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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/07/21 10:49:52 UTC
[GitHub] [flink] ChengkaiYang2022 opened a new pull request, #20331: [FLINK-28121]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
ChengkaiYang2022 opened a new pull request, #20331:
URL: https://github.com/apache/flink/pull/20331
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## What is the purpose of the change
[FLINK-28121]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
## Brief change log
## Verifying this change
Please make sure both new and modified tests in this PR follows the conventions defined in our code quality guide: https://flink.apache.org/contributing/code-style-and-quality-common.html#testing
*(Please pick either of the following options)*
This change is a trivial rework / code cleanup without any test coverage.
*(or)*
This change is already covered by existing tests, such as *(please describe tests)*.
*(or)*
This change added tests and can be verified as follows:
*(example:)*
- *Added integration tests for end-to-end deployment with large payloads (100MB)*
- *Extended integration test for recovery after master (JobManager) failure*
- *Added test that validates that TaskInfo is transferred only once across recoveries*
- *Manually verified the change by running a 4 node cluster with 2 JobManagers and 4 TaskManagers, a stateful streaming program, and killing one JobManager and two TaskManagers during the execution, verifying that recovery happens correctly.*
## Does this pull request potentially affect one of the following parts:
- Dependencies (does it add or upgrade a dependency): (yes / no) no
- The public API, i.e., is any changed class annotated with `@Public(Evolving)`: (yes / no) no
- The serializers: (yes / no / don't know) no
- The runtime per-record code paths (performance sensitive): (yes / no / don't know) no
- Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn, ZooKeeper: (yes / no / don't know)
- The S3 file system connector: (yes / no / don't know) no
## Documentation
- Does this pull request introduce a new feature? (yes / no) no
- If yes, how is the feature documented? (not applicable / docs / JavaDocs / not documented) no
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[GitHub] [flink] ChengkaiYang2022 commented on pull request #20331: [FLINK-28121]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
Posted by GitBox <gi...@apache.org>.
ChengkaiYang2022 commented on PR #20331:
URL: https://github.com/apache/flink/pull/20331#issuecomment-1191337214
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[GitHub] [flink] ChengkaiYang2022 commented on pull request #20331: [FLINK-28121][docs-zh]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
Posted by GitBox <gi...@apache.org>.
ChengkaiYang2022 commented on PR #20331:
URL: https://github.com/apache/flink/pull/20331#issuecomment-1221256587
@coder-zjh Thank your for your feedback! I will solve the problems ASAP!
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[GitHub] [flink] coder-zjh commented on a diff in pull request #20331: [FLINK-28121][docs-zh]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
Posted by GitBox <gi...@apache.org>.
coder-zjh commented on code in PR #20331:
URL: https://github.com/apache/flink/pull/20331#discussion_r950003553
##########
docs/content.zh/docs/dev/table/sourcesSinks.md:
##########
@@ -133,242 +133,209 @@ If you need a feature available only internally within the `org.apache.flink.tab
To learn more, check out [Anatomy of Table Dependencies]({{< ref "docs/dev/configuration/advanced" >}}#anatomy-of-table-dependencies).
{{< /hint >}}
-Extension Points
+<a name="extension-points"></a>
+
+扩展点
----------------
-This section explains the available interfaces for extending Flink's table connectors.
+这一部分主要介绍扩展 Flink table connector 时可能用到的接口。
+
+<a name="dynamic-table-factories"></a>
-### Dynamic Table Factories
+### 动态表的工厂类
-Dynamic table factories are used to configure a dynamic table connector for an external storage system from catalog
-and session information.
+在根据 catalog 与 Flink 运行时上下文信息，为某个外部存储系统配置动态表连接器时，需要用到动态表的工厂类。
-`org.apache.flink.table.factories.DynamicTableSourceFactory` can be implemented to construct a `DynamicTableSource`.
+比如，通过实现 `org.apache.flink.table.factories.DynamicTableSourceFactory` 接口完成一个工厂类，来生产 `DynamicTableSource` 类。
-`org.apache.flink.table.factories.DynamicTableSinkFactory` can be implemented to construct a `DynamicTableSink`.
+通过实现 `org.apache.flink.table.factories.DynamicTableSinkFactory` 接口完成一个工厂类，来生产 `DynamicTableSink` 类。
-By default, the factory is discovered using the value of the `connector` option as the factory identifier
-and Java's Service Provider Interface.
+默认情况下，Java 的 SPI 机制会自动识别这些工厂类，同时将 `connector` 配置项作为工厂类的”标识符“。
-In JAR files, references to new implementations can be added to the service file:
+在 JAR 文件中，需要将实现的工厂类路径放入到下面这个配置文件：
`META-INF/services/org.apache.flink.table.factories.Factory`
-The framework will check for a single matching factory that is uniquely identified by factory identifier
-and requested base class (e.g. `DynamicTableSourceFactory`).
+Flink 会对工厂类逐个进行检查，确保其”标识符“是全局唯一的，并且按照要求实现了上面提到的接口 (比如 `DynamicTableSourceFactory`)。
+
+如果必要的话，也可以在实现 catalog 时绕过上述 SPI 机制识别工厂类的过程。即在实现 catalog 接口时，在`org.apache.flink.table.catalog.Catalog#getFactory` 方法中直接返回工厂类的实例。
+
+<a name="dynamic-table-source"></a>
-The factory discovery process can be bypassed by the catalog implementation if necessary. For this, a
-catalog needs to return an instance that implements the requested base class in `org.apache.flink.table.catalog.Catalog#getFactory`.
+### 动态表的 source 端
-### Dynamic Table Source
+按照定义，动态表是随时间变化的。
-By definition, a dynamic table can change over time.
