klion26 commented on a change in pull request #12012:
URL: https://github.com/apache/flink/pull/12012#discussion_r422440530
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File path: docs/training/etl.zh.md
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@@ -231,13 +200,9 @@ DataStream<Tuple2<Integer, Minutes>> minutesByStartCell =
enrichedNYCRides
});
{% endhighlight %}
-Now it is possible to produce a stream that contains only those rides that are
the longest rides
-ever seen (to that point) for each `startCell`.
+现在就可以对每个 `startCell` 找到最长的行程，并产生一个流。
Review comment:
这个翻译我觉得可以的
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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。
-### `reduce()` and other aggregators
+### `reduce()` 和其他聚合算子
-`maxBy()`, used above, is just one example of a number of aggregator functions
available on Flink's
-`KeyedStream`s. There is also a more general purpose `reduce()` function that
you can use to
-implement your own custom aggregations.
+上面用到的 `maxBy()` 只是 Flink 中 `KeyedStream` 上使用的众多聚合函数中的一个。还有一个更通用的 `reduce()`
函数可以用来实现你的自定义聚合。
{% top %}
-## Stateful Transformations
+## 有状态的转换
-### Why is Flink Involved in Managing State?
+### 为什么 Flink 要参与管理状态？
Review comment:
这个翻译我觉得可以的
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