Optimize ordinal inputs in Values aggregation #127849
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters
dnhatn force-pushed the support-ordinals-values-aggs branch from 5a4fbba to 1fad50d Compare dnhatn added :Analytics/ES|QL 
elasticsearchmachine added Team:Analytics 
| Pinging @elastic/es-analytical-engine (Team:Analytics) |
| Pinging @elastic/es-storage-engine (Team:StorageEngine) |
| Hi @dnhatn, I've created a changelog YAML for you. |
nik9000 approved these changes May 7, 2025
| } | ||
| | ||
| $if(BytesRef)$ | ||
| public static GroupingAggregatorFunction.AddInput wrapAddInput( |
There was a problem hiding this comment.
Maybe make it a static method somewhere else and reference it? So we don't need to edit it without the IDE.
dnhatn force-pushed the support-ordinals-values-aggs branch from a8253f1 to 001fe81 Compare 
| Thanks Nik! |
28 tasks
ywangd pushed a commit to ywangd/elasticsearch that referenced this pull request May 9, 2025
Currently, time-series aggregations use the `values` aggregation to collect dimension values. While we might introduce a specialized aggregation for this in the future, for now, we are using `values`, and the inputs are likely ordinal blocks. This change speeds up the `values` aggregation when the inputs are ordinal-based. Execution time reduced from 461ms to 192ms for 1000 groups. ``` ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 461.938 ± 6.089 ms/op ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 192.898 ± 1.781 ms/op ```
jfreden pushed a commit to jfreden/elasticsearch that referenced this pull request May 12, 2025
Currently, time-series aggregations use the `values` aggregation to collect dimension values. While we might introduce a specialized aggregation for this in the future, for now, we are using `values`, and the inputs are likely ordinal blocks. This change speeds up the `values` aggregation when the inputs are ordinal-based. Execution time reduced from 461ms to 192ms for 1000 groups. ``` ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 461.938 ± 6.089 ms/op ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 192.898 ± 1.781 ms/op ```
dnhatn added a commit to dnhatn/elasticsearch that referenced this pull request Jun 5, 2025
Currently, time-series aggregations use the `values` aggregation to collect dimension values. While we might introduce a specialized aggregation for this in the future, for now, we are using `values`, and the inputs are likely ordinal blocks. This change speeds up the `values` aggregation when the inputs are ordinal-based. Execution time reduced from 461ms to 192ms for 1000 groups. ``` ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 461.938 ± 6.089 ms/op ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 192.898 ± 1.781 ms/op ```
dnhatn added a commit to dnhatn/elasticsearch that referenced this pull request Jun 5, 2025
Currently, time-series aggregations use the `values` aggregation to collect dimension values. While we might introduce a specialized aggregation for this in the future, for now, we are using `values`, and the inputs are likely ordinal blocks. This change speeds up the `values` aggregation when the inputs are ordinal-based. Execution time reduced from 461ms to 192ms for 1000 groups. ``` ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 461.938 ± 6.089 ms/op ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 192.898 ± 1.781 ms/op ```
dnhatn added a commit that referenced this pull request Jun 6, 2025
Currently, time-series aggregations use the `values` aggregation to collect dimension values. While we might introduce a specialized aggregation for this in the future, for now, we are using `values`, and the inputs are likely ordinal blocks. This change speeds up the `values` aggregation when the inputs are ordinal-based. Execution time reduced from 461ms to 192ms for 1000 groups. ``` ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 461.938 ± 6.089 ms/op ValuesAggregatorBenchmark.run BytesRef 10000 avgt 7 192.898 ± 1.781 ms/op ```
dnhatn added a commit that referenced this pull request Jul 21, 2025
Similar to #127849, this change adds an optimized path for leveraging ordinal blocks of intermediate input pages in the Values aggregator. Below are the micro-benchmark results. Before: ``` // 1 raw input page + 1000 intermediate input pages Benchmark (dataType) (groups) Mode Cnt Score Error Units ValuesAggregatorBenchmark.run BytesRef 1 avgt 2 0.382 ms/op ValuesAggregatorBenchmark.run BytesRef 1000 avgt 2 112.293 ms/op ValuesAggregatorBenchmark.run BytesRef 1000000 avgt 2 113182.908 ms/op ``` ``` After: // 1 raw input page + 1000 intermediate input pages Benchmark (dataType) (groups) Mode Cnt Score Error Units ValuesAggregatorBenchmark.run BytesRef 1 avgt 2 0.378 ms/op ValuesAggregatorBenchmark.run BytesRef 1000 avgt 2 34.410 ms/op ValuesAggregatorBenchmark.run BytesRef 1000000 avgt 2 64654.830 ms/op ``` 1K groups: 112 ms -> 34.4ms 1M groups: 113s -> 64s More to come with #130510 Relates #127849
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit. This suggestion is invalid because no changes were made to the code. Suggestions cannot be applied while the pull request is closed. Suggestions cannot be applied while viewing a subset of changes. Only one suggestion per line can be applied in a batch. Add this suggestion to a batch that can be applied as a single commit. Applying suggestions on deleted lines is not supported. You must change the existing code in this line in order to create a valid suggestion. Outdated suggestions cannot be applied. This suggestion has been applied or marked resolved. Suggestions cannot be applied from pending reviews. Suggestions cannot be applied on multi-line comments. Suggestions cannot be applied while the pull request is queued to merge. Suggestion cannot be applied right now. Please check back later.
Currently, time-series aggregations use the
valuesaggregation to collect dimension values. While we might introduce a specialized aggregation for this in the future, for now, we are usingvalues, and the inputs are likely ordinal blocks. This change speeds up thevaluesaggregation when the inputs are ordinal-based.Execution time reduced from 461ms to 192ms for 1000 groups.