dianfu commented on a change in pull request #13498:
URL: https://github.com/apache/flink/pull/13498#discussion_r497227841
##########
File path: docs/dev/python/datastream_tutorial.zh.md
##########
@@ -22,76 +22,77 @@ specific language governing permissions and limitations
under the License.
-->
-Apache Flink offers a DataStream API for building robust, stateful streaming
applications. It provides fine-grained control over state and time, which
allows for the implementation of advanced event-driven systems. In this
step-by-step guide, you’ll learn how to build a simple streaming application
with PyFlink and the DataStream API.
+Apache Flink 提供了 DataStream
API，用于构建健壮的、有状态的流式应用程序。它提供了对状态和时间细粒度控制，从而允许实现高级事件驱动系统。
+在这篇教程中，你将学习如何使用 PyFlink 和 DataStream API 构建一个简单的流式应用程序。
* This will be replaced by the TOC
{:toc}
-## What Will You Be Building?
+## 你将构建什么？
-In this tutorial, you will learn how to write a simple Python DataStream job.
-The pipeline will read data from a non-empty collection and write the results
to the local file system.
+在本教程中，你将学习如何编写一个简单的 Python DataStream 作业。
+例子是从非空集合中读取数据，并将结果写入本地文件系统。
-## Prerequisites
+## 先决条件
-This walkthrough assumes that you have some familiarity with Python, but you
should be able to follow along even if you come from a different programming
language.
+本教程假设你对 Python 有一定的熟悉，但是即使你使用的是不同编程语言，你也应该能够学会。
-## Help, I’m Stuck!
+## 帮助，我很困惑！
-If you get stuck, check out the [community support
resources](https://flink.apache.org/zh/community.html).
-In particular, Apache Flink's [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists) consistently
ranks as one of the most active of any Apache project and a great way to get
help quickly.
+如果你有疑惑，可以查阅 [community support
resources](https://flink.apache.org/zh/community.html)。
Review comment:
```suggestion
如果你有疑惑，可以查阅 [社区支持资源](https://flink.apache.org/zh/community.html)。
```
##########
File path: docs/dev/python/datastream_tutorial.zh.md
##########
@@ -22,76 +22,77 @@ specific language governing permissions and limitations
under the License.
-->
-Apache Flink offers a DataStream API for building robust, stateful streaming
applications. It provides fine-grained control over state and time, which
allows for the implementation of advanced event-driven systems. In this
step-by-step guide, you’ll learn how to build a simple streaming application
with PyFlink and the DataStream API.
+Apache Flink 提供了 DataStream
API，用于构建健壮的、有状态的流式应用程序。它提供了对状态和时间细粒度控制，从而允许实现高级事件驱动系统。
+在这篇教程中，你将学习如何使用 PyFlink 和 DataStream API 构建一个简单的流式应用程序。
* This will be replaced by the TOC
{:toc}
-## What Will You Be Building?
+## 你将构建什么？
-In this tutorial, you will learn how to write a simple Python DataStream job.
-The pipeline will read data from a non-empty collection and write the results
to the local file system.
+在本教程中，你将学习如何编写一个简单的 Python DataStream 作业。
+例子是从非空集合中读取数据，并将结果写入本地文件系统。
-## Prerequisites
+## 先决条件
-This walkthrough assumes that you have some familiarity with Python, but you
should be able to follow along even if you come from a different programming
language.
+本教程假设你对 Python 有一定的熟悉，但是即使你使用的是不同编程语言，你也应该能够学会。
-## Help, I’m Stuck!
+## 帮助，我很困惑！
-If you get stuck, check out the [community support
resources](https://flink.apache.org/zh/community.html).
-In particular, Apache Flink's [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists) consistently
ranks as one of the most active of any Apache project and a great way to get
help quickly.
+如果你有疑惑，可以查阅 [community support
resources](https://flink.apache.org/zh/community.html)。
+特别是，Apache Flink [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists)
一直是最活跃的Apache项目之一，也是快速获得帮助的好方法。
-## How To Follow Along
+## 如何跟进
-If you want to follow along, you will require a computer with:
+如果你想学习，你需要一台装有以下环境的电脑:
* Java 8 or 11
* Python 3.5, 3.6 or 3.7
-Using Python DataStream API requires installing PyFlink, which is available on
[PyPI](https://pypi.org/project/apache-flink/) and can be easily installed
using `pip`.
