hadoop hellow world
实现先启动hadoop。
能进行hello world之前假设你的环境已经搭建完毕（我搭建的伪分布式）
我用hadoop源码中的WordCount作为hadoop的hello world。
（1）我们拿到hadoop源码中的WordCount类代码如下
- package org.apache.hadoop.examples;
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;
import org.apache.hadoop.util.GenericOptionsParser;
public class WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
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();
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);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
（2）我们把这个文件放到hadoop的工作目录下，并在工作目录下面新建文件夹WordCount
cd hadoopdir
mkdir WordCount
（3）
- javac -classpath hadoop-common-0.21.0.jar:lib/commons-cli-1.2.jar:hadoop-mapred-0.21.0.jar -d WordCount WordCount.java
（4）进入到WordCount文件夹中执行
cd WordCount
jar -cvf wordcount.jar org/*
然后把生成的jar拷贝到hadoop工作目录下面
cp wordcount.jar ../
（5）然后在hadoop工作目录下面新建一个input目录 mkdir input，在目录里面新建一个文件vi file1,输入以下内容：
mkdir input
cd input
vi file1
键入如下
hello world
hello hadoop
hello mapreduce
，把该文件上传到hadoop的分布式文件系统中去
./bin/hadoop fs -put input/file* input
[liyan@cctv226 hadoop-0.21.0]$ ./bin/hadoop fs -put input/file* input
12/04/19 10:10:21 INFO security.Groups: Group mapping impl=org.apache.hadoop.security.ShellBasedUnixGroupsMapping; cacheTimeout=300000
12/04/19 10:10:22 WARN conf.Configuration: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
（6）然后我们开始执行
- ./bin/hadoop jar wordcount.jar org.apache.hadoop.examples.WordCount input wordcount_output
（7）最后我们查看运行结果
-
./bin/hadoop jar wordcount.jar org.apache.hadoop.examples.WordCount input wordcount_output
12/04/19 10:11:04 INFO security.Groups: Group mapping impl=org.apache.hadoop.security.ShellBasedUnixGroupsMapping; cacheTimeout=300000
12/04/19 10:11:05 WARN conf.Configuration: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
12/04/19 10:11:05 WARN mapreduce.JobSubmitter: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
12/04/19 10:11:05 INFO input.FileInputFormat: Total input paths to process : 1
12/04/19 10:11:05 WARN conf.Configuration: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
12/04/19 10:11:05 INFO mapreduce.JobSubmitter: number of splits:1
12/04/19 10:11:06 INFO mapreduce.JobSubmitter: adding the following namenodes' delegation tokens:null
12/04/19 10:11:06 INFO mapreduce.Job: Running job: job_201204191009_0001
12/04/19 10:11:07 INFO mapreduce.Job: map 0% reduce 0%
12/04/19 10:11:36 INFO mapreduce.Job: map 100% reduce 0%
12/04/19 10:11:45 INFO mapreduce.Job: map 100% reduce 100%
12/04/19 10:11:48 INFO mapreduce.Job: Job complete: job_201204191009_0001
12/04/19 10:11:48 INFO mapreduce.Job: Counters: 33
FileInputFormatCounters
BYTES_READ=45
FileSystemCounters
FILE_BYTES_READ=59
FILE_BYTES_WRITTEN=150
HDFS_BYTES_READ=148
HDFS_BYTES_WRITTEN=37
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
Job Counters
Data-local map tasks=1
Total time spent by all maps waiting after reserving slots (ms)=0
Total time spent by all reduces waiting after reserving slots (ms)=0
SLOTS_MILLIS_MAPS=22880
SLOTS_MILLIS_REDUCES=6505
Launched map tasks=1
Launched reduce tasks=1
Map-Reduce Framework
Combine input records=6
Combine output records=4
Failed Shuffles=0
GC time elapsed (ms)=17
Map input records=4
Map output bytes=65
Map output records=6
Merged Map outputs=1
Reduce input groups=4
Reduce input records=4
Reduce output records=4
Reduce shuffle bytes=59
Shuffled Maps =1
Spilled Records=8
SPLIT_RAW_BYTES=103
（7）最后我们查看运行结果
./bin/hadoop fs -cat wordcount_output/part-r-00000
hadoop 1
hello 3
mapreduce 1
world 1