Replace floating runner labels with the images they resolve to today
(`ubuntu-latest` -> `ubuntu-24.04`), so OS upgrades happen in an explicit
change instead of whenever GitHub moves a `-latest` label. GitHub moves
`ubuntu-latest` to Ubuntu 26.04 between Oct 19 and Nov 19, 2026.
* Make ValueSource::load_block take &mut self
Value sources were held as `Arc<dyn ValueSource>` and cloned into their
collector, so `load_block` could only take `&self`. A computed source
could not keep per-segment state (caches, scratch buffers) across blocks.
Collectors were built in two phases: `build_nodes` pushed each
`XxxAggReqData` into per-kind vectors of `AggregationsSegmentCtx`, and
each collector builder cloned its request data out of those vectors by
index. The ctx copy had to stay around for name lookups, memory
accounting, and for the collectors that read their data back by index.
Each request node is turned into exactly one collector, so nothing
actually shares a value source. This change makes that ownership
explicit:
- `ValueSource::load_block` takes `&mut self`,
`ValueSourceProvider::for_segment` returns `Box<dyn ValueSource>`.
- The request tree (`AggNode { data: AggNodeData, children }`) owns the
request data. Building collectors consumes the tree, moving the
request data into the collectors instead of cloning it.
- `AggregationsSegmentCtx` only keeps the shared collect-time state
(context and block accessor). `PerRequestAggSegCtx`, `AggKind`,
`idx_in_req_data` and the `push_*`/`get_*_req_data` helpers are gone.
- Cardinality, percentiles, top_hits and missing_term collectors own
their request data instead of reading it from the ctx by index.
- Histogram requests are normalized once, when building the tree. The
flattened terms x histogram path is split into a borrow-only plan step
and a consuming build step, and no longer clones the histogram request
at finalization.
- Request data memory is charged exactly once per node. Previously it
was charged at every nesting level, and range, histogram, filter and
composite builders charged their request data a second time.
- `FilterAggReqData::evaluator` no longer needs an `Rc`, and the
filter, composite and multi_terms request data no longer derive Clone.
* CR comment
* CR comment
---------
Co-authored-by: Paul Masurel <paul.masurel@datadoghq.com>
* Changing the way aggregation access their value.
They now get values via a ValueSource abstraction.
The aggregation collector also gets the possibility to
register ValueSourceProvider describing value columns that are computed
on the fly.
Finally, segment aggregation that require a full column
now manipulates a Arc<dyn ColumnValue> directly.
* CR comments
* Clippy
* Fixing regression
---------
Co-authored-by: Paul Masurel <paul.masurel@datadoghq.com>
Sorted segment merges copied every document's positions for a term into a vector before sorting by mapped document ID. Common terms with many positions therefore consumed memory proportional to documents × positions, outside the indexing writer budget.
Stream shuffled postings through a min-heap with one cursor per input segment and one reusable positions buffer. The mapping preserves each segment's document order; deleted/filtered postings are skipped. The stacked path continues to stream directly.
RegexQuery can be built from a compiled Regex, but RegexPhraseQuery always compiled its patterns with Regex::new and its default DFA state limit. from_regexes takes the compiled regexes directly, so a caller can build them with its own limits, or reuse ones it already compiled.
* Expose the Levenshtein automaton of FuzzyTermQuery
Callers that need the terms a fuzzy query expands to (e.g. to highlight them) had to copy the private DfaWrapper and depend on the same levenshtein_automata version as tantivy, or their expansion could silently diverge from the query's. FuzzyTermQuery::automaton returns the automaton the query matches with, built from the same cached LevenshteinAutomatonBuilder.
* Export DfaWrapper outside of tests
The re-export was still behind #[cfg(test)], so FuzzyTermQuery::automaton returned a type other crates could not name. A doctest now uses it from outside the crate.
* columnar: bench row id to doc id conversion on multivalued columns
* columnar: map multivalued doc ranks to doc ids with a select cursor
MultiValueIndexV2::select_batch_in_place ended with one
OptionalIndex::select call per matched doc, each locating its block from
scratch. The ranks are sorted and deduplicated at that point, so
OptionalIndex::select_batch converts them in one sequential pass with a
select cursor.
