Optimize ordinal inputs in Values aggregation #127849
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dnhatn
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Hi @dnhatn, I've created a changelog YAML for you. |
nik9000
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| public static GroupingAggregatorFunction.AddInput wrapAddInput( |
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Maybe make it a static method somewhere else and reference it? So we don't need to edit it without the IDE.
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Thanks Nik! |
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ywangd
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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 ```
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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
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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
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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
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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
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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
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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.