+使用 Python DataStream API 需要安装 PyFlink，安装地址
[PyPI](https://pypi.org/project/apache-flink/) ，同时也可以使用 `pip` 快速安装。
{% highlight bash %}
$ python -m pip install apache-flink
{% endhighlight %}
-Once PyFlink is installed, you can move on to write a Python DataStream job.
+一旦 PyFlink 安装完成之后，你可以开始编写 Python DataStream 作业。
Review comment:
```suggestion
一旦 PyFlink 安装完成之后，你就可以开始编写 Python DataStream 作业了。
```
##########
File path: docs/dev/python/datastream_tutorial.zh.md
##########
@@ -22,76 +22,77 @@ specific language governing permissions and limitations
under the License.
-->
-Apache Flink offers a DataStream API for building robust, stateful streaming
applications. It provides fine-grained control over state and time, which
allows for the implementation of advanced event-driven systems. In this
step-by-step guide, you’ll learn how to build a simple streaming application
with PyFlink and the DataStream API.
+Apache Flink 提供了 DataStream
API，用于构建健壮的、有状态的流式应用程序。它提供了对状态和时间细粒度控制，从而允许实现高级事件驱动系统。
+在这篇教程中，你将学习如何使用 PyFlink 和 DataStream API 构建一个简单的流式应用程序。
* This will be replaced by the TOC
{:toc}
-## What Will You Be Building?
+## 你将构建什么？
-In this tutorial, you will learn how to write a simple Python DataStream job.
-The pipeline will read data from a non-empty collection and write the results
to the local file system.
+在本教程中，你将学习如何编写一个简单的 Python DataStream 作业。
+例子是从非空集合中读取数据，并将结果写入本地文件系统。
-## Prerequisites
+## 先决条件
-This walkthrough assumes that you have some familiarity with Python, but you
should be able to follow along even if you come from a different programming
language.
+本教程假设你对 Python 有一定的熟悉，但是即使你使用的是不同编程语言，你也应该能够学会。
-## Help, I’m Stuck!
+## 帮助，我很困惑！
-If you get stuck, check out the [community support
resources](https://flink.apache.org/zh/community.html).
-In particular, Apache Flink's [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists) consistently
ranks as one of the most active of any Apache project and a great way to get
help quickly.
+如果你有疑惑，可以查阅 [community support
resources](https://flink.apache.org/zh/community.html)。
+特别是，Apache Flink [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists)
一直是最活跃的Apache项目之一，也是快速获得帮助的好方法。
-## How To Follow Along
+## 如何跟进
Review comment:
```suggestion
## 怎样跟着教程练习
```
##########
File path: docs/dev/python/datastream_tutorial.zh.md
##########
@@ -22,76 +22,77 @@ specific language governing permissions and limitations
under the License.
-->
-Apache Flink offers a DataStream API for building robust, stateful streaming
applications. It provides fine-grained control over state and time, which
allows for the implementation of advanced event-driven systems. In this
step-by-step guide, you’ll learn how to build a simple streaming application
with PyFlink and the DataStream API.
+Apache Flink 提供了 DataStream
API，用于构建健壮的、有状态的流式应用程序。它提供了对状态和时间细粒度控制，从而允许实现高级事件驱动系统。
+在这篇教程中，你将学习如何使用 PyFlink 和 DataStream API 构建一个简单的流式应用程序。
* This will be replaced by the TOC
{:toc}
-## What Will You Be Building?
+## 你将构建什么？
-In this tutorial, you will learn how to write a simple Python DataStream job.
-The pipeline will read data from a non-empty collection and write the results
to the local file system.
+在本教程中，你将学习如何编写一个简单的 Python DataStream 作业。
+例子是从非空集合中读取数据，并将结果写入本地文件系统。
-## Prerequisites
+## 先决条件
-This walkthrough assumes that you have some familiarity with Python, but you
should be able to follow along even if you come from a different programming
language.
+本教程假设你对 Python 有一定的熟悉，但是即使你使用的是不同编程语言，你也应该能够学会。
-## Help, I’m Stuck!
+## 帮助，我很困惑！
-If you get stuck, check out the [community support
resources](https://flink.apache.org/zh/community.html).
