hadoop如何实现x计数器、分区、序列化
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package com.test;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.Counters.Counter;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/*
* 手机号码 流量[类型1、类型2、类型3]
* 13500001234 12,56,78
* 18600001235 32,21,80
* 15800001235 16,33,56
* 13500001234 19,92,73
* 18600001235 53,55,29
* 18600001239 27,77,68
*
* 计算得出
* 手机号 类型1汇总 类型2汇总 类型3汇总
*/
public class WordCount extends Configured implements Tool {
public static class Map extends Mapper {
//避免每调用一次map就创建一次对象
private final Text phoneNum = new Text();
private final StreamWritable streamWritable = new StreamWritable();
private String firstLine = "#_#";
private String lastLine;
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
//获得map输入的第一条记录
if("#_#".equals(firstLine)) {
firstLine = key.toString() + "\t" + line;
}
//获得map输入的最后一条记录
lastLine = key.toString() + "\t" + line;
//13500001234手机号码总共在多少行出现【自定义计数器】
Counter helloCounter = (Counter) context.getCounter("Words", "13500001234");
if(line.contains("13500001234")) {
helloCounter.increment(1L);
}
String[] strs = line.split("\t");
//手机号码
phoneNum.set(strs[0]);
//流量
String[] stream = strs[1].split(",");
streamWritable.set(Long.parseLong(stream[0]), Long.parseLong(stream[1]), Long.parseLong(stream[2]));
context.write(phoneNum, streamWritable);
}
protected void cleanup(org.apache.hadoop.mapreduce.Mapper.Context context) throws IOException ,InterruptedException {
//获得map输入的第一条记录
System.out.println(firstLine);
//获得map输出的最后一条记录
System.out.println(lastLine);
};
}
public static class Reduce extends Reducer {
//避免每调用一次reduce就创建一次对象
private StreamWritable streamWritable = new StreamWritable();
/*
* map函数执行结束后，map输出的一共有4个，分别是,,
* 分区，默认只有一个分区 job.setPartitionerClass
* 排序 ,,
* 分组 把相同key的value放到一个集合中 ，每一组调用一次reduce函数
* 归约（可选） job.setCombinerClass
*/
public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
long stream1 = 0;
long stream2 = 0;
long stream3 = 0;
Iterator it = values.iterator();
while(it.hasNext()) {
streamWritable = it.next();
stream1 = stream1 + streamWritable.getStream1();
stream2 = stream2 + streamWritable.getStream2();
stream3 = stream3 + streamWritable.getStream3();
}
streamWritable.set(stream1, stream2, stream3);
context.write(key, streamWritable);
}
}
public int run(String[] args) throws Exception {
Configuration conf = this.getConf();
Job job = new Job(conf);
job.setJarByClass(WordCount.class);
job.setJobName(WordCount.class.getSimpleName());
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//如果没有配置，默认值是1
job.setNumReduceTasks(1);
//指定map产生的数据按照什么规则分配到不同的reduce中，如果没有配置，默认是HashPartitioner.class
job.setPartitionerClass(MyPartitioner.class);
//FileInputFormat.getSplits决定map任务数量，XxxInputFormat.RecordReader处理每一个split，得到map输入的key、value
//默认是TextInputFormat
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
job.setMapperClass(Map.class);
job.setCombinerClass(Reduce.class);
job.setReducerClass(Reduce.class);
//当reduce输出类型与map输出类型一致时，map的输出类型可以不设置
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(StreamWritable.class);
//reduce的输出类型一定要设置
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(StreamWritable.class);
job.waitForCompletion(true);
return job.isSuccessful()?0:1;
}
public static void main(String[] args) throws Exception {
int exit = ToolRunner.run(new WordCount(), args);
System.exit(exit);
}
}
//自定义Partitioner
class MyPartitioner extends Partitioner {
@Override
//返回值表示，分配到第几个reduce任务中
public int getPartition(Text key, StreamWritable value, int numPartitions) {
//13500001234手机号码分到第1个reduce，其余的分到第二个reduce
if("13500001234".equals(key.toString())) {
return 0;
} else {
return 1;
}
}
}
//自定义序列化类[处理手机流量]
//Serializable：Java序列化的信息非常臃肿，比如存在层层类继承的时候，继承关系序列化出去，还需要序列化回来。
//hadoop的Writable轻量很多
class StreamWritable implements Writable {
private long stream1;
private long stream2;
private long stream3;
public long getStream1() {
return stream1;
}
public void setStream1(long stream1) {
this.stream1 = stream1;
}
public long getStream2() {
return stream2;
}
public void setStream2(long stream2) {
this.stream2 = stream2;
}
public long getStream3() {
return stream3;
}
public void setStream3(long stream3) {
this.stream3 = stream3;
}
public StreamWritable() {
}
public StreamWritable(long stream1, long stream2, long stream3) {
this.set(stream1, stream2, stream3);
}
public void set(long stream1, long stream2, long stream3) {
this.stream1 = stream1;
this.stream2 = stream2;
this.stream3 = stream3;
}
@Override
public void write(DataOutput out) throws IOException {
out.writeLong(stream1);//写出顺序和读入顺序一一对应
out.writeLong(stream2);
out.writeLong(stream3);
}
@Override
public void readFields(DataInput in) throws IOException {
this.stream1 = in.readLong();//写出顺序和读入顺序一一对应
this.stream2 = in.readLong();
this.stream3 = in.readLong();
}
//输出的时候会调用toString方法
@Override
public String toString() {
return this.stream1+"\t"+this.stream2+"\t"+this.stream3;
}
} 以上是“hadoop如何实现x计数器、分区、序列化”这篇文章的所有内容，感谢各位的阅读！相信大家都有了一定的了解，希望分享的内容对大家有所帮助，如果还想学习更多知识，欢迎关注创新互联行业资讯频道！
本文名称：hadoop如何实现x计数器、分区、序列化
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