大数据教程（9.6）map端join实现

时间 2019-11-11

标签数据教程 9.6 map join 实现栏目 SQL 繁體版

原文原文链接

上一篇文章讲了mapreduce配合实现join，本节博主将讲述在map端的join实现；java

1、需求apache

实现两个“表”的join操做，其中一个表数据量小，一个表很大，这种场景在实际中很是常见，好比“订单日志” join “产品信息”centos

2、分析api

--原理阐述：适用于关联表中有小表的情形；能够将小表分发到全部的map节点，这样，map节点就能够在本地对本身所读到的大表数据进行join并输出最终结果，能够大大提升join操做的并发度，加快处理速度缓存

--示例：先在mapper类中预先定义好小表，进行join服务器

--并用distributedcache机制将小表的数据分发到每个maptask执行节点，从而每个maptask节点能够从本地加载到小表的数据，进而在本地便可实现join并发

3、代码实现app

package com.empire.hadoop.mr.mapsidejoin;

import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.net.URI;
import java.util.HashMap;
import java.util.Map;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class MapSideJoin {

    public static class MapSideJoinMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
        // 用一个hashmap来加载保存产品信息表
        Map<String, String> pdInfoMap = new HashMap<String, String>();

        Text                k         = new Text();

        /**
         * 经过阅读父类Mapper的源码，发现 setup方法是在maptask处理数据以前调用一次 能够用来作一些初始化工做
         */
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream("product.txt")));
            String line;
            while (StringUtils.isNotEmpty(line = br.readLine())) {
                String[] fields = line.split("\t");
                pdInfoMap.put(fields[0], fields[2]);
            }
            br.close();
        }

        // 因为已经持有完整的产品信息表，因此在map方法中就能实现join逻辑了
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String orderLine = value.toString();
            String[] fields = orderLine.split("\t");
            String pdName = pdInfoMap.get(fields[1]);
            k.set(orderLine + "\t" + pdName);
            context.write(k, NullWritable.get());
        }

    }

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();

        Job job = Job.getInstance(conf);

        job.setJarByClass(MapSideJoin.class);
        //job.setJar("D:/mapsidejoin.jar");

        job.setMapperClass(MapSideJoinMapper.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 指定须要缓存一个文件到全部的maptask运行节点工做目录
        /* job.addArchiveToClassPath(archive); */// 缓存jar包到task运行节点的classpath中
        /* job.addFileToClassPath(file); */// 缓存普通文件到task运行节点的classpath中
        /* job.addCacheArchive(uri); */// 缓存压缩包文件到task运行节点的工做目录
        /* job.addCacheFile(uri) */// 缓存普通文件到task运行节点的工做目录

        // 将产品表文件缓存到task工做节点的工做目录中去
        //job.addCacheFile(new URI("file:/D:/srcdata/mapjoincache/pdts.txt"));
        job.addCacheFile(new URI("hdfs://centos-aaron-h1:9000/rjoin/mapjoincache/product.txt"));

        //map端join的逻辑不须要reduce阶段，设置reducetask数量为0
        job.setNumReduceTasks(0);

        boolean res = job.waitForCompletion(true);
        System.exit(res ? 0 : 1);

    }

}

4、执行程序ide

#上传jar

Alt+p
lcd d:/
put  mapsidejoin.jar

#准备hadoop处理的数据文件

cd /home/hadoop/apps/hadoop-2.9.1
hadoop fs  -mkdir -p /rjoin/mapjoinsideinput
hadoop fs  -mkdir -p /rjoin/mapjoincache
hdfs dfs -put  order.txt  /rjoin/mapjoinsideinput
hdfs dfs -put  product.txt  /rjoin/mapjoincache


#运行mapsidejoin程序

hadoop jar mapsidejoin.jar  com.empire.hadoop.mr.mapsidejoin.MapSideJoin /rjoin/mapjoinsideinput /rjoin/mapjoinsideoutput

5、运行效果oop

IPC Parameter Sending Thread #0] DEBUG org.apache.hadoop.ipc.Client - IPC Client (1880635722) connection to centos-aaron-h2/192.168.29.145:37772 from hadoop sending #87 org.apache.hadoop.mapreduce.v2.api.MRClientProtocolPB.getCounters
[IPC Client (1880635722) connection to centos-aaron-h2/192.168.29.145:37772 from hadoop] DEBUG org.apache.hadoop.ipc.Client - IPC Client (1880635722) connection to centos-aaron-h2/192.168.29.145:37772 from hadoop got value #87
[main] DEBUG org.apache.hadoop.ipc.ProtobufRpcEngine - Call: getCounters took 36ms
[main] INFO org.apache.hadoop.mapreduce.Job - Counters: 30
	File System Counters
		FILE: Number of bytes read=0
		FILE: Number of bytes written=189612
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=218
		HDFS: Number of bytes written=108
		HDFS: Number of read operations=5
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
	Job Counters 
		Launched map tasks=1
		Data-local map tasks=1
		Total time spent by all maps in occupied slots (ms)=3057
		Total time spent by all reduces in occupied slots (ms)=0
		Total time spent by all map tasks (ms)=3057
		Total vcore-milliseconds taken by all map tasks=3057
		Total megabyte-milliseconds taken by all map tasks=3130368
	Map-Reduce Framework
		Map input records=4
		Map output records=4
		Input split bytes=125
		Spilled Records=0
		Failed Shuffles=0
		Merged Map outputs=0
		GC time elapsed (ms)=99
		CPU time spent (ms)=350
		Physical memory (bytes) snapshot=117669888
		Virtual memory (bytes) snapshot=845942784
		Total committed heap usage (bytes)=16121856
	File Input Format Counters 
		Bytes Read=93
	File Output Format Counters 
		Bytes Written=108
[main] DEBUG org.apache.hadoop.security.UserGroupInformation - PrivilegedAction as:hadoop (auth:SIMPLE) from:org.apache.hadoop.mapreduce.Job.updateStatus(Job.java:328)
[IPC Parameter Sending Thread #0] DEBUG org.apache.hadoop.ipc.Client - IPC Client (1880635722) connection to centos-aaron-h2/192.168.29.145:37772 from hadoop sending #88 org.apache.hadoop.mapreduce.v2.api.MRClientProtocolPB.getJobReport
[IPC Client (1880635722) connection to centos-aaron-h2/192.168.29.145:37772 from hadoop] DEBUG org.apache.hadoop.ipc.Client - IPC Client (1880635722) connection to centos-aaron-h2/192.168.29.145:37772 from hadoop got value #88
[main] DEBUG org.apache.hadoop.ipc.ProtobufRpcEngine - Call: getJobReport took 0ms
[pool-4-thread-1] DEBUG org.apache.hadoop.ipc.Client - stopping client from cache: org.apache.hadoop.ipc.Client@303c7016
[Thread-3] DEBUG org.apache.hadoop.util.ShutdownHookManager - ShutdownHookManger complete shutdown.

6、运行结果

[hadoop@centos-aaron-h1 ~]$  hdfs dfs -cat  /rjoin/mapjoinsideoutput/part-m-00000
1001    20150710        P0001   2       小米5
1002    20150710        P0001   3       小米5
1002    20150710        P0002   3       锤子T1
1003    20150710        P0003   3       锤子

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