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https://issues.apache.org/jira/browse/FLINK-7289?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16107105#comment-16107105
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Vinay commented on FLINK-7289:
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Actually, I was expecting that the memory will be reclaimed when the job is
canceled, but that is not happening currently, so when you run the job again
you might end up getting the TM's to be killed. That is why the above commands
will clear the cache and in turn, the memory used (verified the results by
using free -m on each node).
As Flink is currently unaware of the memory used by RocksDB , there is no way
to deallocate the memory used by it.
Ideally, if the job is canceled Flink should first flush the in-memory state to
disk.
I am not sure if this case should be handled at the Flink side or the resource
manager (YARN in this case) should do it.
> 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: State Backends, Checkpointing
> Affects Versions: 1.2.0, 1.3.0, 1.4.0
> Reporter: Stefan Richter
>
> 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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