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Mapreduce源码分析（二）：MapTask及LineRecordReader读取文件的工作机制，源码详解

程序员文章站 2022-07-14 17:29:51
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MapTask及LineRecordReader读取文件的工作机制，源码详解

MapTask
当ApplicationMaster运行一个MapTask的时候，MapTask会构造一个NewTrackingRecordReader对象
该对象中有个属性是private final org.apache.hadoop.mapreduce.RecordReader<K,V> real;，创建对象时会调用构造方法

private <INKEY,INVALUE,OUTKEY,OUTVALUE>
  void runNewMapper(final JobConf job,
                    final TaskSplitIndex splitIndex,
                    final TaskUmbilicalProtocol umbilical,
                    TaskReporter reporter
                    ) throws IOException, ClassNotFoundException,
                             InterruptedException {
    // 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 mapper
    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
    org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE> inputFormat =
      (org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE>)
        ReflectionUtils.newInstance(taskContext.getInputFormatClass(), job);
    // rebuild the input split
    org.apache.hadoop.mapreduce.InputSplit split = null;
    split = getSplitDetails(new Path(splitIndex.getSplitLocation()),
        splitIndex.getStartOffset());
    LOG.info("Processing split: " + split);
	//代码1:创建一个RecordReader对象input时会调用NewTrackingRecordReader这个构造方法
	//在NewTrackingRecordReader这个构造方法里会调用createRecordReader(split, taskContext)这个方法
	//用来指定分隔符，下面给出源码
    org.apache.hadoop.mapreduce.RecordReader<INKEY,INVALUE> input =
      new NewTrackingRecordReader<INKEY,INVALUE>(split, inputFormat, reporter, taskContext);
    
    job.setBoolean(JobContext.SKIP_RECORDS, isSkipping());
    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);
    }

    org.apache.hadoop.mapreduce.MapContext<INKEY, INVALUE, OUTKEY, OUTVALUE> 
    mapContext = 
      new MapContextImpl<INKEY, INVALUE, OUTKEY, OUTVALUE>(job, getTaskID(), 
          input, output, 
          committer, 
          reporter, split);

    org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE>.Context 
        mapperContext = 
          new WrappedMapper<INKEY, INVALUE, OUTKEY, OUTVALUE>().getMapContext(
              mapContext);

    try {
      //代码2：调用LineRecordReader 中的initialize初始化切片的开始位置和结束位置，后面有源码
      input.initialize(split, mapperContext);
      //代码3：初始化后就开始读数据量，请看后面的run方法
      mapper.run(mapperContext);
      mapPhase.complete();
      setPhase(TaskStatus.Phase.SORT);
      statusUpdate(umbilical);
      input.close();
      input = null;
      output.close(mapperContext);
      output = null;
    } finally {
      closeQuietly(input);
      closeQuietly(output, mapperContext);
    }
  }

NewTrackingRecordReader构造方法，用来创建RecordReader的

//代码1调用的代码
 NewTrackingRecordReader(org.apache.hadoop.mapreduce.InputSplit split,
        org.apache.hadoop.mapreduce.InputFormat<K, V> inputFormat,
        TaskReporter reporter,
        org.apache.hadoop.mapreduce.TaskAttemptContext taskContext)
        throws InterruptedException, IOException {
      this.reporter = reporter;
      this.inputRecordCounter = reporter
          .getCounter(TaskCounter.MAP_INPUT_RECORDS);
      this.fileInputByteCounter = reporter
          .getCounter(FileInputFormatCounter.BYTES_READ);

      List <Statistics> matchedStats = null;
      if (split instanceof org.apache.hadoop.mapreduce.lib.input.FileSplit) {
        matchedStats = getFsStatistics(((org.apache.hadoop.mapreduce.lib.input.FileSplit) split)
            .getPath(), taskContext.getConfiguration());
      }
      fsStats = matchedStats;

      long bytesInPrev = getInputBytes(fsStats);
      //代码1.1：调用InputFormat中的RecordReader方法，实际上调用的时它的子类TextInputFormat中createRecordReader方法 
      this.real = inputFormat.createRecordReader(split, taskContext);
      long bytesInCurr = getInputBytes(fsStats);
      fileInputByteCounter.increment(bytesInCurr - bytesInPrev);
    }

TextInputFormat
public class TextInputFormat extends FileInputFormat<LongWritable, Text>

@Override
//代码1.1调用的代码
  public RecordReader<LongWritable, Text> createRecordReader(InputSplit split,TaskAttemptContext context) {
  	//textinputformat.record.delimiter自定义分割符，默认以回车作为一行的结束符
    String delimiter = context.getConfiguration().get("textinputformat.record.delimiter");
    byte[] recordDelimiterBytes = null;
    if (null != delimiter)
      recordDelimiterBytes = delimiter.getBytes(Charsets.UTF_8);
      //返回一个RecordReader对象
    return new LineRecordReader(recordDelimiterBytes);
  }

textinputformat.record.delimiter指的是读取一行的数据的终止符号，即遇到textinputformat.record.delimiter所包含的字符时，该一行的读取结束。
可以通过Configuration的set()方法来设置自定义的终止符，如果没有设置 textinputformat.record.delimiter，那么Hadoop就采用以CR，LF或者CRLF作为终止符，这一点可以查看LineReader的readDefaultLine方法 。
在MapTask执行run()方法之前会执行Initialize初始化方法，如下

