Hadoop基础教程-第6章 MapReduce入门（6.2 解读WordCount）（草稿）

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第6章 MapReduce入门

6.2 解读WordCount

WordCount程序就是MapReduce的HelloWord程序。通过对WordCount程序分析，我们可以了解MapReduce程序的基本结构和执行过程。

6.2.1 WordCount设计思路

WordCount程序很好的体现了MapReduce编程思想。
一般来说，本文作为MapReduce的输入，MapReduce会将文本进行切分处理并将行号作为输入键值对的键，文本内容作为键值对的值，经map方法处理后，输出中间结果为<word,1>形式。MapReduce会默认按键值分发给reduce方法，在完成计数并输出最后结果<word,count>

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6.2.2 MapReduce运行方式

MapReduce运行方式分为本地运行和服务端运行两种。
本地运行多指本地Windows环境，方便开发调试。
而服务端运行，多用于实际生产环境。

6.2.3 编写代码

（1）创建Java 项目

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（2）修改Hadoop源码
注意，在Windows本地运行MapReduce程序时，需要修改Hadoop源码。如果在Linux服务器运行，则不需要修改Hadoop源码。

修改Hadoop源码，其实就是简单修改一下Hadoop的NativeIO类的源码

下载对应hadoop源代码，hadoop-2.7.3-src.tar.gz解压，hadoop-2.7.3-src\hadoop-common-project\hadoop-common\src\main\java\org\apache\hadoop\io\nativeio下NativeIO.java 复制到对应的Eclipse的project.
修改代码

    public static boolean access(String path, AccessRight desiredAccess)        throws IOException {        return true;        //return access0(path, desiredAccess.accessRight());    }

如果不修改NativeIO类的源码，在Windows本地运行MapReduce程序会产生异常：

log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).log4j:WARN Please initialize the log4j system properly.log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.Exception in thread "main" java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z    at org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Native Method)    at org.apache.hadoop.io.nativeio.NativeIO$Windows.access(NativeIO.java:609)    at org.apache.hadoop.fs.FileUtil.canRead(FileUtil.java:977)    at org.apache.hadoop.util.DiskChecker.checkAccessByFileMethods(DiskChecker.java:187)    at org.apache.hadoop.util.DiskChecker.checkDirAccess(DiskChecker.java:174)    at org.apache.hadoop.util.DiskChecker.checkDir(DiskChecker.java:108)    at org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.confChanged(LocalDirAllocator.java:285)    at org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.getLocalPathForWrite(LocalDirAllocator.java:344)    at org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:150)    at org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:131)    at org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:115)    at org.apache.hadoop.mapred.LocalDistributedCacheManager.setup(LocalDistributedCacheManager.java:125)    at org.apache.hadoop.mapred.LocalJobRunner$Job.<init>(LocalJobRunner.java:163)    at org.apache.hadoop.mapred.LocalJobRunner.submitJob(LocalJobRunner.java:731)    at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:240)    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1290)    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1287)    at java.security.AccessController.doPrivileged(Native Method)    at javax.security.auth.Subject.doAs(Unknown Source)    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1698)    at org.apache.hadoop.mapreduce.Job.submit(Job.java:1287)    at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1308)    at cn.hadron.mr.RunJob.main(RunJob.java:33)

（3）定义Mapper类

package cn.hadron.mr;import java.io.IOException;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.util.StringUtils;//4个泛型参数：前两个表示map的输入键值对的key和value的类型，后两个表示输出键值对的key和value的类型public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{    //该方法循环调用，从文件的split中读取每行调用一次，把该行所在的下标为key，该行的内容为value    protected void map(LongWritable key, Text value,Context context)            throws IOException, InterruptedException {        String[] words = StringUtils.split(value.toString(), ' ');        for(String w :words){            context.write(new Text(w), new IntWritable(1));        }    }}

