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Subject [GitHub] [flink] Myasuka commented on a change in pull request #10498: [FLINK-14495][docs] Add documentation for memory control of RocksDB state backend
Date Fri, 31 Jan 2020 15:49:14 GMT
Myasuka commented on a change in pull request #10498: [FLINK-14495][docs] Add documentation
for memory control of RocksDB state backend
URL: https://github.com/apache/flink/pull/10498#discussion_r373516138

 File path: docs/ops/state/large_state_tuning.md
 @@ -210,6 +211,67 @@ and not from the JVM. Any memory you assign to RocksDB will have to
be accounted
 of the TaskManagers by the same amount. Not doing that may result in YARN/Mesos/etc terminating
the JVM processes for
 allocating more memory than configured.
+### Bounding RocksDB Memory Usage
+RocksDB allocates native memory outside of the JVM, which could lead the process to exceed
the total memory budget.
+This can be especially problematic in containerized environments such as Kubernetes that
kill processes who exceed their memory budgets.
+Flink limit total memory usage of RocksDB instance(s) per slot by leveraging shareable [cache](https://github.com/facebook/rocksdb/wiki/Block-Cache)
+and [write buffer manager](https://github.com/facebook/rocksdb/wiki/Write-Buffer-Manager)
among all instances in a single slot by default.
+The shared cache will place an upper limit on the [three components](https://github.com/facebook/rocksdb/wiki/Memory-usage-in-RocksDB)
that use the majority of memory
+when RocksDB is deployed as a state backend: block cache, index and bloom filters, and MemTables.

+This feature is enabled by default along with managed memory. Flink will use the managed
memory budget as the per-slot memory limit for RocksDB state backend(s).
+Flink also provides two parameters to tune the memory fraction of MemTable and index &
filters along with the bounding RocksDB memory usage feature:
+  - `state.backend.rocksdb.memory.write-buffer-ratio`, by default `0.5`, which means 50%
of the given memory would be used by write buffer manager.
+  - `state.backend.rocksdb.memory.high-prio-pool-ratio`, by default `0.1`, which means 10%
of the given memory would be set as high priority for index and filters in shared block cache.
+  We strongly suggest not to set this to zero, to prevent index and filters from competing
against data blocks for staying in cache and causing performance issues.
+  Moreover, the L0 level filter and index are pinned into the cache by default to mitigate
performance problems,
+  more details please refer to the [RocksDB-documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache#caching-index-filter-and-compression-dictionary-blocks).
+<span class="label label-info">Note</span> When bounded RocksDB memory usage
is enabled by default,
+the shared `cache` and `write buffer manager` will override customized settings of block
cache and write buffer via `PredefinedOptions` and `OptionsFactory`.
+*Experts only*: To control memory manually instead of using managed memory, user can set
`state.backend.rocksdb.memory.managed` as `false` and control via `ColumnFamilyOptions`.
+Or to save some manual calculation, through the `state.backend.rocksdb.memory.fixed-per-slot`
option which will override `state.backend.rocksdb.memory.managed` when configured.
+With the later method, please tune down `taskmanager.memory.managed.size` or `taskmanager.memory.managed.fraction`
to `0` 
+and increase `taskmanager.memory.task.off-heap.size` by "`taskmanager.numberOfTaskSlots`
* `state.backend.rocksdb.memory.fixed-per-slot`" accordingly.
+#### Tune performance when bounding RocksDB memory usage.
+There might existed performance regression compared with previous no-memory-limit case if
you have too many states per slot.
+- If you observed this behavior and not running jobs in containerized environment or don't
care about the over-limit memory usage.
+The easiest way to wipe out the performance regression is to disable memory bound for RocksDB,
e.g. turn `state.backend.rocksdb.memory.managed` as `false`.
+Moreover, please refer to [memory configuration migration guide](WIP) to know how to keep
backward compatibility to previous memory configuration.
 Review comment:
   @azagrebin @xintongsong Please fill in the link to memory configuration migration guide
~~after that guide have been merged~~ when that guide is merging.

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