2.2.3 hadoop体系之离线计算-mapreduce分布式计算-MapReduce分区

清疚 2022-12-13 01:50 154阅读 0赞

**目录**

1.复习MapReduce的8个步骤

2.MapReduce中的分区

2.1 新需求：需要分别reduce

3.IDEA实现分区-JAVA

3.1 实现自定义Partitioner-MyPartitioner.java

3.2 设置主函数：定义分区+设置分区数目

3.3 打成jar包

4.运行并且查看结果

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

# 1.复习MapReduce的8个步骤 #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1N1eWViaXViaXU_size_16_color_FFFFFF_t_70][]

# 2.MapReduce中的分区 #

在MapReduce中, 通过我们指定分区, 会将同一个分区的数据发送到同一个 Reduce 当中进行处理。例如: 为了数据的统计, 可以把一批类似的数据发送到同一个 Reduce 当中, 在同一个Reduce 当中统计相同类型的数据, 就可以实现类似的数据分区和统计等 其实就是相同类型的数据, 有共性的数据, 送到一起去处理。

Reduce 当中默认的分区只有一个。包括我们上次写的wordcount案例，都是一个分区一个reduce。

## 2.1 新需求：需要分别reduce ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1N1eWViaXViaXU_size_16_color_FFFFFF_t_70 1][]

# 3.IDEA实现分区-JAVA #

## 3.1 实现自定义Partitioner-MyPartitioner.java ##

主要的逻辑就在这里, 这也是这个案例的意义, 通过 Partitioner 将数据分发给不同的 Reducer

package com.ucas.mapredece;
    
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Partitioner;
    
    /**
     * @author GONG
     * @version 1.0
     * @date 2020/10/9 15:44
     */
    public class MyPartitioner extends Partitioner<Text, LongWritable> {
        /*
            Text:表示K2
            LongWritable:表示V2
            i：表示reduce的个数
         */
        @Override
        public int getPartition(Text text, LongWritable longWritable, int i) {
            //如果单词长度大于等于5，进入第一分区，否则进入第二分区
            if (text.toString().length() >= 5) {
                return 0;
            } else {
                return 1;
            }
        }
    }

## 3.2 设置主函数：定义分区+设置分区数目 ##

package com.ucas.mapredece;
    
    
    import org.apache.hadoop.conf.Configuration;
    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.lib.input.TextInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
    import org.apache.hadoop.util.Tool;
    import org.apache.hadoop.util.ToolRunner;
    import org.apache.hadoop.conf.Configured;
    
    public class JobMain extends Configured implements Tool {
    
        @Override
        public int run(String[] args) throws Exception {
    
            Job job = Job.getInstance(super.getConf(), JobMain.class.getSimpleName());
            //打包到集群上面运行时候，必须要添加以下配置，指定程序的main函数
            job.setJarByClass(JobMain.class);
            //第一步：读取输入文件解析成key，value对
            job.setInputFormatClass(TextInputFormat.class);
            TextInputFormat.addInputPath(job, new Path("hdfs://192.168.0.101:8020/wordcount"));
            //第二步：设置我们的mapper类
            job.setMapperClass(WordCountMapper.class);
            //设置我们map阶段完成之后的输出类型
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(LongWritable.class);
            //第三步，第四步，第五步，第六步
            //第三步：设置分区规则
            job.setPartitionerClass(MyPartitioner.class);
    
            //第七步：设置我们的reduce类
            job.setReducerClass(WordCountReducer.class);
            //设置我们reduce阶段完成之后的输出类型
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(LongWritable.class);
    
            //两个分区用到两个reduce，设置reduce个数
            job.setNumReduceTasks(2);
    
            //第八步：设置输出类以及输出路径
            job.setOutputFormatClass(TextOutputFormat.class);
            TextOutputFormat.setOutputPath(job, new Path("hdfs://192.168.0.101:8020/wordcount_out"));
            //上面那个路径时不允许存在的，会帮我们自动创建这个文件夹
            boolean b = job.waitForCompletion(true);
            return b ? 0 : 1;
        }
    
        /**
         * 程序main函数的入口类
         *
         * @param args
         * @throws Exception
         */
        public static void main(String[] args) throws Exception {
            Configuration configuration = new Configuration();
            Tool tool = new JobMain();
            int run = ToolRunner.run(configuration, tool, args);
            System.exit(run);
        }
    }

