[FLINK-13683] Translate "Code Style - Component Guide" page into Chinese
This closes #247
diff --git a/contributing/code-style-and-quality-components.zh.md b/contributing/code-style-and-quality-components.zh.md
index 820ab3a..30378e6 100644
--- a/contributing/code-style-and-quality-components.zh.md
+++ b/contributing/code-style-and-quality-components.zh.md
@@ -1,5 +1,5 @@
---
-title: "Apache Flink Code Style and Quality Guide — Components"
+title: "Apache Flink 代码样式和质量指南 — 组件"
---
{% include code-style-navbar.zh.md %}
@@ -9,24 +9,23 @@
-## 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> 所有属于可能要跨作业标准的执行行为的配置。可以将其想像成 Ops 的工作人员或为其他团队提供流处理平台的工作人员设置的参数。
-* <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）
```
taskmanager: {
@@ -48,96 +47,95 @@
}
```
-* 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，我们正在努力使数据源（source）的实现更简单。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.
-
-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（使用 `bin/flink run path/to/myExample.jar --param1 … --param2` 运行程序）。
-### 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)）。并不是所有的部分都能在网上找到，但可以通过网络查找确认。
-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。
-#### 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，多次修改相同代码会增加负担。
+ * 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。
+* 确保测试每个功能的可空性。
-Avoid full integration tests
+尽量避免集成测试
-* 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.
+* 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，使用单元测试来验证 planner 产生的优化计划。或者直接测试算子的运行时行为。
-#### 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 Operator Names/IDs 将发生变化，并且无法匹配和恢复状态。
+* 导致流传输管道的优化物理计划改变的每个 bug 修复均会破坏兼容性。
+* 因此，导致不同优化计划的此类更改目前仅可以合并到大版本中。
-#### 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。
+* 如果要将类转换为 Java，不要使用 .copy() 或 apply() 等 case class 的功能进行构造。
+* 纯 Scala 面向用户的 API 应该使用纯 Scala 集合/迭代/等与 Scala 自然和惯用的（“scalaesk”）集成。
