User Defined Hadoop DataType
目錄
-
- User Defined Hadoop DataType
- 目錄
- 需求
- 實作
- 運作
需求
有時候 Hadoop 内置的資料類型不能滿足我們的要求,這個時候就需要自定義類型了。
假設輸入檔案是很多電話号碼,每行一個:
13612345678
13051812535
13051812535
13912345677
13412345678
要求按照如下格式輸出
其中的 China Mobile 和 1,都是算出來的。
實作
需要一個電話号碼類 TelNo,需要實作 WritableComparable 接口。
// TelNo.java
package com.stephen.hadoop;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;
public class TelNo implements WritableComparable<TelNo>{
private String no;
private String operator;
private Integer times;
private transient final int BEGINPOS = ;
private transient final int ENDPOS = ;
public TelNo() {}
@Override
public void write(DataOutput out) throws IOException {
out.writeUTF(no);
out.writeUTF(operator);
out.writeInt(times);
}
@Override
public void readFields(DataInput in) throws IOException {
no = in.readUTF();
operator = in.readUTF();
times = in.readInt();
}
@Override
public int compareTo(TelNo o) {
return this.no.compareTo(o.getNo());
}
public boolean equals(Object o) {
if( !(o instanceof TelNo)) {
return false;
}
TelNo other = (TelNo) o;
return this.no.compareTo(other.getNo()) == ;
}
public int hashCode() {
return no.hashCode();
}
public Integer getTimes() {
return times;
}
public void setTimes(Integer times) {
this.times = times;
}
public void setNo(String no) {
this.no = no;
}
public String getNo() {
return no;
}
public String getOperator() {
String header = no.substring(BEGINPOS, ENDPOS);
if (header.compareTo("130") >= ) {
if (header.compareTo("135") <= ) {
operator = "***China Mobile***";
} else if (header.compareTo("137") <= ) {
operator = "***China Unicom***";
} else if (header.compareTo("139") <= ) {
operator = "***China Telecom***";
} else {
operator = "***Invalid Operator***";
}
}
return operator;
}
@Override
public String toString() {
return "is subscribed from " + getOperator() + ", appearing " + times + " times";
}
}
MapReduce 實作如下(Partitioner 類沒有使用)
// TelNoCategorizerTool.java
package com.stephen.hadoop;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
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.mapreduce.lib.output.LazyOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
public class TelNoCategorizerTool extends Configured implements Tool {
public static class TelNoMapper extends
Mapper<LongWritable, Text, Text, LongWritable> {
private Text telno = new Text();
private final static LongWritable one = new LongWritable();
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String newkey = value.toString();
telno.set(newkey);
context.write(telno, one);
}
}
public static class TelNoReducer extends
Reducer<Text, LongWritable, Text, TelNo> {
private TelNo telNo = new TelNo();
public void reduce(Text key, Iterable<LongWritable> values,
Context context) throws IOException, InterruptedException {
int sum = ;
for (LongWritable val : values) {
sum += val.get();
}
telNo.setNo(key.toString());
telNo.setTimes(sum);
context.write(key, telNo);
}
}
public static class OperatorPartitioner<K, V> extends Partitioner<K, V> {
private static final List<String> mobileNumList = new ArrayList<>();
private static final List<String> unicomNumList = new ArrayList<>();
private static final List<String> telecomNumList = new ArrayList<>();
static {
mobileNumList.add("130");
mobileNumList.add("131");
mobileNumList.add("132");
mobileNumList.add("133");
mobileNumList.add("134");
mobileNumList.add("135");
unicomNumList.add("136");
unicomNumList.add("137");
telecomNumList.add("138");
telecomNumList.add("139");
}
@Override
public int getPartition(K key, V value, int numReduceTasks) {
String telNoHead = key.toString().substring(, );
if (mobileNumList.contains(telNoHead)) {
return ;
} else if (unicomNumList.contains(telNoHead)) {
return ;
} else if (telecomNumList.contains(telNoHead)) {
return ;
} else {
return ;
}
}
}
@Override
public int run(String[] args) throws Exception {
Configuration conf = this.getConf();
Job job = Job.getInstance(conf, "Telno Categorizer");
job.setJarByClass(TelNoCategorizerTool.class);
job.setMapperClass(TelNoMapper.class);
job.setReducerClass(TelNoReducer.class);
// 隻對 Mapper 生效
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(LongWritable.class);
/**
* 這兩個方法對 Mapper 和 Reducer 都生效
* 是以要在上面單獨指定 Mapper 的Key 和 Value 的格式
* 沒有 setReduceOutputKeyClass...方法
*/
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(TelNo.class);
LazyOutputFormat.setOutputFormatClass(job, TextOutputFormat.class);
job.setPartitionerClass(OperatorPartitioner.class);
FileInputFormat.addInputPath(job, new Path(args[]));
FileOutputFormat.setOutputPath(job, new Path(args[]));
return job.waitForCompletion(true) ? : ;
}
public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new Configuration(),
new TelNoCategorizerTool(), args);
System.exit(exitCode);
}
}
運作
執行一下:
hadoop jar TelNoCategorizerTool.jar com.stephen.hadoop.TelNoCategorizerTool /user/stephen/input/ /user/stephen/output
檢視結果:
hadoop fs -cat /user/stephen/output/part-r-
#output
is subscribed from ***China Mobile***, appearing times
is subscribed from ***China Mobile***, appearing times
is subscribed from ***China Unicom***, appearing times
is subscribed from ***China Telecom***, appearing times
如果想要分區:
hadoop jar TelNoCategorizerTool.jar com.stephen.hadoop.TelNoCategorizerTool -D mapreduce.job.reduces= /user/stephen/input/ /user/stephen/output
能看到 3 個檔案(使用了 LazyOutputFormat,不會輸出空記錄),分别包含了分區後的記錄。
hadoop fs -ls /user/stephen/output/
#output
-rw-r--r-- root supergroup -- : /user/stephen/output/_SUCCESS
-rw-r--r-- root supergroup -- : /user/stephen/output/part-r-
-rw-r--r-- root supergroup -- : /user/stephen/output/part-r-
-rw-r--r-- root supergroup -- : /user/stephen/output/part-r-
3 個檔案的内容合并起來就是之前的 part-r-00000 的内容。