09．Mapreduce实例——ChainMapReduce小
实验原理
一些复杂的任务难以用一次MapReduce处理完成，需要多次MapReduce才能完成任务。Hadoop2.0开始MapReduce作业支持链式处理，类似于工厂的的生产线，每一个阶段都有特定的任务要处理，比如提供原配件——>组装——打印出厂日期，等等。通过这样进一步的分工，从而提高了生成效率，我们Hadoop中的链式MapReduce也是如此，这些Mapper可以像水流一样，一级一级向后处理，有点类似于Linux的管道。前一个Mapper的输出结果直接可以作为下一个Mapper的输入，形成一个流水线。
链式MapReduce的执行规则：整个Job中只能有一个Reducer，在Reducer前面可以有一个或者多个Mapper，在Reducer的后面可以有0个或者多个Mapper。
Hadoop2.0支持的链式处理MapReduce作业有一下三种：
（1）顺序链接MapReduce作业
类似于Unix中的管道：mapreduce-1 | mapreduce-2 | mapreduce-3 ......，每一个阶段创建一个job，并将当前输入路径设为前一个的输出。在最后阶段删除链上生成的中间数据。
（2）具有复杂依赖的MapReduce链接
若mapreduce-1处理一个数据集， mapreduce-2 处理另一个数据集，而mapreduce-3对前两个做内部连结。这种情况通过Job和JobControl类管理非线性作业间的依赖。如x.addDependingJob(y)意味着x在y完成前不会启动。
（3）预处理和后处理的链接
一般将预处理和后处理写为Mapper任务。可以自己进行链接或使用ChainMapper和ChainReducer类，生成得作业表达式类似于：
MAP+ | REDUCE | MAP*
如以下作业： Map1 | Map2 | Reduce | Map3 | Map4，把Map2和Reduce视为MapReduce作业核心。Map1作为前处理，Map3， Map4作为后处理。ChainMapper使用模式：（预处理作业），ChainReducer使用模式：（设置Reducer并添加后处理Mapper）
本实验中用到的就是第三种作业模式：预处理和后处理的链接，生成得作业表达式类似于 Map1 | Map2 | Reduce | Map3
1.建表，逗号分隔
2.本地新建/data/mapreduce10目录。
mkdir -p /data/mapreduce10
3.将表上传到虚拟机中
4.上传并解压hadoop2lib文件
5.在HDFS上新建/mymapreduce10/in目录，然后将Linux本地/data/mapreduce10目录下的goods_0文件导入到HDFS的/mymapreduce10/in目录中。
hadoop fs -mkdir -p /mymapreduce10/in
hadoop fs -put /data/mapreduce10/goods_0 /mymapreduce10/in
6.IDEA中编写Java代码
package mapreduce9;
import java.io.IOException;
import java.net.URI;
import org.apache.hadoop.conf.Configuration;
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.Reducer;
import org.apache.hadoop.mapreduce.lib.chain.ChainMapper;
import org.apache.hadoop.mapreduce.lib.chain.ChainReducer;
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.mapreduce.lib.partition.HashPartitioner;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.io.DoubleWritable;
public class ChainMapReduce {
private static final String INPUTPATH = "hdfs://192.168.149.10:9000/mymapreduce10/in/goods_0";
private static final String OUTPUTPATH = "hdfs://192.168.149.10:9000/mymapreduce10/out";
public static void main(String[] args) {
try {
Configuration conf = new Configuration();
FileSystem fileSystem = FileSystem.get(new URI(OUTPUTPATH), conf);
if (fileSystem.exists(new Path(OUTPUTPATH))) {
fileSystem.delete(new Path(OUTPUTPATH), true);
}
Job job = new Job(conf, ChainMapReduce.class.getSimpleName());
FileInputFormat.addInputPath(job, new Path(INPUTPATH));
job.setInputFormatClass(TextInputFormat.class);
ChainMapper.addMapper(job, FilterMapper1.class, LongWritable.class, Text.class, Text.class, DoubleWritable.class, conf);
ChainMapper.addMapper(job, FilterMapper2.class, Text.class, DoubleWritable.class, Text.class, DoubleWritable.class, conf);
ChainReducer.setReducer(job, SumReducer.class, Text.class, DoubleWritable.class, Text.class, DoubleWritable.class, conf);
ChainReducer.addMapper(job, FilterMapper3.class, Text.class, DoubleWritable.class, Text.class, DoubleWritable.class, conf);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(DoubleWritable.class);
job.setPartitionerClass(HashPartitioner.class);
job.setNumReduceTasks(1);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(DoubleWritable.class);
FileOutputFormat.setOutputPath(job, new Path(OUTPUTPATH));
job.setOutputFormatClass(TextOutputFormat.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
} catch (Exception e) {
e.printStackTrace();
}
}
public static class FilterMapper1 extends Mapper<LongWritable, Text, Text, DoubleWritable>
{
private Text outKey = new Text();
private DoubleWritable outValue = new DoubleWritable();
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, DoubleWritable>.Context
context)
throws IOException,InterruptedException {
String line =
value.toString();
if (line.length() > 0) {
String[] splits =
line.split(",");
double visit = Double.parseDouble(splits[1].trim());
if (visit <= 600) {
outKey.set(splits[0]);
outValue.set(visit);
context.write(outKey, outValue);
}
}
}
}
public static class FilterMapper2 extends Mapper<Text, DoubleWritable, Text, DoubleWritable> {
@Override
protected void map(Text key, DoubleWritable value, Mapper<Text, DoubleWritable, Text, DoubleWritable>.Context context)
throws IOException,InterruptedException {
if (value.get() < 100) {
context.write(key, value);
}
}
}
public
static class SumReducer
extends Reducer<Text, DoubleWritable, Text, DoubleWritable> {
private DoubleWritable outValue = new DoubleWritable();
@Override
protected void reduce(Text key, Iterable<DoubleWritable> values, Reducer<Text, DoubleWritable, Text, DoubleWritable>.Context context)
throws IOException, InterruptedException {
double sum = 0;
for (DoubleWritable val : values) {
sum += val.get();
}
outValue.set(sum);
context.write(key, outValue);
}
}
public
static class FilterMapper3
extends Mapper<Text, DoubleWritable, Text, DoubleWritable> {
@Override
protected void map(Text key, DoubleWritable value, Mapper<Text, DoubleWritable, Text, DoubleWritable>.Context context)
throws IOException, InterruptedException {
if (key.toString().length() < 3) {
System.out.println("写出去的内容为：" + key.toString() +"++++"+ value.toString());
context.write(key, value);
}
}
}
}
7.将hadoop2lib目录中的jar包，拷贝到hadoop2lib目录下。
8.拷贝log4j.properties文件
9.运行结果
hadoop fs -ls /mymapreduce10/out
hadoop fs -cat /mymapreduce10/out/part-r-00000
