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28 Commits

Author SHA1 Message Date
PSeitz-dd
65b5a1a306 one collector per agg request instead per bucket (#2759)
* improve bench

* add more tests for new collection type

* one collector per agg request instead per bucket

In this refactoring a collector knows in which bucket of the parent
their data is in. This allows to convert the previous approach of one
collector per bucket to one collector per request.

low card bucket optimization

* reduce dynamic dispatch, faster term agg

* use radix map, fix prepare_max_bucket

use paged term map in term agg
use special no sub agg term map impl

* specialize columntype in stats

* remove stacktrace bloat, use &mut helper

increase cache to 2048

* cleanup

remove clone
move data in term req, single doc opt for stats

* add comment

* share column block accessor

* simplify fetch block in column_block_accessor

* split subaggcache into two trait impls

* move partitions to heap

* fix name, add comment

---------

Co-authored-by: Pascal Seitz <pascal.seitz@gmail.com>
2026-01-06 11:50:55 +01:00
ChangRui-Ryan
db2ecc6057 fix Column.first method parameter type (#2792) 2026-01-05 10:03:01 +01:00
Paul Masurel
77505c3d03 Making stemming optional. (#2791)
Fixed code and CI to run on no default features.

Co-authored-by: Paul Masurel <paul.masurel@datadoghq.com>
2026-01-02 12:40:42 +01:00
PSeitz
735c588f4f fix union performance regression (#2790)
* add inlines

* fix union performance regression

Remove unwrap from hotpath generates better assembly.

closes #2788
2026-01-02 12:06:51 +01:00
PSeitz
242a1531bf fix flaky test (#2784)
Signed-off-by: Pascal Seitz <pascal.seitz@gmail.com>
2026-01-02 11:30:51 +01:00
trinity-1686a
6443b63177 document 1bit hole and some queries supporting running with just fastfield (#2779)
* add small doc on some queries using fast field when not indexed

* document 1 unused bit in skiplist
2026-01-02 10:32:37 +01:00
Stu Hood
4987495ee4 Add an erased SortKeyComputer to sort on types which are not known until runtime (#2770)
* Remove PartialOrd bound on compared values.

* Fix declared `SortKey` type of `impl<..> SortKeyComputer for (HeadSortKeyComputer, TailSortKeyComputer)`

* Add a SortByOwnedValue implementation to provide a type-erased column.

* Add support for comparing mismatched `OwnedValue` types.

* Support JSON columns.

* Refer to https://github.com/quickwit-oss/tantivy/issues/2776

* Rename to `SortByErasedType`.

* Comment on transitivity.

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

* Fix clippy warnings in new code.

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
2026-01-02 10:28:47 +01:00
Paul Masurel
b11605f045 Addressing clippy comments (#2789)
Co-authored-by: Paul Masurel <paul.masurel@datadoghq.com>
2025-12-31 18:02:00 +01:00
ChangRui-Ryan
75d7989cc6 add benchmark for boolean query with range sub query (#2787) 2025-12-31 12:00:53 +01:00
PSeitz
923f0508f2 seek_exact + cost based intersection (#2538)
* seek_exact + cost based intersection

Adds `seek_exact` and `cost` to `DocSet` for a more efficient intersection.
Unlike `seek`, `seek_exact` does not require the DocSet to advance to the next hit, if the target does not exist.

`cost` allows to address the different DocSet types and their cost
model and is used to determine the DocSet that drives the intersection.
E.g. fast field range queries may do a full scan. Phrase queries load the positions to check if a we have a hit.
They both have a higher cost than their size_hint would suggest.

Improves `size_hint` estimation for intersection and union, by having a
estimation based on random distribution with a co-location factor.

Refactor range query benchmark.

Closes #2531

*Future Work*

Implement `seek_exact` for BufferedUnionScorer and RangeDocSet (fast field range queries)
Evaluate replacing `seek` with `seek_exact` to reduce code complexity

* Apply suggestions from code review

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

* add API contract verfication

* impl seek_exact on union

* rename seek_exact

* add mixed AND OR test, fix buffered_union

* Add a proptest of BooleanQuery. (#2690)

* fix build

* Increase the document count.

* fix merge conflict

* fix debug assert

* Fix compilation errors after rebase

- Remove duplicate proptest_boolean_query module
- Remove duplicate cost() method implementations
- Fix TopDocs API usage (add .order_by_score())
- Remove duplicate imports
- Remove unused variable assignments

---------

Co-authored-by: Paul Masurel <paul@quickwit.io>
Co-authored-by: Pascal Seitz <pascal.seitz@datadoghq.com>
Co-authored-by: Stu Hood <stuhood@gmail.com>
2025-12-30 14:43:25 +01:00
ChangRui-Ryan
e0b62e00ac optimize RangeDocSet for non-overlapping query ranges (#2783) 2025-12-29 16:55:28 +01:00
Stu Hood
ce97beb86f Add support for natural-order-with-none-highest in TopDocs::order_by (#2780)
* Add `ComparatorEnum::NaturalNoneHigher`.

* Fix comments.
2025-12-23 09:22:20 +01:00
Stu Hood
c0f21a45ae Use a strict comparison in TopNComputer (#2777)
* Remove `(Partial)Ord` from `ComparableDoc`, and unify comparison between `TopNComputer` and `Comparator`.

* Doc cleanups.

* Require Ord for `ComparableDoc`.

* Semantics are actually _ascending_ DocId order.

* Adjust docs again for ascending DocId order.

* minor change

---------

Co-authored-by: Paul Masurel <paul.masurel@datadoghq.com>
2025-12-18 12:13:23 +01:00
Moe
73657dff77 fix: fixed integer overflow in ExpUnrolledLinkedList for large datasets (#2735)
* Fixed the overflow issue.

* Fixed lint issues.

* Applied PR fixes.

* Fixed a lint issue.
2025-12-16 22:57:12 +01:00
Moe
e3c9be1f92 fix: boolean query incorrectly dropping documents when AllScorer is present (#2760)
* Fixed the range issue.

* Fixed the second all scorer issue

* Improved docs + tests

* Improved code.

* Fixed lint issues.

* Improved tests + logic based on PR comments.

* Fixed lint issues.

* Increase the document count.

* Improved the prop-tests

* Expand the index size, and remove unused parameter.

---------

Co-authored-by: Stu Hood <stuhood@gmail.com>
2025-12-16 22:52:02 +01:00
Ming
ba61ed6ef3 fix: vint buffer can overflow (#2778)
* fix vint overflow

* comment
2025-12-16 22:50:41 +01:00
trinity-1686a
d0e1600135 fix bug with minimum_should_match and AllScorer (#2774) 2025-12-14 10:10:45 +01:00
PSeitz-dd
e9020d17d4 fix coverage (#2769) 2025-12-11 11:35:58 +01:00
PSeitz-dd
5ba0031f7d move rand_distr to dev_dep (#2772) 2025-12-11 18:23:50 +08:00
Philippe Noël
22dde8f9ae chore: Make some delete-related functions public (#46) (#2766)
Co-authored-by: Ming <ming.ying.nyc@gmail.com>
2025-12-11 01:22:15 +01:00
Philippe Noël
14cc24614e Make DeleteMeta pub (#2765)
Co-authored-by: Ming Ying <ming.ying.nyc@gmail.com>
2025-12-11 00:11:03 +01:00
Philippe Noël
8a1079b2dc expose AddOperation and with_max_doc (#7) (#2762)
Co-authored-by: Ming <ming.ying.nyc@gmail.com>
2025-12-11 00:10:42 +01:00
Philippe Noël
794ff1ffc9 chore: Make Language hashable (#79) (#2763)
Co-authored-by: Ming <ming.ying.nyc@gmail.com>
2025-12-10 15:38:43 +01:00
PSeitz-dd
c6912ce89a Handle JSON fields and columnar in space_usage (#2761)
return field names in space_usage instead of `Field`
more detailed info for columns
2025-12-10 20:33:33 +08:00
PSeitz
618e3bd11b Term and IndexingTerm cleanup (#2750)
* refactor term

* add deprecated functions

---------

Co-authored-by: Pascal Seitz <pascal.seitz@datadoghq.com>
2025-12-05 09:48:40 +08:00
PSeitz
b2f99c6217 add term->histogram benchmark (#2758)
* add term->histogram benchmark

* add more term aggs

---------

Co-authored-by: Pascal Seitz <pascal.seitz@datadoghq.com>
2025-12-04 02:29:37 +01:00
PSeitz
76de5bab6f fix unsafe warnings (#2757) 2025-12-03 20:15:21 +08:00
rustmailer
b7eb31162b docs: add usage example to README (#2743) 2025-12-02 21:56:57 +01:00
108 changed files with 5806 additions and 2272 deletions

View File

@@ -15,11 +15,11 @@ jobs:
steps:
- uses: actions/checkout@v4
- name: Install Rust
run: rustup toolchain install nightly-2024-07-01 --profile minimal --component llvm-tools-preview
run: rustup toolchain install nightly-2025-12-01 --profile minimal --component llvm-tools-preview
- uses: Swatinem/rust-cache@v2
- uses: taiki-e/install-action@cargo-llvm-cov
- name: Generate code coverage
run: cargo +nightly-2024-07-01 llvm-cov --all-features --workspace --doctests --lcov --output-path lcov.info
run: cargo +nightly-2025-12-01 llvm-cov --all-features --workspace --doctests --lcov --output-path lcov.info
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v3
continue-on-error: true

View File

@@ -59,10 +59,10 @@ jobs:
strategy:
matrix:
features: [
{ label: "all", flags: "mmap,stopwords,lz4-compression,zstd-compression,failpoints" },
{ label: "quickwit", flags: "mmap,quickwit,failpoints" }
]
features:
- { label: "all", flags: "mmap,stopwords,lz4-compression,zstd-compression,failpoints,stemmer" }
- { label: "quickwit", flags: "mmap,quickwit,failpoints" }
- { label: "none", flags: "" }
name: test-${{ matrix.features.label}}
@@ -80,7 +80,21 @@ jobs:
- uses: Swatinem/rust-cache@v2
- name: Run tests
run: cargo +stable nextest run --features ${{ matrix.features.flags }} --verbose --workspace
run: |
# if matrix.feature.flags is empty then run on --lib to avoid compiling examples
# (as most of them rely on mmap) otherwise run all
if [ -z "${{ matrix.features.flags }}" ]; then
cargo +stable nextest run --lib --no-default-features --verbose --workspace
else
cargo +stable nextest run --features ${{ matrix.features.flags }} --no-default-features --verbose --workspace
fi
- name: Run doctests
run: cargo +stable test --doc --features ${{ matrix.features.flags }} --verbose --workspace
run: |
# if matrix.feature.flags is empty then run on --lib to avoid compiling examples
# (as most of them rely on mmap) otherwise run all
if [ -z "${{ matrix.features.flags }}" ]; then
echo "no doctest for no feature flag"
else
cargo +stable test --doc --features ${{ matrix.features.flags }} --verbose --workspace
fi

View File

@@ -37,7 +37,7 @@ 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"
rust-stemmers = { version = "1.2.0", optional = true }
downcast-rs = "2.0.1"
bitpacking = { version = "0.9.2", default-features = false, features = [
"bitpacker4x",
@@ -75,12 +75,12 @@ typetag = "0.2.21"
winapi = "0.3.9"
[dev-dependencies]
binggan = "0.14.0"
binggan = "0.14.2"
rand = "0.8.5"
maplit = "1.0.2"
matches = "0.1.9"
pretty_assertions = "1.2.1"
proptest = "1.0.0"
proptest = "1.7.0"
test-log = "0.2.10"
futures = "0.3.21"
paste = "1.0.11"
@@ -113,7 +113,8 @@ debug-assertions = true
overflow-checks = true
[features]
default = ["mmap", "stopwords", "lz4-compression", "columnar-zstd-compression"]
default = ["mmap", "stopwords", "lz4-compression", "columnar-zstd-compression", "stemmer"]
stemmer = ["rust-stemmers"]
mmap = ["fs4", "tempfile", "memmap2"]
stopwords = []
@@ -173,6 +174,18 @@ harness = false
name = "exists_json"
harness = false
[[bench]]
name = "range_query"
harness = false
[[bench]]
name = "and_or_queries"
harness = false
[[bench]]
name = "range_queries"
harness = false
[[bench]]
name = "bool_queries_with_range"
harness = false

View File

@@ -123,6 +123,7 @@ You can also find other bindings on [GitHub](https://github.com/search?q=tantivy
- [seshat](https://github.com/matrix-org/seshat/): A matrix message database/indexer
- [tantiny](https://github.com/baygeldin/tantiny): Tiny full-text search for Ruby
- [lnx](https://github.com/lnx-search/lnx): adaptable, typo tolerant search engine with a REST API
- [Bichon](https://github.com/rustmailer/bichon): A lightweight, high-performance Rust email archiver with WebUI
- and [more](https://github.com/search?q=tantivy)!
### On average, how much faster is Tantivy compared to Lucene?

View File

@@ -1,5 +1,6 @@
use binggan::plugins::PeakMemAllocPlugin;
use binggan::{black_box, InputGroup, PeakMemAlloc, INSTRUMENTED_SYSTEM};
use rand::distributions::WeightedIndex;
use rand::prelude::SliceRandom;
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
@@ -53,27 +54,33 @@ fn bench_agg(mut group: InputGroup<Index>) {
register!(group, stats_f64);
register!(group, extendedstats_f64);
register!(group, percentiles_f64);
register!(group, terms_few);
register!(group, terms_many);
register!(group, terms_7);
register!(group, terms_all_unique);
register!(group, terms_150_000);
register!(group, terms_many_top_1000);
register!(group, terms_many_order_by_term);
register!(group, terms_many_with_top_hits);
register!(group, terms_all_unique_with_avg_sub_agg);
register!(group, terms_many_with_avg_sub_agg);
register!(group, terms_few_with_avg_sub_agg);
register!(group, terms_status_with_avg_sub_agg);
register!(group, terms_status_with_histogram);
register!(group, terms_zipf_1000);
register!(group, terms_zipf_1000_with_histogram);
register!(group, terms_zipf_1000_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, terms_status_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_status);
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, histogram_with_term_agg_few);
register!(group, histogram_with_term_agg_status);
register!(group, avg_and_range_with_avg_sub_agg);
// Filter aggregation benchmarks
@@ -132,12 +139,12 @@ fn extendedstats_f64(index: &Index) {
}
fn percentiles_f64(index: &Index) {
let agg_req = json!({
"mypercentiles": {
"percentiles": {
"field": "score_f64",
"percents": [ 95, 99, 99.9 ]
"mypercentiles": {
"percentiles": {
"field": "score_f64",
"percents": [ 95, 99, 99.9 ]
}
}
}
});
execute_agg(index, agg_req);
}
@@ -152,10 +159,10 @@ fn cardinality_agg(index: &Index) {
});
execute_agg(index, agg_req);
}
fn terms_few_with_cardinality_agg(index: &Index) {
fn terms_status_with_cardinality_agg(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "text_few_terms" },
"terms": { "field": "text_few_terms_status" },
"aggs": {
"cardinality": {
"cardinality": {
@@ -168,13 +175,20 @@ fn terms_few_with_cardinality_agg(index: &Index) {
execute_agg(index, agg_req);
}
fn terms_few(index: &Index) {
fn terms_7(index: &Index) {
let agg_req = json!({
"my_texts": { "terms": { "field": "text_few_terms" } },
"my_texts": { "terms": { "field": "text_few_terms_status" } },
});
execute_agg(index, agg_req);
}
fn terms_many(index: &Index) {
fn terms_all_unique(index: &Index) {
let agg_req = json!({
"my_texts": { "terms": { "field": "text_all_unique_terms" } },
});
execute_agg(index, agg_req);
}
fn terms_150_000(index: &Index) {
let agg_req = json!({
"my_texts": { "terms": { "field": "text_many_terms" } },
});
@@ -222,11 +236,10 @@ fn terms_many_with_avg_sub_agg(index: &Index) {
});
execute_agg(index, agg_req);
}
fn terms_few_with_avg_sub_agg(index: &Index) {
fn terms_all_unique_with_avg_sub_agg(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "text_few_terms" },
"terms": { "field": "text_all_unique_terms" },
"aggs": {
"average_f64": { "avg": { "field": "score_f64" } }
}
@@ -234,6 +247,60 @@ fn terms_few_with_avg_sub_agg(index: &Index) {
});
execute_agg(index, agg_req);
}
fn terms_status_with_histogram(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "text_few_terms_status" },
"aggs": {
"histo": {"histogram": { "field": "score_f64", "interval": 10 }}
}
}
});
execute_agg(index, agg_req);
}
fn terms_zipf_1000_with_histogram(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "text_1000_terms_zipf" },
"aggs": {
"histo": {"histogram": { "field": "score_f64", "interval": 10 }}
}
}
});
execute_agg(index, agg_req);
}
fn terms_status_with_avg_sub_agg(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "text_few_terms_status" },
"aggs": {
"average_f64": { "avg": { "field": "score_f64" } }
}
},
});
execute_agg(index, agg_req);
}
fn terms_zipf_1000_with_avg_sub_agg(index: &Index) {
let agg_req = json!({
"my_texts": {
"terms": { "field": "text_1000_terms_zipf" },
"aggs": {
"average_f64": { "avg": { "field": "score_f64" } }
}
},
});
execute_agg(index, agg_req);
}
fn terms_zipf_1000(index: &Index) {
let agg_req = json!({
"my_texts": { "terms": { "field": "text_1000_terms_zipf" } },
});
execute_agg(index, agg_req);
}
fn terms_many_json_mixed_type_with_avg_sub_agg(index: &Index) {
let agg_req = json!({
@@ -290,7 +357,7 @@ fn range_agg_with_avg_sub_agg(index: &Index) {
execute_agg(index, agg_req);
}
fn range_agg_with_term_agg_few(index: &Index) {
fn range_agg_with_term_agg_status(index: &Index) {
let agg_req = json!({
"rangef64": {
"range": {
@@ -305,7 +372,7 @@ fn range_agg_with_term_agg_few(index: &Index) {
]
},
"aggs": {
"my_texts": { "terms": { "field": "text_few_terms" } },
"my_texts": { "terms": { "field": "text_few_terms_status" } },
}
},
});
@@ -361,12 +428,12 @@ fn histogram_with_avg_sub_agg(index: &Index) {
});
execute_agg(index, agg_req);
}
fn histogram_with_term_agg_few(index: &Index) {
fn histogram_with_term_agg_status(index: &Index) {
let agg_req = json!({
"rangef64": {
"histogram": { "field": "score_f64", "interval": 10 },
"aggs": {
"my_texts": { "terms": { "field": "text_few_terms" } }
"my_texts": { "terms": { "field": "text_few_terms_status" } }
}
}
});
@@ -411,6 +478,13 @@ fn get_collector(agg_req: Aggregations) -> AggregationCollector {
}
fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
// Flag to use existing index
let reuse_index = std::env::var("REUSE_AGG_BENCH_INDEX").is_ok();
if reuse_index && std::path::Path::new("agg_bench").exists() {
return Index::open_in_dir("agg_bench");
}
// crreate dir
std::fs::create_dir_all("agg_bench")?;
let mut schema_builder = Schema::builder();
let text_fieldtype = tantivy::schema::TextOptions::default()
.set_indexing_options(
@@ -419,20 +493,47 @@ fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
.set_stored();
let text_field = schema_builder.add_text_field("text", text_fieldtype);
let json_field = schema_builder.add_json_field("json", FAST);
let text_field_all_unique_terms =
schema_builder.add_text_field("text_all_unique_terms", STRING | FAST);
let text_field_many_terms = schema_builder.add_text_field("text_many_terms", STRING | FAST);
let text_field_few_terms = schema_builder.add_text_field("text_few_terms", STRING | FAST);
let text_field_few_terms_status =
schema_builder.add_text_field("text_few_terms_status", STRING | FAST);
let text_field_1000_terms_zipf =
schema_builder.add_text_field("text_1000_terms_zipf", 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"];
// use tmp dir
let index = if reuse_index {
Index::create_in_dir("agg_bench", schema_builder.build())?
} else {
Index::create_from_tempdir(schema_builder.build())?
};
// Approximate log proportions
let status_field_data = [
("INFO", 8000),
("ERROR", 300),
("WARN", 1200),
("DEBUG", 500),
("OK", 500),
("CRITICAL", 20),
("EMERGENCY", 1),
];
let log_level_distribution =
WeightedIndex::new(status_field_data.iter().map(|item| item.1)).unwrap();
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<_>>();
// Prepare 1000 unique terms sampled using a Zipf distribution.
// Exponent ~1.1 approximates top-20 terms covering around ~20%.
let terms_1000: Vec<String> = (1..=1000).map(|i| format!("term_{i}")).collect();
let zipf_1000 = rand_distr::Zipf::new(1000, 1.1f64).unwrap();
{
let mut rng = StdRng::from_seed([1u8; 32]);
let mut index_writer = index.writer_with_num_threads(1, 200_000_000)?;
@@ -442,15 +543,25 @@ fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
index_writer.add_document(doc!())?;
}
if cardinality == Cardinality::Multivalued {
let log_level_sample_a = status_field_data[log_level_distribution.sample(&mut rng)].0;
let log_level_sample_b = status_field_data[log_level_distribution.sample(&mut rng)].0;
let idx_a = zipf_1000.sample(&mut rng) as usize - 1;
let idx_b = zipf_1000.sample(&mut rng) as usize - 1;
let term_1000_a = &terms_1000[idx_a];
let term_1000_b = &terms_1000[idx_b];
index_writer.add_document(doc!(
json_field => json!({"mixed_type": 10.0}),
json_field => json!({"mixed_type": 10.0}),
text_field => "cool",
text_field => "cool",
text_field_all_unique_terms => "cool",
text_field_all_unique_terms => "coolo",
text_field_many_terms => "cool",
text_field_many_terms => "cool",
text_field_few_terms => "cool",
text_field_few_terms => "cool",
text_field_few_terms_status => log_level_sample_a,
text_field_few_terms_status => log_level_sample_b,
text_field_1000_terms_zipf => term_1000_a.as_str(),
text_field_1000_terms_zipf => term_1000_b.as_str(),
score_field => 1u64,
score_field => 1u64,
score_field_f64 => lg_norm.sample(&mut rng),
@@ -475,8 +586,10 @@ fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
index_writer.add_document(doc!(
text_field => "cool",
json_field => json,
text_field_all_unique_terms => format!("unique_term_{}", rng.gen::<u64>()),
text_field_many_terms => many_terms_data.choose(&mut rng).unwrap().to_string(),
text_field_few_terms => few_terms_data.choose(&mut rng).unwrap().to_string(),
text_field_few_terms_status => status_field_data[log_level_distribution.sample(&mut rng)].0,
text_field_1000_terms_zipf => terms_1000[zipf_1000.sample(&mut rng) as usize - 1].as_str(),
score_field => val as u64,
score_field_f64 => lg_norm.sample(&mut rng),
score_field_i64 => val as i64,
@@ -528,7 +641,7 @@ fn filter_agg_all_query_with_sub_aggs(index: &Index) {
"avg_score": { "avg": { "field": "score" } },
"stats_score": { "stats": { "field": "score_f64" } },
"terms_text": {
"terms": { "field": "text_few_terms" }
"terms": { "field": "text_few_terms_status" }
}
}
}
@@ -544,7 +657,7 @@ fn filter_agg_term_query_with_sub_aggs(index: &Index) {
"avg_score": { "avg": { "field": "score" } },
"stats_score": { "stats": { "field": "score_f64" } },
"terms_text": {
"terms": { "field": "text_few_terms" }
"terms": { "field": "text_few_terms_status" }
}
}
}

View File

@@ -0,0 +1,288 @@
use binggan::{black_box, BenchGroup, BenchRunner};
use rand::prelude::*;
use rand::rngs::StdRng;
use rand::SeedableRng;
use tantivy::collector::{Collector, Count, DocSetCollector, TopDocs};
use tantivy::query::{Query, QueryParser};
use tantivy::schema::{Schema, FAST, INDEXED, TEXT};
use tantivy::{doc, Index, Order, ReloadPolicy, Searcher};
#[derive(Clone)]
struct BenchIndex {
#[allow(dead_code)]
index: Index,
searcher: Searcher,
query_parser: QueryParser,
}
fn build_shared_indices(num_docs: usize, p_title_a: f32, distribution: &str) -> BenchIndex {
// Unified schema
let mut schema_builder = Schema::builder();
let f_title = schema_builder.add_text_field("title", TEXT);
let f_num_rand = schema_builder.add_u64_field("num_rand", INDEXED);
let f_num_asc = schema_builder.add_u64_field("num_asc", INDEXED);
let f_num_rand_fast = schema_builder.add_u64_field("num_rand_fast", INDEXED | FAST);
let f_num_asc_fast = schema_builder.add_u64_field("num_asc_fast", INDEXED | FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema.clone());
// Populate index with stable RNG for reproducibility.
let mut rng = StdRng::from_seed([7u8; 32]);
{
let mut writer = index.writer_with_num_threads(1, 4_000_000_000).unwrap();
match distribution {
"dense" => {
for doc_id in 0..num_docs {
// Always add title to avoid empty documents
let title_token = if rng.gen_bool(p_title_a as f64) {
"a"
} else {
"b"
};
let num_rand = rng.gen_range(0u64..1000u64);
let num_asc = (doc_id / 10000) as u64;
writer
.add_document(doc!(
f_title=>title_token,
f_num_rand=>num_rand,
f_num_asc=>num_asc,
f_num_rand_fast=>num_rand,
f_num_asc_fast=>num_asc,
))
.unwrap();
}
}
"sparse" => {
for doc_id in 0..num_docs {
// Always add title to avoid empty documents
let title_token = if rng.gen_bool(p_title_a as f64) {
"a"
} else {
"b"
};
let num_rand = rng.gen_range(0u64..10000000u64);
let num_asc = doc_id as u64;
writer
.add_document(doc!(
f_title=>title_token,
f_num_rand=>num_rand,
f_num_asc=>num_asc,
f_num_rand_fast=>num_rand,
f_num_asc_fast=>num_asc,
))
.unwrap();
}
}
_ => {
panic!("Unsupported distribution type");
}
}
writer.commit().unwrap();
}
// Prepare reader/searcher once.
let reader = index
.reader_builder()
.reload_policy(ReloadPolicy::Manual)
.try_into()
.unwrap();
let searcher = reader.searcher();
// Build query parser for title field
let qp_title = QueryParser::for_index(&index, vec![f_title]);
BenchIndex {
index,
searcher,
query_parser: qp_title,
}
}
fn main() {
// Prepare corpora with varying scenarios
let scenarios = vec![
(
"dense and 99% a".to_string(),
10_000_000,
0.99,
"dense",
0,
9,
),
(
"dense and 99% a".to_string(),
10_000_000,
0.99,
"dense",
990,
999,
),
(
"sparse and 99% a".to_string(),
10_000_000,
0.99,
"sparse",
0,
9,
),
(
"sparse and 99% a".to_string(),
10_000_000,
0.99,
"sparse",
9_999_990,
9_999_999,
),
];
let mut runner = BenchRunner::new();
for (scenario_id, n, p_title_a, num_rand_distribution, range_low, range_high) in scenarios {
// Build index for this scenario
let bench_index = build_shared_indices(n, p_title_a, num_rand_distribution);
// Create benchmark group
let mut group = runner.new_group();
// Now set the name (this moves scenario_id)
group.set_name(scenario_id);
// Define all four field types
let field_names = ["num_rand", "num_asc", "num_rand_fast", "num_asc_fast"];
// Define the three terms we want to test with
let terms = ["a", "b", "z"];
// Generate all combinations of terms and field names
let mut queries = Vec::new();
for &term in &terms {
for &field_name in &field_names {
let query_str = format!(
"{} AND {}:[{} TO {}]",
term, field_name, range_low, range_high
);
queries.push((query_str, field_name.to_string()));
}
}
let query_str = format!(
"{}:[{} TO {}] AND {}:[{} TO {}]",
"num_rand_fast", range_low, range_high, "num_asc_fast", range_low, range_high
);
queries.push((query_str, "num_asc_fast".to_string()));
// Run all benchmark tasks for each query and its corresponding field name
for (query_str, field_name) in queries {
run_benchmark_tasks(&mut group, &bench_index, &query_str, &field_name);
}
group.run();
}
}
/// Run all benchmark tasks for a given query string and field name
fn run_benchmark_tasks(
bench_group: &mut BenchGroup,
bench_index: &BenchIndex,
query_str: &str,
field_name: &str,
) {
// Test count
add_bench_task(bench_group, bench_index, query_str, Count, "count");
// Test all results
add_bench_task(
bench_group,
bench_index,
query_str,
DocSetCollector,
"all results",
);
// Test top 100 by the field (if it's a FAST field)
if field_name.ends_with("_fast") {
// Ascending order
{
let collector_name = format!("top100_by_{}_asc", field_name);
let field_name_owned = field_name.to_string();
add_bench_task(
bench_group,
bench_index,
query_str,
TopDocs::with_limit(100).order_by_fast_field::<u64>(field_name_owned, Order::Asc),
&collector_name,
);
}
// Descending order
{
let collector_name = format!("top100_by_{}_desc", field_name);
let field_name_owned = field_name.to_string();
add_bench_task(
bench_group,
bench_index,
query_str,
TopDocs::with_limit(100).order_by_fast_field::<u64>(field_name_owned, Order::Desc),
&collector_name,
);
}
}
}
fn add_bench_task<C: Collector + 'static>(
bench_group: &mut BenchGroup,
bench_index: &BenchIndex,
query_str: &str,
collector: C,
collector_name: &str,
) {
let task_name = format!("{}_{}", query_str.replace(" ", "_"), collector_name);
let query = bench_index.query_parser.parse_query(query_str).unwrap();
let search_task = SearchTask {
searcher: bench_index.searcher.clone(),
collector,
query,
};
bench_group.register(task_name, move |_| black_box(search_task.run()));
}
struct SearchTask<C: Collector> {
searcher: Searcher,
collector: C,
query: Box<dyn Query>,
}
impl<C: Collector> SearchTask<C> {
#[inline(never)]
pub fn run(&self) -> usize {
let result = self.searcher.search(&self.query, &self.collector).unwrap();
if let Some(count) = (&result as &dyn std::any::Any).downcast_ref::<usize>() {
*count
} else if let Some(top_docs) = (&result as &dyn std::any::Any)
.downcast_ref::<Vec<(Option<u64>, tantivy::DocAddress)>>()
{
top_docs.len()
} else if let Some(top_docs) =
(&result as &dyn std::any::Any).downcast_ref::<Vec<(u64, tantivy::DocAddress)>>()
{
top_docs.len()
} else if let Some(doc_set) = (&result as &dyn std::any::Any)
.downcast_ref::<std::collections::HashSet<tantivy::DocAddress>>()
{
doc_set.len()
} else {
eprintln!(
"Unknown collector result type: {:?}",
std::any::type_name::<C::Fruit>()
);
0
}
}
}

365
benches/range_queries.rs Normal file
View File

@@ -0,0 +1,365 @@
use std::ops::Bound;
use binggan::{black_box, BenchGroup, BenchRunner};
use rand::prelude::*;
use rand::rngs::StdRng;
use rand::SeedableRng;
use tantivy::collector::{Count, DocSetCollector, TopDocs};
use tantivy::query::RangeQuery;
use tantivy::schema::{Schema, FAST, INDEXED};
use tantivy::{doc, Index, Order, ReloadPolicy, Searcher, Term};
#[derive(Clone)]
struct BenchIndex {
#[allow(dead_code)]
index: Index,
searcher: Searcher,
}
fn build_shared_indices(num_docs: usize, distribution: &str) -> BenchIndex {
// Schema with fast fields only
let mut schema_builder = Schema::builder();
let f_num_rand_fast = schema_builder.add_u64_field("num_rand_fast", INDEXED | FAST);
let f_num_asc_fast = schema_builder.add_u64_field("num_asc_fast", INDEXED | FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema.clone());
// Populate index with stable RNG for reproducibility.
let mut rng = StdRng::from_seed([7u8; 32]);
{
let mut writer = index.writer_with_num_threads(1, 4_000_000_000).unwrap();
match distribution {
"dense" => {
for doc_id in 0..num_docs {
let num_rand = rng.gen_range(0u64..1000u64);
let num_asc = (doc_id / 10000) as u64;
writer
.add_document(doc!(
f_num_rand_fast=>num_rand,
f_num_asc_fast=>num_asc,
))
.unwrap();
}
}
"sparse" => {
for doc_id in 0..num_docs {
let num_rand = rng.gen_range(0u64..10000000u64);
let num_asc = doc_id as u64;
writer
.add_document(doc!(
f_num_rand_fast=>num_rand,
f_num_asc_fast=>num_asc,
))
.unwrap();
}
}
_ => {
panic!("Unsupported distribution type");
}
}
writer.commit().unwrap();
}
// Prepare reader/searcher once.
let reader = index
.reader_builder()
.reload_policy(ReloadPolicy::Manual)
.try_into()
.unwrap();
let searcher = reader.searcher();
BenchIndex { index, searcher }
}
fn main() {
// Prepare corpora with varying scenarios
let scenarios = vec![
// Dense distribution - random values in small range (0-999)
(
"dense_values_search_low_value_range".to_string(),
10_000_000,
"dense",
0,
9,
),
(
"dense_values_search_high_value_range".to_string(),
10_000_000,
"dense",
990,
999,
),
(
"dense_values_search_out_of_range".to_string(),
10_000_000,
"dense",
1000,
1002,
),
(
"sparse_values_search_low_value_range".to_string(),
10_000_000,
"sparse",
0,
9,
),
(
"sparse_values_search_high_value_range".to_string(),
10_000_000,
"sparse",
9_999_990,
9_999_999,
),
(
"sparse_values_search_out_of_range".to_string(),
10_000_000,
"sparse",
10_000_000,
10_000_002,
),
];
let mut runner = BenchRunner::new();
for (scenario_id, n, num_rand_distribution, range_low, range_high) in scenarios {
// Build index for this scenario
let bench_index = build_shared_indices(n, num_rand_distribution);
// Create benchmark group
let mut group = runner.new_group();
// Now set the name (this moves scenario_id)
group.set_name(scenario_id);
// Define fast field types
let field_names = ["num_rand_fast", "num_asc_fast"];
// Generate range queries for fast fields
for &field_name in &field_names {
// Create the range query
let field = bench_index.searcher.schema().get_field(field_name).unwrap();
let lower_term = Term::from_field_u64(field, range_low);
let upper_term = Term::from_field_u64(field, range_high);
let query = RangeQuery::new(Bound::Included(lower_term), Bound::Included(upper_term));
run_benchmark_tasks(
&mut group,
&bench_index,
query,
field_name,
range_low,
range_high,
);
}
group.run();
}
}
/// Run all benchmark tasks for a given range query and field name
fn run_benchmark_tasks(
bench_group: &mut BenchGroup,
bench_index: &BenchIndex,
query: RangeQuery,
field_name: &str,
range_low: u64,
range_high: u64,
) {
// Test count
add_bench_task_count(
bench_group,
bench_index,
query.clone(),
"count",
field_name,
range_low,
range_high,
);
// Test top 100 by the field (ascending order)
{
let collector_name = format!("top100_by_{}_asc", field_name);
let field_name_owned = field_name.to_string();
add_bench_task_top100_asc(
bench_group,
bench_index,
query.clone(),
&collector_name,
field_name,
range_low,
range_high,
field_name_owned,
);
}
// Test top 100 by the field (descending order)
{
let collector_name = format!("top100_by_{}_desc", field_name);
let field_name_owned = field_name.to_string();
add_bench_task_top100_desc(
bench_group,
bench_index,
query,
&collector_name,
field_name,
range_low,
range_high,
field_name_owned,
);
}
}
fn add_bench_task_count(
bench_group: &mut BenchGroup,
bench_index: &BenchIndex,
query: RangeQuery,
collector_name: &str,
field_name: &str,
range_low: u64,
range_high: u64,
) {
let task_name = format!(
"range_{}_[{} TO {}]_{}",
field_name, range_low, range_high, collector_name
);
let search_task = CountSearchTask {
searcher: bench_index.searcher.clone(),
query,
};
bench_group.register(task_name, move |_| black_box(search_task.run()));
}
fn add_bench_task_docset(
bench_group: &mut BenchGroup,
bench_index: &BenchIndex,
query: RangeQuery,
collector_name: &str,
field_name: &str,
range_low: u64,
range_high: u64,
) {
let task_name = format!(
"range_{}_[{} TO {}]_{}",
field_name, range_low, range_high, collector_name
);
let search_task = DocSetSearchTask {
searcher: bench_index.searcher.clone(),
query,
};
bench_group.register(task_name, move |_| black_box(search_task.run()));
}
fn add_bench_task_top100_asc(
bench_group: &mut BenchGroup,
bench_index: &BenchIndex,
query: RangeQuery,
collector_name: &str,
field_name: &str,
range_low: u64,
range_high: u64,
field_name_owned: String,
) {
let task_name = format!(
"range_{}_[{} TO {}]_{}",
field_name, range_low, range_high, collector_name
);
let search_task = Top100AscSearchTask {
searcher: bench_index.searcher.clone(),
query,
field_name: field_name_owned,
};
bench_group.register(task_name, move |_| black_box(search_task.run()));
}
fn add_bench_task_top100_desc(
bench_group: &mut BenchGroup,
bench_index: &BenchIndex,
query: RangeQuery,
collector_name: &str,
field_name: &str,
range_low: u64,
range_high: u64,
field_name_owned: String,
) {
let task_name = format!(
"range_{}_[{} TO {}]_{}",
field_name, range_low, range_high, collector_name
);
let search_task = Top100DescSearchTask {
searcher: bench_index.searcher.clone(),
query,
field_name: field_name_owned,
};
bench_group.register(task_name, move |_| black_box(search_task.run()));
}
struct CountSearchTask {
searcher: Searcher,
query: RangeQuery,
}
impl CountSearchTask {
#[inline(never)]
pub fn run(&self) -> usize {
self.searcher.search(&self.query, &Count).unwrap()
}
}
struct DocSetSearchTask {
searcher: Searcher,
query: RangeQuery,
}
impl DocSetSearchTask {
#[inline(never)]
pub fn run(&self) -> usize {
let result = self.searcher.search(&self.query, &DocSetCollector).unwrap();
result.len()
}
}
struct Top100AscSearchTask {
searcher: Searcher,
query: RangeQuery,
field_name: String,
}
impl Top100AscSearchTask {
#[inline(never)]
pub fn run(&self) -> usize {
let collector =
TopDocs::with_limit(100).order_by_fast_field::<u64>(&self.field_name, Order::Asc);
let result = self.searcher.search(&self.query, &collector).unwrap();
for (_score, doc_address) in &result {
let _doc: tantivy::TantivyDocument = self.searcher.doc(*doc_address).unwrap();
}
result.len()
}
}
struct Top100DescSearchTask {
searcher: Searcher,
query: RangeQuery,
field_name: String,
}
impl Top100DescSearchTask {
#[inline(never)]
pub fn run(&self) -> usize {
let collector =
TopDocs::with_limit(100).order_by_fast_field::<u64>(&self.field_name, Order::Desc);
let result = self.searcher.search(&self.query, &collector).unwrap();
for (_score, doc_address) in &result {
let _doc: tantivy::TantivyDocument = self.searcher.doc(*doc_address).unwrap();
}
result.len()
}
}

260
benches/range_query.rs Normal file
View File

@@ -0,0 +1,260 @@
use std::fmt::Display;
use std::net::Ipv6Addr;
use std::ops::RangeInclusive;
use binggan::plugins::PeakMemAllocPlugin;
use binggan::{black_box, BenchRunner, OutputValue, PeakMemAlloc, INSTRUMENTED_SYSTEM};
use columnar::MonotonicallyMappableToU128;
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use tantivy::collector::{Count, TopDocs};
use tantivy::query::QueryParser;
use tantivy::schema::*;
use tantivy::{doc, Index};
#[global_allocator]
pub static GLOBAL: &PeakMemAlloc<std::alloc::System> = &INSTRUMENTED_SYSTEM;
fn main() {
bench_range_query();
}
fn bench_range_query() {
let index = get_index_0_to_100();
let mut runner = BenchRunner::new();
runner.add_plugin(PeakMemAllocPlugin::new(GLOBAL));
runner.set_name("range_query on u64");
let field_name_and_descr: Vec<_> = vec![
("id", "Single Valued Range Field"),
("ids", "Multi Valued Range Field"),
];
let range_num_hits = vec![
("90_percent", get_90_percent()),
("10_percent", get_10_percent()),
("1_percent", get_1_percent()),
];
test_range(&mut runner, &index, &field_name_and_descr, range_num_hits);
runner.set_name("range_query on ip");
let field_name_and_descr: Vec<_> = vec![
("ip", "Single Valued Range Field"),
("ips", "Multi Valued Range Field"),
];
let range_num_hits = vec![
("90_percent", get_90_percent_ip()),
("10_percent", get_10_percent_ip()),
("1_percent", get_1_percent_ip()),
];
test_range(&mut runner, &index, &field_name_and_descr, range_num_hits);
}
fn test_range<T: Display>(
runner: &mut BenchRunner,
index: &Index,
field_name_and_descr: &[(&str, &str)],
range_num_hits: Vec<(&str, RangeInclusive<T>)>,
) {
for (field, suffix) in field_name_and_descr {
let term_num_hits = vec![
("", ""),
("1_percent", "veryfew"),
("10_percent", "few"),
("90_percent", "most"),
];
let mut group = runner.new_group();
group.set_name(suffix);
// all intersect combinations
for (range_name, range) in &range_num_hits {
for (term_name, term) in &term_num_hits {
let index = &index;
let test_name = if term_name.is_empty() {
format!("id_range_hit_{}", range_name)
} else {
format!(
"id_range_hit_{}_intersect_with_term_{}",
range_name, term_name
)
};
group.register(test_name, move |_| {
let query = if term_name.is_empty() {
"".to_string()
} else {
format!("AND id_name:{}", term)
};
black_box(execute_query(field, range, &query, index));
});
}
}
group.run();
}
}
fn get_index_0_to_100() -> Index {
let mut rng = StdRng::from_seed([1u8; 32]);
let num_vals = 100_000;
let docs: Vec<_> = (0..num_vals)
.map(|_i| {
let id_name = if rng.gen_bool(0.01) {
"veryfew".to_string() // 1%
} else if rng.gen_bool(0.1) {
"few".to_string() // 9%
} else {
"most".to_string() // 90%
};
Doc {
id_name,
id: rng.gen_range(0..100),
// Multiply by 1000, so that we create most buckets in the compact space
// The benches depend on this range to select n-percent of elements with the
// methods below.
ip: Ipv6Addr::from_u128(rng.gen_range(0..100) * 1000),
}
})
.collect();
create_index_from_docs(&docs)
}
#[derive(Clone, Debug)]
pub struct Doc {
pub id_name: String,
pub id: u64,
pub ip: Ipv6Addr,
}
pub fn create_index_from_docs(docs: &[Doc]) -> Index {
let mut schema_builder = Schema::builder();
let id_u64_field = schema_builder.add_u64_field("id", INDEXED | STORED | FAST);
let ids_u64_field =
schema_builder.add_u64_field("ids", NumericOptions::default().set_fast().set_indexed());
let id_f64_field = schema_builder.add_f64_field("id_f64", INDEXED | STORED | FAST);
let ids_f64_field = schema_builder.add_f64_field(
"ids_f64",
NumericOptions::default().set_fast().set_indexed(),
);
let id_i64_field = schema_builder.add_i64_field("id_i64", INDEXED | STORED | FAST);
let ids_i64_field = schema_builder.add_i64_field(
"ids_i64",
NumericOptions::default().set_fast().set_indexed(),
);
let text_field = schema_builder.add_text_field("id_name", STRING | STORED);
let text_field2 = schema_builder.add_text_field("id_name_fast", STRING | STORED | FAST);
let ip_field = schema_builder.add_ip_addr_field("ip", FAST);
let ips_field = schema_builder.add_ip_addr_field("ips", FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
{
let mut index_writer = index.writer_with_num_threads(1, 50_000_000).unwrap();
for doc in docs.iter() {
index_writer
.add_document(doc!(
ids_i64_field => doc.id as i64,
ids_i64_field => doc.id as i64,
ids_f64_field => doc.id as f64,
ids_f64_field => doc.id as f64,
ids_u64_field => doc.id,
ids_u64_field => doc.id,
id_u64_field => doc.id,
id_f64_field => doc.id as f64,
id_i64_field => doc.id as i64,
text_field => doc.id_name.to_string(),
text_field2 => doc.id_name.to_string(),
ips_field => doc.ip,
ips_field => doc.ip,
ip_field => doc.ip,
))
.unwrap();
}
index_writer.commit().unwrap();
}
index
}
fn get_90_percent() -> RangeInclusive<u64> {
0..=90
}
fn get_10_percent() -> RangeInclusive<u64> {
0..=10
}
fn get_1_percent() -> RangeInclusive<u64> {
10..=10
}
fn get_90_percent_ip() -> RangeInclusive<Ipv6Addr> {
let start = Ipv6Addr::from_u128(0);
let end = Ipv6Addr::from_u128(90 * 1000);
start..=end
}
fn get_10_percent_ip() -> RangeInclusive<Ipv6Addr> {
let start = Ipv6Addr::from_u128(0);
let end = Ipv6Addr::from_u128(10 * 1000);
start..=end
}
fn get_1_percent_ip() -> RangeInclusive<Ipv6Addr> {
let start = Ipv6Addr::from_u128(10 * 1000);
let end = Ipv6Addr::from_u128(10 * 1000);
start..=end
}
struct NumHits {
count: usize,
}
impl OutputValue for NumHits {
fn column_title() -> &'static str {
"NumHits"
}
fn format(&self) -> Option<String> {
Some(self.count.to_string())
}
}
fn execute_query<T: Display>(
field: &str,
id_range: &RangeInclusive<T>,
suffix: &str,
index: &Index,
) -> NumHits {
let gen_query_inclusive = |from: &T, to: &T| {
format!(
"{}:[{} TO {}] {}",
field,
&from.to_string(),
&to.to_string(),
suffix
)
};
let query = gen_query_inclusive(id_range.start(), id_range.end());
execute_query_(&query, index)
}
fn execute_query_(query: &str, index: &Index) -> NumHits {
let query_from_text = |text: &str| {
QueryParser::for_index(index, vec![])
.parse_query(text)
.unwrap()
};
let query = query_from_text(query);
let reader = index.reader().unwrap();
let searcher = reader.searcher();
let num_hits = searcher
.search(&query, &(TopDocs::with_limit(10).order_by_score(), Count))
.unwrap()
.1;
NumHits { count: num_hits }
}

