• MapReduce(五)


                                                        MapReduce的(五)

    1.MapReduce的多表关联查询。

            根据文本数据格式。查询多个文本中的内容关联。查询。

    2.MapReduce的多任务窜执行的使用

        多任务的串联执行问题,主要是要建立controlledjob,然后建组管理起来。留意多线程因效率而导致执行结束时间不一致的问题。

    -------------------------------------------------- -------------------------------------------------- ----------------------------


    MapReduce的的的多表关联查询

    数据:

    ctoryname地址
    北京红星1 
    深圳迅雷3 
    广州本田2 
    北京瑞星1 
    广州发展银行2 
    腾讯3 
    北京银行5
    addressID地址名称
    1北京
    2广州
    3深圳
    4西安

    代码:

    包com.huhu.day05; 
    
    import java.io.IOException; 
    
    导入org.apache.hadoop.conf.Configuration; 
    import org.apache.hadoop.fs.FileSystem; 
    import org.apache.hadoop.fs.Path; 
    import org.apache.hadoop.io.LongWritable; 
    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.output.FileOutputFormat; 
    import org.apache.hadoop.util.GenericOptionsParser; 
    import org.apache.hadoop.util.Tool; 
    import org.apache.hadoop.util.ToolRunner;
    
    import com.huhu.day04.ProgenyCount; 
    
    / ** 
     * *厂名厂址为北京红星1 
     * 
     *地址addressID地址名称1北京
     * 
     *从工厂选择factory.factoryname,address.addressname,地址在哪里
     * factory.addressed = address.addressID 
     * 
     *流程1.读取这2个文件?1个mapreduce 2.mapper 2个:map--同时可以处理2个文件代码3.map输出kv k:id 
     * v:t1:北京红星1 k:id v:t2:1北京4.降低价值{t1:北京红
     *星1,t2:1北京} 
     * 
     * @作者huhu_k 
     * 
     * / 
    公共类扩展ToolRunner implements Tool { 
    
    	私人配置conf; 
    
    	公共静态类MyMapper扩展Mapper <LongWritable,文本,文本,文本> { 
    
    		@覆盖
    		protected void map(LongWritable key,Text value,Context context)throws IOException,InterruptedException {
    			String [] line = value.toString()。split(“ t”); 
    			如果(line [0] .matches(“\ d”)){ 
    				// k:1 v:t1:1:北京
    				context.write(new Text(line [0]),new Text(“t1”+ line [0] +“:”+ line [1])); 
    			} else { 
    				// k:1 v:t2:beijingredstar:1 
    				context.write(new Text(line [1]),new Text(“t2”+ line [0] +“:”+ line [1])) ; 
    			} 
    		} 
    	} 
    
    	公共静态类MyReduce扩展减速器{ 
    
    		@覆盖
    		保护无效设置(上下文上下文)抛出IOException,InterruptedException { 
    			context.write(new Text(“factoryname  t  t”),新文本(“地址名称”)); 
    		} 
    
    		@覆盖
    		protected void reduce(Text key,Iterable <Text> values,Context context)
    				抛出IOException,InterruptedException {
    			String fsc =“”; 
    			String addr =“”; 
    			for(Text s:values){ 
    				String line = s.toString(); 
    				if(line.contains(“t1”)){ 
    					addr = line.split(“:”)[1]; 
    				} else if(line.contains(“t2”)){ 
    					fsc = line.split(“:”)[0]; 
    				} 
    			} 
    
    			if(!fsc.equals(“”)&&!addr.equals(“”)){ 
    				context.write(new Text(fsc),new Text(addr)); 
    			} 
    		} 
    
    		@覆盖
    		保护无效清理(上下文上下文)抛出IOException,InterruptedException { 
    		} 
    	} 
    
    	公共静态无效的主要(字符串[]参数)抛出异常{ 
    		多重连接t = new MutipleJoin(); 
    		配置conf = t.getConf (); 
    		String [] other = new GenericOptionsParser(conf,args).getRemainingArgs();
    		if(other.length!= 2){ 
    			System.err.println(“number is fail”); 
    		} 
    		int run = ToolRunner.run(conf,t,args); 
    		System.exit(运行); 
    	} 
    
    	@覆盖
    	public Configuration getConf(){ 
    		if(conf!= null){ 
    			返回conf; 
    		} 
    		返回新的配置(); 
    	} 
    
    	@覆盖
    	public void setConf(Configuration arg0){ 
    
    	} 
    
    	@覆盖
    	公共诠释运行(字符串[]其他)抛出异常{ 
    		配置con = getConf(); 
    		Job job = Job.getInstance(con); 
    		job.setJarByClass(ProgenyCount.class); 
    		job.setMapperClass(MyMapper.class); 
    		job.setMapOutputKeyClass(Text.class); 
    		job.setMapOutputValueClass(Text.class); 
    
