大數據教程（9.5）用MR實現sql中的jion邏輯

時間  2019-11-10
標籤 數據 教程 9.5 實現 sql jion 邏輯 欄目 SQL 简体版
原文   原文鏈接

        上一篇博客講解了使用jar -jar的方式來運行提交MR程序，以及經過修改YarnRunner的源碼來實現MR的windows開發環境提交到集羣的方式。本篇博主將分享sql中常見的join操做。java

        1、需求sql

               訂單數據表t_order：apache

idwindows

datecentos

pidapi

amount服務器

1001app

20150710ide

P0001oop

2

1002

20150710

P0001

3

1002

20150710

P0002

3

               商品信息表t_product：

id

pname

category_id

price

P0001

小米5

1000

2

P0002

錘子T1

1000

3

假如數據量巨大，兩表的數據是以文件的形式存儲在HDFS中，須要用mapreduce程序來實現一下SQL查詢運算：

select  a.id,a.date,b.name,b.category_id,b.price from t_order a join t_product b on a.pid = b.id

實現機制：經過將關聯的條件做爲map輸出的key，將兩表知足join條件的數據並攜帶數據所來源的文件信息，發往同一個reduce task，在reduce中進行數據的串聯

        2、實現代碼

               join後的輸出類：

package com.empire.hadoop.mr.rjoin;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.Writable;

/**
 * 類 InfoBean.java的實現描述：實體類
 * 
 * @author arron 2018年12月10日 下午11:51:27
 */
public class InfoBean implements Writable {

    private int    order_id;
    private String dateString;
    private String p_id;
    private int    amount;
    private String pname;
    private int    category_id;
    private float  price;

    // flag=0表示這個對象是封裝訂單表記錄
    // flag=1表示這個對象是封裝產品信息記錄
    private String flag;

    public InfoBean() {
    }

    public void set(int order_id, String dateString, String p_id, int amount, String pname, int category_id,
                    float price, String flag) {
        this.order_id = order_id;
        this.dateString = dateString;
        this.p_id = p_id;
        this.amount = amount;
        this.pname = pname;
        this.category_id = category_id;
        this.price = price;
        this.flag = flag;
    }

    public int getOrder_id() {
        return order_id;
    }

    public void setOrder_id(int order_id) {
        this.order_id = order_id;
    }

    public String getDateString() {
        return dateString;
    }

    public void setDateString(String dateString) {
        this.dateString = dateString;
    }

    public String getP_id() {
        return p_id;
    }

    public void setP_id(String p_id) {
        this.p_id = p_id;
    }

    public int getAmount() {
        return amount;
    }

    public void setAmount(int amount) {
        this.amount = amount;
    }

    public String getPname() {
        return pname;
    }

    public void setPname(String pname) {
        this.pname = pname;
    }

    public int getCategory_id() {
        return category_id;
    }

    public void setCategory_id(int category_id) {
        this.category_id = category_id;
    }

    public float getPrice() {
        return price;
    }

    public void setPrice(float price) {
        this.price = price;
    }

    public String getFlag() {
        return flag;
    }

    public void setFlag(String flag) {
        this.flag = flag;
    }

    /**
     * private int order_id; private String dateString; private int p_id;
     * private int amount; private String pname; private int category_id;
     * private float price;
     */
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeInt(order_id);
        out.writeUTF(dateString);
        out.writeUTF(p_id);
        out.writeInt(amount);
        out.writeUTF(pname);
        out.writeInt(category_id);
        out.writeFloat(price);
        out.writeUTF(flag);

    }

    @Override
    public void readFields(DataInput in) throws IOException {
        this.order_id = in.readInt();
        this.dateString = in.readUTF();
        this.p_id = in.readUTF();
        this.amount = in.readInt();
        this.pname = in.readUTF();
        this.category_id = in.readInt();
        this.price = in.readFloat();
        this.flag = in.readUTF();

    }

    @Override
    public String toString() {
        return "order_id=" + order_id + ", dateString=" + dateString + ", p_id=" + p_id + ", amount=" + amount
                + ", pname=" + pname + ", category_id=" + category_id + ", price=" + price + ", flag=" + flag;
    }

}

              mapreduce主程序類：

package com.empire.hadoop.mr.rjoin;

import java.io.IOException;
import java.util.ArrayList;

import org.apache.commons.beanutils.BeanUtils;
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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**
 * 訂單表和商品表合到一塊兒
order.txt(訂單id, 日期, 商品編號, 數量)
	1001	20150710	P0001	2
	1002	20150710	P0001	3
	1002	20150710	P0002	3
	1003	20150710	P0003	3
product.txt(商品編號, 商品名字, 價格, 數量)
	P0001	小米5	1001	2
	P0002	錘子T1	1000	3
	P0003	錘子	1002	4
 */
public class RJoin {

	static class RJoinMapper extends Mapper<LongWritable, Text, Text, InfoBean> {
		InfoBean bean = new InfoBean();
		Text k = new Text();