代码说明：

Mapper类用于读取数据输入并执行map方法，编写Mapper类需要继承org.apache.hadoop.mapreduce.Mapper类，并且根据相应问题实现map方法。
Mapper类的4个泛型参数：前两个表示map的输入键值对的key和value的类型，后两个表示输出键值对的key和value的类型
MapReduce计算框架会将键值对作为参数传递给map方法。该方法有3个参数，第1个是Object类型（一般使用LongWritable类型）参数，代表行号，第2个是Object类型参数（一般使用Text类型），代表该行内容，第3个Context参数，代表上下文。
Context类全名是org.apache.hadoop.mapreduce.Mapper.Context，也就是说Context类是Mapper类的静态内容类，在Mapper类中可以直接使用Context类。
在map方法中使用StringUtils的split方法，按空格将输入行内容分割成单词，然后通过Context类的write方法将其作为中间结果输出。

（4）定义Reducer类

package cn.hadron.mr;import java.io.IOException;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{    /**     * Map过程输出<key,values>中key为单个单词，而values是对应单词的计数值所组成的列表，Map的输出就是Reduce的输入，     * 每组调用一次，这一组数据特点：key相同，value可能有多个。     * /所以reduce方法只要遍历values并求和，即可得到某个单词的总次数。     */    protected void reduce(Text key, Iterable<IntWritable> values,Context context)            throws IOException, InterruptedException {        int sum =0;        for(IntWritable i: values){            sum=sum+i.get();        }        context.write(key, new IntWritable(sum));    }}

代码说明：

Reducer类用于接收Mapper输出的中间结果作为Reducer类的输入，并执行reduce方法。
Reducer类的4个泛型参数：前2个代表reduce方法输入的键值对类型（对应map输出类型），后2个代表reduce方法输出键值对的类型
reduce方法参数：key是单个单词，values是对应单词的计数值所组成的列表，Context类型是org.apache.hadoop.mapreduce.Reducer.Context，是Reducer的上下文。

（6）定义主方法（主类）

package cn.hadron.mr;import 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.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;public class RunJob {    public static void main(String[] args) {        //设置环境变量HADOOP_USER_NAME，其值是root        System.setProperty("HADOOP_USER_NAME", "root");        //Configuration类包含了Hadoop的配置        Configuration config =new Configuration();        //设置fs.defaultFS        config.set("fs.defaultFS", "hdfs://192.168.80.131:9000");        //设置yarn.resourcemanager节点        config.set("yarn.resourcemanager.hostname", "node1");        try {            FileSystem fs =FileSystem.get(config);            Job job =Job.getInstance(config);            job.setJarByClass(RunJob.class);            job.setJobName("wc");            //设置Mapper类            job.setMapperClass(WordCountMapper.class);            //设置Reduce类            job.setReducerClass(WordCountReducer.class);            //设置reduce方法输出key的类型            job.setOutputKeyClass(Text.class);            //设置reduce方法输出value的类型            job.setOutputValueClass(IntWritable.class);            //指定输入路径            FileInputFormat.addInputPath(job, new Path("/user/root/input/"));            //指定输出路径（会自动创建）            Path outpath =new Path("/user/root/output/");            //输出路径是MapReduce自动创建的，如果存在则需要先删除            if(fs.exists(outpath)){                fs.delete(outpath, true);            }            FileOutputFormat.setOutputPath(job, outpath);            //提交任务，等待执行完成            boolean f= job.waitForCompletion(true);            if(f){                System.out.println("job任务执行成功");            }        } catch (Exception e) {            e.printStackTrace();        }    }}

（6）本地运行

执行结果：
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[root@node1 ~]# hdfs dfs -ls /user/root/outputFound 2 items-rw-r--r--   3 root supergroup          0 2017-05-28 09:01 /user/root/output/_SUCCESS-rw-r--r--   3 root supergroup         46 2017-05-28 09:01 /user/root/output/part-r-00000[root@node1 ~]# hdfs dfs -cat /user/root/output/part-r-00000Hadoop  2Hello   2Hi      1Java    2World   1world   1[root@node1 ~]#

