RocMarshal commented on a change in pull request #18641:
URL: https://github.com/apache/flink/pull/18641#discussion_r813971115
##########
File path: docs/content.zh/docs/connectors/datastream/formats/avro.md
##########
@@ -39,23 +40,23 @@ The serialization framework of Flink is able to handle
classes generated from Av
</dependency>
```
-In order to read data from an Avro file, you have to specify an
`AvroInputFormat`.
+如果读取 Avro 文件数据，你必须指定 `AvroInputFormat`。
-**Example**:
+**示例**：
```java
AvroInputFormat<User> users = new AvroInputFormat<User>(in, User.class);
DataStream<User> usersDS = env.createInput(users);
```
-Note that `User` is a POJO generated by Avro. Flink also allows to perform
string-based key selection of these POJOs. For example:
+注意，`User` 是一个通过 Avro schema生成的 POJO 类。Flink 还允许选择 POJO 中字符串类型的键。例如：
```java
usersDS.keyBy("name")
```
-Note that using the `GenericData.Record` type is possible with Flink, but not
recommended. Since the record contains the full schema, its very data intensive
and thus probably slow to use.
+注意，在 Flink 中可能会使用 `GenericData.Record` 类型，但是不推荐使用。由于该类型的记录中包含了完整的
schema，导致数据非常密集，使用起来可能很慢。
Review comment:
`可能会`->`可以`
`由于`->`因为` ？
##########
File path: docs/content.zh/docs/connectors/datastream/formats/avro.md
##########
@@ -39,23 +40,23 @@ The serialization framework of Flink is able to handle
classes generated from Av
</dependency>
```
-In order to read data from an Avro file, you have to specify an
`AvroInputFormat`.
+如果读取 Avro 文件数据，你必须指定 `AvroInputFormat`。
-**Example**:
+**示例**：
```java
AvroInputFormat<User> users = new AvroInputFormat<User>(in, User.class);
DataStream<User> usersDS = env.createInput(users);
```
-Note that `User` is a POJO generated by Avro. Flink also allows to perform
string-based key selection of these POJOs. For example:
+注意，`User` 是一个通过 Avro schema生成的 POJO 类。Flink 还允许选择 POJO 中字符串类型的键。例如：
```java
usersDS.keyBy("name")
```
-Note that using the `GenericData.Record` type is possible with Flink, but not
recommended. Since the record contains the full schema, its very data intensive
and thus probably slow to use.
+注意，在 Flink 中可能会使用 `GenericData.Record` 类型，但是不推荐使用。由于该类型的记录中包含了完整的
schema，导致数据非常密集，使用起来可能很慢。
-Flink's POJO field selection also works with POJOs generated from Avro.
However, the usage is only possible if the field types are written correctly to
the generated class. If a field is of type `Object` you can not use the field
as a join or grouping key.
-Specifying a field in Avro like this `{"name": "type_double_test", "type":
"double"},` works fine, however specifying it as a UNION-type with only one
field (`{"name": "type_double_test", "type": ["double"]},`) will generate a
field of type `Object`. Note that specifying nullable types (`{"name":
"type_double_test", "type": ["null", "double"]},`) is possible!
+Flink 中在 Avro schema 生成的 POJO 类上也可以进行 POJO
字段选择。因此，只有在生成的类中正确写入字段类型时，才可以使用字段选择。如果一个字段类型是 `Object` 类型，你不可以选择该字段进行 join 或者
group by 操作。
+在 Avro 中类似 `{"name": "type_double_test", "type": "double"},` 这样指定字段是可行的，但是类似
(`{"name": "type_double_test", "type": ["double"]},`) 这样指定字段，字段类型就会生成为 `Object`
类型。注意，类似 (`{"name": "type_double_test", "type": ["null", "double"]},`) 这样指定
nullable 类型字段，也是有可能的!
Review comment:
```
在 Avro 中如 `{"name": "type_double_test", "type": "double"},` 这样指定字段是可行的，但是如
(`{"name": "type_double_test", "type": ["double"]},`) 这样指定包含一个字段的复合类型就会生成
`Object` 类型的字段。注意，如 (`{"name": "type_double_test", "type": ["null",
"double"]},`) 这样指定 nullable 类型字段也是可能产生 `Object` 类型的!
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/avro.md
##########
@@ -39,23 +40,23 @@ The serialization framework of Flink is able to handle
classes generated from Av
</dependency>
```
-In order to read data from an Avro file, you have to specify an
`AvroInputFormat`.
+如果读取 Avro 文件数据，你必须指定 `AvroInputFormat`。
-**Example**:
+**示例**：
```java
AvroInputFormat<User> users = new AvroInputFormat<User>(in, User.class);
DataStream<User> usersDS = env.createInput(users);
```
-Note that `User` is a POJO generated by Avro. Flink also allows to perform
string-based key selection of these POJOs. For example:
+注意，`User` 是一个通过 Avro schema生成的 POJO 类。Flink 还允许选择 POJO 中字符串类型的键。例如：
```java
usersDS.keyBy("name")
```
-Note that using the `GenericData.Record` type is possible with Flink, but not
recommended. Since the record contains the full schema, its very data intensive
and thus probably slow to use.
+注意，在 Flink 中可能会使用 `GenericData.Record` 类型，但是不推荐使用。由于该类型的记录中包含了完整的
schema，导致数据非常密集，使用起来可能很慢。
-Flink's POJO field selection also works with POJOs generated from Avro.
However, the usage is only possible if the field types are written correctly to
the generated class. If a field is of type `Object` you can not use the field
as a join or grouping key.
-Specifying a field in Avro like this `{"name": "type_double_test", "type":
"double"},` works fine, however specifying it as a UNION-type with only one
field (`{"name": "type_double_test", "type": ["double"]},`) will generate a
field of type `Object`. Note that specifying nullable types (`{"name":
"type_double_test", "type": ["null", "double"]},`) is possible!
+Flink 中在 Avro schema 生成的 POJO 类上也可以进行 POJO
字段选择。因此，只有在生成的类中正确写入字段类型时，才可以使用字段选择。如果一个字段类型是 `Object` 类型，你不可以选择该字段进行 join 或者
group by 操作。
Review comment:
```suggestion
Flink 的 POJO 字段选择也适用于从 Avro schema 生成的 POJO 类。但是，只有将字段类型正确写入生成的类时，才能使用。如果字段是
`Object` 类型，则不能将该字段用作 join 键或 grouping 键。
```
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