View File

@@ -19,7 +19,7 @@ fn u32_to_i32(val: u32) -> i32 {
#[inline]
unsafe fn u32_to_i32_avx2(vals_u32x8s: DataType) -> DataType {
const HIGHEST_BIT_MASK: DataType = from_u32x8([HIGHEST_BIT; NUM_LANES]);
op_xor(vals_u32x8s, HIGHEST_BIT_MASK)
unsafe { op_xor(vals_u32x8s, HIGHEST_BIT_MASK) }
}
pub fn filter_vec_in_place(range: RangeInclusive<u32>, offset: u32, output: &mut Vec<u32>) {
@@ -66,17 +66,19 @@ unsafe fn filter_vec_avx2_aux(
]);
const SHIFT: __m256i = from_u32x8([NUM_LANES as u32; NUM_LANES]);
for _ in 0..num_words {
let word = load_unaligned(input);
let word = u32_to_i32_avx2(word);
let keeper_bitset = compute_filter_bitset(word, range_simd.clone());
let added_len = keeper_bitset.count_ones();
let filtered_doc_ids = compact(ids, keeper_bitset);
store_unaligned(output_tail as *mut __m256i, filtered_doc_ids);
output_tail = output_tail.offset(added_len as isize);
ids = op_add(ids, SHIFT);
input = input.offset(1);
unsafe {
let word = load_unaligned(input);
let word = u32_to_i32_avx2(word);
let keeper_bitset = compute_filter_bitset(word, range_simd.clone());
let added_len = keeper_bitset.count_ones();
let filtered_doc_ids = compact(ids, keeper_bitset);
store_unaligned(output_tail as *mut __m256i, filtered_doc_ids);
output_tail = output_tail.offset(added_len as isize);
ids = op_add(ids, SHIFT);
input = input.offset(1);
}
}
output_tail.offset_from(output) as usize
unsafe { output_tail.offset_from(output) as usize }
}
#[inline]
@@ -92,8 +94,7 @@ unsafe fn compute_filter_bitset(val: __m256i, range: std::ops::RangeInclusive<__
let too_low = op_greater(*range.start(), val);
let too_high = op_greater(val, *range.end());
let inside = op_or(too_low, too_high);
255 - std::arch::x86_64::_mm256_movemask_ps(std::mem::transmute::<DataType, __m256>(inside))
as u8
255 - std::arch::x86_64::_mm256_movemask_ps(_mm256_castsi256_ps(inside)) as u8
}
union U8x32 {

View File

@@ -29,12 +29,20 @@ impl<T: PartialOrd + Copy + std::fmt::Debug + Send + Sync + 'static + Default>
}
}
#[inline]
pub fn fetch_block_with_missing(&mut self, docs: &[u32], accessor: &Column<T>, missing: T) {
pub fn fetch_block_with_missing(
&mut self,
docs: &[u32],
accessor: &Column<T>,
missing: Option<T>,
) {
self.fetch_block(docs, accessor);
// no missing values
if accessor.index.get_cardinality().is_full() {
return;
}
let Some(missing) = missing else {
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

View File

@@ -85,8 +85,8 @@ impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
}
#[inline]
pub fn first(&self, row_id: RowId) -> Option<T> {
self.values_for_doc(row_id).next()
pub fn first(&self, doc_id: DocId) -> Option<T> {
self.values_for_doc(doc_id).next()
}
/// Load the first value for each docid in the provided slice.

View File

@@ -41,12 +41,6 @@ fn transform_range_before_linear_transformation(
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);

View File

@@ -3,7 +3,8 @@ use std::sync::Arc;
use std::{fmt, io};
use common::file_slice::FileSlice;
use common::{ByteCount, DateTime, HasLen, OwnedBytes};
use common::{ByteCount, DateTime, OwnedBytes};
use serde::{Deserialize, Serialize};
use crate::column::{BytesColumn, Column, StrColumn};
use crate::column_values::{StrictlyMonotonicFn, monotonic_map_column};
@@ -317,10 +318,89 @@ impl DynamicColumnHandle {
}
pub fn num_bytes(&self) -> ByteCount {
self.file_slice.len().into()
self.file_slice.num_bytes()
}
/// Legacy helper returning the column space usage.
pub fn column_and_dictionary_num_bytes(&self) -> io::Result<ColumnSpaceUsage> {
self.space_usage()
}
/// Return the space usage of the column, optionally broken down by dictionary and column
/// values.
///
/// For dictionary encoded columns (strings and bytes), this splits the total footprint into
/// the dictionary and the remaining column data (including index and values).
/// For all other column types, the dictionary size is `None` and the column size
/// equals the total bytes.
pub fn space_usage(&self) -> io::Result<ColumnSpaceUsage> {
let total_num_bytes = self.num_bytes();
let dynamic_column = self.open()?;
let dictionary_num_bytes = match &dynamic_column {
DynamicColumn::Bytes(bytes_column) => bytes_column.dictionary().num_bytes(),
DynamicColumn::Str(str_column) => str_column.dictionary().num_bytes(),
_ => {
return Ok(ColumnSpaceUsage::new(self.num_bytes(), None));
}
};
assert!(dictionary_num_bytes <= total_num_bytes);
let column_num_bytes =
ByteCount::from(total_num_bytes.get_bytes() - dictionary_num_bytes.get_bytes());
Ok(ColumnSpaceUsage::new(
column_num_bytes,
Some(dictionary_num_bytes),
))
}
pub fn column_type(&self) -> ColumnType {
self.column_type
}
}
/// Represents space usage of a column.
///
/// `column_num_bytes` tracks the column payload (index, values and footer).
/// For dictionary encoded columns, `dictionary_num_bytes` captures the dictionary footprint.
/// [`ColumnSpaceUsage::total_num_bytes`] returns the sum of both parts.
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct ColumnSpaceUsage {
column_num_bytes: ByteCount,
dictionary_num_bytes: Option<ByteCount>,
}
impl ColumnSpaceUsage {
pub(crate) fn new(
column_num_bytes: ByteCount,
dictionary_num_bytes: Option<ByteCount>,
) -> Self {
ColumnSpaceUsage {
column_num_bytes,
dictionary_num_bytes,
}
}
pub fn column_num_bytes(&self) -> ByteCount {
self.column_num_bytes
}
pub fn dictionary_num_bytes(&self) -> Option<ByteCount> {
self.dictionary_num_bytes
}
pub fn total_num_bytes(&self) -> ByteCount {
self.column_num_bytes + self.dictionary_num_bytes.unwrap_or_default()
}
/// Merge two space usage values by summing their components.
pub fn merge(&self, other: &ColumnSpaceUsage) -> ColumnSpaceUsage {
let dictionary_num_bytes = match (self.dictionary_num_bytes, other.dictionary_num_bytes) {
(Some(lhs), Some(rhs)) => Some(lhs + rhs),
(Some(val), None) | (None, Some(val)) => Some(val),
(None, None) => None,
};
ColumnSpaceUsage {
column_num_bytes: self.column_num_bytes + other.column_num_bytes,
dictionary_num_bytes,
}
}
}

View File

@@ -48,7 +48,7 @@ pub use columnar::{
use sstable::VoidSSTable;
pub use value::{NumericalType, NumericalValue};
pub use self::dynamic_column::{DynamicColumn, DynamicColumnHandle};
pub use self::dynamic_column::{ColumnSpaceUsage, DynamicColumn, DynamicColumnHandle};
pub type RowId = u32;
pub type DocId = u32;

View File

@@ -60,7 +60,7 @@ fn test_dataframe_writer_bool() {
let DynamicColumn::Bool(bool_col) = dyn_bool_col else {
panic!();
};
let vals: Vec<Option<bool>> = (0..5).map(|row_id| bool_col.first(row_id)).collect();
let vals: Vec<Option<bool>> = (0..5).map(|doc_id| bool_col.first(doc_id)).collect();
assert_eq!(&vals, &[None, Some(false), None, Some(true), None,]);
}
@@ -108,7 +108,7 @@ fn test_dataframe_writer_ip_addr() {
let DynamicColumn::IpAddr(ip_col) = dyn_bool_col else {
panic!();
};
let vals: Vec<Option<Ipv6Addr>> = (0..5).map(|row_id| ip_col.first(row_id)).collect();
let vals: Vec<Option<Ipv6Addr>> = (0..5).map(|doc_id| ip_col.first(doc_id)).collect();
assert_eq!(
&vals,
&[
@@ -169,7 +169,7 @@ fn test_dictionary_encoded_str() {
let DynamicColumn::Str(str_col) = col_handles[0].open().unwrap() else {
panic!();
};
let index: Vec<Option<u64>> = (0..5).map(|row_id| str_col.ords().first(row_id)).collect();
let index: Vec<Option<u64>> = (0..5).map(|doc_id| str_col.ords().first(doc_id)).collect();
assert_eq!(index, &[None, Some(0), None, Some(2), Some(1)]);
assert_eq!(str_col.num_rows(), 5);
let mut term_buffer = String::new();
@@ -204,7 +204,7 @@ fn test_dictionary_encoded_bytes() {
panic!();
};
let index: Vec<Option<u64>> = (0..5)
.map(|row_id| bytes_col.ords().first(row_id))
.map(|doc_id| bytes_col.ords().first(doc_id))
.collect();
assert_eq!(index, &[None, Some(0), None, Some(2), Some(1)]);
assert_eq!(bytes_col.num_rows(), 5);

View File

@@ -181,6 +181,14 @@ pub struct BitSet {
len: u64,
max_value: u32,
}
impl std::fmt::Debug for BitSet {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("BitSet")
.field("len", &self.len)
.field("max_value", &self.max_value)
.finish()
}
}
fn num_buckets(max_val: u32) -> u32 {
max_val.div_ceil(64u32)

View File

@@ -1,4 +1,4 @@
use columnar::{Column, ColumnType, StrColumn};
use columnar::{Column, ColumnBlockAccessor, ColumnType, StrColumn};
use common::BitSet;
use rustc_hash::FxHashSet;
use serde::Serialize;
@@ -10,16 +10,16 @@ use crate::aggregation::accessor_helpers::{
};
use crate::aggregation::agg_req::{Aggregation, AggregationVariants, Aggregations};
use crate::aggregation::bucket::{
FilterAggReqData, HistogramAggReqData, HistogramBounds, IncludeExcludeParam,
MissingTermAggReqData, RangeAggReqData, SegmentFilterCollector, SegmentHistogramCollector,
SegmentRangeCollector, TermMissingAgg, TermsAggReqData, TermsAggregation,
build_segment_filter_collector, build_segment_range_collector, FilterAggReqData,
HistogramAggReqData, HistogramBounds, IncludeExcludeParam, MissingTermAggReqData,
RangeAggReqData, SegmentHistogramCollector, TermMissingAgg, TermsAggReqData, TermsAggregation,
TermsAggregationInternal,
};
use crate::aggregation::metric::{
AverageAggregation, CardinalityAggReqData, CardinalityAggregationReq, CountAggregation,
ExtendedStatsAggregation, MaxAggregation, MetricAggReqData, MinAggregation,
SegmentCardinalityCollector, SegmentExtendedStatsCollector, SegmentPercentilesCollector,
SegmentStatsCollector, StatsAggregation, StatsType, SumAggregation, TopHitsAggReqData,
build_segment_stats_collector, AverageAggregation, CardinalityAggReqData,
CardinalityAggregationReq, CountAggregation, ExtendedStatsAggregation, MaxAggregation,
MetricAggReqData, MinAggregation, SegmentCardinalityCollector, SegmentExtendedStatsCollector,
SegmentPercentilesCollector, StatsAggregation, StatsType, SumAggregation, TopHitsAggReqData,
TopHitsSegmentCollector,
};
use crate::aggregation::segment_agg_result::{
@@ -35,6 +35,7 @@ pub struct AggregationsSegmentCtx {
/// Request data for each aggregation type.
pub per_request: PerRequestAggSegCtx,
pub context: AggContextParams,
pub column_block_accessor: ColumnBlockAccessor<u64>,
}
impl AggregationsSegmentCtx {
@@ -107,21 +108,14 @@ impl AggregationsSegmentCtx {
.as_deref()
.expect("range_req_data slot is empty (taken)")
}
#[inline]
pub(crate) fn get_filter_req_data(&self, idx: usize) -> &FilterAggReqData {
self.per_request.filter_req_data[idx]
.as_deref()
.expect("filter_req_data slot is empty (taken)")
}
// ---------- mutable getters ----------
#[inline]
pub(crate) fn get_term_req_data_mut(&mut self, idx: usize) -> &mut TermsAggReqData {
self.per_request.term_req_data[idx]
.as_deref_mut()
.expect("term_req_data slot is empty (taken)")
pub(crate) fn get_metric_req_data_mut(&mut self, idx: usize) -> &mut MetricAggReqData {
&mut self.per_request.stats_metric_req_data[idx]
}
#[inline]
pub(crate) fn get_cardinality_req_data_mut(
&mut self,
@@ -129,10 +123,7 @@ impl AggregationsSegmentCtx {
) -> &mut CardinalityAggReqData {
&mut self.per_request.cardinality_req_data[idx]
}
#[inline]
pub(crate) fn get_metric_req_data_mut(&mut self, idx: usize) -> &mut MetricAggReqData {
&mut self.per_request.stats_metric_req_data[idx]
}
#[inline]
pub(crate) fn get_histogram_req_data_mut(&mut self, idx: usize) -> &mut HistogramAggReqData {
self.per_request.histogram_req_data[idx]
@@ -142,21 +133,6 @@ impl AggregationsSegmentCtx {
// ---------- take / put (terms, histogram, range) ----------
/// Move out the boxed Terms request at `idx`, leaving `None`.
#[inline]
pub(crate) fn take_term_req_data(&mut self, idx: usize) -> Box<TermsAggReqData> {
self.per_request.term_req_data[idx]
.take()
.expect("term_req_data slot is empty (taken)")
}
/// Put back a Terms request into an empty slot at `idx`.
#[inline]
pub(crate) fn put_back_term_req_data(&mut self, idx: usize, value: Box<TermsAggReqData>) {
debug_assert!(self.per_request.term_req_data[idx].is_none());
self.per_request.term_req_data[idx] = Some(value);
}
/// Move out the boxed Histogram request at `idx`, leaving `None`.
#[inline]
pub(crate) fn take_histogram_req_data(&mut self, idx: usize) -> Box<HistogramAggReqData> {
@@ -320,6 +296,7 @@ impl PerRequestAggSegCtx {
/// Convert the aggregation tree into a serializable struct representation.
/// Each node contains: { name, kind, children }.
#[allow(dead_code)]
pub fn get_view_tree(&self) -> Vec<AggTreeViewNode> {
fn node_to_view(node: &AggRefNode, pr: &PerRequestAggSegCtx) -> AggTreeViewNode {
let mut children: Vec<AggTreeViewNode> =
@@ -345,12 +322,19 @@ impl PerRequestAggSegCtx {
pub(crate) fn build_segment_agg_collectors_root(
req: &mut AggregationsSegmentCtx,
) -> crate::Result<Box<dyn SegmentAggregationCollector>> {
build_segment_agg_collectors(req, &req.per_request.agg_tree.clone())
build_segment_agg_collectors_generic(req, &req.per_request.agg_tree.clone())
}
pub(crate) fn build_segment_agg_collectors(
req: &mut AggregationsSegmentCtx,
nodes: &[AggRefNode],
) -> crate::Result<Box<dyn SegmentAggregationCollector>> {
build_segment_agg_collectors_generic(req, nodes)
}
fn build_segment_agg_collectors_generic(
req: &mut AggregationsSegmentCtx,
nodes: &[AggRefNode],
) -> crate::Result<Box<dyn SegmentAggregationCollector>> {
let mut collectors = Vec::new();
for node in nodes.iter() {
@@ -388,6 +372,8 @@ pub(crate) fn build_segment_agg_collector(
Ok(Box::new(SegmentCardinalityCollector::from_req(
req_data.column_type,
node.idx_in_req_data,
req_data.accessor.clone(),
req_data.missing_value_for_accessor,
)))
}
AggKind::StatsKind(stats_type) => {
@@ -398,20 +384,21 @@ pub(crate) fn build_segment_agg_collector(
| StatsType::Count
| StatsType::Max
| StatsType::Min
| StatsType::Stats => Ok(Box::new(SegmentStatsCollector::from_req(
node.idx_in_req_data,
))),
StatsType::ExtendedStats(sigma) => {
Ok(Box::new(SegmentExtendedStatsCollector::from_req(
req_data.field_type,
sigma,
node.idx_in_req_data,
req_data.missing,
)))
}
StatsType::Percentiles => Ok(Box::new(
SegmentPercentilesCollector::from_req_and_validate(node.idx_in_req_data)?,
| StatsType::Stats => build_segment_stats_collector(req_data),
StatsType::ExtendedStats(sigma) => Ok(Box::new(
SegmentExtendedStatsCollector::from_req(req_data, sigma),
)),
StatsType::Percentiles => {
let req_data = req.get_metric_req_data_mut(node.idx_in_req_data);
Ok(Box::new(
SegmentPercentilesCollector::from_req_and_validate(
req_data.field_type,
req_data.missing_u64,
req_data.accessor.clone(),
node.idx_in_req_data,
),
))
}
}
}
AggKind::TopHits => {
@@ -428,12 +415,8 @@ pub(crate) fn build_segment_agg_collector(
AggKind::DateHistogram => Ok(Box::new(SegmentHistogramCollector::from_req_and_validate(
req, node,
)?)),
AggKind::Range => Ok(Box::new(SegmentRangeCollector::from_req_and_validate(
req, node,
)?)),
AggKind::Filter => Ok(Box::new(SegmentFilterCollector::from_req_and_validate(
req, node,
)?)),
AggKind::Range => Ok(build_segment_range_collector(req, node)?),
AggKind::Filter => build_segment_filter_collector(req, node),
}
}
@@ -493,6 +476,7 @@ pub(crate) fn build_aggregations_data_from_req(
let mut data = AggregationsSegmentCtx {
per_request: Default::default(),
context,
column_block_accessor: ColumnBlockAccessor::default(),
};
for (name, agg) in aggs.iter() {
@@ -521,9 +505,9 @@ fn build_nodes(
let idx_in_req_data = data.push_range_req_data(RangeAggReqData {
accessor,
field_type,
column_block_accessor: Default::default(),
name: agg_name.to_string(),
req: range_req.clone(),
is_top_level,
});
let children = build_children(&req.sub_aggregation, reader, segment_ordinal, data)?;
Ok(vec![AggRefNode {
@@ -541,9 +525,7 @@ fn build_nodes(
let idx_in_req_data = data.push_histogram_req_data(HistogramAggReqData {
accessor,
field_type,
column_block_accessor: Default::default(),
name: agg_name.to_string(),
sub_aggregation_blueprint: None,
req: histo_req.clone(),
is_date_histogram: false,
bounds: HistogramBounds {
@@ -568,9 +550,7 @@ fn build_nodes(
let idx_in_req_data = data.push_histogram_req_data(HistogramAggReqData {
accessor,
field_type,
column_block_accessor: Default::default(),
name: agg_name.to_string(),
sub_aggregation_blueprint: None,
req: histo_req,
is_date_histogram: true,
bounds: HistogramBounds {
@@ -650,7 +630,6 @@ fn build_nodes(
let idx_in_req_data = data.push_metric_req_data(MetricAggReqData {
accessor,
field_type,
column_block_accessor: Default::default(),
name: agg_name.to_string(),
collecting_for,
missing: *missing,
@@ -678,7 +657,6 @@ fn build_nodes(
let idx_in_req_data = data.push_metric_req_data(MetricAggReqData {
accessor,
field_type,
column_block_accessor: Default::default(),
name: agg_name.to_string(),
collecting_for: StatsType::Percentiles,
missing: percentiles_req.missing,
@@ -753,6 +731,7 @@ fn build_nodes(
segment_reader: reader.clone(),
evaluator,
matching_docs_buffer,
is_top_level,
});
let children = build_children(&req.sub_aggregation, reader, segment_ordinal, data)?;
Ok(vec![AggRefNode {
@@ -895,7 +874,7 @@ fn build_terms_or_cardinality_nodes(
});
}
// Add one node per accessor to mirror previous behavior and allow per-type missing handling.
// Add one node per accessor
for (accessor, column_type) in column_and_types {
let missing_value_for_accessor = if use_special_missing_agg {
None
@@ -926,11 +905,8 @@ fn build_terms_or_cardinality_nodes(
column_type,
str_dict_column: str_dict_column.clone(),
missing_value_for_accessor,
column_block_accessor: Default::default(),
name: agg_name.to_string(),
req: TermsAggregationInternal::from_req(req),
// Will be filled later when building collectors
sub_aggregation_blueprint: None,
sug_aggregations: sub_aggs.clone(),
allowed_term_ids,
is_top_level,
@@ -943,7 +919,6 @@ fn build_terms_or_cardinality_nodes(
column_type,
str_dict_column: str_dict_column.clone(),
missing_value_for_accessor,
column_block_accessor: Default::default(),
name: agg_name.to_string(),
req: req.clone(),
});

View File

@@ -2,15 +2,441 @@ use serde_json::Value;
use crate::aggregation::agg_req::{Aggregation, Aggregations};
use crate::aggregation::agg_result::AggregationResults;
use crate::aggregation::buf_collector::DOC_BLOCK_SIZE;
use crate::aggregation::collector::AggregationCollector;
use crate::aggregation::intermediate_agg_result::IntermediateAggregationResults;
use crate::aggregation::tests::{get_test_index_2_segments, get_test_index_from_values_and_terms};
use crate::aggregation::DistributedAggregationCollector;
use crate::docset::COLLECT_BLOCK_BUFFER_LEN;
use crate::query::{AllQuery, TermQuery};
use crate::schema::{IndexRecordOption, Schema, FAST};
use crate::{Index, IndexWriter, Term};
// The following tests ensure that each bucket aggregation type correctly functions as a
// sub-aggregation of another bucket aggregation in two scenarios:
// 1) The parent has more buckets than the child sub-aggregation
// 2) The child sub-aggregation has more buckets than the parent
//
// These scenarios exercise the bucket id mapping and sub-aggregation routing logic.
#[test]
fn test_terms_as_subagg_parent_more_vs_child_more() -> crate::Result<()> {
let index = get_test_index_2_segments(false)?;
// Case A: parent has more buckets than child
// Parent: range with 4 buckets
// Child: terms on text -> 2 buckets
let agg_parent_more: Aggregations = serde_json::from_value(json!({
"parent_range": {
"range": {
"field": "score",
"ranges": [
{"to": 3.0},
{"from": 3.0, "to": 7.0},
{"from": 7.0, "to": 20.0},
{"from": 20.0}
]
},
"aggs": {
"child_terms": {"terms": {"field": "text", "order": {"_key": "asc"}}}
}
}
}))
.unwrap();
let res = crate::aggregation::tests::exec_request(agg_parent_more, &index)?;
// Exact expected structure and counts
assert_eq!(
res["parent_range"]["buckets"],
json!([
{
"key": "*-3",
"doc_count": 1,
"to": 3.0,
"child_terms": {
"buckets": [
{"doc_count": 1, "key": "cool"}
],
"sum_other_doc_count": 0
}
},
{
"key": "3-7",
"doc_count": 3,
"from": 3.0,
"to": 7.0,
"child_terms": {
"buckets": [
{"doc_count": 2, "key": "cool"},
{"doc_count": 1, "key": "nohit"}
],
"sum_other_doc_count": 0
}
},
{
"key": "7-20",
"doc_count": 3,
"from": 7.0,
"to": 20.0,
"child_terms": {
"buckets": [
{"doc_count": 3, "key": "cool"}
],
"sum_other_doc_count": 0
}
},
{
"key": "20-*",
"doc_count": 2,
"from": 20.0,
"child_terms": {
"buckets": [
{"doc_count": 1, "key": "cool"},
{"doc_count": 1, "key": "nohit"}
],
"sum_other_doc_count": 0
}
}
])
);
// Case B: child has more buckets than parent
// Parent: histogram on score with large interval -> 1 bucket
// Child: terms on text -> 2 buckets (cool/nohit)
let agg_child_more: Aggregations = serde_json::from_value(json!({
"parent_hist": {
"histogram": {"field": "score", "interval": 100.0},
"aggs": {
"child_terms": {"terms": {"field": "text", "order": {"_key": "asc"}}}
}
}
}))
.unwrap();
let res = crate::aggregation::tests::exec_request(agg_child_more, &index)?;
assert_eq!(
res["parent_hist"],
json!({
"buckets": [
{
"key": 0.0,
"doc_count": 9,
"child_terms": {
"buckets": [
{"doc_count": 7, "key": "cool"},
{"doc_count": 2, "key": "nohit"}
],
"sum_other_doc_count": 0
}
}
]
})
);
Ok(())
}
#[test]
fn test_range_as_subagg_parent_more_vs_child_more() -> crate::Result<()> {
let index = get_test_index_2_segments(false)?;
// Case A: parent has more buckets than child
// Parent: range with 5 buckets
// Child: coarse range with 3 buckets
let agg_parent_more: Aggregations = serde_json::from_value(json!({
"parent_range": {
"range": {
"field": "score",
"ranges": [
{"to": 3.0},
{"from": 3.0, "to": 7.0},
{"from": 7.0, "to": 11.0},
{"from": 11.0, "to": 20.0},
{"from": 20.0}
]
},
"aggs": {
"child_range": {
"range": {
"field": "score",
"ranges": [
{"to": 3.0},
{"from": 3.0, "to": 20.0}
]
}
}
}
}
}))
.unwrap();
let res = crate::aggregation::tests::exec_request(agg_parent_more, &index)?;
assert_eq!(
res["parent_range"]["buckets"],
json!([
{"key": "*-3", "doc_count": 1, "to": 3.0,
"child_range": {"buckets": [
{"key": "*-3", "doc_count": 1, "to": 3.0},
{"key": "3-20", "doc_count": 0, "from": 3.0, "to": 20.0},
{"key": "20-*", "doc_count": 0, "from": 20.0}
]}
},
{"key": "3-7", "doc_count": 3, "from": 3.0, "to": 7.0,
"child_range": {"buckets": [
{"key": "*-3", "doc_count": 0, "to": 3.0},
{"key": "3-20", "doc_count": 3, "from": 3.0, "to": 20.0},
{"key": "20-*", "doc_count": 0, "from": 20.0}
]}
},
{"key": "7-11", "doc_count": 1, "from": 7.0, "to": 11.0,
"child_range": {"buckets": [
{"key": "*-3", "doc_count": 0, "to": 3.0},
{"key": "3-20", "doc_count": 1, "from": 3.0, "to": 20.0},
{"key": "20-*", "doc_count": 0, "from": 20.0}
]}
},
{"key": "11-20", "doc_count": 2, "from": 11.0, "to": 20.0,
"child_range": {"buckets": [
{"key": "*-3", "doc_count": 0, "to": 3.0},
{"key": "3-20", "doc_count": 2, "from": 3.0, "to": 20.0},
{"key": "20-*", "doc_count": 0, "from": 20.0}
]}
},
{"key": "20-*", "doc_count": 2, "from": 20.0,
"child_range": {"buckets": [
{"key": "*-3", "doc_count": 0, "to": 3.0},
{"key": "3-20", "doc_count": 0, "from": 3.0, "to": 20.0},
{"key": "20-*", "doc_count": 2, "from": 20.0}
]}
}
])
);
// Case B: child has more buckets than parent
// Parent: terms on text (2 buckets)
// Child: range with 4 buckets
let agg_child_more: Aggregations = serde_json::from_value(json!({
"parent_terms": {
"terms": {"field": "text"},
"aggs": {
"child_range": {
"range": {
"field": "score",
"ranges": [
{"to": 3.0},
{"from": 3.0, "to": 7.0},
{"from": 7.0, "to": 20.0}
]
}
}
}
}
}))
.unwrap();
let res = crate::aggregation::tests::exec_request(agg_child_more, &index)?;
assert_eq!(
res["parent_terms"],
json!({
"buckets": [
{
"key": "cool",
"doc_count": 7,
"child_range": {
"buckets": [
{"key": "*-3", "doc_count": 1, "to": 3.0},
{"key": "3-7", "doc_count": 2, "from": 3.0, "to": 7.0},
{"key": "7-20", "doc_count": 3, "from": 7.0, "to": 20.0},
{"key": "20-*", "doc_count": 1, "from": 20.0}
]
}
},
{
"key": "nohit",
"doc_count": 2,
"child_range": {
"buckets": [
{"key": "*-3", "doc_count": 0, "to": 3.0},
{"key": "3-7", "doc_count": 1, "from": 3.0, "to": 7.0},
{"key": "7-20", "doc_count": 0, "from": 7.0, "to": 20.0},
{"key": "20-*", "doc_count": 1, "from": 20.0}
]
}
}
],
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0
})
);
Ok(())
}
#[test]
fn test_histogram_as_subagg_parent_more_vs_child_more() -> crate::Result<()> {
let index = get_test_index_2_segments(false)?;
// Case A: parent has more buckets than child
// Parent: range with several ranges
// Child: histogram with large interval (single bucket per parent)
let agg_parent_more: Aggregations = serde_json::from_value(json!({
"parent_range": {
"range": {
"field": "score",
"ranges": [
{"to": 3.0},
{"from": 3.0, "to": 7.0},
{"from": 7.0, "to": 11.0},
{"from": 11.0, "to": 20.0},
{"from": 20.0}
]
},
"aggs": {
"child_hist": {"histogram": {"field": "score", "interval": 100.0}}
}
}
}))
.unwrap();
let res = crate::aggregation::tests::exec_request(agg_parent_more, &index)?;
assert_eq!(
res["parent_range"]["buckets"],
json!([
{"key": "*-3", "doc_count": 1, "to": 3.0,
"child_hist": {"buckets": [ {"key": 0.0, "doc_count": 1} ]}
},
{"key": "3-7", "doc_count": 3, "from": 3.0, "to": 7.0,
"child_hist": {"buckets": [ {"key": 0.0, "doc_count": 3} ]}
},
{"key": "7-11", "doc_count": 1, "from": 7.0, "to": 11.0,
"child_hist": {"buckets": [ {"key": 0.0, "doc_count": 1} ]}
},
{"key": "11-20", "doc_count": 2, "from": 11.0, "to": 20.0,
"child_hist": {"buckets": [ {"key": 0.0, "doc_count": 2} ]}
},
{"key": "20-*", "doc_count": 2, "from": 20.0,
"child_hist": {"buckets": [ {"key": 0.0, "doc_count": 2} ]}
}
])
);
// Case B: child has more buckets than parent
// Parent: terms on text -> 2 buckets
// Child: histogram with small interval -> multiple buckets including empties
let agg_child_more: Aggregations = serde_json::from_value(json!({
"parent_terms": {
"terms": {"field": "text"},
"aggs": {
"child_hist": {"histogram": {"field": "score", "interval": 10.0}}
}
}
}))
.unwrap();
let res = crate::aggregation::tests::exec_request(agg_child_more, &index)?;
assert_eq!(
res["parent_terms"],
json!({
"buckets": [
{
"key": "cool",
"doc_count": 7,
"child_hist": {
"buckets": [
{"key": 0.0, "doc_count": 4},
{"key": 10.0, "doc_count": 2},
{"key": 20.0, "doc_count": 0},
{"key": 30.0, "doc_count": 0},
{"key": 40.0, "doc_count": 1}
]
}
},
{
"key": "nohit",
"doc_count": 2,
"child_hist": {
"buckets": [
{"key": 0.0, "doc_count": 1},
{"key": 10.0, "doc_count": 0},
{"key": 20.0, "doc_count": 0},
{"key": 30.0, "doc_count": 0},
{"key": 40.0, "doc_count": 1}
]
}
}
],
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0
})
);
Ok(())
}
#[test]
fn test_date_histogram_as_subagg_parent_more_vs_child_more() -> crate::Result<()> {
let index = get_test_index_2_segments(false)?;
// Case A: parent has more buckets than child
// Parent: range with several buckets
// Child: date_histogram with 30d -> single bucket per parent
let agg_parent_more: Aggregations = serde_json::from_value(json!({
"parent_range": {
"range": {
"field": "score",
"ranges": [
{"to": 3.0},
{"from": 3.0, "to": 7.0},
{"from": 7.0, "to": 11.0},
{"from": 11.0, "to": 20.0},
{"from": 20.0}
]
},
"aggs": {
"child_date_hist": {"date_histogram": {"field": "date", "fixed_interval": "30d"}}
}
}
}))
.unwrap();
let res = crate::aggregation::tests::exec_request(agg_parent_more, &index)?;
let buckets = res["parent_range"]["buckets"].as_array().unwrap();
// Verify each parent bucket has exactly one child date bucket with matching doc_count
for bucket in buckets {
let parent_count = bucket["doc_count"].as_u64().unwrap();
let child_buckets = bucket["child_date_hist"]["buckets"].as_array().unwrap();
assert_eq!(child_buckets.len(), 1);
assert_eq!(child_buckets[0]["doc_count"], parent_count);
}
// Case B: child has more buckets than parent
// Parent: terms on text (2 buckets)
// Child: date_histogram with 1d -> multiple buckets
let agg_child_more: Aggregations = serde_json::from_value(json!({
"parent_terms": {
"terms": {"field": "text"},
"aggs": {
"child_date_hist": {"date_histogram": {"field": "date", "fixed_interval": "1d"}}
}
}
}))
.unwrap();
let res = crate::aggregation::tests::exec_request(agg_child_more, &index)?;
let buckets = res["parent_terms"]["buckets"].as_array().unwrap();
// cool bucket
assert_eq!(buckets[0]["key"], "cool");
let cool_buckets = buckets[0]["child_date_hist"]["buckets"].as_array().unwrap();
assert_eq!(cool_buckets.len(), 3);
assert_eq!(cool_buckets[0]["doc_count"], 1); // day 0
assert_eq!(cool_buckets[1]["doc_count"], 4); // day 1
assert_eq!(cool_buckets[2]["doc_count"], 2); // day 2
// nohit bucket
assert_eq!(buckets[1]["key"], "nohit");
let nohit_buckets = buckets[1]["child_date_hist"]["buckets"].as_array().unwrap();
assert_eq!(nohit_buckets.len(), 2);
assert_eq!(nohit_buckets[0]["doc_count"], 1); // day 1
assert_eq!(nohit_buckets[1]["doc_count"], 1); // day 2
Ok(())
}
fn get_avg_req(field_name: &str) -> Aggregation {
serde_json::from_value(json!({
"avg": {
@@ -25,6 +451,10 @@ fn get_collector(agg_req: Aggregations) -> AggregationCollector {
}
// *** EVERY BUCKET-TYPE SHOULD BE TESTED HERE ***
// Note: The flushng part of these tests are outdated, since the buffering change after converting
// the collection into one collector per request instead of per bucket.
//
// However they are useful as they test a complex aggregation requests.
fn test_aggregation_flushing(
merge_segments: bool,
use_distributed_collector: bool,
@@ -37,8 +467,9 @@ fn test_aggregation_flushing(
let reader = index.reader()?;
assert_eq!(DOC_BLOCK_SIZE, 64);
// In the tree we cache Documents of DOC_BLOCK_SIZE, before passing them down as one block.
assert_eq!(COLLECT_BLOCK_BUFFER_LEN, 64);
// In the tree we cache documents of COLLECT_BLOCK_BUFFER_LEN before passing them down as one
// block.
//
// Build a request so that on the first level we have one full cache, which is then flushed.
// The same cache should have some residue docs at the end, which are flushed (Range 0-70)

View File

@@ -6,10 +6,14 @@ use serde::{Deserialize, Deserializer, Serialize, Serializer};
use crate::aggregation::agg_data::{
build_segment_agg_collectors, AggRefNode, AggregationsSegmentCtx,
};
use crate::aggregation::cached_sub_aggs::{
CachedSubAggs, HighCardSubAggCache, LowCardSubAggCache, SubAggCache,
};
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResult, IntermediateAggregationResults, IntermediateBucketResult,
};
use crate::aggregation::segment_agg_result::{CollectorClone, SegmentAggregationCollector};
use crate::aggregation::segment_agg_result::{BucketIdProvider, SegmentAggregationCollector};
use crate::aggregation::BucketId;
use crate::docset::DocSet;
use crate::query::{AllQuery, EnableScoring, Query, QueryParser};
use crate::schema::Schema;
@@ -404,15 +408,18 @@ pub struct FilterAggReqData {
pub evaluator: DocumentQueryEvaluator,
/// Reusable buffer for matching documents to minimize allocations during collection
pub matching_docs_buffer: Vec<DocId>,
/// True if this filter aggregation is at the top level of the aggregation tree (not nested).
pub is_top_level: bool,
}
impl FilterAggReqData {
pub(crate) fn get_memory_consumption(&self) -> usize {
// Estimate: name + segment reader reference + bitset + buffer capacity
self.name.len()
+ std::mem::size_of::<SegmentReader>()
+ self.evaluator.bitset.len() / 8 // BitSet memory (bits to bytes)
+ self.matching_docs_buffer.capacity() * std::mem::size_of::<DocId>()
+ std::mem::size_of::<SegmentReader>()
+ self.evaluator.bitset.len() / 8 // BitSet memory (bits to bytes)
+ self.matching_docs_buffer.capacity() * std::mem::size_of::<DocId>()
+ std::mem::size_of::<bool>()
}
}
@@ -489,17 +496,24 @@ impl Debug for DocumentQueryEvaluator {
}
}
/// Segment collector for filter aggregation
pub struct SegmentFilterCollector {
/// Document count in this bucket
#[derive(Debug, Clone, PartialEq, Copy)]
struct DocCount {
doc_count: u64,
bucket_id: BucketId,
}
/// Segment collector for filter aggregation
pub struct SegmentFilterCollector<C: SubAggCache> {
/// Document counts per parent bucket
parent_buckets: Vec<DocCount>,
/// Sub-aggregation collectors
sub_aggregations: Option<Box<dyn SegmentAggregationCollector>>,
sub_aggregations: Option<CachedSubAggs<C>>,
bucket_id_provider: BucketIdProvider,
/// Accessor index for this filter aggregation (to access FilterAggReqData)
accessor_idx: usize,
}
impl SegmentFilterCollector {
impl<C: SubAggCache> SegmentFilterCollector<C> {
/// Create a new filter segment collector following the new agg_data pattern
pub(crate) fn from_req_and_validate(
req: &mut AggregationsSegmentCtx,
@@ -511,47 +525,75 @@ impl SegmentFilterCollector {
} else {
None
};
let sub_agg_collector = sub_agg_collector.map(CachedSubAggs::new);
Ok(SegmentFilterCollector {
doc_count: 0,
parent_buckets: Vec::new(),
sub_aggregations: sub_agg_collector,
accessor_idx: node.idx_in_req_data,
bucket_id_provider: BucketIdProvider::default(),
})
}
}
impl Debug for SegmentFilterCollector {
pub(crate) fn build_segment_filter_collector(
req: &mut AggregationsSegmentCtx,
node: &AggRefNode,
) -> crate::Result<Box<dyn SegmentAggregationCollector>> {
let is_top_level = req.per_request.filter_req_data[node.idx_in_req_data]
.as_ref()
.expect("filter_req_data slot is empty")
.is_top_level;
if is_top_level {
Ok(Box::new(
SegmentFilterCollector::<LowCardSubAggCache>::from_req_and_validate(req, node)?,
))
} else {
Ok(Box::new(
SegmentFilterCollector::<HighCardSubAggCache>::from_req_and_validate(req, node)?,
))
}
}
impl<C: SubAggCache> Debug for SegmentFilterCollector<C> {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("SegmentFilterCollector")
.field("doc_count", &self.doc_count)
.field("buckets", &self.parent_buckets)
.field("has_sub_aggs", &self.sub_aggregations.is_some())
.field("accessor_idx", &self.accessor_idx)
.finish()
}
}
impl CollectorClone for SegmentFilterCollector {
fn clone_box(&self) -> Box<dyn SegmentAggregationCollector> {
// For now, panic - this needs proper implementation with weight recreation
panic!("SegmentFilterCollector cloning not yet implemented - requires weight recreation")
}
}
impl SegmentAggregationCollector for SegmentFilterCollector {
impl<C: SubAggCache> SegmentAggregationCollector for SegmentFilterCollector<C> {
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
let mut sub_results = IntermediateAggregationResults::default();
let bucket_opt = self.parent_buckets.get(parent_bucket_id as usize);
if let Some(sub_aggs) = self.sub_aggregations {
sub_aggs.add_intermediate_aggregation_result(agg_data, &mut sub_results)?;
if let Some(sub_aggs) = &mut self.sub_aggregations {
sub_aggs
.get_sub_agg_collector()
.add_intermediate_aggregation_result(
agg_data,
&mut sub_results,
// Here we create a new bucket ID for sub-aggregations if the bucket doesn't
// exist, so that sub-aggregations can still produce results (e.g., zero doc
// count)
bucket_opt
.map(|bucket| bucket.bucket_id)
.unwrap_or(self.bucket_id_provider.next_bucket_id()),
)?;
}
// Create the filter bucket result
let filter_bucket_result = IntermediateBucketResult::Filter {
doc_count: self.doc_count,
doc_count: bucket_opt.map(|b| b.doc_count).unwrap_or(0),
sub_aggregations: sub_results,
};
@@ -570,32 +612,17 @@ impl SegmentAggregationCollector for SegmentFilterCollector {
Ok(())
}
fn collect(&mut self, doc: DocId, agg_data: &mut AggregationsSegmentCtx) -> crate::Result<()> {
// Access the evaluator from FilterAggReqData
let req_data = agg_data.get_filter_req_data(self.accessor_idx);
// O(1) BitSet lookup to check if document matches filter
if req_data.evaluator.matches_document(doc) {
self.doc_count += 1;
// If we have sub-aggregations, collect on them for this filtered document
if let Some(sub_aggs) = &mut self.sub_aggregations {
sub_aggs.collect(doc, agg_data)?;
}
}
Ok(())
}
#[inline]
fn collect_block(
fn collect(
&mut self,
docs: &[DocId],
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
if docs.is_empty() {
return Ok(());
}
let mut bucket = self.parent_buckets[parent_bucket_id as usize];
// Take the request data to avoid borrow checker issues with sub-aggregations
let mut req = agg_data.take_filter_req_data(self.accessor_idx);
@@ -604,18 +631,24 @@ impl SegmentAggregationCollector for SegmentFilterCollector {
req.evaluator
.filter_batch(docs, &mut req.matching_docs_buffer);
self.doc_count += req.matching_docs_buffer.len() as u64;
bucket.doc_count += req.matching_docs_buffer.len() as u64;
// Batch process sub-aggregations if we have matches
if !req.matching_docs_buffer.is_empty() {
if let Some(sub_aggs) = &mut self.sub_aggregations {
// Use collect_block for better sub-aggregation performance
sub_aggs.collect_block(&req.matching_docs_buffer, agg_data)?;
for &doc_id in &req.matching_docs_buffer {
sub_aggs.push(bucket.bucket_id, doc_id);
}
}
}
// Put the request data back
agg_data.put_back_filter_req_data(self.accessor_idx, req);
if let Some(sub_aggs) = &mut self.sub_aggregations {
sub_aggs.check_flush_local(agg_data)?;
}
// put back bucket
self.parent_buckets[parent_bucket_id as usize] = bucket;
Ok(())
}
@@ -626,6 +659,21 @@ impl SegmentAggregationCollector for SegmentFilterCollector {
}
Ok(())
}
fn prepare_max_bucket(
&mut self,
max_bucket: BucketId,
_agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
while self.parent_buckets.len() <= max_bucket as usize {
let bucket_id = self.bucket_id_provider.next_bucket_id();
self.parent_buckets.push(DocCount {
doc_count: 0,
bucket_id,
});
}
Ok(())
}
}
/// Intermediate result for filter aggregation
@@ -1519,9 +1567,9 @@ mod tests {
let searcher = reader.searcher();
let agg = json!({
"test": {
"filter": deserialized,
"aggs": { "count": { "value_count": { "field": "brand" } } }
"test": {
"filter": deserialized,
"aggs": { "count": { "value_count": { "field": "brand" } } }
}
});

