klion26 commented on a change in pull request #247:
URL: https://github.com/apache/flink-web/pull/247#discussion_r425647065
##########
File path: contributing/code-style-and-quality-components.zh.md
##########
@@ -48,96 +48,96 @@ How to name config keys:
}
```
-* The resulting config keys should hence be:
+* 因此生成的配置键应该是：
- **NOT** `"taskmanager.detailed.network.metrics"`
+ **不是** `"taskmanager.detailed.network.metrics"`
- **But rather** `"taskmanager.network.detailed-metrics"`
+ **而是** `"taskmanager.network.detailed-metrics"`
-### Connectors
+### 连接器
-Connectors are historically hard to implement and need to deal with many
aspects of threading, concurrency, and checkpointing.
+连接器历来很难实现，需要处理线程、并发和检查点的许多方面。
Review comment:
这里的 "threading" 我理解翻译成 ”多线程“？现在的翻译”处理线程“感觉话没有说完一样
##########
File path: contributing/code-style-and-quality-components.zh.md
##########
@@ -48,96 +48,96 @@ How to name config keys:
}
```
-* The resulting config keys should hence be:
+* 因此生成的配置键应该是：
- **NOT** `"taskmanager.detailed.network.metrics"`
+ **不是** `"taskmanager.detailed.network.metrics"`
- **But rather** `"taskmanager.network.detailed-metrics"`
+ **而是** `"taskmanager.network.detailed-metrics"`
-### Connectors
+### 连接器
-Connectors are historically hard to implement and need to deal with many
aspects of threading, concurrency, and checkpointing.
+连接器历来很难实现，需要处理线程、并发和检查点的许多方面。
-As part of
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
we are working on making this much simpler for sources. New sources should not
have to deal with any aspect of concurrency/threading and checkpointing any
more.
+作为
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
的一部分，我们正在努力让这些源（source）更加简单。新的源（source）应该不再处理并发/线程和检查点的任何方面。
Review comment:
”新的源（source）应该“ 可以直接写成 ”新的源“ 因为前面已经翻译过一次了。
##########
File path: contributing/code-style-and-quality-components.zh.md
##########
@@ -9,24 +9,24 @@ title: "Apache Flink Code Style and Quality Guide —
Components"
-## Component Specific Guidelines
+## 组件特定指南
-_Additional guidelines about changes in specific components._
+_关于特定组件更改的附加指南。_
-### Configuration Changes
+### 配置更改
-Where should the config option go?
+配置选项应该放在哪里？
-* <span style="text-decoration:underline;">‘flink-conf.yaml’:</span> All
configuration that pertains to execution behavior that one may want to
standardize across jobs. Think of it as parameters someone would set wearing an
“ops” hat, or someone that provides a stream processing platform to other teams.
+* <span style="text-decoration:underline;">‘flink-conf.yaml’:</span>
文件中的内容是包括那些能够跨 job 共用的标准配置，因为配置在这个文件中的参数会被多个 job 共用。可以想像成 Ops
的工作人员，或者管理一个数据平台的工作人员那样，所以把通用的配置都放到 flink-conf.yaml 文件中。
Review comment:
“可以想像成 Ops 的工作人员，或者管理一个数据平台的工作人员那样，所以把通用的配置都放到 flink-conf.yaml 文件中。”
这句话有点想口语，尤其是 ”工作人员那样“ 这里，咱们有更好的翻译吗？另外后面的 ”所以把XXX“ 这些感觉有点多余。
##########
File path: contributing/code-style-and-quality-components.zh.md
##########
@@ -48,96 +48,96 @@ How to name config keys:
}
```
-* The resulting config keys should hence be:
+* 因此生成的配置键应该是：
- **NOT** `"taskmanager.detailed.network.metrics"`
+ **不是** `"taskmanager.detailed.network.metrics"`
- **But rather** `"taskmanager.network.detailed-metrics"`
+ **而是** `"taskmanager.network.detailed-metrics"`
-### Connectors
+### 连接器
-Connectors are historically hard to implement and need to deal with many
aspects of threading, concurrency, and checkpointing.
+连接器历来很难实现，需要处理线程、并发和检查点的许多方面。
-As part of
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
we are working on making this much simpler for sources. New sources should not
have to deal with any aspect of concurrency/threading and checkpointing any
more.
