MapReduce流程讲解以及源码分析

MapReduce流程讲解

一.简介

对于用户来说只需要书写map操作和reduce操作

mapreduce计算数据的时间较长

整个过程分为map和reduce,map负责处理原始数据，reduce负责处理map数据

二.原理分析

1.map过程

block:块-->物理上的概念,默认是128M

split:切片-->本次map任务要处理的数据的大小;默认大小等于block的大小

maptask:map的任务-->一个split对应一个maptask

处理的都是切片数据,属于最原始的数据

kvbuffer:maptask临时结果输出的目的地.它是内存中的一块环形的空间,默认大小是100M;设置阈值,默认大小是80%,达到阈值之后进行溢写

spill:将环形数据缓冲区的临时结果写出到硬盘上;如果文件足够大，那么会溢写出很多的小文件，大概80M左右

partation:分区的数量和reduce的数量完全相同;

溢写的时候回提前计算出key所对应的的分区reduce

sort:排序先按照partation进行排序;然后按照key进行快速排序

merge:就是将溢写的多个小文件合并成一个大文件;使用归并算法，先按照分区，然后按照key进行归并排序

2.reduce

fetch:从maptask节点拉取reduce需要的数据

reducetask:我们必须将key相同临时数据拉取到同一个reducetask中进行计算;一个箱子里可以有不同的key，但是相同key必须在一个箱子里

output:因为每次产生结果不一样，为了防止产生的文件过大出现问题;每次产生的结果会默认存放到HDFS上;

3.搭建MapReduce集群

搭建的环境完全基于HA的环境

修改mapred-site.xml文件

cp mapred-site.xml.template mapred-site.xml

vim mapred-site.xml

<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>

修改yarn-site.xml文件

vim yarn-site.xml

<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.resourcemanager.ha.enabled</name>
<value>true</value>
</property>
<property>
<name>yarn.resourcemanager.cluster-id</name>
<value>mr_shsxt</value>
</property>
<property>
<name>yarn.resourcemanager.ha.rm-ids</name>
<value>rm1,rm2</value>
</property>
<property>
<name>yarn.resourcemanager.hostname.rm1</name>
<value>node03</value>
</property>
<property>
<name>yarn.resourcemanager.hostname.rm2</name>
<value>node01</value>
</property>
<property>
<name>yarn.resourcemanager.zk-address</name>
<value>node01:2181,node02:2181,node03:2181</value>
</property>

拷贝配置文件(yarn-mapred)到其他节点

scp mapred-site.xml yarn-site.xml root@node02:pwd

scp mapred-site.xml yarn-site.xml root@node02:`pwd

先启动Zookeeper

zkServer.sh start

zkServer.sh status

启动DFS

start-all.sh( start-dfs.sh start-yarn.sh)

在备用节点输入命令

yarn-daemon.sh start resourcemanager

MapReduce源码分析

1.提交工作至客户端(client)

1.1创建job类

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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;
​
/**
 * 作业类的主类
 * @author Administrator
 *
 */
public class WordCountJob {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //获取配置文件对象
        Configuration configuration = new Configuration(true);
        //创建本次的作业对象
        Job job = Job.getInstance(configuration);
        //设置作业Jar的主类
        job.setJarByClass(WordCountJob.class);
        //设置作业的名称
        job.setJobName("shsxt-WorkCount");
        //设置ReduceTask的数量
        job.setNumReduceTasks(3);
        //设置输入路径(要处理的文件路径)
        FileInputFormat.setInputPaths(job, new Path("/shsxt/java/武动乾坤.txt"));
        //设置切片的大小
        //      CombineTextInputFormat.setMaxInputSplitSize(job, 1024 * 1024 * 100);
        //      CombineTextInputFormat.setMinInputSplitSize(job, 1024 * 1024 * 1);
        //设置输入路径(结果要输出的位置)
        FileOutputFormat.setOutputPath(job, 
           new Path("/shsxt/java/武动乾坤_result" + System.currentTimeMillis()));
        //定义Map的输出格式
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        //设置Mapper类
        job.setMapperClass(WordCountMapper.class);
        //设置Reducer类
        job.setReducerClass(WordCountReducer.class);
        //提交作业
        job.waitForCompletion(true);
    }
    //  MapTask
​
}
​

• 文件必须上传到Hadoop上并且路径必须正确 设置的job名称必须和导出的jar包名称一致

1.2创建mapper类

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
​
public class WordCountMapper extends Mapper<Object, Text, Text, IntWritable> {
    private Text text = new Text();
    private IntWritable one = new IntWritable(1);
    @Override
    protected void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {
        //对数据进行切分
        String[] ss = value.toString().split("\\W+");
        for (String string : ss) {
            text.set(string);
            context.write(text, one);
        }
​
    }
​
}

1.3创建reduce类

import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private Text text = new Text("ly");
    private int count = 1000;
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
        //获取所有的key对应的值
        Iterator<IntWritable> iterator = values.iterator();
        //创建计数器
        int count = 0;
        //遍历
        while (iterator.hasNext()) {
            count += iterator.next().get();
        }
        context.write(key, new IntWritable(count));
    }
}
​