+在读取动态表时，表中数据可以是以下情况之一：
+- changelog 流（支持有界或无界），在 changelog 流结束前，所有的改变都会被源源不断地消费，由 `ScanTableSource` 接口表示。
+- 处于一直变换或数据量很大的外部表，其中的数据一般不会被全量读取，除非是在查询某个值时，由 `LookupTableSource` 接口表示。
-When reading a dynamic table, the content can either be considered as:
-- A changelog (finite or infinite) for which all changes are consumed continuously until the changelog
- is exhausted. This is represented by the `ScanTableSource` interface.
-- A continuously changing or very large external table whose content is usually never read entirely
- but queried for individual values when necessary. This is represented by the `LookupTableSource`
- interface.
+一个类可以同时实现这两个接口，Planner 会根据查询的 Query 选择相应接口中的方法。
-A class can implement both of these interfaces at the same time. The planner decides about their usage depending
-on the specified query.
+<a name= "scan-table-source"></a>
#### Scan Table Source
-A `ScanTableSource` scans all rows from an external storage system during runtime.
+在运行期间，`ScanTableSource` 接口会按行扫描外部存储系统中所有数据。
-The scanned rows don't have to contain only insertions but can also contain updates and deletions. Thus,
-the table source can be used to read a (finite or infinite) changelog. The returned _changelog mode_ indicates
-the set of changes that the planner can expect during runtime.
+被扫描的数据可以是 insert、update、delete 三种操作类型，因此数据源可以用作读取 changelog （支持有界或无界）。在运行时，返回的 **_changelog mode_** 表示 Planner 要处理的操作类型。
-For regular batch scenarios, the source can emit a bounded stream of insert-only rows.
+在常规批处理的场景下，数据源可以处理 insert-only 操作类型的有界数据流。
-For regular streaming scenarios, the source can emit an unbounded stream of insert-only rows.
+在常规流处理的场景下，数据源可以处理 insert-only 操作类型的无界数据流。
-For change data capture (CDC) scenarios, the source can emit bounded or unbounded streams with insert,
-update, and delete rows.
+在变更日志数据捕获（即 CDC）场景下，数据源可以处理 insert、update、delete 操作类型的有界或无界数据流。
-A table source can implement further ability interfaces such as `SupportsProjectionPushDown` that might
-mutate an instance during planning. All abilities can be found in the `org.apache.flink.table.connector.source.abilities`
-package and are listed in the [source abilities table](#source-abilities).
+可以实现更多的功能接口来优化数据源，比如实现 `SupportsProjectionPushDown` 接口，这样在运行时在 source 端就处理数据。在 `org.apache.flink.table.connector.source.abilities` 包下可以找到各种功能接口，更多内容可查看 [source abilities table](#source-abilities)。
-The runtime implementation of a `ScanTableSource` must produce internal data structures. Thus, records
-must be emitted as `org.apache.flink.table.data.RowData`. The framework provides runtime converters such
-that a source can still work on common data structures and perform a conversion at the end.
+实现 `ScanTableSource` 接口的类必须能够生产 Flink 内部数据结构，因此每条记录都会按照`org.apache.flink.table.data.RowData` 的方式进行处理。Flink 运行时提供了转换机制保证 source 端可以处理常见的数据结构，并且在最后进行转换。
+
+<a name="lookup-table-source"></a>
#### Lookup Table Source
-A `LookupTableSource` looks up rows of an external storage system by one or more keys during runtime.
+在运行期间，`LookupTableSource` 接口会在外部存储系统中按照 key 进行查找。
+
+相比于`ScanTableSource`，`LookupTableSource` 接口不会全量读取表中数据，只会在需要时向外部存储（其中的数据有可能会一直变化）发起查询请求，惰性地获取数据。
-Compared to `ScanTableSource`, the source does not have to read the entire table and can lazily fetch individual
-values from a (possibly continuously changing) external table when necessary.
+同时相较于`ScanTableSource`，`LookupTableSource` 接口目前只支持处理 insert-only 数据流。
-Compared to `ScanTableSource`, a `LookupTableSource` does only support emitting insert-only changes currently.
+暂时不支持扩展功能接口，可查看 `org.apache.flink.table.connector.source.LookupTableSource` 中的文档了解更多。
-Further abilities are not supported. See the documentation of `org.apache.flink.table.connector.source.LookupTableSource`
-for more information.
+`LookupTableSource` 的实现方法可以是 `TableFunction` 或者 `AsyncTableFunction`，Flink运行时会根据要查询的 key 值，调用这个实现方法进行查询。
-The runtime implementation of a `LookupTableSource` is a `TableFunction` or `AsyncTableFunction`. The function
-will be called with values for the given lookup keys during runtime.
+<a name="source-abilities"></a>
-#### Source Abilities
+#### source 端的功能接口
<table class="table table-bordered">
<thead>
<tr>
- <th class="text-left" style="width: 25%">Interface</th>
- <th class="text-center" style="width: 75%">Description</th>
+ <th class="text-left" style="width: 25%">接口</th>
+ <th class="text-center" style="width: 75%">描述</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsFilterPushDown.java'>SupportsFilterPushDown</a></td>
- <td>Enables to push down the filter into the <code>DynamicTableSource</code>. For efficiency, a source can
- push filters further down in order to be close to the actual data generation.</td>
+ <td>支持将过滤条件下推到 <code>DynamicTableSource</code>。为了更高效处理数据，source 端会将过滤条件下推，以便在数据产生时就处理。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsLimitPushDown.java'>SupportsLimitPushDown</a></td>
- <td>Enables to push down a limit (the expected maximum number of produced records) into a <code>DynamicTableSource</code>.</td>
+ <td>支持将 limit 下推到（期望生产的最大数据条数）<code>DynamicTableSource</code>。</td>
Review Comment:
"支持将 limit 下推到（期望生产的最大数据条数）" 括号中内容靠近limit本身可能好点：
"支持将 limit（期望生产的最大数据条数）下推到"
##########
docs/content.zh/docs/dev/table/sourcesSinks.md:
##########
@@ -133,242 +133,209 @@ If you need a feature available only internally within the `org.apache.flink.tab
To learn more, check out [Anatomy of Table Dependencies]({{< ref "docs/dev/configuration/advanced" >}}#anatomy-of-table-dependencies).
