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MapReduce(四) 典型编程场景（二）

一、MapJoin-DistributedCache 应用

1、mapreduce join 介绍

在各种实际业务场景中，按照某个关键字对两份数据进行连接是非常常见的。如果两份数据 都比较小，那么可以直接在内存中完成连接。如果是大数据量的呢？ 显然，在内存中进行连 接会发生 OOM。 MapReduce 可以用来解决大数据量的链接
MapReduce 的 Join 操作主要分两类： MapJoin 和 ReduceJoin

先看 ReduceJoin:
（1）map 阶段，两份数据 data1 和 data2 会被 map 分别读入，解析成以链接字段为 key 以查 询字段为 value 的 key-value 对，并标明数据来源是 data1 还是 data2。
（2）reduce 阶段， reducetask 会接收来自 data1 和 data2 的相同 key 的数据，在 reduce 端进 行乘积链接， 最直接的影响是很消耗内存，导致 OOM

再看 MapJoin:
MapJoin 适用于有一份数据较小的连接情况。做法是直接把该小份数据直接全部加载到内存 当中，按链接关键字建立索引。 然后大份数据就作为 MapTask 的输入，对 map()方法的每次 输入都去内存当中直接去匹配连接。 然后把连接结果按 key 输出，这种方法要使用 hadoop
中的 DistributedCache 把小份数据分布到各个计算节点，每个 maptask 执行任务的节点都需 要加载该数据到内存，并且按连接关键字建立索引 
（map读的是大表数据，在读大表之前，把小表数据放到内存当中，用setup方法）

2、需求

现有两份数据 movies.dat 和 ratings.dat 数据样式分别为：

Movies.dat：
     1::Toy Story (1995)::Animation|Children's|Comedy
     2::Jumanji (1995)::Adventure|Children's|Fantasy
     3::Grumpier Old Men (1995)::Comedy|Romance
     字段含义： movieid, moviename, movietype

Ratings.dat
    1::1193::5::978300760
    1::661::3::978302109
    1::914::3::978301968

字段含义： userid, movieid, rate, timestamp

现要求对两表进行连接，要求输出最终的结果有以上六个字段：
movieid, userid, rate, moviename, movietype, timestamp

3、实现

第一步：封装 MovieRate,方便数据的排序和序列化

package com.ghgj.mr.mymapjoin;

import java.io.DataInput;

import java.io.DataOutput;

import java.io.IOException;

import org.apache.hadoop.io.WritableComparable;

public class MovieRate implements WritableComparable<MovieRate>{

private String movieid;

private String userid;

private int rate;

private String movieName;

private String movieType;

private long ts;

public String getMovieid() {

return movieid;

}

public void setMovieid(String movieid) {

this.movieid = movieid;

}

public String getUserid() {

return userid;

}

public void setUserid(String userid) {

this.userid = userid;

}

public int getRate() {

return rate;

}

public void setRate(int rate) {

this.rate = rate;

}

public String getMovieName() {

return movieName;

}

public void setMovieName(String movieName) {

this.movieName = movieName;

}

public String getMovieType() {

return movieType;

}

public void setMovieType(String movieType) {

this.movieType = movieType;

}

public long getTs() {

return ts;

}

public void setTs(long ts) {

this.ts = ts;

}

public MovieRate() {

}

public MovieRate(String movieid, String userid, int rate, String movieName,

String movieType, long ts) {

this.movieid = movieid;

this.userid = userid;

this.rate = rate;

this.movieName = movieName;

this.movieType = movieType;

this.ts = ts;

}

@Override

public String toString() {

return movieid + "\t" + userid + "\t" + rate + "\t" + movieName

+ "\t" + movieType + "\t" + ts;

}

@Override

public void write(DataOutput out) throws IOException {

out.writeUTF(movieid);

out.writeUTF(userid);

out.writeInt(rate);

out.writeUTF(movieName);

out.writeUTF(movieType);

out.writeLong(ts);

