yangyichao-mango commented on a change in pull request #12237:
URL: https://github.com/apache/flink/pull/12237#discussion_r427056510
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File path: docs/training/streaming_analytics.zh.md
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@@ -27,125 +27,101 @@ under the License.
* This will be replaced by the TOC
{:toc}
-## Event Time and Watermarks
+## 事件时间和水印
-### Introduction
+### 简介
-Flink explicitly supports three different notions of time:
+Flink 明确的支持以下三种事件时间:
-* _event time:_ the time when an event occurred, as recorded by the device
producing (or storing) the event
+* _事件时间:_ 事件产生的时间，记录的是设备生产(或者存储)事件的时间
-* _ingestion time:_ a timestamp recorded by Flink at the moment it ingests the
event
+* _摄取时间:_ Flink 提取事件时记录的时间戳
-* _processing time:_ the time when a specific operator in your pipeline is
processing the event
+* _处理时间:_ Flink 中通过特定的操作处理事件的时间
-For reproducible results, e.g., when computing the maximum price a stock
reached during the first
-hour of trading on a given day, you should use event time. In this way the
result won't depend on
-when the calculation is performed. This kind of real-time application is
sometimes performed using
-processing time, but then the results are determined by the events that happen
to be processed
-during that hour, rather than the events that occurred then. Computing
analytics based on processing
-time causes inconsistencies, and makes it difficult to re-analyze historic
data or test new
-implementations.
+为了获得可重现的结果，例如在计算过去的特定一天里第一个小时股票的最高价格时，我们应该使用事件时间。这样的话，无论
+什么时间去计算都不会影响输出结果。然而有些人，在实时计算应用时使用处理时间，这样的话，输出结果就会被处理时间点所决
+定，而不是事件的生成时间。基于处理时间会导致多次计算的结果不一致，也可能会导致重新分析历史数据和测试变得异常困难。
-### Working with Event Time
+### 使用事件时间
-By default, Flink will use processing time. To change this, you can set the
Time Characteristic:
+Flink 在默认情况下使用处理时间。也可以通过如下配置来告诉 Flink 选择哪种事件时间:
{% highlight java %}
final StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
{% endhighlight %}
-If you want to use event time, you will also need to supply a Timestamp
Extractor and Watermark
-Generator that Flink will use to track the progress of event time. This will
be covered in the
-section below on [Working with Watermarks]({% link
-training/streaming_analytics.zh.md %}#working-with-watermarks), but first we
should explain what
-watermarks are.
+如果想要使用事件时间，则需要额外给 Flink 提供一个时间戳的提取器和水印，Flink 将使用它们来跟踪事件时间的进度。这
+将在选节[使用水印]({% linktutorials/streaming_analytics.md %}#使用水印)中介绍，但是首先我们需要解释一下
+水印是什么。
-### Watermarks
+### 水印
Review comment:
```suggestion
### Watermarks
```
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