思考
想到全局排序,是否第一想到的是,从map端收集数据,shuffle到reduce来,设置一个reduce,再对reduce中的数据排序,显然这样和单机器并没有什么区别,要知道mapreduce框架默认是对key来排序的,当然也可以将value放到key上面来达到对value排序,最后在reduce时候对调回去,另外排序是针对相同分区,即一个reduce来排序的,这样其实也不能充分运用到集群的并行,那么如何更优雅地实现全局排序呢?
摘要
hadoop中的排序分为部分排序,全局排序,辅助排序,二次排序等,本文主要介绍如何实现key全局排序,共有三种实现方式:
- 设置一个reduce
- 利用自定义partition 将数据按顺序分批次分流到多个分区
- 利用框架自实现TotalOrderPartitioner 分区器来实现
实现
首先准备一些输入数据:https://github.com/hulichao/bigdata-code/tree/master/data/job,如下,
/data/job/file.txt
2
32
654
32
15
756
65223
通过设置一 个reduce来实现全局排序
利用一个reduce来实现全局排序,可以说不需要做什么特别的操作,mapper,reduce,driver实现如下:
package com.hoult.mr.job;
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 java.io.IOException;
public class JobMapper extends Mapper {
@Override
protected void map(LongWritable key, Text value,
Context context) throws IOException, InterruptedException {
IntWritable intWritable = new IntWritable(Integer.parseInt(value.toString()));
context.write(intWritable, intWritable);
}
}
package com.hoult.mr.job;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class JobReducer extends
Reducer {
private int index = 0;//全局排序计数器
@Override
protected void reduce(IntWritable key, Iterable values,
Context context) throws IOException, InterruptedException {
for (IntWritable value : values)
context.write(new IntWritable(++index), value);
}
}
package com.hoult.mr.job;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class JobDriver extends Configured implements Tool {
@Override
public int run(String[] args) throws Exception {
if (args.length != 2) {
System.err.println("input-path output-path");
System.exit(1);
}
Job job = Job.getInstance(getConf());
job.setJarByClass(JobDriver.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(JobMapper.class);
job.setReducerClass(JobReducer.class);
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(NullWritable.class);
//使用一个reduce来排序
job.setNumReduceTasks(1);
job.setJobName("JobDriver");
return job.waitForCompletion(true) ? 0 : 1;
}
public static void main(String[] args)throws Exception{
// int exitCode = ToolRunner.run(new JobDriver(), args);
int exitCode = ToolRunner.run(new JobDriver(), new String[] {"data/job/", "data/job/output"});
System.exit(exitCode);
}
}
//加了排序索引,最后输出一个文件,内容如下:
1 2
2 6
3 15
4 22
5 26
6 32
7 32
8 54
9 92
10 650
11 654
12 756
13 5956
14 65223
PS; 以上通过hadoop自带的ToolRunner工具来启动任务,后续代码涉及到重复的不再列出,只针对差异性的代码。
利用自定义partition 将数据按顺序分批次分流到多个分区
通过自定义分区如何保证数据的全局有序呢?我们知道key值分区,会通过默认分区函数HashPartition将不同范围的key发送到不同的reduce,所以利用这一点,这样来实现分区器,例如有数据分布在1-1亿,可以将1-1000万的数据让reduce1来跑,1000万+1-2000万的数据来让reduce2来跑。。。。最后可以对这十个文件,按顺序组合即可得到所有数据按分区有序的全局排序数据,由于数据量较小,采用分11个分区,分别是1-1000,10001-2000,。跟第一种方式实现不同的有下面两个点,
//partitionner实现
package com.hoult.mr.job;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Partitioner;
/**
* @author hulichao
* @date 20-9-20
**/
public class JobPartitioner extends Partitioner {
@Override
public int getPartition(IntWritable key, IntWritable value, int numPartitions) {
int keyValue = Integer.parseInt(key.toString());
for (int i = 0; i < 10; i++) {
if (keyValue < 1000 * (i+1) && keyValue >= 1000 * (i-1)) {
System.out.println("key:" + keyValue + ", part:" + i);
return i;
}
}
return 10;
}
}
//driver处需要增加:
//设置自定义分区器
job.setPartitionerClass(JobPartitioner.class);
//driver处需要修改reduce数量
job.setNumReduceTasks(10);
执行程序,结果会产生10个文件,文件内有序。
part-r-00000
part-r-00001
part-r-00002
part-r-00003
part-r-00004
part-r-00005
part-r-00006
part-r-00007
part-r-00008
part-r-00009
注意:需要注意一点,partition含有数据的分区要小于等于reduce数,否则会包Illegal partiion错误。另外缺点分区的实现如果对数据知道较少可能会导致数据倾斜和OOM问题。
利用框架自实现TotalOrderPartitioner 分区器来实现
既然想到了第二种自定义方式,其实可以解决多数倾斜问题,但是实际上,在数据分布不了解之前,对数据的分布评估,只能去试,看结果值有哪些,进而自定义分区器,这不就是取样吗,针对取样然后实现分区器这种方式,hadoop已经帮我们实现好了,并且解决了数据倾斜和OOM 问题,那就是TotalOrderPartitioner
类,其类提供了数据采样器,对key值进行部分采样,然后按照采样结果寻找key值的最佳分割点,从而将key均匀分布在不同分区中。
TotalOrderPartitioner