		@Override
		protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

			String line = value.toString();

			FileSplit inputSplit = (FileSplit) context.getInputSplit();
			String name = inputSplit.getPath().getName();
			// 經過文件名判斷是哪一種數據
			String pid = "";
			if (name.startsWith("order")) {
				String[] fields = line.split("\t");
				// id date pid amount
				pid = fields[2];
				bean.set(Integer.parseInt(fields[0]), fields[1], pid, Integer.parseInt(fields[3]), "", 0, 0, "0");

			} else {
				String[] fields = line.split("\t");
				// id pname category_id price
				pid = fields[0];
				bean.set(0, "", pid, 0, fields[1], Integer.parseInt(fields[2]), Float.parseFloat(fields[3]), "1");

			}
			k.set(pid);
			context.write(k, bean);
		}

	}

	static class RJoinReducer extends Reducer<Text, InfoBean, InfoBean, NullWritable> {

		@Override
		protected void reduce(Text pid, Iterable<InfoBean> beans, Context context) throws IOException, InterruptedException {
			InfoBean pdBean = new InfoBean();
			ArrayList<InfoBean> orderBeans = new ArrayList<InfoBean>();

			for (InfoBean bean : beans) {
				if ("1".equals(bean.getFlag())) {	//產品的
					try {
						BeanUtils.copyProperties(pdBean, bean);
					} catch (Exception e) {
						e.printStackTrace();
					}
				} else {
					InfoBean odbean = new InfoBean();
					try {
						BeanUtils.copyProperties(odbean, bean);
						orderBeans.add(odbean);
					} catch (Exception e) {
						e.printStackTrace();
					}
				}

			}

			// 拼接兩類數據造成最終結果
			for (InfoBean bean : orderBeans) {

				bean.setPname(pdBean.getPname());
				bean.setCategory_id(pdBean.getCategory_id());
				bean.setPrice(pdBean.getPrice());

				context.write(bean, NullWritable.get());
			}
		}
	}

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		
		conf.set("mapred.textoutputformat.separator", "\t");
		
		Job job = Job.getInstance(conf);

		// 指定本程序的jar包所在的本地路徑
		// job.setJarByClass(RJoin.class);
//		job.setJar("D:/join.jar");

		job.setJarByClass(RJoin.class);
		// 指定本業務job要使用的mapper/Reducer業務類
		job.setMapperClass(RJoinMapper.class);
		job.setReducerClass(RJoinReducer.class);

		// 指定mapper輸出數據的kv類型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(InfoBean.class);

		// 指定最終輸出的數據的kv類型
		job.setOutputKeyClass(InfoBean.class);
		job.setOutputValueClass(NullWritable.class);

		// 指定job的輸入原始文件所在目錄
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		// 指定job的輸出結果所在目錄
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 將job中配置的相關參數，以及job所用的java類所在的jar包，提交給yarn去運行
		/* job.submit(); */
		boolean res = job.waitForCompletion(true);
		System.exit(res ? 0 : 1);

	}
}

        3、運行程序

#上傳jar

Alt+p
lcd d:/
put  rjoin.jar

#準備hadoop處理的數據文件

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

#運行rjoin程序

hadoop jar rjoin.jar  com.empire.hadoop.mr.rjoin.RJoin /rjoin/input /rjoin/outputs