2.map源码分析

1.split 切片

解析job类中-->job.waitForCompletion(true);

/**
*Submit the job to the cluster and wait for it to finish
*提交该工作到集群并等待完成
*/
job.waitForCompletion(true);
// JobState是个枚举类只有两种状态 public static enum JobState {DEFINE, RUNNING};             //如果状态是DEFINE (定义的) 则直接提交
    if (state == JobState.DEFINE) {
      //(------------------------------submit())
        submit();
    }
//Monitor a job and print status in real-time as progress is made and tasks fail.
//实时监测当前进程的状态
 if (verbose) {
      monitorAndPrintJob();

解析job类中-->submit();

//(------------------------------submit())
//监测任务的状态
 ensureState(JobState.DEFINE);
//设置使用新的API
setUseNewAPI();
//通过配置文件创建新的对象
connect();
//(----------------------连接)
if (cluster == null) {
      cluster = 
        ugi.doAs(new PrivilegedExceptionAction<Cluster>() {
                   public Cluster run()
                          throws IOException, InterruptedException, 
                                 ClassNotFoundException {
                     return new Cluster(getConfiguration());
                   }
                 });
    }
//通过分布式系统和客户端获取任务提交器
 final JobSubmitter submitter = 
        getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
 status = ugi.doAs(new PrivilegedExceptionAction<JobStatus>() {
      //开启多线程提交任务
     public JobStatus run() throws IOException, InterruptedException, 
      ClassNotFoundException {
          //正式提交任务
          //进入(-----------------submitter)
        return submitter.submitJobInternal(Job.this, cluster);
      }
    });
//任务进入运行状态
state = JobState.RUNNING;

解析submitter

//Internal method for submitting jobs to the system
//使用内部的方法将任务提交到分布式系统
//validate the jobs output specs 
//验证任务输出空间
    checkSpecs(job);
//生成新的jobid并且设置进去
 JobID jobId = submitClient.getNewJobID();
    job.setJobID(jobId);
//设置job的提交路径
 Path submitJobDir = new Path(jobStagingArea, jobId.toString());
//Create the splits for the job
//创建给任务的切片
//(---------------------------writeSplits)
int maps = writeSplits(job, submitJobDir);
  List<InputSplit> splits = input.getSplits(job);
//将切片的集合转换成数组
    T[] array = (T[]) splits.toArray(new InputSplit[splits.size()]);
    // sort the splits into order based on size, so that the biggest go first
    //对切片进行排序将最大的放在前面
    Arrays.sort(array, new SplitComparator());
    //创建切片文件
    JobSplitWriter.createSplitFiles(jobSubmitDir, conf, 
        jobSubmitDir.getFileSystem(conf), array);
    //返回切片的个数
    return array.length;
//最终提交任务(设置好切片的数量以及maptask的数量)
 status = submitClient.submitJob(
          jobId, submitJobDir.toString(), job.getCredentials());

解析writeSplits

//获取配置文件
 Configuration conf = job.getConfiguration();
//Create an object for the given class and initialize it from conf
//通过反射工具类使用InputFormatClass()类创建类并使用conf配置文件进行初始化
//JobContext job = org.apache.hadoop.mapreduce.task.JobContextImpl
//input = org.apache.hadoop.mapreduce.lib.input.TextInputFormat.class
InputFormat<?, ?> input =
      ReflectionUtils.newInstance(job.getInputFormatClass(), conf);
//通过输入格式对象获取切片
// getSplits = org.apache.hadoop.mapreduce.lib.input.FileInputFormat
//Generate the list of files and make them into FileSplits
//将切片放入到文件切片中
 List<InputSplit> splits = input.getSplits(job);
//设置切片的最大值和最小值
 long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
 long maxSize = getMaxSplitSize(job);
//创建list
List<InputSplit> splits = new ArrayList<InputSplit>();
//获取需要处理的文件
List<FileStatus> files = listStatus(job);
//遍历
 for (FileStatus file: files) {
     //获取文件的路径
      Path path = file.getPath();
     //获取文件的大小
      long length = file.getLen();
     //判断文件是否为空
      if (length != 0) {
         //当前文件所对应的块
        BlockLocation[] blkLocations;
        if (file instanceof LocatedFileStatus) {
          blkLocations = ((LocatedFileStatus) file).getBlockLocations();
        } else {
          FileSystem fs = path.getFileSystem(job.getConfiguration());
          blkLocations = fs.getFileBlockLocations(file, 0, length);
        }
        //判断文件是否能被切片,压缩的文件不能进行切片
        if (isSplitable(job, path)) {
          //Get the block size of the file
          //获得块的数量
          long blockSize = file.getBlockSize();
         // 获取切片的最终大小
         //(-----------------------------computeSplitSize)
          long splitSize = computeSplitSize(blockSize, minSize, maxSize);
           //剩下的长度 
          long bytesRemaining = length;
            //判断剩下的是否能够切片,依据是不超过数据的10%不切,
            //即就是最后一个切片最大是140.8M,最小切片是12.9M
          while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
              //第一个切片的索引为0
            int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
              //将切片放到集合里,获取片的地址.路径
            splits.add(makeSplit(path, length-bytesRemaining, splitSize,
                        blkLocations[blkIndex].getHosts(),
                        blkLocations[blkIndex].getCachedHosts()));
              //获取剩余部分的长度
            bytesRemaining -= splitSize;
          }
            //最后一个切片的大小(0,1.1]*splitSize,将最后一个切片放入集合中
          if (bytesRemaining != 0) {
            int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
            splits.add(makeSplit(path, length-bytesRemaining, bytesRemaining,
                       blkLocations[blkIndex].getHosts(),
                       blkLocations[blkIndex].getCachedHosts()));
          }
        } else { 
            // not splitable
          splits.add(makeSplit(path, 0, length, blkLocations[0].getHosts(),
                      blkLocations[0].getCachedHosts()));
        }
      } else { 
        //Create empty hosts array for zero length files
        //如果文件为空就创建一个空的文件夹
        splits.add(makeSplit(path, 0, length, new String[0]));
      }
    }
 // Save the number of input files for metrics/loadgen
    job.getConfiguration().setLong(NUM_INPUT_FILES, files.size());
    sw.stop();
    if (LOG.isDebugEnabled()) {
      LOG.debug("Total # of splits generated by getSplits: " + splits.size()
          + ", TimeTaken: " + sw.elapsedMillis());
    }
    //返回带有切片的list集合
    return splits;
  }