View File

@@ -1,6 +1,6 @@
use std::cmp::Ordering;
use columnar::{Column, ColumnBlockAccessor, ColumnType};
use columnar::{Column, ColumnType};
use rustc_hash::FxHashMap;
use serde::{Deserialize, Serialize};
use tantivy_bitpacker::minmax;
@@ -8,14 +8,14 @@ use tantivy_bitpacker::minmax;
use crate::aggregation::agg_data::{
build_segment_agg_collectors, AggRefNode, AggregationsSegmentCtx,
};
use crate::aggregation::agg_limits::MemoryConsumption;
use crate::aggregation::agg_req::Aggregations;
use crate::aggregation::agg_result::BucketEntry;
use crate::aggregation::cached_sub_aggs::{CachedSubAggs, HighCardCachedSubAggs};
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResult, IntermediateAggregationResults, IntermediateBucketResult,
IntermediateHistogramBucketEntry,
};
use crate::aggregation::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::segment_agg_result::{BucketIdProvider, SegmentAggregationCollector};
use crate::aggregation::*;
use crate::TantivyError;
@@ -26,13 +26,8 @@ pub struct HistogramAggReqData {
pub accessor: Column<u64>,
/// The field type of the fast field.
pub field_type: ColumnType,
/// The column block accessor to access the fast field values.
pub column_block_accessor: ColumnBlockAccessor<u64>,
/// The name of the aggregation.
pub name: String,
/// The sub aggregation blueprint, used to create sub aggregations for each bucket.
/// Will be filled during initialization of the collector.
pub sub_aggregation_blueprint: Option<Box<dyn SegmentAggregationCollector>>,
/// The histogram aggregation request.
pub req: HistogramAggregation,
/// True if this is a date_histogram aggregation.
@@ -257,18 +252,24 @@ impl HistogramBounds {
pub(crate) struct SegmentHistogramBucketEntry {
pub key: f64,
pub doc_count: u64,
pub bucket_id: BucketId,
}
impl SegmentHistogramBucketEntry {
pub(crate) fn into_intermediate_bucket_entry(
self,
sub_aggregation: Option<Box<dyn SegmentAggregationCollector>>,
sub_aggregation: &mut Option<HighCardCachedSubAggs>,
agg_data: &AggregationsSegmentCtx,
) -> crate::Result<IntermediateHistogramBucketEntry> {
let mut sub_aggregation_res = IntermediateAggregationResults::default();
if let Some(sub_aggregation) = sub_aggregation {
sub_aggregation
.add_intermediate_aggregation_result(agg_data, &mut sub_aggregation_res)?;
.get_sub_agg_collector()
.add_intermediate_aggregation_result(
agg_data,
&mut sub_aggregation_res,
self.bucket_id,
)?;
}
Ok(IntermediateHistogramBucketEntry {
key: self.key,
@@ -278,27 +279,38 @@ impl SegmentHistogramBucketEntry {
}
}
#[derive(Clone, Debug, Default)]
struct HistogramBuckets {
pub buckets: FxHashMap<i64, SegmentHistogramBucketEntry>,
}
/// The collector puts values from the fast field into the correct buckets and does a conversion to
/// the correct datatype.
#[derive(Clone, Debug)]
#[derive(Debug)]
pub struct SegmentHistogramCollector {
/// The buckets containing the aggregation data.
buckets: FxHashMap<i64, SegmentHistogramBucketEntry>,
sub_aggregations: FxHashMap<i64, Box<dyn SegmentAggregationCollector>>,
/// One Histogram bucket per parent bucket id.
parent_buckets: Vec<HistogramBuckets>,
sub_agg: Option<HighCardCachedSubAggs>,
accessor_idx: usize,
bucket_id_provider: BucketIdProvider,
}
impl SegmentAggregationCollector for SegmentHistogramCollector {
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
let name = agg_data
.get_histogram_req_data(self.accessor_idx)
.name
.clone();
let bucket = self.into_intermediate_bucket_result(agg_data)?;
// TODO: avoid prepare_max_bucket here and handle empty buckets.
self.prepare_max_bucket(parent_bucket_id, agg_data)?;
let histogram = std::mem::take(&mut self.parent_buckets[parent_bucket_id as usize]);
let bucket = self.add_intermediate_bucket_result(agg_data, histogram)?;
results.push(name, IntermediateAggregationResult::Bucket(bucket))?;
Ok(())
@@ -307,44 +319,40 @@ impl SegmentAggregationCollector for SegmentHistogramCollector {
#[inline]
fn collect(
&mut self,
doc: crate::DocId,
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
self.collect_block(&[doc], agg_data)
}
#[inline]
fn collect_block(
&mut self,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
let mut req = agg_data.take_histogram_req_data(self.accessor_idx);
let req = agg_data.take_histogram_req_data(self.accessor_idx);
let mem_pre = self.get_memory_consumption();
let buckets = &mut self.parent_buckets[parent_bucket_id as usize].buckets;
let bounds = req.bounds;
let interval = req.req.interval;
let offset = req.offset;
let get_bucket_pos = |val| get_bucket_pos_f64(val, interval, offset) as i64;
req.column_block_accessor.fetch_block(docs, &req.accessor);
for (doc, val) in req
agg_data
.column_block_accessor
.fetch_block(docs, &req.accessor);
for (doc, val) in agg_data
.column_block_accessor
.iter_docid_vals(docs, &req.accessor)
{
let val = f64_from_fastfield_u64(val, &req.field_type);
let val = f64_from_fastfield_u64(val, req.field_type);
let bucket_pos = get_bucket_pos(val);
if bounds.contains(val) {
let bucket = self.buckets.entry(bucket_pos).or_insert_with(|| {
let bucket = buckets.entry(bucket_pos).or_insert_with(|| {
let key = get_bucket_key_from_pos(bucket_pos as f64, interval, offset);
SegmentHistogramBucketEntry { key, doc_count: 0 }
SegmentHistogramBucketEntry {
key,
doc_count: 0,
bucket_id: self.bucket_id_provider.next_bucket_id(),
}
});
bucket.doc_count += 1;
if let Some(sub_aggregation_blueprint) = req.sub_aggregation_blueprint.as_ref() {
self.sub_aggregations
.entry(bucket_pos)
.or_insert_with(|| sub_aggregation_blueprint.clone())
.collect(doc, agg_data)?;
if let Some(sub_agg) = &mut self.sub_agg {
sub_agg.push(bucket.bucket_id, doc);
}
}
}
@@ -358,14 +366,30 @@ impl SegmentAggregationCollector for SegmentHistogramCollector {
.add_memory_consumed(mem_delta as u64)?;
}
if let Some(sub_agg) = &mut self.sub_agg {
sub_agg.check_flush_local(agg_data)?;
}
Ok(())
}
fn flush(&mut self, agg_data: &mut AggregationsSegmentCtx) -> crate::Result<()> {
for sub_aggregation in self.sub_aggregations.values_mut() {
if let Some(sub_aggregation) = &mut self.sub_agg {
sub_aggregation.flush(agg_data)?;
}
Ok(())
}
fn prepare_max_bucket(
&mut self,
max_bucket: BucketId,
_agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
while self.parent_buckets.len() <= max_bucket as usize {
self.parent_buckets.push(HistogramBuckets {
buckets: FxHashMap::default(),
});
}
Ok(())
}
}
@@ -373,22 +397,19 @@ impl SegmentAggregationCollector for SegmentHistogramCollector {
impl SegmentHistogramCollector {
fn get_memory_consumption(&self) -> usize {
let self_mem = std::mem::size_of::<Self>();
let sub_aggs_mem = self.sub_aggregations.memory_consumption();
let buckets_mem = self.buckets.memory_consumption();
self_mem + sub_aggs_mem + buckets_mem
let buckets_mem = self.parent_buckets.len() * std::mem::size_of::<HistogramBuckets>();
self_mem + buckets_mem
}
/// Converts the collector result into a intermediate bucket result.
pub fn into_intermediate_bucket_result(
self,
fn add_intermediate_bucket_result(
&mut self,
agg_data: &AggregationsSegmentCtx,
histogram: HistogramBuckets,
) -> crate::Result<IntermediateBucketResult> {
let mut buckets = Vec::with_capacity(self.buckets.len());
let mut buckets = Vec::with_capacity(histogram.buckets.len());
for (bucket_pos, bucket) in self.buckets {
let bucket_res = bucket.into_intermediate_bucket_entry(
self.sub_aggregations.get(&bucket_pos).cloned(),
agg_data,
);
for bucket in histogram.buckets.into_values() {
let bucket_res = bucket.into_intermediate_bucket_entry(&mut self.sub_agg, agg_data);
buckets.push(bucket_res?);
}
@@ -408,7 +429,7 @@ impl SegmentHistogramCollector {
agg_data: &mut AggregationsSegmentCtx,
node: &AggRefNode,
) -> crate::Result<Self> {
let blueprint = if !node.children.is_empty() {
let sub_agg = if !node.children.is_empty() {
Some(build_segment_agg_collectors(agg_data, &node.children)?)
} else {
None
@@ -423,13 +444,13 @@ impl SegmentHistogramCollector {
max: f64::MAX,
});
req_data.offset = req_data.req.offset.unwrap_or(0.0);
req_data.sub_aggregation_blueprint = blueprint;
let sub_agg = sub_agg.map(CachedSubAggs::new);
Ok(Self {
buckets: Default::default(),
sub_aggregations: Default::default(),
parent_buckets: Default::default(),
sub_agg,
accessor_idx: node.idx_in_req_data,
bucket_id_provider: BucketIdProvider::default(),
})
}
}

View File

@@ -1,18 +1,22 @@
use std::fmt::Debug;
use std::ops::Range;
use columnar::{Column, ColumnBlockAccessor, ColumnType};
use columnar::{Column, ColumnType};
use rustc_hash::FxHashMap;
use serde::{Deserialize, Serialize};
use crate::aggregation::agg_data::{
build_segment_agg_collectors, AggRefNode, AggregationsSegmentCtx,
};
use crate::aggregation::agg_limits::AggregationLimitsGuard;
use crate::aggregation::cached_sub_aggs::{
CachedSubAggs, HighCardSubAggCache, LowCardCachedSubAggs, LowCardSubAggCache, SubAggCache,
};
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResult, IntermediateAggregationResults, IntermediateBucketResult,
IntermediateRangeBucketEntry, IntermediateRangeBucketResult,
};
use crate::aggregation::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::segment_agg_result::{BucketIdProvider, SegmentAggregationCollector};
use crate::aggregation::*;
use crate::TantivyError;
@@ -23,12 +27,12 @@ pub struct RangeAggReqData {
pub accessor: Column<u64>,
/// The type of the fast field.
pub field_type: ColumnType,
/// The column block accessor to access the fast field values.
pub column_block_accessor: ColumnBlockAccessor<u64>,
/// The range aggregation request.
pub req: RangeAggregation,
/// The name of the aggregation.
pub name: String,
/// Whether this is a top-level aggregation.
pub is_top_level: bool,
}
impl RangeAggReqData {
@@ -151,19 +155,47 @@ pub(crate) struct SegmentRangeAndBucketEntry {
/// The collector puts values from the fast field into the correct buckets and does a conversion to
/// the correct datatype.
#[derive(Clone, Debug)]
pub struct SegmentRangeCollector {
pub struct SegmentRangeCollector<C: SubAggCache> {
/// The buckets containing the aggregation data.
buckets: Vec<SegmentRangeAndBucketEntry>,
/// One for each ParentBucketId
parent_buckets: Vec<Vec<SegmentRangeAndBucketEntry>>,
column_type: ColumnType,
pub(crate) accessor_idx: usize,
sub_agg: Option<CachedSubAggs<C>>,
/// Here things get a bit weird. We need to assign unique bucket ids across all
/// parent buckets. So we keep track of the next available bucket id here.
/// This allows a kind of flattening of the bucket ids across all parent buckets.
/// E.g. in nested aggregations:
/// Term Agg -> Range aggregation -> Stats aggregation
/// E.g. the Term Agg creates 3 buckets ["INFO", "ERROR", "WARN"], each of these has a Range
/// aggregation with 4 buckets. The Range aggregation will create buckets with ids:
/// - INFO: 0,1,2,3
/// - ERROR: 4,5,6,7
/// - WARN: 8,9,10,11
///
/// This allows the Stats aggregation to have unique bucket ids to refer to.
bucket_id_provider: BucketIdProvider,
limits: AggregationLimitsGuard,
}
impl<C: SubAggCache> Debug for SegmentRangeCollector<C> {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("SegmentRangeCollector")
.field("parent_buckets_len", &self.parent_buckets.len())
.field("column_type", &self.column_type)
.field("accessor_idx", &self.accessor_idx)
.field("has_sub_agg", &self.sub_agg.is_some())
.finish()
}
}
/// TODO: Bad naming, there's also SegmentRangeAndBucketEntry
#[derive(Clone)]
pub(crate) struct SegmentRangeBucketEntry {
pub key: Key,
pub doc_count: u64,
pub sub_aggregation: Option<Box<dyn SegmentAggregationCollector>>,
// pub sub_aggregation: Option<Box<dyn SegmentAggregationCollector>>,
pub bucket_id: BucketId,
/// The from range of the bucket. Equals `f64::MIN` when `None`.
pub from: Option<f64>,
/// The to range of the bucket. Equals `f64::MAX` when `None`. Open interval, `to` is not
@@ -184,48 +216,50 @@ impl Debug for SegmentRangeBucketEntry {
impl SegmentRangeBucketEntry {
pub(crate) fn into_intermediate_bucket_entry(
self,
agg_data: &AggregationsSegmentCtx,
) -> crate::Result<IntermediateRangeBucketEntry> {
let mut sub_aggregation_res = IntermediateAggregationResults::default();
if let Some(sub_aggregation) = self.sub_aggregation {
sub_aggregation
.add_intermediate_aggregation_result(agg_data, &mut sub_aggregation_res)?
} else {
Default::default()
};
let sub_aggregation = IntermediateAggregationResults::default();
Ok(IntermediateRangeBucketEntry {
key: self.key.into(),
doc_count: self.doc_count,
sub_aggregation: sub_aggregation_res,
sub_aggregation_res: sub_aggregation,
from: self.from,
to: self.to,
})
}
}
impl SegmentAggregationCollector for SegmentRangeCollector {
impl<C: SubAggCache> SegmentAggregationCollector for SegmentRangeCollector<C> {
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
self.prepare_max_bucket(parent_bucket_id, agg_data)?;
let field_type = self.column_type;
let name = agg_data
.get_range_req_data(self.accessor_idx)
.name
.to_string();
let buckets: FxHashMap<SerializedKey, IntermediateRangeBucketEntry> = self
.buckets
let buckets = std::mem::take(&mut self.parent_buckets[parent_bucket_id as usize]);
let buckets: FxHashMap<SerializedKey, IntermediateRangeBucketEntry> = buckets
.into_iter()
.map(move |range_bucket| {
Ok((
range_to_string(&range_bucket.range, &field_type)?,
range_bucket
.bucket
.into_intermediate_bucket_entry(agg_data)?,
))
.map(|range_bucket| {
let bucket_id = range_bucket.bucket.bucket_id;
let mut agg = range_bucket.bucket.into_intermediate_bucket_entry()?;
if let Some(sub_aggregation) = &mut self.sub_agg {
sub_aggregation
.get_sub_agg_collector()
.add_intermediate_aggregation_result(
agg_data,
&mut agg.sub_aggregation_res,
bucket_id,
)?;
}
Ok((range_to_string(&range_bucket.range, &field_type)?, agg))
})
.collect::<crate::Result<_>>()?;
@@ -242,73 +276,114 @@ impl SegmentAggregationCollector for SegmentRangeCollector {
#[inline]
fn collect(
&mut self,
doc: crate::DocId,
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
self.collect_block(&[doc], agg_data)
}
#[inline]
fn collect_block(
&mut self,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
// Take request data to avoid borrow conflicts during sub-aggregation
let mut req = agg_data.take_range_req_data(self.accessor_idx);
let req = agg_data.take_range_req_data(self.accessor_idx);
req.column_block_accessor.fetch_block(docs, &req.accessor);
agg_data
.column_block_accessor
.fetch_block(docs, &req.accessor);
for (doc, val) in req
let buckets = &mut self.parent_buckets[parent_bucket_id as usize];
for (doc, val) in agg_data
.column_block_accessor
.iter_docid_vals(docs, &req.accessor)
{
let bucket_pos = self.get_bucket_pos(val);
let bucket = &mut self.buckets[bucket_pos];
let bucket_pos = get_bucket_pos(val, buckets);
let bucket = &mut buckets[bucket_pos];
bucket.bucket.doc_count += 1;
if let Some(sub_agg) = bucket.bucket.sub_aggregation.as_mut() {
sub_agg.collect(doc, agg_data)?;
if let Some(sub_agg) = self.sub_agg.as_mut() {
sub_agg.push(bucket.bucket.bucket_id, doc);
}
}
agg_data.put_back_range_req_data(self.accessor_idx, req);
if let Some(sub_agg) = self.sub_agg.as_mut() {
sub_agg.check_flush_local(agg_data)?;
}
Ok(())
}
fn flush(&mut self, agg_data: &mut AggregationsSegmentCtx) -> crate::Result<()> {
for bucket in self.buckets.iter_mut() {
if let Some(sub_agg) = bucket.bucket.sub_aggregation.as_mut() {
sub_agg.flush(agg_data)?;
}
if let Some(sub_agg) = self.sub_agg.as_mut() {
sub_agg.flush(agg_data)?;
}
Ok(())
}
fn prepare_max_bucket(
&mut self,
max_bucket: BucketId,
agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
while self.parent_buckets.len() <= max_bucket as usize {
let new_buckets = self.create_new_buckets(agg_data)?;
self.parent_buckets.push(new_buckets);
}
Ok(())
}
}
/// Build a concrete `SegmentRangeCollector` with either a Vec- or HashMap-backed
/// bucket storage, depending on the column type and aggregation level.
pub(crate) fn build_segment_range_collector(
agg_data: &mut AggregationsSegmentCtx,
node: &AggRefNode,
) -> crate::Result<Box<dyn SegmentAggregationCollector>> {
let accessor_idx = node.idx_in_req_data;
let req_data = agg_data.get_range_req_data(node.idx_in_req_data);
let field_type = req_data.field_type;
// TODO: A better metric instead of is_top_level would be the number of buckets expected.
// E.g. If range agg is not top level, but the parent is a bucket agg with less than 10 buckets,
// we can are still in low cardinality territory.
let is_low_card = req_data.is_top_level && req_data.req.ranges.len() <= 64;
let sub_agg = if !node.children.is_empty() {
Some(build_segment_agg_collectors(agg_data, &node.children)?)
} else {
None
};
if is_low_card {
Ok(Box::new(SegmentRangeCollector::<LowCardSubAggCache> {
sub_agg: sub_agg.map(LowCardCachedSubAggs::new),
column_type: field_type,
accessor_idx,
parent_buckets: Vec::new(),
bucket_id_provider: BucketIdProvider::default(),
limits: agg_data.context.limits.clone(),
}))
} else {
Ok(Box::new(SegmentRangeCollector::<HighCardSubAggCache> {
sub_agg: sub_agg.map(CachedSubAggs::new),
column_type: field_type,
accessor_idx,
parent_buckets: Vec::new(),
bucket_id_provider: BucketIdProvider::default(),
limits: agg_data.context.limits.clone(),
}))
}
}
impl SegmentRangeCollector {
pub(crate) fn from_req_and_validate(
req_data: &mut AggregationsSegmentCtx,
node: &AggRefNode,
) -> crate::Result<Self> {
let accessor_idx = node.idx_in_req_data;
let (field_type, ranges) = {
let req_view = req_data.get_range_req_data(node.idx_in_req_data);
(req_view.field_type, req_view.req.ranges.clone())
};
impl<C: SubAggCache> SegmentRangeCollector<C> {
pub(crate) fn create_new_buckets(
&mut self,
agg_data: &AggregationsSegmentCtx,
) -> crate::Result<Vec<SegmentRangeAndBucketEntry>> {
let field_type = self.column_type;
let req_data = agg_data.get_range_req_data(self.accessor_idx);
// The range input on the request is f64.
// We need to convert to u64 ranges, because we read the values as u64.
// The mapping from the conversion is monotonic so ordering is preserved.
let sub_agg_prototype = if !node.children.is_empty() {
Some(build_segment_agg_collectors(req_data, &node.children)?)
} else {
None
};
let buckets: Vec<_> = extend_validate_ranges(&ranges, &field_type)?
let buckets: Vec<_> = extend_validate_ranges(&req_data.req.ranges, &field_type)?
.iter()
.map(|range| {
let bucket_id = self.bucket_id_provider.next_bucket_id();
let key = range
.key
.clone()
@@ -317,20 +392,20 @@ impl SegmentRangeCollector {
let to = if range.range.end == u64::MAX {
None
} else {
Some(f64_from_fastfield_u64(range.range.end, &field_type))
Some(f64_from_fastfield_u64(range.range.end, field_type))
};
let from = if range.range.start == u64::MIN {
None
} else {
Some(f64_from_fastfield_u64(range.range.start, &field_type))
Some(f64_from_fastfield_u64(range.range.start, field_type))
};
let sub_aggregation = sub_agg_prototype.clone();
// let sub_aggregation = sub_agg_prototype.clone();
Ok(SegmentRangeAndBucketEntry {
range: range.range.clone(),
bucket: SegmentRangeBucketEntry {
doc_count: 0,
sub_aggregation,
bucket_id,
key,
from,
to,
@@ -339,27 +414,20 @@ impl SegmentRangeCollector {
})
.collect::<crate::Result<_>>()?;
req_data.context.limits.add_memory_consumed(
self.limits.add_memory_consumed(
buckets.len() as u64 * std::mem::size_of::<SegmentRangeAndBucketEntry>() as u64,
)?;
Ok(SegmentRangeCollector {
buckets,
column_type: field_type,
accessor_idx,
})
}
#[inline]
fn get_bucket_pos(&self, val: u64) -> usize {
let pos = self
.buckets
.binary_search_by_key(&val, |probe| probe.range.start)
.unwrap_or_else(|pos| pos - 1);
debug_assert!(self.buckets[pos].range.contains(&val));
pos
Ok(buckets)
}
}
#[inline]
fn get_bucket_pos(val: u64, buckets: &[SegmentRangeAndBucketEntry]) -> usize {
let pos = buckets
.binary_search_by_key(&val, |probe| probe.range.start)
.unwrap_or_else(|pos| pos - 1);
debug_assert!(buckets[pos].range.contains(&val));
pos
}
/// Converts the user provided f64 range value to fast field value space.
///
@@ -456,7 +524,7 @@ pub(crate) fn range_to_string(
let val = i64::from_u64(val);
format_date(val)
} else {
Ok(f64_from_fastfield_u64(val, field_type).to_string())
Ok(f64_from_fastfield_u64(val, *field_type).to_string())
}
};
@@ -486,7 +554,7 @@ mod tests {
pub fn get_collector_from_ranges(
ranges: Vec<RangeAggregationRange>,
field_type: ColumnType,
) -> SegmentRangeCollector {
) -> SegmentRangeCollector<HighCardSubAggCache> {
let req = RangeAggregation {
field: "dummy".to_string(),
ranges,
@@ -506,30 +574,33 @@ mod tests {
let to = if range.range.end == u64::MAX {
None
} else {
Some(f64_from_fastfield_u64(range.range.end, &field_type))
Some(f64_from_fastfield_u64(range.range.end, field_type))
};
let from = if range.range.start == u64::MIN {
None
} else {
Some(f64_from_fastfield_u64(range.range.start, &field_type))
Some(f64_from_fastfield_u64(range.range.start, field_type))
};
SegmentRangeAndBucketEntry {
range: range.range.clone(),
bucket: SegmentRangeBucketEntry {
doc_count: 0,
sub_aggregation: None,
key,
from,
to,
bucket_id: 0,
},
}
})
.collect();
SegmentRangeCollector {
buckets,
parent_buckets: vec![buckets],
column_type: field_type,
accessor_idx: 0,
sub_agg: None,
bucket_id_provider: Default::default(),
limits: AggregationLimitsGuard::default(),
}
}
@@ -776,7 +847,7 @@ mod tests {
let buckets = vec![(10f64..20f64).into(), (30f64..40f64).into()];
let collector = get_collector_from_ranges(buckets, ColumnType::F64);
let buckets = collector.buckets;
let buckets = collector.parent_buckets[0].clone();
assert_eq!(buckets[0].range.start, u64::MIN);
assert_eq!(buckets[0].range.end, 10f64.to_u64());
assert_eq!(buckets[1].range.start, 10f64.to_u64());
@@ -799,7 +870,7 @@ mod tests {
];
let collector = get_collector_from_ranges(buckets, ColumnType::F64);
let buckets = collector.buckets;
let buckets = collector.parent_buckets[0].clone();
assert_eq!(buckets[0].range.start, u64::MIN);
assert_eq!(buckets[0].range.end, 10f64.to_u64());
assert_eq!(buckets[1].range.start, 10f64.to_u64());
@@ -814,7 +885,7 @@ mod tests {
let buckets = vec![(-10f64..-1f64).into()];
let collector = get_collector_from_ranges(buckets, ColumnType::F64);
let buckets = collector.buckets;
let buckets = collector.parent_buckets[0].clone();
assert_eq!(&buckets[0].bucket.key.to_string(), "*--10");
assert_eq!(&buckets[buckets.len() - 1].bucket.key.to_string(), "-1-*");
}
@@ -823,7 +894,7 @@ mod tests {
let buckets = vec![(0f64..10f64).into()];
let collector = get_collector_from_ranges(buckets, ColumnType::F64);
let buckets = collector.buckets;
let buckets = collector.parent_buckets[0].clone();
assert_eq!(&buckets[0].bucket.key.to_string(), "*-0");
assert_eq!(&buckets[buckets.len() - 1].bucket.key.to_string(), "10-*");
}
@@ -832,7 +903,7 @@ mod tests {
fn range_binary_search_test_u64() {
let check_ranges = |ranges: Vec<RangeAggregationRange>| {
let collector = get_collector_from_ranges(ranges, ColumnType::U64);
let search = |val: u64| collector.get_bucket_pos(val);
let search = |val: u64| get_bucket_pos(val, &collector.parent_buckets[0]);
assert_eq!(search(u64::MIN), 0);
assert_eq!(search(9), 0);
@@ -878,7 +949,7 @@ mod tests {
let ranges = vec![(10.0..100.0).into()];
let collector = get_collector_from_ranges(ranges, ColumnType::F64);
let search = |val: u64| collector.get_bucket_pos(val);
let search = |val: u64| get_bucket_pos(val, &collector.parent_buckets[0]);
assert_eq!(search(u64::MIN), 0);
assert_eq!(search(9f64.to_u64()), 0);
@@ -890,63 +961,3 @@ mod tests {
// the max value
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use itertools::Itertools;
use rand::seq::SliceRandom;
use rand::thread_rng;
use super::*;
use crate::aggregation::bucket::range::tests::get_collector_from_ranges;
const TOTAL_DOCS: u64 = 1_000_000u64;
const NUM_DOCS: u64 = 50_000u64;
fn get_collector_with_buckets(num_buckets: u64, num_docs: u64) -> SegmentRangeCollector {
let bucket_size = num_docs / num_buckets;
let mut buckets: Vec<RangeAggregationRange> = vec![];
for i in 0..num_buckets {
let bucket_start = (i * bucket_size) as f64;
buckets.push((bucket_start..bucket_start + bucket_size as f64).into())
}
get_collector_from_ranges(buckets, ColumnType::U64)
}
fn get_rand_docs(total_docs: u64, num_docs_returned: u64) -> Vec<u64> {
let mut rng = thread_rng();
let all_docs = (0..total_docs - 1).collect_vec();
let mut vals = all_docs
.as_slice()
.choose_multiple(&mut rng, num_docs_returned as usize)
.cloned()
.collect_vec();
vals.sort();
vals
}
fn bench_range_binary_search(b: &mut test::Bencher, num_buckets: u64) {
let collector = get_collector_with_buckets(num_buckets, TOTAL_DOCS);
let vals = get_rand_docs(TOTAL_DOCS, NUM_DOCS);
b.iter(|| {
let mut bucket_pos = 0;
for val in &vals {
bucket_pos = collector.get_bucket_pos(*val);
}
bucket_pos
})
}
#[bench]
fn bench_range_100_buckets(b: &mut test::Bencher) {
bench_range_binary_search(b, 100)
}
#[bench]
fn bench_range_10_buckets(b: &mut test::Bencher) {
bench_range_binary_search(b, 10)
}
}

File diff suppressed because it is too large Load Diff

View File

@@ -5,11 +5,13 @@ use crate::aggregation::agg_data::{
build_segment_agg_collectors, AggRefNode, AggregationsSegmentCtx,
};
use crate::aggregation::bucket::term_agg::TermsAggregation;
use crate::aggregation::cached_sub_aggs::{CachedSubAggs, HighCardCachedSubAggs};
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResult, IntermediateAggregationResults, IntermediateBucketResult,
IntermediateKey, IntermediateTermBucketEntry, IntermediateTermBucketResult,
};
use crate::aggregation::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::segment_agg_result::{BucketIdProvider, SegmentAggregationCollector};
use crate::aggregation::BucketId;
/// Special aggregation to handle missing values for term aggregations.
/// This missing aggregation will check multiple columns for existence.
@@ -35,41 +37,55 @@ impl MissingTermAggReqData {
}
}
/// The specialized missing term aggregation.
#[derive(Default, Debug, Clone)]
pub struct TermMissingAgg {
struct MissingCount {
missing_count: u32,
bucket_id: BucketId,
}
/// The specialized missing term aggregation.
#[derive(Default, Debug)]
pub struct TermMissingAgg {
accessor_idx: usize,
sub_agg: Option<Box<dyn SegmentAggregationCollector>>,
sub_agg: Option<HighCardCachedSubAggs>,
/// Idx = parent bucket id, Value = missing count for that bucket
missing_count_per_bucket: Vec<MissingCount>,
bucket_id_provider: BucketIdProvider,
}
impl TermMissingAgg {
pub(crate) fn new(
req_data: &mut AggregationsSegmentCtx,
agg_data: &mut AggregationsSegmentCtx,
node: &AggRefNode,
) -> crate::Result<Self> {
let has_sub_aggregations = !node.children.is_empty();
let accessor_idx = node.idx_in_req_data;
let sub_agg = if has_sub_aggregations {
let sub_aggregation = build_segment_agg_collectors(req_data, &node.children)?;
let sub_aggregation = build_segment_agg_collectors(agg_data, &node.children)?;
Some(sub_aggregation)
} else {
None
};
let sub_agg = sub_agg.map(CachedSubAggs::new);
let bucket_id_provider = BucketIdProvider::default();
Ok(Self {
accessor_idx,
sub_agg,
..Default::default()
missing_count_per_bucket: Vec::new(),
bucket_id_provider,
})
}
}
impl SegmentAggregationCollector for TermMissingAgg {
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
self.prepare_max_bucket(parent_bucket_id, agg_data)?;
let req_data = agg_data.get_missing_term_req_data(self.accessor_idx);
let term_agg = &req_data.req;
let missing = term_agg
@@ -80,13 +96,16 @@ impl SegmentAggregationCollector for TermMissingAgg {
let mut entries: FxHashMap<IntermediateKey, IntermediateTermBucketEntry> =
Default::default();
let missing_count = &self.missing_count_per_bucket[parent_bucket_id as usize];
let mut missing_entry = IntermediateTermBucketEntry {
doc_count: self.missing_count,
doc_count: missing_count.missing_count,
sub_aggregation: Default::default(),
};
if let Some(sub_agg) = self.sub_agg {
if let Some(sub_agg) = &mut self.sub_agg {
let mut res = IntermediateAggregationResults::default();
sub_agg.add_intermediate_aggregation_result(agg_data, &mut res)?;
sub_agg
.get_sub_agg_collector()
.add_intermediate_aggregation_result(agg_data, &mut res, missing_count.bucket_id)?;
missing_entry.sub_aggregation = res;
}
entries.insert(missing.into(), missing_entry);
@@ -109,30 +128,52 @@ impl SegmentAggregationCollector for TermMissingAgg {
fn collect(
&mut self,
doc: crate::DocId,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
let bucket = &mut self.missing_count_per_bucket[parent_bucket_id as usize];
let req_data = agg_data.get_missing_term_req_data(self.accessor_idx);
let has_value = req_data
.accessors
.iter()
.any(|(acc, _)| acc.index.has_value(doc));
if !has_value {
self.missing_count += 1;
if let Some(sub_agg) = self.sub_agg.as_mut() {
sub_agg.collect(doc, agg_data)?;
for doc in docs {
let doc = *doc;
let has_value = req_data
.accessors
.iter()
.any(|(acc, _)| acc.index.has_value(doc));
if !has_value {
bucket.missing_count += 1;
if let Some(sub_agg) = self.sub_agg.as_mut() {
sub_agg.push(bucket.bucket_id, doc);
}
}
}
if let Some(sub_agg) = self.sub_agg.as_mut() {
sub_agg.check_flush_local(agg_data)?;
}
Ok(())
}
fn collect_block(
fn prepare_max_bucket(
&mut self,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
max_bucket: BucketId,
_agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
for doc in docs {
self.collect(*doc, agg_data)?;
while self.missing_count_per_bucket.len() <= max_bucket as usize {
let bucket_id = self.bucket_id_provider.next_bucket_id();
self.missing_count_per_bucket.push(MissingCount {
missing_count: 0,
bucket_id,
});
}
Ok(())
}
fn flush(&mut self, agg_data: &mut AggregationsSegmentCtx) -> crate::Result<()> {
if let Some(sub_agg) = self.sub_agg.as_mut() {
sub_agg.flush(agg_data)?;
}
Ok(())
}

View File

@@ -1,87 +0,0 @@
use super::intermediate_agg_result::IntermediateAggregationResults;
use super::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::agg_data::AggregationsSegmentCtx;
use crate::DocId;
#[cfg(test)]
pub(crate) const DOC_BLOCK_SIZE: usize = 64;
#[cfg(not(test))]
pub(crate) const DOC_BLOCK_SIZE: usize = 256;
pub(crate) type DocBlock = [DocId; DOC_BLOCK_SIZE];
/// BufAggregationCollector buffers documents before calling collect_block().
#[derive(Clone)]
pub(crate) struct BufAggregationCollector {
pub(crate) collector: Box<dyn SegmentAggregationCollector>,
staged_docs: DocBlock,
num_staged_docs: usize,
}
impl std::fmt::Debug for BufAggregationCollector {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
f.debug_struct("SegmentAggregationResultsCollector")
.field("staged_docs", &&self.staged_docs[..self.num_staged_docs])
.field("num_staged_docs", &self.num_staged_docs)
.finish()
}
}
impl BufAggregationCollector {
pub fn new(collector: Box<dyn SegmentAggregationCollector>) -> Self {
Self {
collector,
num_staged_docs: 0,
staged_docs: [0; DOC_BLOCK_SIZE],
}
}
}
impl SegmentAggregationCollector for BufAggregationCollector {
#[inline]
fn add_intermediate_aggregation_result(
self: Box<Self>,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
) -> crate::Result<()> {
Box::new(self.collector).add_intermediate_aggregation_result(agg_data, results)
}
#[inline]
fn collect(
&mut self,
doc: crate::DocId,
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
self.staged_docs[self.num_staged_docs] = doc;
self.num_staged_docs += 1;
if self.num_staged_docs == self.staged_docs.len() {
self.collector
.collect_block(&self.staged_docs[..self.num_staged_docs], agg_data)?;
self.num_staged_docs = 0;
}
Ok(())
}
#[inline]
fn collect_block(
&mut self,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
self.collector.collect_block(docs, agg_data)?;
Ok(())
}
#[inline]
fn flush(&mut self, agg_data: &mut AggregationsSegmentCtx) -> crate::Result<()> {
self.collector
.collect_block(&self.staged_docs[..self.num_staged_docs], agg_data)?;
self.num_staged_docs = 0;
self.collector.flush(agg_data)?;
Ok(())
}
}

