1996fanrui commented on a change in pull request #9454: [FLINK-13644][docs-zh]
Translate "State Backends" page into Chinese
URL: https://github.com/apache/flink/pull/9454#discussion_r315497072
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File path: docs/ops/state/state_backends.zh.md
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@@ -22,114 +22,111 @@ specific language governing permissions and limitations
under the License.
-->
-Programs written in the [Data Stream API]({{ site.baseurl
}}/dev/datastream_api.html) often hold state in various forms:
+用 [Data Stream API]({{ site.baseurl }}/zh/dev/datastream_api.html)
编写的程序通常以各种形式保存状态：
-- Windows gather elements or aggregates until they are triggered
-- Transformation functions may use the key/value state interface to store
values
-- Transformation functions may implement the `CheckpointedFunction` interface
to make their local variables fault tolerant
+- 在 Window 触发之前要么收集元素、要么聚合
+- 转换函数可以使用 key/value 格式的状态接口来存储状态
+- 转换函数可以实现 `CheckpointedFunction` 接口，使其本地变量具有容错能力
-See also [state section]({{ site.baseurl }}/dev/stream/state/index.html) in
the streaming API guide.
+另请参阅 Streaming API 指南中的 [状态部分]({{ site.baseurl
}}/zh/dev/stream/state/index.html) 。
-When checkpointing is activated, such state is persisted upon checkpoints to
guard against data loss and recover consistently.
-How the state is represented internally, and how and where it is persisted
upon checkpoints depends on the
-chosen **State Backend**.
+CheckPoint 开启时，CheckPoint 持续地进行来防止数据丢失，并且能够完整地恢复。
+在 Flink 内部以及 CheckPoint 上，状态如何存储以及存储在哪里取决于选择的 **State Backend**。
* ToC
{:toc}
-## Available State Backends
+## 可用的 State Backends
-Out of the box, Flink bundles these state backends:
+Flink 内置了以下这些开箱即用的 state backends ：
- *MemoryStateBackend*
- *FsStateBackend*
- *RocksDBStateBackend*
-If nothing else is configured, the system will use the MemoryStateBackend.
+如果不设置，默认使用 MemoryStateBackend。
-### The MemoryStateBackend
+### MemoryStateBackend
-The *MemoryStateBackend* holds data internally as objects on the Java heap.
Key/value state and window operators hold hash tables
-that store the values, triggers, etc.
+*MemoryStateBackend* 将数据存储在 Java 堆中。 Key/value 的状态值和窗口算子的触发器都是通过 hash table
来存储。
-Upon checkpoints, this state backend will snapshot the state and send it as
part of the checkpoint acknowledgement messages to the
-JobManager (master), which stores it on its heap as well.
+在 CheckPoint 时，State Backend 对状态进行快照，并将快照信息作为 CheckPoint 应答消息的一部分发送给
JobManager(master)，同时 JobManager 也将快照信息存储在堆内存中。
-The MemoryStateBackend can be configured to use asynchronous snapshots. While
we strongly encourage the use of asynchronous snapshots to avoid blocking
pipelines, please note that this is currently enabled
-by default. To disable this feature, users can instantiate a
`MemoryStateBackend` with the corresponding boolean flag in the constructor set
to `false`(this should only used for debug), e.g.:
+MemoryStateBackend 能配置异步快照。强烈建议使用异步快照来防止数据流阻塞，注意，异步快照默认是开启的。
+用户可以在实例化 `MemoryStateBackend` 的时候，将相应布尔类型的构造参数设置为 `false` 来关闭异步快照（仅在 debug
的时候使用），例如：
{% highlight java %}
new MemoryStateBackend(MAX_MEM_STATE_SIZE, false);
{% endhighlight %}
-Limitations of the MemoryStateBackend:
+MemoryStateBackend 的限制：
- - The size of each individual state is by default limited to 5 MB. This
value can be increased in the constructor of the MemoryStateBackend.
- - Irrespective of the configured maximal state size, the state cannot be
larger than the akka frame size (see [Configuration]({{ site.baseurl
}}/ops/config.html)).
- - The aggregate state must fit into the JobManager memory.
+ - 默认情况下，每个独立的状态大小限制是 5 MB。在 MemoryStateBackend 的构造器中可以增加其大小。
+ - 无论配置的 `MAX_MEM_STATE_SIZE` 有多大，都不能大于 akka frame 大小（看[配置参数]({{ site.baseurl
}}/zh/ops/config.html)）。
+ - 聚合后的状态大小必须小于 JobManager 的内存。
-The MemoryStateBackend is encouraged for:
+MemoryStateBackend 适用场景：
- - Local development and debugging
- - Jobs that do hold little state, such as jobs that consist only of
record-at-a-time functions (Map, FlatMap, Filter, ...). The Kafka Consumer
requires very little state.
