自定义inputformat和outputformat

1. 自定义inputFormat

1.1 需求

无论hdfs还是mapreduce，对于小文件都有损效率，实践中，又难免面临处理大量小文件的场景，此时，就需要有相应解决方案

1.2 分析

小文件的优化无非以下几种方式：

1、 在数据采集的时候，就将小文件或小批数据合成大文件再上传HDFS

2、 在业务处理之前，在HDFS上使用mapreduce程序对小文件进行合并

3、 在mapreduce处理时，可采用combineInputFormat提高效率

实现

本节实现的是上述第二种方式

程序的核心机制：

自定义一个InputFormat

改写RecordReader，实现一次读取一个完整文件封装为KV

在输出时使用SequenceFileOutPutFormat输出合并文件

代码如下：

自定义InputFromat

package cn.itcast.bigdata.combinefile;

import java.io.IOException;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.NullWritable;
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, BytesWritable>{

    @Override
    protected boolean isSplitable(JobContext context, Path file) {
        return false;
    }

    @Override
    public RecordReader<NullWritable, BytesWritable> createRecordReader(
            InputSplit split, TaskAttemptContext context) throws IOException,
            InterruptedException {
        WholeFileRecordReader reader = new WholeFileRecordReader();
        reader.initialize(split, context);
        return reader;
    }

}

package cn.itcast.bigdata.combinefile;

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.BytesWritable;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.NullWritable;
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;

/**
 *
 * RecordReader的核心工作逻辑：
 * 通过nextKeyValue()方法去读取数据构造将返回的key   value
 * 通过getCurrentKey 和 getCurrentValue来返回上面构造好的key和value
 *
 *
 * @author
 *
 */
class WholeFileRecordReader extends RecordReader<NullWritable, BytesWritable> {
    private FileSplit fileSplit;
    private Configuration conf;
    private BytesWritable value = new BytesWritable();
    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) {
            byte[] contents = new byte[(int) fileSplit.getLength()];
            Path file = fileSplit.getPath();
            FileSystem fs = file.getFileSystem(conf);
            FSDataInputStream in = null;
            try {
                in = fs.open(file);
                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 BytesWritable getCurrentValue() throws IOException,
            InterruptedException {
        return value;
    }

    /**
     * 返回当前进度
     */
    @Override
    public float getProgress() throws IOException {
        return processed ? 1.0f : 0.0f;
    }

    @Override
    public void close() throws IOException {
        // do nothing
    }
}

package cn.itcast.bigdata.combinefile;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class SmallFilesToSequenceFileConverter extends Configured implements Tool {
    static class SequenceFileMapper extends
            Mapper<NullWritable, BytesWritable, Text, BytesWritable> {
        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, BytesWritable value,
                Context context) throws IOException, InterruptedException {
            context.write(filenameKey, value);
        }
    }

    @Override
    public int run(String[] args) throws Exception {
        Configuration conf = new Configuration();
        /*System.setProperty("HADOOP_USER_NAME", "hadoop");*/
        String[] otherArgs = new GenericOptionsParser(conf, args)
                .getRemainingArgs();
        if (otherArgs.length != 2) {
            System.err.println("Usage: combinefiles <in> <out>");
            System.exit(2);
        }

        Job job = Job.getInstance(conf,"combine small files to sequencefile");
        job.setJarByClass(SmallFilesToSequenceFileConverter.class);

        job.setInputFormatClass(WholeFileInputFormat.class);
        job.setOutputFormatClass(SequenceFileOutputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(BytesWritable.class);
        job.setMapperClass(SequenceFileMapper.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        return job.waitForCompletion(true) ? 0 : 1;
    }

    public static void main(String[] args) throws Exception {
        args=new String[]{"c:/wordcount/smallinput","c:/wordcount/smallout"};
        int exitCode = ToolRunner.run(new SmallFilesToSequenceFileConverter(),
                args);
        System.exit(exitCode);

    }
}

自定义outputFormat

需求

现有一些原始日志需要做增强解析处理，流程：

1、 从原始日志文件中读取数据

2、 根据日志中的一个URL字段到外部知识库中获取信息增强到原始日志

3、 如果成功增强，则输出到增强结果目录；如果增强失败，则抽取原始数据中URL字段输出到待爬清单目录

2.2 分析

程序的关键点是要在一个mapreduce程序中根据数据的不同输出两类结果到不同目录，这类灵活的输出需求可以通过自定义outputformat来实现

2.3 实现

实现要点：

1、 在mapreduce中访问外部资源

2、 自定义outputformat，改写其中的recordwriter，改写具体输出数据的方法write()

