dianfu commented on a change in pull request #16061:
URL: https://github.com/apache/flink/pull/16061#discussion_r646249784
##########
File path: docs/content.zh/docs/dev/python/overview.md
##########
@@ -29,36 +29,33 @@ under the License.
{{< img src="/fig/pyflink.svg" alt="PyFlink" class="offset" width="50%" >}}
-PyFlink is a Python API for Apache Flink that allows you to build scalable
batch and streaming
-workloads, such as real-time data processing pipelines, large-scale
exploratory data analysis,
-Machine Learning (ML) pipelines and ETL processes.
-If you're already familiar with Python and libraries such as Pandas, then
PyFlink makes it simpler
-to leverage the full capabilities of the Flink ecosystem. Depending on the
level of abstraction you
-need, there are two different APIs that can be used in PyFlink:
+PyFlink 是 Apache Flink 的 Python
API，你可以使用它构建可扩展的批处理和流工作负载，例如实时数据处理管道、大规模探索性数据分析、机器学习（ML）管道和 ETL 处理。
Review comment:
```suggestion
PyFlink 是 Apache Flink 的 Python
API，你可以使用它构建可扩展的批处理和流处理任务，例如实时数据处理管道、大规模探索性数据分析、机器学习（ML）管道和 ETL 处理。
```
##########
File path: docs/content.zh/docs/dev/python/overview.md
##########
@@ -29,36 +29,33 @@ under the License.
{{< img src="/fig/pyflink.svg" alt="PyFlink" class="offset" width="50%" >}}
-PyFlink is a Python API for Apache Flink that allows you to build scalable
batch and streaming
-workloads, such as real-time data processing pipelines, large-scale
exploratory data analysis,
-Machine Learning (ML) pipelines and ETL processes.
-If you're already familiar with Python and libraries such as Pandas, then
PyFlink makes it simpler
-to leverage the full capabilities of the Flink ecosystem. Depending on the
level of abstraction you
-need, there are two different APIs that can be used in PyFlink:
+PyFlink 是 Apache Flink 的 Python
API，你可以使用它构建可扩展的批处理和流工作负载，例如实时数据处理管道、大规模探索性数据分析、机器学习（ML）管道和 ETL 处理。
+如果你已经熟悉 Python 和 Pandas 等库，那么 PyFlink 可以让你更轻松地利用 Flink 生态系统的全部功能。
+根据你需要的抽象级别，有两种不同的 API 可以在 PyFlink 中使用：
-* The **PyFlink Table API** allows you to write powerful relational queries in
a way that is similar to using SQL or working with tabular data in Python.
-* At the same time, the **PyFlink DataStream API** gives you lower-level
control over the core building blocks of Flink, [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) and [time]({{< ref
"docs/concepts/time" >}}), to build more complex stream processing use cases.
+* **PyFlink Table API** 允许使用类似于 SQL 或在 Python 中处理表格数据的方式编写强大的关系查询。
+* 与此同时，**PyFlink DataStream API** 提供对 Flink 的核心构建块 [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) 和 [time]({{< ref
"docs/concepts/time" >}}) 进行较低级别（粒度）的控制，以便构建更复杂的流处理用例。
{{< columns >}}
-### Try PyFlink
+### 尝试 PyFlink
-If you’re interested in playing around with Flink, try one of our tutorials:
+如果你有兴趣使用 PyFlink，可以尝试以下任意教程：
-* [Intro to PyFlink DataStream API]({{< ref
"docs/dev/python/datastream_tutorial" >}})
-* [Intro to PyFlink Table API]({{< ref "docs/dev/python/table_api_tutorial"
>}})
+* [PyFlink DataStream API 介绍]({{< ref "docs/dev/python/datastream_tutorial"
>}})
+* [PyFlink Table API 介绍]({{< ref "docs/dev/python/table_api_tutorial" >}})
<--->
-### Explore PyFlink
+### 探究 PyFlink
Review comment:
```suggestion
### 深入 PyFlink
```
##########
File path: docs/content.zh/docs/dev/python/overview.md
##########
@@ -29,36 +29,33 @@ under the License.
{{< img src="/fig/pyflink.svg" alt="PyFlink" class="offset" width="50%" >}}
-PyFlink is a Python API for Apache Flink that allows you to build scalable
batch and streaming
-workloads, such as real-time data processing pipelines, large-scale
exploratory data analysis,
-Machine Learning (ML) pipelines and ETL processes.