+作为
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
的一部分，我们正在努力让这些源（source）更加简单。新的源（source）应该不再处理并发/线程和检查点的任何方面。
-A similar FLIP can be expected for sinks in the near future.
+预计在不久的将来，类似的 FLIP 可以用于接收器。
-### Examples
+### 示例
-Examples should be self-contained and not require systems other than Flink to
run. Except for examples that show how to use specific connectors, like the
Kafka connector. Sources/sinks that are ok to use are
`StreamExecutionEnvironment.socketTextStream`, which should not be used in
production but is quite handy for exploring how things work, and file-based
sources/sinks. (For streaming, there is the continuous file source)
+示例应该是自包含的，不需要运行 Flink 以外的系统。除了显示如何使用具体的连接器的示例，比如 Kafka 连接器。源/接收器可以使用
`StreamExecutionEnvironment.socketTextStream`
，这个不应该在生产中使用，但对于研究示例如何运行的是相当方便的，以及基于文件的源/接收器。（对于流，有连续的文件源）
-Examples should also not be pure toy-examples but strike a balance between
real-world code and purely abstract examples. The WordCount example is quite
long in the tooth by now but it’s a good showcase of simple code that
highlights functionality and can do useful things.
+示例也不应该是纯粹的玩具示例，而是在现实世界的代码和纯粹的抽象示例之间取得平衡。WordCount
示例到现在已经很久了，但它是一个很好的功能突出并可以做有用事情的简单代码示例。
-Examples should also be heavy in comments. They should describe the general
idea of the example in the class-level Javadoc and describe what is happening
and what functionality is used throughout the code. The expected input data and
output data should also be described.
+示例中应该有不少的注释。他们可以在类级 Javadoc
中描述示例的总体思路，并且描述正在发生什么和整个代码里使用了什么功能。还应描述预期的输入数据和输出数据。
-Examples should include parameter parsing, so that you can run an example
(from the Jar that is created for each example using `bin/flink run
path/to/myExample.jar --param1 … --param2`.
+示例应该包括参数解析，以便你可以运行一个示例（从为每个示例所创建的 Jar 使用`bin/flink run path/to/myExample.jar
--param1 … --param2`。
-### Table & SQL API
+### 表和 SQL API
-#### Semantics
+#### 语义
-**The SQL standard should be the main source of truth.**
+**SQL 标准应该是事实的主要来源。**
-* Syntax, semantics, and features should be aligned with SQL!
-* We don’t need to reinvent the wheel. Most problems have already been
discussed industry-wide and written down in the SQL standard.
-* We rely on the newest standard (SQL:2016 or ISO/IEC 9075:2016 when writing
this document
[[download]](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)
). Not every part is available online but a quick web search might help here.
+* 语法、语义和功能应该和 SQL 保持一致！
+* 我们不需要重造轮子。大部分问题都已经在业界广泛讨论过并写在 SQL 标准中了。
+* 我们依靠最新的标准（在写这篇文档时 SQL:2016 or ISO/IEC 9075:2016
[[下载]](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)）。并非每个部分都可在线获取，但快速网络搜索可能对此有所帮助。
-Discuss divergence from the standard or vendor-specific interpretations.
+讨论与标准或厂商特定解释的差异。
-* Once a syntax or behavior is defined it cannot be undone easily.
-* Contributions that need to extent or interpret the standard need a thorough
discussion with the community.
-* Please help committers by performing some initial research about how other
vendors such as Postgres, Microsoft SQL Server, Oracle, Hive, Calcite, Beam are
handling such cases.
+* 一旦定义了语法或行为就不能轻易撤销。
+* 需要扩展或解释标准的贡献需要与社区进行深入的讨论。
+* 请通过一些对 Postgres、Microsoft SQL Server、Oracle、Hive、Calcite、Beam
等其他厂商如何处理此类案例进行初步的探讨来帮助提交者。
-Consider the Table API as a bridge between the SQL and Java/Scala programming
world.
+将 Table API 视为 SQL 和 Java/Scala 编程世界之间的桥梁。
-* The Table API is an Embedded Domain Specific Language for analytical
programs following the relational model.
-It is not required to strictly follow the SQL standard in regards of syntax
and names, but can be closer to the way a programming language would do/name
functions and features, if that helps make it feel more intuitive.
