YngwieWang commented on a change in pull request #9299: [FLINK-13405][docs-zh]
Translate "Basic API Concepts" page into Chinese
URL: https://github.com/apache/flink/pull/9299#discussion_r317603113
##########
File path: docs/dev/api_concepts.zh.md
##########
@@ -739,164 +646,125 @@ class WordWithCount(var word: String, var count: Int) {
val input = env.fromElements(
new WordWithCount("hello", 1),
- new WordWithCount("world", 2)) // Case Class Data Set
+ new WordWithCount("world", 2)) // Case Class 数据集
-input.keyBy("word")// key by field expression "word"
+input.keyBy("word")// 以字段表达式“word”为键
{% endhighlight %}
</div>
</div>
-#### Primitive Types
+#### 基本数据类型
-Flink supports all Java and Scala primitive types such as `Integer`, `String`,
and `Double`.
+Flink 支持所有 Java 和 Scala 的基本数据类型如 `Integer`、 `String`、和 `Double`。
-#### General Class Types
+#### 常规的类
-Flink supports most Java and Scala classes (API and custom).
-Restrictions apply to classes containing fields that cannot be serialized,
like file pointers, I/O streams, or other native
-resources. Classes that follow the Java Beans conventions work well in general.
+Flink 支持大部分 Java 和 Scala 的类（API 和自定义）。
+除了包含无法序列化的字段的类，如文件指针，I / O流或其他本地资源。遵循 Java Beans 约定的类通常可以很好地工作。
-All classes that are not identified as POJO types (see POJO requirements
above) are handled by Flink as general class types.
-Flink treats these data types as black boxes and is not able to access their
content (i.e., for efficient sorting). General types are de/serialized using
the serialization framework [Kryo](https://github.com/EsotericSoftware/kryo).
+Flink 对于所有未识别为 POJO 类型的类（请参阅上面对于的 POJO 要求）都作为常规类处理。
+Flink 将这些数据类型视为黑盒，并且无法访问其内容（为了诸如高效排序等目的）。常规类使用
[Kryo](https://github.com/EsotericSoftware/kryo) 序列化框架进行序列化和反序列化。
-#### Values
+#### 值
-*Value* types describe their serialization and deserialization manually.
Instead of going through a
-general purpose serialization framework, they provide custom code for those
operations by means of
-implementing the `org.apache.flinktypes.Value` interface with the methods
`read` and `write`. Using
-a Value type is reasonable when general purpose serialization would be highly
inefficient. An
-example would be a data type that implements a sparse vector of elements as an
array. Knowing that
-the array is mostly zero, one can use a special encoding for the non-zero
elements, while the
-general purpose serialization would simply write all array elements.
+*值* 类型手工描述其序列化和反序列化。它们不是通过通用序列化框架，而是通过使用 `read` 和 `write` 方法实现
`org.apache.flinktypes.Value`
接口来为这些操作提供自定义编码。当通用序列化效率非常低时，使用值类型是合理的。例如，用数组实现稀疏向量。已知数组大部分元素为零，就可以对非零元素使用特殊编码，而通用序列化只会简单地将所有数组元素都写入。
-The `org.apache.flinktypes.CopyableValue` interface supports manual internal
cloning logic in a
-similar way.
+`org.apache.flinktypes.CopyableValue` 接口以类似的方式支持内部手工克隆逻辑。
-Flink comes with pre-defined Value types that correspond to basic data types.
(`ByteValue`,
-`ShortValue`, `IntValue`, `LongValue`, `FloatValue`, `DoubleValue`,
`StringValue`, `CharValue`,
-`BooleanValue`). These Value types act as mutable variants of the basic data
types: Their value can
-be altered, allowing programmers to reuse objects and take pressure off the
garbage collector.
+Flink 有与基本数据类型对应的预定义值类型。（`ByteValue`、
+`ShortValue`、 `IntValue`、`LongValue`、 `FloatValue`、`DoubleValue`、
`StringValue`、`CharValue`、
+`BooleanValue`）。这些值类型充当基本数据类型的可变变体：它们的值可以改变，允许程序员重用对象并减轻垃圾回收器的压力。
-#### Hadoop Writables
+#### Hadoop Writable
-You can use types that implement the `org.apache.hadoop.Writable` interface.
The serialization logic
-defined in the `write()`and `readFields()` methods will be used for
serialization.
+可以使用实现了 `org.apache.hadoop.Writable` 接口的类型。它们会使用 `write()` 和 `readFields()`
方法中定义的序列化逻辑。
-#### Special Types
+#### 特殊类型
-You can use special types, including Scala's `Either`, `Option`, and `Try`.
-The Java API has its own custom implementation of `Either`.
-Similarly to Scala's `Either`, it represents a value of two possible types,
*Left* or *Right*.
-`Either` can be useful for error handling or operators that need to output two
different types of records.
+可以使用特殊类型，包括 Scala 的 `Either`、`Option` 和 `Try`。
+Java API 有对 `Either` 的自定义实现。
+类似于 Scala 的 `Either`，它表示一个具有 *Left* 或 *Right* 两种可能类型的值。
+`Either` 可用于错误处理或需要输出两种不同类型记录的算子。
-#### Type Erasure & Type Inference
+#### 类型擦除和类型推断
-*Note: This Section is only relevant for Java.*
+*请注意： 本节只与 Java 有关。*
-The Java compiler throws away much of the generic type information after
compilation. This is
-known as *type erasure* in Java. It means that at runtime, an instance of an
object does not know
-its generic type any more. For example, instances of `DataStream<String>` and
`DataStream<Long>` look the
-same to the JVM.
+Java 编译器在编译后抛弃了大量泛型类型信息。这在 Java 中被称作 *类型擦除*。它意味着在运行时，对象的实例已经不知道它的泛型类型了。例如
`DataStream<String>` 和 `DataStream<Long>` 的实例在 JVM 看来是一样的。
-Flink requires type information at the time when it prepares the program for
execution (when the
-main method of the program is called). The Flink Java API tries to reconstruct
the type information
-that was thrown away in various ways and store it explicitly in the data sets
and operators. You can
-retrieve the type via `DataStream.getType()`. The method returns an instance
of `TypeInformation`,
-which is Flink's internal way of representing types.
+Flink 在准备程序执行时（程序的 main 方法被调用时）需要类型信息。Flink Java API
尝试重建以各种方式丢弃的类型信息，并将其显式存储在数据集和算子中。你可以通过 `DataStream.getType()` 获取数据类型。此方法返回
`TypeInformation` 的一个实例，这是 Flink 内部表示类型地方式。
-The type inference has its limits and needs the "cooperation" of the
programmer in some cases.
-Examples for that are methods that create data sets from collections, such as
-`ExecutionEnvironment.fromCollection(),` where you can pass an argument that
describes the type. But
-also generic functions like `MapFunction<I, O>` may need extra type
information.
+类型推断有其局限性，在某些情况下需要程序员的“配合”。
+这方面的示例是从集合创建数据集的方法，例如
`ExecutionEnvironment.fromCollection()`，你可以在这里传递一个描述类型的参数。 但是像 `MapFunction<I,
O>` 这样的泛型函数可能还需要额外的类型信息。
Review comment:
修正了。
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