排序
排序是MapReduce的核心技术。
1.准备
示例：按照气温字段对天气数据集排序。由于气温字段是有符号的整数，所以不能将该字段视为Text对象并以字典顺序排序。反之，用顺序文件存储数据，其IntWritable键代表气温(并且正确排序)，其Text值就是数据行。
MapReduce作业只包含map任务，它过滤输入数据并移除空数据行的记录。各个map创建并输出一个块压缩的顺序文件。
代码如下
package com.zhen.mapreduce.sort.preprocessor;
import java.io.IOException;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.SequenceFile.CompressionType;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.GzipCodec;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* @author FengZhen
* @date 2018年9月9日
* 过滤掉无用数据并使用顺序文件存储数据
*/
public class SortDataPreprocessor extends Configured implements Tool{
static class CleanerMapper extends Mapper<LongWritable, Text, IntWritable, Text>{
private RecordParser recordParser = new RecordParser();
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, IntWritable, Text>.Context context)
throws IOException, InterruptedException {
recordParser.parse(value.toString());
if (recordParser.isValidTemperature()) {
context.write(new IntWritable(recordParser.getTemperature()), new Text(recordParser.getCity()));
}
}
}
public int run(String[] args) throws Exception {
Job job = Job.getInstance(getConf());
job.setJobName("SortDataPreprocessor");
job.setJarByClass(SortDataPreprocessor.class);
job.setMapperClass(CleanerMapper.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(Text.class);
job.setNumReduceTasks(0);
job.setOutputFormatClass(SequenceFileOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
SequenceFileOutputFormat.setOutputPath(job, new Path(args[1]));
//是否被压缩都会被输出
SequenceFileOutputFormat.setCompressOutput(job, true);
SequenceFileOutputFormat.setOutputCompressorClass(job, GzipCodec.class);
SequenceFileOutputFormat.setOutputCompressionType(job, CompressionType.BLOCK);
return job.waitForCompletion(true) ? 0 : 1;
}
public static void main(String[] args) throws Exception {
String[] params = new String[]{"hdfs://fz/user/hdfs/MapReduce/data/sort/preprocessor/input","hdfs://fz/user/hdfs/MapReduce/data/sort/preprocessor/output"};
int exitCode = ToolRunner.run(new SortDataPreprocessor(), params);
System.exit(exitCode);
}
}
package com.zhen.mapreduce.sort.preprocessor;
import java.io.Serializable;
/**
* @author FengZhen
* @date 2018年9月9日
* 解析MapReduce中map的数据
*/
public class RecordParser implements Serializable{
private static final long serialVersionUID = 1L;
/**
* 城市
*/
private String city;
/**
* 气温
*/
private Integer temperature;
/**
* 解析
* @param value
*/
public void parse(String value) {
String[] values = value.split(",");
if (values.length >= 2) {
city = values[0];
temperature = Integer.valueOf(values[1]);
}
}
/**
* 校验是否合格
* @return
*/
public boolean isValidTemperature() {
return null != temperature;
}
public String getCity() {
return city;
}
public void setCity(String city) {
this.city = city;
}
public int getTemperature() {
return temperature;
}
public void setTemperature(Integer temperature) {
this.temperature = temperature;
}
}
打jar包上传至服务器执行
scp /Users/FengZhen/Desktop/Hadoop/file/Sort.jar [email protected]:/usr/local/test/mr hadoop jar Sort.jar com.zhen.mapreduce.sort.SortDataPreprocessor
2.部分排序
当有多个reduce任务时，产生多个已排序的输出文件。但是如何将这些小文件合并成一个有序的文件却并非易事。
3.全排序
如何使用Hadoop产生一个全局排序的文件？最简单的方法是使用一个分区(a single partition)。但该方法在处理大型文件时效率极低，因为一台机器必须处理所有输出文件，从而完全丧失了MapReduce所提供的并行架构的优势。
事实上仍有替代方案：首先，创建一系列排好序的文件；其次，串联这些文件；最后，生成一个全局排序的文件。主要的思路是使用一个partitioner来描述输出的全局排序。
示例：以气温排序为例
给定一个partitioner，四个分区，第一个分区的温度范围在0-10，第二个在11-20，第三个在21-30，第四个在31-40.
