klion26 commented on a change in pull request #9805: [FLINK-11635][docs-zh]
translate dev/stream/state/checkpointing into Chinese
URL: https://github.com/apache/flink/pull/9805#discussion_r334224444
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File path: docs/dev/stream/state/checkpointing.zh.md
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@@ -25,146 +25,138 @@ under the License.
* ToC
{:toc}
-Every function and operator in Flink can be **stateful** (see [working with
state](state.html) for details).
-Stateful functions store data across the processing of individual
elements/events, making state a critical building block for
-any type of more elaborate operation.
+Flink 中的每个方法或算子都能够是**有状态的**（阅读 [working with state](state.html) 查看详细）。
+状态化的方法在处理单个 元素/事件 的时候存储数据，让状态成为使各个类型的算子更加精细的重要部分。
+为了让状态容错，Flink 需要为状态添加**Checkpoint（检查点）**。Checkpoint 使得 Flink
能够恢复状态和在流中的位置，从而向应用提供和无故障执行时一样的语义。
-In order to make state fault tolerant, Flink needs to **checkpoint** the
state. Checkpoints allow Flink to recover state and positions
-in the streams to give the application the same semantics as a failure-free
execution.
+[Documentation on streaming fault tolerance]({{ site.baseurl
}}/zh/internals/stream_checkpointing.html) 介绍了 Flink 流计算容错机制内部的技术原理。
-The [documentation on streaming fault tolerance]({{ site.baseurl
}}/internals/stream_checkpointing.html) describes in detail the technique
behind Flink's streaming fault tolerance mechanism.
+## 前提条件
-## Prerequisites
+Flink 的 Checkpoint 机制会和持久化存储进行交互，交换流与状态。一般需要：
-Flink's checkpointing mechanism interacts with durable storage for streams and
state. In general, it requires:
+ - 一个能够回放一段时间内数据的持久化数据源，例如持久化消息队列（例如 Apache Kafka、RabbitMQ、 Amazon Kinesis、
Google PubSub 等）或文件系统（例如 HDFS、 S3、 GFS、 NFS、 Ceph 等）。
+ - 存放状态的持久化存储，通常为分布式文件系统（比如 HDFS、 S3、 GFS、 NFS、 Ceph 等）。
- - A *persistent* (or *durable*) data source that can replay records for a
certain amount of time. Examples for such sources are persistent messages
queues (e.g., Apache Kafka, RabbitMQ, Amazon Kinesis, Google PubSub) or file
systems (e.g., HDFS, S3, GFS, NFS, Ceph, ...).
- - A persistent storage for state, typically a distributed filesystem (e.g.,
HDFS, S3, GFS, NFS, Ceph, ...)
+## 激活与配置 Checkpoint
+默认情况下，Checkpoint 是禁用的。通过调用 `StreamExecutionEnvironment` 的
`enableCheckpointing(n)` 来激活 Checkpoint，里面的 *n* 是进行 Checkpoint 的间隔，单位毫秒。
-## Enabling and Configuring Checkpointing
+Checkpoint 其他的属性包括：
-By default, checkpointing is disabled. To enable checkpointing, call
`enableCheckpointing(n)` on the `StreamExecutionEnvironment`, where *n* is the
checkpoint interval in milliseconds.
-
-Other parameters for checkpointing include:
-
- - *exactly-once vs. at-least-once*: You can optionally pass a mode to the
`enableCheckpointing(n)` method to choose between the two guarantee levels.
- Exactly-once is preferable for most applications. At-least-once may be
relevant for certain super-low-latency (consistently few milliseconds)
applications.
-
- - *checkpoint timeout*: The time after which a checkpoint-in-progress is
aborted, if it did not complete by then.
-
- - *minimum time between checkpoints*: To make sure that the streaming
application makes a certain amount of progress between checkpoints,
- one can define how much time needs to pass between checkpoints. If this
value is set for example to *5000*, the next checkpoint will be
- started no sooner than 5 seconds after the previous checkpoint completed,
regardless of the checkpoint duration and the checkpoint interval.
- Note that this implies that the checkpoint interval will never be smaller
than this parameter.
+ - *精确一次（exactly-once） 对比 至少一次（at-least-once）*：你可以选择向
`enableCheckpointing(n)` 方法中传入一个模式来选择使用两种保证等级中的哪一种。
+ 对于大多数应用来说，精确一次是较好的选择。至少一次可能与某些延迟超低（始终只有几毫秒）的应用的关联较大。
+
+ - *Checkpoint超时（checkpoint timeout）*：如果过了这个时间，还在进行中的 checkpoint 操作就会被抛弃。
Review comment:
```suggestion
- *Checkpoint 超时（checkpoint timeout）*：如果过了这个时间，还在进行中的 checkpoint 操作就会被抛弃。
```
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