[jira] [Commented] (FLINK-20496) RocksDB partitioned index filter option

["YufeiLiu (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-20496?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17244445#comment-17244445 ] 

YufeiLiu commented on FLINK-20496:
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What do you think about this, and I'd like to take this ticket if it's necessary. [~ liyu]

> RocksDB partitioned index filter option
> ---------------------------------------
>
>                 Key: FLINK-20496
>                 URL: https://issues.apache.org/jira/browse/FLINK-20496
>             Project: Flink
>          Issue Type: Improvement
>          Components: Runtime / State Backends
>            Reporter: YufeiLiu
>            Priority: Major
>
>   When using RocksDBStateBackend and enabling {{state.backend.rocksdb.memory.managed}} and {{state.backend.rocksdb.memory.fixed-per-slot}}, flink will strictly limited rocksdb memory usage which contains "write buffer" and "block cache". With these options rocksdb stores index and filters in block cache, because in default options index/filters can grows unlimited.
>   But it's lead another issue, if high-priority cache(configure by {{state.backend.rocksdb.memory.high-prio-pool-ratio}}) can't fit all index/filters blocks, it will load all metadata from disk when cache missed, and program went extremely slow. According to [Partitioned Index Filters|https://github.com/facebook/rocksdb/wiki/Partitioned-Index-Filters][1], we can enable two-level index having acceptable performance when index/filters cache missed. 
>   Enable these options can get over 10x faster in my case[2], I think we can add an option {{state.backend.rocksdb.partitioned-index-filters}} and default value is false, so we can use this feature easily.
> [1] Partitioned Index Filters: https://github.com/facebook/rocksdb/wiki/Partitioned-Index-Filters
> [2] Deduplicate scenario, state.backend.rocksdb.memory.fixed-per-slot=256M, SSD, elapsed time 4.91ms -> 0.33ms.



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