carp84 commented on a change in pull request #12096:
URL: https://github.com/apache/flink/pull/12096#discussion_r427048919
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File path: docs/dev/stream/state/index.zh.md
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@@ -25,34 +25,17 @@ specific language governing permissions and limitations
under the License.
-->
-Stateful functions and operators store data across the processing of
individual elements/events, making state a critical building block for
-any type of more elaborate operation.
-
-For example:
-
- - When an application searches for certain event patterns, the state will
store the sequence of events encountered so far.
- - When aggregating events per minute/hour/day, the state holds the pending
aggregates.
- - When training a machine learning model over a stream of data points, the
state holds the current version of the model parameters.
- - When historic data needs to be managed, the state allows efficient access
to events that occurred in the past.
-
-Flink needs to be aware of the state in order to make state fault tolerant
using [checkpoints](checkpointing.html) and to allow [savepoints]({{
site.baseurl }}/ops/state/savepoints.html) of streaming applications.
-
-Knowledge about the state also allows for rescaling Flink applications,
meaning that Flink takes care of redistributing state across parallel instances.
-
-The [queryable state](queryable_state.html) feature of Flink allows you to
access state from outside of Flink during runtime.
-
-When working with state, it might also be useful to read about [Flink's state
backends]({{ site.baseurl }}/ops/state/state_backends.html). Flink provides
different state backends that specify how and where state is stored. State can
be located on Java's heap or off-heap. Depending on your state backend, Flink
can also *manage* the state for the application, meaning Flink deals with the
memory management (possibly spilling to disk if necessary) to allow
applications to hold very large state. State backends can be configured without
changing your application logic.
-
+你将在本节中了解到 Flink 提供的用于编写有状态程序的 API，想了解更多有状态流处理的概念，请查看[有状态的流处理]({% link
concepts/stateful-stream-processing.zh.md %})
{% top %}
-Where to go next?
+接下来看什么?
-----------------
-* [Working with State](state.html): Shows how to use state in a Flink
application and explains the different kinds of state.
-* [The Broadcast State Pattern](broadcast_state.html): Explains how to connect
a broadcast stream with a non-broadcast stream and use state to exchange
information between them.
-* [Checkpointing](checkpointing.html): Describes how to enable and configure
checkpointing for fault tolerance.
-* [Queryable State](queryable_state.html): Explains how to access state from
outside of Flink during runtime.
-* [State Schema Evolution](schema_evolution.html): Shows how schema of state
types can be evolved.
-* [Custom Serialization for Managed State](custom_serialization.html):
Discusses how to implement custom serializers, especially for schema evolution.
+* [Working with State](state.html): 描述了如何在 Flink 应用程序中使用状态，以及不同类型的状态。
+* [The Broadcast State 模式](broadcast_state.html): 描述了如何将广播流和非广播流进行连接从而交换数据。
+* [Checkpointing](checkpointing.html): 介绍了如何开启和配置 checkpoint，以实现状态容错。
+* [可查询状态](queryable_state.html): 介绍了如何从外围访问 Flink 的状态。
Review comment:
这个我们保留英文（Queryable State）吧
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