    		//默认分区
    		// job.setPartitionerClass(HashPartitioner.class); 
    
    		job.setReducerClass(MyReduce.class); 
    		job.setOutputKeyClass(Text.class); 
    		job.setOutputValueClass(Text.class); 
    
    		FileInputFormat.addInputPath(job,new Path(“hdfs:// ry-hadoop1:8020 / in / day05”)); 
    		Path path = new Path(“hdfs:// ry-hadoop1:8020 / out / day05.txt”); 
    		FileSystem fs = FileSystem.get(getConf()); 
    		if(fs.exists(path)){ 
    			fs.delete(path,true); 
    		} 
    		FileOutputFormat.setOutputPath(job,path); 
    
    		返回job.waitForCompletion(true)?0:1; 
    	} 
    
    }

    运行结果:


    将有规律的数据进行关联查询。


    二。MapReduce的的多任务窜改的使用

    WordCount_Mapper
    包com.huhu.day05; 
    
    import java.io.IOException; 
    
    import org.apache.hadoop.io.IntWritable; 
    import org.apache.hadoop.io.LongWritable; 
    import org.apache.hadoop.io.Text; 
    导入org.apache.hadoop.mapreduce.Mapper; 
    
    公共类WordCount_Mapper扩展映射器<LongWritable,Text,Text,IntWritable> { 
    
    	private final IntWritable one = new IntWritable(1); 
    
    	@覆盖
    	保护无效映射(LongWritable键,文本值,映射器<LongWritable,文本,文本,IntWritable> .Context上下文)
    			抛出IOException,InterruptedException { 
    		String [] line = value.toString()。split(“”); 
    		for(String s:line){ 
    			context.write(new Text(s),one); 
    		} 
    	} 
    }


    WordCount_Reduce

    包com.huhu.day05; 
    
    import java.io.IOException; 
    
    import org.apache.hadoop.io.IntWritable; 
    import org.apache.hadoop.io.Text; 
    import org.apache.hadoop.mapreduce.Reducer; 
    
    公共类WordCount_Reducer扩展Reducer <Text,IntWritable,Text,IntWritable> { 
    
    	@覆盖
    	protected void reduce(Text key,Iterable <IntWritable> values,Context context)
    			抛出IOException,InterruptedException { 
    
    		int sum = 0; 
    
    		for(IntWritable i:values){ 
    			sum + = i.get(); 
    		} 
    		context.write(key,new IntWritable(sum)); 
    	} 
    }
    

    Top10_Mapper

    包com.huhu.day05; 
    
    import java.io.IOException; 
    
    import org.apache.hadoop.io.IntWritable; 
    import org.apache.hadoop.io.LongWritable; 
    import org.apache.hadoop.io.Text; 
    导入org.apache.hadoop.mapreduce.Mapper; 
    
    公共类Top10_Mapper扩展了Mapper <LongWritable,Text,Text,IntWritable> { 
    
    	@覆盖
    	protected void map(LongWritable key,Text value,Context context)throws IOException,InterruptedException { 
    		String [] line = value.toString()。split(“ t”); 
    		context.write(new Text(line [0]),new IntWritable(Integer.parseInt(line [1]))); 
    	} 
    }
    

    Top10_Reducer

    包com.huhu.day05; 
    
    import java.io.IOException; 
    import java.util.TreeSet; 
    
    import org.apache.hadoop.io.IntWritable; 
    import org.apache.hadoop.io.NullWritable; 
    import org.apache.hadoop.io.Text; 
    import org.apache.hadoop.mapreduce.Reducer; 
    
    import com.huhu.day05.pojo.WordCount; 
    
    公共类Top10_Reducer扩展Reducer <Text,IntWritable,WordCount,NullWritable> { 
    
    	private TreeSet <WordCount> set = new TreeSet <>(); 
    
    	@覆盖
    	protected void reduce(Text key,Iterable <IntWritable> values,Context context)
    			抛出IOException,InterruptedException { 
    
    		for(IntWritable v:values){ 
    			System.err.println(v.toString()+“----- -----------”);
    			set.add(new WordCount(key.toString(),Integer.parseInt(v.toString()))); 
    		} 
    
    		if(10 <set.size()){ 
    			set.remove(set.last()); 
    		} 
    
    	} 
    
    	@覆盖
    	保护无效清理(上下文上下文)抛出IOException,InterruptedException { 
    		for(WordCount w:set){ 
    			context.write(w,NullWritable.get()); 
    		} 
    	} 
    }
    

    WordCountTop_Cuan

    包com.huhu.day05; 
    
