一、基础信息

1.1 组件版本

• JDK：  1.8
• Flink:    1.16.0
• Hive:     3.1.2
• Hadoop:  3.2.2

二、准备工作

2.1 新建Scalar Function

package com.hw.flink.udf;

import org.apache.flink.table.functions.ScalarFunction;

public class MySuffixFunction extends ScalarFunction {

    public String eval(String input) {
        return input.concat("-HamaWhite");
    }
}

Maven依赖如下: 

<dependencies>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-table-common</artifactId>
        <version>1.16.0</version>
        <scope>provided</scope>
    </dependency>
</dependencies>

2.2 构建Jar包

把上述代码打包为 flink1.16-udf-demo.jar。

2.3 上传HDFS

$ hadoop fs -put flink1.16-udf-demo.jar .
$ hadoop fs -ls /user/deploy 
-rw-r--r--   1 deploy supergroup 1228 2022-12-05 20:03 /user/deploy/flink1.16-udf-demo.jar

三、验证函数

参考《flink-docs-release-1.16#create-function》

3.1 新建及测试函数

EnvironmentSettings settings = EnvironmentSettings.inStreamingMode();
TableEnvironment tableEnv = TableEnvironment.create(settings);

// use the hive catalog
String catalogName = "hive";
HiveCatalog hiveCatalog = new HiveCatalog(catalogName, "flink_demo", "conf/hive");
tableEnv.registerCatalog(catalogName, hiveCatalog);
tableEnv.useCatalog(catalogName);

tableEnv.executeSql("DROP FUNCTION IF EXISTS my_suffix_udf_jar");

// create function my_suffix_udf_jar
tableEnv.executeSql("CREATE FUNCTION IF NOT EXISTS my_suffix_udf_jar " +
        "AS 'com.hw.flink.udf.MySuffixFunction' " +
        "USING JAR 'hdfs://xxx.xxx.xxx.xxx:9000/user/deploy/flink1.16-udf-demo.jar'"
);

// use function my_suffix_udf_jar
tableEnv.executeSql("select my_suffix_udf_jar('hw')").print();

最后一行的运行结果是:

+----+--------------------------------+
| op |                         EXPR$0 |
+----+--------------------------------+
| +I |                   hw-HamaWhite |
+----+--------------------------------+
1 row in set

3.2 查看Hive Meta数据库

上文用的是Hive Catalog，Flink创建函数的时候会把函数信息注册到Hive Meta数据库中，存储在FUNCS和FUNC_RU表中。查看数据如下:

mysql> select * from FUNCS where FUNC_NAME  like '%jar%' \G;
*************************** 1. row ***************************
    FUNC_ID: 764
 CLASS_NAME: com.hw.flink.udf.MySuffixFunction
CREATE_TIME: 1670242907
      DB_ID: 11
  FUNC_NAME: my_suffix_udf_jar
  FUNC_TYPE: 1
 OWNER_NAME: NULL
 OWNER_TYPE: GROUP
1 row in set (0.00 sec)

mysql> select * from FUNC_RU \G;
*************************** 1. row ***************************
      FUNC_ID: 764
RESOURCE_TYPE: 1
 RESOURCE_URI: hdfs://xxx.xxx.xxx.xxx:9000/user/deploy/flink1.16-udf-demo.jar
  INTEGER_IDX: 0

四、Flink中使用Hive UDF

参考《flink-docs-release-1.16#hive_functions》

4.1  自定义Hive UDF

package com.hw.hive.udf;

import org.apache.hadoop.hive.ql.exec.UDF;

public class MyPrefixFunction extends UDF {

    public String evaluate(String input) {
        return "HamaWhite-".concat(input);
    }
}

Maven依赖如下: 

<dependencies>
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-exec</artifactId>
        <version>3.1.2</version>
        <scope>provided</scope>
    </dependency>
</dependencies>

4.2 构建Jar包

把上述代码打包为 hive3-udf-demo.jar。

4.3 上传HDFS

$ hadoop fs -put hive3-udf-demo.jar .

4.4 在Hive SQL 中创建UDF

use flink_demo;

CREATE FUNCTION my_prefix_udf_jar AS 'com.hw.hive.udf.MyPrefixFunction' 
USING JAR 'hdfs://xxx.xxx.xxx.xxx:9000/user/deploy/hive3-udf-demo.jar';

4.5 Flink中测试Hive UDF

在3.1的测试代码中增加测试代码:

注: 经验证，官网中提到的 tableEnv.loadModule("hive", new HiveModule("3.1.2")) 不用写，Flink也能正常访问到Hive UDF。

# use hive udf
tableEnv.executeSql("select my_prefix_udf_jar('hw')").print();

 最后一行的运行结果是:

+----+--------------------------------+
| op |                         EXPR$0 |
+----+--------------------------------+
| +I |                   HamaWhite-hw |
+----+--------------------------------+
1 row in set

经测试，在Flink中是可以同时用Hive和Flink的UDF的。例如:

# use flink udf
tableEnv.executeSql("select my_suffix_udf_jar('hw')").print();

# use hive udf
tableEnv.executeSql("select my_prefix_udf_jar('hw')").print();

# mixed use flink and hive udf
tableEnv.executeSql("select my_suffix_udf_jar(my_prefix_udf_jar('hw'))").print();

五、参考文献

1. flink-docs-release-1.16#create-function
2. flink-docs-release-1.16#hive_functions