JingGe commented on a change in pull request #18646:
URL: https://github.com/apache/flink/pull/18646#discussion_r828939974
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -61,146 +62,144 @@ To read Avro records, you will need to add the
`parquet-avro` dependency:
</dependency>
```
-This format is compatible with the new Source that can be used in both batch
and streaming execution modes.
-Thus, you can use this format for two kinds of data:
+此格式与新的 Source 兼容，可以同时在批和流模式下使用。
+因此，你可使用此格式处理以下两类数据：
-- Bounded data: lists all files and reads them all.
-- Unbounded data: monitors a directory for new files that appear.
+- 批处理模式下的有界读取
Review comment:
```suggestion
- 有界数据: 列出所有文件并全部读取。
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -61,146 +62,144 @@ To read Avro records, you will need to add the
`parquet-avro` dependency:
</dependency>
```
-This format is compatible with the new Source that can be used in both batch
and streaming execution modes.
-Thus, you can use this format for two kinds of data:
+此格式与新的 Source 兼容，可以同时在批和流模式下使用。
+因此，你可使用此格式处理以下两类数据：
-- Bounded data: lists all files and reads them all.
-- Unbounded data: monitors a directory for new files that appear.
+- 批处理模式下的有界读取
+- 流处理模式下的连续读取：监控目录中出现的新文件
Review comment:
```suggestion
- 无界数据：监控目录中出现的新文件
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -218,17 +217,17 @@ The following example uses the example schema
[testdata.avsc](https://github.com
]
```
-You will use the Avro Maven plugin to generate the `Address` Java class:
+你可以使用 Avro Maven plugin 生成 `Address` Java 类。
```java
@org.apache.avro.specific.AvroGenerated
public class Address extends org.apache.avro.specific.SpecificRecordBase
implements org.apache.avro.specific.SpecificRecord {
- // generated code...
+ // 生成的代码...
}
```
-You will create an `AvroParquetRecordFormat` via `AvroParquetReaders` for Avro
Specific record
-and then create a DataStream containing Parquet records as Avro Specific
records.
+你可以通过 `AvroParquetReaders` 为 Avro Specific 记录创建 `AvroParquetRecordFormat`，
+然后创建一个包含 Parquet 记录作为 Avro Specific 记录的 DateStream。
Review comment:
```suggestion
然后创建一个包含由 Avro Specific records 格式构成的 Parquet records 的 DateStream。
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -61,146 +62,144 @@ To read Avro records, you will need to add the
`parquet-avro` dependency:
</dependency>
```
-This format is compatible with the new Source that can be used in both batch
and streaming execution modes.
-Thus, you can use this format for two kinds of data:
+此格式与新的 Source 兼容，可以同时在批和流模式下使用。
+因此，你可使用此格式处理以下两类数据：
-- Bounded data: lists all files and reads them all.
-- Unbounded data: monitors a directory for new files that appear.
+- 批处理模式下的有界读取
+- 流处理模式下的连续读取：监控目录中出现的新文件
{{< hint info >}}
-When you start a File Source it is configured for bounded data by default.
-To configure the File Source for unbounded data, you must additionally call
-`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`.
+当你开启一个 File Source，会被默认为有界读取。
+如果你想在连续读取模式下使用 File Source，你必须额外调用
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`。
{{< /hint >}}
**Vectorized reader**
```java
-
// Parquet rows are decoded in batches
FileSource.forBulkFileFormat(BulkFormat,Path...)
-
// Monitor the Paths to read data as unbounded data
FileSource.forBulkFileFormat(BulkFormat,Path...)
- .monitorContinuously(Duration.ofMillis(5L))
- .build();
-
+.monitorContinuously(Duration.ofMillis(5L))
+.build();
```
**Avro Parquet reader**
```java
-
// Parquet rows are decoded in batches
FileSource.forRecordStreamFormat(StreamFormat,Path...)
-
// Monitor the Paths to read data as unbounded data
FileSource.forRecordStreamFormat(StreamFormat,Path...)
.monitorContinuously(Duration.ofMillis(5L))
.build();
-
-
```
{{< hint info >}}
-Following examples are all configured for bounded data.
-To configure the File Source for unbounded data, you must additionally call
-`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`.