提供了三个采样器如下:
- SplitSampler 分片采样器,从数据分片中采样数据,该采样器不适合已经排好序的数据
- RandomSampler随机采样器,按照设置好的采样率从一个数据集中采样
- IntervalSampler间隔采样机,以固定的间隔从分片中采样数据,对于已经排好序的数据效果非常好
采样器实现了K[] getSample(InputFormat
代码实现:
//mapper和driver的类型略有不同
package com.hoult.mr.job.totalsort;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
/**
* @author hulichao
* @date 20-9-20
**/
public class TotalMapper extends Mapper {
@Override
protected void map(Text key, Text value,
Context context) throws IOException, InterruptedException {
System.out.println("key:" + key.toString() + ", value:" + value.toString());
context.write(key, new IntWritable(Integer.parseInt(key.toString())));
}
}
package com.hoult.mr.job.totalsort;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
/**
* @author hulichao
* @date 20-9-20
**/
public class TotalReducer extends Reducer {
@Override
protected void reduce(Text key, Iterable values,
Context context) throws IOException, InterruptedException {
for (IntWritable value : values)
context.write(value, NullWritable.get());
}
}
//比较器
package com.hoult.mr.job.totalsort;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
/**
* 自定义比较器来比较key的顺序
* @author hulichao
* @date 20-9-20
**/
public class KeyComparator extends WritableComparator {
protected KeyComparator() {
super(Text.class, true);
}
@Override
public int compare(WritableComparable w1, WritableComparable w2) {
int num1 = Integer.valueOf(w1.toString());
int num2 = Integer.valueOf(w2.toString());
return num1 - num2;
}
}
package com.hoult.mr.job.totalsort;
//driver 实现
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
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.input.KeyValueTextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.InputSampler;
import org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* @author hulichao
* @date 20-9-20
**/
public class TotalDriver extends Configured implements Tool {
@Override
public int run(String[] args) throws Exception {
Configuration conf = new Configuration();
//设置非分区排序
conf.set("mapreduce.totalorderpartitioner.naturalorder", "false");
Job job = Job.getInstance(conf, "Total Driver");
job.setJarByClass(TotalDriver.class);
//设置读取文件的路径,都是从HDFS中读取。读取文件路径从脚本文件中传进来
FileInputFormat.addInputPath(job,new Path(args[0]));
//设置mapreduce程序的输出路径,MapReduce的结果都是输入到文件中
FileOutputFormat.setOutputPath(job,new Path(args[1]));
job.setInputFormatClass(KeyValueTextInputFormat.class);
//设置比较器,用于比较数据的大小,然后按顺序排序,该例子主要用于比较两个key的大小
job.setSortComparatorClass(KeyComparator.class);
job.setNumReduceTasks(10);//设置reduce数量
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(NullWritable.class);
//设置保存partitions文件的路径
TotalOrderPartitioner.setPartitionFile(job.getConfiguration(), new Path(args[2]));
//key值采样,0.01是采样率,
InputSampler.Sampler sampler = new InputSampler.RandomSampler<>(0.1, 3, 100);
//将采样数据写入到分区文件中
InputSampler.writePartitionFile(job, sampler);
job.setMapperClass(TotalMapper.class);
job.setReducerClass(TotalReducer.class);
//设置分区类。
job.setPartitionerClass(TotalOrderPartitioner.class);
return job.waitForCompletion(true) ? 0 : 1;
}
public static void main(String[] args)throws Exception{
// int exitCode = ToolRunner.run(new TotalDriver(), new String[] {"data/job/input", "data/job/output", "data/job/partition","data/job/partitio2"});
int exitCode = ToolRunner.run(new TotalDriver(), args);
System.exit(exitCode);
}
}
结果和第二种实现类似,需要注意只在集群测试时候才有效,本地测试可能会报错
2020-09-20 16:36:10,664 WARN [org.apache.hadoop.util.NativeCodeLoader] - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: 0
at com.hoult.mr.job.totalsort.TotalDriver.run(TotalDriver.java:32)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:76)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:90)
at com.hoult.mr.job.totalsort.TotalDriver.main(TotalDriver.java:60)
吴邪,小三爷,混迹于后台,大数据,人工智能领域的小菜鸟。
更多请关注