{{< /hint >}}
-Extension Points
+<a name="extension-points"></a>
+
+扩展点
----------------
-This section explains the available interfaces for extending Flink's table connectors.
+这一部分主要介绍扩展 Flink table connector 时可能用到的接口。
+
+<a name="dynamic-table-factories"></a>
-### Dynamic Table Factories
+### 动态表的工厂类
-Dynamic table factories are used to configure a dynamic table connector for an external storage system from catalog
-and session information.
+在根据 catalog 与 Flink 运行时上下文信息，为某个外部存储系统配置动态表连接器时，需要用到动态表的工厂类。
-`org.apache.flink.table.factories.DynamicTableSourceFactory` can be implemented to construct a `DynamicTableSource`.
+比如，通过实现 `org.apache.flink.table.factories.DynamicTableSourceFactory` 接口完成一个工厂类，来生产 `DynamicTableSource` 类。
-`org.apache.flink.table.factories.DynamicTableSinkFactory` can be implemented to construct a `DynamicTableSink`.
+通过实现 `org.apache.flink.table.factories.DynamicTableSinkFactory` 接口完成一个工厂类，来生产 `DynamicTableSink` 类。
-By default, the factory is discovered using the value of the `connector` option as the factory identifier
-and Java's Service Provider Interface.
+默认情况下，Java 的 SPI 机制会自动识别这些工厂类，同时将 `connector` 配置项作为工厂类的”标识符“。
-In JAR files, references to new implementations can be added to the service file:
+在 JAR 文件中，需要将实现的工厂类路径放入到下面这个配置文件：
`META-INF/services/org.apache.flink.table.factories.Factory`
-The framework will check for a single matching factory that is uniquely identified by factory identifier
-and requested base class (e.g. `DynamicTableSourceFactory`).
+Flink 会对工厂类逐个进行检查，确保其”标识符“是全局唯一的，并且按照要求实现了上面提到的接口 (比如 `DynamicTableSourceFactory`)。
+
+如果必要的话，也可以在实现 catalog 时绕过上述 SPI 机制识别工厂类的过程。即在实现 catalog 接口时，在`org.apache.flink.table.catalog.Catalog#getFactory` 方法中直接返回工厂类的实例。
+
+<a name="dynamic-table-source"></a>
-The factory discovery process can be bypassed by the catalog implementation if necessary. For this, a
-catalog needs to return an instance that implements the requested base class in `org.apache.flink.table.catalog.Catalog#getFactory`.
+### 动态表的 source 端
-### Dynamic Table Source
+按照定义，动态表是随时间变化的。
-By definition, a dynamic table can change over time.
+在读取动态表时，表中数据可以是以下情况之一：
+- changelog 流（支持有界或无界），在 changelog 流结束前，所有的改变都会被源源不断地消费，由 `ScanTableSource` 接口表示。
+- 处于一直变换或数据量很大的外部表，其中的数据一般不会被全量读取，除非是在查询某个值时，由 `LookupTableSource` 接口表示。
-When reading a dynamic table, the content can either be considered as:
-- A changelog (finite or infinite) for which all changes are consumed continuously until the changelog
- is exhausted. This is represented by the `ScanTableSource` interface.
-- A continuously changing or very large external table whose content is usually never read entirely
- but queried for individual values when necessary. This is represented by the `LookupTableSource`
- interface.
+一个类可以同时实现这两个接口，Planner 会根据查询的 Query 选择相应接口中的方法。
-A class can implement both of these interfaces at the same time. The planner decides about their usage depending
-on the specified query.
+<a name= "scan-table-source"></a>
#### Scan Table Source
-A `ScanTableSource` scans all rows from an external storage system during runtime.
+在运行期间，`ScanTableSource` 接口会按行扫描外部存储系统中所有数据。
-The scanned rows don't have to contain only insertions but can also contain updates and deletions. Thus,
-the table source can be used to read a (finite or infinite) changelog. The returned _changelog mode_ indicates
-the set of changes that the planner can expect during runtime.
+被扫描的数据可以是 insert、update、delete 三种操作类型，因此数据源可以用作读取 changelog （支持有界或无界）。在运行时，返回的 **_changelog mode_** 表示 Planner 要处理的操作类型。
-For regular batch scenarios, the source can emit a bounded stream of insert-only rows.
+在常规批处理的场景下，数据源可以处理 insert-only 操作类型的有界数据流。
-For regular streaming scenarios, the source can emit an unbounded stream of insert-only rows.
+在常规流处理的场景下，数据源可以处理 insert-only 操作类型的无界数据流。
-For change data capture (CDC) scenarios, the source can emit bounded or unbounded streams with insert,
-update, and delete rows.
+在变更日志数据捕获（即 CDC）场景下，数据源可以处理 insert、update、delete 操作类型的有界或无界数据流。
-A table source can implement further ability interfaces such as `SupportsProjectionPushDown` that might
-mutate an instance during planning. All abilities can be found in the `org.apache.flink.table.connector.source.abilities`
-package and are listed in the [source abilities table](#source-abilities).
+可以实现更多的功能接口来优化数据源，比如实现 `SupportsProjectionPushDown` 接口，这样在运行时在 source 端就处理数据。在 `org.apache.flink.table.connector.source.abilities` 包下可以找到各种功能接口，更多内容可查看 [source abilities table](#source-abilities)。
-The runtime implementation of a `ScanTableSource` must produce internal data structures. Thus, records
-must be emitted as `org.apache.flink.table.data.RowData`. The framework provides runtime converters such
-that a source can still work on common data structures and perform a conversion at the end.
