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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/03/14 15:18:59 UTC
[GitHub] [flink] MartijnVisser commented on a change in pull request #19083: [FLINK-26604][doc] add more information for Avro records support and clean up redundant content of bounded and unbounded data
MartijnVisser commented on a change in pull request #19083:
URL: https://github.com/apache/flink/pull/19083#discussion_r826047567
##########
File path: docs/content/docs/connectors/datastream/formats/parquet.md
##########
@@ -30,7 +30,7 @@ under the License.
Flink supports reading [Parquet](https://parquet.apache.org/) files,
producing {{< javadoc file="org/apache/flink/table/data/RowData.html" name="Flink RowData">}} and producing [Avro](https://avro.apache.org/) records.
-To use the format you need to add the Flink Parquet dependency to your project:
+To use the format you need to add the flink-parquet dependency to your project for reading Flink RowData:
Review comment:
```suggestion
To use the format you need to add the `flink-parquet` dependency to your project:
```
##########
File path: docs/content/docs/connectors/datastream/formats/parquet.md
##########
@@ -39,46 +39,71 @@ To use the format you need to add the Flink Parquet dependency to your project:
<version>{{< version >}}</version>
</dependency>
```
-
+
+For reading Avro records, parquet-avro dependency is required additionally:
+
+```xml
+<dependency>
+ <groupId>org.apache.parquet</groupId>
+ <artifactId>parquet-avro</artifactId>
+ <version>${flink.format.parquet.version}</version>
+ <optional>true</optional>
+ <exclusions>
+ <exclusion>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-client</artifactId>
+ </exclusion>
+ <exclusion>
+ <groupId>it.unimi.dsi</groupId>
+ <artifactId>fastutil</artifactId>
+ </exclusion>
+ </exclusions>
Review comment:
Are these exclusions only needed when using this in combination with `flink-parquet` ? Or can we already exclude these by default?
##########
File path: docs/content/docs/connectors/datastream/formats/parquet.md
##########
@@ -39,46 +39,71 @@ To use the format you need to add the Flink Parquet dependency to your project:
<version>{{< version >}}</version>
</dependency>
```
-
+
+For reading Avro records, parquet-avro dependency is required additionally:
+
+```xml
+<dependency>
+ <groupId>org.apache.parquet</groupId>
+ <artifactId>parquet-avro</artifactId>
+ <version>${flink.format.parquet.version}</version>
+ <optional>true</optional>
+ <exclusions>
+ <exclusion>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-client</artifactId>
+ </exclusion>
+ <exclusion>
+ <groupId>it.unimi.dsi</groupId>
+ <artifactId>fastutil</artifactId>
+ </exclusion>
+ </exclusions>
+</dependency>
+```
+
This format is compatible with the new Source that can be used in both batch and streaming modes.
Thus, you can use this format for two kinds of data:
-- Bounded data
-- Unbounded data: monitors a directory for new files that appear
+- Bounded data: lists all files and reads them all.
+- Unbounded data: monitors a directory for new files that appear.
-## Flink RowData
+By default, a File Source is created in the bounded mode, to turn the source into the continuous unbounded mode you can call
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)` additionally .
-#### Bounded data example
+**Batch mode**
-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.
+```java
+
+// reads bounded data of records from files at a time
+FileSource.forBulkFileFormat(BulkFormat,Path...)
+
+// reads unbounded data of records from files by monitoring the Paths
+FileSource.forBulkFileFormat(BulkFormat,Path...)
+ .monitorContinuously(Duration.ofMillis(5L))
+ .build();
+
+```
+
+**Streaming mode**
```java
-final LogicalType[] fieldTypes =
- 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 =
- FileSource.forBulkFileFormat(format, /* Flink Path */)
- .build();
-final DataStream<RowData> stream =
- env.fromSource(source, WatermarkStrategy.noWatermarks(), "file-source");
+// reads bounded data of records from files at a time
+FileSource.forRecordStreamFormat(StreamFormat,Path...)
+
+// reads unbounded data of records from files by monitoring the Paths
+FileSource.forRecordStreamFormat(StreamFormat,Path...)
+ .monitorContinuously(Duration.ofMillis(5L))
+ .build();
+
+
```
-#### Unbounded data example
+From now on, this document will only show you examples for bounded data. You can add a call of
Review comment:
If we only show examples for either bounded or unbounded data, I think I would prefer to have the examples show unbounded data and explain how you could apply them to bounded data. Since batch is a special case of streaming from a Flink perspective.
##########
File path: docs/content/docs/connectors/datastream/formats/parquet.md
##########
@@ -329,22 +296,94 @@ final DataStream<GenericRecord> stream =
env.fromSource(source, WatermarkStrategy.noWatermarks(), "file-source");
```
-#### Unbounded data example
+#### Prerequisite of Parquet files
+
+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.
+
+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.
+
+```java
+// avro schema with namespace
+final String schema =
+ "{\"type\": \"record\", "
+ + "\"name\": \"User\", "
+ + "\"namespace\": \"org.apache.flink.formats.parquet.avro\", "
+ + "\"fields\": [\n"
+ + " {\"name\": \"name\", \"type\": \"string\" },\n"
+ + " {\"name\": \"favoriteNumber\", \"type\": [\"int\", \"null\"] },\n"
+ + " {\"name\": \"favoriteColor\", \"type\": [\"string\", \"null\"] }\n"
+ + " ]\n"
+ + " }";
+
+```
+
+Parquet files created with this schema will contain meta information like:
-This example, similar to the bounded batch example, uses the same POJO Java class `Datum`
-and monitors for the new files every second to read Avro Reflect records from Parquet files
-infinitely as new files are added to the directory.