-In particular, Apache Flink's [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists) consistently
ranks as one of the most active of any Apache project and a great way to get
help quickly.
+如果你有疑惑，可以查阅 [community support
resources](https://flink.apache.org/zh/community.html)。
+特别是，Apache Flink [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists)
一直是最活跃的Apache项目之一，也是快速获得帮助的好方法。
-## How To Follow Along
+## 如何跟进
-If you want to follow along, you will require a computer with:
Review comment:
首先，你需要在你的电脑上准备以下环境：
##########
File path: docs/dev/python/datastream_tutorial.zh.md
##########
@@ -22,76 +22,77 @@ specific language governing permissions and limitations
under the License.
-->
-Apache Flink offers a DataStream API for building robust, stateful streaming
applications. It provides fine-grained control over state and time, which
allows for the implementation of advanced event-driven systems. In this
step-by-step guide, you’ll learn how to build a simple streaming application
with PyFlink and the DataStream API.
+Apache Flink 提供了 DataStream
API，用于构建健壮的、有状态的流式应用程序。它提供了对状态和时间细粒度控制，从而允许实现高级事件驱动系统。
+在这篇教程中，你将学习如何使用 PyFlink 和 DataStream API 构建一个简单的流式应用程序。
* This will be replaced by the TOC
{:toc}
-## What Will You Be Building?
+## 你将构建什么？
-In this tutorial, you will learn how to write a simple Python DataStream job.
-The pipeline will read data from a non-empty collection and write the results
to the local file system.
+在本教程中，你将学习如何编写一个简单的 Python DataStream 作业。
+例子是从非空集合中读取数据，并将结果写入本地文件系统。
-## Prerequisites
+## 先决条件
-This walkthrough assumes that you have some familiarity with Python, but you
should be able to follow along even if you come from a different programming
language.
+本教程假设你对 Python 有一定的熟悉，但是即使你使用的是不同编程语言，你也应该能够学会。
-## Help, I’m Stuck!
+## 帮助，我很困惑！
-If you get stuck, check out the [community support
resources](https://flink.apache.org/zh/community.html).
-In particular, Apache Flink's [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists) consistently
ranks as one of the most active of any Apache project and a great way to get
help quickly.
+如果你有疑惑，可以查阅 [community support
resources](https://flink.apache.org/zh/community.html)。
+特别是，Apache Flink [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists)
一直是最活跃的Apache项目之一，也是快速获得帮助的好方法。
Review comment:
```suggestion
特别是，Apache Flink
[用户邮件列表](https://flink.apache.org/zh/community.html#mailing-lists)
一直被评为所有Apache项目中最活跃的一个，也是快速获得帮助的好方法。
```
##########
File path: docs/dev/python/datastream_tutorial.zh.md
##########
@@ -22,76 +22,77 @@ specific language governing permissions and limitations
under the License.
-->
-Apache Flink offers a DataStream API for building robust, stateful streaming
applications. It provides fine-grained control over state and time, which
allows for the implementation of advanced event-driven systems. In this
step-by-step guide, you’ll learn how to build a simple streaming application
with PyFlink and the DataStream API.
+Apache Flink 提供了 DataStream
API，用于构建健壮的、有状态的流式应用程序。它提供了对状态和时间细粒度控制，从而允许实现高级事件驱动系统。
+在这篇教程中，你将学习如何使用 PyFlink 和 DataStream API 构建一个简单的流式应用程序。
* This will be replaced by the TOC
{:toc}
-## What Will You Be Building?
+## 你将构建什么？
-In this tutorial, you will learn how to write a simple Python DataStream job.
-The pipeline will read data from a non-empty collection and write the results
to the local file system.
+在本教程中，你将学习如何编写一个简单的 Python DataStream 作业。
+例子是从非空集合中读取数据，并将结果写入本地文件系统。
-## Prerequisites
+## 先决条件
-This walkthrough assumes that you have some familiarity with Python, but you
should be able to follow along even if you come from a different programming
language.
+本教程假设你对 Python 有一定的熟悉，但是即使你使用的是不同编程语言，你也应该能够学会。
-## Help, I’m Stuck!
+## 帮助，我很困惑！
-If you get stuck, check out the [community support
resources](https://flink.apache.org/zh/community.html).