}

@Override

public void readFields(DataInput in) throws IOException {

this.movieid = in.readUTF();

this.userid = in.readUTF();

this.rate = in.readInt();

this.movieName = in.readUTF();

this.movieType = in.readUTF();

this.ts = in.readLong();

}

@Override

public int compareTo(MovieRate mr) {

int it = mr.getMovieid().compareTo(this.movieid);

if(it == 0){

return mr.getUserid().compareTo(this.userid);

}else{

return it;

}

}

}

第二步：编写mapreduce程序

package com.ghgj.mr.mymapjoin;

import java.io.BufferedReader;

import java.io.FileReader;

import java.io.IOException;

import java.net.URI;

import java.util.HashMap;

import java.util.Map;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IOUtils;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.NullWritable;

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.filecache.DistributedCache;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class MovieRatingMapJoinMR {

public static void main(String[] args) throws Exception {

Configuration conf = new Configuration();

conf.set("fs.defaultFS", "hdfs://hadoop02:9000");

System.setProperty("HADOOP_USER_NAME","hadoop");

Job job = Job.getInstance(conf);
// job.setJarByClass(MovieRatingMapJoinMR.class);
job.setJar("/home/hadoop/mrmr.jar");
job.setMapperClass(MovieRatingMapJoinMRMapper.class);
job.setMapOutputKeyClass(MovieRate.class);
job.setMapOutputValueClass(NullWritable.class);
// job.setReducerClass(MovieRatingMapJoinMReducer.class);
// job.setOutputKeyClass(MovieRate.class);
// job.setOutputValueClass(NullWritable.class);
job.setNumReduceTasks(0);
String minInput = args[0];
String maxInput = args[1];
String output = args[2];
FileInputFormat.setInputPaths(job, new Path(maxInput));
Path outputPath = new Path(output);
FileSystem fs = FileSystem.get(conf);
if(fs.exists(outputPath)){
fs.delete(outputPath, true);
}
FileOutputFormat.setOutputPath(job, outputPath);
URI uri = new Path(minInput).toUri();
job.addCacheFile(uri);
boolean status = job.waitForCompletion(true);
System.exit(status?0:1);
}

　　MapReduce(四) 典型编程场景（二）-LMLPHP

MapReduce(四) 典型编程场景（二）-LMLPHP

二、自定义 OutputFormat—数据分类输出

实现：自定义 OutputFormat，改写其中的 RecordWriter，改写具体输出数据的方法 write()

package com.ghgj.mr.score_outputformat;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.FSDataOutputStream;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IOUtils;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.RecordWriter;

import org.apache.hadoop.mapreduce.TaskAttemptContext;

import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class MyScoreOutputFormat extends TextOutputFormat<Text, NullWritable>{

	@Override

	public RecordWriter<Text, NullWritable> getRecordWriter(

			TaskAttemptContext job) throws IOException, InterruptedException {

		Configuration configuration = job.getConfiguration();

		FileSystem fs = FileSystem.get(configuration);

		Path p1 = new Path("/score1/outpu1");

		Path p2 = new Path("/score2/outpu2");

		if(fs.exists(p1)){

			fs.delete(p1, true);

		}

		if(fs.exists(p2)){

			fs.delete(p2, true);

		}

		FSDataOutputStream fsdout1 = fs.create(p1);

		FSDataOutputStream fsdout2 = fs.create(p2);

		return new MyRecordWriter(fsdout1, fsdout2);

	}

	static class MyRecordWriter extends RecordWriter<Text, NullWritable>{

		FSDataOutputStream dout1 = null;

		FSDataOutputStream dout2 = null;

		public MyRecordWriter(FSDataOutputStream dout1, FSDataOutputStream dout2) {

			super();

			this.dout1 = dout1;

			this.dout2 = dout2;

		}

		@Override

		public void write(Text key, NullWritable value) throws IOException,

				InterruptedException {

			// TODO Auto-generated method stub

			String[] strs = key.toString().split("::");

			if(strs[0].equals("1")){

				dout1.writeBytes(strs[1]+"\n");

			}else{

				dout2.writeBytes(strs[1]+"\n");

