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https://issues.apache.org/jira/browse/FLINK-7289?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16109411#comment-16109411
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Stefan Richter commented on FLINK-7289:
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Hi,
I understand that cache memory is not freed, and this sounds exactly like what
I would expect. I would also expect that the consumed cache memory will not go
down as long as it is still below available cache memory configured from the OS
perspective. If this becomes a problem under YARN, this sounds like a problem
of the cluster setup to me. Take this opinion with a grain of sand, I am not an
expert on YARN or container setups.
Then this should not be an end user problem, but also not a Flink problem. It
sounds like an administrator and configuration problem to me. For example, this
caching scenario should also apply to all other filesystem reads / writes and
not only to RocksDB. Manually dropping OS file caches should never been
required from any application or user, and if so it seems like this is fixing
the symptoms of a different problem. We can try to figure out more about root
problem and then maybe come up with a better documentation for the setup and
configuration. But I would disagree about introducing cache cleaning to Flink
because writing to {{/proc/sys/vm/drop_caches}} is an operation that requires
root privileges.
Can you provide us with some more information, e.g. a detailed breakdown of the
os memory consumption after the process ended and the logs about the killed
containers?
> 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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