-* The Table API might have some non-SQL features (e.g. map(), flatMap(), etc.)
but should nevertheless “feel like SQL”. Functions and operations should have
equal semantics and naming if possible.
+* Table API 是一种嵌入式域特定语言，用于遵循关系模型的分析程序。
+在语法和名称方面不需要严格遵循 SQL 标准，但如果这有助于使其感觉更直观，那么可以更接近编程语言的方式/命名函数和功能。
+* Table API 可能有一些非 SQL 功能（例如 map()、flatMap() 等），但应该还是“感觉像 SQL
”。如果可能，函数和算子应该有相等的语义和命名。
-#### Common mistakes
+#### 常见错误
-* Support SQL’s type system when adding a feature.
- * A SQL function, connector, or format should natively support most SQL
types from the very beginning.
- * Unsupported types lead to confusion, limit the usability, and create
overhead by touching the same code paths multiple times.
- * For example, when adding a `SHIFT_LEFT` function, make sure that the
contribution is general enough not only for `INT` but also `BIGINT` or
`TINYINT`.
+* 添加功能时支持 SQL 的类型系统。
+ * SQL 函数、连接器或格式化从一开始就应该原生的支持大多数 SQL 类型。
+ * 不支持的类型会导致混淆，限制可用性，并通过多次接触相同代码路径产生开销。
+ * 例如，当添加 `SHIFT_LEFT` 函数时，确保贡献足够通用，不仅适用于 `INT` 也适用于 `BIGINT` 或 `TINYINT`。
-#### Testing
+#### 测试
-Test for nullability.
+测试为空性
-* SQL natively supports `NULL` for almost every operation and has a 3-valued
boolean logic.
-* Make sure to test every feature for nullability as well.
+* 几乎每个操作，SQL 都原生支持 `NULL`，并具有3值布尔逻辑。
Review comment:
```suggestion
* 几乎每个操作，SQL 都原生支持 `NULL`，并具有 3 值布尔逻辑。
```
##########
File path: contributing/code-style-and-quality-components.zh.md
##########
@@ -48,96 +48,96 @@ How to name config keys:
}
```
-* The resulting config keys should hence be:
+* 因此生成的配置键应该是：
- **NOT** `"taskmanager.detailed.network.metrics"`
+ **不是** `"taskmanager.detailed.network.metrics"`
- **But rather** `"taskmanager.network.detailed-metrics"`
+ **而是** `"taskmanager.network.detailed-metrics"`
-### Connectors
+### 连接器
-Connectors are historically hard to implement and need to deal with many
aspects of threading, concurrency, and checkpointing.
+连接器历来很难实现，需要处理线程、并发和检查点的许多方面。
-As part of
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
we are working on making this much simpler for sources. New sources should not
have to deal with any aspect of concurrency/threading and checkpointing any
more.
+作为
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
的一部分，我们正在努力让这些源（source）更加简单。新的源（source）应该不再处理并发/线程和检查点的任何方面。
-A similar FLIP can be expected for sinks in the near future.
+预计在不久的将来，类似的 FLIP 可以用于接收器。
Review comment:
”预计在不久的将来，会有类似针对 Sink 的 FLIP“ 你觉得这样会好一些吗？
Sink 要么不翻译，如果翻译的话，建议在第一次翻译的时候写成 ”翻译后的中文（Sink）“这样其他人知道这个中文的原文是啥
另外 Sink 翻译成 ”接收器“ 感觉有点奇怪，你可以参考下之前是怎么翻译的，或者可以不翻译
##########
File path: contributing/code-style-and-quality-components.zh.md
##########
@@ -9,24 +9,24 @@ title: "Apache Flink Code Style and Quality Guide —
Components"
-## Component Specific Guidelines
+## 组件特定指南
-_Additional guidelines about changes in specific components._
+_关于特定组件更改的附加指南。_
-### Configuration Changes
+### 配置更改
-Where should the config option go?
+配置选项应该放在哪里？
-* <span style="text-decoration:underline;">‘flink-conf.yaml’:</span> All
configuration that pertains to execution behavior that one may want to
standardize across jobs. Think of it as parameters someone would set wearing an
“ops” hat, or someone that provides a stream processing platform to other teams.
