apache · MartijnVisser · Feb 11, 2026 · Feb 12, 2026
diff --git a/.github/workflows/docs.sh b/.github/workflows/docs.sh
@@ -89,10 +89,21 @@ if [ -f  ./flink-python/dev/lint-python.sh ]; then
     # Just completely ignore sudo in conda.
     unset SUDO_UID SUDO_GID SUDO_USER
 
-    # build python docs
+    # Set base URL for cross-references to main Flink docs (used by sphinx extlinks)
+    export FLINK_DOCS_BASE_URL="https://nightlies.apache.org/flink/flink-docs-${BRANCH}"
+
+    # build English python docs
     # disable the gateway, because otherwise it tries to find FLINK_HOME to access Java classes
     PYFLINK_GATEWAY_DISABLED=1 ./flink-python/dev/lint-python.sh -i "sphinx"
 
-    # move python docs
+    # build Chinese python docs into _build/html/zh/ subdirectory
+    UV_HOME="${FLINK_UV_HOME:-./flink-python/dev/.uv}"
+    pushd flink-python/docs
+    source "$UV_HOME/bin/activate"
+    make zh
+    deactivate
+    popd
+
+    # move python docs (English at root, Chinese at zh/)
     mv flink-python/docs/_build/html docs/target/api/python
 fi
diff --git a/.gitignore b/.gitignore
@@ -44,6 +44,11 @@ flink-python/pyflink.egg-info/
 flink-python/apache_flink.egg-info/
 flink-python/apache-flink-libraries/apache_flink_libraries.egg-info/
 flink-python/docs/_build
+flink-python/docs/locales/**/*.mo
+flink-python/docs/locales/**/LC_MESSAGES/*.po
+flink-python/docs/locales/**/reference/
+flink-python/docs/locales/**/examples/
+flink-python/docs/reference/pyflink.*/api/
 flink-python/.tox/
 flink-python/dev/download
 flink-python/dev/.conda/

diff --git a/docs/content.zh/docs/deployment/repls/python_shell.md b/docs/content.zh/docs/deployment/repls/python_shell.md
@@ -1,7 +1,8 @@
 ---
 title: "Python REPL"
-weight: 7
-type: docs
+layout: redirect
+redirect_to: /api/python/user_guide/python_repl.html
+bookHidden: true
 aliases:
   - /zh/deployment/repls/python_shell.html
   - /zh/apis/python_shell.html
@@ -25,184 +26,3 @@ KIND, either express or implied.  See the License for the
 specific language governing permissions and limitations
 under the License.
 -->
-
-# Python REPL
-
-Flink附带了一个集成的交互式Python Shell。
-它既能够运行在本地启动的local模式，也能够运行在集群启动的cluster模式下。
-本地安装Flink，请看[本地安装]({{< ref "docs/deployment/resource-providers/standalone/overview" >}})页面。
-您也可以从源码安装Flink，请看[从源码构建 Flink]({{< github_repo >}}#building-apache-flink-from-source)页面。
-
-<span class="label label-info">注意</span> Python Shell会调用"python"命令。关于 PyFlink 安装说明，请参考[第一步指南]({{< ref "docs/getting-started/local_installation" >}})。
-
-你可以通过PyPi安装PyFlink，然后使用Python Shell:
-
-```bash
-# 安装 PyFlink
-$ python -m pip install apache-flink
-# 执行脚本
-$ pyflink-shell.sh local
-```
-
-关于如何在一个Cluster集群上运行Python shell，可以参考启动章节介绍。
-
-## 使用
-
-当前Python shell支持Table API的功能。
-在启动之后，Table Environment的相关内容将会被自动加载。
-可以通过变量"bt_env"来使用BatchTableEnvironment，通过变量"st_env"来使用StreamTableEnvironment。
-
-### Table API
-
-下面是一个通过Python Shell 运行的简单示例:
-{{< tabs "7207dd60-97bf-461b-b2a5-fcc3dea507c6" >}}
-{{< tab "stream" >}}
-```python
->>> import tempfile
->>> import os
