RollsBean commented on a change in pull request #16490:
URL: https://github.com/apache/flink/pull/16490#discussion_r675332516
##########
File path: docs/content.zh/docs/dev/datastream/application_parameters.md
##########
@@ -124,18 +118,17 @@ ParameterTool parameters = ParameterTool.fromArgs(args);
final ExecutionEnvironment env =
ExecutionEnvironment.getExecutionEnvironment();
env.getConfig().setGlobalJobParameters(parameters);
```
-
-Access them in any rich user function:
+在任意富函数中访问参数：
```java
public static final class Tokenizer extends RichFlatMapFunction<String,
Tuple2<String, Integer>> {
@Override
public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {
- ParameterTool parameters = (ParameterTool)
- getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
- parameters.getRequired("input");
- // .. do more ..
+ ParameterTool parameters = (ParameterTool)
+
getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
Review comment:
这里多了一行，你用 md 编辑器查看一下
##########
File path: docs/content.zh/docs/dev/datastream/application_parameters.md
##########
@@ -93,29 +88,28 @@ parameter.getNumberOfParameters()
// .. there are more methods available.
```
-You can use the return values of these methods directly in the `main()` method
of the client submitting the application.
-For example, you could set the parallelism of a operator like this:
+你可以在提交应用程序时直接在客户端的 `main()` 方法中使用这些方法的返回值。例如，你可以这样设置算子的并行度：
```java
ParameterTool parameters = ParameterTool.fromArgs(args);
int parallelism = parameters.get("mapParallelism", 2);
DataStream<Tuple2<String, Integer>> counts = text.flatMap(new
Tokenizer()).setParallelism(parallelism);
```
-Since the `ParameterTool` is serializable, you can pass it to the functions
itself:
+由于 `ParameterTool` 是序列化的，你可以将其传递给函数本身：
```java
ParameterTool parameters = ParameterTool.fromArgs(args);
DataStream<Tuple2<String, Integer>> counts = text.flatMap(new
Tokenizer(parameters));
```
-and then use it inside the function for getting values from the command line.
+然后在函数内使用它以获取命令行的传递的参数。
-#### Register the parameters globally
+#### 全局注册参数
-Parameters registered as global job parameters in the `ExecutionConfig` can be
accessed as configuration values from the JobManager web interface and in all
functions defined by the user.
+从 JobManager web 界面和用户定义的所有函数中可以以配置值的方式访问在 `ExecutionConfig` 中注册为全局作业参数。
Review comment:
```suggestion
从 JobManager web 界面和所有用户定义的函数中可以以配置值的方式访问在 `ExecutionConfig` 中注册的全局作业参数。
```
##########
File path: docs/content.zh/docs/dev/datastream/application_parameters.md
##########
@@ -24,28 +24,23 @@ specific language governing permissions and limitations
under the License.
-->
-# Handling Application Parameters
+# 应用程序参数处理
-
-
-Handling Application Parameters
+应用程序参数处理
-------------------------------
-Almost all Flink applications, both batch and streaming, rely on external
configuration parameters.
-They are used to specify input and output sources (like paths or addresses),
system parameters (parallelism, runtime configuration), and application
specific parameters (typically used within user functions).
+几乎所有的批和流的 Flink
应用程序，都依赖于外部配置参数。这些配置参数可以用于指定输入和输出源（如路径或地址）、系统参数（并行度，运行时配置）和特定的应用程序参数（通常使用在用户自定义函数）。
-Flink provides a simple utility called `ParameterTool` to provide some basic
tooling for solving these problems.
-Please note that you don't have to use the `ParameterTool` described here.
Other frameworks such as [Commons
CLI](https://commons.apache.org/proper/commons-cli/) and
-[argparse4j](http://argparse4j.sourceforge.net/) also work well with Flink.
+为解决以上问题，Flink 提供一个名为 `Parametertool` 的简单公共类，其中包含了一些基本的工具。请注意，这里说的
`Parametertool` 并不是必须使用的。[Commons
CLI](https://commons.apache.org/proper/commons-cli/) 和
[argparse4j](http://argparse4j.sourceforge.net/) 等其他框架也可以非常好地兼容 Flink。
+### `ParameterTool` 读取配置值
-### Getting your configuration values into the `ParameterTool`
+`ParameterTool` 已经定义好了一组静态方法，用于读取配置信息。该工具类内部使用了 `Map<string，string>`
类型，这样使得它可以很容易的与你的配置集成在一起。
Review comment:
```suggestion
`ParameterTool` 已经定义好了一组静态方法，用于读取配置信息。该工具类内部使用了 `Map<string，string>`
类型，这样使得它可以很容易地与你的配置集成在一起。
```
##########
File path: docs/content.zh/docs/dev/datastream/application_parameters.md
##########
@@ -124,18 +118,17 @@ ParameterTool parameters = ParameterTool.fromArgs(args);
final ExecutionEnvironment env =
ExecutionEnvironment.getExecutionEnvironment();
env.getConfig().setGlobalJobParameters(parameters);
```
-
-Access them in any rich user function:
+在任意富函数中访问参数：
```java
public static final class Tokenizer extends RichFlatMapFunction<String,
Tuple2<String, Integer>> {
@Override
public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {
- ParameterTool parameters = (ParameterTool)
- getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
- parameters.getRequired("input");
- // .. do more ..
+ ParameterTool parameters = (ParameterTool)
+
getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
Review comment:
这里多了一行，你用 md 编辑器看一下渲染后的效果
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