Comments (2)
Pls note that in this cluster I've raised backtrack_limit from 1_000_000 to 1_000_000_000 to work around Error::BacktrackLimitExceeded. So there's indeed very large regex state which requires sizable runtime memory.
Because the memory pool never contracts in size, it may explain the large memory footprint.
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Each executor within the same operator will create its own expressions, which are usually identical but consume multiple resources. Moreover, if the same expression node is present in different operators or streaming jobs, it can also be reused.
We may consider perform some caching on different granularities:
- cache the results of
build_expr
based on their protobuf input - cache the "context"s that are believed to be memory-consuming based on its own input, like
RegexpContext
in this case
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Related Issues (20)
- Let join amplification logs be configurable
- Bug: Some system table's data can be queried across different database
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