Hive 用户自定义函数 UDF，UDAF

Hive有UDF：（普通）UDF，用户自定义聚合函数（UDAF）以及用户自定义生表函数（UDTF）。它们所接受的输入和生产的输出的数据行的数量的不同的。

UDF

UDF操作作用于单个数据行，且产生一个数据行作为输出。

例：strip

我们这里创造一个strip UDF

package Hive_UDF;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.hive.ql.exec.UDF;
import org.apache.hadoop.io.Text;

public class Strip extends UDF {
    private Text result = new Text();

    public Text evaluate(Text str) {
        if (str == null) {
            return null;
        }
        result.set(StringUtils.strip(str.toString()));
        return result;
    }

    public Text evaluate(Text str, String stripChars) {
        if (str == null) {
            return null;
        }
        result.set(StringUtils.strip(str.toString(), stripChars));
        return result;
    }
}

在hive shell中引入该jar包：

add jar /home/Strip.jar;

生成函数：

create temporary function strip as 'Hive_UDF.Strip';

使用函数1：

select strip("   bee   ") from item;

得到结果：

……
bee
bee
bee
Time taken: 0.768 seconds, Fetched: 26000 row(s)

使用函数2：

select strip("banana","ab") from item;

得到结果：

……
nan
nan
nan
Time taken: 0.359 seconds, Fetched: 26000 row(s)

可以看到，得到的结果都是2600行。
我们看下item表有多少行：

hive> select count(1) from item;
Query ID = root_20160816104545_9ef6e618-b004-470f-b5a7-15af22075407
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=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Job = job_1471250735061_0001, Tracking URL = http://holodesk01:8088/proxy/application_1471250735061_0001/
Kill Command = /usr/lib/hadoop/bin/hadoop job  -kill job_1471250735061_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2016-08-16 10:46:16,504 Stage-1 map = 0%,  reduce = 0%
2016-08-16 10:46:30,241 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.37 sec
2016-08-16 10:46:38,697 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 4.62 sec
MapReduce Total cumulative CPU time: 4 seconds 620 msec
Ended Job = job_1471250735061_0001
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 4.62 sec   HDFS Read: 7326445 HDFS Write: 6 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 620 msec
OK
26000
Time taken: 53.591 seconds, Fetched: 1 row(s)

所以侧面验证了UDF的确是“作用于单个数据行，且产生一个数据行作为输出”

UDAF

UDAF 接受多个输入数据行，并产生一个输出数据行。
一个计算函数必须实现以下5个方法：

• init()： 该方法负责初始化计算函数并重设它的内部状态 。
• iterate()： 每次对一个新值进行聚合计算时会调用该方法。
• terminatePartial()： Hive需要部分聚合结果时会调用该方法。
• merge()： Hive需要将部分聚合结果和另外部分聚合结果合并时会调用该方法。
• terminate()： 需要最终结果时会调用该方法

例：求最大整数UDAF数据流

这里写图片描述

例：Mean

package Hive_UDF;

import org.apache.hadoop.hive.ql.exec.UDAF;
import org.apache.hadoop.hive.ql.exec.UDAFEvaluator;
import org.apache.hadoop.io.FloatWritable;

@SuppressWarnings("deprecation")
public class Mean extends UDAF {
    public static class MeanFloatUDAFEvaluator implements UDAFEvaluator {
        public static class PartialResult {
            float sum;
            long count;
        }

        private PartialResult partialresult;

        @Override
        public void init() {
            // TODO Auto-generated method stub
            partialresult = null;
        }

        public boolean iterate(FloatWritable value) {
            if (value == null) {
                return true;
            }
            if (partialresult == null) {
                partialresult = new PartialResult();
            }

            partialresult.sum += value.get();
            ++partialresult.count;
            return true;
        }

        public PartialResult terminatePartial() {
            return partialresult;
        }

        public boolean merge(PartialResult other) {
            if (other == null) {
                return true;
            }
            if (partialresult == null) {
                partialresult = new PartialResult();
            }

            partialresult.sum += other.sum;
            partialresult.count += other.count;

            return true;
        }

        public FloatWritable terminate() {
            if (partialresult == null) {
                return null;
            }
            return new FloatWritable(partialresult.sum / partialresult.count);
        }
    }
}

使用函数：

select mymean(i_current_price) from item;

得到结果：

Query ID = root_20160816175757_e063c1f7-5817-406a-b448-3a291a14a4a7
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=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Job = job_1471250735061_0002, Tracking URL = http://holodesk01:8088/proxy/application_1471250735061_0002/
Kill Command = /usr/lib/hadoop/bin/hadoop job  -kill job_1471250735061_0002
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2016-08-16 17:57:31,690 Stage-1 map = 0%,  reduce = 0%
2016-08-16 17:57:46,309 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.29 sec
2016-08-16 17:57:54,706 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 5.77 sec
MapReduce Total cumulative CPU time: 5 seconds 770 msec
Ended Job = job_1471250735061_0002
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 5.77 sec   HDFS Read: 7326868 HDFS Write: 10 SUCCESS
Total MapReduce CPU Time Spent: 5 seconds 770 msec
OK
9.4731455
Time taken: 39.621 seconds, Fetched: 1 row(s)