-In particular, Apache Flink's [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists) consistently
ranks as one of the most active of any Apache project and a great way to get
help quickly.
+如果你有疑惑，可以查阅 [community support
resources](https://flink.apache.org/zh/community.html)。
+特别是，Apache Flink [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists)
一直是最活跃的Apache项目之一，也是快速获得帮助的好方法。
-## How To Follow Along
+## 如何跟进
-If you want to follow along, you will require a computer with:
+如果你想学习，你需要一台装有以下环境的电脑:
* Java 8 or 11
* Python 3.5, 3.6 or 3.7
-Using Python DataStream API requires installing PyFlink, which is available on
[PyPI](https://pypi.org/project/apache-flink/) and can be easily installed
using `pip`.
+使用 Python DataStream API 需要安装 PyFlink，安装地址
[PyPI](https://pypi.org/project/apache-flink/) ，同时也可以使用 `pip` 快速安装。
Review comment:
```suggestion
使用 Python DataStream API 需要安装 PyFlink，PyFlink 发布在
[PyPI](https://pypi.org/project/apache-flink/) 上，可以通过 `pip` 快速安装。
```
##########
File path: docs/dev/python/datastream_tutorial.zh.md
##########
@@ -22,76 +22,77 @@ specific language governing permissions and limitations
under the License.
-->
-Apache Flink offers a DataStream API for building robust, stateful streaming
applications. It provides fine-grained control over state and time, which
allows for the implementation of advanced event-driven systems. In this
step-by-step guide, you’ll learn how to build a simple streaming application
with PyFlink and the DataStream API.
+Apache Flink 提供了 DataStream
API，用于构建健壮的、有状态的流式应用程序。它提供了对状态和时间细粒度控制，从而允许实现高级事件驱动系统。
+在这篇教程中，你将学习如何使用 PyFlink 和 DataStream API 构建一个简单的流式应用程序。
* This will be replaced by the TOC
{:toc}
-## What Will You Be Building?
+## 你将构建什么？
-In this tutorial, you will learn how to write a simple Python DataStream job.
-The pipeline will read data from a non-empty collection and write the results
to the local file system.
+在本教程中，你将学习如何编写一个简单的 Python DataStream 作业。
+例子是从非空集合中读取数据，并将结果写入本地文件系统。
-## Prerequisites
+## 先决条件
-This walkthrough assumes that you have some familiarity with Python, but you
should be able to follow along even if you come from a different programming
language.
+本教程假设你对 Python 有一定的熟悉，但是即使你使用的是不同编程语言，你也应该能够学会。
-## Help, I’m Stuck!
+## 帮助，我很困惑！
-If you get stuck, check out the [community support
resources](https://flink.apache.org/zh/community.html).
-In particular, Apache Flink's [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists) consistently
ranks as one of the most active of any Apache project and a great way to get
help quickly.
+如果你有疑惑，可以查阅 [community support
resources](https://flink.apache.org/zh/community.html)。
+特别是，Apache Flink [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists)
一直是最活跃的Apache项目之一，也是快速获得帮助的好方法。
-## How To Follow Along
+## 如何跟进
-If you want to follow along, you will require a computer with:
+如果你想学习，你需要一台装有以下环境的电脑:
* Java 8 or 11
* Python 3.5, 3.6 or 3.7
-Using Python DataStream API requires installing PyFlink, which is available on
[PyPI](https://pypi.org/project/apache-flink/) and can be easily installed
using `pip`.
+使用 Python DataStream API 需要安装 PyFlink，安装地址
[PyPI](https://pypi.org/project/apache-flink/) ，同时也可以使用 `pip` 快速安装。
{% highlight bash %}
$ python -m pip install apache-flink
{% endhighlight %}
-Once PyFlink is installed, you can move on to write a Python DataStream job.
+一旦 PyFlink 安装完成之后，你可以开始编写 Python DataStream 作业。
-## Writing a Flink Python DataStream API Program
+## 编写一个 Flink Python DataStream API 程序
-DataStream API applications begin by declaring an execution environment
(`StreamExecutionEnvironment`), the context in which a streaming program is
executed. This is what you will use to set the properties of your job (e.g.
default parallelism, restart strategy), create your sources and finally trigger
the execution of the job.
