Java操作Hive系列1-Hive UDF

男娘i 2022-08-31 15:24 164阅读 0赞

### 文章目录 ###

*  一.创建Hive工程
 *   *  1.1 新建Java工程
     *  1.2 将Hive的lib下所有jar包放到lib目录下
     *  1.3 导入hadoop-common jar包
     *  1.4 将jar包添加到工程
 *  二.编写java代码并打包
 *  三. 注册UDF
 *   *  3.1 上传上一步的jar文件到服务器
     *  3.2 注册UDF
     *  3.3 Hive UDF测试
     *  3.4 创建永久性的函数

# 一.创建Hive工程 #

## 1.1 新建Java工程 ##

我们这里为了简单就不用Maven了，而是直接使用lib包并把我们需要的jar包放到lib当中。如下所示。  
（lib这个目录是手工创建的）  
![image.png][]

## 1.2 将Hive的lib下所有jar包放到lib目录下 ##

下载地址:https://downloads.apache.org/hive/hive-2.3.9/  
**备注:需要选择对应的版本**  
![image.png][image.png 1]

我们需要把hive的lib下的所有的包放到lib下，hive的jar包所在的位置如下图所示:  
**截图未截全**  
![image.png][image.png 2]

## 1.3 导入hadoop-common jar包 ##

下载地址:https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-common/3.0.3/  
**备注:需要选择对应的版本**  
![image.png][image.png 3]

## 1.4 将jar包添加到工程 ##

我们把jar拷到hive-udf工程下的lib包后记得选中所有的jar包，然后右键在菜单中把鼠标放到"Build path"上，这时会出现子菜单，然后点击"Add to Build path"添加到工程当中。

![image.png][image.png 4]

![image.png][image.png 5]

# 二.编写java代码并打包 #

**代码:**

package myUdf;
    
    import org.apache.hadoop.hive.ql.exec.UDF;
    import org.apache.hadoop.io.Text;
    
    /**
     * @author  只是甲
     * @date    2021-07-26
     * @remark
     */
    
    public class getDegree extends UDF {
    	public String evaluate(int degreetype) {
    		/* 1  -- 小学
    		2  -- 初中
    		3  -- 职业高中
    		4  -- 中专
    		5  -- 高中
    		6  -- 大专
    		7  -- 本科
    		8  -- 硕士
    		9 -- 博士
    		*/
    		String result;
    		
    		if (degreetype == 1) {
    			result = "小学";
    		} else if  (degreetype == 2) {
    			result = "初中";
    		} else if  (degreetype == 3) {
    			result = "职业高中";
    		} else if  (degreetype == 4) {
    			result = "中专";
    		} else if  (degreetype == 5) {
    			result = "高中";
    		} else if  (degreetype == 6) {
    			result = "大专";
    		} else if  (degreetype == 7) {
    			result = "本科";
    		} else if  (degreetype == 8) {
    			result = "硕士";
    		} else if  (degreetype == 9) {
    			result = "博士";
    		} else {
    			result = "N/A";
    		}
    		
    		return result;
    	}
    
    }

**工程概要如下:**  
![image.png][image.png 6]

**导出jar包：**  
选中Project HiveUDF，右键，然后选中Export  
![image.png][image.png 7]

![image.png][image.png 8]

# 三. 注册UDF #

## 3.1 上传上一步的jar文件到服务器 ##

![image.png][image.png 9]

## 3.2 注册UDF ##

接着我们需要注册UDF，通知hive我现在注册了一个UDF，要调用这个UDF。注册UDF需要在hive视图下进行，因此我们首先启动hive的bin目录下启动hive。

代码:

-- 添加jar包
    add jar /home/java/getDegree.jar;
    -- 查看现有的jar包
    list jars;
    -- 创建临时函数
    create temporary function GetDegree as 'myUdf.getDegree';

测试记录:

hive> add jar /home/java/getDegree.jar;
    Added [/home/java/getDegree.jar] to class path
    Added resources: [/home/java/getDegree.jar]
    hive> drop function GetDegree;
    hive> 
        > 
        > list jars;
    /home/java/getDegree.jar
    hive> create temporary function GetDegree as 'myUdf.getDegree';
    OK
    Time taken: 0.023 seconds
    hive>

## 3.3 Hive UDF测试 ##

数据准备:

degree_type
    1  -- 小学
    2  -- 初中
    3  -- 职业高中
    4  -- 中专
    5  -- 高中
    6  -- 大专
    7  -- 本科
    8  -- 硕士
    9 -- 博士
    
    
    create table user_info(id int,degree_type int);
    insert into user_info values (1,3);
    insert into user_info values (2,1);
    insert into user_info values (3,6);
    insert into user_info values (4,4);
    insert into user_info values (5,5);
    insert into user_info values (6,9);
    insert into user_info values (7,8);
    insert into user_info values (8,2);
    insert into user_info values (9,7);
    
    
    hive> 
        > select * from user_info;
    OK
    user_info.id    user_info.degree_type
    1       3
    2       1
    3       6
    4       4
    5       5
    6       9
    7       8
    8       2
    9       7
    Time taken: 0.088 seconds, Fetched: 9 row(s)