6.2.4 服务端运行

（1）修改源码

上面代码中的主方法是根据本地运行设计的，如果要在服务器端运行，可以适当简化。
参照官方源码
http://hadoop.apache.org/docs/r2.7.3/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html

将Mapper类和Reducer类写成主类的静态内部类

package cn.hadron.mr;import java.io.IOException;import java.util.StringTokenizer;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;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;public class WordCount {    //4种形式的参数，分别用来指定map的输入key值类型、输入value值类型、输出key值类型和输出value值类型    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {        private final static IntWritable one = new IntWritable(1);        private Text word = new Text();        //map方法中value值存储的是文本文件中的一行（以回车符为行结束标记），而key值为该行的首字母相对于文本文件的首地址的偏移量        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {            StringTokenizer itr = new StringTokenizer(value.toString());            //StringTokenizer类将每一行拆分成为一个个的单词，并将<word,1>作为map方法的结果输出            while (itr.hasMoreTokens()) {                word.set(itr.nextToken());                context.write(word, one);            }        }    }    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {        private IntWritable result = new IntWritable();        //Map过程输出<key,values>中key为单个单词，而values是对应单词的计数值所组成的列表，Map的输出就是Reduce的输入，        //所以reduce方法只要遍历values并求和，即可得到某个单词的总次数。        public void reduce(Text key, Iterable<IntWritable> values, Context context)                throws IOException, InterruptedException {            int sum = 0;            for (IntWritable val : values) {                sum += val.get();            }            result.set(sum);            context.write(key, result);        }    }    //执行MapReduce任务    public static void main(String[] args) throws Exception {        Configuration conf = new Configuration();        Job job = Job.getInstance(conf, "wordCount");        job.setJarByClass(WordCount.class);        job.setMapperClass(TokenizerMapper.class);        job.setCombinerClass(IntSumReducer.class);        job.setReducerClass(IntSumReducer.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);        //命令行输入的第一个参数是输入路径，第二个参数是输出路径        FileInputFormat.addInputPath(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        System.exit(job.waitForCompletion(true) ? 0 : 1);    }}

（2）导出jar包

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（3）上传到服务器端运行
和前面一样，通过xftp将刚刚导出到桌面的wordcount.jar包上传到node1节点
这里写图片描述

[root@node1 ~]# hadoop jar wordcount.jar cn.hadron.mr.WordCount input output17/05/28 10:41:41 INFO client.RMProxy: Connecting to ResourceManager at node1/192.168.80.131:8032Exception in thread "main" org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory hdfs://node1:9000/user/root/output already exists    at org.apache.hadoop.mapreduce.lib.output.FileOutputFormat.checkOutputSpecs(FileOutputFormat.java:146)    at org.apache.hadoop.mapreduce.JobSubmitter.checkSpecs(JobSubmitter.java:266)    at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:139)    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1290)    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1287)    at java.security.AccessController.doPrivileged(Native Method)    at javax.security.auth.Subject.doAs(Subject.java:422)    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1698)    at org.apache.hadoop.mapreduce.Job.submit(Job.java:1287)    at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1308)    at cn.hadron.mr.WordCount.main(WordCount.java:59)    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)    at java.lang.reflect.Method.invoke(Method.java:498)    at org.apache.hadoop.util.RunJar.run(RunJar.java:221)    at org.apache.hadoop.util.RunJar.main(RunJar.java:136)

这是由于output目录已经存在，删除即可

[root@node1 ~]# hdfs dfs -rmr /user/root/outputrmr: DEPRECATED: Please use 'rm -r' instead.17/05/28 10:42:01 INFO fs.TrashPolicyDefault: Namenode trash configuration: Deletion interval = 0 minutes, Emptier interval = 0 minutes.Deleted /user/root/output