Every Column::get_docids_for_value_range call on a multivalued column goes
through here, e.g. fast field range queries.
Snippet only kept a copy of the fragment text, so callers that need to extend the fragment or map it back to the source (e.g. to keep punctuation that the tokenizer leaves outside the last token) had to search for it, which picks the wrong occurrence when the same text appears earlier.
Following up on #3135, the regexes are compiled on first use and kept in the query, so a query reused across searchers, or cloned, determinizes each pattern once. RegexPhraseQuery::regexes exposes them for callers that inspect the patterns before searching, e.g. to count a phrase's per-segment expansions against max_expansions, instead of compiling them a second time.
* jitexpr
* Added possible necessary conditions to jitexpr.
A function makes it possible to infer a necessary query from an expression to match.
We can then accelerate queries involving a calculated field by not even evaluating the expression
on docs that trivially do not match.
* CR comment
* CR comment
* Fixing unit tests
---------
Co-authored-by: Paul Masurel <paul.masurel@datadoghq.com>
RegexPhraseWeight::phrase_scorer compiled every phrase term's regex again for each segment. Determinizing a wide pattern (e.g. an alternation of typo or morphology expansions) costs milliseconds to tens of milliseconds, so on a multi-segment index the query was dominated by recompiling the same automata. The regexes are now compiled in RegexPhraseQuery::regex_phrase_weight and shared through Arc.
An invalid pattern is now reported when the weight is built, instead of when the first segment is scored.
Use scalar decoding for ranges overlapping the final partial load.
Clarify the 64-value aggregation block specialization and name the
generic decoding chunk size.
Apply nightly formatting to the touched benchmark and columnar code.
Avoid formatting keys and finalizing sub-aggregations for buckets
discarded by the final size limit. Preallocate the retained bucket vector
and reuse the cutoff helper with intermediate tuple keys.
Preserve final-key ordering and finalize the ordering metric when sorting
by a sub-aggregation. Cover mixed key types, empty sums, and discarded
document counts in tests.
Resolve retained string ordinals in sorted batches per field instead
of decoding dictionary blocks separately for each tuple component.
Reuse decoding for repeated ordinals while preserving tuple order
and existing numeric and missing-value handling.
Probe the accumulated map only for incoming keys rather than rehashing every accumulated key on each merge. This removes quadratic work when folding many disjoint multi-terms results. The same helper serves range and composite buckets; existing bucket values still merge left-to-right and the wire representation and pruning rules are unchanged.
Cover overlapping/disjoint tuple keys, empty inputs, recursive range subaggregations, postcard round trips, error/count bookkeeping, and merging after pruning.
Same benchmark and configuration as 0062d2f0d (Apple M4 Max, rustc 1.98.0):
Median milliseconds for 10 / 100 / 1000 inputs:
shared 8: 0.006365 / 0.0656 / 0.6449
disjoint 16: 0.0126 / 0.1486 / 1.6882
disjoint 160: 0.1336 / 1.5558 / 20.4019
At 1000 inputs this is 52.4x and 61.6x faster for the disjoint cases, with unchanged measured peak allocation. Shared-key control remains within approximately 2% of baseline.
Validation: 308 aggregation tests pass with default features and 308 with quickwit; changed-file rustfmt and git diff checks pass. Clippy --lib --bench agg_bench passes with the pre-existing clippy::drop_non_drop warning allowed (unchanged drop(add_document) in the benchmark).
Run the existing many-segment aggregation benchmarks at 100 and 1,000
segments with one million total documents. Add multi-terms and nested
terms cases for status/Zipf and high-cardinality/Zipf combinations,
including top-500 requests, to expose merge costs across many segments.
The nested top-500 case limits outer buckets, while multi-terms limits
tuples globally.
Run both groups with: cargo bench --bench agg_bench -- _segments
David Yaffe
Paul MasurelandPaul Masurel
Pascal Seitz
François Massot
PSeitz-dd
Luca Cominardi
Walter Woodall
trinity-1686a
palmoni5
omerb-vega
PSeitzandPaul Masurel