When a string cardinality aggregation is nested it end up being applied to different buckets.
Dictionary encoding relies on a different dictionaries for each segment.
As a result, during segment collection, we only collect term ordinals in a HashSet, and decode them in the
term dictionary at the end of collection.
Before this PR, this decoding phase was done once for each bucket, causing the same work to be done over and over. This PR introduce a coupon cache. The HLL sketch relies on a hash of the string values.
We populate the cache before bucket collection, and get our values from it.
This PR also rename "caching" "buffering" in aggregation (it was never caching), and does several cleanups.
* 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
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Co-authored-by: Pascal Seitz <pascal.seitz@gmail.com>
This introduce an optimization of top level term aggregation on field with a low cardinality.
We then use a Vec as the underlying map.
In addition, we buffer subaggregations.
---------
Co-authored-by: Pascal Seitz <pascal.seitz@datadoghq.com>
Co-authored-by: Paul Masurel <paul@quickwit.io>
* Initial impl
* Added `Filter` impl in `build_single_agg_segment_collector_with_reader` + Added tests
* Added `Filter(FilterBucketResult)` + Made tests work.
* Fixed type issues.
* Fixed a test.
* 8a7a73a: Pass `segment_reader`
* Added more tests.
* Improved parsing + tests
* refactoring
* Added more tests.
* refactoring: moved parsing code under QueryParser
* Use Tantivy syntax instead of ES
* Added a sanity check test.
* Simplified impl + tests
* Added back tests in a more maintable way
* nitz.
* nitz
* implemented very simple fast-path
* improved a comment
* implemented fast field support
* Used `BoundsRange`
* Improved fast field impl + tests
* Simplified execution.
* Fixed exports + nitz
* Improved the tests to check to the expected result.
* Improved test by checking the whole result JSON
* Removed brittle perf checks.
* Added efficiency verification tests.
* Added one more efficiency check test.
* Improved the efficiency tests.
* Removed unnecessary parsing code + added direct Query obj
* Fixed tests.
* Improved tests
* Fixed code structure
* Fixed lint issues
* nitz.
* nitz
* nitz.
* nitz.
* nitz.
* Added an example
* Fixed PR comments.
* Applied PR comments + nitz
* nitz.
* Improved the code.
* Fixed a perf issue.
* Added batch processing.
* Made the example more interesting
* Fixed bucket count
* Renamed Direct to CustomQuery
* Fixed lint issues.
* No need for scorer to be an `Option`
* nitz
* Used BitSet
* Added an optimization for AllQuery
* Fixed merge issues.
* Fixed lint issues.
* Added benchmark for FILTER
* Removed the Option wrapper.
* nitz.
* Applied PR comments.
* Fixed the AllQuery optimization
* Applied PR comments.
* feat: used `erased_serde` to allow filter query to be serialized
* further improved a comment
* Added back tests.
* removed an unused method
* removed an unused method
* Added documentation
* nitz.
* Added query builder.
* Fixed a comment.
* Applied PR comments.
* Fixed doctest issues.
* Added ser/de
* Removed bench in test
* Fixed a lint issue.
* add nested histogram-termagg benchmark
* Replace AggregationsWithAccessor with AggData
With AggregationsWithAccessor pre-computation and caching was done on the collector level.
If you have 10000 sub collectors (e.g. a term aggregation with sub aggregations) this is very inefficient.
`AggData` instead moves the data from the collector to a node which reflects the cardinality of the request tree instead of the cardinality of the segment collector.
It also moves the global struct shared with all aggregations in to aggregation specific structs. So each aggregation has its own space to store cached data and aggregation specific information.
This also breaks up the dependency to the elastic search aggregation structure somewhat.
Due to lifetime issues, we move the agg request specific object out of `AggData` during the collection and move it back at the end (for now). That's some unnecessary work, which costs CPU.
This allows better caching and will also pave the way for another potential optimization, by separating the collector and its storage. Currently we allocate a new collector for each sub aggregation bucket (for nested aggregations), but ideally we would have just one collector instance.
* renames
* move request data to agg request files
---------
Co-authored-by: Pascal Seitz <pascal.seitz@datadoghq.com>
* first version of extended stats along with its tests
* using IntermediateExtendStats instead of IntermediateStats with all tests passing
* Created struct for request and response
* first test with extended_stats
* kahan summation and tests with approximate equality
* version ready for merge
* removed approx dependency
* refactor for using ExtendedStats only when needed
* interim version
* refined version with code formatted
* refactored a struct
* cosmetic refactor
* fix after merge
* fix format
* added extended_stat bench
* merge and new benchmark for extended stats
* split stat segment collectors
* wrapped intermediate extended stat with a box to limit memory usage
* Revert "wrapped intermediate extended stat with a box to limit memory usage"
This reverts commit 5b4aa9f393.
* some code reformat, commented kahan summation
* refactor after review
* refactor after code review
* fix after incorrectly restoring kahan summation
* modifications for code review + bug fix in merge_fruit
* refactor assert_nearly_equals macro
* update after code review
---------
Co-authored-by: Giovanni Cuccu <gcuccu@imolainformatica.it>
