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From "Vinay (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-7289) Memory allocation of RocksDB can be problematic in container environments
Date Tue, 01 Aug 2017 16:43:00 GMT

    [ https://issues.apache.org/jira/browse/FLINK-7289?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16109261#comment-16109261

Vinay commented on FLINK-7289:

Hi Stephan,

I agree with what you are saying, But I am saying this from the end user perspective. The
user will assume that enough memory is available when the job gets canceled or killed and
will re-run it.

I am just suggesting that if Flink could somehow clean the memory or flush it disk when the
job is canceled or killed.

> 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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