## 3.3 打成jar包 ##

先clean一下以前的东西，再双击packet打包

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1N1eWViaXViaXU_size_16_color_FFFFFF_t_70 2][]

# 4.运行并且查看结果 #

将jar发送到node01中的 /export/software

进入：cd /export/software

**运行命令：hadoop jar day03\_mapreduce\_wordcount-1.0-SNAPSHOT.jar com.ucas.mapredece.JobMain**

[root@node01 software]# hadoop jar day03_mapreduce_wordcount-1.0-SNAPSHOT.jar com.ucas.mapredece.JobMain
    2020-10-10 00:08:46,441 INFO client.RMProxy: Connecting to ResourceManager at node01/192.168.0.101:8032
    2020-10-10 00:08:47,468 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/root/.staging/job_1602247634978_0002
    2020-10-10 00:08:47,823 INFO input.FileInputFormat: Total input files to process : 1
    2020-10-10 00:08:47,990 INFO mapreduce.JobSubmitter: number of splits:1
    2020-10-10 00:08:48,052 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
    2020-10-10 00:08:48,342 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1602247634978_0002
    2020-10-10 00:08:48,345 INFO mapreduce.JobSubmitter: Executing with tokens: []
    2020-10-10 00:08:48,636 INFO conf.Configuration: resource-types.xml not found
    2020-10-10 00:08:48,636 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
    2020-10-10 00:08:48,757 INFO impl.YarnClientImpl: Submitted application application_1602247634978_0002
    2020-10-10 00:08:48,835 INFO mapreduce.Job: The url to track the job: http://node01:8088/proxy/application_1602247634978_0002/
    2020-10-10 00:08:48,836 INFO mapreduce.Job: Running job: job_1602247634978_0002
    2020-10-10 00:09:00,140 INFO mapreduce.Job: Job job_1602247634978_0002 running in uber mode : false
    2020-10-10 00:09:00,156 INFO mapreduce.Job:  map 0% reduce 0%
    2020-10-10 00:09:10,638 INFO mapreduce.Job:  map 100% reduce 0%
    2020-10-10 00:09:18,756 INFO mapreduce.Job:  map 100% reduce 50%
    2020-10-10 00:09:19,772 INFO mapreduce.Job:  map 100% reduce 100%
    2020-10-10 00:09:25,853 INFO mapreduce.Job: Job job_1602247634978_0002 completed successfully
    2020-10-10 00:09:26,115 INFO mapreduce.Job: Counters: 53
    	File System Counters
    		FILE: Number of bytes read=203
    		FILE: Number of bytes written=647888
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=185
    		HDFS: Number of bytes written=70
    		HDFS: Number of read operations=13
    		HDFS: Number of large read operations=0
    		HDFS: Number of write operations=4
    	Job Counters 
    		Launched map tasks=1
    		Launched reduce tasks=2
    		Data-local map tasks=1
    		Total time spent by all maps in occupied slots (ms)=7940
    		Total time spent by all reduces in occupied slots (ms)=11782
    		Total time spent by all map tasks (ms)=7940
    		Total time spent by all reduce tasks (ms)=11782
    		Total vcore-milliseconds taken by all map tasks=7940
    		Total vcore-milliseconds taken by all reduce tasks=11782
    		Total megabyte-milliseconds taken by all map tasks=8130560
    		Total megabyte-milliseconds taken by all reduce tasks=12064768
    	Map-Reduce Framework
    		Map input records=4
    		Map output records=12
    		Map output bytes=167
    		Map output materialized bytes=203
    		Input split bytes=114
    		Combine input records=0
    		Combine output records=0
    		Reduce input groups=9
    		Reduce shuffle bytes=203
    		Reduce input records=12
    		Reduce output records=9
    		Spilled Records=24
    		Shuffled Maps =2
    		Failed Shuffles=0
    		Merged Map outputs=2
    		GC time elapsed (ms)=279
    		CPU time spent (ms)=2730
    		Physical memory (bytes) snapshot=604041216
    		Virtual memory (bytes) snapshot=7283023872
    		Total committed heap usage (bytes)=318500864
    		Peak Map Physical memory (bytes)=364597248
    		Peak Map Virtual memory (bytes)=2409140224
    		Peak Reduce Physical memory (bytes)=122572800
    		Peak Reduce Virtual memory (bytes)=2436947968
    	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=71
    	File Output Format Counters 
    		Bytes Written=70
    [root@node01 software]#

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1N1eWViaXViaXU_size_16_color_FFFFFF_t_70 3][]

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