wuchong commented on a change in pull request #8276: [FLINK-12314] [docs-zh]
Translate the "Type Serialization" page into …
URL: https://github.com/apache/flink/pull/8276#discussion_r281012770
##########
File path: docs/dev/types_serialization.zh.md
##########
@@ -246,98 +226,89 @@ public class AppendOne<T> implements MapFunction<T,
Tuple2<T, Long>> {
}
{% endhighlight %}
-There are cases where Flink cannot reconstruct all generic type information.
In that case, a user has to help out via *type hints*.
+在某些情况下，Flink 无法重建所有泛型类型信息。 在这种情况下，用户必须通过*类型提示*来解决问题。
+#### Java API 中的类型提示
-#### Type Hints in the Java API
-
-In cases where Flink cannot reconstruct the erased generic type information,
the Java API
-offers so called *type hints*. The type hints tell the system the type of
-the data stream or data set produced by a function:
+在 Flink 无法重建被擦除的泛型类型信息的情况下，Java API 需要提供所谓的*类型提示*。
+类型提示告诉系统 DateStream 或者 DateSet 产生的类型：
{% highlight java %}
DataSet<SomeType> result = dataSet
.map(new MyGenericNonInferrableFunction<Long, SomeType>())
.returns(SomeType.class);
{% endhighlight %}
-The `returns` statement specifies the produced type, in this case via a class.
The hints support
-type definition via
-
-* Classes, for non-parameterized types (no generics)
-* TypeHints in the form of `returns(new TypeHint<Tuple2<Integer,
SomeType>>(){})`. The `TypeHint` class
- can capture generic type information and preserve it for the runtime (via an
anonymous subclass).
+在上面情况下 `returns` 表达通过 Class 类型指出产生的类型。通过下面方式支持类型提示：
+* 对于非参数化的类型（没有泛型）的 Class 类型
+* 以 `returns(new TypeHint<Tuple2<Integer, SomeType>>(){})` 方式进行类型提示。
+ `TypeHint` 类可以捕获泛型的类型信息并且保存到执行期间（通过匿名子类）。
-#### Type extraction for Java 8 lambdas
+#### Java 8 lambdas 的类型提取
-Type extraction for Java 8 lambdas works differently than for non-lambdas,
because lambdas are not associated
-with an implementing class that extends the function interface.
+Java 8 lambdas 的类型提取与非-lambdas 不同，因为 lambdas 与扩展函数接口的实现类没有关联。
-Currently, Flink tries to figure out which method implements the lambda and
uses Java's generic signatures to
-determine the parameter types and the return type. However, these signatures
are not generated for lambdas
-by all compilers (as of writing this document only reliably by the Eclipse JDT
compiler from 4.5 onwards).
+Flink 目前试图找出实现 lambda 的方法，并使用 Java 的泛型签名来确定参数类型和返回类型。
+但是，并非所有编译器都为 lambda 生成这些签名（此文档写作时，只有 Eclipse JDT 编译器从4.5开始可靠支持）。
-#### Serialization of POJO types
+#### POJO 类型的序列化
-The PojoTypeInformation is creating serializers for all the fields inside the
POJO. Standard types such as
-int, long, String etc. are handled by serializers we ship with Flink.
-For all other types, we fall back to Kryo.
+PojoTypeInformation 为 POJO 中的所有字段创建序列化器。Flink 标准类型如 int、long、String 等由 Flink
序列化器处理。
+对于所有其他类型，我们回退到 Kryo。
-If Kryo is not able to handle the type, you can ask the PojoTypeInfo to
serialize the POJO using Avro.
-To do so, you have to call
+对于 Kryo 不能处理的类型，你可以要求 PojoTypeInfo 使用 Avro 对 POJO 进行序列化。
+需要通过下面的代码开启。
{% highlight java %}
final ExecutionEnvironment env =
ExecutionEnvironment.getExecutionEnvironment();
env.getConfig().enableForceAvro();
{% endhighlight %}
-Note that Flink is automatically serializing POJOs generated by Avro with the
Avro serializer.
+请注意，Flink 会使用 Avro 序列化器自动序列化 Avro 生成的 POJO。
-If you want your **entire** POJO Type to be treated by the Kryo serializer, set
+通过下面设置可以让你的**整个** POJO 类型被 Kryo 序列化器处理。
{% highlight java %}
final ExecutionEnvironment env =
ExecutionEnvironment.getExecutionEnvironment();
env.getConfig().enableForceKryo();
{% endhighlight %}
-If Kryo is not able to serialize your POJO, you can add a custom serializer to
Kryo, using
+如果 Kryo 不能序列化你的 POJO，可以通过下面的代码添加自定义的序列化器
{% highlight java %}
env.getConfig().addDefaultKryoSerializer(Class<?> type, Class<? extends
Serializer<?>> serializerClass)
{% endhighlight %}
-There are different variants of these methods available.
+这些方法有不同的变体可供选择。
-## Disabling Kryo Fallback
+## 禁止回退到 Kryo
-There are cases when programs may want to explicitly avoid using Kryo as a
fallback for generic types. The most
-common one is wanting to ensure that all types are efficiently serialized
either through Flink's own serializers,
-or via user-defined custom serializers.
+对于泛型信息，程序可能希望在一些情况下显示的避免使用 Kryo。最常见的场景是，用户想要确保所有的类型都可以通过 Flink 自身
+或者用户自定义的序列化器高效的进行序列化操作。
-The setting below will raise an exception whenever a data type is encountered
that would go through Kryo:
+下面的设置将引起通过 Kryo 的数据类型抛出异常：
{% highlight java %}
env.getConfig().disableGenericTypes();
{% endhighlight %}
-## Defining Type Information using a Factory
+## 使用工厂方法定义类型信息
-A type information factory allows for plugging-in user-defined type
information into the Flink type system.
-You have to implement `org.apache.flink.api.common.typeinfo.TypeInfoFactory`
to return your custom type information.
-The factory is called during the type extraction phase if the corresponding
type has been annotated
-with the `@org.apache.flink.api.common.typeinfo.TypeInfo` annotation.
+类型信息工厂允许将用户定义的类型信息插入 Flink 类型系统。
+你可以通过实现 `org.apache.flink.api.common.typeinfo.TypeInfoFactory` 来返回自定义的类型信息工厂。
+如果相应的类型已使用 `@ org.apache.flink.api.common.typeinfo.TypeInfo`
注释进行注释，则在类型提取阶段调用自定义
Review comment:
```suggestion
如果相应的类型已指定了 `@org.apache.flink.api.common.typeinfo.TypeInfo` 注解，则在类型提取阶段会调用
TypeInfo 注解指定的
```
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