+* <span style="text-decoration:underline;">‘flink-conf.yaml’:</span>
文件中的内容是包括那些能够跨 job 共用的标准配置，因为配置在这个文件中的参数会被多个 job 共用。可以想像成 Ops
的工作人员，或者管理一个数据平台的工作人员那样，所以把通用的配置都放到 flink-conf.yaml 文件中。
-* <span style="text-decoration:underline;">‘ExecutionConfig’</span>:
Parameters specific to an individual Flink application, needed by the operators
during execution. Typical examples are watermark interval, serializer
parameters, object reuse.
-* <span style="text-decoration:underline;">ExecutionEnvironment (in
code)</span>: Everything that is specific to an individual Flink application
and is only needed to build program / dataflow, not needed inside the operators
during execution.
+* <span style="text-decoration:underline;">‘ExecutionConfig’</span>:
执行期间算子需要特定于单个 Flink 应用程序的参数，典型的例子是水印间隔，序列化参数，对象重用。
+* <span style="text-decoration:underline;">ExecutionEnvironment (在代码里)</span>:
所有特定于单个 Flink 应用程序的东西，仅在构建程序/数据流时需要，在算子执行期间不需要。
-How to name config keys:
+如何命名配置键：
-* Config key names should be hierarchical.
- Think of the configuration as nested objects (JSON style)
+* 配置键名应该分层级。
+ 将配置视为嵌套对象（ JSON 样式）
Review comment:
```suggestion
将配置视为嵌套对象（JSON 样式）
```
##########
File path: contributing/code-style-and-quality-components.zh.md
##########
@@ -48,96 +48,96 @@ How to name config keys:
}
```
-* The resulting config keys should hence be:
+* 因此生成的配置键应该是：
- **NOT** `"taskmanager.detailed.network.metrics"`
+ **不是** `"taskmanager.detailed.network.metrics"`
- **But rather** `"taskmanager.network.detailed-metrics"`
+ **而是** `"taskmanager.network.detailed-metrics"`
-### Connectors
+### 连接器
-Connectors are historically hard to implement and need to deal with many
aspects of threading, concurrency, and checkpointing.
+连接器历来很难实现，需要处理线程、并发和检查点的许多方面。
-As part of
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
we are working on making this much simpler for sources. New sources should not
have to deal with any aspect of concurrency/threading and checkpointing any
more.
+作为
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
的一部分，我们正在努力让这些源（source）更加简单。新的源（source）应该不再处理并发/线程和检查点的任何方面。
-A similar FLIP can be expected for sinks in the near future.
+预计在不久的将来，类似的 FLIP 可以用于接收器。
-### Examples
+### 示例
-Examples should be self-contained and not require systems other than Flink to
run. Except for examples that show how to use specific connectors, like the
Kafka connector. Sources/sinks that are ok to use are
`StreamExecutionEnvironment.socketTextStream`, which should not be used in
production but is quite handy for exploring how things work, and file-based
sources/sinks. (For streaming, there is the continuous file source)
+示例应该是自包含的，不需要运行 Flink 以外的系统。除了显示如何使用具体的连接器的示例，比如 Kafka 连接器。源/接收器可以使用
`StreamExecutionEnvironment.socketTextStream`
，这个不应该在生产中使用，但对于研究示例如何运行的是相当方便的，以及基于文件的源/接收器。（对于流，有连续的文件源）
-Examples should also not be pure toy-examples but strike a balance between
real-world code and purely abstract examples. The WordCount example is quite
long in the tooth by now but it’s a good showcase of simple code that
highlights functionality and can do useful things.
+示例也不应该是纯粹的玩具示例，而是在现实世界的代码和纯粹的抽象示例之间取得平衡。WordCount
示例到现在已经很久了，但它是一个很好的功能突出并可以做有用事情的简单代码示例。
-Examples should also be heavy in comments. They should describe the general
idea of the example in the class-level Javadoc and describe what is happening
and what functionality is used throughout the code. The expected input data and
output data should also be described.
+示例中应该有不少的注释。他们可以在类级 Javadoc
中描述示例的总体思路，并且描述正在发生什么和整个代码里使用了什么功能。还应描述预期的输入数据和输出数据。
-Examples should include parameter parsing, so that you can run an example
(from the Jar that is created for each example using `bin/flink run
path/to/myExample.jar --param1 … --param2`.