			}

		}

		@Override

		public void close(TaskAttemptContext context) throws IOException,

				InterruptedException {

			IOUtils.closeStream(dout2);

			IOUtils.closeStream(dout1);

		}

	}

}

　　

package com.ghgj.mr.score_outputformat;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.conf.Configured;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

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.input.TextInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.Tool;

import org.apache.hadoop.util.ToolRunner;

public class ScoreOutputFormatMR extends Configured implements Tool{

	// 这个run方法就相当于Driver

	@Override

	public int run(String[] args) throws Exception {

		Configuration conf = new Configuration();

		conf.set("fs.defaultFS", "hdfs://hadoop02:9000");

		System.setProperty("HADOOP_USER_NAME", "hadoop");

		Job job = Job.getInstance(conf);

		job.setMapperClass(ScoreOutputFormatMRMapper.class);

		job.setMapOutputKeyClass(Text.class);

		job.setMapOutputValueClass(NullWritable.class);

		job.setNumReduceTasks(0);

		// 这就是默认的输入输出组件

		job.setInputFormatClass(TextInputFormat.class);

		// 这是默认往外输出数据的组件

//		job.setOutputFormatClass(TextOutputFormat.class);

		job.setOutputFormatClass(MyScoreOutputFormat.class);

		FileInputFormat.setInputPaths(job, new Path("/scorefmt"));

		Path output = new Path("/scorefmt/output");

		FileSystem fs = FileSystem.get(conf);

		if(fs.exists(output)){

			fs.delete(output, true);

		}

		FileOutputFormat.setOutputPath(job, output);

		boolean status = job.waitForCompletion(true);

		return status?0:1;

	}

	public static void main(String[] args) throws Exception {

		int run = new ToolRunner().run(new ScoreOutputFormatMR(), args);

		System.exit(run);

	}

	static class ScoreOutputFormatMRMapper extends Mapper<LongWritable,  Text, Text, NullWritable>{

		@Override

		protected void map(LongWritable key, Text value,

				Mapper<LongWritable, Text, Text, NullWritable>.Context context)

				throws IOException, InterruptedException {

			String[] split = value.toString().split("\t");

			if(split.length-2 >= 6){

				context.write(new Text("1::"+value.toString()), NullWritable.get());

			}else{

				context.write(new Text("2::"+value.toString()), NullWritable.get());

			}

		}

	}

}

三、自定义 InputFormat—小文件合并

第一步：自定义InputFormat

package com.ghgj.mr.format.input;

import java.io.IOException;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.InputSplit;

import org.apache.hadoop.mapreduce.JobContext;

import org.apache.hadoop.mapreduce.RecordReader;

import org.apache.hadoop.mapreduce.TaskAttemptContext;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

public class WholeFileInputFormat extends FileInputFormat<NullWritable, Text> {

	// 设置每个小文件不可分片,保证一个小文件生成一个key-value键值对

	@Override

	protected boolean isSplitable(JobContext context, Path file) {

		return false;

	}

	@Override

	public RecordReader<NullWritable, Text> createRecordReader(

			InputSplit split, TaskAttemptContext context) throws IOException,

			InterruptedException {

		WholeFileRecordReader reader = new WholeFileRecordReader();

		reader.initialize(split, context);

		return reader;

	}

}

　　第二步：编写自定义的 RecordReader

package com.ghgj.mr.format.input;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.FSDataInputStream;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IOUtils;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.InputSplit;

import org.apache.hadoop.mapreduce.RecordReader;

import org.apache.hadoop.mapreduce.TaskAttemptContext;

import org.apache.hadoop.mapreduce.lib.input.FileSplit;

class WholeFileRecordReader extends RecordReader<NullWritable, Text> {

	private FileSplit fileSplit;

	private Configuration conf;

	private Text value = new Text();

	private boolean processed = false;

	@Override

	public void initialize(InputSplit split, TaskAttemptContext context)

			throws IOException, InterruptedException {

		this.fileSplit = (FileSplit) split;

		this.conf = context.getConfiguration();

