[
https://issues.apache.org/jira/browse/FLINK-7289?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16109261#comment-16109261
]
Vinay edited comment on FLINK-7289 at 8/1/17 4:42 PM:
------------------------------------------------------
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
to disk when the job is canceled or killed.
was (Author: vinaypatil18):
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.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
