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

["Yu Li (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FLINK-7289?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Yu Li updated FLINK-7289:
-------------------------
    Release Note: After FLINK-7289, we could control the memory usage of RocksDB state backend. By default user could set the RocksDB memory boundary through `taskmanager.memory.managed.size` or `taskmanager.memory.managed.fraction`, tune the write/read memory ratio through `state.backend.rocksdb.memory.write-buffer-ratio` (by default 0.5) and the reserved memory fraction for index/filters through `state.backend.rocksdb.memory.high-prio-pool-ratio` (by default 0.1). We also supply a `state.backend.rocksdb.memory.fixed-per-slot` configuration for manually control, but for experts only. More details, please refer to the Flink documents.

Adding release note.

> 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: Runtime / State Backends
>    Affects Versions: 1.2.0, 1.3.0, 1.4.0, 1.7.2, 1.8.2, 1.9.0
>            Reporter: Stefan Richter
>            Priority: Major
>              Labels: pull-request-available
>             Fix For: 1.10.0
>
>         Attachments: completeRocksdbConfig.txt
>
>          Time Spent: 20m
>  Remaining Estimate: 0h
>
> 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
(v8.3.4#803005)