YngwieWang commented on a change in pull request #9350: [FLINK-13485]
[chinese-translation] Translate "Table API Example Walkthrough" page into
Chinese
URL: https://github.com/apache/flink/pull/9350#discussion_r310369186
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File path: docs/getting-started/walkthroughs/table_api.zh.md
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@@ -352,13 +356,13 @@ Running the updated query will produce identical results
as before.
> 2, 2019-01-01 04:00:00.0, $760.76
{% endhighlight %}
-## Once More, With Streaming!
+## 通过流的方式再来一次！
-Because Flink's Table API offers consistent syntax and semantics for both
batch and streaming, migrating from one to the other requires just two steps.
+因为Flink的Table API为批和流提供了相同的语法和语义，从一种方式迁移到另一种方式只需要两步。
-The first step is to replace the batch `ExecutionEnvironment` with its
streaming counterpart, `StreamExecutionEnvironment`, which creates a continuous
streaming Job.
-It includes stream-specific configurations, such as the time characteristic,
which when set to [event time]({{ site.baseurl }}/dev/event_time.html)
guarantees consistent results even when faced with out-of-order events or a Job
failure.
-This is what will be used by your `Tumble` window when grouping records.
+第一步就是把批环境使用的 `ExecutionEnvironment` 替换成它在流环境中对应的
`StreamExecutionEnvironment`。`StreamExecutionEnvironment`可以创建一个连续不断的流任务。
+它包含了流环境专属的配置，比如时间特性。当这个属性被设置成 [事件时间]({{ site.baseurl
}}/dev/event_time.html)时，它能保证在遇到乱序事件或者作业失败了也能输出一致性的结果。
Review comment:
```suggestion
它包含特定于流处理的配置，比如时间特性。当这个属性被设置成 [事件时间]({{ site.baseurl
}}/zh/dev/event_time.html)时，它能保证即使遭遇乱序事件或者作业失败的情况也能输出一致的结果。
```
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