ranqiqiang commented on a change in pull request #16478:
URL: https://github.com/apache/flink/pull/16478#discussion_r670099338
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File path: docs/content.zh/docs/concepts/stateful-stream-processing.md
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@@ -24,342 +24,227 @@ under the License.
# 有状态流处理
-## What is State?
+## 什么是状态？
-While many operations in a dataflow simply look at one individual *event at a
-time* (for example an event parser), some operations remember information
-across multiple events (for example window operators). These operations are
-called **stateful**.
+虽然数据流中的很多操作一次只着眼于一个单独的事件（例如事件解析器），但有些操作会记住多个事件的信息（例如窗口算子）。
+这些操作称为**有状态的**（stateful）。
-Some examples of stateful operations:
+有状态操作的一些示例：
- - 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.
+ - 当应用程序搜索某些事件模式时，状态将存储到目前为止遇到的事件序列。
+ - 当每分钟/每小时/每天聚合事件时，状态会持有待处理的聚合。
+ - 当在数据点的流上训练一个机器学习模型时，状态会保存模型参数的当前版本。
+ - 当需要管理历史数据时，状态允许有效访问过去发生的事件。
-Flink needs to be aware of the state in order to make it fault tolerant using
+Flink 需要知道状态以便使用
[checkpoints]({{< ref "docs/dev/datastream/fault-tolerance/checkpointing" >}})
-and [savepoints]({{< ref "docs/ops/state/savepoints" >}}).
+和 [savepoints]({{< ref "docs/ops/state/savepoints" >}}) 进行容错。
-Knowledge about the state also allows for rescaling Flink applications, meaning
-that Flink takes care of redistributing state across parallel instances.
+关于状态的知识也允许我们重新调节 Flink 应用程序，这意味着 Flink 负责跨并行实例重新分布状态。
-[Queryable state]({{< ref
"docs/dev/datastream/fault-tolerance/queryable_state" >}}) allows you to access
state from outside of Flink during runtime.
+[可查询的状态]({{< ref "docs/dev/datastream/fault-tolerance/queryable_state"
>}})允许你在运行时从 Flink 外部访问状态。
-When working with state, it might also be useful to read about [Flink's state
-backends]({{< ref "docs/ops/state/state_backends" >}}). Flink
-provides different state backends that specify how and where state is stored.
+在使用状态时，阅读 [Flink 的状态后端]({{< ref "docs/ops/state/state_backends" >}})可能也很有用。
+Flink 提供了不同的状态后端，用于指定状态存储的方式和位置。
{{< top >}}
-## Keyed State
-
-Keyed state is maintained in what can be thought of as an embedded key/value
-store. The state is partitioned and distributed strictly together with the
-streams that are read by the stateful operators. Hence, access to the key/value
-state is only possible on *keyed streams*, i.e. after a keyed/partitioned data
-exchange, and is restricted to the values associated with the current event's
-key. Aligning the keys of streams and state makes sure that all state updates
-are local operations, guaranteeing consistency without transaction overhead.
-This alignment also allows Flink to redistribute the state and adjust the
-stream partitioning transparently.
-
-{{< img src="/fig/state_partitioning.svg" alt="State and Partitioning"
class="offset" width="50%" >}}
-
-Keyed State is further organized into so-called *Key Groups*. Key Groups are
-the atomic unit by which Flink can redistribute Keyed State; there are exactly
-as many Key Groups as the defined maximum parallelism. During execution each
-parallel instance of a keyed operator works with the keys for one or more Key
-Groups.
-
-## State Persistence
-
-Flink implements fault tolerance using a combination of **stream replay** and
-**checkpointing**. A checkpoint marks a specific point in each of the
-input streams along with the corresponding state for each of the operators. A
-streaming dataflow can be resumed from a checkpoint while maintaining
-consistency *(exactly-once processing semantics)* by restoring the state of the
-operators and replaying the records from the point of the checkpoint.
-
-The checkpoint interval is a means of trading off the overhead of fault
-tolerance during execution with the recovery time (the number of records that
-need to be replayed).
-
-The fault tolerance mechanism continuously draws snapshots of the distributed
-streaming data flow. For streaming applications with small state, these
-snapshots are very light-weight and can be drawn frequently without much impact
-on performance. The state of the streaming applications is stored at a
-configurable place, usually in a distributed file system.
-
-In case of a program failure (due to machine-, network-, or software failure),
-Flink stops the distributed streaming dataflow. The system then restarts the
-operators and resets them to the latest successful checkpoint. The input
-streams are reset to the point of the state snapshot. Any records that are
-processed as part of the restarted parallel dataflow are guaranteed to not have
-affected the previously checkpointed state.
+## 键控状态（Keyed State）
+
+键控状态被维护在一个可以认为是键/值存储的地方。状态和有状态算子读取的流一起被严格地分区和分布。
Review comment:
keyed state 感觉可以不翻译，我看其他翻译里面都保留这个关键字了。
keyed 是对state 的一种定语，就是以key 存储，查找的一种state 类型
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