liying919 commented on a change in pull request #12012:
URL: https://github.com/apache/flink/pull/12012#discussion_r421887139
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File path: docs/training/etl.zh.md
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@@ -262,65 +227,51 @@ The output stream now contains a record for each key
every time the duration rea
...
1> (50797,12M)
-### (Implicit) State
+### （隐式的）状态
-This is the first example in this training that involves stateful streaming.
Though the state is
-being handled transparently, Flink has to keep track of the maximum duration
for each distinct
-key.
+这是培训中第一个包含状态的流的例子。尽管状态的处理是透明的，Flink必须跟踪每个不同的键的最大时长。
-Whenever state gets involved in your application, you should think about how
large the state might
-become. Whenever the key space is unbounded, then so is the amount of state
Flink will need.
+只要应用中有状态，你就应该考虑状态的大小。如果键值的数量是无限的，那 Flink 的状态需要的空间也同样是无限的。
-When working with streams, it generally makes more sense to think in terms of
aggregations over
-finite windows, rather than over the entire stream.
+当我们在流上作业时，考虑有限窗口的聚合往往比整个流聚合更有意义。
Review comment:
此处改为"在流处理场景中“，这样确实通顺一点
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