需求

过滤输入的log日志中是否包含java

包含java的网站输出到e:/java.log中
不包含java的网站输出到e:/other.log中

输入数据 : log.txt

java.org
jdxia
java.com
x.com
java

输出预期: java.log other.log

OutputFormat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.
常见是OutputFormat实现类

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.他的键和值可以是任意类型,因为TextOutputFormat调用toString()方法把他们转换为字符串
SequenceFileOutputFormat
SequenceFileOutputFormat将它的输出写为一个顺序文件.如果输出需要作为后续MapReduce任务的输入,这便是一种很好的输出格式,因为他的格式紧凑,很容易被压缩
自定义OutputFormat
根据用户需求,自定义实现输出

代码

自定义OutputFormat步骤

自定义一个类继承FileOutputFormat
改写recordwrite,具体改写输出数据的方法write()

自定义一个OutputFormat

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;
import java.io.IOException;
public class FilterOutputFormat extends FileOutputFormat<Text, NullWritable> {
    @Override
    public RecordWriter<Text, NullWritable> getRecordWriter(TaskAttemptContext job) throws IOException, InterruptedException {
        //创建一个RecordWriter
        return new FilterRecordWriter(job);
    }
}

import org.apache.hadoop.fs.FSDataOutputStream;
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.RecordWriter;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import java.io.IOException;
public class FilterRecordWriter extends RecordWriter<Text, NullWritable> {
    FSDataOutputStream javaOut = 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 javaPath = new Path("/Users/jdxia/Desktop/website/data/java.log");
            Path otherPath = new Path("/Users/jdxia/Desktop/website/data/other.log");
            //3. 创建输出流
            javaOut = fs.create(javaPath);
            otherOut = fs.create(otherPath);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
    @Override
    public void write(Text key, NullWritable value) throws IOException, InterruptedException {
        //判断是否包含"java"输出到不同文件
        if (key.toString().contains("java")) {
            javaOut.write(key.toString().getBytes());
        } else {
            otherOut.write(key.toString().getBytes());
        }
    }
    @Override
    public void close(TaskAttemptContext context) throws IOException, InterruptedException {
        //关闭资源,流不关文件是空的
        if (javaOut != null) {
            javaOut.close();
        }
        if (otherOut != null) {
            otherOut.close();
        }
    }
}

Mapper类

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> {
    Text k = new Text();
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //获取一行
        String line = value.toString();
        k.set(line);
        //写出
        context.write(k, NullWritable.get());
    }
}

Reducer类

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> {
    @Override
    protected void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
        String k = key.toString();
        k += "\r\n";
        context.write(new Text(k), NullWritable.get());
    }
}

驱动类

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

import java.io.IOException;

public class FilterDriver {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(FilterDriver.class);
        job.setMapperClass(FilterMapper.class);
        job.setReducerClass(FilterReducer.class);

        //输入输出组件
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(FilterOutputFormat.class);

        //Map的输出
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);

        //reduce的输出
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        //告诉框架，我们要处理的数据文件在那个路径下
        FileInputFormat.setInputPaths(job, new Path("/Users/jdxia/Desktop/website/data/input/"));

        //如果有这个文件夹就删除
        Path out = new Path("/Users/jdxia/Desktop/website/data/output/");
        FileSystem fileSystem = FileSystem.get(conf);
        if (fileSystem.exists(out)) {
            fileSystem.delete(out, true);
        }
        //告诉框架，我们的处理结果要输出到什么地方
        FileOutputFormat.setOutputPath(job, out);

        //虽然自定义OutputFormat,但是因为我们的OutputFormat继承自FileOutputFormat
        //而FileOutputFormat要输出一个_SUCCESS文件,所以这里还需要指定一个输出目录
        FileOutputFormat.setOutputPath(job, new Path("/Users/jdxia/Desktop/website/data/output/ "));

        boolean res = job.waitForCompletion(true);

        System.exit(res ? 0 : 1);
    }
}

注意

自定义OutputFormat时,注意recordWriter中的close方法必须关闭流资源.否则输出的文件内容中数据为空

自定义outputFormat

需求

OutputFormat接口实现类

代码

注意