**测试Hive UDF:**

hive> 
        > 
        > select id,GetDegree(degree_type) from user_info order by id;
    Query ID = root_20210726144704_e3235df0-fa86-4eec-8fcd-b5a5e87234cf
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks determined at compile time: 1
    In order to change the average load for a reducer (in bytes):
      set hive.exec.reducers.bytes.per.reducer=<number>
    In order to limit the maximum number of reducers:
      set hive.exec.reducers.max=<number>
    In order to set a constant number of reducers:
      set mapreduce.job.reduces=<number>
    21/07/26 14:47:04 INFO client.ConfiguredRMFailoverProxyProvider: Failing over to rm69
    Starting Job = job_1627269473896_0006, Tracking URL = http://hp3:8088/proxy/application_1627269473896_0006/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1627269473896_0006
    Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
    2021-07-26 14:47:10,383 Stage-1 map = 0%,  reduce = 0%
    2021-07-26 14:47:16,584 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 5.19 sec
    2021-07-26 14:47:22,759 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.9 sec
    MapReduce Total cumulative CPU time: 6 seconds 900 msec
    Ended Job = job_1627269473896_0006
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.9 sec   HDFS Read: 12073 HDFS Write: 342 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 6 seconds 900 msec
    OK
    1       职业高中
    2       小学
    3       大专
    4       中专
    5       高中
    6       博士
    7       硕士
    8       初中
    9       本科
    Time taken: 20.596 seconds, Fetched: 9 row(s)
    hive>

## 3.4 创建永久性的函数 ##

上面一个步骤我们创建的是临时的函数，退出当前session后，该函数不可用。

[root@hp1 java]# hive
    WARNING: Use "yarn jar" to launch YARN applications.
    SLF4J: Class path contains multiple SLF4J bindings.
    SLF4J: Found binding in [jar:file:/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/jars/log4j-slf4j-impl-2.8.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: Found binding in [jar:file:/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/jars/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
    SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
    
    Logging initialized using configuration in jar:file:/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/jars/hive-common-2.1.1-cdh6.3.1.jar!/hive-log4j2.properties Async: false
    
    WARNING: Hive CLI is deprecated and migration to Beeline is recommended.
    hive> 
        > use test;
    OK
    Time taken: 1.154 seconds
    hive> 
        > select id,GetDegree(degree_type) from user_info order by id;
    FAILED: SemanticException [Error 10011]: Invalid function GetDegree

**此时我们可以来创建永久性的函数**

-- OS端操作
    sudo -u hdfs  hadoop  fs -mkdir /user/hive/lib
    sudo -u hdfs  hadoop fs -put /home/java/getDegree.jar /user/hive/lib
    
    -- Hive命令行操作
    CREATE FUNCTION GetDegree AS 'myUdf.getDegree' USING JAR 'hdfs:///user/hive/lib/getDegree.jar';
    
    select id,GetDegree(degree_type) from user_info order by id;

**测试记录:**

[root@hp1 ~]# sudo -u hdfs  hadoop  fs -mkdir /user/hive/lib
    [root@hp1 ~]# 
    [root@hp1 ~]# 
    [root@hp1 ~]# sudo -u hdfs  hadoop fs -put /home/java/getDegree.jar /user/hive/lib
    
    hive> 
        > 
        > CREATE FUNCTION GetDegree AS 'myUdf.getDegree' USING JAR 'hdfs://hp1:9866/user/hive/lib/getDegree.jar';
    End of File Exception between local host is: "hp1/10.31.1.123"; destination host is: "hp1":9866; : java.io.EOFException; For more details see:  http://wiki.apache.org/hadoop/EOFException
    Failed to register test.getdegree using class myUdf.getDegree
    FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.FunctionTask
    hive> 
        > CREATE FUNCTION GetDegree AS 'myUdf.getDegree' USING JAR 'hdfs:///user/hive/lib/getDegree.jar';
    Added [/tmp/4e1c389c-4053-4197-9364-93a01840309c_resources/getDegree.jar] to class path
    Added resources: [hdfs:///user/hive/lib/getDegree.jar]
    OK
    Time taken: 0.203 seconds
    hive> select id,GetDegree(degree_type) from user_info order by id;
    Query ID = root_20210726152617_b3da9372-0724-4126-8d62-f58fa55afc42
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks determined at compile time: 1
    In order to change the average load for a reducer (in bytes):
      set hive.exec.reducers.bytes.per.reducer=<number>
    In order to limit the maximum number of reducers:
      set hive.exec.reducers.max=<number>
    In order to set a constant number of reducers:
      set mapreduce.job.reduces=<number>
    21/07/26 15:26:18 INFO client.ConfiguredRMFailoverProxyProvider: Failing over to rm69
    Starting Job = job_1627269473896_0007, Tracking URL = http://hp3:8088/proxy/application_1627269473896_0007/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1627269473896_0007
    Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
    2021-07-26 15:26:26,852 Stage-1 map = 0%,  reduce = 0%
    2021-07-26 15:26:33,129 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.6 sec
    2021-07-26 15:26:38,290 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 7.12 sec
    MapReduce Total cumulative CPU time: 7 seconds 120 msec
    Ended Job = job_1627269473896_0007
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 7.12 sec   HDFS Read: 11873 HDFS Write: 342 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 7 seconds 120 msec
    OK
    1       职业高中
    2       小学
    3       大专
    4       中专
    5       高中
    6       博士
    7       硕士
    8       初中
    9       本科
    Time taken: 22.51 seconds, Fetched: 9 row(s)
    hive>

参考:

1.  https://cwiki.apache.org/confluence/display/Hive/HivePlugins
2.  https://blog.csdn.net/u012453843/article/details/53058193
3.  https://www.cnblogs.com/xuziyu/p/10754592.html

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