+实现 `ScanTableSource` 接口的类必须能够生产 Flink 内部数据结构，因此每条记录都会按照`org.apache.flink.table.data.RowData` 的方式进行处理。Flink 运行时提供了转换机制保证 source 端可以处理常见的数据结构，并且在最后进行转换。
+
+<a name="lookup-table-source"></a>
#### Lookup Table Source
-A `LookupTableSource` looks up rows of an external storage system by one or more keys during runtime.
+在运行期间，`LookupTableSource` 接口会在外部存储系统中按照 key 进行查找。
+
+相比于`ScanTableSource`，`LookupTableSource` 接口不会全量读取表中数据，只会在需要时向外部存储（其中的数据有可能会一直变化）发起查询请求，惰性地获取数据。
-Compared to `ScanTableSource`, the source does not have to read the entire table and can lazily fetch individual
-values from a (possibly continuously changing) external table when necessary.
+同时相较于`ScanTableSource`，`LookupTableSource` 接口目前只支持处理 insert-only 数据流。
-Compared to `ScanTableSource`, a `LookupTableSource` does only support emitting insert-only changes currently.
+暂时不支持扩展功能接口，可查看 `org.apache.flink.table.connector.source.LookupTableSource` 中的文档了解更多。
-Further abilities are not supported. See the documentation of `org.apache.flink.table.connector.source.LookupTableSource`
-for more information.
+`LookupTableSource` 的实现方法可以是 `TableFunction` 或者 `AsyncTableFunction`，Flink运行时会根据要查询的 key 值，调用这个实现方法进行查询。
-The runtime implementation of a `LookupTableSource` is a `TableFunction` or `AsyncTableFunction`. The function
-will be called with values for the given lookup keys during runtime.
+<a name="source-abilities"></a>
-#### Source Abilities
+#### source 端的功能接口
<table class="table table-bordered">
<thead>
<tr>
- <th class="text-left" style="width: 25%">Interface</th>
- <th class="text-center" style="width: 75%">Description</th>
+ <th class="text-left" style="width: 25%">接口</th>
+ <th class="text-center" style="width: 75%">描述</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsFilterPushDown.java'>SupportsFilterPushDown</a></td>
- <td>Enables to push down the filter into the <code>DynamicTableSource</code>. For efficiency, a source can
- push filters further down in order to be close to the actual data generation.</td>
+ <td>支持将过滤条件下推到 <code>DynamicTableSource</code>。为了更高效处理数据，source 端会将过滤条件下推，以便在数据产生时就处理。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsLimitPushDown.java'>SupportsLimitPushDown</a></td>
- <td>Enables to push down a limit (the expected maximum number of produced records) into a <code>DynamicTableSource</code>.</td>
+ <td>支持将 limit 下推到（期望生产的最大数据条数）<code>DynamicTableSource</code>。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsPartitionPushDown.java'>SupportsPartitionPushDown</a></td>
- <td>Enables to pass available partitions to the planner and push down partitions into a <code>DynamicTableSource</code>.
- During the runtime, the source will only read data from the passed partition list for efficiency.</td>
+ <td>支持将可用的分区信息提供给 planner 并且将分区信息下推到 <code>DynamicTableSource</code>。在运行时为了更高效处理数据，source 端会只从提供的分区列表中读取数据。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsProjectionPushDown.java'>SupportsProjectionPushDown</a> </td>
- <td>Enables to push down a (possibly nested) projection into a <code>DynamicTableSource</code>. For efficiency,
- a source can push a projection further down in order to be close to the actual data generation. If the source
- also implements <code>SupportsReadingMetadata</code>, the source will also read the required metadata only.
+ <td>支持将查询列(可嵌套)下推到 <code>DynamicTableSource</code>。为了更高效处理数据，source 端会将查询列下推，以便在数据产生时就处理。如果 source 端同时实现了 <code>SupportsReadingMetadata</code>，那么 source 端也会读取相对应列的元数据信息。
</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsReadingMetadata.java'>SupportsReadingMetadata</a></td>
- <td>Enables to read metadata columns from a <code>DynamicTableSource</code>. The source
- is responsible to add the required metadata at the end of the produced rows. This includes
- potentially forwarding metadata column from contained formats.</td>
+ <td>支持通过 <code>DynamicTableSource</code> 读取列的元数据信息。source 端会在生产数据行时，在最后添加相应的元数据信息，其中包括元数据的格式信息。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsWatermarkPushDown.java'>SupportsWatermarkPushDown</a></td>
- <td>Enables to push down a watermark strategy into a <code>DynamicTableSource</code>. The watermark
- strategy is a builder/factory for timestamp extraction and watermark generation. During the runtime, the
- watermark generator is located inside the source and is able to generate per-partition watermarks.</td>
+ <td>支持将水印策略下推到 <code>DynamicTableSource</code>。水印策略可以通过工厂模式或 Builder 模式来构建，用于抽取时间戳以及水印的生成。在运行时，source 端内部的水印生成器会为每个分区生产水印。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsSourceWatermark.java'>SupportsSourceWatermark</a></td>
- <td>Enables to fully rely on the watermark strategy provided by the <code>ScanTableSource</code>
- itself. Thus, a <code>CREATE TABLE</code> DDL is able to use <code>SOURCE_WATERMARK()</code> which
- is a built-in marker function that will be detected by the planner and translated into a call
- to this interface if available.</td>
+ <td>支持使用 <code>ScanTableSource</code> 中提供的水印策略。当使用 <code>CREATE TABLE</code> DDL 时，<可以使用></可以使用> <code>SOURCE_WATERMARK()</code> 来告诉 planner 调用这个接口中的水印策略方法。</td>
</tr>
</tbody>
</table>
-<span class="label label-danger">Attention</span> The interfaces above are currently only available for
-`ScanTableSource`, not for `LookupTableSource`.
