hadoop之mapreduce快速上手

　　需求描述将每一行文本数据变成<单词,1>这样的k，v数据
　　将相同单词的一组k，v数据进行聚合：累加所有的v
　　例如
　　可以创建多个文件，内容格式如下hello java hello python …
　　经过分析计算生成结果:hello 2 java 1 python 1前期准备
　　数据准备：a.txt
　　hello java
　　hello python
　　hello hadoop
　　hello sparkb.txt c.txt
　　hello hadoop
　　hello python
　　hello python
　　hello pythonc.txt
　　hello hadoop
　　word hadoop
　　hello spark
　　hello spark
　　启动hadoop（我这里namenode和datanode都在localhost上）hadoop-daemon.sh start namenode dataname
　　启动yarn（也是在localhost启动）start-yarn.sh
　　在hadoop中创建目录，并将a.txt b.txt c.txt上传hadoop fs -mkdir -p /wordcount/input hadoop fs -put *txt /wordcount/input
　　处理maptask的类，每读取一行调用一次，主要是将一行：hello java，转化为：（hello,1）（java,1）（hello,1）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; public class WordcountMapper extends Mapper{ @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String[] words = line.split(＂ ＂); for(String word:words){ context.write(new Text(word), new IntWritable(1)); } } }执行reducetask类，处理maptask返回结果，进行聚合处理import java.io.IOException; import java.util.Iterator; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class WordcountReducer extends Reducer{ @Override protected void reduce(Text key, Iterable values,Context context) throws IOException, InterruptedException { int count = 0; Iterator iterator = values.iterator(); while(iterator.hasNext()){ IntWritable value = iterator.next(); count += value.get(); } context.write(key, new IntWritable(count)); } }调度程序，将job进行执行在local上（local为hadoop本地虚拟出来的）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.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class JobSubmitterLinuxToYarn { public static void main(String[] args) throws Exception { Configuration conf = new Configuration();  # conf.set(＂mapreduce.framework.name＂, ＂yarn＂); # 设置job提交到yarn运行，默认local Job job = Job.getInstance(conf); job.setJarByClass(JobSubmitterLinuxToYarn.class); job.setMapperClass(WordcountMapper.class); job.setReducerClass(WordcountReducer.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.setInputPaths(job, new Path(＂/wordcount/input＂)); FileOutputFormat.setOutputPath(job, new Path(＂/wordcount/output＂)); job.setNumReduceTasks(3); boolean res = job.waitForCompletion(true); System.exit(res?0:1); } }在linux上进行编译打包.java
　　1.1将代码上传至服务器
　　1.2开始进行编译打包[root@node1 java_jar]# javac *.java -cp $(hadoop classpath)[root@node1 java_jar]# vi MANIFEST.MF #添加 Main-Class: JobSubmitterLinuxToYarn[root@node1 java_jar]# jar -cvfm mr.jar MANIFEST.MF *.class [root@node1 java_jar]# ls
　　开始执行任务[root@node1 java_jar]# hadoop jar mr.jar JobSubmitterLinuxToYarn控制台输出
　　查看hadoop目录[root@node1 java_jar]# hadoop fs -ls /wordcount/output [root@node1 java_jar]# hadoop fs -cat /wordcount/output/par*
　　如果你是在yarn上调度，可以打开 http://node1:8088/cluster
　　控制台输出