+DataStream API
应用程序首先声明一个执行环境（`StreamExecutionEnvironment`），是流式程序执行的上下文。你将使用它来设置作业的属性(例如默认并行性、重启策略)、创建源，并最终触发作业的执行。
Review comment:
```suggestion
DataStream API
应用程序首先需要声明一个执行环境（`StreamExecutionEnvironment`），这是流式程序执行的上下文。你将通过它来设置作业的属性（例如默认并发度、重启策略等）、创建源、并最终触发作业的执行。
```
##########
File path: docs/dev/python/datastream_tutorial.zh.md
##########
@@ -22,76 +22,77 @@ specific language governing permissions and limitations
under the License.
-->
-Apache Flink offers a DataStream API for building robust, stateful streaming
applications. It provides fine-grained control over state and time, which
allows for the implementation of advanced event-driven systems. In this
step-by-step guide, you’ll learn how to build a simple streaming application
with PyFlink and the DataStream API.
+Apache Flink 提供了 DataStream
API，用于构建健壮的、有状态的流式应用程序。它提供了对状态和时间细粒度控制，从而允许实现高级事件驱动系统。
+在这篇教程中，你将学习如何使用 PyFlink 和 DataStream API 构建一个简单的流式应用程序。
* This will be replaced by the TOC
{:toc}
-## What Will You Be Building?
+## 你将构建什么？
-In this tutorial, you will learn how to write a simple Python DataStream job.
-The pipeline will read data from a non-empty collection and write the results
to the local file system.
+在本教程中，你将学习如何编写一个简单的 Python DataStream 作业。
+例子是从非空集合中读取数据，并将结果写入本地文件系统。
-## Prerequisites
+## 先决条件
-This walkthrough assumes that you have some familiarity with Python, but you
should be able to follow along even if you come from a different programming
language.
+本教程假设你对 Python 有一定的熟悉，但是即使你使用的是不同编程语言，你也应该能够学会。
-## Help, I’m Stuck!
+## 帮助，我很困惑！
-If you get stuck, check out the [community support
resources](https://flink.apache.org/zh/community.html).
-In particular, Apache Flink's [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists) consistently
ranks as one of the most active of any Apache project and a great way to get
help quickly.
+如果你有疑惑，可以查阅 [community support
resources](https://flink.apache.org/zh/community.html)。
+特别是，Apache Flink [user mailing
list](https://flink.apache.org/zh/community.html#mailing-lists)
一直是最活跃的Apache项目之一，也是快速获得帮助的好方法。
-## How To Follow Along
+## 如何跟进
-If you want to follow along, you will require a computer with:
+如果你想学习，你需要一台装有以下环境的电脑:
* Java 8 or 11
* Python 3.5, 3.6 or 3.7
-Using Python DataStream API requires installing PyFlink, which is available on
[PyPI](https://pypi.org/project/apache-flink/) and can be easily installed
using `pip`.
+使用 Python DataStream API 需要安装 PyFlink，安装地址
[PyPI](https://pypi.org/project/apache-flink/) ，同时也可以使用 `pip` 快速安装。
{% highlight bash %}
$ python -m pip install apache-flink
{% endhighlight %}
-Once PyFlink is installed, you can move on to write a Python DataStream job.
+一旦 PyFlink 安装完成之后，你可以开始编写 Python DataStream 作业。
-## Writing a Flink Python DataStream API Program
+## 编写一个 Flink Python DataStream API 程序
-DataStream API applications begin by declaring an execution environment
(`StreamExecutionEnvironment`), the context in which a streaming program is
executed. This is what you will use to set the properties of your job (e.g.
default parallelism, restart strategy), create your sources and finally trigger
the execution of the job.
+DataStream API
应用程序首先声明一个执行环境（`StreamExecutionEnvironment`），是流式程序执行的上下文。你将使用它来设置作业的属性(例如默认并行性、重启策略)、创建源，并最终触发作业的执行。
{% highlight python %}
env = StreamExecutionEnvironment.get_execution_environment()
env.set_parallelism(1)
{% endhighlight %}
-Once a `StreamExecutionEnvironment` is created, you can use it to declare your
_source_. Sources ingest data from external systems, such as Apache Kafka,
Rabbit MQ, or Apache Pulsar, into Flink Jobs.
