九、MapReduce--input源码分析

当job提交至yarn之后，就会开始调度运行map任务，这里开始讲解map输入的源码分析。
一个map任务的入口就是 MapTask.class 中的run() 方法

1、首先看看MapTask.run() 方法

MapTask.class

//---------------------------------MapTask.javapublic void run(JobConf job, TaskUmbilicalProtocol umbilical) throws IOException, ClassNotFoundException, InterruptedException {     this.umbilical = umbilical;    if (this.isMapTask()) {        if (this.conf.getNumReduceTasks() == 0) {            this.mapPhase = this.getProgress().addPhase("map", 1.0F);        } else {            this.mapPhase = this.getProgress().addPhase("map", 0.667F);            this.sortPhase = this.getProgress().addPhase("sort", 0.333F);        }    }    TaskReporter reporter = this.startReporter(umbilical);    boolean useNewApi = job.getUseNewMapper();    //进行map任务的初始化    this.initialize(job, this.getJobID(), reporter, useNewApi);    if (this.jobCleanup) {        this.runJobCleanupTask(umbilical, reporter);    } else if (this.jobSetup) {        this.runJobSetupTask(umbilical, reporter);    } else if (this.taskCleanup) {        this.runTaskCleanupTask(umbilical, reporter);    } else {        //启动map任务，判断是使用新的还是旧的api        if (useNewApi) {            this.runNewMapper(job, this.splitMetaInfo, umbilical, reporter);        } else {            this.runOldMapper(job, this.splitMetaInfo, umbilical, reporter);        }        this.done(umbilical, reporter);    }}

上面重点有两个方法，一个是 this.initialize()以及 this.runNewMapper()。

2、下面看看this.initialize()

//---------------------------------Task.javapublic void initialize(JobConf job, JobID id, Reporter reporter, boolean useNewApi) throws IOException, ClassNotFoundException, InterruptedException {    //创建task以及job上下文对象    this.jobContext = new JobContextImpl(job, id, reporter);    this.taskContext = new TaskAttemptContextImpl(job, this.taskId, reporter);    //将task任务的状态改为正在运行    if (this.getState() == org.apache.hadoop.mapred.TaskStatus.State.UNASSIGNED) {        this.setState(org.apache.hadoop.mapred.TaskStatus.State.RUNNING);    }    if (useNewApi) {        if (LOG.isDebugEnabled()) {            LOG.debug("using new api for output committer");        }        //获取job中配置的输出格式类，并通过反射获取该类的Class对象        this.outputFormat = (OutputFormat)ReflectionUtils.newInstance(this.taskContext.getOutputFormatClass(), job);        //通过outputformat类获取commiter        this.committer = this.outputFormat.getOutputCommitter(this.taskContext);    } else {        this.committer = this.conf.getOutputCommitter();    }    //从FileOutputFormat获取任务结果输出路径。    /*    可能有的人会奇怪，为啥mapper这里要获取outputformat 的输出路径。    首先我们要知道，一个MapReduce任务可以只有mapper，而没有reducer的，    那么这时候程序的输出是有mapper直接输出的，这时候自然就需要知道输出的路径，这里就派上用场了    */    Path outputPath = FileOutputFormat.getOutputPath(this.conf);    if (outputPath != null) {        if (this.committer instanceof FileOutputCommitter) {            FileOutputFormat.setWorkOutputPath(this.conf, ((FileOutputCommitter)this.committer).getTaskAttemptPath(this.taskContext));        } else {            FileOutputFormat.setWorkOutputPath(this.conf, outputPath);        }    }    this.committer.setupTask(this.taskContext);    Class clazz = this.conf.getClass("mapreduce.job.process-tree.class", (Class)null, ResourceCalculatorProcessTree.class);    this.pTree = ResourceCalculatorProcessTree.getResourceCalculatorProcessTree((String)System.getenv().get("JVM_PID"), clazz, this.conf);    LOG.info(" Using ResourceCalculatorProcessTree : " + this.pTree);    if (this.pTree != null) {        this.pTree.updateProcessTree();        this.initCpuCumulativeTime = this.pTree.getCumulativeCpuTime();    }}

这个方法主要做了一些初始化工作，比如创建上下文对象，获取输出outputFormat类，以及路径等。

3、下面接着看看this.runNewMapper()