-If you're already familiar with Python and libraries such as Pandas, then
PyFlink makes it simpler
-to leverage the full capabilities of the Flink ecosystem. Depending on the
level of abstraction you
-need, there are two different APIs that can be used in PyFlink:
+PyFlink 是 Apache Flink 的 Python
API，你可以使用它构建可扩展的批处理和流工作负载，例如实时数据处理管道、大规模探索性数据分析、机器学习（ML）管道和 ETL 处理。
+如果你已经熟悉 Python 和 Pandas 等库，那么 PyFlink 可以让你更轻松地利用 Flink 生态系统的全部功能。
+根据你需要的抽象级别，有两种不同的 API 可以在 PyFlink 中使用：
-* The **PyFlink Table API** allows you to write powerful relational queries in
a way that is similar to using SQL or working with tabular data in Python.
-* At the same time, the **PyFlink DataStream API** gives you lower-level
control over the core building blocks of Flink, [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) and [time]({{< ref
"docs/concepts/time" >}}), to build more complex stream processing use cases.
+* **PyFlink Table API** 允许使用类似于 SQL 或在 Python 中处理表格数据的方式编写强大的关系查询。
Review comment:
```suggestion
* **PyFlink Table API** 允许你使用类似于 SQL 或在 Python 中处理表格数据的方式编写强大的关系查询。
```
##########
File path: docs/content.zh/docs/dev/python/overview.md
##########
@@ -29,36 +29,33 @@ under the License.
{{< img src="/fig/pyflink.svg" alt="PyFlink" class="offset" width="50%" >}}
-PyFlink is a Python API for Apache Flink that allows you to build scalable
batch and streaming
-workloads, such as real-time data processing pipelines, large-scale
exploratory data analysis,
-Machine Learning (ML) pipelines and ETL processes.
-If you're already familiar with Python and libraries such as Pandas, then
PyFlink makes it simpler
-to leverage the full capabilities of the Flink ecosystem. Depending on the
level of abstraction you
-need, there are two different APIs that can be used in PyFlink:
+PyFlink 是 Apache Flink 的 Python
API，你可以使用它构建可扩展的批处理和流工作负载，例如实时数据处理管道、大规模探索性数据分析、机器学习（ML）管道和 ETL 处理。
+如果你已经熟悉 Python 和 Pandas 等库，那么 PyFlink 可以让你更轻松地利用 Flink 生态系统的全部功能。
+根据你需要的抽象级别，有两种不同的 API 可以在 PyFlink 中使用：
Review comment:
```suggestion
根据你需要的抽象级别的不同，有两种不同的 API 可以在 PyFlink 中使用：
```
##########
File path: docs/content.zh/docs/dev/python/overview.md
##########
@@ -29,36 +29,33 @@ under the License.
Review comment:
title: Overview -> title: 概览
##########
File path: docs/content.zh/docs/dev/python/overview.md
##########
@@ -29,36 +29,33 @@ under the License.
{{< img src="/fig/pyflink.svg" alt="PyFlink" class="offset" width="50%" >}}
-PyFlink is a Python API for Apache Flink that allows you to build scalable
batch and streaming
-workloads, such as real-time data processing pipelines, large-scale
exploratory data analysis,
-Machine Learning (ML) pipelines and ETL processes.
-If you're already familiar with Python and libraries such as Pandas, then
PyFlink makes it simpler
-to leverage the full capabilities of the Flink ecosystem. Depending on the
level of abstraction you
-need, there are two different APIs that can be used in PyFlink:
+PyFlink 是 Apache Flink 的 Python
API，你可以使用它构建可扩展的批处理和流工作负载，例如实时数据处理管道、大规模探索性数据分析、机器学习（ML）管道和 ETL 处理。
+如果你已经熟悉 Python 和 Pandas 等库，那么 PyFlink 可以让你更轻松地利用 Flink 生态系统的全部功能。
Review comment:
```suggestion
如果你对 Python 和 Pandas 等库已经比较熟悉，那么 PyFlink 可以让你更轻松地利用 Flink 生态系统的全部功能。
```
##########
File path: docs/content.zh/docs/dev/python/overview.md
##########
@@ -29,36 +29,33 @@ under the License.
