klion26 commented on a change in pull request #11971:
URL: https://github.com/apache/flink/pull/11971#discussion_r422456361
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -104,63 +99,56 @@ public class Example {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
-
+
DataStream<Person> flintstones = env.fromElements(
new Person("Fred", 35),
new Person("Wilma", 35),
new Person("Pebbles", 2));
-
+
DataStream<Person> adults = flintstones.filter(new
FilterFunction<Person>() {
@Override
public boolean filter(Person person) throws Exception {
return person.age >= 18;
}
});
-
+
adults.print();
-
+
env.execute();
}
-
+
public static class Person {
public String name;
public Integer age;
public Person() {};
-
+
public Person(String name, Integer age) {
this.name = name;
this.age = age;
};
-
+
public String toString() {
return this.name.toString() + ": age " + this.age.toString();
};
}
}
{% endhighlight %}
-### Stream execution environment
+### Stream 执行环境
-Every Flink application needs an execution environment, `env` in this example.
Streaming
-applications need to use a `StreamExecutionEnvironment`.
+每个 Flink 应用都需要有执行环境，在该示例中为 `env` 。流式应用需要用到 `StreamExecutionEnvironment`。
-The DataStream API calls made in your application build a job graph that is
attached to the
-`StreamExecutionEnvironment`. When `env.execute()` is called this graph is
packaged up and sent to
-the Flink Master, which parallelizes the job and distributes slices of it to
the Task Managers for
-execution. Each parallel slice of your job will be executed in a *task slot*.
+DataStream API 将你的应用构建为一个由 `StreamExecutionEnvironment` 生成的 job graph。当调用
`env.execute()` 时此 graph 就被打包并发送到 Flink Master 上，后者对作业并行处理并将其切片分发给 Task Manager
来执行。每个作业并行切片将在 *task slot* 中执行。
Review comment:
我暂时没有更好的建议，“子任务”我可以，看其他人是否有什么更好的建议
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