computeSplitSize解析

//计算块的大小的方法
//如何让Splitsize>blockSize(将MinSize大于blockSize)
//如果让Splitsize<blockSize(将maxSize小于blockSize)
Math.max(minSize, Math.min(maxSize, blockSize));

2.maptask

//  所属包org.apache.hadoop.mapred
  public void run(final JobConf job, final TaskUmbilicalProtocol umbilical)
    throws IOException, ClassNotFoundException, InterruptedException {
    this.umbilical = umbilical;
    if (isMapTask()) {
      // If there are no reducers then there won't be any sort. Hence the map 
      // phase will govern the entire attempt's progress.
      //如果没有reducers,只进行map操作
      if (conf.getNumReduceTasks() == 0) {
        mapPhase = getProgress().addPhase("map", 1.0f);
      } else {
        // If there are reducers then the entire attempt's progress will be 
        // split between the map phase (67%) and the sort phase (33%).
          //设置溢写
        mapPhase = getProgress().addPhase("map", 0.667f);
        sortPhase  = getProgress().addPhase("sort", 0.333f);
      }
    }
    TaskReporter reporter = startReporter(umbilical);
    //判断是否使用新的API
    boolean useNewApi = job.getUseNewMapper();
      //进行数据初始化
    initialize(job, getJobID(), reporter, useNewApi);
​
    // check if it is a cleanupJobTask
    if (jobCleanup) {
      runJobCleanupTask(umbilical, reporter);
      return;
    }
    if (jobSetup) {
      runJobSetupTask(umbilical, reporter);
      return;
    }
    if (taskCleanup) {
      runTaskCleanupTask(umbilical, reporter);
      return;
    }
    //(------------------------------- runNewMapper)
    if (useNewApi) {
      runNewMapper(job, splitMetaInfo, umbilical, reporter);
    } else {
      runOldMapper(job, splitMetaInfo, umbilical, reporter);
    }
    done(umbilical, reporter);
  }
​