+```text
+creator: parquet-mr version 1.12.2 (build 77e30c8093386ec52c3cfa6c34b7ef3321322c94)
+extra: parquet.avro.schema =
+{"type":"record","name":"User","namespace":"org.apache.flink.formats.parquet.avro","fields":[{"name":"name","type":"string"},{"name":"favoriteNumber","type":["int","null"]},{"name":"favoriteColor","type":["string","null"]}]}
+extra: writer.model.name = avro
+
+file schema: org.apache.flink.formats.parquet.avro.User
+--------------------------------------------------------------------------------
+name: REQUIRED BINARY L:STRING R:0 D:0
+favoriteNumber: OPTIONAL INT32 R:0 D:1
+favoriteColor: OPTIONAL BINARY L:STRING R:0 D:1
+
+row group 1: RC:3 TS:143 OFFSET:4
+--------------------------------------------------------------------------------
+name: BINARY UNCOMPRESSED DO:0 FPO:4 SZ:47/47/1.00 VC:3 ENC:PLAIN,BIT_PACKED ST:[min: Jack, max: Tom, num_nulls: 0]
+favoriteNumber: INT32 UNCOMPRESSED DO:0 FPO:51 SZ:41/41/1.00 VC:3 ENC:RLE,PLAIN,BIT_PACKED ST:[min: 1, max: 3, num_nulls: 0]
+favoriteColor: BINARY UNCOMPRESSED DO:0 FPO:92 SZ:55/55/1.00 VC:3 ENC:RLE,PLAIN,BIT_PACKED ST:[min: green, max: yellow, num_nulls: 0]
+
+```
+
+With the User class defined in the package org.apache.flink.formats.parquet.avro:
Review comment:
```suggestion
With the `User` class defined in the package org.apache.flink.formats.parquet.avro:
```
##########
File path: docs/content/docs/connectors/datastream/formats/parquet.md
##########
@@ -39,46 +39,71 @@ To use the format you need to add the Flink Parquet dependency to your project:
<version>{{< version >}}</version>
</dependency>
```
-
+
+For reading Avro records, parquet-avro dependency is required additionally:
+
+```xml
+<dependency>
+ <groupId>org.apache.parquet</groupId>
+ <artifactId>parquet-avro</artifactId>
+ <version>${flink.format.parquet.version}</version>
+ <optional>true</optional>
+ <exclusions>
+ <exclusion>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-client</artifactId>
+ </exclusion>
+ <exclusion>
+ <groupId>it.unimi.dsi</groupId>
+ <artifactId>fastutil</artifactId>
+ </exclusion>
+ </exclusions>
+</dependency>
+```
+
This format is compatible with the new Source that can be used in both batch and streaming modes.
Thus, you can use this format for two kinds of data:
-- Bounded data
-- Unbounded data: monitors a directory for new files that appear
+- Bounded data: lists all files and reads them all.
+- Unbounded data: monitors a directory for new files that appear.
-## Flink RowData
+By default, a File Source is created in the bounded mode, to turn the source into the continuous unbounded mode you can call
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)` additionally .
-#### Bounded data example
+**Batch mode**
Review comment:
I think Flink still uses Bounded and Unbounded data. Batch mode is referring to the execution mode
##########
File path: docs/content/docs/connectors/datastream/formats/parquet.md
##########
@@ -39,46 +39,71 @@ To use the format you need to add the Flink Parquet dependency to your project:
<version>{{< version >}}</version>
</dependency>
```
-
+
+For reading Avro records, parquet-avro dependency is required additionally:
Review comment:
```suggestion
To read Avro records, you will need to add the `parquet-avro` dependency:
```
##########
File path: docs/content/docs/connectors/datastream/formats/parquet.md
##########
@@ -39,46 +39,71 @@ To use the format you need to add the Flink Parquet dependency to your project:
<version>{{< version >}}</version>
</dependency>
```
-
+
+For reading Avro records, parquet-avro dependency is required additionally:
+
+```xml
+<dependency>
+ <groupId>org.apache.parquet</groupId>
+ <artifactId>parquet-avro</artifactId>
+ <version>${flink.format.parquet.version}</version>
+ <optional>true</optional>
+ <exclusions>
+ <exclusion>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-client</artifactId>
+ </exclusion>
+ <exclusion>
+ <groupId>it.unimi.dsi</groupId>
+ <artifactId>fastutil</artifactId>
+ </exclusion>
+ </exclusions>
+</dependency>
+```
+
This format is compatible with the new Source that can be used in both batch and streaming modes.
Thus, you can use this format for two kinds of data:
-- Bounded data
-- Unbounded data: monitors a directory for new files that appear
+- Bounded data: lists all files and reads them all.
+- Unbounded data: monitors a directory for new files that appear.
-## Flink RowData
+By default, a File Source is created in the bounded mode, to turn the source into the continuous unbounded mode you can call
+`AbstractFileSource.AbstractFileSourceBuilder.monitorContinuously(Duration)` additionally .
-#### Bounded data example
+**Batch mode**
-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.
+```java
+
+// reads bounded data of records from files at a time
+FileSource.forBulkFileFormat(BulkFormat,Path...)
+
+// reads unbounded data of records from files by monitoring the Paths
+FileSource.forBulkFileFormat(BulkFormat,Path...)
+ .monitorContinuously(Duration.ofMillis(5L))
+ .build();
+
+```
+
+**Streaming mode**
Review comment:
```suggestion
**Unbounded data**
```
##########
File path: docs/content/docs/connectors/datastream/formats/parquet.md
##########
@@ -329,22 +296,94 @@ final DataStream<GenericRecord> stream =
env.fromSource(source, WatermarkStrategy.noWatermarks(), "file-source");
```
-#### Unbounded data example
+#### Prerequisite of Parquet files
+
+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.
+
+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.
Review comment:
```suggestion
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.
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.
```
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