_value_index_builder.record_row(0u32);
|
||||
multivalued_value_index_builder.record_value();
|
||||
multivalued_value_index_builder.record_value();
|
||||
let serialized_multivalue_index = multivalued_value_index_builder.finish(1u32);
|
||||
let start_offsets: Vec<u32> = serialized_multivalue_index
|
||||
.start_offsets
|
||||
.boxed_iter()
|
||||
.collect();
|
||||
assert_eq!(&start_offsets, &[0, 2]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_multivalued_value_index_builder() {
|
||||
let mut multivalued_value_index_builder = MultivaluedIndexBuilder::default();
|
||||
@@ -149,25 +194,15 @@ mod tests {
|
||||
multivalued_value_index_builder.record_value();
|
||||
multivalued_value_index_builder.record_row(2u32);
|
||||
multivalued_value_index_builder.record_value();
|
||||
assert_eq!(
|
||||
multivalued_value_index_builder
|
||||
.finish(4u32)
|
||||
.iter()
|
||||
.copied()
|
||||
.collect::<Vec<u32>>(),
|
||||
vec![0, 0, 2, 3, 3]
|
||||
);
|
||||
multivalued_value_index_builder.reset();
|
||||
multivalued_value_index_builder.record_row(2u32);
|
||||
multivalued_value_index_builder.record_value();
|
||||
multivalued_value_index_builder.record_value();
|
||||
assert_eq!(
|
||||
multivalued_value_index_builder
|
||||
.finish(4u32)
|
||||
.iter()
|
||||
.copied()
|
||||
.collect::<Vec<u32>>(),
|
||||
vec![0, 0, 0, 2, 2]
|
||||
);
|
||||
let SerializableMultivalueIndex {
|
||||
doc_ids_with_values,
|
||||
start_offsets,
|
||||
} = multivalued_value_index_builder.finish(4u32);
|
||||
assert_eq!(doc_ids_with_values.num_rows, 4u32);
|
||||
let doc_ids_with_values: Vec<u32> =
|
||||
doc_ids_with_values.non_null_row_ids.boxed_iter().collect();
|
||||
assert_eq!(&doc_ids_with_values, &[1u32, 2u32]);
|
||||
let start_offsets: Vec<u32> = start_offsets.boxed_iter().collect();
|
||||
assert_eq!(&start_offsets[..], &[0, 2, 3]);
|
||||
}
|
||||
}
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user