{{< img src="/fig/pyflink.svg" alt="PyFlink" class="offset" width="50%" >}}
-PyFlink is a Python API for Apache Flink that allows you to build scalable
batch and streaming
-workloads, such as real-time data processing pipelines, large-scale
exploratory data analysis,
-Machine Learning (ML) pipelines and ETL processes.
-If you're already familiar with Python and libraries such as Pandas, then
PyFlink makes it simpler
-to leverage the full capabilities of the Flink ecosystem. Depending on the
level of abstraction you
-need, there are two different APIs that can be used in PyFlink:
+PyFlink 是 Apache Flink 的 Python
API，你可以使用它构建可扩展的批处理和流工作负载，例如实时数据处理管道、大规模探索性数据分析、机器学习（ML）管道和 ETL 处理。
+如果你已经熟悉 Python 和 Pandas 等库，那么 PyFlink 可以让你更轻松地利用 Flink 生态系统的全部功能。
+根据你需要的抽象级别，有两种不同的 API 可以在 PyFlink 中使用：
-* The **PyFlink Table API** allows you to write powerful relational queries in
a way that is similar to using SQL or working with tabular data in Python.
-* At the same time, the **PyFlink DataStream API** gives you lower-level
control over the core building blocks of Flink, [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) and [time]({{< ref
"docs/concepts/time" >}}), to build more complex stream processing use cases.
+* **PyFlink Table API** 允许使用类似于 SQL 或在 Python 中处理表格数据的方式编写强大的关系查询。
+* 与此同时，**PyFlink DataStream API** 提供对 Flink 的核心构建块 [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) 和 [time]({{< ref
"docs/concepts/time" >}}) 进行较低级别（粒度）的控制，以便构建更复杂的流处理用例。
{{< columns >}}
-### Try PyFlink
+### 尝试 PyFlink
-If you’re interested in playing around with Flink, try one of our tutorials:
+如果你有兴趣使用 PyFlink，可以尝试以下任意教程：
-* [Intro to PyFlink DataStream API]({{< ref
"docs/dev/python/datastream_tutorial" >}})
-* [Intro to PyFlink Table API]({{< ref "docs/dev/python/table_api_tutorial"
>}})
+* [PyFlink DataStream API 介绍]({{< ref "docs/dev/python/datastream_tutorial"
>}})
+* [PyFlink Table API 介绍]({{< ref "docs/dev/python/table_api_tutorial" >}})
<--->
-### Explore PyFlink
+### 探究 PyFlink
-The reference documentation covers all the details. Some starting points:
+参考文档涵盖了 PyFlink 所有细节。一些起始链接如下：
* [PyFlink DataStream API]({{< ref "docs/dev/python/table/table_environment"
>}})
* [PyFlink Table API & SQL]({{< ref "docs/dev/python/datastream/operators"
>}})
{{< /columns >}}
-### Get Help with PyFlink
+### 获取有关 PyFlink 的帮助
-If you get stuck, check out our [community support
resources](https://flink.apache.org/community.html). In particular, Apache
Flink’s user mailing list is consistently ranked as one of the most active of
any Apache project, and is a great way to get help quickly.
+如果你遇到困难，请查看我们的[社区支持资源](https://flink.apache.org/community.html)。特别是 Apache
Flink 的用户邮件列表，Apache Flink 的用户邮件列表一直被列为所有 Apache 项目中最活跃的项目邮件列表之一，是快速获得帮助的好方法。
Review comment:
```suggestion
如果你遇到困难，请查看我们的[社区支持资源](https://flink.apache.org/community.html)，特别是 Apache
Flink 的用户邮件列表，Apache Flink 的用户邮件列表一直是所有 Apache 项目中最活跃的项目邮件列表之一，是快速获得帮助的好方法。
```
##########
File path: docs/content.zh/docs/dev/python/overview.md
##########
@@ -29,36 +29,33 @@ under the License.
{{< img src="/fig/pyflink.svg" alt="PyFlink" class="offset" width="50%" >}}
-PyFlink is a Python API for Apache Flink that allows you to build scalable
batch and streaming
-workloads, such as real-time data processing pipelines, large-scale
exploratory data analysis,
-Machine Learning (ML) pipelines and ETL processes.