代码实现如下：

数据库获取数据的工具

package cn.itcast.bigdata.mr.logenhance;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.Statement;
import java.util.HashMap;
import java.util.Map;

public class DBLoader {

    public static void dbLoader(Map<String, String> ruleMap) throws Exception {

        Connection conn = null;
        Statement st = null;
        ResultSet res = null;

        try {
            Class.forName("com.mysql.jdbc.Driver");
            conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/urldb", "root", "root");
            st = conn.createStatement();
            res = st.executeQuery("select url,content from url_rule");
            while (res.next()) {
                ruleMap.put(res.getString(1), res.getString(2));
            }

        } finally {
            try{
                if(res!=null){
                    res.close();
                }
                if(st!=null){
                    st.close();
                }
                if(conn!=null){
                    conn.close();
                }

            }catch(Exception e){
                e.printStackTrace();
            }
        }

    }

}

package cn.itcast.bigdata.mr.logenhance;

import java.io.IOException;

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

/**
 * maptask或者reducetask在最终输出时，先调用OutputFormat的getRecordWriter方法拿到一个RecordWriter
 * 然后再调用RecordWriter的write(k,v)方法将数据写出
 *
 * @author
 *
 */
public class LogEnhanceOutputFormat extends FileOutputFormat<Text, NullWritable> {

    @Override
    public RecordWriter<Text, NullWritable> getRecordWriter(TaskAttemptContext context) throws IOException, InterruptedException {

        FileSystem fs = FileSystem.get(context.getConfiguration());

        Path enhancePath = new Path("D:/temp/en/log.dat");
        Path tocrawlPath = new Path("D:/temp/crw/url.dat");

        FSDataOutputStream enhancedOs = fs.create(enhancePath);
        FSDataOutputStream tocrawlOs = fs.create(tocrawlPath);

        return new EnhanceRecordWriter(enhancedOs, tocrawlOs);
    }

    /**
     * 构造一个自己的recordwriter
     *
     * @author
     *
     */
    static class EnhanceRecordWriter extends RecordWriter<Text, NullWritable> {
        FSDataOutputStream enhancedOs = null;
        FSDataOutputStream tocrawlOs = null;

        public EnhanceRecordWriter(FSDataOutputStream enhancedOs, FSDataOutputStream tocrawlOs) {
            super();
            this.enhancedOs = enhancedOs;
            this.tocrawlOs = tocrawlOs;
        }

        @Override
        public void write(Text key, NullWritable value) throws IOException, InterruptedException {
            String result = key.toString();
            // 如果要写出的数据是待爬的url，则写入待爬清单文件 /logenhance/tocrawl/url.dat
            if (result.contains("tocrawl")) {
                tocrawlOs.write(result.getBytes());
            } else {
                // 如果要写出的数据是增强日志，则写入增强日志文件 /logenhance/enhancedlog/log.dat
                enhancedOs.write(result.getBytes());
            }

        }

        @Override
        public void close(TaskAttemptContext context) throws IOException, InterruptedException {
            if (tocrawlOs != null) {
                tocrawlOs.close();
            }
            if (enhancedOs != null) {
                enhancedOs.close();
            }

        }

    }

}

package cn.itcast.bigdata.mr.logenhance;

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
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.Counter;
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.output.FileOutputFormat;

public class LogEnhance {

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

        Map<String, String> ruleMap = new HashMap<String, String>();

        Text k = new Text();
        NullWritable v = NullWritable.get();

        // 从数据库中加载规则信息倒ruleMap中
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {

            try {
                DBLoader.dbLoader(ruleMap);
            } catch (Exception e) {
                e.printStackTrace();
            }

        }

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 获取一个计数器用来记录不合法的日志行数, 组名, 计数器名称
            Counter counter = context.getCounter("malformed", "malformedline");
            String line = value.toString();
            String[] fields = StringUtils.split(line, "\t");
            try {
                String url = fields[26];
                String content_tag = ruleMap.get(url);
                // 判断内容标签是否为空，如果为空，则只输出url到待爬清单；如果有值，则输出到增强日志
                if (content_tag == null) {
                    k.set(url + "\t" + "tocrawl" + "\n");
                    context.write(k, v);
                } else {
                    k.set(line + "\t" + content_tag + "\n");
                    context.write(k, v);
                }

            } catch (Exception exception) {
                counter.increment(1);
            }
        }

    }

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

        Configuration conf = new Configuration();

        Job job = Job.getInstance(conf);

        job.setJarByClass(LogEnhance.class);

        job.setMapperClass(LogEnhanceMapper.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        // 要控制不同的内容写往不同的目标路径，可以采用自定义outputformat的方法
        job.setOutputFormatClass(LogEnhanceOutputFormat.class);

        FileInputFormat.setInputPaths(job, new Path("D:/srcdata/webloginput/"));

        // 尽管我们用的是自定义outputformat，但是它是继承制fileoutputformat
        // 在fileoutputformat中，必须输出一个_success文件，所以在此还需要设置输出path
        FileOutputFormat.setOutputPath(job, new Path("D:/temp/output/"));

        // 不需要reducer
        job.setNumReduceTasks(0);

        job.waitForCompletion(true);
        System.exit(0);

    }

}