    导入org.apache.hadoop.conf.Configuration; 
    import org.apache.hadoop.fs.FileSystem; 
    import org.apache.hadoop.fs.Path; 
    import org.apache.hadoop.io.IntWritable; 
    import org.apache.hadoop.io.NullWritable; 
    import org.apache.hadoop.io.Text; 
    import org.apache.hadoop.mapred.jobcontrol.JobControl; 
    import org.apache.hadoop.mapreduce.Job; 
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; 
    import org.apache.hadoop.mapreduce.lib.jobcontrol.ControlledJob; 
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 
    import org.apache.hadoop.util.GenericOptionsParser; 
    import org.apache.hadoop.util.Tool; 
    import org.apache.hadoop.util.ToolRunner;
    
    import com.huhu.day05.pojo.WordCount; 
    
    公共类WordCountTop_Cuan扩展ToolRunner实现工具{ 
    
    	私人配置con; 
    
    	@覆盖
    	public配置getConf(){ 
    		如果(con!= null)
    			返回con; 
    		返回新的配置(); 
    	} 
    
    	@覆盖
    	public void setConf(Configuration arg0){ 
    	} 
    
    	@覆盖
    	公共诠释运行(字符串[] arg0)抛出异常{ 
    		配置con = getConf();} 
    		Job WordJob = Job.getInstance(con,“WordCount Job”); 
    		WordJob.setJarByClass(WordCountTop_Cuan.class); 
    		WordJob.setMapperClass(WordCount_Mapper.class); 
    		WordJob.setMapOutputKeyClass(Text.class); 
    		WordJob.setMapOutputValueClass(IntWritable.class); 
    
    		WordJob.setReducerClass(WordCount_Reducer.class);
    		WordJob.setOutputKeyClass(WordCount.class); 
    		WordJob.setOutputValueClass(NullWritable.class); 
    
    		FileInputFormat.addInputPath(WordJob,new Path(“hdfs:// ry-hadoop1:8020 / in / ihaveadream.txt”)); 
    		Path path = new Path(“hdfs:// ry-hadoop1:8020 / out / Word_job.txt”); 
    		FileSystem fs = FileSystem.get(getConf()); 
    		if(fs.exists(path)){ 
    			fs.delete(path,true); 
    		} 
    		FileOutputFormat.setOutputPath(WordJob,path); 
    
    		Job TopJob = Job.getInstance(con,“Top10 Job”); 
    		TopJob.setJarByClass(WordCountTop_Cuan.class); 
    		TopJob.setMapperClass(Top10_Mapper.class); 
    		TopJob.setMapOutputKeyClass(Text.class); 
    		TopJob.setMapOutputValueClass(IntWritable.class);
    
    		TopJob.setReducerClass(Top10_Reducer.class); 
    		TopJob.setOutputKeyClass(WordCount.class); 
    		TopJob.setOutputValueClass(NullWritable.class); 
    
    		FileInputFormat.addInputPath(TOPJOB,路径); 
    		Path paths = new Path(“hdfs:// ry-hadoop1:8020 / out / Top_Job.txt”); 
    		if(fs.exists(paths)){ 
    			fs.delete(paths,true); 
    		} 
    		FileOutputFormat.setOutputPath(TopJob,paths); 
    
    		//重点
    		ControlledJob controlledWC = new ControlledJob(WordJob.getConfiguration()); 
    		ControlledJob controlledTP = new ControlledJob(TopJob.getConfiguration()); 
    
    		// JobTop依赖JobWC 
    		controlledTP.addDependingJob(controlledWC); 
    		//定义控制器
    		JobControl jobControl =新的JobControl(“WordCount和Top”); 
    		jobControl.addJob(controlledWC); 
    		jobControl.addJob(controlledTP); 
    
    		线程线程=新线程(JobControl作业控制); 
    		thread.start(); 
    
    		而{(jobControl.allFinished()!)
    			了了Thread.sleep(1000); 
    		} 
    
    		jobControl.stop(); 
    
    		返回0; 
    	} 
    
    	公共静态无效的主要(字符串[]参数)抛出异常{ 
    
    		WordCountTop_Cuan wc = new WordCountTop_Cuan(); 
    		配置conf = wc.getConf(); 
    		String [] other = new GenericOptionsParser(conf,args).getRemainingArgs(); 
    		int run = ToolRunner.run(conf,wc,other); 
    		System.exit(运行); 
    	} 
    }

    运行结果:




    我是在本地运行的,如果在Hadoop的的上运行输入命令

    hadoop jar xxx.jar /in/xx.txt /out/Word_Job.txt /out/Top_Job.txt

    此时Top_Job依赖于Word_Job

    因为Top_Job的输入路径是Word_Job的输出路径。当线程只启动一个工作,Top_job等待Word_Job运行完,Top_Job开始运行。


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  • 原文地址:https://www.cnblogs.com/meiLinYa/p/9252101.html
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