runNewMapper解析

//创建任务上下文对象
taskContext =
      new org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl(job, 
                                                                  getTaskID(),
                                                                  reporter);
// make a mapper
//通过反射创建mapper类
//获取到的是wordcountmapper这个类
    org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE> mapper =
      (org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE>)
        ReflectionUtils.newInstance(taskContext.getMapperClass(), job);
// make the input format
//所属包  org.apache.hadoop.mapreduce.lib.input.TextInputFormat
    org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE> inputFormat =
      (org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE>)
        ReflectionUtils.newInstance(taskContext.getInputFormatClass(), job);
//记录读取器,使用的类类似于装饰器模式
 input = new NewTrackingRecordReader (split, inputFormat, reporter, taskContext);
//真正的读取数据的内部组件
//(-------------------------createRecordReader)
this.real = inputFormat.createRecordReader(split, taskContext);
//数据写出器
 org.apache.hadoop.mapreduce.RecordWriter output = null;
    // get an output object
    if (job.getNumReduceTasks() == 0) {
      output = 
        new NewDirectOutputCollector(taskContext, job, umbilical, reporter);
    } else {
      output = new NewOutputCollector(taskContext, job, umbilical, reporter);
    }
//创建一个收集器
collector = createSortingCollector(job, reporter);
//创建收集器org.apache.hadoop.mapred.MapOutputBuffer.class
MapOutputCollector<KEY, VALUE> collector =
          ReflectionUtils.newInstance(subclazz, job);
//收集器初始化
//(------------------------------ collector.init(context))
 collector.init(context);
//创建分区 org.apache.hadoop.mapreduce.lib.partition.HashPartitioner.class
//(-------------------HashPartitioner.class)
 partitions = jobContext.getNumReduceTasks();
      if (partitions > 1) {
        partitioner = (org.apache.hadoop.mapreduce.Partitioner<K,V>)
          ReflectionUtils.newInstance(jobContext.getPartitionerClass(), job);
      } else {
        partitioner = new org.apache.hadoop.mapreduce.Partitioner<K,V>() {
          @Override
          public int getPartition(K key, V value, int numPartitions) {
            return partitions - 1;
          }
//创建mapper的上下文对象
 mapContext = 
      new MapContextImpl<INKEY, INVALUE, OUTKEY, OUTVALUE>(job,
                                                           getTaskID(), 
                                                           input, output, 
                                                           committer, 
                                                           reporter, split);
//创建mapperContext的包装类
mapperContext = 
          new WrappedMapper<INKEY, INVALUE, OUTKEY, OUTVALUE>().getMapContext(
              mapContext);
try {
    //行读取器初始化
    //(----------------------------------input.initialize)
      input.initialize(split, mapperContext);
    //(------------------mapper.run)
      mapper.run(mapperContext);
      mapPhase.complete();
      setPhase(TaskStatus.Phase.SORT);
    //更新状态
      statusUpdate(umbilical);
    //关闭行读取器
      input.close();
      input = null;
      output.close(mapperContext);
    //数据刷出
    //关闭写出器
      output = null;
    } finally {
      closeQuietly(input);
      closeQuietly(output, mapperContext);
    }

解析HashPartitioner.class

public class HashPartitioner<K, V> extends Partitioner<K, V> {
​
  /** Use {@link Object#hashCode()} to partition. */
  public int getPartition(K key, V value,
                          int numReduceTasks) {
      //取余运算防止负数
    return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
  }
​
}

解析 collector.init(context)

  public void init(MapOutputCollector.Context context
                    ) throws IOException, ClassNotFoundException {
      job = context.getJobConf();
      reporter = context.getReporter();
      mapTask = context.getMapTask();
      mapOutputFile = mapTask.getMapOutputFile();
      sortPhase = mapTask.getSortPhase();
      spilledRecordsCounter = reporter.getCounter(TaskCounter.SPILLED_RECORDS);
      partitions = job.getNumReduceTasks();
      rfs = ((LocalFileSystem)FileSystem.getLocal(job)).getRaw();
​
      //sanity checks
      final float spillper =
        job.getFloat(JobContext.MAP_SORT_SPILL_PERCENT, (float)0.8);
      final int sortmb = job.getInt(JobContext.IO_SORT_MB, 100);
      indexCacheMemoryLimit = job.getInt(JobContext.INDEX_CACHE_MEMORY_LIMIT,
                                         INDEX_CACHE_MEMORY_LIMIT_DEFAULT);
      //确定溢写范围
      if (spillper > (float)1.0 || spillper <= (float)0.0) {
        throw new IOException("Invalid \"" + JobContext.MAP_SORT_SPILL_PERCENT +
            "\": " + spillper);
      }
      if ((sortmb & 0x7FF) != sortmb) {
        throw new IOException(
            "Invalid \"" + JobContext.IO_SORT_MB + "\": " + sortmb);
      }
      sorter = ReflectionUtils.newInstance(job.getClass("map.sort.class",
            QuickSort.class, IndexedSorter.class), job);
      // buffers and accounting
      int maxMemUsage = sortmb << 20;
      maxMemUsage -= maxMemUsage % METASIZE;
      //环形缓冲区,默认大小100M
      kvbuffer = new byte[maxMemUsage];
      bufvoid = kvbuffer.length;
      kvmeta = ByteBuffer.wrap(kvbuffer)
         .order(ByteOrder.nativeOrder())
         .asIntBuffer();
      setEquator(0);
      bufstart = bufend = bufindex = equator;
      kvstart = kvend = kvindex;
​
      maxRec = kvmeta.capacity() / NMETA;
      softLimit = (int)(kvbuffer.length * spillper);
      bufferRemaining = softLimit;
      LOG.info(JobContext.IO_SORT_MB + ": " + sortmb);
      LOG.info("soft limit at " + softLimit);
      LOG.info("bufstart = " + bufstart + "; bufvoid = " + bufvoid);
      LOG.info("kvstart = " + kvstart + "; length = " + maxRec);
​
      // k/v serialization
      comparator = job.getOutputKeyComparator();
      //获取key和value的类型
      keyClass = (Class<K>)job.getMapOutputKeyClass();
      valClass = (Class<V>)job.getMapOutputValueClass();
      serializationFactory = new SerializationFactory(job);
      keySerializer = serializationFactory.getSerializer(keyClass);
      keySerializer.open(bb);
      valSerializer = serializationFactory.getSerializer(valClass);
      valSerializer.open(bb);
​
      // output counters
      mapOutputByteCounter = reporter.getCounter(TaskCounter.MAP_OUTPUT_BYTES);
      mapOutputRecordCounter =
        reporter.getCounter(TaskCounter.MAP_OUTPUT_RECORDS);
      fileOutputByteCounter = reporter
          .getCounter(TaskCounter.MAP_OUTPUT_MATERIALIZED_BYTES);
​
      // compression
      if (job.getCompressMapOutput()) {
        Class<? extends CompressionCodec> codecClass =
          job.getMapOutputCompressorClass(DefaultCodec.class);
        codec = ReflectionUtils.newInstance(codecClass, job);
      } else {
        codec = null;
      }
​
      // combiner
      final Counters.Counter combineInputCounter =
        reporter.getCounter(TaskCounter.COMBINE_INPUT_RECORDS);
      combinerRunner = CombinerRunner.create(job, getTaskID(), 
                                             combineInputCounter,
                                             reporter, null);
      if (combinerRunner != null) {
        final Counters.Counter combineOutputCounter =
          reporter.getCounter(TaskCounter.COMBINE_OUTPUT_RECORDS);
        combineCollector= new CombineOutputCollector<K,V>(combineOutputCounter, reporter, job);
      } else {
        combineCollector = null;
      }
      spillInProgress = false;
      minSpillsForCombine = job.getInt(JobContext.MAP_COMBINE_MIN_SPILLS, 3);
      //溢写线程
      spillThread.setDaemon(true);
      spillThread.setName("SpillThread");
      spillLock.lock();
      try {
        spillThread.start();
        while (!spillThreadRunning) {
          spillDone.await();
        }
      } catch (InterruptedException e) {
        throw new IOException("Spill thread failed to initialize", e);
      } finally {
        spillLock.unlock();
      }
      if (sortSpillException != null) {
        throw new IOException("Spill thread failed to initialize",sortSpillException);
      }
    }
​

