RollsBean commented on a change in pull request #16478:
URL: https://github.com/apache/flink/pull/16478#discussion_r672128643
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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一系列事件。
+ - 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。
Review comment:
@hackergin , “Flink takes care of redistributing state across parallel
instances.” 是不是翻译成 “这意味着 Flink 会跨多个并行实例重新分配状态” 更符合原意，前面说“状态”支持 Flink 扩缩容，那
Flink 就需要能在并行实例中分配状态
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