klion26 commented on a change in pull request #247: URL: https://github.com/apache/flink-web/pull/247#discussion_r437135017
########## File path: contributing/code-style-and-quality-components.zh.md ########## @@ -48,96 +47,95 @@ How to name config keys: } ``` -* The resulting config keys should hence be: +* 因此生成的配置键应该： - **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，需要处理多线程、并发和检查点等许多方面。 -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数据源（source）更加简单。新的数据源应该不必处理并发/线程和检查点的任何方面。 Review comment: `我们正在努力让这些数据源（source）更加简单` 这里改成 `实现数据源` 或者 `数据源实现` 等会更好一些吗？前面说 `连接器很难实现`，这里可以对应起来 ########## File path: contributing/code-style-and-quality-components.zh.md ########## @@ -48,96 +47,95 @@ How to name config keys: } ``` -* The resulting config keys should hence be: +* 因此生成的配置键应该： - **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，需要处理多线程、并发和检查点等许多方面。 -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数据源（source）更加简单。新的数据源应该不必处理并发/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，会有类似针对数据汇（sink）的 FLIP。 -### 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。数据源/数据汇可以使用 `StreamExecutionEnvironment.socketTextStream`，这个不应该在生产中使用，但对于研究示例如何运行是相当方便的，以及基于文件的数据源/数据源。（对于流，Flink 提供了连续的文件数据源读取数据） +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 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 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. +示例应该包括参数解析，以便你可以运行一个示例（使用 `bin/flink run path/to/myExample.jar --param1 … --param2` 运行程序）。 -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`. +### 表和 SQL API -### Table & SQL API +#### 语义 -#### Semantics +**SQL 标准应该是事实的主要来源。** -**The SQL standard should be the main source of truth.** +* 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（在写这篇文档时使用 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: `但快速网络搜索可能对此有所帮助` 这个可以有一个更好的描述吗？现在这样读起来有一点突兀。 这里的意思应该上 标准可能在网上找不到（并不是所有的部分都能找到），但是首先应该通过网络查找确认？ ########## File path: contributing/code-style-and-quality-components.zh.md ########## @@ -48,96 +47,95 @@ How to name config keys: } ``` -* The resulting config keys should hence be: +* 因此生成的配置键应该： - **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，需要处理多线程、并发和检查点等许多方面。 -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数据源（source）更加简单。新的数据源应该不必处理并发/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，会有类似针对数据汇（sink）的 FLIP。 -### 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。数据源/数据汇可以使用 `StreamExecutionEnvironment.socketTextStream`，这个不应该在生产中使用，但对于研究示例如何运行是相当方便的，以及基于文件的数据源/数据源。（对于流，Flink 提供了连续的文件数据源读取数据） +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 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 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. +示例应该包括参数解析，以便你可以运行一个示例（使用 `bin/flink run path/to/myExample.jar --param1 … --param2` 运行程序）。 -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`. +### 表和 SQL API -### Table & SQL API +#### 语义 -#### Semantics +**SQL 标准应该是事实的主要来源。** -**The SQL standard should be the main source of truth.** +* 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（在写这篇文档时使用 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。 -* 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. +讨论与标准或厂商特定解释的差异。 -Discuss divergence from the standard or vendor-specific interpretations. +* 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。 -* 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. +将 Table API 视为 SQL 和 Java/Scala 编程世界之间的桥梁。 -Consider the Table API as a bridge between the SQL and Java/Scala programming world. +* 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），但还是应该“感觉像 SQL”。如果可能，函数和算子应该有相等的语义和命名。 -* 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. +#### 常见错误 -#### Common mistakes +* 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。 Review comment: 这句话不完全确定： `并通过多次接触相同代码路径产生开销` 这个地方时说多次的修改代码会增加负担（修改时算一次）？ ########## File path: contributing/code-style-and-quality-components.zh.md ########## @@ -48,96 +47,95 @@ How to name config keys: } ``` -* The resulting config keys should hence be: +* 因此生成的配置键应该： - **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，需要处理多线程、并发和检查点等许多方面。 -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数据源（source）更加简单。新的数据源应该不必处理并发/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，会有类似针对数据汇（sink）的 FLIP。 -### 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。数据源/数据汇可以使用 `StreamExecutionEnvironment.socketTextStream`，这个不应该在生产中使用，但对于研究示例如何运行是相当方便的，以及基于文件的数据源/数据源。（对于流，Flink 提供了连续的文件数据源读取数据） +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 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 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. +示例应该包括参数解析，以便你可以运行一个示例（使用 `bin/flink run path/to/myExample.jar --param1 … --param2` 运行程序）。 -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`. +### 表和 SQL API -### Table & SQL API +#### 语义 -#### Semantics +**SQL 标准应该是事实的主要来源。** -**The SQL standard should be the main source of truth.** +* 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（在写这篇文档时使用 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。 -* 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. +讨论与标准或厂商特定解释的差异。 -Discuss divergence from the standard or vendor-specific interpretations. +* 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。 -* 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. +将 Table API 视为 SQL 和 Java/Scala 编程世界之间的桥梁。 -Consider the Table API as a bridge between the SQL and Java/Scala programming world. +* 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），但还是应该“感觉像 SQL”。如果可能，函数和算子应该有相等的语义和命名。 -* 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. +#### 常见错误 -#### Common mistakes +* 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`。 -* 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`. +#### 测试 -#### Testing +测试为空性 -Test for nullability. +* 几乎每个操作，SQL 都原生支持 `NULL`，并具有 3 值布尔逻辑。 +* 确保测试每个功能的可空性。 -* 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. +尽量避免集成测试 -Avoid full integration tests +* 启动一个 Flink 集群并且对 SQL 查询生成的代码进行编译会很耗时。 +* 避免对 planner 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。或者直接测试算子的运行时行为。 Review comment: 这句话中的 planner 是不是也可以不翻译 ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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