xintongsong commented on a change in pull request #9805: [FLINK-14227]
translate dev/stream/state/checkpointing into Chinese
URL: https://github.com/apache/flink/pull/9805#discussion_r329421500
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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被**状态化**（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**检查点（Checkpoint）**。检查点允许 Flink
恢复状态，并且能够在流中定位，让应用像无故障一样的运行。
-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
}}/internals/stream_checkpointing.html) 介绍了 Flink 流计算容错机制的内部技术原理。
-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 的检查点机制会与流和状态的持久化存储交互。一般需要：
-Flink's checkpointing mechanism interacts with durable storage for streams and
state. In general, it requires:
+ - 一个能够在一定时间内重新得到记录的持久化数据源，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）。
Review comment:
一个能够回放一段时间内数据的持久化数据源
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