+示例应该包括参数解析，以便你可以运行一个示例（从为每个示例所创建的 Jar 使用`bin/flink run path/to/myExample.jar
--param1 … --param2`。
-### Table & SQL API
+### 表和 SQL API
-#### Semantics
+#### 语义
-**The SQL standard should be the main source of truth.**
+**SQL 标准应该是事实的主要来源。**
-* Syntax, semantics, and features should be aligned with SQL!
-* We don’t need to reinvent the wheel. Most problems have already been
discussed industry-wide and written down in the SQL standard.
-* We rely on the newest standard (SQL:2016 or ISO/IEC 9075:2016 when writing
this document
[[download]](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)
). Not every part is available online but a quick web search might help here.
+* 语法、语义和功能应该和 SQL 保持一致！
+* 我们不需要重造轮子。大部分问题都已经在业界广泛讨论过并写在 SQL 标准中了。
+* 我们依靠最新的标准（在写这篇文档时 SQL:2016 or ISO/IEC 9075:2016
[[下载]](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)）。并非每个部分都可在线获取，但快速网络搜索可能对此有所帮助。
-Discuss divergence from the standard or vendor-specific interpretations.
+讨论与标准或厂商特定解释的差异。
-* Once a syntax or behavior is defined it cannot be undone easily.
-* Contributions that need to extent or interpret the standard need a thorough
discussion with the community.
-* Please help committers by performing some initial research about how other
vendors such as Postgres, Microsoft SQL Server, Oracle, Hive, Calcite, Beam are
handling such cases.
+* 一旦定义了语法或行为就不能轻易撤销。
+* 需要扩展或解释标准的贡献需要与社区进行深入的讨论。
+* 请通过一些对 Postgres、Microsoft SQL Server、Oracle、Hive、Calcite、Beam
等其他厂商如何处理此类案例进行初步的探讨来帮助提交者。
-Consider the Table API as a bridge between the SQL and Java/Scala programming
world.
+将 Table API 视为 SQL 和 Java/Scala 编程世界之间的桥梁。
-* The Table API is an Embedded Domain Specific Language for analytical
programs following the relational model.
-It is not required to strictly follow the SQL standard in regards of syntax
and names, but can be closer to the way a programming language would do/name
functions and features, if that helps make it feel more intuitive.
-* The Table API might have some non-SQL features (e.g. map(), flatMap(), etc.)
but should nevertheless “feel like SQL”. Functions and operations should have
equal semantics and naming if possible.
+* Table API 是一种嵌入式域特定语言，用于遵循关系模型的分析程序。
+在语法和名称方面不需要严格遵循 SQL 标准，但如果这有助于使其感觉更直观，那么可以更接近编程语言的方式/命名函数和功能。
+* Table API 可能有一些非 SQL 功能（例如 map()、flatMap() 等），但应该还是“感觉像 SQL
”。如果可能，函数和算子应该有相等的语义和命名。
-#### Common mistakes
+#### 常见错误
-* Support SQL’s type system when adding a feature.
- * A SQL function, connector, or format should natively support most SQL
types from the very beginning.
- * Unsupported types lead to confusion, limit the usability, and create
overhead by touching the same code paths multiple times.
- * For example, when adding a `SHIFT_LEFT` function, make sure that the
contribution is general enough not only for `INT` but also `BIGINT` or
`TINYINT`.
+* 添加功能时支持 SQL 的类型系统。
+ * SQL 函数、连接器或格式化从一开始就应该原生的支持大多数 SQL 类型。
+ * 不支持的类型会导致混淆，限制可用性，并通过多次接触相同代码路径产生开销。
+ * 例如，当添加 `SHIFT_LEFT` 函数时，确保贡献足够通用，不仅适用于 `INT` 也适用于 `BIGINT` 或 `TINYINT`。
-#### Testing
+#### 测试
-Test for nullability.
+测试为空性
-* SQL natively supports `NULL` for almost every operation and has a 3-valued
boolean logic.
-* Make sure to test every feature for nullability as well.
+* 几乎每个操作，SQL 都原生支持 `NULL`，并具有3值布尔逻辑。
+* 也确保测试每个功能的可空性.
-Avoid full integration tests
+避免仅用集成测试
-* Spawning a Flink mini-cluster and performing compilation of generated code
for a SQL query is expensive.
-* Avoid integration tests for planner tests or variations of API calls.