这样可以保证第i个分区的键小于第i+1个分区的键，保证了完全有序，但是会出现数据分布不均的情况。
获得气温分布信息意味着可以建立一系列分布非常均匀的分区。但由于该操作需要遍历整个数据集，因此并不实用。通过对键空间进行采样，就可较为均匀地划分数据集。采样的核心思想是只查看一小部分键，获得键的近似分布，并由此构建分区。Hadoop已经内置了若干采样器。
InputSampler类实现了Sampler接口，该接口的唯一成员方法(getSampler)有两个输入参数(一个InputFormat对象和一个Job对象)，返回一系列样本键。
代码如下
package com.zhen.mapreduce.sort.totalPartitioner;
import java.net.URI;
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.SequenceFile.CompressionType;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.GzipCodec;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
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 FengZhen
* @date 2018年9月9日
* 根据分区全排序
*/
public class SortByTemperatureUsingTotalOrderPartitioner extends Configured implements Tool{
public int run(String[] args) throws Exception {
Job job = Job.getInstance(getConf());
job.setJobName("SortByTemperatureUsingTotalOrderPartitioner");
job.setJarByClass(SortByTemperatureUsingTotalOrderPartitioner.class);
job.setInputFormatClass(SequenceFileInputFormat.class);
job.setOutputFormatClass(SequenceFileOutputFormat.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputFormatClass(SequenceFileOutputFormat.class);
SequenceFileInputFormat.setInputPaths(job, new Path(args[0]));
SequenceFileOutputFormat.setOutputPath(job, new Path(args[1]));
//是否被压缩都会被输出
SequenceFileOutputFormat.setCompressOutput(job, true);
SequenceFileOutputFormat.setOutputCompressorClass(job, GzipCodec.class);
SequenceFileOutputFormat.setOutputCompressionType(job, CompressionType.BLOCK);
job.setPartitionerClass(TotalOrderPartitioner.class);
/**
* 采样率设为 0.1
* 最大样本数 10000
* 最大分区数 10
* 这也是InputSampler作为应用程序运行时的默认设置
* 只要任意一个限制条件满足，即停止采样。
*/
InputSampler.Sampler<IntWritable, Text> sampler = new InputSampler.RandomSampler(0.1, 10000, 10);
InputSampler.writePartitionFile(job, sampler);
//为了和集群上运行的其他任务共享分区文件，InputSampler需要将其所写的分区文件加到分布式缓存中。
Configuration conf = job.getConfiguration();
String partitionFile = TotalOrderPartitioner.getPartitionFile(conf);
URI partitionUri = new URI(partitionFile + "#" + TotalOrderPartitioner.DEFAULT_PATH);
job.addCacheFile(partitionUri);
job.createSymlink();
return job.waitForCompletion(true) ? 0 : 1;
}
public static void main(String[] args) throws Exception {
String[] params = new String[]{"hdfs://fz/user/hdfs/MapReduce/data/sort/preprocessor/output","hdfs://fz/user/hdfs/MapReduce/data/sort/SortByTemperatureUsingTotalOrderPartitioner/output"};
int exitCode = ToolRunner.run(new SortByTemperatureUsingTotalOrderPartitioner(), params);
System.exit(exitCode);
}
}
4.辅助排序
MapReduce框架在记录到达reducer之前按键对记录排序，但键所对应的值并没有被排序。
示例：键升序，键相同的值升序
代码如下
package com.zhen.mapreduce.sort.secondarySort;
import java.io.IOException;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
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.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* @author FengZhen
* @date 2018年9月9日
* 对键排序后的值排序
*/
public class MaxTemperatureUsingSecondarySort extends Configured implements Tool{
static class MaxTemperatureMapper extends Mapper<LongWritable, Text, IntPair, NullWritable>{
private RecordParser recordParser = new RecordParser();
@Override
protected void map(LongWritable key, Text value,
Mapper<LongWritable, Text, IntPair, NullWritable>.Context context)
throws IOException, InterruptedException {
recordParser.parse(value.toString());
if (recordParser.isValidTemperature()) {
context.write(new IntPair(recordParser.getYear(), recordParser.getTemperature()), NullWritable.get());
}
}
}
static class MaxTemperatureReducer extends Reducer<IntPair, NullWritable, IntPair, NullWritable>{
@Override
protected void reduce(IntPair key, Iterable<NullWritable> values,
Reducer<IntPair, NullWritable, IntPair, NullWritable>.Context context)
throws IOException, InterruptedException {
context.write(key, NullWritable.get());
}
}
/**
* 创建一个自定义的partitioner以按照组合键的守字段（年份）进行分区
* @author FengZhen
*
*/
public static class FirstPartitioner extends Partitioner<IntWritable, IntWritable>{
@Override
public int getPartition(IntWritable key, IntWritable value, int numPartitions) {
return Math.abs(key.get() * 127) % numPartitions;
}
}
/**
* 按照年份（升序）和气温（降序）排列键
* @author FengZhen
*
*/
public static class KeyComparator extends WritableComparator{
public KeyComparator() {
super(IntPair.class, true);
}
@Override
public int compare(WritableComparable a, WritableComparable b) {
