[jira] [Comment Edited] (FLINK-7289) Memory allocation of RocksDB can be problematic in container environments

["Vinay (JIRA)" <ji...@apache.org>]

    [ 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)