        4、運行效果

[main] DEBUG org.apache.hadoop.ipc.ProtobufRpcEngine - Call: getJobReport took 5ms
[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 (1318427113) connection to centos-aaron-h3/192.168.29.146:34672 from hadoop sending #116 org.apache.hadoop.mapreduce.v2.api.MRClientProtocolPB.getJobReport
[IPC Client (1318427113) connection to centos-aaron-h3/192.168.29.146:34672 from hadoop] DEBUG org.apache.hadoop.ipc.Client - IPC Client (1318427113) connection to centos-aaron-h3/192.168.29.146:34672 from hadoop got value #116
[main] DEBUG org.apache.hadoop.ipc.ProtobufRpcEngine - Call: getJobReport took 7ms
[main] INFO org.apache.hadoop.mapreduce.Job - Job job_1544487152077_0003 completed successfully
[main] DEBUG org.apache.hadoop.security.UserGroupInformation - PrivilegedAction as:hadoop (auth:SIMPLE) from:org.apache.hadoop.mapreduce.Job.getCounters(Job.java:817)
[IPC Parameter Sending Thread #0] DEBUG org.apache.hadoop.ipc.Client - IPC Client (1318427113) connection to centos-aaron-h3/192.168.29.146:34672 from hadoop sending #117 org.apache.hadoop.mapreduce.v2.api.MRClientProtocolPB.getCounters
[IPC Client (1318427113) connection to centos-aaron-h3/192.168.29.146:34672 from hadoop] DEBUG org.apache.hadoop.ipc.Client - IPC Client (1318427113) connection to centos-aaron-h3/192.168.29.146:34672 from hadoop got value #117
[main] DEBUG org.apache.hadoop.ipc.ProtobufRpcEngine - Call: getCounters took 111ms
[main] INFO org.apache.hadoop.mapreduce.Job - Counters: 49
	File System Counters
		FILE: Number of bytes read=339
		FILE: Number of bytes written=569177
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=378
		HDFS: Number of bytes written=452
		HDFS: Number of read operations=9
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
	Job Counters 
		Launched map tasks=2
		Launched reduce tasks=1
		Data-local map tasks=2
		Total time spent by all maps in occupied slots (ms)=17791
		Total time spent by all reduces in occupied slots (ms)=3709
		Total time spent by all map tasks (ms)=17791
		Total time spent by all reduce tasks (ms)=3709
		Total vcore-milliseconds taken by all map tasks=17791
		Total vcore-milliseconds taken by all reduce tasks=3709
		Total megabyte-milliseconds taken by all map tasks=18217984
		Total megabyte-milliseconds taken by all reduce tasks=3798016
	Map-Reduce Framework
		Map input records=7
		Map output records=7
		Map output bytes=319
		Map output materialized bytes=345
		Input split bytes=230
		Combine input records=0
		Combine output records=0
		Reduce input groups=3
		Reduce shuffle bytes=345
		Reduce input records=7
		Reduce output records=4
		Spilled Records=14
		Shuffled Maps =2
		Failed Shuffles=0
		Merged Map outputs=2
		GC time elapsed (ms)=552
		CPU time spent (ms)=3590
		Physical memory (bytes) snapshot=554237952
		Virtual memory (bytes) snapshot=2538106880
		Total committed heap usage (bytes)=259047424
	Shuffle Errors
		BAD_ID=0
		CONNECTION=0
		IO_ERROR=0
		WRONG_LENGTH=0
		WRONG_MAP=0
		WRONG_REDUCE=0
	File Input Format Counters 
		Bytes Read=148
	File Output Format Counters 
		Bytes Written=452
[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 (1318427113) connection to centos-aaron-h3/192.168.29.146:34672 from hadoop sending #118 org.apache.hadoop.mapreduce.v2.api.MRClientProtocolPB.getJobReport
[IPC Client (1318427113) connection to centos-aaron-h3/192.168.29.146:34672 from hadoop] DEBUG org.apache.hadoop.ipc.Client - IPC Client (1318427113) connection to centos-aaron-h3/192.168.29.146:34672 from hadoop got value #118
[main] DEBUG org.apache.hadoop.ipc.ProtobufRpcEngine - Call: getJobReport took 2ms
[pool-4-thread-1] DEBUG org.apache.hadoop.ipc.Client - stopping client from cache: org.apache.hadoop.ipc.Client@4b5fd811
[Thread-3] DEBUG org.apache.hadoop.util.ShutdownHookManager - ShutdownHookManger complete shutdown.

        5、運行結果

[hadoop@centos-aaron-h1 ~]$ hdfs dfs -ls /rjoin/outputs
Found 2 items
-rw-r--r--   2 hadoop supergroup          0 2018-12-11 08:44 /rjoin/outputs/_SUCCESS
-rw-r--r--   2 hadoop supergroup        452 2018-12-11 08:44 /rjoin/outputs/part-r-00000
[hadoop@centos-aaron-h1 ~]$ hdfs dfs -cat  /rjoin/outputs/part-r-00000
order_id=1002, dateString=20150710, p_id=P0001, amount=3, pname=小米5, category_id=1001, price=2.0, flag=0
order_id=1001, dateString=20150710, p_id=P0001, amount=2, pname=小米5, category_id=1001, price=2.0, flag=0
order_id=1002, dateString=20150710, p_id=P0002, amount=3, pname=錘子T1, category_id=1000, price=3.0, flag=0
order_id=1003, dateString=20150710, p_id=P0003, amount=3, pname=錘子, category_id=1002, price=4.0, flag=0
[hadoop@centos-aaron-h1 ~]$

        6、補充知識

        mapreduce程序輸出的日誌路徑通常爲:

/home/hadoop/apps/hadoop-2.9.1/logs/userlogs/application_1544487152077_0004/container_1544487152077_0004_01_000003；

        其中/home/hadoop/apps/hadoop-2.9.1/logs/爲hadoop的安裝目錄下的logs。

 

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