View File

@@ -0,0 +1,245 @@
use std::fmt::Debug;
use super::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::agg_data::AggregationsSegmentCtx;
use crate::aggregation::bucket::MAX_NUM_TERMS_FOR_VEC;
use crate::aggregation::BucketId;
use crate::DocId;
/// A cache for sub-aggregations, storing doc ids per bucket id.
/// Depending on the cardinality of the parent aggregation, we use different
/// storage strategies.
///
/// ## Low Cardinality
/// Cardinality here refers to the number of unique flattened buckets that can be created
/// by the parent aggregation.
/// Flattened buckets are the result of combining all buckets per collector
/// into a single list of buckets, where each bucket is identified by its BucketId.
///
/// ## Usage
/// Since this is caching for sub-aggregations, it is only used by bucket
/// aggregations.
///
/// TODO: consider using a more advanced data structure for high cardinality
/// aggregations.
/// What this datastructure does in general is to group docs by bucket id.
#[derive(Debug)]
pub(crate) struct CachedSubAggs<C: SubAggCache> {
cache: C,
sub_agg_collector: Box<dyn SegmentAggregationCollector>,
num_docs: usize,
}
pub type LowCardCachedSubAggs = CachedSubAggs<LowCardSubAggCache>;
pub type HighCardCachedSubAggs = CachedSubAggs<HighCardSubAggCache>;
const FLUSH_THRESHOLD: usize = 2048;
/// A trait for caching sub-aggregation doc ids per bucket id.
/// Different implementations can be used depending on the cardinality
/// of the parent aggregation.
pub trait SubAggCache: Debug {
fn new() -> Self;
fn push(&mut self, bucket_id: BucketId, doc_id: DocId);
fn flush_local(
&mut self,
sub_agg: &mut Box<dyn SegmentAggregationCollector>,
agg_data: &mut AggregationsSegmentCtx,
force: bool,
) -> crate::Result<()>;
}
impl<Backend: SubAggCache + Debug> CachedSubAggs<Backend> {
pub fn new(sub_agg: Box<dyn SegmentAggregationCollector>) -> Self {
Self {
cache: Backend::new(),
sub_agg_collector: sub_agg,
num_docs: 0,
}
}
pub fn get_sub_agg_collector(&mut self) -> &mut Box<dyn SegmentAggregationCollector> {
&mut self.sub_agg_collector
}
#[inline]
pub fn push(&mut self, bucket_id: BucketId, doc_id: DocId) {
self.cache.push(bucket_id, doc_id);
self.num_docs += 1;
}
/// Check if we need to flush based on the number of documents cached.
/// If so, flushes the cache to the provided aggregation collector.
pub fn check_flush_local(
&mut self,
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
if self.num_docs >= FLUSH_THRESHOLD {
self.cache
.flush_local(&mut self.sub_agg_collector, agg_data, false)?;
self.num_docs = 0;
}
Ok(())
}
/// Note: this _does_ flush the sub aggregations.
pub fn flush(&mut self, agg_data: &mut AggregationsSegmentCtx) -> crate::Result<()> {
if self.num_docs != 0 {
self.cache
.flush_local(&mut self.sub_agg_collector, agg_data, true)?;
self.num_docs = 0;
}
self.sub_agg_collector.flush(agg_data)?;
Ok(())
}
}
/// Number of partitions for high cardinality sub-aggregation cache.
const NUM_PARTITIONS: usize = 16;
#[derive(Debug)]
pub(crate) struct HighCardSubAggCache {
/// This weird partitioning is used to do some cheap grouping on the bucket ids.
/// bucket ids are dense, e.g. when we don't detect the cardinality as low cardinality,
/// but there are just 16 bucket ids, each bucket id will go to its own partition.
///
/// We want to keep this cheap, because high cardinality aggregations can have a lot of
/// buckets, and there may be nothing to group.
partitions: Box<[PartitionEntry; NUM_PARTITIONS]>,
}
impl HighCardSubAggCache {
#[inline]
fn clear(&mut self) {
for partition in self.partitions.iter_mut() {
partition.clear();
}
}
}
#[derive(Debug, Clone, Default)]
struct PartitionEntry {
bucket_ids: Vec<BucketId>,
docs: Vec<DocId>,
}
impl PartitionEntry {
#[inline]
fn clear(&mut self) {
self.bucket_ids.clear();
self.docs.clear();
}
}
impl SubAggCache for HighCardSubAggCache {
fn new() -> Self {
Self {
partitions: Box::new(core::array::from_fn(|_| PartitionEntry::default())),
}
}
fn push(&mut self, bucket_id: BucketId, doc_id: DocId) {
let idx = bucket_id % NUM_PARTITIONS as u32;
let slot = &mut self.partitions[idx as usize];
slot.bucket_ids.push(bucket_id);
slot.docs.push(doc_id);
}
fn flush_local(
&mut self,
sub_agg: &mut Box<dyn SegmentAggregationCollector>,
agg_data: &mut AggregationsSegmentCtx,
_force: bool,
) -> crate::Result<()> {
let mut max_bucket = 0u32;
for partition in self.partitions.iter() {
if let Some(&local_max) = partition.bucket_ids.iter().max() {
max_bucket = max_bucket.max(local_max);
}
}
sub_agg.prepare_max_bucket(max_bucket, agg_data)?;
for slot in self.partitions.iter() {
if !slot.bucket_ids.is_empty() {
// Reduce dynamic dispatch overhead by collecting a full partition in one call.
sub_agg.collect_multiple(&slot.bucket_ids, &slot.docs, agg_data)?;
}
}
self.clear();
Ok(())
}
}
#[derive(Debug)]
pub(crate) struct LowCardSubAggCache {
/// Cache doc ids per bucket for sub-aggregations.
///
/// The outer Vec is indexed by BucketId.
per_bucket_docs: Vec<Vec<DocId>>,
}
impl LowCardSubAggCache {
#[inline]
fn clear(&mut self) {
for v in &mut self.per_bucket_docs {
v.clear();
}
}
}
impl SubAggCache for LowCardSubAggCache {
fn new() -> Self {
Self {
per_bucket_docs: Vec::new(),
}
}
fn push(&mut self, bucket_id: BucketId, doc_id: DocId) {
let idx = bucket_id as usize;
if self.per_bucket_docs.len() <= idx {
self.per_bucket_docs.resize_with(idx + 1, Vec::new);
}
self.per_bucket_docs[idx].push(doc_id);
}
fn flush_local(
&mut self,
sub_agg: &mut Box<dyn SegmentAggregationCollector>,
agg_data: &mut AggregationsSegmentCtx,
force: bool,
) -> crate::Result<()> {
// Pre-aggregated: call collect per bucket.
let max_bucket = (self.per_bucket_docs.len() as BucketId).saturating_sub(1);
sub_agg.prepare_max_bucket(max_bucket, agg_data)?;
// The threshold above which we flush buckets individually.
// Note: We need to make sure that we don't lock ourselves into a situation where we hit
// the FLUSH_THRESHOLD, but never flush any buckets. (except the final flush)
let mut bucket_treshold = FLUSH_THRESHOLD / (self.per_bucket_docs.len().max(1) * 2);
const _: () = {
// MAX_NUM_TERMS_FOR_VEC threshold is used for term aggregations
// Note: There may be other flexible values, for other aggregations, but we can use the
// const value here as a upper bound. (better than nothing)
let bucket_treshold_limit = FLUSH_THRESHOLD / (MAX_NUM_TERMS_FOR_VEC as usize * 2);
assert!(
bucket_treshold_limit > 0,
"Bucket threshold must be greater than 0"
);
};
if force {
bucket_treshold = 0;
}
for (bucket_id, docs) in self
.per_bucket_docs
.iter()
.enumerate()
.filter(|(_, docs)| docs.len() > bucket_treshold)
{
sub_agg.collect(bucket_id as BucketId, docs, agg_data)?;
}
self.clear();
Ok(())
}
}

View File

@@ -1,9 +1,9 @@
use super::agg_req::Aggregations;
use super::agg_result::AggregationResults;
use super::buf_collector::BufAggregationCollector;
use super::cached_sub_aggs::LowCardCachedSubAggs;
use super::intermediate_agg_result::IntermediateAggregationResults;
use super::segment_agg_result::SegmentAggregationCollector;
use super::AggContextParams;
// group buffering strategy is chosen explicitly by callers; no need to hash-group on the fly.
use crate::aggregation::agg_data::{
build_aggregations_data_from_req, build_segment_agg_collectors_root, AggregationsSegmentCtx,
};
@@ -136,7 +136,7 @@ fn merge_fruits(
/// `AggregationSegmentCollector` does the aggregation collection on a segment.
pub struct AggregationSegmentCollector {
aggs_with_accessor: AggregationsSegmentCtx,
agg_collector: BufAggregationCollector,
agg_collector: LowCardCachedSubAggs,
error: Option<TantivyError>,
}
@@ -151,8 +151,11 @@ impl AggregationSegmentCollector {
) -> crate::Result<Self> {
let mut agg_data =
build_aggregations_data_from_req(agg, reader, segment_ordinal, context.clone())?;
let result =
BufAggregationCollector::new(build_segment_agg_collectors_root(&mut agg_data)?);
let mut result =
LowCardCachedSubAggs::new(build_segment_agg_collectors_root(&mut agg_data)?);
result
.get_sub_agg_collector()
.prepare_max_bucket(0, &agg_data)?; // prepare for bucket zero
Ok(AggregationSegmentCollector {
aggs_with_accessor: agg_data,
@@ -170,26 +173,31 @@ impl SegmentCollector for AggregationSegmentCollector {
if self.error.is_some() {
return;
}
if let Err(err) = self
self.agg_collector.push(0, doc);
match self
.agg_collector
.collect(doc, &mut self.aggs_with_accessor)
.check_flush_local(&mut self.aggs_with_accessor)
{
self.error = Some(err);
Ok(_) => {}
Err(e) => {
self.error = Some(e);
}
}
}
/// The query pushes the documents to the collector via this method.
///
/// Only valid for Collectors that ignore docs
fn collect_block(&mut self, docs: &[DocId]) {
if self.error.is_some() {
return;
}
if let Err(err) = self
.agg_collector
.collect_block(docs, &mut self.aggs_with_accessor)
{
self.error = Some(err);
match self.agg_collector.get_sub_agg_collector().collect(
0,
docs,
&mut self.aggs_with_accessor,
) {
Ok(_) => {}
Err(e) => {
self.error = Some(e);
}
}
}
@@ -200,10 +208,13 @@ impl SegmentCollector for AggregationSegmentCollector {
self.agg_collector.flush(&mut self.aggs_with_accessor)?;
let mut sub_aggregation_res = IntermediateAggregationResults::default();
Box::new(self.agg_collector).add_intermediate_aggregation_result(
&self.aggs_with_accessor,
&mut sub_aggregation_res,
)?;
self.agg_collector
.get_sub_agg_collector()
.add_intermediate_aggregation_result(
&self.aggs_with_accessor,
&mut sub_aggregation_res,
0,
)?;
Ok(sub_aggregation_res)
}

View File

@@ -792,7 +792,7 @@ pub struct IntermediateRangeBucketEntry {
/// The number of documents in the bucket.
pub doc_count: u64,
/// The sub_aggregation in this bucket.
pub sub_aggregation: IntermediateAggregationResults,
pub sub_aggregation_res: IntermediateAggregationResults,
/// The from range of the bucket. Equals `f64::MIN` when `None`.
pub from: Option<f64>,
/// The to range of the bucket. Equals `f64::MAX` when `None`.
@@ -811,7 +811,7 @@ impl IntermediateRangeBucketEntry {
key: self.key.into(),
doc_count: self.doc_count,
sub_aggregation: self
.sub_aggregation
.sub_aggregation_res
.into_final_result_internal(req, limits)?,
to: self.to,
from: self.from,
@@ -857,7 +857,8 @@ impl MergeFruits for IntermediateTermBucketEntry {
impl MergeFruits for IntermediateRangeBucketEntry {
fn merge_fruits(&mut self, other: IntermediateRangeBucketEntry) -> crate::Result<()> {
self.doc_count += other.doc_count;
self.sub_aggregation.merge_fruits(other.sub_aggregation)?;
self.sub_aggregation_res
.merge_fruits(other.sub_aggregation_res)?;
Ok(())
}
}
@@ -887,7 +888,7 @@ mod tests {
IntermediateRangeBucketEntry {
key: IntermediateKey::Str(key.to_string()),
doc_count: *doc_count,
sub_aggregation: Default::default(),
sub_aggregation_res: Default::default(),
from: None,
to: None,
},
@@ -920,7 +921,7 @@ mod tests {
doc_count: *doc_count,
from: None,
to: None,
sub_aggregation: get_sub_test_tree(&[(
sub_aggregation_res: get_sub_test_tree(&[(
sub_aggregation_key.to_string(),
*sub_aggregation_count,
)]),

View File

@@ -52,10 +52,8 @@ pub struct IntermediateAverage {
impl IntermediateAverage {
/// Creates a new [`IntermediateAverage`] instance from a [`SegmentStatsCollector`].
pub(crate) fn from_collector(collector: SegmentStatsCollector) -> Self {
Self {
stats: collector.stats,
}
pub(crate) fn from_stats(stats: IntermediateStats) -> Self {
Self { stats }
}
/// Merges the other intermediate result into self.
pub fn merge_fruits(&mut self, other: IntermediateAverage) {

View File

@@ -2,7 +2,7 @@ use std::collections::hash_map::DefaultHasher;
use std::hash::{BuildHasher, Hasher};
use columnar::column_values::CompactSpaceU64Accessor;
use columnar::{Column, ColumnBlockAccessor, ColumnType, Dictionary, StrColumn};
use columnar::{Column, ColumnType, Dictionary, StrColumn};
use common::f64_to_u64;
use hyperloglogplus::{HyperLogLog, HyperLogLogPlus};
use rustc_hash::FxHashSet;
@@ -106,8 +106,6 @@ pub struct CardinalityAggReqData {
pub str_dict_column: Option<StrColumn>,
/// The missing value normalized to the internal u64 representation of the field type.
pub missing_value_for_accessor: Option<u64>,
/// The column block accessor to access the fast field values.
pub(crate) column_block_accessor: ColumnBlockAccessor<u64>,
/// The name of the aggregation.
pub name: String,
/// The aggregation request.
@@ -135,45 +133,34 @@ impl CardinalityAggregationReq {
}
}
#[derive(Clone, Debug, PartialEq)]
#[derive(Clone, Debug)]
pub(crate) struct SegmentCardinalityCollector {
cardinality: CardinalityCollector,
entries: FxHashSet<u64>,
buckets: Vec<SegmentCardinalityCollectorBucket>,
accessor_idx: usize,
/// The column accessor to access the fast field values.
accessor: Column<u64>,
/// The column_type of the field.
column_type: ColumnType,
/// The missing value normalized to the internal u64 representation of the field type.
missing_value_for_accessor: Option<u64>,
}
impl SegmentCardinalityCollector {
pub fn from_req(column_type: ColumnType, accessor_idx: usize) -> Self {
#[derive(Clone, Debug, PartialEq, Default)]
pub(crate) struct SegmentCardinalityCollectorBucket {
cardinality: CardinalityCollector,
entries: FxHashSet<u64>,
}
impl SegmentCardinalityCollectorBucket {
pub fn new(column_type: ColumnType) -> Self {
Self {
cardinality: CardinalityCollector::new(column_type as u8),
entries: Default::default(),
accessor_idx,
entries: FxHashSet::default(),
}
}
fn fetch_block_with_field(
&mut self,
docs: &[crate::DocId],
agg_data: &mut CardinalityAggReqData,
) {
if let Some(missing) = agg_data.missing_value_for_accessor {
agg_data.column_block_accessor.fetch_block_with_missing(
docs,
&agg_data.accessor,
missing,
);
} else {
agg_data
.column_block_accessor
.fetch_block(docs, &agg_data.accessor);
}
}
fn into_intermediate_metric_result(
mut self,
agg_data: &AggregationsSegmentCtx,
req_data: &CardinalityAggReqData,
) -> crate::Result<IntermediateMetricResult> {
let req_data = &agg_data.get_cardinality_req_data(self.accessor_idx);
if req_data.column_type == ColumnType::Str {
let fallback_dict = Dictionary::empty();
let dict = req_data
@@ -194,6 +181,7 @@ impl SegmentCardinalityCollector {
term_ids.push(term_ord as u32);
}
}
term_ids.sort_unstable();
dict.sorted_ords_to_term_cb(term_ids.iter().map(|term| *term as u64), |term| {
self.cardinality.sketch.insert_any(&term);
@@ -227,16 +215,49 @@ impl SegmentCardinalityCollector {
}
}
impl SegmentCardinalityCollector {
pub fn from_req(
column_type: ColumnType,
accessor_idx: usize,
accessor: Column<u64>,
missing_value_for_accessor: Option<u64>,
) -> Self {
Self {
buckets: vec![SegmentCardinalityCollectorBucket::new(column_type); 1],
column_type,
accessor_idx,
accessor,
missing_value_for_accessor,
}
}
fn fetch_block_with_field(
&mut self,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) {
agg_data.column_block_accessor.fetch_block_with_missing(
docs,
&self.accessor,
self.missing_value_for_accessor,
);
}
}
impl SegmentAggregationCollector for SegmentCardinalityCollector {
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
self.prepare_max_bucket(parent_bucket_id, agg_data)?;
let req_data = &agg_data.get_cardinality_req_data(self.accessor_idx);
let name = req_data.name.to_string();
// take the bucket in buckets and replace it with a new empty one
let bucket = std::mem::take(&mut self.buckets[parent_bucket_id as usize]);
let intermediate_result = self.into_intermediate_metric_result(agg_data)?;
let intermediate_result = bucket.into_intermediate_metric_result(req_data)?;
results.push(
name,
IntermediateAggregationResult::Metric(intermediate_result),
@@ -247,27 +268,20 @@ impl SegmentAggregationCollector for SegmentCardinalityCollector {
fn collect(
&mut self,
doc: crate::DocId,
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
self.collect_block(&[doc], agg_data)
}
fn collect_block(
&mut self,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
let req_data = agg_data.get_cardinality_req_data_mut(self.accessor_idx);
self.fetch_block_with_field(docs, req_data);
self.fetch_block_with_field(docs, agg_data);
let bucket = &mut self.buckets[parent_bucket_id as usize];
let col_block_accessor = &req_data.column_block_accessor;
if req_data.column_type == ColumnType::Str {
let col_block_accessor = &agg_data.column_block_accessor;
if self.column_type == ColumnType::Str {
for term_ord in col_block_accessor.iter_vals() {
self.entries.insert(term_ord);
bucket.entries.insert(term_ord);
}
} else if req_data.column_type == ColumnType::IpAddr {
let compact_space_accessor = req_data
} else if self.column_type == ColumnType::IpAddr {
let compact_space_accessor = self
.accessor
.values
.clone()
@@ -282,16 +296,29 @@ impl SegmentAggregationCollector for SegmentCardinalityCollector {
})?;
for val in col_block_accessor.iter_vals() {
let val: u128 = compact_space_accessor.compact_to_u128(val as u32);
self.cardinality.sketch.insert_any(&val);
bucket.cardinality.sketch.insert_any(&val);
}
} else {
for val in col_block_accessor.iter_vals() {
self.cardinality.sketch.insert_any(&val);
bucket.cardinality.sketch.insert_any(&val);
}
}
Ok(())
}
fn prepare_max_bucket(
&mut self,
max_bucket: BucketId,
_agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
if max_bucket as usize >= self.buckets.len() {
self.buckets.resize_with(max_bucket as usize + 1, || {
SegmentCardinalityCollectorBucket::new(self.column_type)
});
}
Ok(())
}
}
#[derive(Clone, Debug, Serialize, Deserialize)]

View File

@@ -52,10 +52,8 @@ pub struct IntermediateCount {
impl IntermediateCount {
/// Creates a new [`IntermediateCount`] instance from a [`SegmentStatsCollector`].
pub(crate) fn from_collector(collector: SegmentStatsCollector) -> Self {
Self {
stats: collector.stats,
}
pub(crate) fn from_stats(stats: IntermediateStats) -> Self {
Self { stats }
}
/// Merges the other intermediate result into self.
pub fn merge_fruits(&mut self, other: IntermediateCount) {

View File

@@ -8,10 +8,9 @@ use crate::aggregation::agg_data::AggregationsSegmentCtx;
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResult, IntermediateAggregationResults, IntermediateMetricResult,
};
use crate::aggregation::metric::MetricAggReqData;
use crate::aggregation::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::*;
use crate::{DocId, TantivyError};
use crate::TantivyError;
/// A multi-value metric aggregation that computes a collection of extended statistics
/// on numeric values that are extracted
@@ -318,51 +317,28 @@ impl IntermediateExtendedStats {
}
}
#[derive(Clone, Debug, PartialEq)]
#[derive(Clone, Debug)]
pub(crate) struct SegmentExtendedStatsCollector {
name: String,
missing: Option<u64>,
field_type: ColumnType,
pub(crate) extended_stats: IntermediateExtendedStats,
pub(crate) accessor_idx: usize,
val_cache: Vec<u64>,
accessor: columnar::Column<u64>,
buckets: Vec<IntermediateExtendedStats>,
sigma: Option<f64>,
}
impl SegmentExtendedStatsCollector {
pub fn from_req(
field_type: ColumnType,
sigma: Option<f64>,
accessor_idx: usize,
missing: Option<f64>,
) -> Self {
let missing = missing.and_then(|val| f64_to_fastfield_u64(val, &field_type));
pub fn from_req(req: &MetricAggReqData, sigma: Option<f64>) -> Self {
let missing = req
.missing
.and_then(|val| f64_to_fastfield_u64(val, &req.field_type));
Self {
field_type,
extended_stats: IntermediateExtendedStats::with_sigma(sigma),
accessor_idx,
name: req.name.clone(),
field_type: req.field_type,
accessor: req.accessor.clone(),
missing,
val_cache: Default::default(),
}
}
#[inline]
pub(crate) fn collect_block_with_field(
&mut self,
docs: &[DocId],
req_data: &mut MetricAggReqData,
) {
if let Some(missing) = self.missing.as_ref() {
req_data.column_block_accessor.fetch_block_with_missing(
docs,
&req_data.accessor,
*missing,
);
} else {
req_data
.column_block_accessor
.fetch_block(docs, &req_data.accessor);
}
for val in req_data.column_block_accessor.iter_vals() {
let val1 = f64_from_fastfield_u64(val, &self.field_type);
self.extended_stats.collect(val1);
buckets: vec![IntermediateExtendedStats::with_sigma(sigma); 16],
sigma,
}
}
}
@@ -370,15 +346,18 @@ impl SegmentExtendedStatsCollector {
impl SegmentAggregationCollector for SegmentExtendedStatsCollector {
#[inline]
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
let name = agg_data.get_metric_req_data(self.accessor_idx).name.clone();
let name = self.name.clone();
self.prepare_max_bucket(parent_bucket_id, agg_data)?;
let extended_stats = std::mem::take(&mut self.buckets[parent_bucket_id as usize]);
results.push(
name,
IntermediateAggregationResult::Metric(IntermediateMetricResult::ExtendedStats(
self.extended_stats,
extended_stats,
)),
)?;
@@ -388,39 +367,36 @@ impl SegmentAggregationCollector for SegmentExtendedStatsCollector {
#[inline]
fn collect(
&mut self,
doc: crate::DocId,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
let req_data = agg_data.get_metric_req_data(self.accessor_idx);
if let Some(missing) = self.missing {
let mut has_val = false;
for val in req_data.accessor.values_for_doc(doc) {
let val1 = f64_from_fastfield_u64(val, &self.field_type);
self.extended_stats.collect(val1);
has_val = true;
}
if !has_val {
self.extended_stats
.collect(f64_from_fastfield_u64(missing, &self.field_type));
}
} else {
for val in req_data.accessor.values_for_doc(doc) {
let val1 = f64_from_fastfield_u64(val, &self.field_type);
self.extended_stats.collect(val1);
}
let mut extended_stats = self.buckets[parent_bucket_id as usize].clone();
agg_data
.column_block_accessor
.fetch_block_with_missing(docs, &self.accessor, self.missing);
for val in agg_data.column_block_accessor.iter_vals() {
let val1 = f64_from_fastfield_u64(val, self.field_type);
extended_stats.collect(val1);
}
// store back
self.buckets[parent_bucket_id as usize] = extended_stats;
Ok(())
}
#[inline]
fn collect_block(
fn prepare_max_bucket(
&mut self,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
max_bucket: BucketId,
_agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
let req_data = agg_data.get_metric_req_data_mut(self.accessor_idx);
self.collect_block_with_field(docs, req_data);
if self.buckets.len() <= max_bucket as usize {
self.buckets.resize_with(max_bucket as usize + 1, || {
IntermediateExtendedStats::with_sigma(self.sigma)
});
}
Ok(())
}
}

View File

@@ -52,10 +52,8 @@ pub struct IntermediateMax {
impl IntermediateMax {
/// Creates a new [`IntermediateMax`] instance from a [`SegmentStatsCollector`].
pub(crate) fn from_collector(collector: SegmentStatsCollector) -> Self {
Self {
stats: collector.stats,
}
pub(crate) fn from_stats(stats: IntermediateStats) -> Self {
Self { stats }
}
/// Merges the other intermediate result into self.
pub fn merge_fruits(&mut self, other: IntermediateMax) {

View File

@@ -52,10 +52,8 @@ pub struct IntermediateMin {
impl IntermediateMin {
/// Creates a new [`IntermediateMin`] instance from a [`SegmentStatsCollector`].
pub(crate) fn from_collector(collector: SegmentStatsCollector) -> Self {
Self {
stats: collector.stats,
}
pub(crate) fn from_stats(stats: IntermediateStats) -> Self {
Self { stats }
}
/// Merges the other intermediate result into self.
pub fn merge_fruits(&mut self, other: IntermediateMin) {

View File

@@ -31,7 +31,7 @@ use std::collections::HashMap;
pub use average::*;
pub use cardinality::*;
use columnar::{Column, ColumnBlockAccessor, ColumnType};
use columnar::{Column, ColumnType};
pub use count::*;
pub use extended_stats::*;
pub use max::*;
@@ -55,8 +55,6 @@ pub struct MetricAggReqData {
pub field_type: ColumnType,
/// The missing value normalized to the internal u64 representation of the field type.
pub missing_u64: Option<u64>,
/// The column block accessor to access the fast field values.
pub column_block_accessor: ColumnBlockAccessor<u64>,
/// The column accessor to access the fast field values.
pub accessor: Column<u64>,
/// Used when converting to intermediate result

View File

@@ -7,10 +7,9 @@ use crate::aggregation::agg_data::AggregationsSegmentCtx;
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResult, IntermediateAggregationResults, IntermediateMetricResult,
};
use crate::aggregation::metric::MetricAggReqData;
use crate::aggregation::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::*;
use crate::{DocId, TantivyError};
use crate::TantivyError;
/// # Percentiles
///
@@ -131,10 +130,16 @@ impl PercentilesAggregationReq {
}
}
#[derive(Clone, Debug, PartialEq)]
#[derive(Clone, Debug)]
pub(crate) struct SegmentPercentilesCollector {
pub(crate) percentiles: PercentilesCollector,
pub(crate) buckets: Vec<PercentilesCollector>,
pub(crate) accessor_idx: usize,
/// The type of the field.
pub field_type: ColumnType,
/// The missing value normalized to the internal u64 representation of the field type.
pub missing_u64: Option<u64>,
/// The column accessor to access the fast field values.
pub accessor: Column<u64>,
}
#[derive(Clone, Serialize, Deserialize)]
@@ -229,33 +234,18 @@ impl PercentilesCollector {
}
impl SegmentPercentilesCollector {
pub fn from_req_and_validate(accessor_idx: usize) -> crate::Result<Self> {
Ok(Self {
percentiles: PercentilesCollector::new(),
pub fn from_req_and_validate(
field_type: ColumnType,
missing_u64: Option<u64>,
accessor: Column<u64>,
accessor_idx: usize,
) -> Self {
Self {
buckets: Vec::with_capacity(64),
field_type,
missing_u64,
accessor,
accessor_idx,
})
}
#[inline]
pub(crate) fn collect_block_with_field(
&mut self,
docs: &[DocId],
req_data: &mut MetricAggReqData,
) {
if let Some(missing) = req_data.missing_u64.as_ref() {
req_data.column_block_accessor.fetch_block_with_missing(
docs,
&req_data.accessor,
*missing,
);
} else {
req_data
.column_block_accessor
.fetch_block(docs, &req_data.accessor);
}
for val in req_data.column_block_accessor.iter_vals() {
let val1 = f64_from_fastfield_u64(val, &req_data.field_type);
self.percentiles.collect(val1);
}
}
}
@@ -263,12 +253,18 @@ impl SegmentPercentilesCollector {
impl SegmentAggregationCollector for SegmentPercentilesCollector {
#[inline]
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
let name = agg_data.get_metric_req_data(self.accessor_idx).name.clone();
let intermediate_metric_result = IntermediateMetricResult::Percentiles(self.percentiles);
self.prepare_max_bucket(parent_bucket_id, agg_data)?;
// Swap collector with an empty one to avoid cloning
let percentiles_collector = std::mem::take(&mut self.buckets[parent_bucket_id as usize]);
let intermediate_metric_result =
IntermediateMetricResult::Percentiles(percentiles_collector);
results.push(
name,
@@ -281,40 +277,33 @@ impl SegmentAggregationCollector for SegmentPercentilesCollector {
#[inline]
fn collect(
&mut self,
doc: crate::DocId,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
let req_data = agg_data.get_metric_req_data(self.accessor_idx);
let percentiles = &mut self.buckets[parent_bucket_id as usize];
agg_data.column_block_accessor.fetch_block_with_missing(
docs,
&self.accessor,
self.missing_u64,
);
if let Some(missing) = req_data.missing_u64 {
let mut has_val = false;
for val in req_data.accessor.values_for_doc(doc) {
let val1 = f64_from_fastfield_u64(val, &req_data.field_type);
self.percentiles.collect(val1);
has_val = true;
}
if !has_val {
self.percentiles
.collect(f64_from_fastfield_u64(missing, &req_data.field_type));
}
} else {
for val in req_data.accessor.values_for_doc(doc) {
let val1 = f64_from_fastfield_u64(val, &req_data.field_type);
self.percentiles.collect(val1);
}
for val in agg_data.column_block_accessor.iter_vals() {
let val1 = f64_from_fastfield_u64(val, self.field_type);
percentiles.collect(val1);
}
Ok(())
}
#[inline]
fn collect_block(
fn prepare_max_bucket(
&mut self,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
max_bucket: BucketId,
_agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
let req_data = agg_data.get_metric_req_data_mut(self.accessor_idx);
self.collect_block_with_field(docs, req_data);
while self.buckets.len() <= max_bucket as usize {
self.buckets.push(PercentilesCollector::new());
}
Ok(())
}
}

View File

@@ -1,5 +1,6 @@
use std::fmt::Debug;
use columnar::{Column, ColumnType};
use serde::{Deserialize, Serialize};
use super::*;
@@ -7,10 +8,9 @@ use crate::aggregation::agg_data::AggregationsSegmentCtx;
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResult, IntermediateAggregationResults, IntermediateMetricResult,
};
use crate::aggregation::metric::MetricAggReqData;
use crate::aggregation::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::*;
use crate::{DocId, TantivyError};
use crate::TantivyError;
/// A multi-value metric aggregation that computes a collection of statistics on numeric values that
/// are extracted from the aggregated documents.
@@ -83,7 +83,7 @@ impl Stats {
/// Intermediate result of the stats aggregation that can be combined with other intermediate
/// results.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
#[derive(Clone, Copy, Debug, PartialEq, Serialize, Deserialize)]
pub struct IntermediateStats {
/// The number of extracted values.
pub(crate) count: u64,
@@ -187,75 +187,75 @@ pub enum StatsType {
Percentiles,
}
fn create_collector<const TYPE_ID: u8>(
req: &MetricAggReqData,
) -> Box<dyn SegmentAggregationCollector> {
Box::new(SegmentStatsCollector::<TYPE_ID> {
name: req.name.clone(),
collecting_for: req.collecting_for,
is_number_or_date_type: req.is_number_or_date_type,
missing_u64: req.missing_u64,
accessor: req.accessor.clone(),
buckets: vec![IntermediateStats::default()],
})
}
/// Build a concrete `SegmentStatsCollector` depending on the column type.
pub(crate) fn build_segment_stats_collector(
req: &MetricAggReqData,
) -> crate::Result<Box<dyn SegmentAggregationCollector>> {
match req.field_type {
ColumnType::I64 => Ok(create_collector::<{ ColumnType::I64 as u8 }>(req)),
ColumnType::U64 => Ok(create_collector::<{ ColumnType::U64 as u8 }>(req)),
ColumnType::F64 => Ok(create_collector::<{ ColumnType::F64 as u8 }>(req)),
ColumnType::Bool => Ok(create_collector::<{ ColumnType::Bool as u8 }>(req)),
ColumnType::DateTime => Ok(create_collector::<{ ColumnType::DateTime as u8 }>(req)),
ColumnType::Bytes => Ok(create_collector::<{ ColumnType::Bytes as u8 }>(req)),
ColumnType::Str => Ok(create_collector::<{ ColumnType::Str as u8 }>(req)),
ColumnType::IpAddr => Ok(create_collector::<{ ColumnType::IpAddr as u8 }>(req)),
}
}
#[repr(C)]
#[derive(Clone, Debug)]
pub(crate) struct SegmentStatsCollector {
pub(crate) stats: IntermediateStats,
pub(crate) accessor_idx: usize,
pub(crate) struct SegmentStatsCollector<const COLUMN_TYPE_ID: u8> {
pub(crate) missing_u64: Option<u64>,
pub(crate) accessor: Column<u64>,
pub(crate) is_number_or_date_type: bool,
pub(crate) buckets: Vec<IntermediateStats>,
pub(crate) name: String,
pub(crate) collecting_for: StatsType,
}
impl SegmentStatsCollector {
pub fn from_req(accessor_idx: usize) -> Self {
Self {
stats: IntermediateStats::default(),
accessor_idx,
}
}
#[inline]
pub(crate) fn collect_block_with_field(
&mut self,
docs: &[DocId],
req_data: &mut MetricAggReqData,
) {
if let Some(missing) = req_data.missing_u64.as_ref() {
req_data.column_block_accessor.fetch_block_with_missing(
docs,
&req_data.accessor,
*missing,
);
} else {
req_data
.column_block_accessor
.fetch_block(docs, &req_data.accessor);
}
if req_data.is_number_or_date_type {
for val in req_data.column_block_accessor.iter_vals() {
let val1 = f64_from_fastfield_u64(val, &req_data.field_type);
self.stats.collect(val1);
}
} else {
for _val in req_data.column_block_accessor.iter_vals() {
// we ignore the value and simply record that we got something
self.stats.collect(0.0);
}
}
}
}
impl SegmentAggregationCollector for SegmentStatsCollector {
impl<const COLUMN_TYPE_ID: u8> SegmentAggregationCollector
for SegmentStatsCollector<COLUMN_TYPE_ID>
{
#[inline]
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
let req = agg_data.get_metric_req_data(self.accessor_idx);
let name = req.name.clone();
let name = self.name.clone();
let intermediate_metric_result = match req.collecting_for {
self.prepare_max_bucket(parent_bucket_id, agg_data)?;
let stats = self.buckets[parent_bucket_id as usize];
let intermediate_metric_result = match self.collecting_for {
StatsType::Average => {
IntermediateMetricResult::Average(IntermediateAverage::from_collector(*self))
IntermediateMetricResult::Average(IntermediateAverage::from_stats(stats))
}
StatsType::Count => {
IntermediateMetricResult::Count(IntermediateCount::from_collector(*self))
IntermediateMetricResult::Count(IntermediateCount::from_stats(stats))
}
StatsType::Max => IntermediateMetricResult::Max(IntermediateMax::from_collector(*self)),
StatsType::Min => IntermediateMetricResult::Min(IntermediateMin::from_collector(*self)),
StatsType::Stats => IntermediateMetricResult::Stats(self.stats),
StatsType::Sum => IntermediateMetricResult::Sum(IntermediateSum::from_collector(*self)),
StatsType::Max => IntermediateMetricResult::Max(IntermediateMax::from_stats(stats)),
StatsType::Min => IntermediateMetricResult::Min(IntermediateMin::from_stats(stats)),
StatsType::Stats => IntermediateMetricResult::Stats(stats),
StatsType::Sum => IntermediateMetricResult::Sum(IntermediateSum::from_stats(stats)),
_ => {
return Err(TantivyError::InvalidArgument(format!(
"Unsupported stats type for stats aggregation: {:?}",
req.collecting_for
self.collecting_for
)))
}
};
@@ -271,41 +271,67 @@ impl SegmentAggregationCollector for SegmentStatsCollector {
#[inline]
fn collect(
&mut self,
doc: crate::DocId,
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
let req_data = agg_data.get_metric_req_data(self.accessor_idx);
if let Some(missing) = req_data.missing_u64 {
let mut has_val = false;
for val in req_data.accessor.values_for_doc(doc) {
let val1 = f64_from_fastfield_u64(val, &req_data.field_type);
self.stats.collect(val1);
has_val = true;
}
if !has_val {
self.stats
.collect(f64_from_fastfield_u64(missing, &req_data.field_type));
}
} else {
for val in req_data.accessor.values_for_doc(doc) {
let val1 = f64_from_fastfield_u64(val, &req_data.field_type);
self.stats.collect(val1);
}
}
Ok(())
}
#[inline]
fn collect_block(
&mut self,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
let req_data = agg_data.get_metric_req_data_mut(self.accessor_idx);
self.collect_block_with_field(docs, req_data);
// TODO: remove once we fetch all values for all bucket ids in one go
if docs.len() == 1 && self.missing_u64.is_none() {
collect_stats::<COLUMN_TYPE_ID>(
&mut self.buckets[parent_bucket_id as usize],
self.accessor.values_for_doc(docs[0]),
self.is_number_or_date_type,
)?;
return Ok(());
}
agg_data.column_block_accessor.fetch_block_with_missing(
docs,
&self.accessor,
self.missing_u64,
);
collect_stats::<COLUMN_TYPE_ID>(
&mut self.buckets[parent_bucket_id as usize],
agg_data.column_block_accessor.iter_vals(),
self.is_number_or_date_type,
)?;
Ok(())
}
fn prepare_max_bucket(
&mut self,
max_bucket: BucketId,
_agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
let required_buckets = (max_bucket as usize) + 1;
if self.buckets.len() < required_buckets {
self.buckets
.resize_with(required_buckets, IntermediateStats::default);
}
Ok(())
}
}
#[inline]
fn collect_stats<const COLUMN_TYPE_ID: u8>(
stats: &mut IntermediateStats,
vals: impl Iterator<Item = u64>,
is_number_or_date_type: bool,
) -> crate::Result<()> {
if is_number_or_date_type {
for val in vals {
let val1 = convert_to_f64::<COLUMN_TYPE_ID>(val);
stats.collect(val1);
}
} else {
for _val in vals {
// we ignore the value and simply record that we got something
stats.collect(0.0);
}
}
Ok(())
}
#[cfg(test)]

View File

@@ -52,10 +52,8 @@ pub struct IntermediateSum {
impl IntermediateSum {
/// Creates a new [`IntermediateSum`] instance from a [`SegmentStatsCollector`].
pub(crate) fn from_collector(collector: SegmentStatsCollector) -> Self {
Self {
stats: collector.stats,
}
pub(crate) fn from_stats(stats: IntermediateStats) -> Self {
Self { stats }
}
/// Merges the other intermediate result into self.
pub fn merge_fruits(&mut self, other: IntermediateSum) {

View File

@@ -15,12 +15,11 @@ use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResult, IntermediateMetricResult,
};
use crate::aggregation::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::AggregationError;
use crate::aggregation::{AggregationError, BucketId};
use crate::collector::sort_key::ReverseComparator;
use crate::collector::TopNComputer;
use crate::schema::OwnedValue;
use crate::{DocAddress, DocId, SegmentOrdinal};
// duplicate import removed; already imported above
/// Contains all information required by the TopHitsSegmentCollector to perform the
/// top_hits aggregation on a segment.
@@ -472,7 +471,10 @@ impl TopHitsTopNComputer {
/// Create a new TopHitsCollector
pub fn new(req: &TopHitsAggregationReq) -> Self {
Self {
top_n: TopNComputer::new(req.size + req.from.unwrap_or(0)),
top_n: TopNComputer::new_with_comparator(
req.size + req.from.unwrap_or(0),
ReverseComparator,
),
req: req.clone(),
}
}
@@ -518,7 +520,8 @@ impl TopHitsTopNComputer {
pub(crate) struct TopHitsSegmentCollector {
segment_ordinal: SegmentOrdinal,
accessor_idx: usize,
top_n: TopNComputer<Vec<DocValueAndOrder>, DocAddress, ReverseComparator>,
buckets: Vec<TopNComputer<Vec<DocValueAndOrder>, DocAddress, ReverseComparator>>,
num_hits: usize,
}
impl TopHitsSegmentCollector {
@@ -527,19 +530,29 @@ impl TopHitsSegmentCollector {
accessor_idx: usize,
segment_ordinal: SegmentOrdinal,
) -> Self {
let num_hits = req.size + req.from.unwrap_or(0);
Self {
top_n: TopNComputer::new(req.size + req.from.unwrap_or(0)),
num_hits,
segment_ordinal,
accessor_idx,
buckets: vec![TopNComputer::new_with_comparator(num_hits, ReverseComparator); 1],
}
}
fn into_top_hits_collector(
self,
fn get_top_hits_computer(
&mut self,
parent_bucket_id: BucketId,
value_accessors: &HashMap<String, Vec<DynamicColumn>>,
req: &TopHitsAggregationReq,
) -> TopHitsTopNComputer {
if parent_bucket_id as usize >= self.buckets.len() {
return TopHitsTopNComputer::new(req);
}
let top_n = std::mem::replace(
&mut self.buckets[parent_bucket_id as usize],
TopNComputer::new(0),
);
let mut top_hits_computer = TopHitsTopNComputer::new(req);
let top_results = self.top_n.into_vec();
let top_results = top_n.into_vec();
for res in top_results {
let doc_value_fields = req.get_document_field_data(value_accessors, res.doc.doc_id);
@@ -554,54 +567,24 @@ impl TopHitsSegmentCollector {
top_hits_computer
}
/// TODO add a specialized variant for a single sort field
fn collect_with(
&mut self,
doc_id: crate::DocId,
req: &TopHitsAggregationReq,
accessors: &[(Column<u64>, ColumnType)],
) -> crate::Result<()> {
let sorts: Vec<DocValueAndOrder> = req
.sort
.iter()
.enumerate()
.map(|(idx, KeyOrder { order, .. })| {
let order = *order;
let value = accessors
.get(idx)
.expect("could not find field in accessors")
.0
.values_for_doc(doc_id)
.next();
DocValueAndOrder { value, order }
})
.collect();
self.top_n.push(
sorts,
DocAddress {
segment_ord: self.segment_ordinal,
doc_id,
},
);
Ok(())
}
}
impl SegmentAggregationCollector for TopHitsSegmentCollector {
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut crate::aggregation::intermediate_agg_result::IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
let req_data = agg_data.get_top_hits_req_data(self.accessor_idx);
let value_accessors = &req_data.value_accessors;
let intermediate_result = IntermediateMetricResult::TopHits(
self.into_top_hits_collector(value_accessors, &req_data.req),
);
let intermediate_result = IntermediateMetricResult::TopHits(self.get_top_hits_computer(
parent_bucket_id,
value_accessors,
&req_data.req,
));
results.push(
req_data.name.to_string(),
IntermediateAggregationResult::Metric(intermediate_result),
@@ -611,26 +594,56 @@ impl SegmentAggregationCollector for TopHitsSegmentCollector {
/// TODO: Consider a caching layer to reduce the call overhead
fn collect(
&mut self,
doc_id: crate::DocId,
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
let req_data = agg_data.get_top_hits_req_data(self.accessor_idx);
self.collect_with(doc_id, &req_data.req, &req_data.accessors)?;
Ok(())
}
fn collect_block(
&mut self,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
let top_n = &mut self.buckets[parent_bucket_id as usize];
let req_data = agg_data.get_top_hits_req_data(self.accessor_idx);
// TODO: Consider getting fields with the column block accessor.
for doc in docs {
self.collect_with(*doc, &req_data.req, &req_data.accessors)?;
let req = &req_data.req;
let accessors = &req_data.accessors;
for &doc_id in docs {
// TODO: this is terrible, a new vec is allocated for every doc
// We can fetch blocks instead
// We don't need to store the order for every value
let sorts: Vec<DocValueAndOrder> = req
.sort
.iter()
.enumerate()
.map(|(idx, KeyOrder { order, .. })| {
let order = *order;
let value = accessors
.get(idx)
.expect("could not find field in accessors")
.0
.values_for_doc(doc_id)
.next();
DocValueAndOrder { value, order }
})
.collect();
top_n.push(
sorts,
DocAddress {
segment_ord: self.segment_ordinal,
doc_id,
},
);
}
Ok(())
}
fn prepare_max_bucket(
&mut self,
max_bucket: BucketId,
_agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
self.buckets.resize(
(max_bucket as usize) + 1,
TopNComputer::new_with_comparator(self.num_hits, ReverseComparator),
);
Ok(())
}
}
#[cfg(test)]
@@ -746,7 +759,7 @@ mod tests {
],
"from": 0,
}
}
}
}))
.unwrap();
@@ -875,7 +888,7 @@ mod tests {
"mixed.*",
],
}
}
}
}))?;
let collector = AggregationCollector::from_aggs(d, Default::default());

