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https://issues.apache.org/jira/browse/FLINK-7289?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16932200#comment-16932200
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Truong Duc Kien commented on FLINK-7289:
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In my experience, using jemalloc can reduce RocksDB usage by a lot, due to
reduction in memory fragmentation. Jemalloc's profiler is also very useful in
tracking down memory issues. To use jemalloc, we set the LD_PRELOAD environment
variable before starting Task Managers.
Settings
{{cache_index_and_filter_blocks = true}}
can reduce memory usage a bit, because indexes and filters are no longer pinned
in memory. However, you'd pay a very high cost when cache misses: we've
encounter orders of magnitude increase in latency when accessing state if this
happens. Do not use if your job is latency-sensitive.
> Memory allocation of RocksDB can be problematic in container environments
> -------------------------------------------------------------------------
>
> Key: FLINK-7289
> URL: https://issues.apache.org/jira/browse/FLINK-7289
> Project: Flink
> Issue Type: Improvement
> Components: Runtime / State Backends
> Affects Versions: 1.2.0, 1.3.0, 1.4.0
> Reporter: Stefan Richter
> Priority: Major
> Attachments: completeRocksdbConfig.txt
>
>
> Flink's RocksDB based state backend allocates native memory. The amount of
> allocated memory by RocksDB is not under the control of Flink or the JVM and
> can (theoretically) grow without limits.
> In container environments, this can be problematic because the process can
> exceed the memory budget of the container, and the process will get killed.
> Currently, there is no other option than trusting RocksDB to be well behaved
> and to follow its memory configurations. However, limiting RocksDB's memory
> usage is not as easy as setting a single limit parameter. The memory limit is
> determined by an interplay of several configuration parameters, which is
> almost impossible to get right for users. Even worse, multiple RocksDB
> instances can run inside the same process and make reasoning about the
> configuration also dependent on the Flink job.
> Some information about the memory management in RocksDB can be found here:
> https://github.com/facebook/rocksdb/wiki/Memory-usage-in-RocksDB
> https://github.com/facebook/rocksdb/wiki/RocksDB-Tuning-Guide
> We should try to figure out ways to help users in one or more of the
> following ways:
> - Some way to autotune or calculate the RocksDB configuration.
> - Conservative default values.
> - Additional documentation.
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