+<span class="label label-danger">注意</span>上述接口当前只适用于 `ScanTableSource`，不适用于`LookupTableSource`。
+
+<a name="dynamic-table-sink"></a>
-### Dynamic Table Sink
+### 动态表的 sink 端
-By definition, a dynamic table can change over time.
+按照定义，动态表是随时间变化的。
-When writing a dynamic table, the content can always be considered as a changelog (finite or infinite)
-for which all changes are written out continuously until the changelog is exhausted. The returned _changelog mode_
-indicates the set of changes that the sink accepts during runtime.
+当写入一个动态表时，数据流可以被看作是 changelog （有界或无界都可），在 changelog 结束前，所有的变更都会被持续写入。在运行时，返回的 **_changelog mode_** 会显示 sink 端支持的数据操作类型。
-For regular batch scenarios, the sink can solely accept insert-only rows and write out bounded streams.
+在常规批处理的场景下，sink 端可以持续接收 insert-only 操作类型的数据，并写入到有界数据流中。
-For regular streaming scenarios, the sink can solely accept insert-only rows and can write out unbounded streams.
+在常规流处理的场景下，sink 端可以持续接收 insert-only 操作类型的数据，并写入到无界数据流中。
-For change data capture (CDC) scenarios, the sink can write out bounded or unbounded streams with insert,
-update, and delete rows.
+在变更日志数据捕获（即 CDC）场景下，sink 端可以将 insert、update、delete 操作类型的数据写入有界或无界数据流。
-A table sink can implement further ability interfaces such as `SupportsOverwrite` that might mutate an
-instance during planning. All abilities can be found in the `org.apache.flink.table.connector.sink.abilities`
-package and are listed in the [sink abilities table](#sink-abilities).
+可以实现 `SupportsOverwrite` 等功能接口，在 sink 端处理数据。可以在 `org.apache.flink.table.connector.sink.abilities` 包下找到各种功能接口，更多内容可查看[sink abilities table](#sink-abilities)。
-The runtime implementation of a `DynamicTableSink` must consume internal data structures. Thus, records
-must be accepted as `org.apache.flink.table.data.RowData`. The framework provides runtime converters such
-that a sink can still work on common data structures and perform a conversion at the beginning.
+实现 `DynamicTableSink` 接口的类必须能够处理 Flink 内部数据结构，因此每条记录都会按照 `org.apache.flink.table.data.RowData` 的方式进行处理。Flink 运行时提供了转换机制来保证在最开始进行数据类型转换，以便 sink 端可以处理常见的数据结构。
-#### Sink Abilities
+<a name="sink-abilities"></a>
+
+#### sink 端的功能接口
<table class="table table-bordered">
<thead>
<tr>
- <th class="text-left" style="width: 25%">Interface</th>
- <th class="text-center" style="width: 75%">Description</th>
+ <th class="text-left" style="width: 25%">接口</th>
+ <th class="text-center" style="width: 75%">描述</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsOverwrite.java'>SupportsOverwrite</a></td>
- <td>Enables to overwrite existing data in a <code>DynamicTableSink</code>. By default, if
- this interface is not implemented, existing tables or partitions cannot be overwritten using
- e.g. the SQL <code>INSERT OVERWRITE</code> clause.</td>
+ <td>支持 <code>DynamicTableSink</code> 覆盖写入已存在的数据。默认情况下，如果不实现这个接口，在使用 <code>INSERT OVERWRITE</code> SQL 语法时，已存在的表或分区不会被覆盖写入</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsPartitioning.java'>SupportsPartitioning</a></td>
- <td>Enables to write partitioned data in a <code>DynamicTableSink</code>.</td>
+ <td>支持 <code>DynamicTableSink</code> 写入分区数据。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsWritingMetadata.java'>SupportsWritingMetadata</a></td>
- <td>Enables to write metadata columns into a <code>DynamicTableSource</code>. A table sink is
- responsible for accepting requested metadata columns at the end of consumed rows and persist
- them. This includes potentially forwarding metadata columns to contained formats.</td>
+ <td>支持 <code>DynamicTableSource</code> 写入分区数据。sink 端会在消费数据行时，在最后接受相应的元数据信息并进行持久化，其中包括元数据的格式信息。</td>
Review Comment:
Enables to write metadata columns into a <code>DynamicTableSource</code>.
"写入分区数据" 应是"写入元数据列"。
##########
docs/content.zh/docs/dev/table/sourcesSinks.md:
##########
@@ -133,242 +133,209 @@ If you need a feature available only internally within the `org.apache.flink.tab
To learn more, check out [Anatomy of Table Dependencies]({{< ref "docs/dev/configuration/advanced" >}}#anatomy-of-table-dependencies).
{{< /hint >}}
-Extension Points
+<a name="extension-points"></a>
+
+扩展点
----------------
-This section explains the available interfaces for extending Flink's table connectors.
+这一部分主要介绍扩展 Flink table connector 时可能用到的接口。
+
+<a name="dynamic-table-factories"></a>
-### Dynamic Table Factories
+### 动态表的工厂类
-Dynamic table factories are used to configure a dynamic table connector for an external storage system from catalog
-and session information.
+在根据 catalog 与 Flink 运行时上下文信息，为某个外部存储系统配置动态表连接器时，需要用到动态表的工厂类。
-`org.apache.flink.table.factories.DynamicTableSourceFactory` can be implemented to construct a `DynamicTableSource`.
+比如，通过实现 `org.apache.flink.table.factories.DynamicTableSourceFactory` 接口完成一个工厂类，来生产 `DynamicTableSource` 类。
-`org.apache.flink.table.factories.DynamicTableSinkFactory` can be implemented to construct a `DynamicTableSink`.
+通过实现 `org.apache.flink.table.factories.DynamicTableSinkFactory` 接口完成一个工厂类，来生产 `DynamicTableSink` 类。
-By default, the factory is discovered using the value of the `connector` option as the factory identifier
-and Java's Service Provider Interface.