//---------------------------------MapTask.javaprivate  void runNewMapper(JobConf job, TaskSplitIndex splitIndex, TaskUmbilicalProtocol umbilical, TaskReporter reporter) throws IOException, ClassNotFoundException, InterruptedException {    TaskAttemptContext taskContext = new TaskAttemptContextImpl(job, this.getTaskID(), reporter);    //通过反射获取job中配置的mapper实现类    Mapper mapper = (Mapper)ReflectionUtils.newInstance(taskContext.getMapperClass(), job);    //通过反射获取job中配置的输入格式类,默认是TextInputFormat    InputFormat inputFormat = (InputFormat)ReflectionUtils.newInstance(taskContext.getInputFormatClass(), job);    org.apache.hadoop.mapreduce.InputSplit split = null;    //获取切片详细信息,传入输出路径以及偏移量作为参数.也就是当前mapper处理的某个切片    split = (org.apache.hadoop.mapreduce.InputSplit)this.getSplitDetails(new Path(splitIndex.getSplitLocation()), splitIndex.getStartOffset());    LOG.info("Processing split: " + split);    //获取输入的读取数据文件的 RecordReader 的对象，默认inputformat为TextInputFormat，对应默认的RecordReader为LineRecordReader    org.apache.hadoop.mapreduce.RecordReader input = new MapTask.NewTrackingRecordReader(split, inputFormat, reporter, taskContext);    job.setBoolean("mapreduce.job.skiprecords", this.isSkipping());    RecordWriter output = null;    //获取RecordWriter输出对象    if (job.getNumReduceTasks() == 0) {        output = new MapTask.NewDirectOutputCollector(taskContext, job, umbilical, reporter);    } else {        output = new MapTask.NewOutputCollector(taskContext, job, umbilical, reporter);    }    MapContext mapContext = new MapContextImpl(job, this.getTaskID(), input, (RecordWriter)output, this.committer, reporter, split);    org.apache.hadoop.mapreduce.Mapper.Context mapperContext = (new WrappedMapper()).getMapContext(mapContext);    try {        //初始化RecordReader中的数据        input.initialize(split, mapperContext);        //运行mapper中的run方法，也就是Mapper类中的run方法，开始运行map任务        mapper.run(mapperContext);        this.mapPhase.complete();        this.setPhase(Phase.SORT);        this.statusUpdate(umbilical);        //map运行完，关闭输入、输出流        input.close();        input = null;        ((RecordWriter)output).close(mapperContext);        output = null;    } finally {        this.closeQuietly((org.apache.hadoop.mapreduce.RecordReader)input);        this.closeQuietly((RecordWriter)output, mapperContext);    }}

可以看到，这里就是整个map任务的核心流程，做了以下工作：
（1）获取mapper类对象，下面要执行里面的map方法
（2）获取InputFormat对象，默认是默认inputformat为TextInputFormat
（3）通过InputFormat对象获取RecordReader对象，后面用于读取数据文件
（4）获取用于输出map的结果的RecordWriter对象
（5）获取切片信息，比如切片所在文件的路径，起始偏移量等
（6）初始化切片数据
（7）开始运行mapper中的run()方法
（8）运行完毕，关闭输入流，将结果通过RecordWriter刷写。
（9）刷写完毕后，关闭输入流以及输出流
下面看看其中的核心方法

4、this.getSplitDetails() 获取切片信息

//---------------------------------MapTask.javaprivate  T getSplitDetails(Path file, long offset) throws IOException {    //获取文件系统对象，并打开文件输出流    FileSystem fs = file.getFileSystem(this.conf);    FSDataInputStream inFile = fs.open(file);    //跳过指定的偏移量，也就是从指定偏移量的位置开始读取数据，其实就是切片开始的偏移量    inFile.seek(offset);    String className = StringInterner.weakIntern(Text.readString(inFile));    Class cls;    try {        cls = this.conf.getClassByName(className);    } catch (ClassNotFoundException var13) {        IOException wrap = new IOException("Split class " + className + " not found");        wrap.initCause(var13);        throw wrap;    }    SerializationFactory factory = new SerializationFactory(this.conf);    //反序列化方式打开输入流    Deserializer deserializer = factory.getDeserializer(cls);    deserializer.open(inFile);    T split = deserializer.deserialize((Object)null);    long pos = inFile.getPos();    ((Counter)this.getCounters().findCounter(TaskCounter.SPLIT_RAW_BYTES)).increment(pos - offset);    inFile.close();    //返回切片经过反序列化之后的可读取对象    return split;}