解析mapper.run

 public void run(Context context) throws IOException, InterruptedException {
    //开始任务
     setup(context);
    try {
        //循环调用数据(mapcontext-->mapcontextimpl-->lineRecordWriter.nextKeyValue())
        //(-------------------------context.nextKeyValue())
        while (context.nextKeyValue()) {
        //context.getCurrentKey()-->当前行的索引
       //context.getCurrentValue()-->value当前行的数据
        map(context.getCurrentKey(), context.getCurrentValue(), context);
      }
    } finally {
        //任务结束
      cleanup(context);
    }
  }

解析context.nextKeyValue()

 public boolean nextKeyValue() throws IOException {
    if (key == null) {
        //设置key的类型
      key = new LongWritable();
    }
    //设置当前行开始的索引
    key.set(pos);
    if (value == null) {
      //设置value的类型
      value = new Text();
    }
    int newSize = 0;
    // We always read one extra line, which lies outside the upper
    // split limit i.e. (end - 1)
    //除了最后一行之外,我们总是多读一行
    //getFilePosition确定当前读取的数据定位在切片中
    //in.needAdditionalRecordAfterSplit()即使超出范围,可能下个切片的第一行也是能读出来的
    while (getFilePosition() <= end || in.needAdditionalRecordAfterSplit()) {
      if (pos == 0) {
        //如果是第一行就跳过Utf-8字符
        newSize = skipUtfByteOrderMark();
      } else {
        //读取一行数据,并把它存到value中
        newSize = in.readLine(value, maxLineLength, maxBytesToConsume(pos));
        //将定位修改到下一行的开头
        pos += newSize;
      }
​
      if ((newSize == 0) || (newSize < maxLineLength)) {
        break;
      }
​
      // line too long. try again
      LOG.info("Skipped line of size " + newSize + " at pos " + 
               (pos - newSize));
    }
    if (newSize == 0) {
      key = null;
      value = null;
      return false;
    } else {
      return true;
    }
  }
​

解析createRecordReader

  @Override
  public RecordReader<LongWritable, Text> 
    createRecordReader(InputSplit split,
                       TaskAttemptContext context) {
    String delimiter = context.getConfiguration().get(
        "textinputformat.record.delimiter");
    byte[] recordDelimiterBytes = null;
    if (null != delimiter)
      recordDelimiterBytes = delimiter.getBytes(Charsets.UTF_8);
      //返回一个行读取器
    return new LineRecordReader(recordDelimiterBytes);
  }
​

解析input.initialize

• public void initialize(InputSplit genericSplit,
                           TaskAttemptContext context) throws IOException {
      FileSplit split = (FileSplit) genericSplit;
      Configuration job = context.getConfiguration();
      //每一行最多读取的大小,默认最大读取值是int的最大范围
      this.maxLineLength = job.getInt(MAX_LINE_LENGTH, Integer.MAX_VALUE);
      //The position of the first byte in the file to process
      //获得进程的文件中第一的字节的偏移量
      start = split.getStart();
      //获取当前切片的最后一个字符的位置
      end = start + split.getLength();
      //获取切片的路径
      final Path file = split.getPath();
      // open the file and seek to the start of the split
      final FileSystem fs = file.getFileSystem(job);
      fileIn = fs.open(file);
      
