liyubin117 commented on code in PR #23666:
URL: https://github.com/apache/flink/pull/23666#discussion_r1390673821
##########
docs/content.zh/docs/dev/datastream/dataset_migration.md:
##########
@@ -134,10 +131,9 @@ DataStreamSource<> source =
StreamExecutionEnvironment.createInput(inputFormat)
</tbody>
</table>
-### Sinks
+### 写
Review Comment:
done
##########
docs/content.zh/docs/dev/datastream/dataset_migration.md:
##########
@@ -62,44 +58,45 @@ The first step of migrating an application from DataSet API
to DataStream API is
<tr>
<td>
{{< highlight "java" >}}
-// Create the execution environment
+// 创建执行环境
ExecutionEnvironment.getExecutionEnvironment();
-// Create the local execution environment
+// 创建本地执行环境
ExecutionEnvironment.createLocalEnvironment();
-// Create the collection environment
+// 创建 collection 环境
new CollectionEnvironment();
-// Create the remote environment
+// 创建远程执行环境
ExecutionEnvironment.createRemoteEnvironment(String host, int port, String...
jarFiles);
{{< /highlight >}}
</td>
<td>
{{< highlight "java" >}}
-// Create the execution environment
+// 创建执行环境
StreamExecutionEnvironment.getExecutionEnvironment();
-// Create the local execution environment
+// 创建本地执行环境
StreamExecutionEnvironment.createLocalEnvironment();
-// The collection environment is not supported.
-// Create the remote environment
+// 不支持 collection 环境
+// 创建远程执行环境
StreamExecutionEnvironment.createRemoteEnvironment(String host, int port,
String... jarFiles);
{{< /highlight >}}
</td>
</tr>
</tbody>
</table>
-Unlike DataSet, DataStream supports processing on both bounded and unbounded
data streams. Thus, user needs to explicitly set the execution mode
-to `RuntimeExecutionMode.BATCH` if that is expected.
+与 DataSet 不同，DataStream 支持对有界和无界数据流进行处理。
+
+如果需要的话，用户可以显式地将执行模式设置为 `RuntimeExecutionMode.BATCH`。
```java
StreamExecutionEnvironment executionEnvironment = // [...];
executionEnvironment.setRuntimeMode(RuntimeExecutionMode.BATCH);
```
-## Using the streaming sources and sinks
+## 流读和流写
-### Sources
+### 读
Review Comment:
done
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