可以看到这里主要是返回切片的反序列化之后可以读取的信息对象

5、接着看看 input.initialize()

在看这个方法之前，首先我们看看input这个对象是由哪个类创建的。它是由NewTrackingRecordReader 这个类创建的。这是个静态内部类

//---------------------------------MapTask.javastatic class NewTrackingRecordReader extends org.apache.hadoop.mapreduce.RecordReader {    private final org.apache.hadoop.mapreduce.RecordReader real;    private final org.apache.hadoop.mapreduce.Counter inputRecordCounter;    private final org.apache.hadoop.mapreduce.Counter fileInputByteCounter;    private final TaskReporter reporter;    private final List fsStats;    NewTrackingRecordReader(org.apache.hadoop.mapreduce.InputSplit split, InputFormat inputFormat, TaskReporter reporter, TaskAttemptContext taskContext) throws InterruptedException, IOException {        this.reporter = reporter;        this.inputRecordCounter = reporter.getCounter(TaskCounter.MAP_INPUT_RECORDS);        this.fileInputByteCounter = reporter.getCounter(FileInputFormatCounter.BYTES_READ);        List matchedStats = null;        if (split instanceof org.apache.hadoop.mapreduce.lib.input.FileSplit) {            matchedStats = Task.getFsStatistics(((org.apache.hadoop.mapreduce.lib.input.FileSplit)split).getPath(), taskContext.getConfiguration());        }        this.fsStats = matchedStats;        long bytesInPrev = this.getInputBytes(this.fsStats);        //调用job任务中定义的inputformat类中的createRecordReader方法，获取RecordReader对象。返回的是 LineRecordReader        this.real = inputFormat.createRecordReader(split, taskContext);        long bytesInCurr = this.getInputBytes(this.fsStats);        this.fileInputByteCounter.increment(bytesInCurr - bytesInPrev);    }    ...........}

我们可以看到构造方法中，是调用 inputFormat对象的createRecordReader() 方法来创建RecordReader对象的，上面也说了默认inputFormat为 TextInputFormat。

//---------------------------TextInputFormat.javapublic class TextInputFormat extends FileInputFormat {    public TextInputFormat() {    }    public RecordReader 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);        }        return new LineRecordReader(recordDelimiterBytes);    }

可以清楚看到，返回的就是 LineRecordReader 这个reader类。

接着我们继续看 input.initialize()

static class NewTrackingRecordReader extends org.apache.hadoop.mapreduce.RecordReader {    public void initialize(org.apache.hadoop.mapreduce.InputSplit split, TaskAttemptContext context) throws IOException, InterruptedException {        long bytesInPrev = this.getInputBytes(this.fsStats);        //调用 RecordReader对象的 initialize方法，初始化输入。上面说到默认的是LineRecordReader        //this.real已经在上面初始化了，就是LineRecordReader        this.real.initialize(split, context);        long bytesInCurr = this.getInputBytes(this.fsStats);        this.fileInputByteCounter.increment(bytesInCurr - bytesInPrev);    }}

可以看到，调用 RecordReader中的 initialize 方法，也就是调用LineRecordReader 中的 initialize() 方法，下面看看

//---------------------------------------LineRecordReader.javapublic void initialize(InputSplit genericSplit, TaskAttemptContext context) throws IOException {    FileSplit split = (FileSplit)genericSplit;    Configuration job = context.getConfiguration();    this.maxLineLength = job.getInt("mapreduce.input.linerecordreader.line.maxlength", 2147483647);    //获取切片的数据开始位置以及终止位置    this.start = split.getStart();    this.end = this.start + split.getLength();    //获取切片对应的文件的输入流    Path file = split.getPath();    FileSystem fs = file.getFileSystem(job);    this.fileIn = fs.open(file);    //如果文件有压缩，则用压缩类解压    CompressionCodec codec = (new CompressionCodecFactory(job)).getCodec(file);    //以压缩方式读取切片    if (null != codec) {        this.isCompressedInput = true;        this.decompressor = CodecPool.getDecompressor(codec);        if (codec instanceof SplittableCompressionCodec) {            SplitCompressionInputStream cIn = ((SplittableCompressionCodec)codec).createInputStream(this.fileIn, this.decompressor, this.start, this.end, READ_MODE.BYBLOCK);            this.in = new CompressedSplitLineReader(cIn, job, this.recordDelimiterBytes);            this.start = cIn.getAdjustedStart();            this.end = cIn.getAdjustedEnd();            this.filePosition = cIn;        } else {            if (this.start != 0L) {                throw new IOException("Cannot seek in " + codec.getClass().getSimpleName() + " compressed stream");            }            this.in = new SplitLineReader(codec.createInputStream(this.fileIn, this.decompressor), job, this.recordDelimiterBytes);            this.filePosition = this.fileIn;        }    } else {        //无压缩方式读取切片        this.fileIn.seek(this.start);        //这里很重要，是真正用于读取数据的类        this.in = new UncompressedSplitLineReader(this.fileIn, job, this.recordDelimiterBytes, split.getLength());        this.filePosition = this.fileIn;    }    //对起始偏移量进行修正，并赋值给pos这个偏移量    if (this.start != 0L) {        this.start += (long)this.in.readLine(new Text(), 0, this.maxBytesToConsume(this.start));    }    this.pos = this.start;}