      CompressionCodec codec = new CompressionCodecFactory(job).getCodec(file);
      if (null!=codec) {
        isCompressedInput = true; 
        decompressor = CodecPool.getDecompressor(codec);
        if (codec instanceof SplittableCompressionCodec) {
          final SplitCompressionInputStream cIn =
            ((SplittableCompressionCodec)codec).createInputStream(
              fileIn, decompressor, start, end,
              SplittableCompressionCodec.READ_MODE.BYBLOCK);
          in = new CompressedSplitLineReader(cIn, job,
              this.recordDelimiterBytes);
          start = cIn.getAdjustedStart();
          end = cIn.getAdjustedEnd();
          filePosition = cIn;
        } else {
          in = new SplitLineReader(codec.createInputStream(fileIn,
              decompressor), job, this.recordDelimiterBytes);
          filePosition = fileIn;
        }
      } else {
        fileIn.seek(start);
        in = new UncompressedSplitLineReader(
            fileIn, job, this.recordDelimiterBytes, split.getLength());
        filePosition = fileIn;
      }
      // If this is not the first split, we always throw away first record
      // because we always (except the last split) read one extra line in
      // next() method.
      //如果不是第一个切片,我们读取时就跳过第一行
      //除了最后一行之外,每个切片多读下一个切片的第一行
      if (start != 0) {
        start += in.readLine(new Text(), 0, maxBytesToConsume(start));
      }
      this.pos = start;
    }
    

3.reduce源码分析

1.reducetask

//所属包 org.apache.hadoop.mapred
public void run(JobConf job, final TaskUmbilicalProtocol umbilical)
    throws IOException, InterruptedException, ClassNotFoundException {
    job.setBoolean(JobContext.SKIP_RECORDS, isSkipping());
​
    if (isMapOrReduce()) {
      copyPhase = getProgress().addPhase("copy");
      sortPhase  = getProgress().addPhase("sort");
      reducePhase = getProgress().addPhase("reduce");
    }
    // start thread that will handle communication with parent
    TaskReporter reporter = startReporter(umbilical);
    
    boolean useNewApi = job.getUseNewReducer();
    //(------------------initialize(job, getJobID(), reporter, useNewApi))
    initialize(job, getJobID(), reporter, useNewApi);
​
    // check if it is a cleanupJobTask
    if (jobCleanup) {
      runJobCleanupTask(umbilical, reporter);
      return;
    }
    if (jobSetup) {
      runJobSetupTask(umbilical, reporter);
      return;
    }
    if (taskCleanup) {
      runTaskCleanupTask(umbilical, reporter);
      return;
    }
    
    // Initialize the codec
    codec = initCodec();
    //(---------------------------------RawKeyValueIterator)
    //原生的key和value迭代器
    RawKeyValueIterator rIter = null;
    //洗牌插件,模式使用Shuffle.class
    //所属包org.apache.hadoop.mapreduce.task.reduce.Shuffle.class
    ShuffleConsumerPlugin shuffleConsumerPlugin = null;
    
    Class combinerClass = conf.getCombinerClass();
    CombineOutputCollector combineCollector = 
      (null != combinerClass) ? 
     new CombineOutputCollector(reduceCombineOutputCounter, reporter, conf) : null;
​
    Class<? extends ShuffleConsumerPlugin> clazz =
          job.getClass(MRConfig.SHUFFLE_CONSUMER_PLUGIN, Shuffle.class, ShuffleConsumerPlugin.class);
                    
    shuffleConsumerPlugin = ReflectionUtils.newInstance(clazz, job);
    LOG.info("Using ShuffleConsumerPlugin: " + shuffleConsumerPlugin);
​
    ShuffleConsumerPlugin.Context shuffleContext = 
      new ShuffleConsumerPlugin.Context(getTaskID(), job, FileSystem.getLocal(job), umbilical, 
                  super.lDirAlloc, reporter, codec, 
                  combinerClass, combineCollector, 
                  spilledRecordsCounter, reduceCombineInputCounter,
                  shuffledMapsCounter,
                  reduceShuffleBytes, failedShuffleCounter,
                  mergedMapOutputsCounter,
                  taskStatus, copyPhase, sortPhase, this,
                  mapOutputFile, localMapFiles);
    shuffleConsumerPlugin.init(shuffleContext);
    //(---------------------------shuffleConsumerPlugin.run())
    rIter = shuffleConsumerPlugin.run();
​
    // free up the data structures
    mapOutputFilesOnDisk.clear();
    
    sortPhase.complete();                         // sort is complete
    setPhase(TaskStatus.Phase.REDUCE); 
    statusUpdate(umbilical);
    //获取输出key的类型
    Class keyClass = job.getMapOutputKeyClass();
    //获取输出value的类型
    Class valueClass = job.getMapOutputValueClass();
    //获取分组比较器
    //(----------------------job.getOutputValueGroupingComparator())
    RawComparator comparator = job.getOutputValueGroupingComparator();
​
    if (useNewApi) {
      //(-------------------------------- runNewReducer)
      runNewReducer(job, umbilical, reporter, rIter, comparator, 
                    keyClass, valueClass);
    } else {
      runOldReducer(job, umbilical, reporter, rIter, comparator, 
                    keyClass, valueClass);
    }
​
    shuffleConsumerPlugin.close();
    done(umbilical, reporter);
  }
​