+下面的案例都是基于有界数据的。
+如果你想在连续读取模式下使用 File Source，你必须额外调用
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`。
{{< /hint >}}
+<a name="flink-rowdata"></a>
+
## Flink RowData
-In this example, you will create a DataStream containing Parquet records as
Flink RowDatas. The schema is projected to read only the specified fields
("f7", "f4" and "f99").
-Flink will read records in batches of 500 records. The first boolean parameter
specifies that timestamp columns will be interpreted as UTC.
-The second boolean instructs the application that the projected Parquet fields
names are case-sensitive.
-There is no watermark strategy defined as records do not contain event
timestamps.
+在这个示例中，你将创建由 Parquet 格式的记录构成的 Flink RowDatas DataStream。我们把 schema
信息映射为只读字段（"f7"、"f4" 和 "f99"）。
Review comment:
```suggestion
在此示例中，你将创建由 Parquet 格式的记录构成的 Flink RowDatas DataStream。我们把 schema
信息映射为只读字段（"f7"、"f4" 和 "f99"）。
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -61,146 +62,144 @@ To read Avro records, you will need to add the
`parquet-avro` dependency:
</dependency>
```
-This format is compatible with the new Source that can be used in both batch
and streaming execution modes.
-Thus, you can use this format for two kinds of data:
+此格式与新的 Source 兼容，可以同时在批和流模式下使用。
+因此，你可使用此格式处理以下两类数据：
-- Bounded data: lists all files and reads them all.
-- Unbounded data: monitors a directory for new files that appear.
+- 批处理模式下的有界读取
+- 流处理模式下的连续读取：监控目录中出现的新文件
{{< hint info >}}
-When you start a File Source it is configured for bounded data by default.
-To configure the File Source for unbounded data, you must additionally call
-`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`.
+当你开启一个 File Source，会被默认为有界读取。
+如果你想在连续读取模式下使用 File Source，你必须额外调用
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`。
{{< /hint >}}
**Vectorized reader**
```java
-
// Parquet rows are decoded in batches
FileSource.forBulkFileFormat(BulkFormat,Path...)
-
// Monitor the Paths to read data as unbounded data
FileSource.forBulkFileFormat(BulkFormat,Path...)
- .monitorContinuously(Duration.ofMillis(5L))
- .build();
-
+.monitorContinuously(Duration.ofMillis(5L))
+.build();
```
**Avro Parquet reader**
```java
-
// Parquet rows are decoded in batches
FileSource.forRecordStreamFormat(StreamFormat,Path...)
-
// Monitor the Paths to read data as unbounded data
FileSource.forRecordStreamFormat(StreamFormat,Path...)
.monitorContinuously(Duration.ofMillis(5L))
.build();
-
-
```
{{< hint info >}}
-Following examples are all configured for bounded data.
-To configure the File Source for unbounded data, you must additionally call
-`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`.
+下面的案例都是基于有界数据的。
+如果你想在连续读取模式下使用 File Source，你必须额外调用
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`。
{{< /hint >}}
+<a name="flink-rowdata"></a>
+
## Flink RowData
-In this example, you will create a DataStream containing Parquet records as
Flink RowDatas. The schema is projected to read only the specified fields
("f7", "f4" and "f99").
-Flink will read records in batches of 500 records. The first boolean parameter
specifies that timestamp columns will be interpreted as UTC.
-The second boolean instructs the application that the projected Parquet fields
names are case-sensitive.
-There is no watermark strategy defined as records do not contain event
timestamps.