这里的工作主要是给 RecordReader对象读取文件做初始化工作。主要就是获取切片的输入流对象。
this.in 这里就用于后面读取数据的对象，这里就是完成了这个输入流对象的初始化。

6、接着我们回到3中，看mapper.run() 方法

这个其实就是写的mapper 的run方法：

//------------------------Mapper.java   mapper.run(mapperContext);public void run(Mapper.Context context) throws IOException, InterruptedException {    this.setup(context);    try {        //这里循环读取key和value，给map方法处理        //关键在于 context这个对象,从上面runNewApi中可以看到，是MapContextImpl类型的        while(context.nextKeyValue()) {            this.map(context.getCurrentKey(), context.getCurrentValue(), context);        }    } finally {        this.cleanup(context);    }}

可以看到，这里是个while循环，通过context上下文对象获取KV，然后传入map方法中处理。

7、下面看看 context.nextKeyValue()

从3中可以看到，这个context是 MapContextImpl类型的，看看这个类

//-----------------------MapContextImpl.java..    public class MapContextImpl extends TaskInputOutputContextImpl implements MapContext {    private RecordReader reader;    private InputSplit split;    //构造方法中包括获取 RecordReader对象，以及split    public MapContextImpl(Configuration conf, TaskAttemptID taskid, RecordReader reader, RecordWriter writer, OutputCommitter committer, StatusReporter reporter, InputSplit split) {        super(conf, taskid, writer, committer, reporter);        this.reader = reader;        this.split = split;    }    public InputSplit getInputSplit() {        return this.split;    }    //下面都是调用 RecordReader 中的get方法获取key value    public KEYIN getCurrentKey() throws IOException, InterruptedException {        return this.reader.getCurrentKey();    }    public VALUEIN getCurrentValue() throws IOException, InterruptedException {        return this.reader.getCurrentValue();    }    public boolean nextKeyValue() throws IOException, InterruptedException {       //这里就是调用reader 的方法        return this.reader.nextKeyValue();    }}

在它的构造方法中，主要从3中传入了 split切片，以及 RecordReader对象。下面就是三个获取KV的方法，也就是在 mapper.run() 中调用的方法。

下面看看 this.reader.nextKeyValue()

//----------------------------------LineRecordReader.javapublic boolean nextKeyValue() throws IOException {    if (this.key == null) {        this.key = new LongWritable();    }    //设置key为偏移量    this.key.set(this.pos);    if (this.value == null) {        this.value = new Text();    }    int newSize = 0;    while(this.getFilePosition() <= this.end || this.in.needAdditionalRecordAfterSplit()) {        if (this.pos == 0L) {            newSize = this.skipUtfByteOrderMark();        } else {            /*读取数据到value中。this.in是UncompressedSplitLineReader类型的，在LineRecordReader的initialize方法中初始化了。该类父类为LineReader。*/            //调用 LineRreader 的readline 方法。读一行数据            newSize = this.in.readLine(this.value, this.maxLineLength, this.maxBytesToConsume(this.pos));            this.pos += (long)newSize;        }        if (newSize == 0 || newSize < this.maxLineLength) {            break;        }        LOG.info("Skipped line of size " + newSize + " at pos " + (this.pos - (long)newSize));    }    if (newSize == 0) {        this.key = null;        this.value = null;        return false;    } else {        return true;    }}