解析initialize(job, getJobID(), reporter, useNewApi)

jobContext = new JobContextImpl(job, id, reporter);
taskContext = new TaskAttemptContextImpl(job, taskId, reporter);
outputFormat =ReflectionUtils.newInstance(taskContext.getOutputFormatClass(), job);
committer = outputFormat.getOutputCommitter(taskContext);

解析RawKeyValueIterator和shuffleConsumerPlugin.run()

 public RawKeyValueIterator run() throws IOException, InterruptedException {
    // Scale the maximum events we fetch per RPC call to mitigate OOM issues
    // on the ApplicationMaster when a thundering herd of reducers fetch events
   //设置最大的范围防止出现OOM(内存溢出)问题
    int eventsPerReducer = Math.max(MIN_EVENTS_TO_FETCH,
        MAX_RPC_OUTSTANDING_EVENTS / jobConf.getNumReduceTasks());
    int maxEventsToFetch = Math.min(MAX_EVENTS_TO_FETCH, eventsPerReducer);
​
    // Start the map-completion events fetcher thread
    final EventFetcher<K,V> eventFetcher = 
      new EventFetcher<K,V>(reduceId, umbilical, scheduler, this,
          maxEventsToFetch);
    eventFetcher.start();
    
    // Start the map-output fetcher threads
   //开启拉取线程
    boolean isLocal = localMapFiles != null;
   //如果是本期文件开启1个拉取器,如果是远程的开启5个拉取器
    final int numFetchers = isLocal ? 1 :
      jobConf.getInt(MRJobConfig.SHUFFLE_PARALLEL_COPIES, 5);
    Fetcher<K,V>[] fetchers = new Fetcher[numFetchers];
    if (isLocal) {
      fetchers[0] = new LocalFetcher<K, V>(jobConf, reduceId, scheduler,
          merger, reporter, metrics, this, reduceTask.getShuffleSecret(),
          localMapFiles);
      fetchers[0].start();
    } else {
      for (int i=0; i < numFetchers; ++i) {
        fetchers[i] = new Fetcher<K,V>(jobConf, reduceId, scheduler, merger, 
                                       reporter, metrics, this, 
                                       reduceTask.getShuffleSecret());
            //默认调用run方法;run方法中会调用copyFromHost(host);  
          fetchers[i].start();
      }
    }
    
    // Wait for shuffle to complete successfully
    while (!scheduler.waitUntilDone(PROGRESS_FREQUENCY)) {
      reporter.progress();
      
      synchronized (this) {
        if (throwable != null) {
          throw new ShuffleError("error in shuffle in " + throwingThreadName,
                                 throwable);
        }
      }
    }
​
    // Stop the event-fetcher thread
    eventFetcher.shutDown();
    
    // Stop the map-output fetcher threads
    for (Fetcher<K,V> fetcher : fetchers) {
      fetcher.shutDown();
    }
    
    // stop the scheduler
    scheduler.close();
​
    copyPhase.complete(); // copy is already complete
    taskStatus.setPhase(TaskStatus.Phase.SORT);
    reduceTask.statusUpdate(umbilical);
​
    // Finish the on-going merges...
    RawKeyValueIterator kvIter = null;
    try {
      kvIter = merger.close();
    } catch (Throwable e) {
      throw new ShuffleError("Error while doing final merge " , e);
    }
​
    // Sanity check
    synchronized (this) {
      if (throwable != null) {
        throw new ShuffleError("error in shuffle in " + throwingThreadName,
                               throwable);
      }
    }
    
    return kvIter;
  }
​

解析kvIter = merger.close();

kvIter = merger.close();
//(||||||||||||||||||||||||||||||||||||||||||||||||||||)
final RawComparator<K> comparator =
    (RawComparator<K>)job.getOutputKeyComparator();
return finalMerge(jobConf, rfs, memory, disk);
//(||||||||||||||||||||||||||||||||||||||||||||||||||||)
eturn Merger.merge(job, fs, keyClass, valueClass,
    finalSegments, finalSegments.size(), tmpDir,
    comparator, reporter, spilledRecordsCounter, null, null);
//(||||||||||||||||||||||||||||||||||||||||||||||||||||)
return merge(conf, fs, keyClass, valueClass, segments, mergeFactor, tmpDir,
    comparator, reporter, false, readsCounter, writesCounter,mergePhase);
//(||||||||||||||||||||||||||||||||||||||||||||||||||||)
return new MergeQueue<K, V>(conf, fs, segments, comparator, reporter,
    sortSegments,
    TaskType.REDUCE).merge(keyClass, valueClass,
                        mergeFactor, tmpDir,
                        readsCounter, writesCounter,
                        mergePhase);
//(||||||||||||||||||||||||||||||||||||||||||||||||||||)
//kvIter最终拿到的是org.apache.hadoop.mapred.Merger.MergeQueue
org.apache.hadoop.mapred.Merger.MergeQueue