+默认情况下，Java 的 SPI 机制会自动识别这些工厂类，同时将 `connector` 配置项作为工厂类的”标识符“。
-In JAR files, references to new implementations can be added to the service file:
+在 JAR 文件中，需要将实现的工厂类路径放入到下面这个配置文件：
`META-INF/services/org.apache.flink.table.factories.Factory`
-The framework will check for a single matching factory that is uniquely identified by factory identifier
-and requested base class (e.g. `DynamicTableSourceFactory`).
+Flink 会对工厂类逐个进行检查，确保其”标识符“是全局唯一的，并且按照要求实现了上面提到的接口 (比如 `DynamicTableSourceFactory`)。
+
+如果必要的话，也可以在实现 catalog 时绕过上述 SPI 机制识别工厂类的过程。即在实现 catalog 接口时，在`org.apache.flink.table.catalog.Catalog#getFactory` 方法中直接返回工厂类的实例。
+
+<a name="dynamic-table-source"></a>
-The factory discovery process can be bypassed by the catalog implementation if necessary. For this, a
-catalog needs to return an instance that implements the requested base class in `org.apache.flink.table.catalog.Catalog#getFactory`.
+### 动态表的 source 端
-### Dynamic Table Source
+按照定义，动态表是随时间变化的。
-By definition, a dynamic table can change over time.
+在读取动态表时，表中数据可以是以下情况之一：
+- changelog 流（支持有界或无界），在 changelog 流结束前，所有的改变都会被源源不断地消费，由 `ScanTableSource` 接口表示。
+- 处于一直变换或数据量很大的外部表，其中的数据一般不会被全量读取，除非是在查询某个值时，由 `LookupTableSource` 接口表示。
-When reading a dynamic table, the content can either be considered as:
-- A changelog (finite or infinite) for which all changes are consumed continuously until the changelog
- is exhausted. This is represented by the `ScanTableSource` interface.
-- A continuously changing or very large external table whose content is usually never read entirely
- but queried for individual values when necessary. This is represented by the `LookupTableSource`
- interface.
+一个类可以同时实现这两个接口，Planner 会根据查询的 Query 选择相应接口中的方法。
-A class can implement both of these interfaces at the same time. The planner decides about their usage depending
-on the specified query.
+<a name= "scan-table-source"></a>
#### Scan Table Source
-A `ScanTableSource` scans all rows from an external storage system during runtime.
+在运行期间，`ScanTableSource` 接口会按行扫描外部存储系统中所有数据。
-The scanned rows don't have to contain only insertions but can also contain updates and deletions. Thus,
-the table source can be used to read a (finite or infinite) changelog. The returned _changelog mode_ indicates
-the set of changes that the planner can expect during runtime.
+被扫描的数据可以是 insert、update、delete 三种操作类型，因此数据源可以用作读取 changelog （支持有界或无界）。在运行时，返回的 **_changelog mode_** 表示 Planner 要处理的操作类型。
-For regular batch scenarios, the source can emit a bounded stream of insert-only rows.
+在常规批处理的场景下，数据源可以处理 insert-only 操作类型的有界数据流。
-For regular streaming scenarios, the source can emit an unbounded stream of insert-only rows.
+在常规流处理的场景下，数据源可以处理 insert-only 操作类型的无界数据流。
-For change data capture (CDC) scenarios, the source can emit bounded or unbounded streams with insert,
-update, and delete rows.
+在变更日志数据捕获（即 CDC）场景下，数据源可以处理 insert、update、delete 操作类型的有界或无界数据流。
-A table source can implement further ability interfaces such as `SupportsProjectionPushDown` that might
-mutate an instance during planning. All abilities can be found in the `org.apache.flink.table.connector.source.abilities`
-package and are listed in the [source abilities table](#source-abilities).
+可以实现更多的功能接口来优化数据源，比如实现 `SupportsProjectionPushDown` 接口，这样在运行时在 source 端就处理数据。在 `org.apache.flink.table.connector.source.abilities` 包下可以找到各种功能接口，更多内容可查看 [source abilities table](#source-abilities)。
-The runtime implementation of a `ScanTableSource` must produce internal data structures. Thus, records
-must be emitted as `org.apache.flink.table.data.RowData`. The framework provides runtime converters such
-that a source can still work on common data structures and perform a conversion at the end.
+实现 `ScanTableSource` 接口的类必须能够生产 Flink 内部数据结构，因此每条记录都会按照`org.apache.flink.table.data.RowData` 的方式进行处理。Flink 运行时提供了转换机制保证 source 端可以处理常见的数据结构，并且在最后进行转换。
+
+<a name="lookup-table-source"></a>
#### Lookup Table Source
-A `LookupTableSource` looks up rows of an external storage system by one or more keys during runtime.
+在运行期间，`LookupTableSource` 接口会在外部存储系统中按照 key 进行查找。
+
+相比于`ScanTableSource`，`LookupTableSource` 接口不会全量读取表中数据，只会在需要时向外部存储（其中的数据有可能会一直变化）发起查询请求，惰性地获取数据。
-Compared to `ScanTableSource`, the source does not have to read the entire table and can lazily fetch individual
-values from a (possibly continuously changing) external table when necessary.
+同时相较于`ScanTableSource`，`LookupTableSource` 接口目前只支持处理 insert-only 数据流。
-Compared to `ScanTableSource`, a `LookupTableSource` does only support emitting insert-only changes currently.
+暂时不支持扩展功能接口，可查看 `org.apache.flink.table.connector.source.LookupTableSource` 中的文档了解更多。
-Further abilities are not supported. See the documentation of `org.apache.flink.table.connector.source.LookupTableSource`
-for more information.
+`LookupTableSource` 的实现方法可以是 `TableFunction` 或者 `AsyncTableFunction`，Flink运行时会根据要查询的 key 值，调用这个实现方法进行查询。
-The runtime implementation of a `LookupTableSource` is a `TableFunction` or `AsyncTableFunction`. The function
-will be called with values for the given lookup keys during runtime.
