这篇文章主要为大家展示了“Hadoop如何实现辅助排序”，内容简而易懂，条理清晰，希望能够帮助大家解决疑惑，下面让小编带领大家一起研究并学习一下“Hadoop如何实现辅助排序”这篇文章吧。
1. 样例数据
011990-99999 SIHCCAJAVRI
012650-99999 TYNSET-HANSMOEN
012650-99999 194903241200 111
012650-99999 194903241800 78
011990-99999 195005150700 0
011990-99999 195005151200 22
011990-99999 195005151800 -11
2. 需求
3. 思路、代码
将气象站ID相同的气象站信息和天气信息交由同一个 Reducer 处理，并保证气象站信息首先到达；然后 reduce() 函数从第一行中获取气象台名称，从第二行开始获取天气信息并输出。
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
import org.apache.hadoop.io.WritableUtils;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
/**
* 组合键，此例中用于辅助排序，包括气象站ID和“标记”。
* “标记”是一个虚拟字段，其唯一目的是对记录排序，使气象站的记录比天气记录先到达。
* 虽然可以不指定数据传输次序，并将待处理的记录缓存在内存之中，但应该尽量避免这种情况，
* 因为其中任何一组的记录数量都可能非常庞大，远远超出 reducer 的可用内存量
*/
public class TextPair implements WritableComparable<TextPair> {
private Text first;
private Text second;
public TextPair() {
set(new Text(), new Text());
}
public TextPair(String first, String second) {
set(new Text(first), new Text(second));
}
public TextPair(Text first, Text second) {
set(first, second);
}
public void set(Text first, Text second) {
this.first = first;
this.second = second;
}
public Text getFirst() {
return first;
}
public Text getSecond() {
return second;
}
public void write(DataOutput out) throws IOException {
first.write(out);
second.write(out);
}
public void readFields(DataInput in) throws IOException {
first.readFields(in);
second.readFields(in);
}
@Override
public int hashCode() {
return first.hashCode() * 163 + second.hashCode();
}
@Override
public boolean equals(Object obj) {
if (obj instanceof TextPair) {
TextPair tp = (TextPair) obj;
return first.equals(tp.first) && second.equals(tp.second);
}
return false;
}
@Override
public String toString() {
return first + "\t" + second;
}
public int compareTo(TextPair o) {
int cmp = first.compareTo(o.first);
if (cmp == 0) {
cmp = second.compareTo(o.second);
}
return cmp;
}
// RawComparator 允许直接比较数据流中的记录，无须先把数据流反序列化为对象，这样避免了新建对象的额外开销
// WritableComparator 是对继承自 WritableComparable 类的 RawComparator 的一个通用实现。
public static class FirstComparator extends WritableComparator {
private static final Text.Comparator TEXT_COMPARATOR = new Text.Comparator();
public FirstComparator() {
super(TextPair.class);
}
@Override
public int compare(byte[] b1, int s1, int l1,
byte[] b2, int s2, int l2) {
try {
// firstL1、firstL2 表示每个字节流中第一个 Text 字段的长度
int firstL1 = WritableUtils.decodeVIntSize(b1[s1]) + readVInt(b1, s1);
int firstL2 = WritableUtils.decodeVIntSize(b2[s2]) + readVInt(b2, s2);
return TEXT_COMPARATOR.compare(b1, s1, firstL1, b2, s2, firstL2);
} catch (IOException e) {
throw new IllegalArgumentException(e);
}
}
@Override
public int compare(WritableComparable a, WritableComparable b) {
if (a instanceof TextPair && b instanceof TextPair) {
return ((TextPair) a).first.compareTo(((TextPair) b).first);
}
return super.compare(a, b);
}
}
}
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
/**
* 标记气象站记录的 mapper
*/
public class JoinStationMapper extends Mapper<LongWritable, Text, TextPair, Text> {
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] val = value.toString().split("\\t");
if (val.length == 2) {
context.write(new TextPair(val[0], "0"), new Text(val[1]));
}
}
}
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
/**
* 标记天气记录的 mapper
*/
public class JoinRecordMapper extends Mapper<LongWritable, Text, TextPair, Text> {
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] val = value.toString().split("\\t");
if (val.length == 3) {
context.write(new TextPair(val[0], "1"), new Text(val[1] + "\t" + val[2]));
}
}
}
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
import java.util.Iterator;
/**
* 连接已标记的气象站记录和天气记录的 reducer
*/
public class JoinReducer extends Reducer<TextPair, Text, Text, Text> {
@Override
protected void reduce(TextPair key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
Iterator<Text> iter = values.iterator();
Text stationName = new Text(iter.next()); // reducer 会先接收气象站记录（这里千万不能写成 Text stationName = iter.next(); ）
while (iter.hasNext()) {
Text record = iter.next();
Text outValue = new Text(stationName.toString() + "\t" + record.toString());
context.write(key.getFirst(), outValue);
}
}
}
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.lib.input.MultipleInputs;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class JoinRecordWithStationName {
static class KeyPartitioner extends Partitioner<TextPair, Text> {
@Override
public int getPartition(TextPair textPair, Text text, int numPartitions) {
return (textPair.getFirst().hashCode() & Integer.MAX_VALUE) % numPartitions;
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 3) {
System.err.println("Parameter number is wrong, please enter three parameters：<ncdc input> <station input> <output>");
System.exit(-1);
}
Path ncdcInputPath = new Path(otherArgs[0]);
Path stationInputPath = new Path(otherArgs[1]);
Path outputPath = new Path(otherArgs[2]);
//conf.set("fs.defaultFS", "hdfs://vmnode.zhch:9000");
Job job = Job.getInstance(conf, "JoinRecordWithStationName");
//job.setJar("F:/workspace/AssistRanking/target/AssistRanking-1.0-SNAPSHOT.jar");
job.setJarByClass(JoinRecordWithStationName.class);
MultipleInputs.addInputPath(job, ncdcInputPath, TextInputFormat.class, JoinRecordMapper.class);
MultipleInputs.addInputPath(job, stationInputPath, TextInputFormat.class, JoinStationMapper.class);
FileOutputFormat.setOutputPath(job, outputPath);
//仅按照 first(气象台ID) 分区、分组 （同一分区的记录将被同一个Reducer处理，同一区同一组的记录将被同一个Reducer在同一次reduce()函数调用中处理）
job.setPartitionerClass(KeyPartitioner.class);
job.setGroupingComparatorClass(TextPair.FirstComparator.class);
job.setMapOutputKeyClass(TextPair.class);
job.setReducerClass(JoinReducer.class);
job.setOutputKeyClass(Text.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
4. 运行结果
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