解析job.getOutputValueGroupingComparator()

//获取设置的分组比较器
public RawComparator getOutputValueGroupingComparator() {
    Class<? extends RawComparator> theClass = getClass(
      JobContext.GROUP_COMPARATOR_CLASS, null, RawComparator.class);
    //如果没有设置分组比较器,选择key比较器
    if (theClass == null) {
      return getOutputKeyComparator();
    }
    
    return ReflectionUtils.newInstance(theClass, this);
  }
//如果没key比较器使用key类型自带的比较器 : mapreduce.map.output.key.class

解析 runNewReducer

 private <INKEY,INVALUE,OUTKEY,OUTVALUE> void runNewReducer(JobConf job,
                     final TaskUmbilicalProtocol umbilical,
                     final TaskReporter reporter,
                     RawKeyValueIterator rIter,
                     RawComparator<INKEY> comparator,
                     Class<INKEY> keyClass,
                     Class<INVALUE> valueClass
                     ) throws IOException,InterruptedException, 
                              ClassNotFoundException {
    // wrap value iterator to report progress.
    final RawKeyValueIterator rawIter = rIter;
    rIter = new RawKeyValueIterator() {
      public void close() throws IOException {
        rawIter.close();
      }
      public DataInputBuffer getKey() throws IOException {
        return rawIter.getKey();
      }
      public Progress getProgress() {
        return rawIter.getProgress();
      }
      public DataInputBuffer getValue() throws IOException {
        return rawIter.getValue();
      }
      public boolean next() throws IOException {
        boolean ret = rawIter.next();
        reporter.setProgress(rawIter.getProgress().getProgress());
        return ret;
      }
    };
    // make a task context so we can get the classes
    org.apache.hadoop.mapreduce.TaskAttemptContext taskContext =
      new org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl(job,
          getTaskID(), reporter);
    // make a reducer
    //实际上就是Wordcountreducer.class                              
    org.apache.hadoop.mapreduce.Reducer<INKEY,INVALUE,OUTKEY,OUTVALUE> reducer =
      (org.apache.hadoop.mapreduce.Reducer<INKEY,INVALUE,OUTKEY,OUTVALUE>)
        ReflectionUtils.newInstance(taskContext.getReducerClass(), job);
   //生成写出器
   //所属包org.apache.hadoop.mapreduce.lib.output.TextOutputFormat.LineRecordWriter
    this.real  = return new LineRecordWriter<K, V>(fileOut, keyValueSeparator);           
    org.apache.hadoop.mapreduce.RecordWriter<OUTKEY,OUTVALUE> trackedRW = 
      new NewTrackingRecordWriter<OUTKEY, OUTVALUE>(this, taskContext);
    job.setBoolean("mapred.skip.on", isSkipping());
    job.setBoolean(JobContext.SKIP_RECORDS, isSkipping());
     //(---------------------------- reducerContext)                             
    org.apache.hadoop.mapreduce.Reducer.Context 
         reducerContext = createReduceContext(reducer, job, getTaskID(),
                                               rIter, reduceInputKeyCounter, 
                                               reduceInputValueCounter, 
                                               trackedRW,
                                               committer,
                                               reporter, comparator, keyClass,
                                               valueClass);
    try {
      //(----------------------reducer.run)
      reducer.run(reducerContext);
    } finally {
      trackedRW.close(reducerContext);
    }
  }
  

解析 reducerContext

//org.apache.hadoop.mapreduce.lib.reduce.WrappedReducer
//org.apache.hadoop.mapreduce.task.ReduceContextImpl
reducerContext = new WrappedReducer-->new ReduceContextImpl

解析reducer.run

WrappedReducer.nextkey();
ReduceContextImpl.nextkey();
//拿到反序列化的key
key = keyDeserializer.deserialize(key);
//拿到反序列化的value
value = valueDeserializer.deserialize(value);
//下一个值是否存在
 hasMore = input.next();
//判断下个key是否有值,
if (hasMore) {
    //获得下一个key
      nextKey = input.getKey();
    //比较当前key和下一个key是否一样
      nextKeyIsSame = comparator.compare(currentRawKey.getBytes(), 0, 
                                     currentRawKey.getLength(),
                                     nextKey.getData(),
                                     nextKey.getPosition(),
                                     nextKey.getLength() - nextKey.getPosition()
                                         ) == 0;
    } else {
      nextKeyIsSame = false;
    }
    inputValueCounter.increment(1);
//context.getValues()返回的是一个迭代器return iterable;