YufeiLiu created FLINK-20496: -------------------------------- Summary: 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
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. -- This message was sent by Atlassian Jira (v8.3.4#803005)