->>> import shutil
->>> sink_path = tempfile.gettempdir() + '/streaming.csv'
->>> if os.path.exists(sink_path):
-...     if os.path.isfile(sink_path):
-...         os.remove(sink_path)
-...     else:
-...         shutil.rmtree(sink_path)
->>> s_env.set_parallelism(1)
->>> t = st_env.from_elements([(1, 'hi', 'hello'), (2, 'hi', 'hello')], ['a', 'b', 'c'])
->>> st_env.create_temporary_table("stream_sink", TableDescriptor.for_connector("filesystem")
-...     .schema(Schema.new_builder()
-...         .column("a", DataTypes.BIGINT())
-...         .column("b", DataTypes.STRING())
-...         .column("c", DataTypes.STRING())
-...         .build())
-...     .option("path", path)
-...     .format(FormatDescriptor.for_format("csv")
-...         .option("field-delimiter", ",")
-...         .build())
-...     .build())
->>> t.select(col('a') + 1, col('b'), col('c'))\
-...     .execute_insert("stream_sink").wait()
->>> # 如果作业运行在local模式, 你可以执行以下代码查看结果:
->>> with open(os.path.join(sink_path, os.listdir(sink_path)[0]), 'r') as f:
-...     print(f.read())
-```
-{{< /tab >}}
-{{< tab "batch" >}}
-```python
->>> import tempfile
->>> import os
->>> import shutil
->>> sink_path = tempfile.gettempdir() + '/batch.csv'
->>> if os.path.exists(sink_path):
-...     if os.path.isfile(sink_path):
-...         os.remove(sink_path)
-...     else:
-...         shutil.rmtree(sink_path)
->>> b_env.set_parallelism(1)
->>> t = bt_env.from_elements([(1, 'hi', 'hello'), (2, 'hi', 'hello')], ['a', 'b', 'c'])
->>> st_env.create_temporary_table("batch_sink", TableDescriptor.for_connector("filesystem")
-...     .schema(Schema.new_builder()
-...         .column("a", DataTypes.BIGINT())
-...         .column("b", DataTypes.STRING())
-...         .column("c", DataTypes.STRING())
-...         .build())
-...     .option("path", path)
-...     .format(FormatDescriptor.for_format("csv")
-...         .option("field-delimiter", ",")
-...         .build())
-...     .build())
->>> t.select(col('a') + 1, col('b'), col('c'))\
-...     .execute_insert("batch_sink").wait()
->>> # 如果作业运行在local模式, 你可以执行以下代码查看结果:
->>> with open(os.path.join(sink_path, os.listdir(sink_path)[0]), 'r') as f:
-...     print(f.read())
-```
-{{< /tab >}}
-{{< /tabs >}}
-
-## 启动
-
-查看Python Shell提供的可选参数，可以使用:
-
-```bash
-pyflink-shell.sh --help
-```
-
-### Local
-
-Python Shell运行在local模式下，只需要执行:
-
-```bash
-pyflink-shell.sh local
-```
-
-
-### Remote
-
-Python Shell运行在一个指定的JobManager上，通过关键字`remote`和对应的JobManager
-的地址和端口号来进行指定:
-
-```bash
-pyflink-shell.sh remote <hostname> <portnumber>
-```
-
-### Yarn Python Shell cluster
-
-Python Shell可以运行在YARN集群之上。Python shell在Yarn上部署一个新的Flink集群，并进行连接。除了指定container数量，你也
-可以指定JobManager的内存，YARN应用的名字等参数。
-例如，在一个部署了两个TaskManager的Yarn集群上运行Python Shell:
-
-```bash
-pyflink-shell.sh yarn -n 2
-```
-
-关于所有可选的参数，可以查看本页面底部的完整说明。
-
-
-### Yarn Session
-
-如果你已经通过Flink Yarn Session部署了一个Flink集群，能够通过以下的命令连接到这个集群:
-
-```bash
-pyflink-shell.sh yarn
-```
-
-
-## 完整的参考
-
-```bash
-Flink Python Shell
-使用: pyflink-shell.sh [local|remote|yarn] [options] <args>...