重新运行

[root@node1 ~]# hadoop jar wordcount.jar cn.hadron.mr.WordCount input output17/05/28 10:43:12 INFO client.RMProxy: Connecting to ResourceManager at node1/192.168.80.131:803217/05/28 10:43:14 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.17/05/28 10:43:15 INFO input.FileInputFormat: Total input paths to process : 217/05/28 10:43:15 INFO mapreduce.JobSubmitter: number of splits:217/05/28 10:43:16 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1495804618534_000117/05/28 10:43:17 INFO impl.YarnClientImpl: Submitted application application_1495804618534_000117/05/28 10:43:17 INFO mapreduce.Job: The url to track the job: http://node1:8088/proxy/application_1495804618534_0001/17/05/28 10:43:17 INFO mapreduce.Job: Running job: job_1495804618534_000117/05/28 10:43:43 INFO mapreduce.Job: Job job_1495804618534_0001 running in uber mode : false17/05/28 10:43:43 INFO mapreduce.Job:  map 0% reduce 0%17/05/28 10:44:19 INFO mapreduce.Job:  map 100% reduce 0%17/05/28 10:44:33 INFO mapreduce.Job:  map 100% reduce 100%17/05/28 10:44:35 INFO mapreduce.Job: Job job_1495804618534_0001 completed successfully17/05/28 10:44:36 INFO mapreduce.Job: Counters: 50    File System Counters        FILE: Number of bytes read=89        FILE: Number of bytes written=355368        FILE: Number of read operations=0        FILE: Number of large read operations=0        FILE: Number of write operations=0        HDFS: Number of bytes read=301        HDFS: Number of bytes written=46        HDFS: Number of read operations=9        HDFS: Number of large read operations=0        HDFS: Number of write operations=2    Job Counters         Killed map tasks=1        Launched map tasks=2        Launched reduce tasks=1        Data-local map tasks=2        Total time spent by all maps in occupied slots (ms)=62884        Total time spent by all reduces in occupied slots (ms)=12445        Total time spent by all map tasks (ms)=62884        Total time spent by all reduce tasks (ms)=12445        Total vcore-milliseconds taken by all map tasks=62884        Total vcore-milliseconds taken by all reduce tasks=12445        Total megabyte-milliseconds taken by all map tasks=64393216        Total megabyte-milliseconds taken by all reduce tasks=12743680    Map-Reduce Framework        Map input records=6        Map output records=14        Map output bytes=140        Map output materialized bytes=95        Input split bytes=216        Combine input records=14        Combine output records=7        Reduce input groups=6        Reduce shuffle bytes=95        Reduce input records=7        Reduce output records=6        Spilled Records=14        Shuffled Maps =2        Failed Shuffles=0        Merged Map outputs=2        GC time elapsed (ms)=860        CPU time spent (ms)=10230        Physical memory (bytes) snapshot=503312384        Virtual memory (bytes) snapshot=6236766208        Total committed heap usage (bytes)=301146112    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=85    File Output Format Counters         Bytes Written=46[root@node1 ~]#

查看结果

[root@node1 ~]# hdfs dfs -ls /user/root/outputFound 2 items-rw-r--r--   3 root supergroup          0 2017-05-28 10:44 /user/root/output/_SUCCESS-rw-r--r--   3 root supergroup         46 2017-05-28 10:44 /user/root/output/part-r-00000[root@node1 ~]# hdfs dfs -cat /user/root/output/part-r-00000Hadoop  2Hello   2Hi      1Java    2World   1world   1

问题补充

2017-06-24
今天再次运行之前写的MapReduce程序时，报错：

(null) entry in command string: null chmod 0700

解决办法：
（1）下载hadoop-2.7.3.tar.gz，解压缩。比如解压缩到D盘，hadoop根目录就是D:\hadoop-2.7.3
（2）拷贝debug工具(winutils.exe)到HADOOP_HOME/bin
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（3）设置环境变量

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