-* Instead, use unit tests that validate the optimized plan which comes out of
a planner. Or test the behavior of a runtime operator directly.
+* 生成 Flink 迷你集群并为 SQL 查询执行生成代码的编译是昂贵的。
+* 避免对计划测试或 API 调用的变更进行集成测试。
+* 相反，使用单元测试验证计划器的优化计划。或者直接测试运行时的算子行为。
-#### Compatibility
+#### 兼容性
-Don’t introduce physical plan changes in minor releases!
+不要在次要版本中引入物理计划更改！
-* Backwards compatibility for state in streaming SQL relies on the fact that
the physical execution plan remains stable. Otherwise the generated Operator
Names/IDs change and state cannot be matched and restored.
-* Every bug fix that leads to changes in the optimized physical plan of a
streaming pipeline hences breaks compatibility.
-* As a consequence, changes of the kind that lead to different optimizer plans
can only be merged in major releases for now.
+* 流式 SQL 中状态的向后兼容性依赖于物理执行计划保持稳定的事实。否则，生成的操作名称/IDs将发生变化，并且无法匹配和恢复状态。
+* 每个 bug 修复都会导致流式传输管道的优化物理规划发生变化，从而破坏兼容性。
+* 因此，导致不同优化器计划的更改现在只能在主版本中合并。
-#### Scala / Java interoperability (legacy code parts)
+#### Scala / Java 互操作性（遗留代码部分）
-Keep Java in mind when designing interfaces.
+在设计接口时要牢记 Java。
-* Consider whether a class will need to interact with a Java class in the
future.
-* Use Java collections and Java Optional in interfaces for a smooth
integration with Java code.
-* Don’t use features of case classes such as .copy() or apply() for
construction if a class is subjected to be converted to Java.
-* Pure Scala user-facing APIs should use pure Scala collections/iterables/etc.
for natural and idiomatic (“scalaesk”) integration with Scala.
+* 考虑一个类将来是否需要与 Java 类交互。
+* 在接口中使用 Java 集合和 Java Optional，以便与 Java 代码平滑集成。
+* 如果一个类要被转换为 Java，请不要使用 .copy() 或 apply() 等案例类的功能。
Review comment:
这里 ”案例类“ 是 ”case classes“ 的翻译吗？这个第一次读起来很难对应上，有更好的一个翻译吗？
##########
File path: contributing/code-style-and-quality-components.zh.md
##########
@@ -48,96 +48,96 @@ How to name config keys:
}
```
-* The resulting config keys should hence be:
+* 因此生成的配置键应该是：
- **NOT** `"taskmanager.detailed.network.metrics"`
+ **不是** `"taskmanager.detailed.network.metrics"`
- **But rather** `"taskmanager.network.detailed-metrics"`
+ **而是** `"taskmanager.network.detailed-metrics"`
-### Connectors
+### 连接器
-Connectors are historically hard to implement and need to deal with many
aspects of threading, concurrency, and checkpointing.
+连接器历来很难实现，需要处理线程、并发和检查点的许多方面。
-As part of
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
we are working on making this much simpler for sources. New sources should not
have to deal with any aspect of concurrency/threading and checkpointing any
more.
+作为
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
的一部分，我们正在努力让这些源（source）更加简单。新的源（source）应该不再处理并发/线程和检查点的任何方面。
-A similar FLIP can be expected for sinks in the near future.
+预计在不久的将来，类似的 FLIP 可以用于接收器。
-### Examples
+### 示例
-Examples should be self-contained and not require systems other than Flink to
run. Except for examples that show how to use specific connectors, like the
Kafka connector. Sources/sinks that are ok to use are
`StreamExecutionEnvironment.socketTextStream`, which should not be used in
production but is quite handy for exploring how things work, and file-based
sources/sinks. (For streaming, there is the continuous file source)
+示例应该是自包含的，不需要运行 Flink 以外的系统。除了显示如何使用具体的连接器的示例，比如 Kafka 连接器。源/接收器可以使用
`StreamExecutionEnvironment.socketTextStream`
，这个不应该在生产中使用，但对于研究示例如何运行的是相当方便的，以及基于文件的源/接收器。（对于流，有连续的文件源）
Review comment:
```suggestion
示例应该是自包含的，不需要运行 Flink 以外的系统。除了显示如何使用具体的连接器的示例，比如 Kafka 连接器。源/接收器可以使用
`StreamExecutionEnvironment.socketTextStream`，这个不应该在生产中使用，但对于研究示例如何运行的是相当方便的，以及基于文件的源/接收器。（对于流，有连续的文件源）
```
##########
File path: contributing/code-style-and-quality-components.zh.md
##########
@@ -48,96 +48,96 @@ How to name config keys:
}
```
-* The resulting config keys should hence be:
+* 因此生成的配置键应该是：
- **NOT** `"taskmanager.detailed.network.metrics"`
+ **不是** `"taskmanager.detailed.network.metrics"`
- **But rather** `"taskmanager.network.detailed-metrics"`
+ **而是** `"taskmanager.network.detailed-metrics"`
-### Connectors
+### 连接器
-Connectors are historically hard to implement and need to deal with many
aspects of threading, concurrency, and checkpointing.
