Mrzyxing commented on code in PR #20510:
URL: https://github.com/apache/flink/pull/20510#discussion_r948565208
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docs/content.zh/docs/try-flink/table_api.md:
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@@ -275,38 +270,38 @@ public static Table report(Table transactions) {
}
```
-This defines your application as using one hour tumbling windows based on the
timestamp column.
-So a row with timestamp `2019-06-01 01:23:47` is put in the `2019-06-01
01:00:00` window.
+上面的代码含义为：应用使用滚动窗口，窗口按照指定的时间戳字段划分，区间为一小时。
+所以，时间戳为 `2019-06-01 01:23:47` 的行会进入窗口 `2019-06-01 01:00:00`中。
+不同于其他属性，时间在一个持续不断的流式应用中总是向前移动，因此基于时间的聚合总是不重复的。
-Aggregations based on time are unique because time, as opposed to other
attributes, generally moves forward in a continuous streaming application.
-Unlike `floor` and your UDF, window functions are
[intrinsics](https://en.wikipedia.org/wiki/Intrinsic_function), which allows
the runtime to apply additional optimizations.
-In a batch context, windows offer a convenient API for grouping records by a
timestamp attribute.
+不同于 `floor` 以及 UDF，窗口函数是
[内部的][intrinsics](https://en.wikipedia.org/wiki/Intrinsic_function)，可以运行时优化。
+批环境中，如果需要按照时间属性分组数据，窗口函数也有便利的 API。
-Running the test with this implementation will also pass.
+按此逻辑实现，测试也可以通过。
-## Once More, With Streaming!
+## 再用流式处理一次！
-And that's it, a fully functional, stateful, distributed streaming application!
-The query continuously consumes the stream of transactions from Kafka,
computes the hourly spendings, and emits results as soon as they are ready.
-Since the input is unbounded, the query keeps running until it is manually
stopped.
-And because the Job uses time window-based aggregations, Flink can perform
specific optimizations such as state clean up when the framework knows that no
more records will arrive for a particular window.
+这次的编写的应用是一个功能齐全、有状态的分布式流式应用！
+查询语句持续消费 Kafka 中流式的交易数据，然后计算每小时的消费，最后当窗口结束时立刻提交结果。
Review Comment:
Yes, I do think so, just missing world '流'.
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