-
-命令: local [选项]
-启动一个部署在local的Flink Python shell
-使用:
-    -h,--help   查看所有可选的参数
-命令: remote [选项] <host> <port>
-启动一个部署在remote集群的Flink Python shell
-  <host>
-        JobManager的主机名
-  <port>
-        JobManager的端口号
-
-使用:
-    -h,--help   查看所有可选的参数
-
-命令: yarn [选项]
-启动一个部署在Yarn集群的Flink Python Shell
-使用:
-     -h,--help                       查看所有可选的参数
-     -jm,--jobManagerMemory <arg>    具有可选单元的JobManager
-                                     的container的内存（默认值：MB)
-     -n,--container <arg>            需要分配的YARN container的
-                                     数量 (=TaskManager的数量)            
-     -nm,--name <arg>                自定义YARN Application的名字
-     -qu,--queue <arg>               指定YARN的queue     
-     -s,--slots <arg>                每个TaskManager上slots的数量     
-     -tm,--taskManagerMemory <arg>   具有可选单元的每个TaskManager
-                                     的container的内存（默认值：MB）
--h | --help
-    打印输出使用文档
-```
-
-{{< top >}}
diff --git a/docs/content.zh/docs/dev/datastream/python/_index.md b/docs/content.zh/docs/dev/datastream/python/_index.md
@@ -1,7 +1,8 @@
 ---
-title: Python DataStream API
-bookCollapseSection: true
-weight: 100
+title: "Python DataStream API"
+layout: redirect
+redirect_to: /api/python/user_guide/datastream/
+bookHidden: true
 aliases:
   - /zh/docs/dev/datastream/python/
 ---

diff --git a/docs/content.zh/docs/dev/datastream/python/data_types.md b/docs/content.zh/docs/dev/datastream/python/data_types.md
@@ -1,7 +1,8 @@
 ---
 title: "Data Types"
-weight: 25
-type: docs
+layout: redirect
+redirect_to: /api/python/user_guide/datastream/data_types.html
+bookHidden: true
 aliases:
   - /zh/dev/python/datastream-api-users-guide/data_types.html
 ---
@@ -23,123 +24,3 @@ KIND, either express or implied.  See the License for the
 specific language governing permissions and limitations
 under the License.
 -->
-
-# Data Types
-
-In Apache Flink's Python DataStream API, a data type describes the type of a value in the DataStream ecosystem. 
-It can be used to declare input and output types of operations and informs the system how to serialize elements.
-
-## Pickle Serialization
-
-If the type has not been declared, data would be serialized or deserialized using Pickle. 
-For example, the program below specifies no data types.
-
-```python
-from pyflink.datastream import StreamExecutionEnvironment
-
-
-def processing():
-    env = StreamExecutionEnvironment.get_execution_environment()
-    env.set_parallelism(1)
-    env.from_collection(collection=[(1, 'aaa'), (2, 'bbb')]) \
-        .map(lambda record: (record[0]+1, record[1].upper())) \
-        .print()  # note: print to stdout on the worker machine
-
-    env.execute()
-
-
-if __name__ == '__main__':
-    processing()
-```
-
-However, types need to be specified when:
-
-- Passing Python records to Java operations.
-- Improve serialization and deserialization performance.
-
-### Passing Python records to Java operations
-
-Since Java operators or functions can not identify Python data, types need to be provided to help to convert Python types to Java types for processing.
-For example, types need to be provided if you want to output data using the FileSink which is implemented in Java.
-
-```python
-from pyflink.common.serialization import Encoder
-from pyflink.common.typeinfo import Types
-from pyflink.datastream import StreamExecutionEnvironment
-from pyflink.datastream.connectors.file_system import FileSink
-
-
-def file_sink():
-    env = StreamExecutionEnvironment.get_execution_environment()
-    env.set_parallelism(1)
-    env.from_collection(collection=[(1, 'aaa'), (2, 'bbb')]) \
-        .map(lambda record: (record[0] + 1, record[1].upper()),
-             output_type=Types.ROW([Types.INT(), Types.STRING()])) \
-        .add_sink(FileSink
-                  .for_row_format('/tmp/output', Encoder.simple_string_encoder())
-                  .build())
-
-    env.execute()
-
-
-if __name__ == '__main__':
-    file_sink()
-
-```
-
-### Improve serialization and deserialization performance
-
-Even though data can be serialized and deserialized through Pickle, performance will be better if types are provided.
-Explicit types allow PyFlink to use efficient serializers when moving records through the pipeline.
-
-## Supported Data Types
-
-You can use `pyflink.common.typeinfo.Types` to define types in Python DataStream API.