+连接器历来很难实现，需要处理线程、并发和检查点的许多方面。
-As part of
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
we are working on making this much simpler for sources. New sources should not
have to deal with any aspect of concurrency/threading and checkpointing any
more.
+作为
[FLIP-27](https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface)
的一部分，我们正在努力让这些源（source）更加简单。新的源（source）应该不再处理并发/线程和检查点的任何方面。
-A similar FLIP can be expected for sinks in the near future.
+预计在不久的将来，类似的 FLIP 可以用于接收器。
-### Examples
+### 示例
-Examples should be self-contained and not require systems other than Flink to
run. Except for examples that show how to use specific connectors, like the
Kafka connector. Sources/sinks that are ok to use are
`StreamExecutionEnvironment.socketTextStream`, which should not be used in
production but is quite handy for exploring how things work, and file-based
sources/sinks. (For streaming, there is the continuous file source)
+示例应该是自包含的，不需要运行 Flink 以外的系统。除了显示如何使用具体的连接器的示例，比如 Kafka 连接器。源/接收器可以使用
`StreamExecutionEnvironment.socketTextStream`
，这个不应该在生产中使用，但对于研究示例如何运行的是相当方便的，以及基于文件的源/接收器。（对于流，有连续的文件源）
-Examples should also not be pure toy-examples but strike a balance between
real-world code and purely abstract examples. The WordCount example is quite
long in the tooth by now but it’s a good showcase of simple code that
highlights functionality and can do useful things.
+示例也不应该是纯粹的玩具示例，而是在现实世界的代码和纯粹的抽象示例之间取得平衡。WordCount
示例到现在已经很久了，但它是一个很好的功能突出并可以做有用事情的简单代码示例。
-Examples should also be heavy in comments. They should describe the general
idea of the example in the class-level Javadoc and describe what is happening
and what functionality is used throughout the code. The expected input data and
output data should also be described.
+示例中应该有不少的注释。他们可以在类级 Javadoc
中描述示例的总体思路，并且描述正在发生什么和整个代码里使用了什么功能。还应描述预期的输入数据和输出数据。
-Examples should include parameter parsing, so that you can run an example
(from the Jar that is created for each example using `bin/flink run
path/to/myExample.jar --param1 … --param2`.
+示例应该包括参数解析，以便你可以运行一个示例（从为每个示例所创建的 Jar 使用`bin/flink run path/to/myExample.jar
--param1 … --param2`。
-### Table & SQL API
+### 表和 SQL API
-#### Semantics
+#### 语义
-**The SQL standard should be the main source of truth.**
+**SQL 标准应该是事实的主要来源。**
-* Syntax, semantics, and features should be aligned with SQL!
-* We don’t need to reinvent the wheel. Most problems have already been
discussed industry-wide and written down in the SQL standard.
-* We rely on the newest standard (SQL:2016 or ISO/IEC 9075:2016 when writing
this document
[[download]](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)
). Not every part is available online but a quick web search might help here.
+* 语法、语义和功能应该和 SQL 保持一致！
+* 我们不需要重造轮子。大部分问题都已经在业界广泛讨论过并写在 SQL 标准中了。
+* 我们依靠最新的标准（在写这篇文档时 SQL:2016 or ISO/IEC 9075:2016
[[下载]](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)）。并非每个部分都可在线获取，但快速网络搜索可能对此有所帮助。
Review comment:
改成 “在写这篇文档时使用 SQL:2016...." 你觉得会更好一些吗？
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