+<a name="source-abilities"></a>
-#### Source Abilities
+#### source 端的功能接口
<table class="table table-bordered">
<thead>
<tr>
- <th class="text-left" style="width: 25%">Interface</th>
- <th class="text-center" style="width: 75%">Description</th>
+ <th class="text-left" style="width: 25%">接口</th>
+ <th class="text-center" style="width: 75%">描述</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsFilterPushDown.java'>SupportsFilterPushDown</a></td>
- <td>Enables to push down the filter into the <code>DynamicTableSource</code>. For efficiency, a source can
- push filters further down in order to be close to the actual data generation.</td>
+ <td>支持将过滤条件下推到 <code>DynamicTableSource</code>。为了更高效处理数据，source 端会将过滤条件下推，以便在数据产生时就处理。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsLimitPushDown.java'>SupportsLimitPushDown</a></td>
- <td>Enables to push down a limit (the expected maximum number of produced records) into a <code>DynamicTableSource</code>.</td>
+ <td>支持将 limit 下推到（期望生产的最大数据条数）<code>DynamicTableSource</code>。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsPartitionPushDown.java'>SupportsPartitionPushDown</a></td>
- <td>Enables to pass available partitions to the planner and push down partitions into a <code>DynamicTableSource</code>.
- During the runtime, the source will only read data from the passed partition list for efficiency.</td>
+ <td>支持将可用的分区信息提供给 planner 并且将分区信息下推到 <code>DynamicTableSource</code>。在运行时为了更高效处理数据，source 端会只从提供的分区列表中读取数据。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsProjectionPushDown.java'>SupportsProjectionPushDown</a> </td>
- <td>Enables to push down a (possibly nested) projection into a <code>DynamicTableSource</code>. For efficiency,
- a source can push a projection further down in order to be close to the actual data generation. If the source
- also implements <code>SupportsReadingMetadata</code>, the source will also read the required metadata only.
+ <td>支持将查询列(可嵌套)下推到 <code>DynamicTableSource</code>。为了更高效处理数据，source 端会将查询列下推，以便在数据产生时就处理。如果 source 端同时实现了 <code>SupportsReadingMetadata</code>，那么 source 端也会读取相对应列的元数据信息。
</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsReadingMetadata.java'>SupportsReadingMetadata</a></td>
- <td>Enables to read metadata columns from a <code>DynamicTableSource</code>. The source
- is responsible to add the required metadata at the end of the produced rows. This includes
- potentially forwarding metadata column from contained formats.</td>
+ <td>支持通过 <code>DynamicTableSource</code> 读取列的元数据信息。source 端会在生产数据行时，在最后添加相应的元数据信息，其中包括元数据的格式信息。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsWatermarkPushDown.java'>SupportsWatermarkPushDown</a></td>
- <td>Enables to push down a watermark strategy into a <code>DynamicTableSource</code>. The watermark
- strategy is a builder/factory for timestamp extraction and watermark generation. During the runtime, the
- watermark generator is located inside the source and is able to generate per-partition watermarks.</td>
+ <td>支持将水印策略下推到 <code>DynamicTableSource</code>。水印策略可以通过工厂模式或 Builder 模式来构建，用于抽取时间戳以及水印的生成。在运行时，source 端内部的水印生成器会为每个分区生产水印。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsSourceWatermark.java'>SupportsSourceWatermark</a></td>
- <td>Enables to fully rely on the watermark strategy provided by the <code>ScanTableSource</code>
- itself. Thus, a <code>CREATE TABLE</code> DDL is able to use <code>SOURCE_WATERMARK()</code> which
- is a built-in marker function that will be detected by the planner and translated into a call
- to this interface if available.</td>
+ <td>支持使用 <code>ScanTableSource</code> 中提供的水印策略。当使用 <code>CREATE TABLE</code> DDL 时，<可以使用></可以使用> <code>SOURCE_WATERMARK()</code> 来告诉 planner 调用这个接口中的水印策略方法。</td>
</tr>
</tbody>
</table>
-<span class="label label-danger">Attention</span> The interfaces above are currently only available for
-`ScanTableSource`, not for `LookupTableSource`.
+<span class="label label-danger">注意</span>上述接口当前只适用于 `ScanTableSource`，不适用于`LookupTableSource`。
+
+<a name="dynamic-table-sink"></a>
-### Dynamic Table Sink
+### 动态表的 sink 端
-By definition, a dynamic table can change over time.
+按照定义，动态表是随时间变化的。
-When writing a dynamic table, the content can always be considered as a changelog (finite or infinite)
-for which all changes are written out continuously until the changelog is exhausted. The returned _changelog mode_
-indicates the set of changes that the sink accepts during runtime.
+当写入一个动态表时，数据流可以被看作是 changelog （有界或无界都可），在 changelog 结束前，所有的变更都会被持续写入。在运行时，返回的 **_changelog mode_** 会显示 sink 端支持的数据操作类型。
-For regular batch scenarios, the sink can solely accept insert-only rows and write out bounded streams.
+在常规批处理的场景下，sink 端可以持续接收 insert-only 操作类型的数据，并写入到有界数据流中。
-For regular streaming scenarios, the sink can solely accept insert-only rows and can write out unbounded streams.
+在常规流处理的场景下，sink 端可以持续接收 insert-only 操作类型的数据，并写入到无界数据流中。
-For change data capture (CDC) scenarios, the sink can write out bounded or unbounded streams with insert,
-update, and delete rows.
+在变更日志数据捕获（即 CDC）场景下，sink 端可以将 insert、update、delete 操作类型的数据写入有界或无界数据流。
-A table sink can implement further ability interfaces such as `SupportsOverwrite` that might mutate an
-instance during planning. All abilities can be found in the `org.apache.flink.table.connector.sink.abilities`
-package and are listed in the [sink abilities table](#sink-abilities).