View File

@@ -133,7 +133,7 @@ mod agg_limits;
pub mod agg_req;
pub mod agg_result;
pub mod bucket;
mod buf_collector;
pub(crate) mod cached_sub_aggs;
mod collector;
mod date;
mod error;
@@ -162,6 +162,19 @@ use serde::{Deserialize, Deserializer, Serialize};
use crate::tokenizer::TokenizerManager;
/// A bucket id is a dense identifier for a bucket within an aggregation.
/// It is used to index into a Vec that hold per-bucket data.
///
/// For example, in a terms aggregation, each unique term will be assigned a incremental BucketId.
/// This BucketId will be forwarded to sub-aggregations to identify the parent bucket.
///
/// This allows to have a single AggregationCollector instance per aggregation,
/// that can handle multiple buckets efficiently.
///
/// The API to call sub-aggregations is therefore a &[(BucketId, &[DocId])].
/// For that we'll need a buffer. One Vec per bucket aggregation is needed.
pub type BucketId = u32;
/// Context parameters for aggregation execution
///
/// This struct holds shared resources needed during aggregation execution:
@@ -335,19 +348,37 @@ impl Display for Key {
}
}
pub(crate) fn convert_to_f64<const COLUMN_TYPE_ID: u8>(val: u64) -> f64 {
if COLUMN_TYPE_ID == ColumnType::U64 as u8 {
val as f64
} else if COLUMN_TYPE_ID == ColumnType::I64 as u8
|| COLUMN_TYPE_ID == ColumnType::DateTime as u8
{
i64::from_u64(val) as f64
} else if COLUMN_TYPE_ID == ColumnType::F64 as u8 {
f64::from_u64(val)
} else if COLUMN_TYPE_ID == ColumnType::Bool as u8 {
val as f64
} else {
panic!(
"ColumnType ID {} cannot be converted to f64 metric",
COLUMN_TYPE_ID
)
}
}
/// Inverse of `to_fastfield_u64`. Used to convert to `f64` for metrics.
///
/// # Panics
/// Only `u64`, `f64`, `date`, and `i64` are supported.
pub(crate) fn f64_from_fastfield_u64(val: u64, field_type: &ColumnType) -> f64 {
pub(crate) fn f64_from_fastfield_u64(val: u64, field_type: ColumnType) -> f64 {
match field_type {
ColumnType::U64 => val as f64,
ColumnType::I64 | ColumnType::DateTime => i64::from_u64(val) as f64,
ColumnType::F64 => f64::from_u64(val),
ColumnType::Bool => val as f64,
_ => {
panic!("unexpected type {field_type:?}. This should not happen")
}
ColumnType::U64 => convert_to_f64::<{ ColumnType::U64 as u8 }>(val),
ColumnType::I64 => convert_to_f64::<{ ColumnType::I64 as u8 }>(val),
ColumnType::F64 => convert_to_f64::<{ ColumnType::F64 as u8 }>(val),
ColumnType::Bool => convert_to_f64::<{ ColumnType::Bool as u8 }>(val),
ColumnType::DateTime => convert_to_f64::<{ ColumnType::DateTime as u8 }>(val),
_ => panic!("unexpected type {field_type:?}. This should not happen"),
}
}

View File

@@ -8,25 +8,67 @@ use std::fmt::Debug;
pub(crate) use super::agg_limits::AggregationLimitsGuard;
use super::intermediate_agg_result::IntermediateAggregationResults;
use crate::aggregation::agg_data::AggregationsSegmentCtx;
use crate::aggregation::BucketId;
/// Monotonically increasing provider of BucketIds.
#[derive(Debug, Clone, Default)]
pub struct BucketIdProvider(u32);
impl BucketIdProvider {
/// Get the next BucketId.
pub fn next_bucket_id(&mut self) -> BucketId {
let bucket_id = self.0;
self.0 += 1;
bucket_id
}
}
/// A SegmentAggregationCollector is used to collect aggregation results.
pub trait SegmentAggregationCollector: CollectorClone + Debug {
pub trait SegmentAggregationCollector: Debug {
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()>;
/// Note: The caller needs to call `prepare_max_bucket` before calling `collect`.
fn collect(
&mut self,
doc: crate::DocId,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()>;
fn collect_block(
/// Collect docs for multiple buckets in one call.
/// Minimizes dynamic dispatch overhead when collecting many buckets.
///
/// Note: The caller needs to call `prepare_max_bucket` before calling `collect`.
fn collect_multiple(
&mut self,
bucket_ids: &[BucketId],
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
debug_assert_eq!(bucket_ids.len(), docs.len());
let mut start = 0;
while start < bucket_ids.len() {
let bucket_id = bucket_ids[start];
let mut end = start + 1;
while end < bucket_ids.len() && bucket_ids[end] == bucket_id {
end += 1;
}
self.collect(bucket_id, &docs[start..end], agg_data)?;
start = end;
}
Ok(())
}
/// Prepare the collector for collecting up to BucketId `max_bucket`.
/// This is useful so we can split allocation ahead of time of collecting.
fn prepare_max_bucket(
&mut self,
max_bucket: BucketId,
agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()>;
/// Finalize method. Some Aggregator collect blocks of docs before calling `collect_block`.
@@ -36,26 +78,7 @@ pub trait SegmentAggregationCollector: CollectorClone + Debug {
}
}
/// A helper trait to enable cloning of Box<dyn SegmentAggregationCollector>
pub trait CollectorClone {
fn clone_box(&self) -> Box<dyn SegmentAggregationCollector>;
}
impl<T> CollectorClone for T
where T: 'static + SegmentAggregationCollector + Clone
{
fn clone_box(&self) -> Box<dyn SegmentAggregationCollector> {
Box::new(self.clone())
}
}
impl Clone for Box<dyn SegmentAggregationCollector> {
fn clone(&self) -> Box<dyn SegmentAggregationCollector> {
self.clone_box()
}
}
#[derive(Clone, Default)]
#[derive(Default)]
/// The GenericSegmentAggregationResultsCollector is the generic version of the collector, which
/// can handle arbitrary complexity of sub-aggregations. Ideally we never have to pick this one
/// and can provide specialized versions instead, that remove some of its overhead.
@@ -73,12 +96,13 @@ impl Debug for GenericSegmentAggregationResultsCollector {
impl SegmentAggregationCollector for GenericSegmentAggregationResultsCollector {
fn add_intermediate_aggregation_result(
self: Box<Self>,
&mut self,
agg_data: &AggregationsSegmentCtx,
results: &mut IntermediateAggregationResults,
parent_bucket_id: BucketId,
) -> crate::Result<()> {
for agg in self.aggs {
agg.add_intermediate_aggregation_result(agg_data, results)?;
for agg in &mut self.aggs {
agg.add_intermediate_aggregation_result(agg_data, results, parent_bucket_id)?;
}
Ok(())
@@ -86,23 +110,13 @@ impl SegmentAggregationCollector for GenericSegmentAggregationResultsCollector {
fn collect(
&mut self,
doc: crate::DocId,
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
self.collect_block(&[doc], agg_data)?;
Ok(())
}
fn collect_block(
&mut self,
parent_bucket_id: BucketId,
docs: &[crate::DocId],
agg_data: &mut AggregationsSegmentCtx,
) -> crate::Result<()> {
for collector in &mut self.aggs {
collector.collect_block(docs, agg_data)?;
collector.collect(parent_bucket_id, docs, agg_data)?;
}
Ok(())
}
@@ -112,4 +126,15 @@ impl SegmentAggregationCollector for GenericSegmentAggregationResultsCollector {
}
Ok(())
}
fn prepare_max_bucket(
&mut self,
max_bucket: BucketId,
agg_data: &AggregationsSegmentCtx,
) -> crate::Result<()> {
for collector in &mut self.aggs {
collector.prepare_max_bucket(max_bucket, agg_data)?;
}
Ok(())
}
}

View File

@@ -1,25 +1,48 @@
mod order;
mod sort_by_erased_type;
mod sort_by_score;
mod sort_by_static_fast_value;
mod sort_by_string;
mod sort_key_computer;
pub use order::*;
pub use sort_by_erased_type::SortByErasedType;
pub use sort_by_score::SortBySimilarityScore;
pub use sort_by_static_fast_value::SortByStaticFastValue;
pub use sort_by_string::SortByString;
pub use sort_key_computer::{SegmentSortKeyComputer, SortKeyComputer};
#[cfg(test)]
mod tests {
pub(crate) mod tests {
// By spec, regardless of whether ascending or descending order was requested, in presence of a
// tie, we sort by ascending doc id/doc address.
pub(crate) fn sort_hits<TSortKey: Ord, D: Ord>(
hits: &mut [ComparableDoc<TSortKey, D>],
order: Order,
) {
if order.is_asc() {
hits.sort_by(|l, r| l.sort_key.cmp(&r.sort_key).then(l.doc.cmp(&r.doc)));
} else {
hits.sort_by(|l, r| {
l.sort_key
.cmp(&r.sort_key)
.reverse() // This is descending
.then(l.doc.cmp(&r.doc))
});
}
}
use std::collections::HashMap;
use std::ops::Range;
use crate::collector::sort_key::{SortBySimilarityScore, SortByStaticFastValue, SortByString};
use crate::collector::sort_key::{
SortByErasedType, SortBySimilarityScore, SortByStaticFastValue, SortByString,
};
use crate::collector::{ComparableDoc, DocSetCollector, TopDocs};
use crate::indexer::NoMergePolicy;
use crate::query::{AllQuery, QueryParser};
use crate::schema::{Schema, FAST, TEXT};
use crate::schema::{OwnedValue, Schema, FAST, TEXT};
use crate::{DocAddress, Document, Index, Order, Score, Searcher};
fn make_index() -> crate::Result<Index> {
@@ -294,11 +317,9 @@ mod tests {
(SortBySimilarityScore, score_order),
(SortByString::for_field("city"), city_order),
));
Ok(searcher
.search(&AllQuery, &top_collector)?
.into_iter()
.map(|(f, doc)| (f, ids[&doc]))
.collect())
let results: Vec<((Score, Option<String>), DocAddress)> =
searcher.search(&AllQuery, &top_collector)?;
Ok(results.into_iter().map(|(f, doc)| (f, ids[&doc])).collect())
}
assert_eq!(
@@ -323,6 +344,51 @@ mod tests {
Ok(())
}
#[test]
fn test_order_by_score_then_owned_value() -> crate::Result<()> {
let index = make_index()?;
type SortKey = (Score, OwnedValue);
fn query(
index: &Index,
score_order: Order,
city_order: Order,
) -> crate::Result<Vec<(SortKey, u64)>> {
let searcher = index.reader()?.searcher();
let ids = id_mapping(&searcher);
let top_collector = TopDocs::with_limit(4).order_by::<(Score, OwnedValue)>((
(SortBySimilarityScore, score_order),
(SortByErasedType::for_field("city"), city_order),
));
let results: Vec<((Score, OwnedValue), DocAddress)> =
searcher.search(&AllQuery, &top_collector)?;
Ok(results.into_iter().map(|(f, doc)| (f, ids[&doc])).collect())
}
assert_eq!(
&query(&index, Order::Asc, Order::Asc)?,
&[
((1.0, OwnedValue::Str("austin".to_owned())), 0),
((1.0, OwnedValue::Str("greenville".to_owned())), 1),
((1.0, OwnedValue::Str("tokyo".to_owned())), 2),
((1.0, OwnedValue::Null), 3),
]
);
assert_eq!(
&query(&index, Order::Asc, Order::Desc)?,
&[
((1.0, OwnedValue::Str("tokyo".to_owned())), 2),
((1.0, OwnedValue::Str("greenville".to_owned())), 1),
((1.0, OwnedValue::Str("austin".to_owned())), 0),
((1.0, OwnedValue::Null), 3),
]
);
Ok(())
}
use proptest::prelude::*;
proptest! {
@@ -372,15 +438,10 @@ mod tests {
// Using the TopDocs collector should always be equivalent to sorting, skipping the
// offset, and then taking the limit.
let sorted_docs: Vec<_> = if order.is_desc() {
let mut comparable_docs: Vec<ComparableDoc<_, _, true>> =
let sorted_docs: Vec<_> = {
let mut comparable_docs: Vec<ComparableDoc<_, _>> =
all_results.into_iter().map(|(sort_key, doc)| ComparableDoc { sort_key, doc}).collect();
comparable_docs.sort();
comparable_docs.into_iter().map(|cd| (cd.sort_key, cd.doc)).collect()
} else {
let mut comparable_docs: Vec<ComparableDoc<_, _, false>> =
all_results.into_iter().map(|(sort_key, doc)| ComparableDoc { sort_key, doc}).collect();
comparable_docs.sort();
sort_hits(&mut comparable_docs, order);
comparable_docs.into_iter().map(|cd| (cd.sort_key, cd.doc)).collect()
};
let expected_docs = sorted_docs.into_iter().skip(offset).take(limit).collect::<Vec<_>>();

View File

@@ -1,36 +1,116 @@
use std::cmp::Ordering;
use columnar::MonotonicallyMappableToU64;
use serde::{Deserialize, Serialize};
use crate::collector::{SegmentSortKeyComputer, SortKeyComputer};
use crate::schema::Schema;
use crate::schema::{OwnedValue, Schema};
use crate::{DocId, Order, Score};
fn compare_owned_value<const NULLS_FIRST: bool>(lhs: &OwnedValue, rhs: &OwnedValue) -> Ordering {
match (lhs, rhs) {
(OwnedValue::Null, OwnedValue::Null) => Ordering::Equal,
(OwnedValue::Null, _) => {
if NULLS_FIRST {
Ordering::Less
} else {
Ordering::Greater
}
}
(_, OwnedValue::Null) => {
if NULLS_FIRST {
Ordering::Greater
} else {
Ordering::Less
}
}
(OwnedValue::Str(a), OwnedValue::Str(b)) => a.cmp(b),
(OwnedValue::PreTokStr(a), OwnedValue::PreTokStr(b)) => a.cmp(b),
(OwnedValue::U64(a), OwnedValue::U64(b)) => a.cmp(b),
(OwnedValue::I64(a), OwnedValue::I64(b)) => a.cmp(b),
(OwnedValue::F64(a), OwnedValue::F64(b)) => a.to_u64().cmp(&b.to_u64()),
(OwnedValue::Bool(a), OwnedValue::Bool(b)) => a.cmp(b),
(OwnedValue::Date(a), OwnedValue::Date(b)) => a.cmp(b),
(OwnedValue::Facet(a), OwnedValue::Facet(b)) => a.cmp(b),
(OwnedValue::Bytes(a), OwnedValue::Bytes(b)) => a.cmp(b),
(OwnedValue::IpAddr(a), OwnedValue::IpAddr(b)) => a.cmp(b),
(OwnedValue::U64(a), OwnedValue::I64(b)) => {
if *b < 0 {
Ordering::Greater
} else {
a.cmp(&(*b as u64))
}
}
(OwnedValue::I64(a), OwnedValue::U64(b)) => {
if *a < 0 {
Ordering::Less
} else {
(*a as u64).cmp(b)
}
}
(OwnedValue::U64(a), OwnedValue::F64(b)) => (*a as f64).to_u64().cmp(&b.to_u64()),
(OwnedValue::F64(a), OwnedValue::U64(b)) => a.to_u64().cmp(&(*b as f64).to_u64()),
(OwnedValue::I64(a), OwnedValue::F64(b)) => (*a as f64).to_u64().cmp(&b.to_u64()),
(OwnedValue::F64(a), OwnedValue::I64(b)) => a.to_u64().cmp(&(*b as f64).to_u64()),
(a, b) => {
let ord = a.discriminant_value().cmp(&b.discriminant_value());
// If the discriminant is equal, it's because a new type was added, but hasn't been
// included in this `match` statement.
assert!(
ord != Ordering::Equal,
"Unimplemented comparison for type of {a:?}, {b:?}"
);
ord
}
}
}
/// Comparator trait defining the order in which documents should be ordered.
pub trait Comparator<T>: Send + Sync + std::fmt::Debug + Default {
/// Return the order between two values.
fn compare(&self, lhs: &T, rhs: &T) -> Ordering;
}
/// With the natural comparator, the top k collector will return
/// the top documents in decreasing order.
/// Compare values naturally (e.g. 1 < 2).
///
/// When used with `TopDocs`, which reverses the order, this results in a
/// "Descending" sort (Greatest values first).
///
/// `None` (or Null for `OwnedValue`) values are considered to be smaller than any other value,
/// and will therefore appear last in a descending sort (e.g. `[Some(20), Some(10), None]`).
#[derive(Debug, Copy, Clone, Default, Serialize, Deserialize)]
pub struct NaturalComparator;
impl<T: PartialOrd> Comparator<T> for NaturalComparator {
#[inline(always)]
fn compare(&self, lhs: &T, rhs: &T) -> Ordering {
lhs.partial_cmp(rhs).unwrap()
lhs.partial_cmp(rhs).unwrap_or(Ordering::Equal)
}
}
/// Sorts document in reverse order.
/// A (partial) implementation of comparison for OwnedValue.
///
/// If the sort key is None, it will considered as the lowest value, and will therefore appear
/// first.
/// Intended for use within columns of homogenous types, and so will panic for OwnedValues with
/// mismatched types. The one exception is Null, for which we do define all comparisons.
impl Comparator<OwnedValue> for NaturalComparator {
#[inline(always)]
fn compare(&self, lhs: &OwnedValue, rhs: &OwnedValue) -> Ordering {
compare_owned_value::</* NULLS_FIRST= */ true>(lhs, rhs)
}
}
/// Compare values in reverse (e.g. 2 < 1).
///
/// When used with `TopDocs`, which reverses the order, this results in an
/// "Ascending" sort (Smallest values first).
///
/// `None` is considered smaller than `Some` in the underlying comparator, but because the
/// comparison is reversed, `None` is effectively treated as the lowest value in the resulting
/// Ascending sort (e.g. `[None, Some(10), Some(20)]`).
///
/// The ReverseComparator does not necessarily imply that the sort order is reversed compared
/// to the NaturalComparator. In presence of a tie, both version will retain the higher doc ids.
/// to the NaturalComparator. In presence of a tie on the sort key, documents will always be
/// sorted by ascending `DocId`/`DocAddress` in TopN results, regardless of the sort key's order.
#[derive(Debug, Copy, Clone, Default, Serialize, Deserialize)]
pub struct ReverseComparator;
@@ -43,11 +123,15 @@ where NaturalComparator: Comparator<T>
}
}
/// Sorts document in reverse order, but considers None as having the lowest value.
/// Compare values in reverse, but treating `None` as lower than `Some`.
///
/// When used with `TopDocs`, which reverses the order, this results in an
/// "Ascending" sort (Smallest values first), but with `None` values appearing last
/// (e.g. `[Some(10), Some(20), None]`).
///
/// This is usually what is wanted when sorting by a field in an ascending order.
/// For instance, in a e-commerce website, if I sort by price ascending, I most likely want the
/// cheapest items first, and the items without a price at last.
/// For instance, in an e-commerce website, if sorting by price ascending,
/// the cheapest items would appear first, and items without a price would appear last.
#[derive(Debug, Copy, Clone, Default)]
pub struct ReverseNoneIsLowerComparator;
@@ -107,6 +191,84 @@ impl Comparator<String> for ReverseNoneIsLowerComparator {
}
}
impl Comparator<OwnedValue> for ReverseNoneIsLowerComparator {
#[inline(always)]
fn compare(&self, lhs: &OwnedValue, rhs: &OwnedValue) -> Ordering {
compare_owned_value::</* NULLS_FIRST= */ false>(rhs, lhs)
}
}
/// Compare values naturally, but treating `None` as higher than `Some`.
///
/// When used with `TopDocs`, which reverses the order, this results in a
/// "Descending" sort (Greatest values first), but with `None` values appearing first
/// (e.g. `[None, Some(20), Some(10)]`).
#[derive(Debug, Copy, Clone, Default, Serialize, Deserialize)]
pub struct NaturalNoneIsHigherComparator;
impl<T> Comparator<Option<T>> for NaturalNoneIsHigherComparator
where NaturalComparator: Comparator<T>
{
#[inline(always)]
fn compare(&self, lhs_opt: &Option<T>, rhs_opt: &Option<T>) -> Ordering {
match (lhs_opt, rhs_opt) {
(None, None) => Ordering::Equal,
(None, Some(_)) => Ordering::Greater,
(Some(_), None) => Ordering::Less,
(Some(lhs), Some(rhs)) => NaturalComparator.compare(lhs, rhs),
}
}
}
impl Comparator<u32> for NaturalNoneIsHigherComparator {
#[inline(always)]
fn compare(&self, lhs: &u32, rhs: &u32) -> Ordering {
NaturalComparator.compare(lhs, rhs)
}
}
impl Comparator<u64> for NaturalNoneIsHigherComparator {
#[inline(always)]
fn compare(&self, lhs: &u64, rhs: &u64) -> Ordering {
NaturalComparator.compare(lhs, rhs)
}
}
impl Comparator<f64> for NaturalNoneIsHigherComparator {
#[inline(always)]
fn compare(&self, lhs: &f64, rhs: &f64) -> Ordering {
NaturalComparator.compare(lhs, rhs)
}
}
impl Comparator<f32> for NaturalNoneIsHigherComparator {
#[inline(always)]
fn compare(&self, lhs: &f32, rhs: &f32) -> Ordering {
NaturalComparator.compare(lhs, rhs)
}
}
impl Comparator<i64> for NaturalNoneIsHigherComparator {
#[inline(always)]
fn compare(&self, lhs: &i64, rhs: &i64) -> Ordering {
NaturalComparator.compare(lhs, rhs)
}
}
impl Comparator<String> for NaturalNoneIsHigherComparator {
#[inline(always)]
fn compare(&self, lhs: &String, rhs: &String) -> Ordering {
NaturalComparator.compare(lhs, rhs)
}
}
impl Comparator<OwnedValue> for NaturalNoneIsHigherComparator {
#[inline(always)]
fn compare(&self, lhs: &OwnedValue, rhs: &OwnedValue) -> Ordering {
compare_owned_value::</* NULLS_FIRST= */ false>(lhs, rhs)
}
}
/// An enum representing the different sort orders.
#[derive(Debug, Clone, Copy, Eq, PartialEq, Default)]
pub enum ComparatorEnum {
@@ -115,8 +277,10 @@ pub enum ComparatorEnum {
Natural,
/// Reverse order (See [ReverseComparator])
Reverse,
/// Reverse order by treating None as the lowest value.(See [ReverseNoneLowerComparator])
/// Reverse order by treating None as the lowest value. (See [ReverseNoneLowerComparator])
ReverseNoneLower,
/// Natural order but treating None as the highest value. (See [NaturalNoneIsHigherComparator])
NaturalNoneHigher,
}
impl From<Order> for ComparatorEnum {
@@ -133,6 +297,7 @@ where
ReverseNoneIsLowerComparator: Comparator<T>,
NaturalComparator: Comparator<T>,
ReverseComparator: Comparator<T>,
NaturalNoneIsHigherComparator: Comparator<T>,
{
#[inline(always)]
fn compare(&self, lhs: &T, rhs: &T) -> Ordering {
@@ -140,6 +305,7 @@ where
ComparatorEnum::Natural => NaturalComparator.compare(lhs, rhs),
ComparatorEnum::Reverse => ReverseComparator.compare(lhs, rhs),
ComparatorEnum::ReverseNoneLower => ReverseNoneIsLowerComparator.compare(lhs, rhs),
ComparatorEnum::NaturalNoneHigher => NaturalNoneIsHigherComparator.compare(lhs, rhs),
}
}
}
@@ -322,11 +488,12 @@ impl<TSegmentSortKeyComputer, TSegmentSortKey, TComparator> SegmentSortKeyComput
for SegmentSortKeyComputerWithComparator<TSegmentSortKeyComputer, TComparator>
where
TSegmentSortKeyComputer: SegmentSortKeyComputer<SegmentSortKey = TSegmentSortKey>,
TSegmentSortKey: PartialOrd + Clone + 'static + Sync + Send,
TSegmentSortKey: Clone + 'static + Sync + Send,
TComparator: Comparator<TSegmentSortKey> + 'static + Sync + Send,
{
type SortKey = TSegmentSortKeyComputer::SortKey;
type SegmentSortKey = TSegmentSortKey;
type SegmentComparator = TComparator;
fn segment_sort_key(&mut self, doc: DocId, score: Score) -> Self::SegmentSortKey {
self.segment_sort_key_computer.segment_sort_key(doc, score)
@@ -346,3 +513,55 @@ where
.convert_segment_sort_key(sort_key)
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::schema::OwnedValue;
#[test]
fn test_natural_none_is_higher() {
let comp = NaturalNoneIsHigherComparator;
let null = None;
let v1 = Some(1_u64);
let v2 = Some(2_u64);
// NaturalNoneIsGreaterComparator logic:
// 1. Delegates to NaturalComparator for non-nulls.
// NaturalComparator compare(2, 1) -> 2.cmp(1) -> Greater.
assert_eq!(comp.compare(&v2, &v1), Ordering::Greater);
// 2. Treats None (Null) as Greater than any value.
// compare(None, Some(2)) should be Greater.
assert_eq!(comp.compare(&null, &v2), Ordering::Greater);
// compare(Some(1), None) should be Less.
assert_eq!(comp.compare(&v1, &null), Ordering::Less);
// compare(None, None) should be Equal.
assert_eq!(comp.compare(&null, &null), Ordering::Equal);
}
#[test]
fn test_mixed_ownedvalue_compare() {
let u = OwnedValue::U64(10);
let i = OwnedValue::I64(10);
let f = OwnedValue::F64(10.0);
let nc = NaturalComparator;
assert_eq!(nc.compare(&u, &i), Ordering::Equal);
assert_eq!(nc.compare(&u, &f), Ordering::Equal);
assert_eq!(nc.compare(&i, &f), Ordering::Equal);
let u2 = OwnedValue::U64(11);
assert_eq!(nc.compare(&u2, &f), Ordering::Greater);
let s = OwnedValue::Str("a".to_string());
// Str < U64
assert_eq!(nc.compare(&s, &u), Ordering::Less);
// Str < I64
assert_eq!(nc.compare(&s, &i), Ordering::Less);
// Str < F64
assert_eq!(nc.compare(&s, &f), Ordering::Less);
}
}

View File

@@ -0,0 +1,361 @@
use columnar::{ColumnType, MonotonicallyMappableToU64};
use crate::collector::sort_key::{
NaturalComparator, SortBySimilarityScore, SortByStaticFastValue, SortByString,
};
use crate::collector::{SegmentSortKeyComputer, SortKeyComputer};
use crate::fastfield::FastFieldNotAvailableError;
use crate::schema::OwnedValue;
use crate::{DateTime, DocId, Score};
/// Sort by the boxed / OwnedValue representation of either a fast field, or of the score.
///
/// Using the OwnedValue representation allows for type erasure, and can be useful when sort orders
/// are not known until runtime. But it comes with a performance cost: wherever possible, prefer to
/// use a SortKeyComputer implementation with a known-type at compile time.
#[derive(Debug, Clone)]
pub enum SortByErasedType {
/// Sort by a fast field
Field(String),
/// Sort by score
Score,
}
impl SortByErasedType {
/// Creates a new sort key computer which will sort by the given fast field column, with type
/// erasure.
pub fn for_field(column_name: impl ToString) -> Self {
Self::Field(column_name.to_string())
}
/// Creates a new sort key computer which will sort by score, with type erasure.
pub fn for_score() -> Self {
Self::Score
}
}
trait ErasedSegmentSortKeyComputer: Send + Sync {
fn segment_sort_key(&mut self, doc: DocId, score: Score) -> Option<u64>;
fn convert_segment_sort_key(&self, sort_key: Option<u64>) -> OwnedValue;
}
struct ErasedSegmentSortKeyComputerWrapper<C, F> {
inner: C,
converter: F,
}
impl<C, F> ErasedSegmentSortKeyComputer for ErasedSegmentSortKeyComputerWrapper<C, F>
where
C: SegmentSortKeyComputer<SegmentSortKey = Option<u64>> + Send + Sync,
F: Fn(C::SortKey) -> OwnedValue + Send + Sync + 'static,
{
fn segment_sort_key(&mut self, doc: DocId, score: Score) -> Option<u64> {
self.inner.segment_sort_key(doc, score)
}
fn convert_segment_sort_key(&self, sort_key: Option<u64>) -> OwnedValue {
let val = self.inner.convert_segment_sort_key(sort_key);
(self.converter)(val)
}
}
struct ScoreSegmentSortKeyComputer {
segment_computer: SortBySimilarityScore,
}
impl ErasedSegmentSortKeyComputer for ScoreSegmentSortKeyComputer {
fn segment_sort_key(&mut self, doc: DocId, score: Score) -> Option<u64> {
let score_value: f64 = self.segment_computer.segment_sort_key(doc, score).into();
Some(score_value.to_u64())
}
fn convert_segment_sort_key(&self, sort_key: Option<u64>) -> OwnedValue {
let score_value: u64 = sort_key.expect("This implementation always produces a score.");
OwnedValue::F64(f64::from_u64(score_value))
}
}
impl SortKeyComputer for SortByErasedType {
type SortKey = OwnedValue;
type Child = ErasedColumnSegmentSortKeyComputer;
type Comparator = NaturalComparator;
fn requires_scoring(&self) -> bool {
matches!(self, Self::Score)
}
fn segment_sort_key_computer(
&self,
segment_reader: &crate::SegmentReader,
) -> crate::Result<Self::Child> {
let inner: Box<dyn ErasedSegmentSortKeyComputer> = match self {
Self::Field(column_name) => {
let fast_fields = segment_reader.fast_fields();
// TODO: We currently double-open the column to avoid relying on the implementation
// details of `SortByString` or `SortByStaticFastValue`. Once
// https://github.com/quickwit-oss/tantivy/issues/2776 is resolved, we should
// consider directly constructing the appropriate `SegmentSortKeyComputer` type for
// the column that we open here.
let (_column, column_type) =
fast_fields.u64_lenient(column_name)?.ok_or_else(|| {
FastFieldNotAvailableError {
field_name: column_name.to_owned(),
}
})?;
match column_type {
ColumnType::Str => {
let computer = SortByString::for_field(column_name);
let inner = computer.segment_sort_key_computer(segment_reader)?;
Box::new(ErasedSegmentSortKeyComputerWrapper {
inner,
converter: |val: Option<String>| {
val.map(OwnedValue::Str).unwrap_or(OwnedValue::Null)
},
})
}
ColumnType::U64 => {
let computer = SortByStaticFastValue::<u64>::for_field(column_name);
let inner = computer.segment_sort_key_computer(segment_reader)?;
Box::new(ErasedSegmentSortKeyComputerWrapper {
inner,
converter: |val: Option<u64>| {
val.map(OwnedValue::U64).unwrap_or(OwnedValue::Null)
},
})
}
ColumnType::I64 => {
let computer = SortByStaticFastValue::<i64>::for_field(column_name);
let inner = computer.segment_sort_key_computer(segment_reader)?;
Box::new(ErasedSegmentSortKeyComputerWrapper {
inner,
converter: |val: Option<i64>| {
val.map(OwnedValue::I64).unwrap_or(OwnedValue::Null)
},
})
}
ColumnType::F64 => {
let computer = SortByStaticFastValue::<f64>::for_field(column_name);
let inner = computer.segment_sort_key_computer(segment_reader)?;
Box::new(ErasedSegmentSortKeyComputerWrapper {
inner,
converter: |val: Option<f64>| {
val.map(OwnedValue::F64).unwrap_or(OwnedValue::Null)
},
})
}
ColumnType::Bool => {
let computer = SortByStaticFastValue::<bool>::for_field(column_name);
let inner = computer.segment_sort_key_computer(segment_reader)?;
Box::new(ErasedSegmentSortKeyComputerWrapper {
inner,
converter: |val: Option<bool>| {
val.map(OwnedValue::Bool).unwrap_or(OwnedValue::Null)
},
})
}
ColumnType::DateTime => {
let computer = SortByStaticFastValue::<DateTime>::for_field(column_name);
let inner = computer.segment_sort_key_computer(segment_reader)?;
Box::new(ErasedSegmentSortKeyComputerWrapper {
inner,
converter: |val: Option<DateTime>| {
val.map(OwnedValue::Date).unwrap_or(OwnedValue::Null)
},
})
}
column_type => {
return Err(crate::TantivyError::SchemaError(format!(
"Field `{}` is of type {column_type:?}, which is not supported for \
sorting by owned value yet.",
column_name
)))
}
}
}
Self::Score => Box::new(ScoreSegmentSortKeyComputer {
segment_computer: SortBySimilarityScore,
}),
};
Ok(ErasedColumnSegmentSortKeyComputer { inner })
}
}
pub struct ErasedColumnSegmentSortKeyComputer {
inner: Box<dyn ErasedSegmentSortKeyComputer>,
}
impl SegmentSortKeyComputer for ErasedColumnSegmentSortKeyComputer {
type SortKey = OwnedValue;
type SegmentSortKey = Option<u64>;
type SegmentComparator = NaturalComparator;
#[inline(always)]
fn segment_sort_key(&mut self, doc: DocId, score: Score) -> Option<u64> {
self.inner.segment_sort_key(doc, score)
}
fn convert_segment_sort_key(&self, segment_sort_key: Self::SegmentSortKey) -> OwnedValue {
self.inner.convert_segment_sort_key(segment_sort_key)
}
}
#[cfg(test)]
mod tests {
use crate::collector::sort_key::{ComparatorEnum, SortByErasedType};
use crate::collector::TopDocs;
use crate::query::AllQuery;
use crate::schema::{OwnedValue, Schema, FAST, TEXT};
use crate::Index;
#[test]
fn test_sort_by_owned_u64() {
let mut schema_builder = Schema::builder();
let id_field = schema_builder.add_u64_field("id", FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut writer = index.writer_for_tests().unwrap();
writer.add_document(doc!(id_field => 10u64)).unwrap();
writer.add_document(doc!(id_field => 2u64)).unwrap();
writer.add_document(doc!()).unwrap();
writer.commit().unwrap();
let reader = index.reader().unwrap();
let searcher = reader.searcher();
let collector = TopDocs::with_limit(10)
.order_by((SortByErasedType::for_field("id"), ComparatorEnum::Natural));
let top_docs = searcher.search(&AllQuery, &collector).unwrap();
let values: Vec<OwnedValue> = top_docs.into_iter().map(|(key, _)| key).collect();
assert_eq!(
values,
vec![OwnedValue::U64(10), OwnedValue::U64(2), OwnedValue::Null]
);
let collector = TopDocs::with_limit(10).order_by((
SortByErasedType::for_field("id"),
ComparatorEnum::ReverseNoneLower,
));
let top_docs = searcher.search(&AllQuery, &collector).unwrap();
let values: Vec<OwnedValue> = top_docs.into_iter().map(|(key, _)| key).collect();
assert_eq!(
values,
vec![OwnedValue::U64(2), OwnedValue::U64(10), OwnedValue::Null]
);
}
#[test]
fn test_sort_by_owned_string() {
let mut schema_builder = Schema::builder();
let city_field = schema_builder.add_text_field("city", FAST | TEXT);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut writer = index.writer_for_tests().unwrap();
writer.add_document(doc!(city_field => "tokyo")).unwrap();
writer.add_document(doc!(city_field => "austin")).unwrap();
writer.add_document(doc!()).unwrap();
writer.commit().unwrap();
let reader = index.reader().unwrap();
let searcher = reader.searcher();
let collector = TopDocs::with_limit(10).order_by((
SortByErasedType::for_field("city"),
ComparatorEnum::ReverseNoneLower,
));
let top_docs = searcher.search(&AllQuery, &collector).unwrap();
let values: Vec<OwnedValue> = top_docs.into_iter().map(|(key, _)| key).collect();
assert_eq!(
values,
vec![
OwnedValue::Str("austin".to_string()),
OwnedValue::Str("tokyo".to_string()),
OwnedValue::Null
]
);
}
#[test]
fn test_sort_by_owned_reverse() {
let mut schema_builder = Schema::builder();
let id_field = schema_builder.add_u64_field("id", FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut writer = index.writer_for_tests().unwrap();
writer.add_document(doc!(id_field => 10u64)).unwrap();
writer.add_document(doc!(id_field => 2u64)).unwrap();
writer.add_document(doc!()).unwrap();
writer.commit().unwrap();
let reader = index.reader().unwrap();
let searcher = reader.searcher();
let collector = TopDocs::with_limit(10)
.order_by((SortByErasedType::for_field("id"), ComparatorEnum::Reverse));
let top_docs = searcher.search(&AllQuery, &collector).unwrap();
let values: Vec<OwnedValue> = top_docs.into_iter().map(|(key, _)| key).collect();
assert_eq!(
values,
vec![OwnedValue::Null, OwnedValue::U64(2), OwnedValue::U64(10)]
);
}
#[test]
fn test_sort_by_owned_score() {
let mut schema_builder = Schema::builder();
let body_field = schema_builder.add_text_field("body", TEXT);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut writer = index.writer_for_tests().unwrap();
writer.add_document(doc!(body_field => "a a")).unwrap();
writer.add_document(doc!(body_field => "a")).unwrap();
writer.commit().unwrap();
let reader = index.reader().unwrap();
let searcher = reader.searcher();
let query_parser = crate::query::QueryParser::for_index(&index, vec![body_field]);
let query = query_parser.parse_query("a").unwrap();
// Sort by score descending (Natural)
let collector = TopDocs::with_limit(10)
.order_by((SortByErasedType::for_score(), ComparatorEnum::Natural));
let top_docs = searcher.search(&query, &collector).unwrap();
let values: Vec<f64> = top_docs
.into_iter()
.map(|(key, _)| match key {
OwnedValue::F64(val) => val,
_ => panic!("Wrong type {key:?}"),
})
.collect();
assert_eq!(values.len(), 2);
assert!(values[0] > values[1]);
// Sort by score ascending (ReverseNoneLower)
let collector = TopDocs::with_limit(10).order_by((
SortByErasedType::for_score(),
ComparatorEnum::ReverseNoneLower,
));
let top_docs = searcher.search(&query, &collector).unwrap();
let values: Vec<f64> = top_docs
.into_iter()
.map(|(key, _)| match key {
OwnedValue::F64(val) => val,
_ => panic!("Wrong type {key:?}"),
})
.collect();
assert_eq!(values.len(), 2);
assert!(values[0] < values[1]);
}
}

View File

@@ -63,8 +63,8 @@ impl SortKeyComputer for SortBySimilarityScore {
impl SegmentSortKeyComputer for SortBySimilarityScore {
type SortKey = Score;
type SegmentSortKey = Score;
type SegmentComparator = NaturalComparator;
#[inline(always)]
fn segment_sort_key(&mut self, _doc: DocId, score: Score) -> Score {

View File

@@ -34,9 +34,7 @@ impl<T: FastValue> SortByStaticFastValue<T> {
impl<T: FastValue> SortKeyComputer for SortByStaticFastValue<T> {
type Child = SortByFastValueSegmentSortKeyComputer<T>;
type SortKey = Option<T>;
type Comparator = NaturalComparator;
fn check_schema(&self, schema: &crate::schema::Schema) -> crate::Result<()> {
@@ -84,8 +82,8 @@ pub struct SortByFastValueSegmentSortKeyComputer<T> {
impl<T: FastValue> SegmentSortKeyComputer for SortByFastValueSegmentSortKeyComputer<T> {
type SortKey = Option<T>;
type SegmentSortKey = Option<u64>;
type SegmentComparator = NaturalComparator;
#[inline(always)]
fn segment_sort_key(&mut self, doc: DocId, _score: Score) -> Self::SegmentSortKey {

View File

@@ -30,9 +30,7 @@ impl SortByString {
impl SortKeyComputer for SortByString {
type SortKey = Option<String>;
type Child = ByStringColumnSegmentSortKeyComputer;
type Comparator = NaturalComparator;
fn segment_sort_key_computer(
@@ -50,8 +48,8 @@ pub struct ByStringColumnSegmentSortKeyComputer {
impl SegmentSortKeyComputer for ByStringColumnSegmentSortKeyComputer {
type SortKey = Option<String>;
type SegmentSortKey = Option<TermOrdinal>;
type SegmentComparator = NaturalComparator;
#[inline(always)]
fn segment_sort_key(&mut self, doc: DocId, _score: Score) -> Option<TermOrdinal> {
@@ -60,6 +58,8 @@ impl SegmentSortKeyComputer for ByStringColumnSegmentSortKeyComputer {
}
fn convert_segment_sort_key(&self, term_ord_opt: Option<TermOrdinal>) -> Option<String> {
// TODO: Individual lookups to the dictionary like this are very likely to repeatedly
// decompress the same blocks. See https://github.com/quickwit-oss/tantivy/issues/2776
let term_ord = term_ord_opt?;
let str_column = self.str_column_opt.as_ref()?;
let mut bytes = Vec::new();

View File

@@ -12,13 +12,21 @@ use crate::{DocAddress, DocId, Result, Score, SegmentReader};
/// It is the segment local version of the [`SortKeyComputer`].
pub trait SegmentSortKeyComputer: 'static {
/// The final score being emitted.
type SortKey: 'static + PartialOrd + Send + Sync + Clone;
type SortKey: 'static + Send + Sync + Clone;
/// Sort key used by at the segment level by the `SegmentSortKeyComputer`.
///
/// It is typically small like a `u64`, and is meant to be converted
/// to the final score at the end of the collection of the segment.
type SegmentSortKey: 'static + PartialOrd + Clone + Send + Sync + Clone;
type SegmentSortKey: 'static + Clone + Send + Sync + Clone;
/// Comparator type.
type SegmentComparator: Comparator<Self::SegmentSortKey> + 'static;
/// Returns the segment sort key comparator.
fn segment_comparator(&self) -> Self::SegmentComparator {
Self::SegmentComparator::default()
}
/// Computes the sort key for the given document and score.
fn segment_sort_key(&mut self, doc: DocId, score: Score) -> Self::SegmentSortKey;
@@ -47,7 +55,7 @@ pub trait SegmentSortKeyComputer: 'static {
left: &Self::SegmentSortKey,
right: &Self::SegmentSortKey,
) -> Ordering {
NaturalComparator.compare(left, right)
self.segment_comparator().compare(left, right)
}
/// Implementing this method makes it possible to avoid computing
@@ -81,7 +89,7 @@ pub trait SegmentSortKeyComputer: 'static {
/// the sort key at a segment scale.
pub trait SortKeyComputer: Sync {
/// The sort key type.
type SortKey: 'static + Send + Sync + PartialOrd + Clone + std::fmt::Debug;
type SortKey: 'static + Send + Sync + Clone + std::fmt::Debug;
/// Type of the associated [`SegmentSortKeyComputer`].
type Child: SegmentSortKeyComputer<SortKey = Self::SortKey>;
/// Comparator type.
@@ -136,10 +144,7 @@ where
HeadSortKeyComputer: SortKeyComputer,
TailSortKeyComputer: SortKeyComputer,
{
type SortKey = (
<HeadSortKeyComputer::Child as SegmentSortKeyComputer>::SortKey,
<TailSortKeyComputer::Child as SegmentSortKeyComputer>::SortKey,
);
type SortKey = (HeadSortKeyComputer::SortKey, TailSortKeyComputer::SortKey);
type Child = (HeadSortKeyComputer::Child, TailSortKeyComputer::Child);
type Comparator = (
@@ -188,6 +193,11 @@ where
TailSegmentSortKeyComputer::SegmentSortKey,
);
type SegmentComparator = (
HeadSegmentSortKeyComputer::SegmentComparator,
TailSegmentSortKeyComputer::SegmentComparator,
);
/// A SegmentSortKeyComputer maps to a SegmentSortKey, but it can also decide on
/// its ordering.
///
@@ -269,11 +279,12 @@ impl<T, PreviousScore, NewScore> SegmentSortKeyComputer
for MappedSegmentSortKeyComputer<T, PreviousScore, NewScore>
where
T: SegmentSortKeyComputer<SortKey = PreviousScore>,
PreviousScore: 'static + Clone + Send + Sync + PartialOrd,
NewScore: 'static + Clone + Send + Sync + PartialOrd,
PreviousScore: 'static + Clone + Send + Sync,
NewScore: 'static + Clone + Send + Sync,
{
type SortKey = NewScore;
type SegmentSortKey = T::SegmentSortKey;
type SegmentComparator = T::SegmentComparator;
fn segment_sort_key(&mut self, doc: DocId, score: Score) -> Self::SegmentSortKey {
self.sort_key_computer.segment_sort_key(doc, score)
@@ -463,6 +474,7 @@ where
{
type SortKey = TSortKey;
type SegmentSortKey = TSortKey;
type SegmentComparator = NaturalComparator;
fn segment_sort_key(&mut self, doc: DocId, _score: Score) -> TSortKey {
(self)(doc)

