Hadoop基础教程-第6章 MapReduce入门（6.2 解读WordCount）（草稿）
2017-05-28 17:50
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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>
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
（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：
[root@node1 ~]# hdfs dfs -ls /user/root/output Found 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-00000 Hadoop 2 Hello 2 Hi 1 Java 2 World 1 world 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
（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
[root@node1 ~]# hadoop jar wordcount.jar cn.hadron.mr.WordCount input output 17/05/28 10:41:41 INFO client.RMProxy: Connecting to ResourceManager at node1/192.168.80.131:8032 Exception 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/output rmr: 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 output 17/05/28 10:43:12 INFO client.RMProxy: Connecting to ResourceManager at node1/192.168.80.131:8032 17/05/28 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 : 2 17/05/28 10:43:15 INFO mapreduce.JobSubmitter: number of splits:2 17/05/28 10:43:16 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1495804618534_0001 17/05/28 10:43:17 INFO impl.YarnClientImpl: Submitted application application_1495804618534_0001 17/05/28 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_0001 17/05/28 10:43:43 INFO mapreduce.Job: Job job_1495804618534_0001 running in uber mode : false 17/05/28 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 successfully 17/05/28 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/output Found 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-00000 Hadoop 2 Hello 2 Hi 1 Java 2 World 1 world 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
（3）设置环境变量
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