	}

	@Override

	public boolean nextKeyValue() throws IOException, InterruptedException {

		if (!processed) {

			// 获取 输入逻辑切片的 字节数组

			byte[] contents = new byte[(int) fileSplit.getLength()];

			// 通过 filesplit获取该逻辑切片在文件系统的位置

			Path file = fileSplit.getPath();

			FileSystem fs = file.getFileSystem(conf);

			FSDataInputStream in = null;

			try {

				// 文件系统对象fs打开一个file的输入流

				in = fs.open(file);

				/**

				 *  in是输入流

				 *  contents是存这个流读取的到数的数据的字节数组

				 *

				 */

				IOUtils.readFully(in, contents, 0, contents.length);

				value.set(contents, 0, contents.length);

			} finally {

				IOUtils.closeStream(in);

			}

			processed = true;

			return true;

		}

		return false;

	}

	@Override

	public NullWritable getCurrentKey() throws IOException, InterruptedException {

		return NullWritable.get();

	}

	@Override

	public Text getCurrentValue() throws IOException, InterruptedException {

		return value;

	}

	@Override

	public float getProgress() throws IOException {

		return processed ? 1.0f : 0.0f;

	}

	@Override

	public void close() throws IOException {

		// do nothing

	}

}

　　 第三步：编写mapreduce程序

package com.ghgj.mr.format.input;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.conf.Configured;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.InputSplit;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.input.FileSplit;

import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.Tool;

import org.apache.hadoop.util.ToolRunner;

public class SmallFilesConvertToBigMR extends Configured implements Tool {

	public static void main(String[] args) throws Exception {

		int exitCode = ToolRunner.run(new SmallFilesConvertToBigMR(), args);

		System.exit(exitCode);

	}

	static class SmallFilesConvertToBigMRMapper extends

			Mapper<NullWritable, Text, Text, Text> {

		private Text filenameKey;

		@Override

		protected void setup(Context context) throws IOException,

				InterruptedException {

			InputSplit split = context.getInputSplit();

			Path path = ((FileSplit) split).getPath();

			filenameKey = new Text(path.toString());

		}

		@Override

		protected void map(NullWritable key, Text value, Context context)

				throws IOException, InterruptedException {

			context.write(filenameKey, value);

		}

	}

	static class SmallFilesConvertToBigMRReducer extends

			Reducer<Text, Text, NullWritable, Text> {

		@Override

		protected void reduce(Text filename, Iterable<Text> bytes,

				Context context) throws IOException, InterruptedException {

			context.write(NullWritable.get(), bytes.iterator().next());

		}

	}

	@Override

	public int run(String[] args) throws Exception {

		Configuration conf = new Configuration();

		conf.set("fs.defaultFS", "hdfs://hadoop02:9000");

		System.setProperty("HADOOP_USER_NAME", "hadoop");

		Job job = Job.getInstance(conf, "combine small files to bigfile");

		job.setJarByClass(SmallFilesConvertToBigMR.class);

		job.setMapOutputKeyClass(Text.class);

		job.setMapOutputValueClass(Text.class);

		job.setMapperClass(SmallFilesConvertToBigMRMapper.class);

		job.setOutputKeyClass(NullWritable.class);

		job.setOutputValueClass(Text.class);

		job.setReducerClass(SmallFilesConvertToBigMRReducer.class);

		// TextInputFormat是默认的数据读取组件

//		job.setInputFormatClass(TextInputFormat.class);

		// 不是用默认的读取数据的Format，我使用自定义的 WholeFileInputFormat

		job.setInputFormatClass(WholeFileInputFormat.class);

		Path input = new Path("/smallfiles");

		Path output = new Path("/bigfile");

		FileInputFormat.setInputPaths(job, input);

		FileSystem fs = FileSystem.get(conf);

		if (fs.exists(output)) {

			fs.delete(output, true);

		}

		FileOutputFormat.setOutputPath(job, output);

		int status = job.waitForCompletion(true) ? 0 : 1;

		return status;

	}

}

　　

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