[GitHub] [flink] XBaith commented on a change in pull request #11979: [FLINK-17291][docs] Translate 'docs/training/event_driven.zh.md' to C…

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XBaith commented on a change in pull request #11979:
URL: https://github.com/apache/flink/pull/11979#discussion_r419131197



##########
File path: docs/training/event_driven.zh.md
##########
@@ -29,19 +29,18 @@ under the License.
 
 ## Process Functions
 
-### Introduction
+### 介绍
 
-A `ProcessFunction` combines event processing with timers and state, making it a powerful building
-block for stream processing applications. This is the basis for creating event-driven applications
-with Flink. It is very similar to a `RichFlatMapFunction`, but with the addition of timers.
+`ProcessFunction` 将事件处理与 Timer 和 State 结合在一起，使其成为流处理应用的强大构建模块。
+这是使用 Flink 创建事件驱动的应用程序的基础。它和 `RichFlatMapFunction` 十分相似， 但是增加了 Timer。
 
-### Example
+### 实例

Review comment:
       Do you mean “示例” instead of “实例”？

##########
File path: docs/training/event_driven.zh.md
##########
@@ -29,19 +29,18 @@ under the License.
 
 ## Process Functions
 
-### Introduction
+### 介绍
 
-A `ProcessFunction` combines event processing with timers and state, making it a powerful building
-block for stream processing applications. This is the basis for creating event-driven applications
-with Flink. It is very similar to a `RichFlatMapFunction`, but with the addition of timers.
+`ProcessFunction` 将事件处理与 Timer 和 State 结合在一起，使其成为流处理应用的强大构建模块。
+这是使用 Flink 创建事件驱动的应用程序的基础。它和 `RichFlatMapFunction` 十分相似， 但是增加了 Timer。
 
-### Example
+### 实例
 
-If you've done the
-[hands-on exercise]({% link training/streaming_analytics.zh.md %}#hands-on)
-in the [Streaming Analytics training]({% link training/streaming_analytics.zh.md %}),
-you will recall that it uses a `TumblingEventTimeWindow` to compute the sum of the tips for
-each driver during each hour, like this:
+如果你已经体验了
+[流式分析训练]({% link training/streaming_analytics.zh.md %})
+的[动手实践]({% link training/streaming_analytics.zh.md %}#hands-on)，
+你会忆起，它是采用 `TumblingEventTimeWindow` 来计算每个小时内每个司机的小费总和，

Review comment:
       “忆起”还是说“想起”，我个人觉得“想起”读起来更符合我们平常的说法。亦或者说意译成“你应该记得”

##########
File path: docs/training/event_driven.zh.md
##########
@@ -51,8 +50,8 @@ DataStream<Tuple3<Long, Long, Float>> hourlyTips = fares
         .process(new AddTips());
 {% endhighlight %}
 
-It is reasonably straightforward, and educational, to do the same thing with a
-`KeyedProcessFunction`. Let us begin by replacing the code above with this:
+使用 `KeyedProcessFunction` 去实现相同的效果是合理、直接且有学习意义的。