-The table below shows the types supported now and how to define them:
-
-| PyFlink Type |  Python Type |  Java Type |
-|:-----------------|:-----------------------|:-----------------------|
-|`Types.BOOLEAN()` | `bool` | `java.lang.Boolean` |
-|`Types.BYTE()` | `int` | `java.lang.Byte` |
-|`Types.SHORT()` | `int` | `java.lang.Short` |
-|`Types.INT()` | `int` | `java.lang.Integer` |
-|`Types.LONG()` | `int` | `java.lang.Long` |
-|`Types.FLOAT()` | `float` | `java.lang.Float` |
-|`Types.DOUBLE()` | `float` | `java.lang.Double` |
-|`Types.CHAR()` | `str` | `java.lang.Character` |
-|`Types.STRING()` | `str` | `java.lang.String` |
-|`Types.BIG_INT()` | `int` | `java.math.BigInteger` |
-|`Types.BIG_DEC()` | `decimal.Decimal` | `java.math.BigDecimal` |
-|`Types.INSTANT()` | `pyflink.common.time.Instant` | `java.time.Instant` |
-|`Types.TUPLE()` | `tuple` | `org.apache.flink.api.java.tuple.Tuple0` ~ `org.apache.flink.api.java.tuple.Tuple25` |
-|`Types.ROW()` | `pyflink.common.Row` | `org.apache.flink.types.Row` |
-|`Types.ROW_NAMED()` | `pyflink.common.Row` | `org.apache.flink.types.Row` |
-|`Types.MAP()` | `dict` | `java.util.Map` |
-|`Types.PICKLED_BYTE_ARRAY()` | `The actual unpickled Python object` | `byte[]` |
-|`Types.SQL_DATE()` | `datetime.date` | `java.sql.Date` |
-|`Types.SQL_TIME()` | `datetime.time` | `java.sql.Time` |
-|`Types.SQL_TIMESTAMP()` | `datetime.datetime` | `java.sql.Timestamp` |
-|`Types.LIST()` | `list of Python object` | `java.util.List` |
-
-The table below shows the array types supported:
-
-| PyFlink Array Type |  Python Type |  Java Type |
-|:-----------------|:-----------------------|:-----------------------|
-|`Types.PRIMITIVE_ARRAY(Types.BYTE())` | `bytes` | `byte[]` |
-|`Types.PRIMITIVE_ARRAY(Types.BOOLEAN())` | `list of bool` | `boolean[]` |
-|`Types.PRIMITIVE_ARRAY(Types.SHORT())` | `list of int` | `short[]` |
-|`Types.PRIMITIVE_ARRAY(Types.INT())` | `list of int` | `int[]` |
-|`Types.PRIMITIVE_ARRAY(Types.LONG())` | `list of int` | `long[]` |
-|`Types.PRIMITIVE_ARRAY(Types.FLOAT())` | `list of float` | `float[]` |
-|`Types.PRIMITIVE_ARRAY(Types.DOUBLE())` | `list of float` | `double[]` |
-|`Types.PRIMITIVE_ARRAY(Types.CHAR())` | `list of str` | `char[]` |
-|`Types.BASIC_ARRAY(Types.BYTE())` | `list of int` | `java.lang.Byte[]` |
-|`Types.BASIC_ARRAY(Types.BOOLEAN())` | `list of bool` | `java.lang.Boolean[]` |
-|`Types.BASIC_ARRAY(Types.SHORT())` | `list of int` | `java.lang.Short[]` |
-|`Types.BASIC_ARRAY(Types.INT())` | `list of int` | `java.lang.Integer[]` |
-|`Types.BASIC_ARRAY(Types.LONG())` | `list of int` | `java.lang.Long[]` |
-|`Types.BASIC_ARRAY(Types.FLOAT())` | `list of float` | `java.lang.Float[]` |
-|`Types.BASIC_ARRAY(Types.DOUBLE())` | `list of float` | `java.lang.Double[]` |
-|`Types.BASIC_ARRAY(Types.CHAR())` | `list of str` | `java.lang.Character[]` |
-|`Types.BASIC_ARRAY(Types.STRING())` | `list of str` | `java.lang.String[]` |
-|`Types.OBJECT_ARRAY()` | `list of Python object` | `Array` |