View File

@@ -1,64 +1,22 @@
use std::cmp::Ordering;
use serde::{Deserialize, Serialize};
/// Contains a feature (field, score, etc.) of a document along with the document address.
///
/// It guarantees stable sorting: in case of a tie on the feature, the document
/// address is used.
///
/// The REVERSE_ORDER generic parameter controls whether the by-feature order
/// should be reversed, which is useful for achieving for example largest-first
/// semantics without having to wrap the feature in a `Reverse`.
#[derive(Clone, Default, Serialize, Deserialize)]
pub struct ComparableDoc<T, D, const REVERSE_ORDER: bool = false> {
/// Used only by TopNComputer, which implements the actual comparison via a `Comparator`.
#[derive(Clone, Default, Eq, PartialEq, Serialize, Deserialize)]
pub struct ComparableDoc<T, D> {
/// The feature of the document. In practice, this is
/// is any type that implements `PartialOrd`.
/// is a type which can be compared with a `Comparator<T>`.
pub sort_key: T,
/// The document address. In practice, this is any
/// type that implements `PartialOrd`, and is guaranteed
/// to be unique for each document.
/// The document address. In practice, this is either a `DocId` or `DocAddress`.
pub doc: D,
}
impl<T: std::fmt::Debug, D: std::fmt::Debug, const R: bool> std::fmt::Debug
for ComparableDoc<T, D, R>
{
impl<T: std::fmt::Debug, D: std::fmt::Debug> std::fmt::Debug for ComparableDoc<T, D> {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
f.debug_struct(format!("ComparableDoc<_, _ {R}").as_str())
f.debug_struct("ComparableDoc")
.field("feature", &self.sort_key)
.field("doc", &self.doc)
.finish()
}
}
impl<T: PartialOrd, D: PartialOrd, const R: bool> PartialOrd for ComparableDoc<T, D, R> {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
Some(self.cmp(other))
}
}
impl<T: PartialOrd, D: PartialOrd, const R: bool> Ord for ComparableDoc<T, D, R> {
#[inline]
fn cmp(&self, other: &Self) -> Ordering {
let by_feature = self
.sort_key
.partial_cmp(&other.sort_key)
.map(|ord| if R { ord.reverse() } else { ord })
.unwrap_or(Ordering::Equal);
let lazy_by_doc_address = || self.doc.partial_cmp(&other.doc).unwrap_or(Ordering::Equal);
// In case of a tie on the feature, we sort by ascending
// `DocAddress` in order to ensure a stable sorting of the
// documents.
by_feature.then_with(lazy_by_doc_address)
}
}
impl<T: PartialOrd, D: PartialOrd, const R: bool> PartialEq for ComparableDoc<T, D, R> {
fn eq(&self, other: &Self) -> bool {
self.cmp(other) == Ordering::Equal
}
}
impl<T: PartialOrd, D: PartialOrd, const R: bool> Eq for ComparableDoc<T, D, R> {}

View File

@@ -23,10 +23,9 @@ use crate::{DocAddress, DocId, Order, Score, SegmentReader};
/// The theoretical complexity for collecting the top `K` out of `N` documents
/// is `O(N + K)`.
///
/// This collector does not guarantee a stable sorting in case of a tie on the
/// document score, for stable sorting `PartialOrd` needs to resolve on other fields
/// like docid in case of score equality.
/// Only then, it is suitable for pagination.
/// This collector guarantees a stable sorting in case of a tie on the
/// document score/sort key: The document address (`DocAddress`) is used as a tie breaker.
/// In case of a tie on the sort key, documents are always sorted by ascending `DocAddress`.
///
/// ```rust
/// use tantivy::collector::TopDocs;
@@ -325,7 +324,7 @@ impl TopDocs {
sort_key_computer: impl SortKeyComputer<SortKey = TSortKey> + Send + 'static,
) -> impl Collector<Fruit = Vec<(TSortKey, DocAddress)>>
where
TSortKey: 'static + Clone + Send + Sync + PartialOrd + std::fmt::Debug,
TSortKey: 'static + Clone + Send + Sync + std::fmt::Debug,
{
TopBySortKeyCollector::new(sort_key_computer, self.doc_range())
}
@@ -446,7 +445,7 @@ where
F: 'static + Send + Sync + Fn(&SegmentReader) -> TTweakScoreSortKeyFn,
TTweakScoreSortKeyFn: 'static + Fn(DocId, Score) -> TSortKey,
TweakScoreSegmentSortKeyComputer<TTweakScoreSortKeyFn>:
SegmentSortKeyComputer<SortKey = TSortKey>,
SegmentSortKeyComputer<SortKey = TSortKey, SegmentSortKey = TSortKey>,
TSortKey: 'static + PartialOrd + Clone + Send + Sync + std::fmt::Debug,
{
type SortKey = TSortKey;
@@ -481,6 +480,7 @@ where
{
type SortKey = TSortKey;
type SegmentSortKey = TSortKey;
type SegmentComparator = NaturalComparator;
fn segment_sort_key(&mut self, doc: DocId, score: Score) -> TSortKey {
(self.sort_key_fn)(doc, score)
@@ -500,8 +500,13 @@ where
///
/// For TopN == 0, it will be relative expensive.
///
/// When using the natural comparator, the top N computer returns the top N elements in
/// descending order, as expected for a top N.
/// The TopNComputer will tiebreak by using ascending `D` (DocId or DocAddress):
/// i.e., in case of a tie on the sort key, the `DocId|DocAddress` are always sorted in
/// ascending order, regardless of the `Comparator` used for the `Score` type.
///
/// NOTE: Items must be `push`ed to the TopNComputer in ascending `DocId|DocAddress` order, as the
/// threshold used to eliminate docs does not include the `DocId` or `DocAddress`: this provides
/// the ascending `DocId|DocAddress` tie-breaking behavior without additional comparisons.
#[derive(Serialize, Deserialize)]
#[serde(from = "TopNComputerDeser<Score, D, C>")]
pub struct TopNComputer<Score, D, C> {
@@ -580,6 +585,18 @@ where
}
}
#[inline(always)]
fn compare_for_top_k<TSortKey, D: Ord, C: Comparator<TSortKey>>(
c: &C,
lhs: &ComparableDoc<TSortKey, D>,
rhs: &ComparableDoc<TSortKey, D>,
) -> std::cmp::Ordering {
c.compare(&lhs.sort_key, &rhs.sort_key)
.reverse() // Reverse here because we want top K.
.then_with(|| lhs.doc.cmp(&rhs.doc)) // Regardless of asc/desc, in presence of a tie, we
// sort by doc id
}
impl<TSortKey, D, C> TopNComputer<TSortKey, D, C>
where
D: Ord,
@@ -600,10 +617,13 @@ where
/// Push a new document to the top n.
/// If the document is below the current threshold, it will be ignored.
///
/// NOTE: `push` must be called in ascending `DocId`/`DocAddress` order.
#[inline]
pub fn push(&mut self, sort_key: TSortKey, doc: D) {
if let Some(last_median) = &self.threshold {
if self.comparator.compare(&sort_key, last_median) == Ordering::Less {
// See the struct docs for an explanation of why this comparison is strict.
if self.comparator.compare(&sort_key, last_median) != Ordering::Greater {
return;
}
}
@@ -629,9 +649,7 @@ where
fn truncate_top_n(&mut self) -> TSortKey {
// Use select_nth_unstable to find the top nth score
let (_, median_el, _) = self.buffer.select_nth_unstable_by(self.top_n, |lhs, rhs| {
self.comparator
.compare(&rhs.sort_key, &lhs.sort_key)
.then_with(|| lhs.doc.cmp(&rhs.doc))
compare_for_top_k(&self.comparator, lhs, rhs)
});
let median_score = median_el.sort_key.clone();
@@ -646,11 +664,8 @@ where
if self.buffer.len() > self.top_n {
self.truncate_top_n();
}
self.buffer.sort_unstable_by(|left, right| {
self.comparator
.compare(&right.sort_key, &left.sort_key)
.then_with(|| left.doc.cmp(&right.doc))
});
self.buffer
.sort_unstable_by(|lhs, rhs| compare_for_top_k(&self.comparator, lhs, rhs));
self.buffer
}
@@ -755,6 +770,33 @@ mod tests {
);
}
#[test]
fn test_topn_computer_duplicates() {
let mut computer: TopNComputer<u32, u32, NaturalComparator> =
TopNComputer::new_with_comparator(2, NaturalComparator);
computer.push(1u32, 1u32);
computer.push(1u32, 2u32);
computer.push(1u32, 3u32);
computer.push(1u32, 4u32);
computer.push(1u32, 5u32);
// In the presence of duplicates, DocIds are always ascending order.
assert_eq!(
computer.into_sorted_vec(),
&[
ComparableDoc {
sort_key: 1u32,
doc: 1u32,
},
ComparableDoc {
sort_key: 1u32,
doc: 2u32,
}
]
);
}
#[test]
fn test_topn_computer_no_panic() {
for top_n in 0..10 {
@@ -772,14 +814,17 @@ mod tests {
#[test]
fn test_topn_computer_asc_prop(
limit in 0..10_usize,
docs in proptest::collection::vec((0..100_u64, 0..100_u64), 0..100_usize),
mut docs in proptest::collection::vec((0..100_u64, 0..100_u64), 0..100_usize),
) {
// NB: TopNComputer must receive inputs in ascending DocId order.
docs.sort_by_key(|(_, doc_id)| *doc_id);
let mut computer: TopNComputer<_, _, ReverseComparator> = TopNComputer::new_with_comparator(limit, ReverseComparator);
for (feature, doc) in &docs {
computer.push(*feature, *doc);
}
let mut comparable_docs: Vec<ComparableDoc<u64, u64>> = docs.into_iter().map(|(sort_key, doc)| ComparableDoc { sort_key, doc }).collect::<Vec<_>>();
comparable_docs.sort();
let mut comparable_docs: Vec<ComparableDoc<u64, u64>> =
docs.into_iter().map(|(sort_key, doc)| ComparableDoc { sort_key, doc }).collect();
crate::collector::sort_key::tests::sort_hits(&mut comparable_docs, Order::Asc);
comparable_docs.truncate(limit);
prop_assert_eq!(
computer.into_sorted_vec(),
@@ -1406,15 +1451,10 @@ mod tests {
// Using the TopDocs collector should always be equivalent to sorting, skipping the
// offset, and then taking the limit.
let sorted_docs: Vec<_> = if order.is_desc() {
let mut comparable_docs: Vec<ComparableDoc<_, _, true>> =
let sorted_docs: Vec<_> = {
let mut comparable_docs: Vec<ComparableDoc<_, _>> =
all_results.into_iter().map(|(sort_key, doc)| ComparableDoc { sort_key, doc}).collect();
comparable_docs.sort();
comparable_docs.into_iter().map(|cd| (cd.sort_key, cd.doc)).collect()
} else {
let mut comparable_docs: Vec<ComparableDoc<_, _, false>> =
all_results.into_iter().map(|(sort_key, doc)| ComparableDoc { sort_key, doc}).collect();
comparable_docs.sort();
crate::collector::sort_key::tests::sort_hits(&mut comparable_docs, order);
comparable_docs.into_iter().map(|cd| (cd.sort_key, cd.doc)).collect()
};
let expected_docs = sorted_docs.into_iter().skip(offset).take(limit).collect::<Vec<_>>();

View File

@@ -48,7 +48,15 @@ impl Executor {
F: Sized + Sync + Fn(A) -> crate::Result<R>,
{
match self {
Executor::SingleThread => args.map(f).collect::<crate::Result<_>>(),
Executor::SingleThread => {
// Avoid `collect`, since the stacktrace is blown up by it, which makes profiling
// harder.
let mut result = Vec::with_capacity(args.size_hint().0);
for arg in args {
result.push(f(arg)?);
}
Ok(result)
}
Executor::ThreadPool(pool) => {
let args: Vec<A> = args.collect();
let num_fruits = args.len();

View File

@@ -406,7 +406,7 @@ mod tests {
let mut term = Term::from_field_json_path(field, "color", false);
term.append_type_and_str("red");
assert_eq!(term.serialized_term(), b"\x00\x00\x00\x01jcolor\x00sred")
assert_eq!(term.serialized_value_bytes(), b"color\x00sred".to_vec())
}
#[test]
@@ -416,8 +416,8 @@ mod tests {
term.append_type_and_fast_value(-4i64);
assert_eq!(
term.serialized_term(),
b"\x00\x00\x00\x01jcolor\x00i\x7f\xff\xff\xff\xff\xff\xff\xfc"
term.serialized_value_bytes(),
b"color\x00i\x7f\xff\xff\xff\xff\xff\xff\xfc".to_vec()
)
}
@@ -428,8 +428,8 @@ mod tests {
term.append_type_and_fast_value(4u64);
assert_eq!(
term.serialized_term(),
b"\x00\x00\x00\x01jcolor\x00u\x00\x00\x00\x00\x00\x00\x00\x04"
term.serialized_value_bytes(),
b"color\x00u\x00\x00\x00\x00\x00\x00\x00\x04".to_vec()
)
}
@@ -439,8 +439,8 @@ mod tests {
let mut term = Term::from_field_json_path(field, "color", false);
term.append_type_and_fast_value(4.0f64);
assert_eq!(
term.serialized_term(),
b"\x00\x00\x00\x01jcolor\x00f\xc0\x10\x00\x00\x00\x00\x00\x00"
term.serialized_value_bytes(),
b"color\x00f\xc0\x10\x00\x00\x00\x00\x00\x00".to_vec()
)
}
@@ -450,8 +450,8 @@ mod tests {
let mut term = Term::from_field_json_path(field, "color", false);
term.append_type_and_fast_value(true);
assert_eq!(
term.serialized_term(),
b"\x00\x00\x00\x01jcolor\x00o\x00\x00\x00\x00\x00\x00\x00\x01"
term.serialized_value_bytes(),
b"color\x00o\x00\x00\x00\x00\x00\x00\x00\x01".to_vec()
)
}

View File

@@ -5,7 +5,7 @@ use std::ops::Range;
use common::{BinarySerializable, CountingWriter, HasLen, VInt};
use crate::directory::{FileSlice, TerminatingWrite, WritePtr};
use crate::schema::Field;
use crate::schema::{Field, Schema};
use crate::space_usage::{FieldUsage, PerFieldSpaceUsage};
#[derive(Eq, PartialEq, Hash, Copy, Ord, PartialOrd, Clone, Debug)]
@@ -167,10 +167,11 @@ impl CompositeFile {
.map(|byte_range| self.data.slice(byte_range.clone()))
}
pub fn space_usage(&self) -> PerFieldSpaceUsage {
pub fn space_usage(&self, schema: &Schema) -> PerFieldSpaceUsage {
let mut fields = Vec::new();
for (&field_addr, byte_range) in &self.offsets_index {
let mut field_usage = FieldUsage::empty(field_addr.field);
let field_name = schema.get_field_name(field_addr.field).to_string();
let mut field_usage = FieldUsage::empty(field_name);
field_usage.add_field_idx(field_addr.idx, byte_range.len().into());
fields.push(field_usage);
}

View File

@@ -1,3 +1,5 @@
mod file_watcher;
use std::collections::HashMap;
use std::fmt;
use std::fs::{self, File, OpenOptions};
@@ -7,6 +9,7 @@ use std::path::{Path, PathBuf};
use std::sync::{Arc, RwLock, Weak};
use common::StableDeref;
use file_watcher::FileWatcher;
use fs4::fs_std::FileExt;
#[cfg(all(feature = "mmap", unix))]
pub use memmap2::Advice;
@@ -18,7 +21,6 @@ use crate::core::META_FILEPATH;
use crate::directory::error::{
DeleteError, LockError, OpenDirectoryError, OpenReadError, OpenWriteError,
};
use crate::directory::file_watcher::FileWatcher;
use crate::directory::{
AntiCallToken, Directory, DirectoryLock, FileHandle, Lock, OwnedBytes, TerminatingWrite,
WatchCallback, WatchHandle, WritePtr,

View File

@@ -5,7 +5,6 @@ mod mmap_directory;
mod directory;
mod directory_lock;
mod file_watcher;
pub mod footer;
mod managed_directory;
mod ram_directory;

View File

@@ -40,6 +40,8 @@ pub trait DocSet: Send {
/// of `DocSet` should support it.
///
/// Calling `seek(TERMINATED)` is also legal and is the normal way to consume a `DocSet`.
///
/// `target` has to be larger or equal to `.doc()` when calling `seek`.
fn seek(&mut self, target: DocId) -> DocId {
let mut doc = self.doc();
debug_assert!(doc <= target);
@@ -49,6 +51,33 @@ pub trait DocSet: Send {
doc
}
/// Seeks to the target if possible and returns true if the target is in the DocSet.
///
/// DocSets that already have an efficient `seek` method don't need to implement
/// `seek_into_the_danger_zone`. All wrapper DocSets should forward
/// `seek_into_the_danger_zone` to the underlying DocSet.
///
/// ## API Behaviour
/// If `seek_into_the_danger_zone` is returning true, a call to `doc()` has to return target.
/// If `seek_into_the_danger_zone` is returning false, a call to `doc()` may return any doc
/// between the last doc that matched and target or a doc that is a valid next hit after
/// target. The DocSet is considered to be in an invalid state until
/// `seek_into_the_danger_zone` returns true again.
///
/// `target` needs to be equal or larger than `doc` when in a valid state.
///
/// Consecutive calls are not allowed to have decreasing `target` values.
///
/// # Warning
/// This is an advanced API used by intersection. The API contract is tricky, avoid using it.
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
let current_doc = self.doc();
if current_doc < target {
self.seek(target);
}
self.doc() == target
}
/// Fills a given mutable buffer with the next doc ids from the
/// `DocSet`
///
@@ -94,6 +123,15 @@ pub trait DocSet: Send {
/// which would be the number of documents in the DocSet.
///
/// By default this returns `size_hint()`.
///
/// DocSets may have vastly different cost depending on their type,
/// e.g. an intersection with 10 hits is much cheaper than
/// a phrase search with 10 hits, since it needs to load positions.
///
/// ### Future Work
/// We may want to differentiate `DocSet` costs more more granular, e.g.
/// creation_cost, advance_cost, seek_cost on to get a good estimation
/// what query types to choose.
fn cost(&self) -> u64 {
self.size_hint() as u64
}
@@ -137,6 +175,10 @@ impl DocSet for &mut dyn DocSet {
(**self).seek(target)
}
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
(**self).seek_into_the_danger_zone(target)
}
fn doc(&self) -> u32 {
(**self).doc()
}
@@ -169,6 +211,11 @@ impl<TDocSet: DocSet + ?Sized> DocSet for Box<TDocSet> {
unboxed.seek(target)
}
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
let unboxed: &mut TDocSet = self.borrow_mut();
unboxed.seek_into_the_danger_zone(target)
}
fn fill_buffer(&mut self, buffer: &mut [DocId; COLLECT_BLOCK_BUFFER_LEN]) -> usize {
let unboxed: &mut TDocSet = self.borrow_mut();
unboxed.fill_buffer(buffer)

View File

@@ -8,7 +8,7 @@ use columnar::{
};
use common::ByteCount;
use crate::core::json_utils::encode_column_name;
use crate::core::json_utils::{encode_column_name, json_path_sep_to_dot};
use crate::directory::FileSlice;
use crate::schema::{Field, FieldEntry, FieldType, Schema};
use crate::space_usage::{FieldUsage, PerFieldSpaceUsage};
@@ -39,19 +39,15 @@ impl FastFieldReaders {
self.resolve_column_name_given_default_field(column_name, default_field_opt)
}
pub(crate) fn space_usage(&self, schema: &Schema) -> io::Result<PerFieldSpaceUsage> {
pub(crate) fn space_usage(&self) -> io::Result<PerFieldSpaceUsage> {
let mut per_field_usages: Vec<FieldUsage> = Default::default();
for (field, field_entry) in schema.fields() {
let column_handles = self.columnar.read_columns(field_entry.name())?;
let num_bytes: ByteCount = column_handles
.iter()
.map(|column_handle| column_handle.num_bytes())
.sum();
let mut field_usage = FieldUsage::empty(field);
field_usage.add_field_idx(0, num_bytes);
for (mut field_name, column_handle) in self.columnar.iter_columns()? {
json_path_sep_to_dot(&mut field_name);
let space_usage = column_handle.space_usage()?;
let mut field_usage = FieldUsage::empty(field_name);
field_usage.set_column_usage(space_usage);
per_field_usages.push(field_usage);
}
// TODO fix space usage for JSON fields.
Ok(PerFieldSpaceUsage::new(per_field_usages))
}

View File

@@ -2,7 +2,7 @@ use std::sync::Arc;
use super::{fieldnorm_to_id, id_to_fieldnorm};
use crate::directory::{CompositeFile, FileSlice, OwnedBytes};
use crate::schema::Field;
use crate::schema::{Field, Schema};
use crate::space_usage::PerFieldSpaceUsage;
use crate::DocId;
@@ -37,8 +37,8 @@ impl FieldNormReaders {
}
/// Return a break down of the space usage per field.
pub fn space_usage(&self) -> PerFieldSpaceUsage {
self.data.space_usage()
pub fn space_usage(&self, schema: &Schema) -> PerFieldSpaceUsage {
self.data.space_usage(schema)
}
/// Returns a handle to inner file

View File

@@ -13,9 +13,9 @@ use crate::store::Compressor;
use crate::{Inventory, Opstamp, TrackedObject};
#[derive(Clone, Debug, Serialize, Deserialize)]
struct DeleteMeta {
pub struct DeleteMeta {
num_deleted_docs: u32,
opstamp: Opstamp,
pub opstamp: Opstamp,
}
#[derive(Clone, Default)]
@@ -213,7 +213,7 @@ impl SegmentMeta {
struct InnerSegmentMeta {
segment_id: SegmentId,
max_doc: u32,
deletes: Option<DeleteMeta>,
pub deletes: Option<DeleteMeta>,
/// If you want to avoid the SegmentComponent::TempStore file to be covered by
/// garbage collection and deleted, set this to true. This is used during merge.
#[serde(skip)]
@@ -404,7 +404,10 @@ mod tests {
schema_builder.build()
};
let index_metas = IndexMeta {
index_settings: IndexSettings::default(),
index_settings: IndexSettings {
docstore_compression: Compressor::None,
..Default::default()
},
segments: Vec::new(),
schema,
opstamp: 0u64,
@@ -413,7 +416,7 @@ mod tests {
let json = serde_json::ser::to_string(&index_metas).expect("serialization failed");
assert_eq!(
json,
r#"{"index_settings":{"docstore_compression":"lz4","docstore_blocksize":16384},"segments":[],"schema":[{"name":"text","type":"text","options":{"indexing":{"record":"position","fieldnorms":true,"tokenizer":"default"},"stored":false,"fast":false}}],"opstamp":0}"#
r#"{"index_settings":{"docstore_compression":"none","docstore_blocksize":16384},"segments":[],"schema":[{"name":"text","type":"text","options":{"indexing":{"record":"position","fieldnorms":true,"tokenizer":"default"},"stored":false,"fast":false}}],"opstamp":0}"#
);
let deser_meta: UntrackedIndexMeta = serde_json::from_str(&json).unwrap();
@@ -494,6 +497,8 @@ mod tests {
#[test]
#[cfg(feature = "lz4-compression")]
fn test_index_settings_default() {
use crate::store::Compressor;
let mut index_settings = IndexSettings::default();
assert_eq!(
index_settings,

View File

@@ -46,7 +46,7 @@ impl Segment {
///
/// This method is only used when updating `max_doc` from 0
/// as we finalize a fresh new segment.
pub(crate) fn with_max_doc(self, max_doc: u32) -> Segment {
pub fn with_max_doc(self, max_doc: u32) -> Segment {
Segment {
index: self.index,
meta: self.meta.with_max_doc(max_doc),

View File

@@ -455,11 +455,11 @@ impl SegmentReader {
pub fn space_usage(&self) -> io::Result<SegmentSpaceUsage> {
Ok(SegmentSpaceUsage::new(
self.num_docs(),
self.termdict_composite.space_usage(),
self.postings_composite.space_usage(),
self.positions_composite.space_usage(),
self.fast_fields_readers.space_usage(self.schema())?,
self.fieldnorm_readers.space_usage(),
self.termdict_composite.space_usage(self.schema()),
self.postings_composite.space_usage(self.schema()),
self.positions_composite.space_usage(self.schema()),
self.fast_fields_readers.space_usage()?,
self.fieldnorm_readers.space_usage(self.schema()),
self.get_store_reader(0)?.space_usage(),
self.alive_bitset_opt
.as_ref()

View File

@@ -4,38 +4,37 @@ use std::sync::{Arc, RwLock, Weak};
use super::operation::DeleteOperation;
use crate::Opstamp;
// The DeleteQueue is similar in conceptually to a multiple
// consumer single producer broadcast channel.
//
// All consumer will receive all messages.
//
// Consumer of the delete queue are holding a `DeleteCursor`,
// which points to a specific place of the `DeleteQueue`.
//
// New consumer can be created in two ways
// - calling `delete_queue.cursor()` returns a cursor, that will include all future delete operation
// (and some or none of the past operations... The client is in charge of checking the opstamps.).
// - cloning an existing cursor returns a new cursor, that is at the exact same position, and can
// now advance independently from the original cursor.
/// The DeleteQueue is similar in conceptually to a multiple
/// consumer single producer broadcast channel.
///
/// All consumer will receive all messages.
///
/// Consumer of the delete queue are holding a `DeleteCursor`,
/// which points to a specific place of the `DeleteQueue`.
///
/// New consumer can be created in two ways
/// - calling `delete_queue.cursor()` returns a cursor, that will include all future delete
/// operation (and some or none of the past operations... The client is in charge of checking the
/// opstamps.).
/// - cloning an existing cursor returns a new cursor, that is at the exact same position, and can
/// now advance independently from the original cursor.
#[derive(Default)]
struct InnerDeleteQueue {
writer: Vec<DeleteOperation>,
last_block: Weak<Block>,
}
#[derive(Clone)]
/// The delete queue is a linked list storing delete operations.
///
/// Several consumers can hold a reference to it. Delete operations
/// get dropped/gc'ed when no more consumers are holding a reference
/// to them.
#[derive(Clone, Default)]
pub struct DeleteQueue {
inner: Arc<RwLock<InnerDeleteQueue>>,
}
impl DeleteQueue {
// Creates a new delete queue.
pub fn new() -> DeleteQueue {
DeleteQueue {
inner: Arc::default(),
}
}
fn get_last_block(&self) -> Arc<Block> {
{
// try get the last block with simply acquiring the read lock.
@@ -58,10 +57,10 @@ impl DeleteQueue {
block
}
// Creates a new cursor that makes it possible to
// consume future delete operations.
//
// Past delete operations are not accessible.
/// Creates a new cursor that makes it possible to
/// consume future delete operations.
///
/// Past delete operations are not accessible.
pub fn cursor(&self) -> DeleteCursor {
let last_block = self.get_last_block();
let operations_len = last_block.operations.len();
@@ -71,7 +70,7 @@ impl DeleteQueue {
}
}
// Appends a new delete operations.
/// Appends a new delete operations.
pub fn push(&self, delete_operation: DeleteOperation) {
self.inner
.write()
@@ -169,6 +168,7 @@ struct Block {
next: NextBlock,
}
/// As we process delete operations, keeps track of our position.
#[derive(Clone)]
pub struct DeleteCursor {
block: Arc<Block>,
@@ -261,7 +261,7 @@ mod tests {
#[test]
fn test_deletequeue() {
let delete_queue = DeleteQueue::new();
let delete_queue = DeleteQueue::default();
let make_op = |i: usize| DeleteOperation {
opstamp: i as u64,

View File

@@ -128,7 +128,7 @@ fn compute_deleted_bitset(
/// is `==` target_opstamp.
/// For instance, there was no delete operation between the state of the `segment_entry` and
/// the `target_opstamp`, `segment_entry` is not updated.
pub(crate) fn advance_deletes(
pub fn advance_deletes(
mut segment: Segment,
segment_entry: &mut SegmentEntry,
target_opstamp: Opstamp,
@@ -303,7 +303,7 @@ impl<D: Document> IndexWriter<D> {
let (document_sender, document_receiver) =
crossbeam_channel::bounded(PIPELINE_MAX_SIZE_IN_DOCS);
let delete_queue = DeleteQueue::new();
let delete_queue = DeleteQueue::default();
let current_opstamp = index.load_metas()?.opstamp;

View File

@@ -3,21 +3,21 @@ use std::net::Ipv6Addr;
use columnar::MonotonicallyMappableToU128;
use crate::fastfield::FastValue;
use crate::schema::{Field, Type};
use crate::schema::Field;
/// Term represents the value that the token can take.
/// It's a serialized representation over different types.
/// IndexingTerm is used to represent a term during indexing.
/// It's a serialized representation over field and value.
///
/// It actually wraps a `Vec<u8>`. The first 5 bytes are metadata.
/// 4 bytes are the field id, and the last byte is the type.
/// It actually wraps a `Vec<u8>`. The first 4 bytes are the field.
///
/// The serialized value `ValueBytes` is considered everything after the 4 first bytes (term id).
/// We serialize the field, because we index everything in a single
/// global term dictionary during indexing.
#[derive(Clone)]
pub(crate) struct IndexingTerm<B = Vec<u8>>(B)
where B: AsRef<[u8]>;
/// The number of bytes used as metadata by `Term`.
const TERM_METADATA_LENGTH: usize = 5;
const TERM_METADATA_LENGTH: usize = 4;
impl IndexingTerm {
/// Create a new Term with a buffer with a given capacity.
@@ -31,10 +31,9 @@ impl IndexingTerm {
/// Use `clear_with_field_and_type` in that case.
///
/// Sets field and the type.
pub(crate) fn set_field_and_type(&mut self, field: Field, typ: Type) {
pub(crate) fn set_field(&mut self, field: Field) {
assert!(self.is_empty());
self.0[0..4].clone_from_slice(field.field_id().to_be_bytes().as_ref());
self.0[4] = typ.to_code();
}
/// Is empty if there are no value bytes.
@@ -42,10 +41,10 @@ impl IndexingTerm {
self.0.len() == TERM_METADATA_LENGTH
}
/// Removes the value_bytes and set the field and type code.
pub(crate) fn clear_with_field_and_type(&mut self, typ: Type, field: Field) {
/// Removes the value_bytes and set the field
pub(crate) fn clear_with_field(&mut self, field: Field) {
self.truncate_value_bytes(0);
self.set_field_and_type(field, typ);
self.set_field(field);
}
/// Sets a u64 value in the term.
@@ -122,6 +121,23 @@ impl IndexingTerm {
impl<B> IndexingTerm<B>
where B: AsRef<[u8]>
{
/// Wraps serialized term bytes.
///
/// The input buffer is expected to be the concatenation of the big endian encoded field id
/// followed by the serialized value bytes (type tag + payload).
#[inline]
pub fn wrap(serialized_term: B) -> IndexingTerm<B> {
debug_assert!(serialized_term.as_ref().len() >= TERM_METADATA_LENGTH);
IndexingTerm(serialized_term)
}
/// Returns the field this term belongs to.
#[inline]
pub fn field(&self) -> Field {
let field_id_bytes: [u8; 4] = self.0.as_ref()[..4].try_into().unwrap();
Field::from_field_id(u32::from_be_bytes(field_id_bytes))
}
/// Returns the serialized representation of Term.
/// This includes field_id, value type and value.
///
@@ -136,6 +152,7 @@ where B: AsRef<[u8]>
#[cfg(test)]
mod tests {
use super::IndexingTerm;
use crate::schema::*;
#[test]
@@ -143,42 +160,55 @@ mod tests {
let mut schema_builder = Schema::builder();
schema_builder.add_text_field("text", STRING);
let title_field = schema_builder.add_text_field("title", STRING);
let term = Term::from_field_text(title_field, "test");
let mut term = IndexingTerm::with_capacity(0);
term.set_field(title_field);
term.set_bytes(b"test");
assert_eq!(term.field(), title_field);
assert_eq!(term.typ(), Type::Str);
assert_eq!(term.value().as_str(), Some("test"))
assert_eq!(term.serialized_term(), b"\x00\x00\x00\x01test".to_vec())
}
/// Size (in bytes) of the buffer of a fast value (u64, i64, f64, or date) term.
/// <field> + <type byte> + <value len>
///
/// - <field> is a big endian encoded u32 field id
/// - <type_byte>'s most significant bit expresses whether the term is a json term or not The
/// remaining 7 bits are used to encode the type of the value. If this is a JSON term, the
/// type is the type of the leaf of the json.
/// - <value> is, if this is not the json term, a binary representation specific to the type.
/// If it is a JSON Term, then it is prepended with the path that leads to this leaf value.
const FAST_VALUE_TERM_LEN: usize = 4 + 1 + 8;
const FAST_VALUE_TERM_LEN: usize = 4 + 8;
#[test]
pub fn test_term_u64() {
let mut schema_builder = Schema::builder();
let count_field = schema_builder.add_u64_field("count", INDEXED);
let term = Term::from_field_u64(count_field, 983u64);
let mut term = IndexingTerm::with_capacity(0);
term.set_field(count_field);
term.set_u64(983u64);
assert_eq!(term.field(), count_field);
assert_eq!(term.typ(), Type::U64);
assert_eq!(term.serialized_term().len(), FAST_VALUE_TERM_LEN);
assert_eq!(term.value().as_u64(), Some(983u64))
}
#[test]
pub fn test_term_bool() {
let mut schema_builder = Schema::builder();
let bool_field = schema_builder.add_bool_field("bool", INDEXED);
let term = Term::from_field_bool(bool_field, true);
let term = {
let mut term = IndexingTerm::with_capacity(0);
term.set_field(bool_field);
term.set_bool(true);
term
};
assert_eq!(term.field(), bool_field);
assert_eq!(term.typ(), Type::Bool);
assert_eq!(term.serialized_term().len(), FAST_VALUE_TERM_LEN);
assert_eq!(term.value().as_bool(), Some(true))
}
#[test]
pub fn indexing_term_wrap_extracts_field() {
let field = Field::from_field_id(7u32);
let mut term = IndexingTerm::with_capacity(0);
term.set_field(field);
term.append_bytes(b"abc");
let wrapped = IndexingTerm::wrap(term.serialized_term());
assert_eq!(wrapped.field(), field);
assert_eq!(wrapped.serialized_term(), term.serialized_term());
}
}

View File

@@ -4,6 +4,7 @@
//! `IndexWriter` is the main entry point for that, which created from
//! [`Index::writer`](crate::Index::writer).
/// Delete queue implementation for broadcasting delete operations to consumers.
pub(crate) mod delete_queue;
pub(crate) mod path_to_unordered_id;
@@ -32,12 +33,11 @@ mod stamper;
use crossbeam_channel as channel;
use smallvec::SmallVec;
pub use self::index_writer::{IndexWriter, IndexWriterOptions};
pub use self::index_writer::{advance_deletes, IndexWriter, IndexWriterOptions};
pub use self::log_merge_policy::LogMergePolicy;
pub use self::merge_operation::MergeOperation;
pub use self::merge_policy::{MergeCandidate, MergePolicy, NoMergePolicy};
use self::operation::AddOperation;
pub use self::operation::UserOperation;
pub use self::operation::{AddOperation, DeleteOperation, UserOperation};
pub use self::prepared_commit::PreparedCommit;
pub use self::segment_entry::SegmentEntry;
pub(crate) use self::segment_serializer::SegmentSerializer;

View File

@@ -5,14 +5,20 @@ use crate::Opstamp;
/// Timestamped Delete operation.
pub struct DeleteOperation {
/// Operation stamp.
/// It is used to check whether the delete operation
/// applies to an added document operation.
pub opstamp: Opstamp,
/// Weight is used to define the set of documents to be deleted.
pub target: Box<dyn Weight>,
}
/// Timestamped Add operation.
#[derive(Eq, PartialEq, Debug)]
pub struct AddOperation<D: Document = TantivyDocument> {
/// Operation stamp.
pub opstamp: Opstamp,
/// Document to be added.
pub document: D,
}

View File

@@ -117,7 +117,7 @@ mod tests {
#[test]
fn test_segment_register() {
let inventory = SegmentMetaInventory::default();
let delete_queue = DeleteQueue::new();
let delete_queue = DeleteQueue::default();
let mut segment_register = SegmentRegister::default();
let segment_id_a = SegmentId::generate_random();

View File

@@ -171,7 +171,7 @@ impl SegmentWriter {
let (term_buffer, ctx) = (&mut self.term_buffer, &mut self.ctx);
let postings_writer: &mut dyn PostingsWriter =
self.per_field_postings_writers.get_for_field_mut(field);
term_buffer.clear_with_field_and_type(field_entry.field_type().value_type(), field);
term_buffer.clear_with_field(field);
match field_entry.field_type() {
FieldType::Facet(_) => {
@@ -421,10 +421,9 @@ fn remap_and_write(
#[cfg(test)]
mod tests {
use std::collections::BTreeMap;
use std::path::{Path, PathBuf};
use std::path::Path;
use columnar::ColumnType;
use tempfile::TempDir;
use crate::collector::{Count, TopDocs};
use crate::directory::RamDirectory;
@@ -1067,10 +1066,7 @@ mod tests {
let mut schema_builder = Schema::builder();
schema_builder.add_text_field("title", text_options);
let schema = schema_builder.build();
let tempdir = TempDir::new().unwrap();
let tempdir_path = PathBuf::from(tempdir.path());
Index::create_in_dir(&tempdir_path, schema).unwrap();
let index = Index::open_in_dir(tempdir_path).unwrap();
let index = Index::create_in_ram(schema);
let schema = index.schema();
let mut index_writer = index.writer(50_000_000).unwrap();
let title = schema.get_field("title").unwrap();

View File

@@ -17,6 +17,7 @@
//!
//! ```rust
//! # use std::path::Path;
//! # use std::fs;
//! # use tempfile::TempDir;
//! # use tantivy::collector::TopDocs;
//! # use tantivy::query::QueryParser;
@@ -27,8 +28,11 @@
//! # // Let's create a temporary directory for the
//! # // sake of this example
//! # if let Ok(dir) = TempDir::new() {
//! # run_example(dir.path()).unwrap();
//! # dir.close().unwrap();
//! # let index_path = dir.path().join("index");
//! # // In case the directory already exists, we remove it
//! # let _ = fs::remove_dir_all(&index_path);
//! # fs::create_dir_all(&index_path).unwrap();
//! # run_example(&index_path).unwrap();
//! # }
//! # }
//! #
@@ -203,6 +207,7 @@ mod docset;
mod reader;
#[cfg(test)]
#[cfg(feature = "mmap")]
mod compat_tests;
pub use self::reader::{IndexReader, IndexReaderBuilder, ReloadPolicy, Warmer};
@@ -216,9 +221,7 @@ use once_cell::sync::Lazy;
use serde::{Deserialize, Serialize};
pub use self::docset::{DocSet, COLLECT_BLOCK_BUFFER_LEN, TERMINATED};
#[doc(hidden)]
pub use crate::core::json_utils;
pub use crate::core::{Executor, Searcher, SearcherGeneration};
pub use crate::core::{json_utils, Executor, Searcher, SearcherGeneration};
pub use crate::directory::Directory;
pub use crate::index::{
Index, IndexBuilder, IndexMeta, IndexSettings, InvertedIndexReader, Order, Segment,
@@ -1172,12 +1175,11 @@ pub mod tests {
#[test]
fn test_validate_checksum() -> crate::Result<()> {
let index_path = tempfile::tempdir().expect("dir");
let mut builder = Schema::builder();
let body = builder.add_text_field("body", TEXT | STORED);
let schema = builder.build();
let index = Index::create_in_dir(&index_path, schema)?;
let mut writer: IndexWriter = index.writer(50_000_000)?;
let index = Index::create_in_ram(schema);
let mut writer: IndexWriter = index.writer_for_tests()?;
writer.set_merge_policy(Box::new(NoMergePolicy));
for _ in 0..5000 {
writer.add_document(doc!(body => "foo"))?;

View File

@@ -1,12 +1,15 @@
use bitpacking::{BitPacker, BitPacker4x};
use common::FixedSize;
pub const COMPRESSION_BLOCK_SIZE: usize = BitPacker4x::BLOCK_LEN;
const COMPRESSED_BLOCK_MAX_SIZE: usize = COMPRESSION_BLOCK_SIZE * u32::SIZE_IN_BYTES;
// in vint encoding, each byte stores 7 bits of data, so we need at most 32 / 7 = 4.57 bytes to
// store a u32 in the worst case, rounding up to 5 bytes total
const MAX_VINT_SIZE: usize = 5;
const COMPRESSED_BLOCK_MAX_SIZE: usize = COMPRESSION_BLOCK_SIZE * MAX_VINT_SIZE;
mod vint;
/// Returns the size in bytes of a compressed block, given `num_bits`.
#[inline]
pub fn compressed_block_size(num_bits: u8) -> usize {
(num_bits as usize) * COMPRESSION_BLOCK_SIZE / 8
}
@@ -267,7 +270,6 @@ impl VIntDecoder for BlockDecoder {
#[cfg(test)]
pub(crate) mod tests {
use super::*;
use crate::TERMINATED;
@@ -372,6 +374,13 @@ pub(crate) mod tests {
}
}
}
#[test]
fn test_compress_vint_unsorted_does_not_overflow() {
let mut encoder = BlockEncoder::new();
let input: Vec<u32> = vec![u32::MAX; COMPRESSION_BLOCK_SIZE];
encoder.compress_vint_unsorted(&input);
}
}
#[cfg(all(test, feature = "unstable"))]

View File

@@ -8,7 +8,7 @@ use crate::indexer::path_to_unordered_id::OrderedPathId;
use crate::postings::postings_writer::SpecializedPostingsWriter;
use crate::postings::recorder::{BufferLender, DocIdRecorder, Recorder};
use crate::postings::{FieldSerializer, IndexingContext, IndexingPosition, PostingsWriter};
use crate::schema::{Field, Type, ValueBytes};
use crate::schema::{Field, Type};
use crate::tokenizer::TokenStream;
use crate::DocId;
@@ -79,8 +79,7 @@ impl<Rec: Recorder> PostingsWriter for JsonPostingsWriter<Rec> {
term_buffer.truncate(term_path_len);
term_buffer.append_bytes(term);
let json_value = ValueBytes::wrap(term);
let typ = json_value.typ();
let typ = Type::from_code(term[0]).expect("Invalid type code in JSON term");
if typ == Type::Str {
SpecializedPostingsWriter::<Rec>::serialize_one_term(
term_buffer.as_bytes(),
@@ -107,6 +106,8 @@ impl<Rec: Recorder> PostingsWriter for JsonPostingsWriter<Rec> {
}
}
/// Helper to build the JSON term bytes that land in the term dictionary.
/// Format: `[json path utf8][JSON_END_OF_PATH][type tag][payload]`
struct JsonTermSerializer(Vec<u8>);
impl JsonTermSerializer {
/// Appends a JSON path to the Term.