Review comment:
       ```suggestion
   使用 `KeyedProcessFunction` 去实现相同的操作更加直接且更有学习意义。
   ```

##########
File path: docs/training/event_driven.zh.md
##########
@@ -202,52 +196,48 @@ public void onTimer(
 }
 {% endhighlight %}
 
-Observations:
+观察：
 
-* The `OnTimerContext context` passed in to `onTimer` can be used to determine the current key.
+* 传递给 `onTimer` 的 `OnTimerContext context` 可用于确定当前 key。
 
-* Our pseudo-windows are being triggered when the current watermark reaches the end of each hour, at
-  which point `onTimer` is called. This onTimer method removes the related entry from `sumOfTips`,
-  which has the effect of making it impossible to accommodate late events. This is the equivalent of
-  setting the allowedLateness to zero when working with Flink's time windows.
+* 我们的 pseudo-windows 在当前 Watermark 到达每小时结束时触发，此时调用 `onTimer`。
+  这个 `onTimer` 方法从 `sumOfTips` 中删除相关的条目，这样做的效果是不可能容纳延迟的事件。
+  这相当于在使用 Flink 的时间窗口时将 allowedLateness 设置为零。
 
-### Performance Considerations
+### 性能考虑
 
-Flink provides `MapState` and `ListState` types that are optimized for RocksDB. Where possible,
-these should be used instead of a `ValueState` object holding some sort of collection. The RocksDB
-state backend can append to `ListState` without going through (de)serialization, and for `MapState`, each
-key/value pair is a separate RocksDB object, so `MapState` can be efficiently accessed and updated.
+在可能的情况下，应该使用这些对象，而不是持有某种集合的 `ValueState` 对象。
+Flink 提供了为 RocksDB 优化的 `MapState` 和 `ListState` 类型。
+在可能的情况下，应该使用这些对象，而不是持有某种集合的 `ValueState` 对象。
+RocksDB State Backend 可以附加到 `ListState`，而无需进行（反）序列化，
+对于 `MapState`，每个 key/value 都是一个单独的 RocksDB 对象，因此可以有效地访问和更新 `MapState`。
 
 {% top %}
 
 ## Side Outputs
 
-### Introduction
+### 介绍
 
-There are several good reasons to want to have more than one output stream from a Flink operator, such as reporting:
+有几个很好的理由希望从 Flink operator 获得多个输出流，如下报告条目：

Review comment:
       ```suggestion
   有几个很好的理由希望从 Flink 算子获得多个输出流，如下报告条目：
   ```

##########
File path: docs/training/event_driven.zh.md
##########
@@ -269,38 +259,35 @@ SingleOutputStreamOperator hourlyTips = fares
 hourlyTips.getSideOutput(lateFares).print();
 {% endhighlight %}
 
-Alternatively, you can use two OutputTags with the
-same name to refer to the same side output, but if you do, they must have the same type.
+或者，可以使用两个同名的 OutputTag 来引用同一个旁路输出，但如果这样做，它们必须具有相同的类型。
 
 {% top %}
 
-## Closing Remarks
+## 结语
 
-In this example you have seen how a `ProcessFunction` can be used to reimplement a straightforward time
-window. Of course, if Flink's built-in windowing API meets your needs, by all means, go ahead and
-use it. But if you find yourself considering doing something contorted with Flink's windows, don't
-be afraid to roll your own.
+在本例中，你已经了解了如何使用 `ProcessFunction` 重新实现一个简单的时间窗口。
+当然，如果 Flink 内置的窗口 API 能够满足你的开发需求，那么一定要优先使用它。
+但如果你发现自己在考虑用 Flink 的窗口做些错综复杂的事情，不要害怕自己动手。
 
-Also, `ProcessFunctions` are useful for many other use cases beyond computing analytics. The hands-on
-exercise below provides an example of something completely different.
+此外，`ProcessFunctions` 对于计算分析之外的许多其他用例也很有用。
+下面的实践练习提供了一个完全不同的例子。
 
-Another common use case for ProcessFunctions is for expiring stale state. If you think back to the
-[Rides and Fares Exercise](https://github.com/apache/flink-training/tree/{% if site.is_stable %}release-{{ site.version_title }}{% else %}master{% endif %}/rides-and-fares),
-where a `RichCoFlatMapFunction` is used to compute a simple join, the sample solution assumes that
-the TaxiRides and TaxiFares are perfectly matched, one-to-one for each `rideId`. If an event is lost,
-the other event for the same `rideId` will be held in state forever. This could instead be implemented
-as a `KeyedCoProcessFunction`, and a timer could be used to detect and clear any stale state.
+ProcessFunctions 的另一个常见用例是过期过时 State。如果你回想一下
+[Rides and Fares Exercise](https://github.com/apache/flink-training/tree/{% if site.is_stable %}release-{{ site.version_title }}{% else %}master{% endif %}/rides-and-fares)，
+其中使用 `RichCoFlatMapFunction` 来计算简单 Join，那么示例解决方案假设 TaxiRides 和 TaxiFares 
+完全匹配，每个 `rideId` 一对一。如果某个事件丢失，则同一 `rideId` 的另一个事件将永远保持 State。
+这可以作为 `Keyedcomprocessfunction` 实现，并且可以使用计时器来检测和清除任何过时的 State。

Review comment:
       ```suggestion
   这可以作为 `KeyedCoProcessFunction` 实现，并且可以使用计时器来检测和清除任何过时的 State。
   ```




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