klion26 commented on a change in pull request #8319: [FLINK-11636] [docs-zh]
Translate "State Schema Evolution" into Chinese
URL: https://github.com/apache/flink/pull/8319#discussion_r280969840
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File path: docs/dev/stream/state/schema_evolution.zh.md
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@@ -48,62 +47,52 @@ checkpointedState =
getRuntimeContext().getListState(descriptor);
{% endhighlight %}
</div>
-Under the hood, whether or not the schema of state can be evolved depends on
the serializer used to read / write
-persisted state bytes. Simply put, a registered state's schema can only be
evolved if its serializer properly
-supports it. This is handled transparently by serializers generated by Flink's
type serialization framework
-(current scope of support is listed [below]({{ site.baseurl
}}/dev/stream/state/schema_evolution.html#supported-data-types-for-schema-evolution)).
+在内部，状态是否可以进行升级取决于用于读写持久化状态字节的序列化器。
+简而言之，状态数据结构只有在其序列化器正确支持时才能升级。
+这一过程是被 Flink 的类型序列化框架生成的序列化器透明处理的（[下面]({{ site.baseurl
}}/zh/dev/stream/state/schema_evolution.html#数据结构升级支持的数据类型) 列出了当前的支持范围）。
-If you intend to implement a custom `TypeSerializer` for your state type and
would like to learn how to implement
-the serializer to support state schema evolution, please refer to
-[Custom State Serialization]({{ site.baseurl
}}/dev/stream/state/custom_serialization.html).
-The documentation there also covers necessary internal details about the
interplay between state serializers and Flink's
-state backends to support state schema evolution.
+如果你想要为你的状态类型实现自定义的 `TypeSerializer` 并且想要学习如何实现支持状态数据结构升级的序列化器，
+可以参考 [自定义状态序列化器]({{ site.baseurl
}}/zh/dev/stream/state/custom_serialization.html)。
+本文档也包含一些用于支持状态数据结构升级的状态序列化器与 Flink 状态后端存储相互作用的必要内部细节。
-## Evolving state schema
+## 升级状态数据结构
-To evolve the schema of a given state type, you would take the following steps:
+为了对给定的状态类型进行升级，你需要采取以下几个步骤：
- 1. Take a savepoint of your Flink streaming job.
- 2. Update state types in your application (e.g., modifying your Avro type
schema).
- 3. Restore the job from the savepoint. When accessing state for the first
time, Flink will assess whether or not
- the schema had been changed for the state, and migrate state schema if
necessary.
+ 1. 对 Flink 流作业进行 savepoint 操作。
+ 2. 升级程序中的状态类型（例如：修改你的 Avro 结构）。
+ 3. 从 savepoint 处重启作业。当第一次访问状态数据时，Flink 会评估状态数据结构是否已经改变，并在必要的时候进行状态结构迁移。
Review comment:
`从 savepoint 处重启作业` --> `从 savepoint 恢复作业`
`当第一次访问状态数据时，Flink 会评估状态数据结构是否已经改变，并在必要的时候进行状态结构迁移。` --> `当第一次访问状态数据时，Flink
会判断状态数据 schema 是否已经改变，并进行必要的迁移。`
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