View File

@@ -527,6 +527,7 @@ pub(crate) mod tests {
}
impl<TScorer: Scorer> Scorer for UnoptimizedDocSet<TScorer> {
#[inline]
fn score(&mut self) -> Score {
self.0.score()
}

View File

@@ -11,7 +11,7 @@ use crate::postings::recorder::{BufferLender, Recorder};
use crate::postings::{
FieldSerializer, IndexingContext, InvertedIndexSerializer, PerFieldPostingsWriter,
};
use crate::schema::{Field, Schema, Term, Type};
use crate::schema::{Field, Schema, Type};
use crate::tokenizer::{Token, TokenStream, MAX_TOKEN_LEN};
use crate::DocId;
@@ -59,14 +59,14 @@ pub(crate) fn serialize_postings(
let mut term_offsets: Vec<(Field, OrderedPathId, &[u8], Addr)> =
Vec::with_capacity(ctx.term_index.len());
term_offsets.extend(ctx.term_index.iter().map(|(key, addr)| {
let field = Term::wrap(key).field();
let field = IndexingTerm::wrap(key).field();
if schema.get_field_entry(field).field_type().value_type() == Type::Json {
let byte_range_path = 5..5 + 4;
let byte_range_path = 4..4 + 4;
let unordered_id = u32::from_be_bytes(key[byte_range_path.clone()].try_into().unwrap());
let path_id = unordered_id_to_ordered_id[unordered_id as usize];
(field, path_id, &key[byte_range_path.end..], addr)
} else {
(field, 0.into(), &key[5..], addr)
(field, 0.into(), &key[4..], addr)
}
}));
// Sort by field, path, and term

View File

@@ -6,17 +6,21 @@ use crate::{DocId, Score, TERMINATED};
// doc num bits uses the following encoding:
// given 0b a b cdefgh
// |1|2| 3 |
// |1|2|3| 4 |
// - 1: unused
// - 2: is delta-1 encoded. 0 if not, 1, if yes
// - 3: a 6 bit number in 0..=32, the actual bitwidth
// - 3: unused
// - 4: a 5 bit number in 0..32, the actual bitwidth. Bitpacking could in theory say this is 32
// (requiring a 6th bit), but the biggest doc_id we can want to encode is TERMINATED-1, which can
// be represented on 31b without delta encoding.
fn encode_bitwidth(bitwidth: u8, delta_1: bool) -> u8 {
assert!(bitwidth < 32);
bitwidth | ((delta_1 as u8) << 6)
}
fn decode_bitwidth(raw_bitwidth: u8) -> (u8, bool) {
let delta_1 = ((raw_bitwidth >> 6) & 1) != 0;
let bitwidth = raw_bitwidth & 0x3f;
let bitwidth = raw_bitwidth & 0x1f;
(bitwidth, delta_1)
}
@@ -430,7 +434,7 @@ mod tests {
#[test]
fn test_encode_decode_bitwidth() {
for bitwidth in 0..=32 {
for bitwidth in 0..32 {
for delta_1 in [false, true] {
assert_eq!(
(bitwidth, delta_1),

View File

@@ -23,7 +23,11 @@ pub struct AllWeight;
impl Weight for AllWeight {
fn scorer(&self, reader: &SegmentReader, boost: Score) -> crate::Result<Box<dyn Scorer>> {
let all_scorer = AllScorer::new(reader.max_doc());
Ok(Box::new(BoostScorer::new(all_scorer, boost)))
if boost != 1.0 {
Ok(Box::new(BoostScorer::new(all_scorer, boost)))
} else {
Ok(Box::new(all_scorer))
}
}
fn explain(&self, reader: &SegmentReader, doc: DocId) -> crate::Result<Explanation> {
@@ -58,6 +62,15 @@ impl DocSet for AllScorer {
self.doc
}
fn seek(&mut self, target: DocId) -> DocId {
debug_assert!(target >= self.doc);
self.doc = target;
if self.doc >= self.max_doc {
self.doc = TERMINATED;
}
self.doc
}
fn fill_buffer(&mut self, buffer: &mut [DocId; COLLECT_BLOCK_BUFFER_LEN]) -> usize {
if self.doc() == TERMINATED {
return 0;
@@ -92,6 +105,7 @@ impl DocSet for AllScorer {
}
impl Scorer for AllScorer {
#[inline]
fn score(&mut self) -> Score {
1.0
}

View File

@@ -483,7 +483,7 @@ mod tests {
let checkpoints_for_each_pruning =
compute_checkpoints_for_each_pruning(term_scorers.clone(), top_k);
let checkpoints_manual =
compute_checkpoints_manual(term_scorers.clone(), top_k, 100_000);
compute_checkpoints_manual(term_scorers.clone(), top_k, max_doc as u32);
assert_eq!(checkpoints_for_each_pruning.len(), checkpoints_manual.len());
for (&(left_doc, left_score), &(right_doc, right_score)) in checkpoints_for_each_pruning
.iter()

View File

@@ -97,6 +97,65 @@ fn into_box_scorer<TScoreCombiner: ScoreCombiner>(
}
}
/// Returns the effective MUST scorer, accounting for removed AllScorers.
///
/// When AllScorer instances are removed from must_scorers as an optimization,
/// we must restore the "match all" semantics if the list becomes empty.
fn effective_must_scorer(
must_scorers: Vec<Box<dyn Scorer>>,
removed_all_scorer_count: usize,
max_doc: DocId,
num_docs: u32,
) -> Option<Box<dyn Scorer>> {
if must_scorers.is_empty() {
if removed_all_scorer_count > 0 {
// Had AllScorer(s) only - all docs match
Some(Box::new(AllScorer::new(max_doc)))
} else {
// No MUST constraint at all
None
}
} else {
Some(intersect_scorers(must_scorers, num_docs))
}
}
/// Returns a SHOULD scorer with AllScorer union if any were removed.
///
/// For union semantics (OR): if any SHOULD clause was an AllScorer, the result
/// should include all documents. We restore this by unioning with AllScorer.
///
/// When `scoring_enabled` is false, we can just return AllScorer alone since
/// we don't need score contributions from the should_scorer.
fn effective_should_scorer_for_union<TScoreCombiner: ScoreCombiner>(
should_scorer: SpecializedScorer,
removed_all_scorer_count: usize,
max_doc: DocId,
num_docs: u32,
score_combiner_fn: impl Fn() -> TScoreCombiner,
scoring_enabled: bool,
) -> SpecializedScorer {
if removed_all_scorer_count > 0 {
if scoring_enabled {
// Need to union to get score contributions from both
let all_scorers: Vec<Box<dyn Scorer>> = vec![
into_box_scorer(should_scorer, &score_combiner_fn, num_docs),
Box::new(AllScorer::new(max_doc)),
];
SpecializedScorer::Other(Box::new(BufferedUnionScorer::build(
all_scorers,
score_combiner_fn,
num_docs,
)))
} else {
// Scoring disabled - AllScorer alone is sufficient
SpecializedScorer::Other(Box::new(AllScorer::new(max_doc)))
}
} else {
should_scorer
}
}
enum ShouldScorersCombinationMethod {
// Should scorers are irrelevant.
Ignored,
@@ -193,18 +252,18 @@ impl<TScoreCombiner: ScoreCombiner> BooleanWeight<TScoreCombiner> {
return Ok(SpecializedScorer::Other(Box::new(EmptyScorer)));
}
let minimum_number_should_match = self
let effective_minimum_number_should_match = self
.minimum_number_should_match
.saturating_sub(should_special_scorer_counts.num_all_scorers);
let should_scorers: ShouldScorersCombinationMethod = {
let num_of_should_scorers = should_scorers.len();
if minimum_number_should_match > num_of_should_scorers {
if effective_minimum_number_should_match > num_of_should_scorers {
// We don't have enough scorers to satisfy the minimum number of should matches.
// The request will match no documents.
return Ok(SpecializedScorer::Other(Box::new(EmptyScorer)));
}
match minimum_number_should_match {
match effective_minimum_number_should_match {
0 if num_of_should_scorers == 0 => ShouldScorersCombinationMethod::Ignored,
0 => ShouldScorersCombinationMethod::Optional(scorer_union(
should_scorers,
@@ -226,7 +285,7 @@ impl<TScoreCombiner: ScoreCombiner> BooleanWeight<TScoreCombiner> {
scorer_disjunction(
should_scorers,
score_combiner_fn(),
self.minimum_number_should_match,
effective_minimum_number_should_match,
),
)),
}
@@ -246,53 +305,78 @@ impl<TScoreCombiner: ScoreCombiner> BooleanWeight<TScoreCombiner> {
let include_scorer = match (should_scorers, must_scorers) {
(ShouldScorersCombinationMethod::Ignored, must_scorers) => {
let boxed_scorer: Box<dyn Scorer> = if must_scorers.is_empty() {
// We do not have any should scorers, nor all scorers.
// There are still two cases here.
//
// If this follows the removal of some AllScorers in the should/must clauses,
// then we match all documents.
//
// Otherwise, it is really just an EmptyScorer.
if must_special_scorer_counts.num_all_scorers
+ should_special_scorer_counts.num_all_scorers
> 0
{
Box::new(AllScorer::new(reader.max_doc()))
} else {
Box::new(EmptyScorer)
}
} else {
intersect_scorers(must_scorers, num_docs)
};
// No SHOULD clauses (or they were absorbed into MUST).
// Result depends entirely on MUST + any removed AllScorers.
let combined_all_scorer_count = must_special_scorer_counts.num_all_scorers
+ should_special_scorer_counts.num_all_scorers;
let boxed_scorer: Box<dyn Scorer> = effective_must_scorer(
must_scorers,
combined_all_scorer_count,
reader.max_doc(),
num_docs,
)
.unwrap_or_else(|| Box::new(EmptyScorer));
SpecializedScorer::Other(boxed_scorer)
}
(ShouldScorersCombinationMethod::Optional(should_scorer), must_scorers) => {
if must_scorers.is_empty() && must_special_scorer_counts.num_all_scorers == 0 {
// Optional options are promoted to required if no must scorers exists.
should_scorer
} else {
let must_scorer = intersect_scorers(must_scorers, num_docs);
if self.scoring_enabled {
SpecializedScorer::Other(Box::new(RequiredOptionalScorer::<
_,
_,
TScoreCombiner,
>::new(
must_scorer,
into_box_scorer(should_scorer, &score_combiner_fn, num_docs),
)))
} else {
SpecializedScorer::Other(must_scorer)
// Optional SHOULD: contributes to scoring but not required for matching.
match effective_must_scorer(
must_scorers,
must_special_scorer_counts.num_all_scorers,
reader.max_doc(),
num_docs,
) {
None => {
// No MUST constraint: promote SHOULD to required.
// Must preserve any removed AllScorers from SHOULD via union.
effective_should_scorer_for_union(
should_scorer,
should_special_scorer_counts.num_all_scorers,
reader.max_doc(),
num_docs,
&score_combiner_fn,
self.scoring_enabled,
)
}
Some(must_scorer) => {
// Has MUST constraint: SHOULD only affects scoring.
if self.scoring_enabled {
SpecializedScorer::Other(Box::new(RequiredOptionalScorer::<
_,
_,
TScoreCombiner,
>::new(
must_scorer,
into_box_scorer(should_scorer, &score_combiner_fn, num_docs),
)))
} else {
SpecializedScorer::Other(must_scorer)
}
}
}
}
(ShouldScorersCombinationMethod::Required(should_scorer), mut must_scorers) => {
if must_scorers.is_empty() {
should_scorer
} else {
must_scorers.push(into_box_scorer(should_scorer, &score_combiner_fn, num_docs));
SpecializedScorer::Other(intersect_scorers(must_scorers, num_docs))
(ShouldScorersCombinationMethod::Required(should_scorer), must_scorers) => {
// Required SHOULD: at least `minimum_number_should_match` must match.
// Semantics: (MUST constraint) AND (SHOULD constraint)
match effective_must_scorer(
must_scorers,
must_special_scorer_counts.num_all_scorers,
reader.max_doc(),
num_docs,
) {
None => {
// No MUST constraint: SHOULD alone determines matching.
should_scorer
}
Some(must_scorer) => {
// Has MUST constraint: intersect MUST with SHOULD.
let should_boxed =
into_box_scorer(should_scorer, &score_combiner_fn, num_docs);
SpecializedScorer::Other(intersect_scorers(
vec![must_scorer, should_boxed],
num_docs,
))
}
}
}
};

View File

@@ -9,12 +9,14 @@ pub use self::boolean_weight::BooleanWeight;
#[cfg(test)]
mod tests {
use std::ops::Bound;
use super::*;
use crate::collector::tests::TEST_COLLECTOR_WITH_SCORE;
use crate::collector::TopDocs;
use crate::collector::{Count, TopDocs};
use crate::query::term_query::TermScorer;
use crate::query::{
AllScorer, EmptyScorer, EnableScoring, Intersection, Occur, Query, QueryParser,
AllScorer, EmptyScorer, EnableScoring, Intersection, Occur, Query, QueryParser, RangeQuery,
RequiredOptionalScorer, Scorer, SumCombiner, TermQuery,
};
use crate::schema::*;
@@ -374,4 +376,466 @@ mod tests {
}
Ok(())
}
#[test]
pub fn test_min_should_match_with_all_query() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let text_field = schema_builder.add_text_field("text", TEXT);
let num_field =
schema_builder.add_i64_field("num", NumericOptions::default().set_fast().set_indexed());
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer: IndexWriter = index.writer_for_tests()?;
index_writer.add_document(doc!(text_field => "apple", num_field => 10i64))?;
index_writer.add_document(doc!(text_field => "banana", num_field => 20i64))?;
index_writer.commit()?;
let searcher = index.reader()?.searcher();
let effective_all_match_query: Box<dyn Query> = Box::new(RangeQuery::new(
Bound::Excluded(Term::from_field_i64(num_field, 0)),
Bound::Unbounded,
));
let term_query: Box<dyn Query> = Box::new(TermQuery::new(
Term::from_field_text(text_field, "apple"),
IndexRecordOption::Basic,
));
// in some previous version, we would remove the 2 all_match, but then say we need *4*
// matches out of the 3 term queries, which matches nothing.
let mut bool_query = BooleanQuery::new(vec![
(Occur::Should, effective_all_match_query.box_clone()),
(Occur::Should, effective_all_match_query.box_clone()),
(Occur::Should, term_query.box_clone()),
(Occur::Should, term_query.box_clone()),
(Occur::Should, term_query.box_clone()),
]);
bool_query.set_minimum_number_should_match(4);
let count = searcher.search(&bool_query, &Count)?;
assert_eq!(count, 1);
Ok(())
}
// =========================================================================
// AllScorer Preservation Regression Tests
// =========================================================================
//
// These tests verify the fix for a bug where AllScorer instances (produced by
// queries matching all documents, such as range queries covering all values)
// were incorrectly removed from Boolean query processing, causing documents
// to be unexpectedly excluded from results.
//
// The bug manifested in several scenarios:
// 1. SHOULD + SHOULD where one clause is AllScorer
// 2. MUST (AllScorer) + SHOULD
// 3. Range queries in Boolean clauses when all documents match the range
/// Regression test: SHOULD clause with AllScorer combined with other SHOULD clauses.
///
/// When a SHOULD clause produces an AllScorer (e.g., from a range query matching
/// all documents), the Boolean query should still match all documents.
///
/// Bug before fix: AllScorer was removed during optimization, leaving only the
/// other SHOULD clauses, which incorrectly excluded documents.
#[test]
pub fn test_should_with_all_scorer_regression() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let text_field = schema_builder.add_text_field("text", TEXT);
let num_field =
schema_builder.add_i64_field("num", NumericOptions::default().set_fast().set_indexed());
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer: IndexWriter = index.writer_for_tests()?;
// All docs have num > 0, so range query will return AllScorer
index_writer.add_document(doc!(text_field => "hello", num_field => 10i64))?;
index_writer.add_document(doc!(text_field => "world", num_field => 20i64))?;
index_writer.add_document(doc!(text_field => "hello world", num_field => 30i64))?;
index_writer.add_document(doc!(text_field => "foo", num_field => 40i64))?;
index_writer.add_document(doc!(text_field => "bar", num_field => 50i64))?;
index_writer.add_document(doc!(text_field => "baz", num_field => 60i64))?;
index_writer.commit()?;
let searcher = index.reader()?.searcher();
// Range query matching all docs (returns AllScorer)
let all_match_query: Box<dyn Query> = Box::new(RangeQuery::new(
Bound::Excluded(Term::from_field_i64(num_field, 0)),
Bound::Unbounded,
));
let term_query: Box<dyn Query> = Box::new(TermQuery::new(
Term::from_field_text(text_field, "hello"),
IndexRecordOption::Basic,
));
// Verify range matches all 6 docs
assert_eq!(searcher.search(all_match_query.as_ref(), &Count)?, 6);
// RangeQuery(all) OR TermQuery should match all 6 docs
let bool_query = BooleanQuery::new(vec![
(Occur::Should, all_match_query.box_clone()),
(Occur::Should, term_query.box_clone()),
]);
let count = searcher.search(&bool_query, &Count)?;
assert_eq!(count, 6, "SHOULD with AllScorer should match all docs");
// Order should not matter
let bool_query_reversed = BooleanQuery::new(vec![
(Occur::Should, term_query.box_clone()),
(Occur::Should, all_match_query.box_clone()),
]);
let count_reversed = searcher.search(&bool_query_reversed, &Count)?;
assert_eq!(
count_reversed, 6,
"Order of SHOULD clauses should not matter"
);
Ok(())
}
/// Regression test: MUST clause with AllScorer combined with SHOULD clause.
///
/// When MUST contains an AllScorer, all documents satisfy the MUST constraint.
/// The SHOULD clause should only affect scoring, not filtering.
///
/// Bug before fix: AllScorer was removed, leaving an empty must_scorers vector.
/// intersect_scorers([]) incorrectly returned EmptyScorer, matching 0 documents.
#[test]
pub fn test_must_all_with_should_regression() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let text_field = schema_builder.add_text_field("text", TEXT);
let num_field =
schema_builder.add_i64_field("num", NumericOptions::default().set_fast().set_indexed());
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer: IndexWriter = index.writer_for_tests()?;
// All docs have num > 0, so range query will return AllScorer
index_writer.add_document(doc!(text_field => "apple", num_field => 10i64))?;
index_writer.add_document(doc!(text_field => "banana", num_field => 20i64))?;
index_writer.add_document(doc!(text_field => "cherry", num_field => 30i64))?;
index_writer.add_document(doc!(text_field => "date", num_field => 40i64))?;
index_writer.commit()?;
let searcher = index.reader()?.searcher();
// Range query matching all docs (returns AllScorer)
let all_match_query: Box<dyn Query> = Box::new(RangeQuery::new(
Bound::Excluded(Term::from_field_i64(num_field, 0)),
Bound::Unbounded,
));
let term_query: Box<dyn Query> = Box::new(TermQuery::new(
Term::from_field_text(text_field, "apple"),
IndexRecordOption::Basic,
));
// Verify range matches all 4 docs
assert_eq!(searcher.search(all_match_query.as_ref(), &Count)?, 4);
// MUST(range matching all) AND SHOULD(term) should match all 4 docs
let bool_query = BooleanQuery::new(vec![
(Occur::Must, all_match_query.box_clone()),
(Occur::Should, term_query.box_clone()),
]);
let count = searcher.search(&bool_query, &Count)?;
assert_eq!(count, 4, "MUST AllScorer + SHOULD should match all docs");
Ok(())
}
/// Regression test: Range queries in Boolean clauses when all documents match.
///
/// Range queries can return AllScorer as an optimization when all indexed values
/// fall within the range. This test ensures such queries work correctly in
/// Boolean combinations.
///
/// This is the most common real-world manifestation of the bug, occurring in
/// queries like: (age > 50 OR name = 'Alice') AND status = 'active'
/// when all documents have age > 50.
#[test]
pub fn test_range_query_all_match_in_boolean() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let name_field = schema_builder.add_text_field("name", TEXT);
let age_field =
schema_builder.add_i64_field("age", NumericOptions::default().set_fast().set_indexed());
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer: IndexWriter = index.writer_for_tests()?;
// All documents have age > 50, so range query will return AllScorer
index_writer.add_document(doc!(name_field => "alice", age_field => 55_i64))?;
index_writer.add_document(doc!(name_field => "bob", age_field => 60_i64))?;
index_writer.add_document(doc!(name_field => "charlie", age_field => 70_i64))?;
index_writer.add_document(doc!(name_field => "diana", age_field => 80_i64))?;
index_writer.commit()?;
let searcher = index.reader()?.searcher();
let range_query: Box<dyn Query> = Box::new(RangeQuery::new(
Bound::Excluded(Term::from_field_i64(age_field, 50)),
Bound::Unbounded,
));
let term_query: Box<dyn Query> = Box::new(TermQuery::new(
Term::from_field_text(name_field, "alice"),
IndexRecordOption::Basic,
));
// Verify preconditions
assert_eq!(searcher.search(range_query.as_ref(), &Count)?, 4);
assert_eq!(searcher.search(term_query.as_ref(), &Count)?, 1);
// SHOULD(range) OR SHOULD(term): range matches all, so result is 4
let should_query = BooleanQuery::new(vec![
(Occur::Should, range_query.box_clone()),
(Occur::Should, term_query.box_clone()),
]);
assert_eq!(
searcher.search(&should_query, &Count)?,
4,
"SHOULD range OR term should match all"
);
// MUST(range) AND SHOULD(term): range matches all, term is optional
let must_should_query = BooleanQuery::new(vec![
(Occur::Must, range_query.box_clone()),
(Occur::Should, term_query.box_clone()),
]);
assert_eq!(
searcher.search(&must_should_query, &Count)?,
4,
"MUST range + SHOULD term should match all"
);
Ok(())
}
/// Test multiple AllScorer instances in different clause types.
///
/// Verifies correct behavior when AllScorers appear in multiple positions.
#[test]
pub fn test_multiple_all_scorers() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let text_field = schema_builder.add_text_field("text", TEXT);
let num_field =
schema_builder.add_i64_field("num", NumericOptions::default().set_fast().set_indexed());
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer: IndexWriter = index.writer_for_tests()?;
// All docs have num > 0, so range queries will return AllScorer
index_writer.add_document(doc!(text_field => "doc1", num_field => 10i64))?;
index_writer.add_document(doc!(text_field => "doc2", num_field => 20i64))?;
index_writer.add_document(doc!(text_field => "doc3", num_field => 30i64))?;
index_writer.commit()?;
let searcher = index.reader()?.searcher();
// Two different range queries that both match all docs (return AllScorer)
let all_query1: Box<dyn Query> = Box::new(RangeQuery::new(
Bound::Excluded(Term::from_field_i64(num_field, 0)),
Bound::Unbounded,
));
let all_query2: Box<dyn Query> = Box::new(RangeQuery::new(
Bound::Excluded(Term::from_field_i64(num_field, 5)),
Bound::Unbounded,
));
let term_query: Box<dyn Query> = Box::new(TermQuery::new(
Term::from_field_text(text_field, "doc1"),
IndexRecordOption::Basic,
));
// Multiple AllScorers in SHOULD
let multi_all_should = BooleanQuery::new(vec![
(Occur::Should, all_query1.box_clone()),
(Occur::Should, all_query2.box_clone()),
(Occur::Should, term_query.box_clone()),
]);
assert_eq!(
searcher.search(&multi_all_should, &Count)?,
3,
"Multiple AllScorers in SHOULD"
);
// AllScorer in both MUST and SHOULD
let all_must_and_should = BooleanQuery::new(vec![
(Occur::Must, all_query1.box_clone()),
(Occur::Should, all_query2.box_clone()),
]);
assert_eq!(
searcher.search(&all_must_and_should, &Count)?,
3,
"AllScorer in both MUST and SHOULD"
);
Ok(())
}
}
/// A proptest which generates arbitrary permutations of a simple boolean AST, and then matches
/// the result against an index which contains all permutations of documents with N fields.
#[cfg(test)]
mod proptest_boolean_query {
use std::collections::{BTreeMap, HashSet};
use std::ops::{Bound, Range};
use proptest::collection::vec;
use proptest::prelude::*;
use crate::collector::DocSetCollector;
use crate::query::{AllQuery, BooleanQuery, Occur, Query, RangeQuery, TermQuery};
use crate::schema::{Field, NumericOptions, OwnedValue, Schema, TEXT};
use crate::{DocId, Index, Term};
#[derive(Debug, Clone)]
enum BooleanQueryAST {
/// Matches all documents via AllQuery (wraps AllScorer in BoostScorer)
All,
/// Matches all documents via RangeQuery (returns bare AllScorer)
/// This is the actual trigger for the AllScorer preservation bug
RangeAll,
/// Matches documents where the field has value "true"
Leaf {
field_idx: usize,
},
Union(Vec<BooleanQueryAST>),
Intersection(Vec<BooleanQueryAST>),
}
impl BooleanQueryAST {
fn matches(&self, doc_id: DocId) -> bool {
match self {
BooleanQueryAST::All => true,
BooleanQueryAST::RangeAll => true,
BooleanQueryAST::Leaf { field_idx } => Self::matches_field(doc_id, *field_idx),
BooleanQueryAST::Union(children) => {
children.iter().any(|child| child.matches(doc_id))
}
BooleanQueryAST::Intersection(children) => {
children.iter().all(|child| child.matches(doc_id))
}
}
}
fn matches_field(doc_id: DocId, field_idx: usize) -> bool {
((doc_id as usize) >> field_idx) & 1 == 1
}
fn to_query(&self, fields: &[Field], range_field: Field) -> Box<dyn Query> {
match self {
BooleanQueryAST::All => Box::new(AllQuery),
BooleanQueryAST::RangeAll => {
// Range query that matches all docs (all have value >= 0)
// This returns bare AllScorer, triggering the bug we fixed
Box::new(RangeQuery::new(
Bound::Included(Term::from_field_i64(range_field, 0)),
Bound::Unbounded,
))
}
BooleanQueryAST::Leaf { field_idx } => Box::new(TermQuery::new(
Term::from_field_text(fields[*field_idx], "true"),
crate::schema::IndexRecordOption::Basic,
)),
BooleanQueryAST::Union(children) => {
let sub_queries = children
.iter()
.map(|child| (Occur::Should, child.to_query(fields, range_field)))
.collect();
Box::new(BooleanQuery::new(sub_queries))
}
BooleanQueryAST::Intersection(children) => {
let sub_queries = children
.iter()
.map(|child| (Occur::Must, child.to_query(fields, range_field)))
.collect();
Box::new(BooleanQuery::new(sub_queries))
}
}
}
}
fn doc_ids(num_docs: usize, num_fields: usize) -> Range<DocId> {
let permutations = 1 << num_fields;
let copies = (num_docs as f32 / permutations as f32).ceil() as u32;
0..(permutations * copies)
}
fn create_index_with_boolean_permutations(
num_docs: usize,
num_fields: usize,
) -> (Index, Vec<Field>, Field) {
let mut schema_builder = Schema::builder();
let fields: Vec<Field> = (0..num_fields)
.map(|i| schema_builder.add_text_field(&format!("field_{}", i), TEXT))
.collect();
// Add a numeric field for RangeQuery tests - all docs have value = doc_id
let range_field = schema_builder.add_i64_field(
"range_field",
NumericOptions::default().set_fast().set_indexed(),
);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut writer = index.writer_for_tests().unwrap();
for doc_id in doc_ids(num_docs, num_fields) {
let mut doc: BTreeMap<_, OwnedValue> = BTreeMap::default();
for (field_idx, &field) in fields.iter().enumerate() {
if (doc_id >> field_idx) & 1 == 1 {
doc.insert(field, "true".into());
}
}
// All docs have non-negative values, so RangeQuery(>=0) matches all
doc.insert(range_field, (doc_id as i64).into());
writer.add_document(doc).unwrap();
}
writer.commit().unwrap();
(index, fields, range_field)
}
fn arb_boolean_query_ast(num_fields: usize) -> impl Strategy<Value = BooleanQueryAST> {
// Leaf strategies: term queries, AllQuery, and RangeQuery matching all docs
let leaf = prop_oneof![
(0..num_fields).prop_map(|field_idx| BooleanQueryAST::Leaf { field_idx }),
Just(BooleanQueryAST::All),
Just(BooleanQueryAST::RangeAll),
];
leaf.prop_recursive(
8, // 8 levels of recursion
256, // 256 nodes max
10, // 10 items per collection
|inner| {
prop_oneof![
vec(inner.clone(), 1..10).prop_map(BooleanQueryAST::Union),
vec(inner, 1..10).prop_map(BooleanQueryAST::Intersection),
]
},
)
}
#[test]
fn proptest_boolean_query() {
// In the presence of optimizations around buffering, it can take large numbers of
// documents to uncover some issues.
let num_fields = 8;
let num_docs = 1 << num_fields;
let (index, fields, range_field) =
create_index_with_boolean_permutations(num_docs, num_fields);
let searcher = index.reader().unwrap().searcher();
proptest!(|(ast in arb_boolean_query_ast(num_fields))| {
let query = ast.to_query(&fields, range_field);
let mut matching_docs = HashSet::new();
for doc_id in doc_ids(num_docs, num_fields) {
if ast.matches(doc_id as DocId) {
matching_docs.insert(doc_id as DocId);
}
}
let doc_addresses = searcher.search(&*query, &DocSetCollector).unwrap();
let result_docs: HashSet<DocId> =
doc_addresses.into_iter().map(|doc_address| doc_address.doc_id).collect();
prop_assert_eq!(result_docs, matching_docs);
});
}
}

View File

@@ -104,6 +104,9 @@ impl<S: Scorer> DocSet for BoostScorer<S> {
fn seek(&mut self, target: DocId) -> DocId {
self.underlying.seek(target)
}
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
self.underlying.seek_into_the_danger_zone(target)
}
fn fill_buffer(&mut self, buffer: &mut [DocId; COLLECT_BLOCK_BUFFER_LEN]) -> usize {
self.underlying.fill_buffer(buffer)
@@ -131,6 +134,7 @@ impl<S: Scorer> DocSet for BoostScorer<S> {
}
impl<S: Scorer> Scorer for BoostScorer<S> {
#[inline]
fn score(&mut self) -> Score {
self.underlying.score() * self.boost
}

View File

@@ -137,6 +137,7 @@ impl<TDocSet: DocSet> DocSet for ConstScorer<TDocSet> {
}
impl<TDocSet: DocSet + 'static> Scorer for ConstScorer<TDocSet> {
#[inline]
fn score(&mut self) -> Score {
self.score
}

View File

@@ -62,6 +62,16 @@ impl<T: Scorer> DocSet for ScorerWrapper<T> {
self.current_doc = doc_id;
doc_id
}
fn seek(&mut self, target: DocId) -> DocId {
let doc_id = self.scorer.seek(target);
self.current_doc = doc_id;
doc_id
}
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
let found = self.scorer.seek_into_the_danger_zone(target);
self.current_doc = self.scorer.doc();
found
}
fn doc(&self) -> DocId {
self.current_doc
@@ -163,6 +173,7 @@ impl<TScorer: Scorer, TScoreCombiner: ScoreCombiner> DocSet
impl<TScorer: Scorer, TScoreCombiner: ScoreCombiner> Scorer
for Disjunction<TScorer, TScoreCombiner>
{
#[inline]
fn score(&mut self) -> Score {
self.current_score
}
@@ -297,6 +308,7 @@ mod tests {
}
impl Scorer for DummyScorer {
#[inline]
fn score(&mut self) -> Score {
self.foo.get(self.cursor).map(|x| x.1).unwrap_or(0.0)
}

View File

@@ -55,6 +55,7 @@ impl DocSet for EmptyScorer {
}
impl Scorer for EmptyScorer {
#[inline]
fn score(&mut self) -> Score {
0.0
}

View File

@@ -84,6 +84,7 @@ where
TScorer: Scorer,
TDocSetExclude: DocSet + 'static,
{
#[inline]
fn score(&mut self) -> Score {
self.underlying_docset.score()
}

View File

@@ -1,5 +1,5 @@
use super::size_hint::estimate_intersection;
use crate::docset::{DocSet, TERMINATED};
use crate::query::size_hint::estimate_intersection;
use crate::query::term_query::TermScorer;
use crate::query::{EmptyScorer, Scorer};
use crate::{DocId, Score};
@@ -12,6 +12,9 @@ use crate::{DocId, Score};
/// For better performance, the function uses a
/// specialized implementation if the two
/// shortest scorers are `TermScorer`s.
///
/// num_docs_segment is the number of documents in the segment. It is used for estimating the
/// `size_hint` of the intersection.
pub fn intersect_scorers(
mut scorers: Vec<Box<dyn Scorer>>,
num_docs_segment: u32,
@@ -102,35 +105,48 @@ impl<TDocSet: DocSet> Intersection<TDocSet, TDocSet> {
}
impl<TDocSet: DocSet, TOtherDocSet: DocSet> DocSet for Intersection<TDocSet, TOtherDocSet> {
#[inline]
fn advance(&mut self) -> DocId {
let (left, right) = (&mut self.left, &mut self.right);
let mut candidate = left.advance();
if candidate == TERMINATED {
return TERMINATED;
}
'outer: loop {
loop {
// In the first part we look for a document in the intersection
// of the two rarest `DocSet` in the intersection.
loop {
let right_doc = right.seek(candidate);
candidate = left.seek(right_doc);
if candidate == right_doc {
if right.seek_into_the_danger_zone(candidate) {
break;
}
let right_doc = right.doc();
// TODO: Think about which value would make sense here
// It depends on the DocSet implementation, when a seek would outweigh an advance.
if right_doc > candidate.wrapping_add(100) {
candidate = left.seek(right_doc);
} else {
candidate = left.advance();
}
if candidate == TERMINATED {
return TERMINATED;
}
}
debug_assert_eq!(left.doc(), right.doc());
// test the remaining scorers;
for docset in self.others.iter_mut() {
let seek_doc = docset.seek(candidate);
if seek_doc > candidate {
candidate = left.seek(seek_doc);
continue 'outer;
}
// test the remaining scorers
if self
.others
.iter_mut()
.all(|docset| docset.seek_into_the_danger_zone(candidate))
{
debug_assert_eq!(candidate, self.left.doc());
debug_assert_eq!(candidate, self.right.doc());
debug_assert!(self.others.iter().all(|docset| docset.doc() == candidate));
return candidate;
}
debug_assert_eq!(candidate, self.left.doc());
debug_assert_eq!(candidate, self.right.doc());
debug_assert!(self.others.iter().all(|docset| docset.doc() == candidate));
return candidate;
candidate = left.advance();
}
}
@@ -146,6 +162,20 @@ impl<TDocSet: DocSet, TOtherDocSet: DocSet> DocSet for Intersection<TDocSet, TOt
doc
}
/// Seeks to the target if necessary and checks if the target is an exact match.
///
/// Some implementations may choose to advance past the target if beneficial for performance.
/// The return value is `true` if the target is in the docset, and `false` otherwise.
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
self.left.seek_into_the_danger_zone(target)
&& self.right.seek_into_the_danger_zone(target)
&& self
.others
.iter_mut()
.all(|docset| docset.seek_into_the_danger_zone(target))
}
#[inline]
fn doc(&self) -> DocId {
self.left.doc()
}
@@ -172,6 +202,7 @@ where
TScorer: Scorer,
TOtherScorer: Scorer,
{
#[inline]
fn score(&mut self) -> Score {
self.left.score()
+ self.right.score()
@@ -181,6 +212,8 @@ where
#[cfg(test)]
mod tests {
use proptest::prelude::*;
use super::Intersection;
use crate::docset::{DocSet, TERMINATED};
use crate::postings::tests::test_skip_against_unoptimized;
@@ -270,4 +303,38 @@ mod tests {
let intersection = Intersection::new(vec![a, b, c], 10);
assert_eq!(intersection.doc(), TERMINATED);
}
// Strategy to generate sorted and deduplicated vectors of u32 document IDs
fn sorted_deduped_vec(max_val: u32, max_size: usize) -> impl Strategy<Value = Vec<u32>> {
prop::collection::vec(0..max_val, 0..max_size).prop_map(|mut vec| {
vec.sort();
vec.dedup();
vec
})
}
proptest! {
#[test]
fn prop_test_intersection_consistency(
a in sorted_deduped_vec(100, 10),
b in sorted_deduped_vec(100, 10),
num_docs in 100u32..500u32
) {
let left = VecDocSet::from(a.clone());
let right = VecDocSet::from(b.clone());
let mut intersection = Intersection::new(vec![left, right], num_docs);
let expected: Vec<u32> = a.iter()
.cloned()
.filter(|doc| b.contains(doc))
.collect();
for expected_doc in expected {
assert_eq!(intersection.doc(), expected_doc);
intersection.advance();
}
assert_eq!(intersection.doc(), TERMINATED);
}
}
}

View File

@@ -70,9 +70,83 @@ pub use self::weight::Weight;
#[cfg(test)]
mod tests {
use crate::collector::TopDocs;
use crate::query::phrase_query::tests::create_index;
use crate::query::QueryParser;
use crate::schema::{Schema, TEXT};
use crate::{Index, Term};
use crate::{DocAddress, Index, Term};
#[test]
pub fn test_mixed_intersection_and_union() -> crate::Result<()> {
let index = create_index(&["a b", "a c", "a b c", "b"])?;
let schema = index.schema();
let text_field = schema.get_field("text").unwrap();
let searcher = index.reader()?.searcher();
let do_search = |term: &str| {
let query = QueryParser::for_index(&index, vec![text_field])
.parse_query(term)
.unwrap();
let top_docs: Vec<(f32, DocAddress)> = searcher
.search(&query, &TopDocs::with_limit(10).order_by_score())
.unwrap();
top_docs.iter().map(|el| el.1.doc_id).collect::<Vec<_>>()
};
assert_eq!(do_search("a AND b"), vec![0, 2]);
assert_eq!(do_search("(a OR b) AND C"), vec![2, 1]);
// The intersection code has special code for more than 2 intersections
// left, right + others
// The will place the union in the "others" insersection to that seek_into_the_danger_zone
// is called
assert_eq!(
do_search("(a OR b) AND (c OR a) AND (b OR c)"),
vec![2, 1, 0]
);
Ok(())
}
#[test]
pub fn test_mixed_intersection_and_union_with_skip() -> crate::Result<()> {
// Test 4096 skip in BufferedUnionScorer
let mut data: Vec<&str> = Vec::new();
data.push("a b");
let zz_data = vec!["z z"; 5000];
data.extend_from_slice(&zz_data);
data.extend_from_slice(&["a c"]);
data.extend_from_slice(&zz_data);
data.extend_from_slice(&["a b c", "b"]);
let index = create_index(&data)?;
let schema = index.schema();
let text_field = schema.get_field("text").unwrap();
let searcher = index.reader()?.searcher();
let do_search = |term: &str| {
let query = QueryParser::for_index(&index, vec![text_field])
.parse_query(term)
.unwrap();
let top_docs: Vec<(f32, DocAddress)> = searcher
.search(&query, &TopDocs::with_limit(10).order_by_score())
.unwrap();
top_docs.iter().map(|el| el.1.doc_id).collect::<Vec<_>>()
};
assert_eq!(do_search("a AND b"), vec![0, 10002]);
assert_eq!(do_search("(a OR b) AND C"), vec![10002, 5001]);
// The intersection code has special code for more than 2 intersections
// left, right + others
// The will place the union in the "others" insersection to that seek_into_the_danger_zone
// is called
assert_eq!(
do_search("(a OR b) AND (c OR a) AND (b OR c)"),
vec![10002, 5001, 0]
);
Ok(())
}
#[test]
fn test_query_terms() {

View File

@@ -81,6 +81,7 @@ impl<TPostings: Postings> DocSet for PhraseKind<TPostings> {
}
impl<TPostings: Postings> Scorer for PhraseKind<TPostings> {
#[inline]
fn score(&mut self) -> Score {
match self {
PhraseKind::SinglePrefix { positions, .. } => {
@@ -193,6 +194,14 @@ impl<TPostings: Postings> DocSet for PhrasePrefixScorer<TPostings> {
self.advance()
}
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
if self.phrase_scorer.seek_into_the_danger_zone(target) {
self.matches_prefix()
} else {
false
}
}
fn doc(&self) -> DocId {
self.phrase_scorer.doc()
}
@@ -207,6 +216,7 @@ impl<TPostings: Postings> DocSet for PhrasePrefixScorer<TPostings> {
}
impl<TPostings: Postings> Scorer for PhrasePrefixScorer<TPostings> {
#[inline]
fn score(&mut self) -> Score {
// TODO modify score??
self.phrase_scorer.score()

View File

@@ -382,8 +382,9 @@ impl<TPostings: Postings> PhraseScorer<TPostings> {
PostingsWithOffset::new(postings, (max_offset - offset) as u32)
})
.collect::<Vec<_>>();
let intersection_docset = Intersection::new(postings_with_offsets, num_docs);
let mut scorer = PhraseScorer {
intersection_docset: Intersection::new(postings_with_offsets, num_docs),
intersection_docset,
num_terms: num_docsets,
left_positions: Vec::with_capacity(100),
right_positions: Vec::with_capacity(100),
@@ -529,25 +530,40 @@ impl<TPostings: Postings> DocSet for PhraseScorer<TPostings> {
self.advance()
}
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
debug_assert!(target >= self.doc());
if self.intersection_docset.seek_into_the_danger_zone(target) && self.phrase_match() {
return true;
}
false
}
fn doc(&self) -> DocId {
self.intersection_docset.doc()
}
fn size_hint(&self) -> u32 {
self.intersection_docset.size_hint()
// We adjust the intersection estimate, since actual phrase hits are much lower than where
// the all appear.
// The estimate should depend on average field length, e.g. if the field is really short
// a phrase hit is more likely
self.intersection_docset.size_hint() / (10 * self.num_terms as u32)
}
/// Returns a best-effort hint of the
/// cost to drive the docset.
fn cost(&self) -> u64 {
// Evaluating phrase matches is generally more expensive than simple term matches,
// as it requires loading and comparing positions. Use a conservative multiplier
// based on the number of terms.
// While determing a potential hit is cheap for phrases, evaluating an actual hit is
// expensive since it requires to load positions for a doc and check if they are next to
// each other.
// So the cost estimation would be the number of times we need to check if a doc is a hit *
// 10 * self.num_terms.
self.intersection_docset.size_hint() as u64 * 10 * self.num_terms as u64
}
}
impl<TPostings: Postings> Scorer for PhraseScorer<TPostings> {
#[inline]
fn score(&mut self) -> Score {
let doc = self.doc();
let fieldnorm_id = self.fieldnorm_reader.fieldnorm_id(doc);

