Introduce batched query execution and data-node side reduce (#121885)#126563
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original-brownbear merged 4 commits intoelastic:8.xfrom Apr 10, 2025
original-brownbear:121885-8.x
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Introduce batched query execution and data-node side reduce (#121885)#126563original-brownbear merged 4 commits intoelastic:8.xfrom original-brownbear:121885-8.x
original-brownbear merged 4 commits intoelastic:8.xfrom
original-brownbear:121885-8.x
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This change moves the query phase a single roundtrip per node just like can_match or field_caps work already. A a result of executing multiple shard queries from a single request we can also partially reduce each node's query results on the data node side before responding to the coordinating node. As a result this change significantly reduces the impact of network latencies on the end-to-end query performance, reduces the amount of work done (memory and cpu) on the coordinating node and the network traffic by factors of up to the number of shards per data node! Benchmarking shows up to orders of magnitude improvements in heap and network traffic dimensions in querying across a larger number of shards.
`Lucene.EMPTY_TOP_DOCS` to identify empty to docs results. These were previously null results, but did not need to be send over transport as incremental reduction was performed only on the data node. Now it can happen that the coord node received a merge result with empty top docs, which has nothing interesting for merging, but that can lead to an exception because the type of the empty array does not match the type of other shards results, for instance if the query was sorted by field. To resolve this, we filter out empty top docs results before merging. Closes #126118
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makes sense pulled it in here :) |
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This change moves the query phase a single roundtrip per node just like can_match or field_caps work already. A a result of executing multiple shard queries from a single request we can also partially reduce each node's query results on the data node side before responding to the coordinating node.
As a result this change significantly reduces the impact of network latencies on the end-to-end query performance, reduces the amount of work done (memory and cpu) on the coordinating node and the network traffic by factors of up to the number of shards per data node!
Benchmarking shows up to orders of magnitude improvements in heap and network traffic dimensions in querying across a larger number of shards.
backport of #121885