+在这个示例中，你将创建由 Parquet 格式的记录构成的 Flink RowDatas DataStream。我们把 schema
信息映射为只读字段（"f7"、"f4" 和 "f99"）。
+每个批次读取 500 条记录。其中，第一个布尔类型的参数用来指定是否需要将时间戳列处理为 UTC。
+第二个布尔类型参数用来指定在进行 Parquet 字段映射时，是否要区分大小写。
+这里不需要水印策略，因为记录中不包含事件时间戳。
+
```java
final LogicalType[] fieldTypes =
- new LogicalType[] {
- new DoubleType(), new IntType(), new VarCharType()
- };
+ new LogicalType[] {
+ new DoubleType(), new IntType(), new VarCharType()
+ };
final ParquetColumnarRowInputFormat<FileSourceSplit> format =
- new ParquetColumnarRowInputFormat<>(
- new Configuration(),
- RowType.of(fieldTypes, new String[] {"f7", "f4", "f99"}),
- 500,
- false,
- true);
-final FileSource<RowData> source =
+ new ParquetColumnarRowInputFormat<>(
+ new Configuration(),
+ RowType.of(fieldTypes, new String[] {"f7", "f4", "f99"}),
+ 500,
+ false,
+ true);
+
+final FileSource<RowData> source
FileSource.forBulkFileFormat(format, /* Flink Path */)
.build();
+
final DataStream<RowData> stream =
- env.fromSource(source, WatermarkStrategy.noWatermarks(), "file-source");
+ env.fromSource(source, WatermarkStrategy.noWatermarks(),
"file-source");
```
## Avro Records
-Flink supports producing three types of Avro records by reading Parquet files:
+Flink 支持三种通过读取 Parquet 文件创建 Avro records 的方式：
- [Generic record](https://avro.apache.org/docs/1.10.0/api/java/index.html)
- [Specific record](https://avro.apache.org/docs/1.10.0/api/java/index.html)
- [Reflect
record](https://avro.apache.org/docs/1.10.0/api/java/org/apache/avro/reflect/package-summary.html)
### Generic record
-Avro schemas are defined using JSON. You can get more information about Avro
schemas and types from the [Avro
specification](https://avro.apache.org/docs/1.10.0/spec.html).
-This example uses an Avro schema example similar to the one described in the
[official Avro
tutorial](https://avro.apache.org/docs/1.10.0/gettingstartedjava.html):
+使用 JSON 定义 Avro schemas。你可以从 [Avro
specification](https://avro.apache.org/docs/1.10.0/spec.html) 获取更多关于 Avro
schemas 和类型的信息。
+这个示例使用了 [official Avro
tutorial](https://avro.apache.org/docs/1.10.0/gettingstartedjava.html)
中描述的示例相似的 Avro schema：
```json lines
{"namespace": "example.avro",
- "type": "record",
- "name": "User",
- "fields": [
- {"name": "name", "type": "string"},
- {"name": "favoriteNumber", "type": ["int", "null"]},
- {"name": "favoriteColor", "type": ["string", "null"]}
- ]
+ "type": "record",
+ "name": "User",
+ "fields": [
+ {"name": "name", "type": "string"},
+ {"name": "favoriteNumber", "type": ["int", "null"]},
+ {"name": "favoriteColor", "type": ["string", "null"]}
+ ]
}
```
+这个 schema 定义了一个具有三个属性的的 user 记录：name，favoriteNumber 和 favoriteColor。你可以
+在 [record
specification](https://avro.apache.org/docs/1.10.0/spec.html#schema_record)
找到更多关于如何定义 Avro schema 的详细信息。
-This schema defines a record representing a user with three fields: name,
favoriteNumber, and favoriteColor. You can find more details at [record
specification](https://avro.apache.org/docs/1.10.0/spec.html#schema_record) for
how to define an Avro schema.
-
-In the following example, you will create a DataStream containing Parquet
records as Avro Generic records.
-It will parse the Avro schema based on the JSON string. There are many other
ways to parse a schema, e.g. from java.io.File or java.io.InputStream. Please
refer to [Avro
Schema](https://avro.apache.org/docs/1.10.0/api/java/org/apache/avro/Schema.html)
for details.
-After that, you will create an `AvroParquetRecordFormat` via
`AvroParquetReaders` for Avro Generic records.
+在这个示例中，你将创建由 Parquet 格式的记录构成的 Avro Generic 记录的 DataStream。
Review comment:
```suggestion
在此示例中，你将创建包含由 Avro Generic records 格式构成的 Parquet records 的 DataStream。
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -287,8 +286,8 @@ public class Datum implements Serializable {
}
```
-You will create an `AvroParquetRecordFormat` via `AvroParquetReaders` for Avro
Reflect record
-and then create a DataStream containing Parquet records as Avro Reflect
records.