@Override
	//代码2执行的代码
    public void initialize(org.apache.hadoop.mapreduce.InputSplit split,
                           org.apache.hadoop.mapreduce.TaskAttemptContext context
                           ) throws IOException, InterruptedException {
      long bytesInPrev = getInputBytes(fsStats);
      //代码2.1：调用LineRecordReader中的initialize方法
      real.initialize(split, context);
      long bytesInCurr = getInputBytes(fsStats);
      fileInputByteCounter.increment(bytesInCurr - bytesInPrev);
    }

之前返回的RecordReader对象real调用了它自己的initialize方法real.initialize(split, context);
LineRecordReader类
作用：读取行偏移量作为key, 行记录作为value
public class LineRecordReader extends RecordReader<LongWritable, Text>
主要属性：

  public static final String MAX_LINE_LENGTH = "mapreduce.input.linerecordreader.line.maxlength";
  private long start;	//分片的开始位置
  private long pos;	//行读取器已经读取的字节数
  private long end;	 //分片的结束位置
  private SplitLineReader in;	// 行读取流
  private FSDataInputStream fileIn;	//输入流，关联文件
  private Seekable filePosition;	//定位位置
  private int maxLineLength;	 //最大行长度
  private LongWritable key;	//Key偏移量
  private Text value;	//Value每行文本
  private boolean isCompressedInput;	
  private Decompressor decompressor;
  private byte[] recordDelimiterBytes; //分隔符

initialize方法
initialize函数主要是计算分片的始末位置，以及打开想要的输入流以供读取K-V对，输入流另外处理分片经过压缩的情况
public void initialize(InputSplit genericSplit, TaskAttemptContext context)

//代码2.1调用的代码，初始化切片的起始和结束位置，pos会做偏移量
public void initialize(InputSplit genericSplit,
                         TaskAttemptContext context) throws IOException {
     //将InputSplit类型转为FileSplit 类型
    FileSplit split = (FileSplit) genericSplit;
    //将hadoop的配置文件给job
    Configuration job = context.getConfiguration();
    this.maxLineLength = job.getInt(MAX_LINE_LENGTH, Integer.MAX_VALUE);
    start = split.getStart();
    end = start + split.getLength();
    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 SplitLineReader(fileIn, job, this.recordDelimiterBytes);
      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.
    if (start != 0) {
      start += in.readLine(new Text(), 0, maxBytesToConsume(start));
    }
    this.pos = start;
  }

初始化input后，mapper就开始读取数据量执行mapper.run(mapperContext);方法

//代码3调用的方法
public void run(Context context) throws IOException, InterruptedException {
    setup(context);
    try {
    //代码3.1：判断是否有下一行，调用LineRecordReader中的nextKeyValue方法
      while (context.nextKeyValue()) {
      //调用map方法，并把读取的key和value传入
        map(context.getCurrentKey(), context.getCurrentValue(), context);
      }
    } finally {
      cleanup(context);
    }
  }

调用LineRecordReader中的nextKeyValue方法

//代码3.1调用的方法，用来读取<K,V>,把pos赋值给key，读取的一行数据赋值给value
public boolean nextKeyValue() throws IOException {
    if (key == null) {
      key = new LongWritable();
    }
    key.set(pos);
    if (value == null) {
      value = new Text();
    }
    int newSize = 0;
    // We always read one extra line, which lies outside the upper
    // split limit i.e. (end - 1)
    while (getFilePosition() <= end || in.needAdditionalRecordAfterSplit()) {
      if (pos == 0) {
        newSize = skipUtfByteOrderMark();
      } else {
        newSize = in.readLine(value, maxLineLength, maxBytesToConsume(pos));
        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;
    }
  }

我们通常会继承Mapper，重写一个map方法，上面的map就调用我们重写的方法

整体思路：

  1. ApplicationMaster会创建一个MapTask，MapTask创建一个InputFormat对象，默认TextInputFormat，调用它的createRecordReader(InputSplit split,TaskAttemptContext context)方法，此方法是用来自定义分隔符的，默认时以回车来作为一行的结束符，返回一个RecordReader对象，默认是LineRecordReader类型
  2. 对切片初始化，信息保存在创建的LineRecordReader对象上（start，end，pos（行读取器已经读取的字节数）等信息）
  3. 执行MapTask中的mapper.run(mapperContext);方法开始读取数据
  4. 在run方法中会调用TextInputFormat中的nextKeyValue方法，在此方法中他会给key和value赋值，key是pos信息当作偏移量，value是每一行的文本数据，返回一个boolean值，来判断是否有下一行数据
  5. 如果有数据的话把<K,V>键值对传给map方法，
  6. 我们通常会继承Mapper，重写一个map方法，map中的参数为传进来的<K,V>键值对，key：偏移量，value：每一行的数据。
相关标签： Hadoop hadoop mapreduce java

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