mapreduce学习笔记一：WordCound
MapReduce采用的是“分而治之”的思想，把对大规模数据集的操作，分发给一个主节点管理下的各个从节点共同完成，然后通过整合各个节点的中间结果，得到最终结果。简单来说，MapReduce就是”任务的分解与结果的汇总“。
1.MapReduce的工作原理
在分布式计算中，MapReduce框架负责处理了并行编程里分布式存储、工作调度，负载均衡、容错处理以及网络通信等复杂问题，现在我们把处理过程高度抽象为Map与Reduce两个部分来进行阐述，其中Map部分负责把任务分解成多个子任务，Reduce部分负责把分解后多个子任务的处理结果汇总起来，具体设计思路如下。
（1）Map过程需要继承org.apache.hadoop.mapreduce包中Mapper类，并重写其map方法。通过在map方法中添加两句把key值和value值输出到控制台的代码，可以发现map方法中输入的value值存储的是文本文件中的一行（以回车符为行结束标记），而输入的key值存储的是该行的首字母相对于文本文件的首地址的偏移量。然后用StringTokenizer类将每一行拆分成为一个个的字段，把截取出需要的字段（本实验为买家id字段）设置为key，并将其作为map方法的结果输出。
（2）Reduce过程需要继承org.apache.hadoop.mapreduce包中Reducer类，并重写其reduce方法。Map过程输出的<key,value>键值对先经过shuffle过程把key值相同的所有value值聚集起来形成values，此时values是对应key字段的计数值所组成的列表，然后将<key,values>输入到reduce方法中，reduce方法只要遍历values并求和，即可得到某个单词的总次数。
在main()主函数中新建一个Job对象，由Job对象负责管理和运行MapReduce的一个计算任务，并通过Job的一些方法对任务的参数进行相关的设置。本实验是设置使用将继承Mapper的doMapper类完成Map过程中的处理和使用doReducer类完成Reduce过程中的处理。还设置了Map过程和Reduce过程的输出类型：key的类型为Text，value的类型为IntWritable。任务的输出和输入路径则由字符串指定，并由FileInputFormat和FileOutputFormat分别设定。完成相应任务的参数设定后，即可调用job.waitForCompletion()方法执行任务，其余的工作都交由MapReduce框架处理。
2.java api的文件源码：
首先需要自己在hdfs中上传文件：（可以参开上一篇文件读写的api）
1（这个代码是统计某一列的数量）
package mapreduce; import java.io.IOException; import java.util.StringTokenizer; 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.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 { public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { Configuration conf=new Configuration(); conf.set("dfs.client.use.datanode.hostname", "true"); @SuppressWarnings("deprecation") Job job =new Job(conf,"filter"); job.setJobName("WordCount"); job.setJarByClass(WordCount.class); job.setMapperClass(doMapper.class); job.setReducerClass(doReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); Path in=new Path("hdfs://*自己的hdfs地址*:9000/user/hadoop/input/b.txt"); Path out=new Path("hdfs://*自己的hdfs地址*:9000/user/hadoop/output"); Path path = new Path("hdfs://*自己的hdfs地址*:9000/user/hadoop/output"); FileSystem fileSystem = path.getFileSystem(conf);// 根据path找到这个文件 if (fileSystem.exists(path)) { fileSystem.delete(path, true);// true的意思是，就算output有东西，也一带删除 } FileInputFormat.addInputPath(job, in); FileOutputFormat.setOutputPath(job, out); System.exit(job.waitForCompletion(true) ? 0 : 1); } public static class doMapper extends Mapper<Object, Text, Text, IntWritable>{ @SuppressWarnings("unused") private static Text newKey=new Text(); public static final IntWritable one = new IntWritable(1); public static Text word = new Text(); @Override protected void map(Object key, Text value, Context context) throws IOException, InterruptedException { String line=value.toString(); String arr[]=line.split(" "); newKey.set(arr[0]); context.write(newKey , one); } } public static class doReducer extends Reducer<Text, IntWritable, Text, IntWritable>{ private IntWritable result = new IntWritable(); @Override protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable value : values) { sum += value.get(); } result.set(sum); context.write(key, result); } } }
2.这个是所有的字符的：
package mapreduce; import java.io.IOException; import java.util.StringTokenizer; 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 { public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { Configuration conf=new Configuration(); conf.set("dfs.client.use.datanode.hostname", "true"); @SuppressWarnings("deprecation") Job job =new Job(conf,"filter"); job.setJobName("WordCount"); job.setJarByClass(WordCount.class); job.setMapperClass(doMapper.class); job.setReducerClass(doReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); Path in = new Path("hdfs://**/mymapreduce1/in/buyer_favorite1"); Path out = new Path("hdfs://**/mymapreduce1/out");
Path path = new Path("hdfs://*自己的hdfs地址*:9000/user/hadoop/output"); FileSystem fileSystem = path.getFileSystem(conf);// 根据path找到这个文件 if (fileSystem.exists(path)) { fileSystem.delete(path, true);// true的意思是，就算output有东西，也一带删除 } FileInputFormat.addInputPath(job, in); FileOutputFormat.setOutputPath(job, out); System.exit(job.waitForCompletion(true) ? 0 : 1); } public static class doMapper extends Mapper<Object, Text, Text, IntWritable>{ public static final IntWritable one = new IntWritable(1); public static Text word = new Text(); @Override protected void map(Object key, Text value, Context context) throws IOException, InterruptedException { StringTokenizer tokenizer = new StringTokenizer(value.toString(), "\t"); word.set(tokenizer.nextToken()); context.write(word, one); } } public static class doReducer extends Reducer<Text, IntWritable, Text, IntWritable>{ private IntWritable result = new IntWritable(); @Override protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable value : values) { sum += value.get(); } result.set(sum); context.write(key, result); } } }