+你可以通过 `AvroParquetReaders` 为 Avro Reflect 记录创建一个 `AvroParquetRecordFormat`，
+然后创建一个包含 Parquet 记录作为 Avro Reflect 记录的 DataStream。
Review comment:
```suggestion
然后创建一个包含由 Avro Reflect records 格式构成的 Parquet records 的 DateStream。
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -349,45 +345,44 @@ favoriteColor: BINARY UNCOMPRESSED DO:0 FPO:92
SZ:55/55/1.00 VC:3 ENC:RLE,PLAI
```
-With the `User` class defined in the package
org.apache.flink.formats.parquet.avro:
+包路径 `org.apache.flink.formats.parquet.avro` 下创建的对象如下：
Review comment:
```suggestion
使用包 `org.apache.flink.formats.parquet.avro` 路径下已定义的 User 类：
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -303,14 +302,12 @@ final DataStream<GenericRecord> stream =
env.fromSource(source, WatermarkStrategy.noWatermarks(),
"file-source");
```
-#### Prerequisite for Parquet files
+### 使用 Parquet files 必备条件
-In order to support reading Avro reflect records, the Parquet file must
contain specific meta information.
-The Avro schema used for creating the Parquet data must contain a `namespace`,
-which will be used by the program to identify the concrete Java class for the
reflection process.
+为了支持读取 Avro Reflect 数据，Parquet 文件必须包含特定的 meta 信息。为了生成 Parquet 数据，Avro schema
信息中必须包含 namespace，
+以便让程序在反射执行过程中能确定唯一的 Java Class 对象。
-The following example shows the `User` schema used previously. But this time
it contains a namespace
-pointing to the location(in this case the package), where the `User` class for
the reflection could be found.
+下面的案例展示了上文中的 User 对象的 schema 信息。但是当前案例包含了一个指定文件目录的
namespace（当前案例下的包路径），反射过程中可以找到对应的 User 文件。
Review comment:
```suggestion
下面的案例展示了上文中的 User 对象的 schema 信息。但是当前案例包含了一个指定文件目录的
namespace（当前案例下的包路径），反射过程中可以找到对应的 User 类。
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -349,45 +345,44 @@ favoriteColor: BINARY UNCOMPRESSED DO:0 FPO:92
SZ:55/55/1.00 VC:3 ENC:RLE,PLAI
```
-With the `User` class defined in the package
org.apache.flink.formats.parquet.avro:
+包路径 `org.apache.flink.formats.parquet.avro` 下创建的对象如下：
```java
public class User {
- private String name;
- private Integer favoriteNumber;
- private String favoriteColor;
+ private String name;
+ private Integer favoriteNumber;
+ private String favoriteColor;
- public User() {}
+ public User() {}
- public User(String name, Integer favoriteNumber, String favoriteColor)
{
- this.name = name;
- this.favoriteNumber = favoriteNumber;
- this.favoriteColor = favoriteColor;
- }
+ public User(String name, Integer favoriteNumber, String favoriteColor) {
+ this.name = name;
+ this.favoriteNumber = favoriteNumber;
+ this.favoriteColor = favoriteColor;
+ }
- public String getName() {
- return name;
- }
+ public String getName() {
+ return name;
+ }
- public Integer getFavoriteNumber() {
- return favoriteNumber;
- }
+ public Integer getFavoriteNumber() {
+ return favoriteNumber;
+ }
- public String getFavoriteColor() {
- return favoriteColor;
- }
+ public String getFavoriteColor() {
+ return favoriteColor;
}
+}
```
-you can write the following program to read Avro Reflect records of User type
from parquet files:
+你可以通过下面的程序读取类型为 User 的 Avro Reflect 数据：
Review comment:
```suggestion
你可以通过下面的程序读取类型为 User 的 Avro Reflect records：
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -61,146 +62,144 @@ To read Avro records, you will need to add the
`parquet-avro` dependency:
</dependency>
```
-This format is compatible with the new Source that can be used in both batch
and streaming execution modes.
-Thus, you can use this format for two kinds of data:
+此格式与新的 Source 兼容，可以同时在批和流模式下使用。
+因此，你可使用此格式处理以下两类数据：
-- Bounded data: lists all files and reads them all.
-- Unbounded data: monitors a directory for new files that appear.
+- 批处理模式下的有界读取
+- 流处理模式下的连续读取：监控目录中出现的新文件
{{< hint info >}}
-When you start a File Source it is configured for bounded data by default.
-To configure the File Source for unbounded data, you must additionally call
-`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`.
+当你开启一个 File Source，会被默认为有界读取。
+如果你想在连续读取模式下使用 File Source，你必须额外调用
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`。
{{< /hint >}}
**Vectorized reader**
```java
-
// Parquet rows are decoded in batches
FileSource.forBulkFileFormat(BulkFormat,Path...)
-
// Monitor the Paths to read data as unbounded data
FileSource.forBulkFileFormat(BulkFormat,Path...)