+可以实现 `SupportsOverwrite` 等功能接口，在 sink 端处理数据。可以在 `org.apache.flink.table.connector.sink.abilities` 包下找到各种功能接口，更多内容可查看[sink abilities table](#sink-abilities)。
-The runtime implementation of a `DynamicTableSink` must consume internal data structures. Thus, records
-must be accepted as `org.apache.flink.table.data.RowData`. The framework provides runtime converters such
-that a sink can still work on common data structures and perform a conversion at the beginning.
+实现 `DynamicTableSink` 接口的类必须能够处理 Flink 内部数据结构，因此每条记录都会按照 `org.apache.flink.table.data.RowData` 的方式进行处理。Flink 运行时提供了转换机制来保证在最开始进行数据类型转换，以便 sink 端可以处理常见的数据结构。
-#### Sink Abilities
+<a name="sink-abilities"></a>
+
+#### sink 端的功能接口
<table class="table table-bordered">
<thead>
<tr>
- <th class="text-left" style="width: 25%">Interface</th>
- <th class="text-center" style="width: 75%">Description</th>
+ <th class="text-left" style="width: 25%">接口</th>
+ <th class="text-center" style="width: 75%">描述</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsOverwrite.java'>SupportsOverwrite</a></td>
- <td>Enables to overwrite existing data in a <code>DynamicTableSink</code>. By default, if
- this interface is not implemented, existing tables or partitions cannot be overwritten using
- e.g. the SQL <code>INSERT OVERWRITE</code> clause.</td>
+ <td>支持 <code>DynamicTableSink</code> 覆盖写入已存在的数据。默认情况下，如果不实现这个接口，在使用 <code>INSERT OVERWRITE</code> SQL 语法时，已存在的表或分区不会被覆盖写入</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsPartitioning.java'>SupportsPartitioning</a></td>
- <td>Enables to write partitioned data in a <code>DynamicTableSink</code>.</td>
+ <td>支持 <code>DynamicTableSink</code> 写入分区数据。</td>
</tr>
<tr>
<td><a href='https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsWritingMetadata.java'>SupportsWritingMetadata</a></td>
- <td>Enables to write metadata columns into a <code>DynamicTableSource</code>. A table sink is
- responsible for accepting requested metadata columns at the end of consumed rows and persist
- them. This includes potentially forwarding metadata columns to contained formats.</td>
+ <td>支持 <code>DynamicTableSource</code> 写入分区数据。sink 端会在消费数据行时，在最后接受相应的元数据信息并进行持久化，其中包括元数据的格式信息。</td>
</tr>
</tbody>
</table>
-### Encoding / Decoding Formats
+<a name="encoding--decoding-formats"></a>
+
+### 编码与解码
-Some table connectors accept different formats that encode and decode keys and/or values.
+有的表连接器支持 K/V 型数据的各类编码与解码方式。
-Formats work similar to the pattern `DynamicTableSourceFactory -> DynamicTableSource -> ScanRuntimeProvider`,
-where the factory is responsible for translating options and the source is responsible for creating runtime logic.
+编码与解码格式器的工作原理类似于 `DynamicTableSourceFactory -> DynamicTableSource -> ScanRuntimeProvider`，其中工厂类负责传参，source 负责提供处理逻辑。
-Because formats might be located in different modules, they are discovered using Java's Service Provider
-Interface similar to [table factories](#dynamic-table-factories). In order to discover a format factory,
-the dynamic table factory searches for a factory that corresponds to a factory identifier and connector-specific
-base class.
+由于编码与解码格式器处于不同的代码模块，类似于[table factories](#dynamic-table-factories)，它们都需要通过 Java 的 SPI 机制自动识别为了找到格式器的工厂类，动态表工厂类会根据该格式器工厂类的”标识符“来搜索，并确认其实现了连接器相关的基类。
Review Comment:
"它们都需要通过 Java 的 SPI 机制自动识别为了找到格式器的工厂类"
缺个逗号，"它们都需要通过 Java 的 SPI 机制自动识别。为了找到格式器的工厂类，"
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[GitHub] [flink] ChengkaiYang2022 commented on pull request #20331: [FLINK-28121][docs-zh]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
Posted by GitBox <gi...@apache.org>.
ChengkaiYang2022 commented on PR #20331:
URL: https://github.com/apache/flink/pull/20331#issuecomment-1222362837
@fsk119 Would you help to take a look at this? ^_^
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[GitHub] [flink] flinkbot commented on pull request #20331: [FLINK-28121]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
Posted by GitBox <gi...@apache.org>.
flinkbot commented on PR #20331:
URL: https://github.com/apache/flink/pull/20331#issuecomment-1191359398
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## CI report:
* 90b07d33d12d70928620851c4e1225fda0caa447 UNKNOWN
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[GitHub] [flink] ChengkaiYang2022 commented on pull request #20331: [FLINK-28121][docs-zh]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
Posted by GitBox <gi...@apache.org>.
ChengkaiYang2022 commented on PR #20331:
URL: https://github.com/apache/flink/pull/20331#issuecomment-1216470360
Hi, @gaoyunhaii, could you help to review this PR?
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[GitHub] [flink] MartijnVisser merged pull request #20331: [FLINK-28121][docs-zh]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
Posted by GitBox <gi...@apache.org>.
MartijnVisser merged PR #20331:
URL: https://github.com/apache/flink/pull/20331
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[GitHub] [flink] ChengkaiYang2022 commented on pull request #20331: [FLINK-28121][docs-zh]Translate "Extension Points" and "Full Stack Example" in "User-defined Sources & Sinks" page
Posted by GitBox <gi...@apache.org>.
ChengkaiYang2022 commented on PR #20331:
URL: https://github.com/apache/flink/pull/20331#issuecomment-1221293450
@coder-zjh Thanks for the review! @MartijnVisser would you help to take a look and merge it?
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