大数据框架之Hadoop：MapReduce（三）MapReduce框架原理——OutputFormat数据输出

3.6.1OutputFormat接口实现类

OutputFormat是MapReduce输出的基类，所有实现MapReduce输出都实现了OutputFormat接口。下面我们介绍几种常见的OutputFormat实现类。

1、文本输出TextOutputFormat

默认的输出格式是TextOutputFormat，它把每条记录写为文本行。它的键和值可以是任意类型，疑问TextOutputFormat调用toString()方法把他们转换为字符串。

2、SequenceFileOutputFormat

将SequenceFileOutputFormat输出作为后续MapReduce任务的输入，这便是一种好的输出格式，因为它的格式紧凑，很容易被压缩。

3、自定义OutputFormat

根据用户需求，自定义实现输出。

3.6.2自定义OutputFormat

1、使用场景

为了实现控制最终文件的输出路径和输出格式，可以自定义OutputFormat。

例如：要在一个MapReduce程序中根据数据的不同输出两类结果到不同的目录，这类灵活的输出需求可以通过自定义OutputFormat来实现。

2、自定义OUtputFormat步骤

（1）自定义一个类继承FileOutputFormat。

（2）改写RecordWriter，具体改写输出数据的方法write()。

3.6.3自定义OutputFormat案例实操

1、需求

过滤输入的log日志，包含atguigu的网站输出到e:/atguigu.log，不包含atguigu的网站输出到e:/other.log。

（1）输入数据

http://www.baidu.com
http://www.google.com
http://cn.bing.com
http://www.atguigu.com
http://www.sohu.com
http://www.sina.com
http://www.sin2a.com
http://www.sin2desa.com
http://www.sindsafa.com

（2）期望输出数据

http://www.atguigu.com

http://cn.bing.com
http://www.baidu.com
http://www.google.com
http://www.sin2a.com
http://www.sin2desa.com
http://www.sina.com
http://www.sindsafa.com
http://www.sohu.com

2、需求分析

3、案例实操

（1）编写FilterMapper类

package com.cuiyf41.output;import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;public class FilterMapper extends Mapper<LongWritable, Text, Text, NullWritable> {@Overrideprotected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, NullWritable>.Context context) throws IOException, InterruptedException {// 写出context.write(value, NullWritable.get());}
}

（2）编写FilterReducer类

package com.cuiyf41.output;import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class FilterReducer extends Reducer<Text, NullWritable, Text, NullWritable> {Text k = new Text();@Overrideprotected void reduce(Text key, Iterable<NullWritable> values, Reducer<Text, NullWritable, Text, NullWritable>.Context context) throws IOException, InterruptedException {// 1 获取一行String line = key.toString();// 2 拼接line = line + "\r\n";// 3 设置keyk.set(line);// 4 输出context.write(k, NullWritable.get());}
}

（3）自定义一个OutputFormat类

package com.atguigu.mapreduce.outputformat;
import java.io.IOException;
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.FileOutputFormat;public class FilterOutputFormat extends FileOutputFormat<Text, NullWritable>{@Overridepublic RecordWriter<Text, NullWritable> getRecordWriter(TaskAttemptContext job)			throws IOException, InterruptedException {// 创建一个RecordWriterreturn new FilterRecordWriter(job);}
}

（4）编写RecordWriter类

package com.cuiyf41.output;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 java.io.IOException;public class FilterRecordWriter extends RecordWriter<Text, NullWritable> {FSDataOutputStream atguiguOut = null;FSDataOutputStream otherOut = null;public FilterRecordWriter(TaskAttemptContext job) {// 1 获取文件系统FileSystem fs;try {fs = FileSystem.get(job.getConfiguration());// 2 创建输出文件路径Path atguiguPath = new Path("e:/atguigu.log");Path otherPath = new Path("e:/other.log");// 3 创建输出流atguiguOut = fs.create(atguiguPath);otherOut = fs.create(otherPath);} catch (IOException e) {e.printStackTrace();}}@Overridepublic void write(Text key, NullWritable value) throws IOException, InterruptedException {// 判断是否包含“atguigu”输出到不同文件if (key.toString().contains("atguigu")) {atguiguOut.write(key.toString().getBytes());} else {otherOut.write(key.toString().getBytes());}}@Overridepublic void close(TaskAttemptContext context) throws IOException, InterruptedException {// 关闭资源IOUtils.closeStream(atguiguOut);IOUtils.closeStream(otherOut);}
}

（5）编写FilterDriver类

package com.cuiyf41.output;import org.apache.hadoop.conf.Configuration;
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.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import java.io.IOException;public class FilterDriver {public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {// 输入输出路径需要根据自己电脑上实际的输入输出路径设置args = new String[] { "e:/input/log.txt", "e:/output2" };Configuration conf = new Configuration();Job job = Job.getInstance(conf);job.setJarByClass(FilterDriver.class);job.setMapperClass(FilterMapper.class);job.setReducerClass(FilterReducer.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(NullWritable.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(NullWritable.class);// 要将自定义的输出格式组件设置到job中job.setOutputFormatClass(FilterOutputFormat.class);Path input = new Path(args[0]);Path output = new Path(args[1]);// 如果输出路径存在，则进行删除FileSystem fs = FileSystem.get(conf);if (fs.exists(output)) {fs.delete(output,true);}FileInputFormat.setInputPaths(job, input);// 虽然我们自定义了outputformat，但是因为我们的outputformat继承自fileoutputformat// 而fileoutputformat要输出一个_SUCCESS文件，所以，在这还得指定一个输出目录FileOutputFormat.setOutputPath(job, output);boolean result = job.waitForCompletion(true);System.exit(result ? 0 : 1);}
}