- .monitorContinuously(Duration.ofMillis(5L))
- .build();
-
+.monitorContinuously(Duration.ofMillis(5L))
+.build();
```
**Avro Parquet reader**
```java
-
// Parquet rows are decoded in batches
FileSource.forRecordStreamFormat(StreamFormat,Path...)
-
// Monitor the Paths to read data as unbounded data
FileSource.forRecordStreamFormat(StreamFormat,Path...)
.monitorContinuously(Duration.ofMillis(5L))
.build();
-
-
```
{{< hint info >}}
-Following examples are all configured for bounded data.
-To configure the File Source for unbounded data, you must additionally call
-`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`.
+下面的案例都是基于有界数据的。
+如果你想在连续读取模式下使用 File Source，你必须额外调用
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`。
{{< /hint >}}
+<a name="flink-rowdata"></a>
+
## Flink RowData
-In this example, you will create a DataStream containing Parquet records as
Flink RowDatas. The schema is projected to read only the specified fields
("f7", "f4" and "f99").
-Flink will read records in batches of 500 records. The first boolean parameter
specifies that timestamp columns will be interpreted as UTC.
-The second boolean instructs the application that the projected Parquet fields
names are case-sensitive.
-There is no watermark strategy defined as records do not contain event
timestamps.
+在这个示例中，你将创建由 Parquet 格式的记录构成的 Flink RowDatas DataStream。我们把 schema
信息映射为只读字段（"f7"、"f4" 和 "f99"）。
+每个批次读取 500 条记录。其中，第一个布尔类型的参数用来指定是否需要将时间戳列处理为 UTC。
+第二个布尔类型参数用来指定在进行 Parquet 字段映射时，是否要区分大小写。
+这里不需要水印策略，因为记录中不包含事件时间戳。
+
```java
final LogicalType[] fieldTypes =
- new LogicalType[] {
- new DoubleType(), new IntType(), new VarCharType()
- };
+ new LogicalType[] {
+ new DoubleType(), new IntType(), new VarCharType()
+ };
final ParquetColumnarRowInputFormat<FileSourceSplit> format =
- new ParquetColumnarRowInputFormat<>(
- new Configuration(),
- RowType.of(fieldTypes, new String[] {"f7", "f4", "f99"}),
- 500,
- false,
- true);
-final FileSource<RowData> source =
+ new ParquetColumnarRowInputFormat<>(
+ new Configuration(),
+ RowType.of(fieldTypes, new String[] {"f7", "f4", "f99"}),
+ 500,
+ false,
+ true);
+
+final FileSource<RowData> source
FileSource.forBulkFileFormat(format, /* Flink Path */)
.build();
+
final DataStream<RowData> stream =
- env.fromSource(source, WatermarkStrategy.noWatermarks(), "file-source");
+ env.fromSource(source, WatermarkStrategy.noWatermarks(),
"file-source");
```
## Avro Records
-Flink supports producing three types of Avro records by reading Parquet files:
+Flink 支持三种通过读取 Parquet 文件创建 Avro records 的方式：
- [Generic record](https://avro.apache.org/docs/1.10.0/api/java/index.html)
- [Specific record](https://avro.apache.org/docs/1.10.0/api/java/index.html)
- [Reflect
record](https://avro.apache.org/docs/1.10.0/api/java/org/apache/avro/reflect/package-summary.html)
### Generic record
-Avro schemas are defined using JSON. You can get more information about Avro
schemas and types from the [Avro
specification](https://avro.apache.org/docs/1.10.0/spec.html).
-This example uses an Avro schema example similar to the one described in the
[official Avro
tutorial](https://avro.apache.org/docs/1.10.0/gettingstartedjava.html):
+使用 JSON 定义 Avro schemas。你可以从 [Avro
specification](https://avro.apache.org/docs/1.10.0/spec.html) 获取更多关于 Avro
schemas 和类型的信息。
+这个示例使用了 [official Avro
tutorial](https://avro.apache.org/docs/1.10.0/gettingstartedjava.html)
中描述的示例相似的 Avro schema：
Review comment:
```suggestion
此示例使用了一个在 [official Avro
tutorial](https://avro.apache.org/docs/1.10.0/gettingstartedjava.html)
中描述的示例相似的 Avro schema：
```
##########
File path: docs/content.zh/docs/connectors/datastream/formats/parquet.md
##########
@@ -61,146 +62,144 @@ To read Avro records, you will need to add the
`parquet-avro` dependency:
</dependency>
```
-This format is compatible with the new Source that can be used in both batch
and streaming execution modes.