IntPair ip1 = (IntPair) a;
IntPair ip2 = (IntPair) b;
int cmp = IntPair.compare(ip1.getFirstKey(), ip2.getFirstKey());
if (cmp != 0) {
return cmp;
}
return -IntPair.compare(ip1.getSecondKey(), ip2.getSecondKey());
}
}
/**
* 按年份对键进行分组
* @author FengZhen
*
*/
public static class GroupComparator extends WritableComparator {
protected GroupComparator() {
super(IntPair.class, true);
}
@Override
public int compare(WritableComparable a, WritableComparable b) {
IntPair ip1 = (IntPair) a;
IntPair ip2 = (IntPair) b;
return IntPair.compare(ip1.getFirstKey(), ip2.getFirstKey());
}
}
public int run(String[] args) throws Exception {
Job job = Job.getInstance(getConf());
job.setJobName("MaxTemperatureUsingSecondarySort");
job.setJarByClass(MaxTemperatureUsingSecondarySort.class);
job.setMapperClass(MaxTemperatureMapper.class);
job.setReducerClass(MaxTemperatureReducer.class);
job.setPartitionerClass(FirstPartitioner.class);
job.setSortComparatorClass(KeyComparator.class);
job.setGroupingComparatorClass(GroupComparator.class);
job.setOutputKeyClass(IntPair.class);
job.setOutputValueClass(NullWritable.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
return job.waitForCompletion(true) ? 0 : 1;
}
public static void main(String[] args) throws Exception {
String[] params = new String[] {"hdfs://fz/user/hdfs/MapReduce/data/sort/MaxTemperatureUsingSecondarySort/input", "hdfs://fz/user/hdfs/MapReduce/data/sort/MaxTemperatureUsingSecondarySort/output"};
int exitCode = ToolRunner.run(new MaxTemperatureUsingSecondarySort(), params);
System.exit(exitCode);
}
}
IntPair：自定义组合键
package com.zhen.mapreduce.sort.secondarySort;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;
/**
* 自定义组合键
* map的键排序
* */
public class IntPair implements WritableComparable{
//使用java基本数据类型
private int firstKey;
private int secondKey;
public IntPair() {
}
public IntPair(int firstKey, int secondKey) {
this.firstKey = firstKey;
this.secondKey = secondKey;
}
//必须有默认的构造函数
public int getFirstKey() {
return firstKey;
}
public void setFirstKey(int firstKey) {
this.firstKey = firstKey;
}
public int getSecondKey() {
return secondKey;
}
public void setSecondKey(int secondKey) {
this.secondKey = secondKey;
}
public void readFields(DataInput in) throws IOException {
firstKey = in.readInt();
secondKey = in.readInt();
}
public void write(DataOutput out) throws IOException {
out.writeInt(firstKey);
out.writeInt(secondKey);
}
/**
* map的键的比较就是根据这个方法来进行
* */
public int compareTo(Object o) {
IntPair tInt = (IntPair)o;
//利用这个来控制升序或降序
//this在前为升序
//this在后为降序
return this.getFirstKey() >= (tInt.getFirstKey()) ? -1 : 1;
}
/**
* 比较两个int值大小
* 降序
* @param a
* @param b
* @return
*/
public static int compare(int a, int b) {
return a >= b ? -1 : 1;
}
@Override
public String toString() {
return "IntPair [firstKey=" + firstKey + ", secondKey=" + secondKey + "]";
}
}
RecordParser：解析每条记录
package com.zhen.mapreduce.sort.secondarySort;
import java.io.Serializable;
/**
* @author FengZhen
* @date 2018年9月9日
* 解析MapReduce中map的数据
*/
public class RecordParser implements Serializable{
private static final long serialVersionUID = 1L;
/**
* 年份
*/
private Integer year;
/**
* 气温
*/
private Integer temperature;
/**
* 解析
* @param value
*/
public void parse(String value) {
String[] values = value.split(",");
if (values.length >= 2) {
year = Integer.valueOf(values[0]);
temperature = Integer.valueOf(values[1]);
}
}
/**
* 校验是否合格
* @return
*/
public boolean isValidTemperature() {
return null != temperature;
}
public Integer getYear() {
return year;
}
public void setYear(Integer year) {
this.year = year;
}
public int getTemperature() {
return temperature;
}
public void setTemperature(Integer temperature) {
this.temperature = temperature;
}
}
原始数据如下
1990,14 1980,12 1990,19 1960,11 1960,18 1980,17 1970,24 1970,23 1940,22 1940,35 1930,44 1920,43
输出数据如下：输出数据格式可重写IntPair的toString方法
IntPair [firstKey=1990, secondKey=19] IntPair [firstKey=1990, secondKey=14] IntPair [firstKey=1980, secondKey=17] IntPair [firstKey=1980, secondKey=12] IntPair [firstKey=1970, secondKey=23] IntPair [firstKey=1970, secondKey=24] IntPair [firstKey=1960, secondKey=18] IntPair [firstKey=1960, secondKey=11] IntPair [firstKey=1940, secondKey=35] IntPair [firstKey=1940, secondKey=22] IntPair [firstKey=1930, secondKey=44] IntPair [firstKey=1920, secondKey=43]