+一旦创建了 `StreamExecutionEnvironment` 之后，你可以使用它来声明数据源。数据源从外部系统（如 Apache
Kafka、Rabbit MQ 或 Apache Pulsar）拉取数据到 Flink 作业里。
-To keep things simple, this walkthrough uses a source that is backed by a
collection of elements.
+为了简单起见，本教程使用元素集合作为数据源。
{% highlight python %}
ds = env.from_collection(
collection=[(1, 'aaa'), (2, 'bbb')],
type_info=Types.ROW([Types.INT(), Types.STRING()]))
{% endhighlight %}
-This creates a data stream from the given collection, with the same type as
that of the elements in it (here, a `ROW` type with a INT field and a STRING
field).
+这里从相同类型数据集合中创建数据流（一个带有 INT 和 STRING 类型字段的 `ROW` 类型）。
-You can now perform transformations on this data stream, or just write the
data to an external system using a _sink_. This walkthrough uses the
`StreamingFileSink` sink connector to write the data into a file in the
`/tmp/output` directory.
+你现在可以在这个数据流上执行转换操作，或者使用 _sink_ 将数据写入外部系统。本教程使用 `StreamingFileSink` 将数据写入
`/tmp/output` 文件目录中。
{% highlight python %}
ds.add_sink(StreamingFileSink
.for_row_format('/tmp/output', SimpleStringEncoder())
.build())
{% endhighlight %}
-The last step is to execute the actual PyFlink DataStream API job. PyFlink
applications are built lazily and shipped to the cluster for execution only
once fully formed. To execute an application, you simply call
`env.execute(job_name)`.
+最后一步是执行真实的 PyFlink DataStream API 作业。PyFlink applications
是惰性构建的，并且只有完全构建之后才会提交给集群执行。要执行一个应用程序，你只需简单的调用 `env.execute(job_name)`。
Review comment:
```suggestion
最后一步是执行真实的 PyFlink DataStream API 作业。PyFlink applications
是懒加载的，并且只有在完全构建之后才会提交给集群上执行。要执行一个应用程序，你只需简单地调用 `env.execute(job_name)`。
```
##########
File path: docs/dev/python/datastream_tutorial.zh.md
##########
@@ -116,28 +117,28 @@ if __name__ == '__main__':
tutorial()
{% endhighlight %}
-## Executing a Flink Python DataStream API Program
+## 执行一个 Flink Python DataStream API 程序
-Now that you defined your PyFlink program, you can run it! First, make sure
that the output directory doesn't exist:
+现在你已经编写好 PyFlink 程序，可以运行它了！首先，需要确保输出目录不存在:
{% highlight bash %}
rm -rf /tmp/output
{% endhighlight %}
-Next, you can run the example you just created on the command line:
+接下来，可以使用如下命令运行刚刚创建的示例:
{% highlight bash %}
$ python datastream_tutorial.py
{% endhighlight %}
-The command builds and runs your PyFlink program in a local mini cluster. You
can alternatively submit it to a remote cluster using the instructions detailed
in [Job Submission Examples]({{ site.baseurl
}}/zh/ops/cli.html#job-submission-examples).
+这个命令会在本地集群中构建并运行 PyFlink 程序。你也可以使用 [Job Submission Examples]({{ site.baseurl
}}/zh/ops/cli.html#job-submission-examples) 中描述的命令将其提交到远程集群。
-Finally, you can see the execution result on the command line:
+最后，你可以在命令行上看到执行结果:
{% highlight bash %}
$ find /tmp/output -type f -exec cat {} \;
1,aaa
2,bbb
{% endhighlight %}
-This walkthrough gives you the foundations to get started writing your own
PyFlink DataStream API programs. To learn more about the Python DataStream API,
please refer to [Flink Python API Docs]({{ site.pythondocs_baseurl
}}/api/python) for more details.
+本教程为你开始编写自己的 PyFlink DataStream API 程序提供了基础。为了了解更多关于 Python DataStream API
的使用，请查阅 [Flink Python API Docs]({{ site.pythondocs_baseurl }}/api/python)。
Review comment:
```suggestion
本教程为你开始编写自己的 PyFlink DataStream API 程序提供了基础。如果需要了解更多关于 Python DataStream API
的使用，请查阅 [Flink Python API Docs]({{ site.pythondocs_baseurl }}/api/python)。
```
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