+ - 本地开发和调试。
+ - 状态很小的 Job，例如：由每次只处理一条记录的函数（Map、FlatMap、Filter 等）构成的 Job。Kafka Consumer
仅仅需要非常小的状态。
-### The FsStateBackend
+### FsStateBackend
-The *FsStateBackend* is configured with a file system URL (type, address,
path), such as "hdfs://namenode:40010/flink/checkpoints" or
"file:///data/flink/checkpoints".
+*FsStateBackend* 需要配置一个文件系统的
URL（类型、地址、路径），例如："hdfs://namenode:40010/flink/checkpoints" 或
"file:///data/flink/checkpoints"。
-The FsStateBackend holds in-flight data in the TaskManager's memory. Upon
checkpointing, it writes state snapshots into files in the configured file
system and directory. Minimal metadata is stored in the JobManager's memory
(or, in high-availability mode, in the metadata checkpoint).
+FsStateBackend 将正在运行中的状态数据保存在 TaskManager 的内存中。CheckPoint 时，将状态快照写入到配置的文件系统目录中。
+少量的元数据信息存储到 JobManager 的内存中（高可用模式下，将其写入到 CheckPoint 的元数据文件中）。
-The FsStateBackend uses *asynchronous snapshots by default* to avoid blocking
the processing pipeline while writing state checkpoints. To disable this
feature, users can instantiate a `FsStateBackend` with the corresponding
boolean flag in the constructor set to `false`, e.g.:
+FsStateBackend 默认使用异步快照来防止 CheckPoint 写状态时对数据处理造成阻塞。
+用户可以在实例化 `FsStateBackend` 的时候，将相应布尔类型的构造参数设置为 `false` 来关闭异步快照，例如：
{% highlight java %}
new FsStateBackend(path, false);
{% endhighlight %}
-The FsStateBackend is encouraged for:
+FsStateBackend 适用场景:
- - Jobs with large state, long windows, large key/value states.
- - All high-availability setups.
+ - 状态比较大、窗口比较长、key/value 状态比较大的 Job。
+ - 所有高可用的场景。
-### The RocksDBStateBackend
+### RocksDBStateBackend
-The *RocksDBStateBackend* is configured with a file system URL (type, address,
path), such as "hdfs://namenode:40010/flink/checkpoints" or
"file:///data/flink/checkpoints".
+*RocksDBStateBackend* 需要配置一个文件系统的 URL
（类型、地址、路径），例如："hdfs://namenode:40010/flink/checkpoints" 或
"file:///data/flink/checkpoints"。
-The RocksDBStateBackend holds in-flight data in a
[RocksDB](http://rocksdb.org) database
-that is (per default) stored in the TaskManager data directories. Upon
checkpointing, the whole
-RocksDB database will be checkpointed into the configured file system and
directory. Minimal
-metadata is stored in the JobManager's memory (or, in high-availability mode,
in the metadata checkpoint).
+RocksDBStateBackend 将正在运行中的状态数据保存在 [RocksDB](http://rocksdb.org) 数据库中，RocksDB
数据库默认将数据存储在 TaskManager 的数据目录。
+CheckPoint 时，整个 RocksDB 数据库被 checkpoint 到配置的文件系统目录中。
+少量的元数据信息存储到 JobManager 的内存中（高可用模式下，将其存储到 CheckPoint 的元数据文件中）。
-The RocksDBStateBackend always performs asynchronous snapshots.
+RocksDBStateBackend 只支持异步快照。
-Limitations of the RocksDBStateBackend:
+RocksDBStateBackend 的限制：
- - As RocksDB's JNI bridge API is based on byte[], the maximum supported size
per key and per value is 2^31 bytes each.
- IMPORTANT: states that use merge operations in RocksDB (e.g. ListState) can
silently accumulate value sizes > 2^31 bytes and will then fail on their next
retrieval. This is currently a limitation of RocksDB JNI.
+ - 由于 RocksDB 的 JNI API 构建在 byte[] 数据结构之上, 所以每个 key 和 value 最大支持 2^31 字节。
+ 重要信息: RocksDB 合并操作的状态（例如：ListState）累积数据量大小可以超过 2^31 字节，但是会在下一次恢复任务时失败。这是当前
RocksDB JNI 的限制。
Review comment:
同意
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