View File

@@ -62,6 +62,17 @@ pub(crate) struct RangeDocSet<T> {
const DEFAULT_FETCH_HORIZON: u32 = 128;
impl<T: Send + Sync + PartialOrd + Copy + Debug + 'static> RangeDocSet<T> {
pub(crate) fn new(value_range: RangeInclusive<T>, column: Column<T>) -> Self {
if *value_range.start() > column.max_value() || *value_range.end() < column.min_value() {
return Self {
value_range,
column,
loaded_docs: VecCursor::new(),
next_fetch_start: TERMINATED,
fetch_horizon: DEFAULT_FETCH_HORIZON,
last_seek_pos_opt: None,
};
}
let mut range_docset = Self {
value_range,
column,
@@ -81,6 +92,9 @@ impl<T: Send + Sync + PartialOrd + Copy + Debug + 'static> RangeDocSet<T> {
/// Returns true if more data could be fetched
fn fetch_block(&mut self) {
if self.next_fetch_start >= self.column.num_docs() {
return;
}
const MAX_HORIZON: u32 = 100_000;
while self.loaded_docs.is_empty() {
let finished_to_end = self.fetch_horizon(self.fetch_horizon);
@@ -105,10 +119,10 @@ impl<T: Send + Sync + PartialOrd + Copy + Debug + 'static> RangeDocSet<T> {
fn fetch_horizon(&mut self, horizon: u32) -> bool {
let mut finished_to_end = false;
let limit = self.column.num_docs();
let mut end = self.next_fetch_start + horizon;
if end >= limit {
end = limit;
let num_docs = self.column.num_docs();
let mut fetch_end = self.next_fetch_start + horizon;
if fetch_end >= num_docs {
fetch_end = num_docs;
finished_to_end = true;
}
@@ -116,7 +130,7 @@ impl<T: Send + Sync + PartialOrd + Copy + Debug + 'static> RangeDocSet<T> {
let doc_buffer: &mut Vec<DocId> = self.loaded_docs.get_cleared_data();
self.column.get_docids_for_value_range(
self.value_range.clone(),
self.next_fetch_start..end,
self.next_fetch_start..fetch_end,
doc_buffer,
);
if let Some(last_doc) = last_doc {
@@ -124,7 +138,7 @@ impl<T: Send + Sync + PartialOrd + Copy + Debug + 'static> RangeDocSet<T> {
self.loaded_docs.next();
}
}
self.next_fetch_start = end;
self.next_fetch_start = fetch_end;
finished_to_end
}
@@ -136,9 +150,6 @@ impl<T: Send + Sync + PartialOrd + Copy + Debug + 'static> DocSet for RangeDocSe
if let Some(docid) = self.loaded_docs.next() {
return docid;
}
if self.next_fetch_start >= self.column.num_docs() {
return TERMINATED;
}
self.fetch_block();
self.loaded_docs.current().unwrap_or(TERMINATED)
}
@@ -174,15 +185,25 @@ impl<T: Send + Sync + PartialOrd + Copy + Debug + 'static> DocSet for RangeDocSe
}
fn size_hint(&self) -> u32 {
self.column.num_docs()
// TODO: Implement a better size hint
self.column.num_docs() / 10
}
/// Returns a best-effort hint of the
/// cost to drive the docset.
fn cost(&self) -> u64 {
// Advancing the docset is relatively expensive since it scans the column.
// Keep cost relative to a term query driver; use num_docs as baseline.
self.column.num_docs() as u64
// Advancing the docset is pretty expensive since it scans the whole column, there is no
// index currently (will change with an kd-tree)
// Since we use SIMD to scan the fast field range query we lower the cost a little bit,
// assuming that we hit 10% of the docs like in size_hint.
//
// If we would return a cost higher than num_docs, we would never choose ff range query as
// the driver in a DocSet, when intersecting a term query with a fast field. But
// it's the faster choice when the term query has a lot of docids and the range
// query has not.
//
// Ideally this would take the fast field codec into account
(self.column.num_docs() as f64 * 0.8) as u64
}
}
@@ -236,4 +257,52 @@ mod tests {
let count = searcher.search(&query, &Count).unwrap();
assert_eq!(count, 500);
}
#[test]
fn range_query_no_overlap_optimization() {
let mut schema_builder = schema::SchemaBuilder::new();
let id_field = schema_builder.add_text_field("id", schema::STRING);
let value_field = schema_builder.add_u64_field("value", schema::FAST | schema::INDEXED);
let dir = RamDirectory::default();
let index = IndexBuilder::new()
.schema(schema_builder.build())
.open_or_create(dir)
.unwrap();
{
let mut writer = index.writer(15_000_000).unwrap();
// Add documents with values in the range [10, 20]
for i in 0..100 {
let mut doc = TantivyDocument::new();
doc.add_text(id_field, format!("doc{i}"));
doc.add_u64(value_field, 10 + (i % 11) as u64); // values in range 10-20
writer.add_document(doc).unwrap();
}
writer.commit().unwrap();
}
let reader = index.reader().unwrap();
let searcher = reader.searcher();
// Test a range query [100, 200] that has no overlap with data range [10, 20]
let query = RangeQuery::new(
Bound::Included(Term::from_field_u64(value_field, 100)),
Bound::Included(Term::from_field_u64(value_field, 200)),
);
let count = searcher.search(&query, &Count).unwrap();
assert_eq!(count, 0); // should return 0 results since there's no overlap
// Test another non-overlapping range: [0, 5] while data range is [10, 20]
let query2 = RangeQuery::new(
Bound::Included(Term::from_field_u64(value_field, 0)),
Bound::Included(Term::from_field_u64(value_field, 5)),
);
let count2 = searcher.search(&query2, &Count).unwrap();
assert_eq!(count2, 0); // should return 0 results since there's no overlap
}
}

View File

@@ -1598,449 +1598,3 @@ pub(crate) mod ip_range_tests {
Ok(())
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use test::Bencher;
use super::tests::*;
use super::*;
use crate::collector::Count;
use crate::query::QueryParser;
use crate::Index;
fn get_index_0_to_100() -> Index {
let mut rng = StdRng::from_seed([1u8; 32]);
let num_vals = 100_000;
let docs: Vec<_> = (0..num_vals)
.map(|_i| {
let id_name = if rng.gen_bool(0.01) {
"veryfew".to_string() // 1%
} else if rng.gen_bool(0.1) {
"few".to_string() // 9%
} else {
"many".to_string() // 90%
};
Doc {
id_name,
id: rng.gen_range(0..100),
}
})
.collect();
create_index_from_docs(&docs, false)
}
fn get_90_percent() -> RangeInclusive<u64> {
0..=90
}
fn get_10_percent() -> RangeInclusive<u64> {
0..=10
}
fn get_1_percent() -> RangeInclusive<u64> {
10..=10
}
fn execute_query(
field: &str,
id_range: RangeInclusive<u64>,
suffix: &str,
index: &Index,
) -> usize {
let gen_query_inclusive = |from: &u64, to: &u64| {
format!(
"{}:[{} TO {}] {}",
field,
&from.to_string(),
&to.to_string(),
suffix
)
};
let query = gen_query_inclusive(id_range.start(), id_range.end());
let query_from_text = |text: &str| {
QueryParser::for_index(index, vec![])
.parse_query(text)
.unwrap()
};
let query = query_from_text(&query);
let reader = index.reader().unwrap();
let searcher = reader.searcher();
searcher.search(&query, &(Count)).unwrap()
}
#[bench]
fn bench_id_range_hit_90_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_90_percent(), "", &index));
}
#[bench]
fn bench_id_range_hit_10_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_10_percent(), "", &index));
}
#[bench]
fn bench_id_range_hit_1_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_1_percent(), "", &index));
}
#[bench]
fn bench_id_range_hit_10_percent_intersect_with_10_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_10_percent(), "AND id_name:few", &index));
}
#[bench]
fn bench_id_range_hit_1_percent_intersect_with_10_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_1_percent(), "AND id_name:few", &index));
}
#[bench]
fn bench_id_range_hit_1_percent_intersect_with_90_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_1_percent(), "AND id_name:many", &index));
}
#[bench]
fn bench_id_range_hit_1_percent_intersect_with_1_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_1_percent(), "AND id_name:veryfew", &index));
}
#[bench]
fn bench_id_range_hit_10_percent_intersect_with_90_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_10_percent(), "AND id_name:many", &index));
}
#[bench]
fn bench_id_range_hit_90_percent_intersect_with_90_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_90_percent(), "AND id_name:many", &index));
}
#[bench]
fn bench_id_range_hit_90_percent_intersect_with_10_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_90_percent(), "AND id_name:few", &index));
}
#[bench]
fn bench_id_range_hit_90_percent_intersect_with_1_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("id", get_90_percent(), "AND id_name:veryfew", &index));
}
#[bench]
fn bench_id_range_hit_90_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_90_percent(), "", &index));
}
#[bench]
fn bench_id_range_hit_10_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_10_percent(), "", &index));
}
#[bench]
fn bench_id_range_hit_1_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_1_percent(), "", &index));
}
#[bench]
fn bench_id_range_hit_10_percent_intersect_with_10_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_10_percent(), "AND id_name:few", &index));
}
#[bench]
fn bench_id_range_hit_1_percent_intersect_with_10_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_1_percent(), "AND id_name:few", &index));
}
#[bench]
fn bench_id_range_hit_1_percent_intersect_with_90_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_1_percent(), "AND id_name:many", &index));
}
#[bench]
fn bench_id_range_hit_1_percent_intersect_with_1_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_1_percent(), "AND id_name:veryfew", &index));
}
#[bench]
fn bench_id_range_hit_10_percent_intersect_with_90_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_10_percent(), "AND id_name:many", &index));
}
#[bench]
fn bench_id_range_hit_90_percent_intersect_with_90_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_90_percent(), "AND id_name:many", &index));
}
#[bench]
fn bench_id_range_hit_90_percent_intersect_with_10_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_90_percent(), "AND id_name:few", &index));
}
#[bench]
fn bench_id_range_hit_90_percent_intersect_with_1_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ids", get_90_percent(), "AND id_name:veryfew", &index));
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench_ip {
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use test::Bencher;
use super::ip_range_tests::*;
use super::*;
use crate::collector::Count;
use crate::query::QueryParser;
use crate::Index;
fn get_index_0_to_100() -> Index {
let mut rng = StdRng::from_seed([1u8; 32]);
let num_vals = 100_000;
let docs: Vec<_> = (0..num_vals)
.map(|_i| {
let id = if rng.gen_bool(0.01) {
"veryfew".to_string() // 1%
} else if rng.gen_bool(0.1) {
"few".to_string() // 9%
} else {
"many".to_string() // 90%
};
Doc {
id,
// Multiply by 1000, so that we create many buckets in the compact space
// The benches depend on this range to select n-percent of elements with the
// methods below.
ip: Ipv6Addr::from_u128(rng.gen_range(0..100) * 1000),
}
})
.collect();
create_index_from_ip_docs(&docs)
}
fn get_90_percent() -> RangeInclusive<Ipv6Addr> {
let start = Ipv6Addr::from_u128(0);
let end = Ipv6Addr::from_u128(90 * 1000);
start..=end
}
fn get_10_percent() -> RangeInclusive<Ipv6Addr> {
let start = Ipv6Addr::from_u128(0);
let end = Ipv6Addr::from_u128(10 * 1000);
start..=end
}
fn get_1_percent() -> RangeInclusive<Ipv6Addr> {
let start = Ipv6Addr::from_u128(10 * 1000);
let end = Ipv6Addr::from_u128(10 * 1000);
start..=end
}
fn execute_query(
field: &str,
ip_range: RangeInclusive<Ipv6Addr>,
suffix: &str,
index: &Index,
) -> usize {
let gen_query_inclusive = |from: &Ipv6Addr, to: &Ipv6Addr| {
format!(
"{}:[{} TO {}] {}",
field,
&from.to_string(),
&to.to_string(),
suffix
)
};
let query = gen_query_inclusive(ip_range.start(), ip_range.end());
let query_from_text = |text: &str| {
QueryParser::for_index(index, vec![])
.parse_query(text)
.unwrap()
};
let query = query_from_text(&query);
let reader = index.reader().unwrap();
let searcher = reader.searcher();
searcher.search(&query, &(Count)).unwrap()
}
#[bench]
fn bench_ip_range_hit_90_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_90_percent(), "", &index));
}
#[bench]
fn bench_ip_range_hit_10_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_10_percent(), "", &index));
}
#[bench]
fn bench_ip_range_hit_1_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_1_percent(), "", &index));
}
#[bench]
fn bench_ip_range_hit_10_percent_intersect_with_10_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_10_percent(), "AND id:few", &index));
}
#[bench]
fn bench_ip_range_hit_1_percent_intersect_with_10_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_1_percent(), "AND id:few", &index));
}
#[bench]
fn bench_ip_range_hit_1_percent_intersect_with_90_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_1_percent(), "AND id:many", &index));
}
#[bench]
fn bench_ip_range_hit_1_percent_intersect_with_1_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_1_percent(), "AND id:veryfew", &index));
}
#[bench]
fn bench_ip_range_hit_10_percent_intersect_with_90_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_10_percent(), "AND id:many", &index));
}
#[bench]
fn bench_ip_range_hit_90_percent_intersect_with_90_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_90_percent(), "AND id:many", &index));
}
#[bench]
fn bench_ip_range_hit_90_percent_intersect_with_10_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_90_percent(), "AND id:few", &index));
}
#[bench]
fn bench_ip_range_hit_90_percent_intersect_with_1_percent(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ip", get_90_percent(), "AND id:veryfew", &index));
}
#[bench]
fn bench_ip_range_hit_90_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_90_percent(), "", &index));
}
#[bench]
fn bench_ip_range_hit_10_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_10_percent(), "", &index));
}
#[bench]
fn bench_ip_range_hit_1_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_1_percent(), "", &index));
}
#[bench]
fn bench_ip_range_hit_10_percent_intersect_with_10_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_10_percent(), "AND id:few", &index));
}
#[bench]
fn bench_ip_range_hit_1_percent_intersect_with_10_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_1_percent(), "AND id:few", &index));
}
#[bench]
fn bench_ip_range_hit_1_percent_intersect_with_90_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_1_percent(), "AND id:many", &index));
}
#[bench]
fn bench_ip_range_hit_1_percent_intersect_with_1_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_1_percent(), "AND id:veryfew", &index));
}
#[bench]
fn bench_ip_range_hit_10_percent_intersect_with_90_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_10_percent(), "AND id:many", &index));
}
#[bench]
fn bench_ip_range_hit_90_percent_intersect_with_90_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_90_percent(), "AND id:many", &index));
}
#[bench]
fn bench_ip_range_hit_90_percent_intersect_with_10_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_90_percent(), "AND id:few", &index));
}
#[bench]
fn bench_ip_range_hit_90_percent_intersect_with_1_percent_multi(bench: &mut Bencher) {
let index = get_index_0_to_100();
bench.iter(|| execute_query("ips", get_90_percent(), "AND id:veryfew", &index));
}
}

View File

@@ -56,6 +56,11 @@ where
self.req_scorer.seek(target)
}
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
self.score_cache = None;
self.req_scorer.seek_into_the_danger_zone(target)
}
fn doc(&self) -> DocId {
self.req_scorer.doc()
}
@@ -76,6 +81,7 @@ where
TOptScorer: Scorer,
TScoreCombiner: ScoreCombiner,
{
#[inline]
fn score(&mut self) -> Score {
if let Some(score) = self.score_cache {
return score;

View File

@@ -29,6 +29,7 @@ impl ScoreCombiner for DoNothingCombiner {
fn clear(&mut self) {}
#[inline]
fn score(&self) -> Score {
1.0
}
@@ -49,6 +50,7 @@ impl ScoreCombiner for SumCombiner {
self.score = 0.0;
}
#[inline]
fn score(&self) -> Score {
self.score
}
@@ -86,6 +88,7 @@ impl ScoreCombiner for DisjunctionMaxCombiner {
self.sum = 0.0;
}
#[inline]
fn score(&self) -> Score {
self.max + (self.sum - self.max) * self.tie_breaker
}

View File

@@ -18,6 +18,7 @@ pub trait Scorer: downcast_rs::Downcast + DocSet + 'static {
impl_downcast!(Scorer);
impl Scorer for Box<dyn Scorer> {
#[inline]
fn score(&mut self) -> Score {
self.deref_mut().score()
}

View File

@@ -98,14 +98,17 @@ impl TermScorer {
}
impl DocSet for TermScorer {
#[inline]
fn advance(&mut self) -> DocId {
self.postings.advance()
}
#[inline]
fn seek(&mut self, target: DocId) -> DocId {
self.postings.seek(target)
}
#[inline]
fn doc(&self) -> DocId {
self.postings.doc()
}
@@ -116,6 +119,7 @@ impl DocSet for TermScorer {
}
impl Scorer for TermScorer {
#[inline]
fn score(&mut self) -> Score {
let fieldnorm_id = self.fieldnorm_id();
let term_freq = self.term_freq();

View File

@@ -15,7 +15,7 @@ const HORIZON: u32 = 64u32 * 64u32;
// This function is similar except that it does is not unstable, and
// it does not keep the original vector ordering.
//
// Also, it does not "yield" any elements.
// Elements are dropped and not yielded.
fn unordered_drain_filter<T, P>(v: &mut Vec<T>, mut predicate: P)
where P: FnMut(&mut T) -> bool {
let mut i = 0;
@@ -128,6 +128,7 @@ impl<TScorer: Scorer, TScoreCombiner: ScoreCombiner> BufferedUnionScorer<TScorer
}
}
#[inline]
fn advance_buffered(&mut self) -> bool {
while self.bucket_idx < HORIZON_NUM_TINYBITSETS {
if let Some(val) = self.bitsets[self.bucket_idx].pop_lowest() {
@@ -143,6 +144,12 @@ impl<TScorer: Scorer, TScoreCombiner: ScoreCombiner> BufferedUnionScorer<TScorer
}
false
}
fn is_in_horizon(&self, target: DocId) -> bool {
// wrapping_sub, because target may be < window_start_doc
let gap = target.wrapping_sub(self.window_start_doc);
gap < HORIZON
}
}
impl<TScorer, TScoreCombiner> DocSet for BufferedUnionScorer<TScorer, TScoreCombiner>
@@ -150,6 +157,7 @@ where
TScorer: Scorer,
TScoreCombiner: ScoreCombiner,
{
#[inline]
fn advance(&mut self) -> DocId {
if self.advance_buffered() {
return self.doc;
@@ -217,8 +225,29 @@ where
}
}
// TODO Also implement `count` with deletes efficiently.
fn seek_into_the_danger_zone(&mut self, target: DocId) -> bool {
if self.is_in_horizon(target) {
// Our value is within the buffered horizon and the docset may already have been
// processed and removed, so we need to use seek, which uses the regular advance.
self.seek(target) == target
} else {
// The docsets are not in the buffered range, so we can use seek_into_the_danger_zone
// of the underlying docsets
let is_hit = self
.docsets
.iter_mut()
.any(|docset| docset.seek_into_the_danger_zone(target));
// The API requires the DocSet to be in a valid state when `seek_into_the_danger_zone`
// returns true.
if is_hit {
self.seek(target);
}
is_hit
}
}
#[inline]
fn doc(&self) -> DocId {
self.doc
}
@@ -231,6 +260,7 @@ where
self.docsets.iter().map(|docset| docset.cost()).sum()
}
// TODO Also implement `count` with deletes efficiently.
fn count_including_deleted(&mut self) -> u32 {
if self.doc == TERMINATED {
return 0;
@@ -259,6 +289,7 @@ where
TScoreCombiner: ScoreCombiner,
TScorer: Scorer,
{
#[inline]
fn score(&mut self) -> Score {
self.score
}

View File

@@ -92,6 +92,7 @@ impl<TDocSet: DocSet> DocSet for SimpleUnion<TDocSet> {
}
fn size_hint(&self) -> u32 {
// TODO: use estimate_union
self.docsets
.iter()
.map(|docset| docset.size_hint())

View File

@@ -58,6 +58,31 @@ impl AsRef<OwnedValue> for OwnedValue {
}
}
impl OwnedValue {
/// Returns a u8 discriminant value for the `OwnedValue` variant.
///
/// This can be used to sort `OwnedValue` instances by their type.
pub fn discriminant_value(&self) -> u8 {
match self {
OwnedValue::Null => 0,
OwnedValue::Str(_) => 1,
OwnedValue::PreTokStr(_) => 2,
// It is key to make sure U64, I64, F64 are grouped together in there, otherwise we
// might be breaking transivity.
OwnedValue::U64(_) => 3,
OwnedValue::I64(_) => 4,
OwnedValue::F64(_) => 5,
OwnedValue::Bool(_) => 6,
OwnedValue::Date(_) => 7,
OwnedValue::Facet(_) => 8,
OwnedValue::Bytes(_) => 9,
OwnedValue::Array(_) => 10,
OwnedValue::Object(_) => 11,
OwnedValue::IpAddr(_) => 12,
}
}
}
impl<'a> Value<'a> for &'a OwnedValue {
type ArrayIter = std::slice::Iter<'a, OwnedValue>;
type ObjectIter = ObjectMapIter<'a>;

View File

@@ -98,6 +98,10 @@
//! make it possible to access the value given the doc id rapidly. This is useful if the value
//! of the field is required during scoring or collection for instance.
//!
//! Some queries may leverage Fast fields when run on a field that is not indexed. This can be
//! handy if that kind of request is infrequent, however note that searching on a Fast field is
//! generally much slower than searching in an index.
//!
//! ```
//! use tantivy::schema::*;
//! let mut schema_builder = Schema::builder();

View File

@@ -1,10 +1,11 @@
use std::hash::{Hash, Hasher};
use std::hash::Hash;
use std::net::Ipv6Addr;
use std::{fmt, str};
use columnar::MonotonicallyMappableToU128;
use common::json_path_writer::{JSON_END_OF_PATH, JSON_PATH_SEGMENT_SEP_STR};
use common::JsonPathWriter;
use serde::{Deserialize, Serialize};
use super::date_time_options::DATE_TIME_PRECISION_INDEXED;
use super::{Field, Schema};
@@ -16,23 +17,54 @@ use crate::DateTime;
/// Term represents the value that the token can take.
/// It's a serialized representation over different types.
///
/// It actually wraps a `Vec<u8>`. The first 5 bytes are metadata.
/// 4 bytes are the field id, and the last byte is the type.
///
/// The serialized value `ValueBytes` is considered everything after the 4 first bytes (term id).
#[derive(Clone)]
pub struct Term<B = Vec<u8>>(B)
where B: AsRef<[u8]>;
/// A term is composed of Field and the serialized value bytes.
/// The serialized value bytes themselves start with a one byte type tag followed by the payload.
#[derive(Clone, Eq, PartialEq, Ord, PartialOrd, Hash, Serialize, Deserialize)]
pub struct Term {
field: Field,
serialized_value_bytes: Vec<u8>,
}
/// The number of bytes used as metadata by `Term`.
const TERM_METADATA_LENGTH: usize = 5;
/// The number of bytes used as metadata when serializing a term.
const TERM_TYPE_TAG_LEN: usize = 1;
impl Term {
/// Takes a serialized term and wraps it as a Term.
/// First 4 bytes are the field id
#[deprecated(
note = "we want to avoid working on the serialized representation directly, replace with \
typed API calls (add more if needed) or use serde to serialize/deserialize"
)]
pub fn wrap(serialized: &[u8]) -> Term {
let field_id_bytes: [u8; 4] = serialized[0..4].try_into().unwrap();
let field_id = u32::from_be_bytes(field_id_bytes);
Term {
field: Field::from_field_id(field_id),
serialized_value_bytes: serialized[4..].to_vec(),
}
}
/// Returns the serialized representation of the term.
/// First 4 bytes are the field id
#[deprecated(
note = "we want to avoid working on the serialized representation directly, replace with \
typed API calls (add more if needed) or use serde to serialize/deserialize"
)]
pub fn serialized_term(&self) -> Vec<u8> {
let mut serialized = Vec::with_capacity(4 + self.serialized_value_bytes.len());
serialized.extend(self.field.field_id().to_be_bytes().as_ref());
serialized.extend_from_slice(&self.serialized_value_bytes);
serialized
}
/// Create a new Term with a buffer with a given capacity.
pub fn with_capacity(capacity: usize) -> Term {
let mut data = Vec::with_capacity(TERM_METADATA_LENGTH + capacity);
data.resize(TERM_METADATA_LENGTH, 0u8);
Term(data)
let mut data = Vec::with_capacity(TERM_TYPE_TAG_LEN + capacity);
data.resize(TERM_TYPE_TAG_LEN, 0u8);
Term {
field: Field::from_field_id(0u32),
serialized_value_bytes: data,
}
}
/// Creates a term from a json path.
@@ -89,7 +121,7 @@ impl Term {
fn with_bytes_and_field_and_payload(typ: Type, field: Field, bytes: &[u8]) -> Term {
let mut term = Self::with_capacity(bytes.len());
term.set_field_and_type(field, typ);
term.0.extend_from_slice(bytes);
term.serialized_value_bytes.extend_from_slice(bytes);
term
}
@@ -105,13 +137,13 @@ impl Term {
/// Sets field and the type.
pub(crate) fn set_field_and_type(&mut self, field: Field, typ: Type) {
assert!(self.is_empty());
self.0[0..4].clone_from_slice(field.field_id().to_be_bytes().as_ref());
self.0[4] = typ.to_code();
self.field = field;
self.serialized_value_bytes[0] = typ.to_code();
}
/// Is empty if there are no value bytes.
pub fn is_empty(&self) -> bool {
self.0.len() == TERM_METADATA_LENGTH
self.serialized_value_bytes.len() == TERM_TYPE_TAG_LEN
}
/// Builds a term given a field, and a `Ipv6Addr`-value
@@ -177,7 +209,7 @@ impl Term {
/// Removes the value_bytes and set the type code.
pub fn clear_with_type(&mut self, typ: Type) {
self.truncate_value_bytes(0);
self.0[4] = typ.to_code();
self.serialized_value_bytes[0] = typ.to_code();
}
/// Append a type marker + fast value to a term.
@@ -185,9 +217,10 @@ impl Term {
///
/// It will not clear existing bytes.
pub fn append_type_and_fast_value<T: FastValue>(&mut self, val: T) {
self.0.push(T::to_type().to_code());
self.serialized_value_bytes.push(T::to_type().to_code());
let value = val.to_u64();
self.0.extend(value.to_be_bytes().as_ref());
self.serialized_value_bytes
.extend(value.to_be_bytes().as_ref());
}
/// Append a string type marker + string to a term.
@@ -195,24 +228,25 @@ impl Term {
///
/// It will not clear existing bytes.
pub fn append_type_and_str(&mut self, val: &str) {
self.0.push(Type::Str.to_code());
self.0.extend(val.as_bytes().as_ref());
self.serialized_value_bytes.push(Type::Str.to_code());
self.serialized_value_bytes.extend(val.as_bytes().as_ref());
}
/// Sets the value of a `Bytes` field.
pub fn set_bytes(&mut self, bytes: &[u8]) {
self.truncate_value_bytes(0);
self.0.extend(bytes);
self.serialized_value_bytes.extend(bytes);
}
/// Truncates the value bytes of the term. Value and field type stays the same.
pub fn truncate_value_bytes(&mut self, len: usize) {
self.0.truncate(len + TERM_METADATA_LENGTH);
self.serialized_value_bytes
.truncate(len + TERM_TYPE_TAG_LEN);
}
/// The length of the bytes.
pub fn len_bytes(&self) -> usize {
self.0.len() - TERM_METADATA_LENGTH
self.serialized_value_bytes.len() - TERM_TYPE_TAG_LEN
}
/// Appends value bytes to the Term.
@@ -220,18 +254,9 @@ impl Term {
/// This function returns the segment that has just been added.
#[inline]
pub fn append_bytes(&mut self, bytes: &[u8]) -> &mut [u8] {
let len_before = self.0.len();
self.0.extend_from_slice(bytes);
&mut self.0[len_before..]
}
}
impl<B> Term<B>
where B: AsRef<[u8]>
{
/// Wraps a object holding bytes
pub fn wrap(data: B) -> Term<B> {
Term(data)
let len_before = self.serialized_value_bytes.len();
self.serialized_value_bytes.extend_from_slice(bytes);
&mut self.serialized_value_bytes[len_before..]
}
/// Return the type of the term.
@@ -241,8 +266,7 @@ where B: AsRef<[u8]>
/// Returns the field.
pub fn field(&self) -> Field {
let field_id_bytes: [u8; 4] = (&self.0.as_ref()[..4]).try_into().unwrap();
Field::from_field_id(u32::from_be_bytes(field_id_bytes))
self.field
}
/// Returns the serialized representation of the value.
@@ -252,23 +276,13 @@ where B: AsRef<[u8]>
/// If the term is a u64, its value is encoded according
/// to `byteorder::BigEndian`.
pub fn serialized_value_bytes(&self) -> &[u8] {
&self.0.as_ref()[TERM_METADATA_LENGTH..]
&self.serialized_value_bytes[TERM_TYPE_TAG_LEN..]
}
/// Returns the value of the term.
/// address or JSON path + value. (this does not include the field.)
pub fn value(&self) -> ValueBytes<&[u8]> {
ValueBytes::wrap(&self.0.as_ref()[4..])
}
/// Returns the serialized representation of Term.
/// This includes field_id, value type and value.
///
/// Do NOT rely on this byte representation in the index.
/// This value is likely to change in the future.
#[inline]
pub fn serialized_term(&self) -> &[u8] {
self.0.as_ref()
ValueBytes::wrap(self.serialized_value_bytes.as_ref())
}
}
@@ -452,10 +466,7 @@ where B: AsRef<[u8]>
}
}
/// Returns the serialized representation of Term.
///
/// Do NOT rely on this byte representation in the index.
/// This value is likely to change in the future.
/// Returns the serialized representation of the value bytes including the type tag.
pub fn as_serialized(&self) -> &[u8] {
self.0.as_ref()
}
@@ -508,40 +519,6 @@ where B: AsRef<[u8]>
}
}
impl<B> Ord for Term<B>
where B: AsRef<[u8]>
{
fn cmp(&self, other: &Self) -> std::cmp::Ordering {
self.serialized_term().cmp(other.serialized_term())
}
}
impl<B> PartialOrd for Term<B>
where B: AsRef<[u8]>
{
fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
Some(self.cmp(other))
}
}
impl<B> PartialEq for Term<B>
where B: AsRef<[u8]>
{
fn eq(&self, other: &Self) -> bool {
self.serialized_term() == other.serialized_term()
}
}
impl<B> Eq for Term<B> where B: AsRef<[u8]> {}
impl<B> Hash for Term<B>
where B: AsRef<[u8]>
{
fn hash<H: Hasher>(&self, state: &mut H) {
self.0.as_ref().hash(state)
}
}
fn write_opt<T: std::fmt::Debug>(f: &mut fmt::Formatter, val_opt: Option<T>) -> fmt::Result {
if let Some(val) = val_opt {
write!(f, "{val:?}")?;
@@ -549,13 +526,11 @@ fn write_opt<T: std::fmt::Debug>(f: &mut fmt::Formatter, val_opt: Option<T>) ->
Ok(())
}
impl<B> fmt::Debug for Term<B>
where B: AsRef<[u8]>
{
impl fmt::Debug for Term {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
let field_id = self.field().field_id();
let field_id = self.field.field_id();
write!(f, "Term(field={field_id}, ")?;
let value_bytes = ValueBytes::wrap(&self.0.as_ref()[4..]);
let value_bytes = ValueBytes::wrap(&self.serialized_value_bytes);
value_bytes.debug_value_bytes(f)?;
write!(f, ")",)?;
Ok(())
@@ -578,17 +553,6 @@ mod tests {
assert_eq!(term.value().as_str(), Some("test"))
}
/// Size (in bytes) of the buffer of a fast value (u64, i64, f64, or date) term.
/// <field> + <type byte> + <value len>
///
/// - <field> is a big endian encoded u32 field id
/// - <type_byte>'s most significant bit expresses whether the term is a json term or not The
/// remaining 7 bits are used to encode the type of the value. If this is a JSON term, the
/// type is the type of the leaf of the json.
/// - <value> is, if this is not the json term, a binary representation specific to the type.
/// If it is a JSON Term, then it is prepended with the path that leads to this leaf value.
const FAST_VALUE_TERM_LEN: usize = 4 + 1 + 8;
#[test]
pub fn test_term_u64() {
let mut schema_builder = Schema::builder();
@@ -596,7 +560,7 @@ mod tests {
let term = Term::from_field_u64(count_field, 983u64);
assert_eq!(term.field(), count_field);
assert_eq!(term.typ(), Type::U64);
assert_eq!(term.serialized_term().len(), FAST_VALUE_TERM_LEN);
assert_eq!(term.serialized_value_bytes().len(), 8);
assert_eq!(term.value().as_u64(), Some(983u64))
}
@@ -607,7 +571,7 @@ mod tests {
let term = Term::from_field_bool(bool_field, true);
assert_eq!(term.field(), bool_field);
assert_eq!(term.typ(), Type::Bool);
assert_eq!(term.serialized_term().len(), FAST_VALUE_TERM_LEN);
assert_eq!(term.serialized_value_bytes().len(), 8);
assert_eq!(term.value().as_bool(), Some(true))
}
}

View File

@@ -483,7 +483,7 @@ mod tests {
use super::{collapse_overlapped_ranges, search_fragments, select_best_fragment_combination};
use crate::query::QueryParser;
use crate::schema::{IndexRecordOption, Schema, TextFieldIndexing, TextOptions, TEXT};
use crate::schema::{Schema, TEXT};
use crate::snippet::SnippetGenerator;
use crate::tokenizer::{NgramTokenizer, SimpleTokenizer};
use crate::Index;
@@ -727,8 +727,10 @@ Survey in 2016, 2017, and 2018."#;
Ok(())
}
#[cfg(feature = "stemmer")]
#[test]
fn test_snippet_generator() -> crate::Result<()> {
use crate::schema::{IndexRecordOption, TextFieldIndexing, TextOptions};
let mut schema_builder = Schema::builder();
let text_options = TextOptions::default().set_indexing_options(
TextFieldIndexing::default()

View File

@@ -7,13 +7,14 @@
//! storage-level details into consideration. For example, if your file system block size is 4096
//! bytes, we can under-count actual resultant space usage by up to 4095 bytes per file.
use std::collections::HashMap;
use std::collections::btree_map::Entry;
use std::collections::BTreeMap;
use columnar::ColumnSpaceUsage;
use common::ByteCount;
use serde::{Deserialize, Serialize};
use crate::index::SegmentComponent;
use crate::schema::Field;
/// Enum containing any of the possible space usage results for segment components.
pub enum ComponentSpaceUsage {
@@ -212,17 +213,26 @@ impl StoreSpaceUsage {
/// Multiple indexes are used to handle variable length things, where
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct PerFieldSpaceUsage {
fields: HashMap<Field, FieldUsage>,
fields: BTreeMap<String, FieldUsage>,
total: ByteCount,
}
impl PerFieldSpaceUsage {
pub(crate) fn new(fields: Vec<FieldUsage>) -> PerFieldSpaceUsage {
let total = fields.iter().map(FieldUsage::total).sum();
let field_usage_map: HashMap<Field, FieldUsage> = fields
.into_iter()
.map(|field_usage| (field_usage.field(), field_usage))
.collect();
let mut total = ByteCount::default();
let mut field_usage_map: BTreeMap<String, FieldUsage> = BTreeMap::new();
for field_usage in fields {
total += field_usage.total();
let field_name = field_usage.field_name().to_string();
match field_usage_map.entry(field_name) {
Entry::Vacant(entry) => {
entry.insert(field_usage);
}
Entry::Occupied(mut entry) => {
entry.get_mut().merge(field_usage);
}
}
}
PerFieldSpaceUsage {
fields: field_usage_map,
total,
@@ -230,8 +240,8 @@ impl PerFieldSpaceUsage {
}
/// Per field space usage
pub fn fields(&self) -> impl Iterator<Item = (&Field, &FieldUsage)> {
self.fields.iter()
pub fn fields(&self) -> impl Iterator<Item = &FieldUsage> {
self.fields.values()
}
/// Bytes used by the represented file
@@ -246,20 +256,23 @@ impl PerFieldSpaceUsage {
/// See documentation for [`PerFieldSpaceUsage`] for slightly more information.
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct FieldUsage {
field: Field,
field_name: String,
num_bytes: ByteCount,
/// A field can be composed of more than one piece.
/// These pieces are indexed by arbitrary numbers starting at zero.
/// `self.num_bytes` includes all of `self.sub_num_bytes`.
sub_num_bytes: Vec<Option<ByteCount>>,
/// Space usage of the column for fast fields, if relevant.
column_space_usage: Option<ColumnSpaceUsage>,
}
impl FieldUsage {
pub(crate) fn empty(field: Field) -> FieldUsage {
pub(crate) fn empty(field_name: impl Into<String>) -> FieldUsage {
FieldUsage {
field,
field_name: field_name.into(),
num_bytes: Default::default(),
sub_num_bytes: Vec::new(),
column_space_usage: None,
}
}
@@ -272,9 +285,14 @@ impl FieldUsage {
self.num_bytes += size
}
pub(crate) fn set_column_usage(&mut self, column_space_usage: ColumnSpaceUsage) {
self.num_bytes += column_space_usage.total_num_bytes();
self.column_space_usage = Some(column_space_usage);
}
/// Field
pub fn field(&self) -> Field {
self.field
pub fn field_name(&self) -> &str {
&self.field_name
}
/// Space usage for each index
@@ -282,16 +300,64 @@ impl FieldUsage {
&self.sub_num_bytes[..]
}
/// Returns the number of bytes used by the column payload, if the field is columnar.
pub fn column_num_bytes(&self) -> Option<ByteCount> {
self.column_space_usage
.as_ref()
.map(ColumnSpaceUsage::column_num_bytes)
}
/// Returns the number of bytes used by the dictionary for dictionary-encoded columns.
pub fn dictionary_num_bytes(&self) -> Option<ByteCount> {
self.column_space_usage
.as_ref()
.and_then(ColumnSpaceUsage::dictionary_num_bytes)
}
/// Returns the space usage of the column, if any.
pub fn column_space_usage(&self) -> Option<&ColumnSpaceUsage> {
self.column_space_usage.as_ref()
}
/// Total bytes used for this field in this context
pub fn total(&self) -> ByteCount {
self.num_bytes
}
fn merge(&mut self, other: FieldUsage) {
assert_eq!(self.field_name, other.field_name);
self.num_bytes += other.num_bytes;
if other.sub_num_bytes.len() > self.sub_num_bytes.len() {
self.sub_num_bytes.resize(other.sub_num_bytes.len(), None);
}
for (idx, num_bytes_opt) in other.sub_num_bytes.into_iter().enumerate() {
if let Some(num_bytes) = num_bytes_opt {
match self.sub_num_bytes[idx] {
Some(existing) => self.sub_num_bytes[idx] = Some(existing + num_bytes),
None => self.sub_num_bytes[idx] = Some(num_bytes),
}
}
}
self.column_space_usage =
merge_column_space_usage(self.column_space_usage.take(), other.column_space_usage);
}
}
fn merge_column_space_usage(
left: Option<ColumnSpaceUsage>,
right: Option<ColumnSpaceUsage>,
) -> Option<ColumnSpaceUsage> {
match (left, right) {
(Some(lhs), Some(rhs)) => Some(lhs.merge(&rhs)),
(Some(space), None) | (None, Some(space)) => Some(space),
(None, None) => None,
}
}
#[cfg(test)]
mod test {
use crate::index::Index;
use crate::schema::{Field, Schema, FAST, INDEXED, STORED, TEXT};
use crate::schema::{Schema, FAST, INDEXED, STORED, TEXT};
use crate::space_usage::PerFieldSpaceUsage;
use crate::{IndexWriter, Term};
@@ -307,17 +373,17 @@ mod test {
fn expect_single_field(
field_space: &PerFieldSpaceUsage,
field: &Field,
field: &str,
min_size: u64,
max_size: u64,
) {
assert!(field_space.total() >= min_size);
assert!(field_space.total() <= max_size);
assert_eq!(
vec![(field, field_space.total())],
vec![(field.to_string(), field_space.total())],
field_space
.fields()
.map(|(x, y)| (x, y.total()))
.map(|usage| (usage.field_name().to_string(), usage.total()))
.collect::<Vec<_>>()
);
}
@@ -327,6 +393,7 @@ mod test {
let mut schema_builder = Schema::builder();
let name = schema_builder.add_u64_field("name", FAST | INDEXED);
let schema = schema_builder.build();
let field_name = schema.get_field_name(name).to_string();
let index = Index::create_in_ram(schema);
{
@@ -349,11 +416,11 @@ mod test {
assert_eq!(4, segment.num_docs());
expect_single_field(segment.termdict(), &name, 1, 512);
expect_single_field(segment.postings(), &name, 1, 512);
expect_single_field(segment.termdict(), &field_name, 1, 512);
expect_single_field(segment.postings(), &field_name, 1, 512);
assert_eq!(segment.positions().total(), 0);
expect_single_field(segment.fast_fields(), &name, 1, 512);
expect_single_field(segment.fieldnorms(), &name, 1, 512);
expect_single_field(segment.fast_fields(), &field_name, 1, 512);
expect_single_field(segment.fieldnorms(), &field_name, 1, 512);
// TODO: understand why the following fails
// assert_eq!(0, segment.store().total());
assert_eq!(segment.deletes(), 0);
@@ -365,6 +432,7 @@ mod test {
let mut schema_builder = Schema::builder();
let name = schema_builder.add_text_field("name", TEXT);
let schema = schema_builder.build();
let field_name = schema.get_field_name(name).to_string();
let index = Index::create_in_ram(schema);
{
@@ -389,11 +457,11 @@ mod test {
assert_eq!(4, segment.num_docs());
expect_single_field(segment.termdict(), &name, 1, 512);
expect_single_field(segment.postings(), &name, 1, 512);
expect_single_field(segment.positions(), &name, 1, 512);
expect_single_field(segment.termdict(), &field_name, 1, 512);
expect_single_field(segment.postings(), &field_name, 1, 512);
expect_single_field(segment.positions(), &field_name, 1, 512);
assert_eq!(segment.fast_fields().total(), 0);
expect_single_field(segment.fieldnorms(), &name, 1, 512);
expect_single_field(segment.fieldnorms(), &field_name, 1, 512);
// TODO: understand why the following fails
// assert_eq!(0, segment.store().total());
assert_eq!(segment.deletes(), 0);
@@ -429,10 +497,15 @@ mod test {
assert_eq!(4, segment.num_docs());
assert_eq!(segment.termdict().total(), 0);
assert!(segment.termdict().fields().next().is_none());
assert_eq!(segment.postings().total(), 0);
assert!(segment.postings().fields().next().is_none());
assert_eq!(segment.positions().total(), 0);
assert!(segment.positions().fields().next().is_none());
assert_eq!(segment.fast_fields().total(), 0);
assert!(segment.fast_fields().fields().next().is_none());
assert_eq!(segment.fieldnorms().total(), 0);
assert!(segment.fieldnorms().fields().next().is_none());
assert!(segment.store().total() > 0);
assert!(segment.store().total() < 512);
assert_eq!(segment.deletes(), 0);
@@ -444,6 +517,7 @@ mod test {
let mut schema_builder = Schema::builder();
let name = schema_builder.add_u64_field("name", INDEXED);
let schema = schema_builder.build();
let field_name = schema.get_field_name(name).to_string();
let index = Index::create_in_ram(schema);
{
@@ -474,11 +548,11 @@ mod test {
assert_eq!(2, segment_space_usage.num_docs());
expect_single_field(segment_space_usage.termdict(), &name, 1, 512);
expect_single_field(segment_space_usage.postings(), &name, 1, 512);
expect_single_field(segment_space_usage.termdict(), &field_name, 1, 512);
expect_single_field(segment_space_usage.postings(), &field_name, 1, 512);
assert_eq!(segment_space_usage.positions().total(), 0u64);
assert_eq!(segment_space_usage.fast_fields().total(), 0u64);
expect_single_field(segment_space_usage.fieldnorms(), &name, 1, 512);
expect_single_field(segment_space_usage.fieldnorms(), &field_name, 1, 512);
assert!(segment_space_usage.deletes() > 0);
Ok(())
}

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