-If you're already familiar with Python and libraries such as Pandas, then
PyFlink makes it simpler
-to leverage the full capabilities of the Flink ecosystem. Depending on the
level of abstraction you
-need, there are two different APIs that can be used in PyFlink:
+PyFlink 是 Apache Flink 的 Python
API，你可以使用它构建可扩展的批处理和流工作负载，例如实时数据处理管道、大规模探索性数据分析、机器学习（ML）管道和 ETL 处理。
+如果你已经熟悉 Python 和 Pandas 等库，那么 PyFlink 可以让你更轻松地利用 Flink 生态系统的全部功能。
+根据你需要的抽象级别，有两种不同的 API 可以在 PyFlink 中使用：
-* The **PyFlink Table API** allows you to write powerful relational queries in
a way that is similar to using SQL or working with tabular data in Python.
-* At the same time, the **PyFlink DataStream API** gives you lower-level
control over the core building blocks of Flink, [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) and [time]({{< ref
"docs/concepts/time" >}}), to build more complex stream processing use cases.
+* **PyFlink Table API** 允许使用类似于 SQL 或在 Python 中处理表格数据的方式编写强大的关系查询。
+* 与此同时，**PyFlink DataStream API** 提供对 Flink 的核心构建块 [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) 和 [time]({{< ref
"docs/concepts/time" >}}) 进行较低级别（粒度）的控制，以便构建更复杂的流处理用例。
Review comment:
```suggestion
* 与此同时，**PyFlink DataStream API** 允许你对 Flink 的核心组件 [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) 和 [time]({{< ref
"docs/concepts/time" >}}) 进行细粒度的控制，以便构建更复杂的流处理应用。
```
##########
File path: docs/content.zh/docs/dev/python/overview.md
##########
@@ -29,36 +29,33 @@ under the License.
{{< img src="/fig/pyflink.svg" alt="PyFlink" class="offset" width="50%" >}}
-PyFlink is a Python API for Apache Flink that allows you to build scalable
batch and streaming
-workloads, such as real-time data processing pipelines, large-scale
exploratory data analysis,
-Machine Learning (ML) pipelines and ETL processes.
-If you're already familiar with Python and libraries such as Pandas, then
PyFlink makes it simpler
-to leverage the full capabilities of the Flink ecosystem. Depending on the
level of abstraction you
-need, there are two different APIs that can be used in PyFlink:
+PyFlink 是 Apache Flink 的 Python
API，你可以使用它构建可扩展的批处理和流工作负载，例如实时数据处理管道、大规模探索性数据分析、机器学习（ML）管道和 ETL 处理。
+如果你已经熟悉 Python 和 Pandas 等库，那么 PyFlink 可以让你更轻松地利用 Flink 生态系统的全部功能。
+根据你需要的抽象级别，有两种不同的 API 可以在 PyFlink 中使用：
-* The **PyFlink Table API** allows you to write powerful relational queries in
a way that is similar to using SQL or working with tabular data in Python.
-* At the same time, the **PyFlink DataStream API** gives you lower-level
control over the core building blocks of Flink, [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) and [time]({{< ref
"docs/concepts/time" >}}), to build more complex stream processing use cases.
+* **PyFlink Table API** 允许使用类似于 SQL 或在 Python 中处理表格数据的方式编写强大的关系查询。
+* 与此同时，**PyFlink DataStream API** 提供对 Flink 的核心构建块 [state]({{< ref
"docs/concepts/stateful-stream-processing" >}}) 和 [time]({{< ref
"docs/concepts/time" >}}) 进行较低级别（粒度）的控制，以便构建更复杂的流处理用例。
{{< columns >}}
-### Try PyFlink
+### 尝试 PyFlink
-If you’re interested in playing around with Flink, try one of our tutorials:
+如果你有兴趣使用 PyFlink，可以尝试以下任意教程：
-* [Intro to PyFlink DataStream API]({{< ref
"docs/dev/python/datastream_tutorial" >}})
-* [Intro to PyFlink Table API]({{< ref "docs/dev/python/table_api_tutorial"
>}})
+* [PyFlink DataStream API 介绍]({{< ref "docs/dev/python/datastream_tutorial"
>}})
+* [PyFlink Table API 介绍]({{< ref "docs/dev/python/table_api_tutorial" >}})
<--->
-### Explore PyFlink
+### 探究 PyFlink
-The reference documentation covers all the details. Some starting points:
+参考文档涵盖了 PyFlink 所有细节。一些起始链接如下：
Review comment:
```suggestion
这些参考文档涵盖了 PyFlink 的所有细节，可以从以下链接入手：
```
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