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_r310370268
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File path: docs/getting-started/walkthroughs/table_api.zh.md
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@@ -399,27 +403,27 @@ tEnv.registerTableSource("transactions", new
UnboundedTransactionTableSource)
</div>
</div>
-And that's it, a fully functional, stateful, distributed streaming application!
-The query continuously consumes the stream of transactions, 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.
+这就是一个功能齐全、有状态的分布式流式应用！
+这个查询作业会不断得消费交易事件流，计算每小时的消费额，然后实时得输出结果。
+由于输入是无界的，这个查询作业会一直运行下去，直到被手动停下来。
+因为这个作业使用了基于时间的聚合，Flink可以使用一些特定的优化，比如当系统知道一个特定的窗口不会再有新的数据来临，它就会对状态进行清理。
{% highlight raw %}
-# Query 3 output showing account id, timestamp, and amount
+# 查询三的输出，显示了账户id，时间戳，和消费总额
-# These rows are calculated continuously over the hour
-# and output immediately at the end of the hour
> 1, 2019-01-01 00:00:00.0, $567.87
> 2, 2019-01-01 00:00:00.0, $726.23
-# Flink begins computing these rows as soon as
-# as the first record for the window arrives
+这些行是在这一小时中持续不断得进行计算所得，然后在这一小时结束时立刻被输出
+
> 1, 2019-01-01 01:00:00.0, $686.87
> 2, 2019-01-01 01:00:00.0, $810.06
+当接收到该窗口的第一条数据时，Flink就开始进行计算这些数据了。
Review comment:
```suggestion
# 当接收到该窗口的第一条数据时，Flink 就开始计算了
```
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