-Thus, you can use this format for two kinds of data:
+此格式与新的 Source 兼容，可以同时在批和流模式下使用。
+因此，你可使用此格式处理以下两类数据：
-- Bounded data: lists all files and reads them all.
-- Unbounded data: monitors a directory for new files that appear.
+- 批处理模式下的有界读取
+- 流处理模式下的连续读取：监控目录中出现的新文件
{{< hint info >}}
-When you start a File Source it is configured for bounded data by default.
-To configure the File Source for unbounded data, you must additionally call
-`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`.
+当你开启一个 File Source，会被默认为有界读取。
+如果你想在连续读取模式下使用 File Source，你必须额外调用
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`。
{{< /hint >}}
**Vectorized reader**
```java
-
// Parquet rows are decoded in batches
FileSource.forBulkFileFormat(BulkFormat,Path...)
-
// Monitor the Paths to read data as unbounded data
FileSource.forBulkFileFormat(BulkFormat,Path...)
- .monitorContinuously(Duration.ofMillis(5L))
- .build();
-
+.monitorContinuously(Duration.ofMillis(5L))
+.build();
```
**Avro Parquet reader**
```java
-
// Parquet rows are decoded in batches
FileSource.forRecordStreamFormat(StreamFormat,Path...)
-
// Monitor the Paths to read data as unbounded data
FileSource.forRecordStreamFormat(StreamFormat,Path...)
.monitorContinuously(Duration.ofMillis(5L))
.build();
-
-
```
{{< hint info >}}
-Following examples are all configured for bounded data.
-To configure the File Source for unbounded data, you must additionally call
-`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`.
+下面的案例都是基于有界数据的。
+如果你想在连续读取模式下使用 File Source，你必须额外调用
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)`。
{{< /hint >}}
+<a name="flink-rowdata"></a>
+
## Flink RowData
-In this example, you will create a DataStream containing Parquet records as
Flink RowDatas. The schema is projected to read only the specified fields
("f7", "f4" and "f99").
-Flink will read records in batches of 500 records. The first boolean parameter
specifies that timestamp columns will be interpreted as UTC.
-The second boolean instructs the application that the projected Parquet fields
names are case-sensitive.
-There is no watermark strategy defined as records do not contain event
timestamps.
+在这个示例中，你将创建由 Parquet 格式的记录构成的 Flink RowDatas DataStream。我们把 schema
信息映射为只读字段（"f7"、"f4" 和 "f99"）。
+每个批次读取 500 条记录。其中，第一个布尔类型的参数用来指定是否需要将时间戳列处理为 UTC。
+第二个布尔类型参数用来指定在进行 Parquet 字段映射时，是否要区分大小写。
+这里不需要水印策略，因为记录中不包含事件时间戳。
+
```java
final LogicalType[] fieldTypes =
- new LogicalType[] {
- new DoubleType(), new IntType(), new VarCharType()
- };
+ new LogicalType[] {
+ new DoubleType(), new IntType(), new VarCharType()
+ };
final ParquetColumnarRowInputFormat<FileSourceSplit> format =
- new ParquetColumnarRowInputFormat<>(
- new Configuration(),
- RowType.of(fieldTypes, new String[] {"f7", "f4", "f99"}),
- 500,
- false,
- true);
-final FileSource<RowData> source =
+ new ParquetColumnarRowInputFormat<>(
+ new Configuration(),
+ RowType.of(fieldTypes, new String[] {"f7", "f4", "f99"}),
+ 500,
+ false,
+ true);
+
+final FileSource<RowData> source
FileSource.forBulkFileFormat(format, /* Flink Path */)
.build();
+
final DataStream<RowData> stream =
- env.fromSource(source, WatermarkStrategy.noWatermarks(), "file-source");
+ env.fromSource(source, WatermarkStrategy.noWatermarks(),
"file-source");
```
## Avro Records
-Flink supports producing three types of Avro records by reading Parquet files:
+Flink 支持三种通过读取 Parquet 文件创建 Avro records 的方式：
Review comment:
```suggestion
Flink 支持三种方式来读取 Parquet 文件并创建 Avro records ：
```
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