可以看到，这里已经看到key和value的踪影了。key就是数据偏移量，value就是通过readLine读取的数据。如果有数据返回true，mapper.run() 通过getKey和getValue对应的KV。下面看看 this.in.readLine，也就是 LineReader.readLine()。

8、LineReader.readLine() 按行读取的reader

//---------------------------LineReader.javapublic int readLine(Text str, int maxLineLength, int maxBytesToConsume) throws IOException {    return this.recordDelimiterBytes != null ? this.readCustomLine(str, maxLineLength, maxBytesToConsume) : this.readDefaultLine(str, maxLineLength, maxBytesToConsume);}private int readCustomLine(Text str, int maxLineLength, int maxBytesToConsume) throws IOException {    str.clear();    int txtLength = 0;    long bytesConsumed = 0L;    int delPosn = 0;    int ambiguousByteCount = 0;    do {        int startPosn = this.bufferPosn;        if (this.bufferPosn >= this.bufferLength) {            startPosn = this.bufferPosn = 0;            this.bufferLength = this.fillBuffer(this.in, this.buffer, ambiguousByteCount > 0);            if (this.bufferLength <= 0) {                if (ambiguousByteCount > 0) {                    str.append(this.recordDelimiterBytes, 0, ambiguousByteCount);                    bytesConsumed += (long)ambiguousByteCount;                }                break;            }        }        for(; this.bufferPosn < this.bufferLength; ++this.bufferPosn) {            if (this.buffer[this.bufferPosn] == this.recordDelimiterBytes[delPosn]) {                ++delPosn;                if (delPosn >= this.recordDelimiterBytes.length) {                    ++this.bufferPosn;                    break;                }            } else if (delPosn != 0) {                this.bufferPosn -= delPosn;                if (this.bufferPosn < -1) {                    this.bufferPosn = -1;                }                delPosn = 0;            }        }        int readLength = this.bufferPosn - startPosn;        bytesConsumed += (long)readLength;        int appendLength = readLength - delPosn;        if (appendLength > maxLineLength - txtLength) {            appendLength = maxLineLength - txtLength;        }        bytesConsumed += (long)ambiguousByteCount;        if (appendLength >= 0 && ambiguousByteCount > 0) {            //看到这里就很明显了，将数据追加到 value中            str.append(this.recordDelimiterBytes, 0, ambiguousByteCount);            ambiguousByteCount = 0;            this.unsetNeedAdditionalRecordAfterSplit();        }        if (appendLength > 0) {            str.append(this.buffer, startPosn, appendLength);            txtLength += appendLength;        }        if (this.bufferPosn >= this.bufferLength && delPosn > 0 && delPosn < this.recordDelimiterBytes.length) {            ambiguousByteCount = delPosn;            bytesConsumed -= (long)delPosn;        }    } while(delPosn < this.recordDelimiterBytes.length && bytesConsumed < (long)maxBytesToConsume);    if (bytesConsumed > 2147483647L) {        throw new IOException("Too many bytes before delimiter: " + bytesConsumed);    } else {        return (int)bytesConsumed;    }}

上面重要就是读取数据的过程了，过程过于长，抓住关键的看，其实就是将读取的一行数据追加到 this.value中。

9、总结

至此，map的整个输入流程涉及到两个重要的类
InputFormat -- 处理原始数据并切片；创建RecordReader 对象
RecordReader -- 读取切片中的数据，处理成KV，传递KV给map方法处理

这两个都是抽象类：

public abstract class RecordReader implements Closeable {    public RecordReader() {    }    public abstract void initialize(InputSplit var1, TaskAttemptContext var2) throws IOException, InterruptedException;    public abstract boolean nextKeyValue() throws IOException, InterruptedException;    public abstract KEYIN getCurrentKey() throws IOException, InterruptedException;    public abstract VALUEIN getCurrentValue() throws IOException, InterruptedException;    public abstract float getProgress() throws IOException, InterruptedException;    public abstract void close() throws IOException;}

public abstract class InputFormat {    public InputFormat() {    }    public abstract List getSplits(JobContext var1) throws IOException, InterruptedException;    public abstract RecordReader createRecordReader(InputSplit var1, TaskAttemptContext var2) throws IOException, InterruptedException;}

当我们想自定义inputformat类和recordreader类时，就需要继承这两个类，并实现其中的方法。