遇到的一些错误以及解决办法：
（1）
Exception in thread "main" org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory hdfs://*河蟹*/user/hadoop/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 mapreduce.WordCount.main(WordCount.java:36)
错误原因：output文件夹存在，
解决方法：
把它删除就可以了。也可以加一个判断：
Path path = new Path("hdfs://*自己的hdfs地址*:9000/user/hadoop/output");// 取第1个表示输出目录参数（第0个参数是输入目录）
FileSystem fileSystem = path.getFileSystem(conf);// 根据path找到这个文件
if (fileSystem.exists(path)) {
fileSystem.delete(path, true);// true的意思是，就算output有东西，也一带删除
}
（2）
java.net.ConnectException: Connection timed out: no further information at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method) at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:717) at org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:206) at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:531) at org.apache.hadoop.hdfs.DFSClient.newConnectedPeer(DFSClient.java:3436) at org.apache.hadoop.hdfs.BlockReaderFactory.nextTcpPeer(BlockReaderFactory.java:777) at org.apache.hadoop.hdfs.BlockReaderFactory.getRemoteBlockReaderFromTcp(BlockReaderFactory.java:694) at org.apache.hadoop.hdfs.BlockReaderFactory.build(BlockReaderFactory.java:355) at org.apache.hadoop.hdfs.DFSInputStream.blockSeekTo(DFSInputStream.java:656) at org.apache.hadoop.hdfs.DFSInputStream.readWithStrategy(DFSInputStream.java:882) at org.apache.hadoop.hdfs.DFSInputStream.read(DFSInputStream.java:934) at java.io.DataInputStream.read(DataInputStream.java:149) at org.apache.hadoop.mapreduce.lib.input.UncompressedSplitLineReader.fillBuffer(UncompressedSplitLineReader.java:62) at org.apache.hadoop.util.LineReader.readDefaultLine(LineReader.java:216) at org.apache.hadoop.util.LineReader.readLine(LineReader.java:174) at org.apache.hadoop.mapreduce.lib.input.UncompressedSplitLineReader.readLine(UncompressedSplitLineReader.java:94) at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.skipUtfByteOrderMark(LineRecordReader.java:144) at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.nextKeyValue(LineRecordReader.java:184) at org.apache.hadoop.mapred.MapTask$NewTrackingRecordReader.nextKeyValue(MapTask.java:556) at org.apache.hadoop.mapreduce.task.MapContextImpl.nextKeyValue(MapContextImpl.java:80) at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.nextKeyValue(WrappedMapper.java:91) at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145) at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:787) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341) at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:243) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745)
错误原因：由于操作的主机和hdfs所在的master不是一个主机，所有namenode的地址映射出现问题,而且博主配置的是伪分布式。
解决方法：（博主配置的是伪分布式）把虚拟机的ip就加入到window本机的host文件里面（怎么改window的host参考百度）。
然后加入conf的配置。把namenode变成地址映射。把新建job时候加入conf
import org.apache.hadoop.conf.Configuration;
conf.set("dfs.client.use.datanode.hostname", "true"); @SuppressWarnings("deprecation") Job job =new Job(conf,"filter")
package mapreduce;
import java.io.IOException;
import java.util.StringTokenizer;
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 {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Job job = Job.getInstance();
job.setJobName("WordCount");
job.setJarByClass(WordCount.class);
job.setMapperClass(doMapper.class);
job.setReducerClass(doReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
Path in = new Path("hdfs://localhost:9000/mymapreduce1/in/buyer_favorite1");
Path out = new Path("hdfs://localhost:9000/mymapreduce1/out");
FileInputFormat.addInputPath(job, in);
FileOutputFormat.setOutputPath(job, out);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
public static class doMapper extends Mapper<Object, Text, Text, IntWritable>{
public static final IntWritable one = new IntWritable(1);
public static Text word = new Text();
@Override
protected void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
StringTokenizer tokenizer = new StringTokenizer(value.toString(), "\t");
word.set(tokenizer.nextToken());
context.write(word, one);
}
}
public static class doReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
private IntWritable result = new IntWritable();
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable value : values) {
sum += value.get();
}
result.set(sum);
context.write(key, result);
}
}
}
