昨天，经过几个小时的学习。该MapReduce学习的某一位的方法的第一阶段。即当大多数文件的开头的Data至key-value制图。那是，InputFormat的过程。虽说过程不是非常难，可是也存在非常多细节的。
也非常少会有人对此做比較细腻的研究。学习。今天。就让我来为大家剖析一下这段代码的原理。
我还为此花了一点时间做了几张结构图。便于大家理解。
在这里先声明一下。我研究的MapReduce主要研究的是旧版的API，也就是mapred包下的。
InputFormat最最原始的形式就是一个接口。后面出现的各种Format都是他的衍生类。结构例如以下，仅仅包括最重要的2个方法:
public interface InputFormat<K, V> {
/**
* Logically split the set of input files for the job.
*
* <p>Each {@link InputSplit} is then assigned to an individual {@link Mapper}
* for processing.</p>
*
* <p><i>Note</i>: The split is a <i>logical</i> split of the inputs and the
* input files are not physically split into chunks. For e.g. a split could
* be <i><input-file-path, start, offset></i> tuple.
*
* @param job job configuration.
* @param numSplits the desired number of splits, a hint.
* @return an array of {@link InputSplit}s for the job.
*/
InputSplit[] getSplits(JobConf job, int numSplits) throws IOException;
/**
* Get the {@link RecordReader} for the given {@link InputSplit}.
*
* <p>It is the responsibility of the <code>RecordReader</code> to respect
* record boundaries while processing the logical split to present a
* record-oriented view to the individual task.</p>
*
* @param split the {@link InputSplit}
* @param job the job that this split belongs to
* @return a {@link RecordReader}
*/
RecordReader<K, V> getRecordReader(InputSplit split,
JobConf job,
Reporter reporter) throws IOException;
}
所以后面解说，我也仅仅是会环绕这2个方法进行分析。当然我们用的最多的是从文件里获得输入数据，也就是FileInputFormat这个类。继承关系例如以下:
public abstract class FileInputFormat<K, V> implements InputFormat<K, V>
我们看里面的1个主要方法:
public InputSplit[] getSplits(JobConf job, int numSplits)
返回的类型是一个InputSpilt对象。这是一个抽象的输入Spilt分片概念。结构例如以下:
public interface InputSplit extends Writable {
/**
* Get the total number of bytes in the data of the <code>InputSplit</code>.
*
* @return the number of bytes in the input split.
* @throws IOException
*/
long getLength() throws IOException;
/**
* Get the list of hostnames where the input split is located.
*
* @return list of hostnames where data of the <code>InputSplit</code> is
* located as an array of <code>String</code>s.
* @throws IOException
*/
String[] getLocations() throws IOException;
}
提供了与数据相关的2个方法。后面这个返回的值会被用来传递给RecordReader里面去的。在想理解getSplits方法之前另一个类须要理解，FileStatus，里面包装了一系列的文件基本信息方法:
public class FileStatus implements Writable, Comparable {
private Path path;
private long length;
private boolean isdir;
private short block_replication;
private long blocksize;
private long modification_time;
private long access_time;
private FsPermission permission;
private String owner;
private String group;
.....
看到这里你预计会有点晕了，以下是我做的一张小小类图关系:
能够看到，FileSpilt为了兼容新老版本号，继承了新的抽象类InputSpilt。同一时候附上旧的接口形式的InputSpilt。以下我们看看里面的getspilt核心过程:
/** Splits files returned by {@link #listStatus(JobConf)} when
* they're too big.*/
@SuppressWarnings("deprecation")
public InputSplit[] getSplits(JobConf job, int numSplits)
throws IOException {
//获取全部的状态文件
FileStatus[] files = listStatus(job);
// Save the number of input files in the job-conf
//在job-cof中保存文件的数量
job.setLong(NUM_INPUT_FILES, files.length);
long totalSize = 0;
// compute total size,计算文件总的大小
for (FileStatus file: files) { // check we have valid files
if (file.isDir()) {
//假设是文件夹不是纯文件的直接抛异常
throw new IOException("Not a file: "+ file.getPath());
}
totalSize += file.getLen();
}
//用户期待的划分大小。总大小除以spilt划分数目
long goalSize = totalSize / (numSplits == 0 ?
1 : numSplits);
//获取系统的划分最小值
long minSize = Math.max(job.getLong("mapred.min.split.size", 1),
minSplitSize);
// generate splits
//创建numSplits个FileSpilt文件划分量
ArrayList<FileSplit> splits = new ArrayList<FileSplit>(numSplits);
NetworkTopology clusterMap = new NetworkTopology();
for (FileStatus file: files) {
Path path = file.getPath();
FileSystem fs = path.getFileSystem(job);
long length = file.getLen();
//获取此文件的block的位置列表
BlockLocation[] blkLocations = fs.getFileBlockLocations(file, 0, length);
//假设文件系统可划分
if ((length != 0) && isSplitable(fs, path)) {
//计算此文件的总的block块的大小
long blockSize = file.getBlockSize();
//依据期待大小。最小大小。得出终于的split分片大小
long splitSize = computeSplitSize(goalSize, minSize, blockSize);
long bytesRemaining = length;
//假设剩余待划分字节倍数为划分大小超过1.1的划分比例，则进行拆分
while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
//获取提供数据的splitHost位置
String[] splitHosts = getSplitHosts(blkLocations,
length-bytesRemaining, splitSize, clusterMap);
//加入FileSplit
splits.add(new FileSplit(path, length-bytesRemaining, splitSize,
splitHosts));
//数量降低splitSize大小
bytesRemaining -= splitSize;
}
if (bytesRemaining != 0) {
//加入刚刚剩下的没划分完的部分。此时bytesRemaining已经小于splitSize的1.1倍了
splits.add(new FileSplit(path, length-bytesRemaining, bytesRemaining,
blkLocations[blkLocations.length-1].getHosts()));
}
} else if (length != 0) {
//不划分。直接加入Spilt
String[] splitHosts = getSplitHosts(blkLocations,0,length,clusterMap);
splits.add(new FileSplit(path, 0, length, splitHosts));
} else {
//Create empty hosts array for zero length files
splits.add(new FileSplit(path, 0, length, new String[0]));
}
}
//最后返回FileSplit数组
LOG.debug("Total # of splits: " + splits.size());
return splits.toArray(new FileSplit[splits.size()]);
}
里面有个computerSpiltSize方法非常特殊，考虑了非常多情况。总之最小值不能小于系统设定的最小值。要与期待值，块大小，系统同意最小值:
protected long computeSplitSize(long goalSize, long minSize,
long blockSize) {
return Math.max(minSize, Math.min(goalSize, blockSize));
}
上述过程的对应流程图例如以下:
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3种情况3中年运行流程。
处理完getSpilt方法然后，也就是说已经把数据从文件里转划到InputSpilt中了，接下来就是给RecordRead去取出里面的一条条的记录了。当然这在FileInputFormat是抽象方法，必须由子类实现的，我在这里挑出了2个典型的子类SequenceFileInputFormat，和TextInputFormat。
他们的实现RecordRead方法例如以下：
public RecordReader<K, V> getRecordReader(InputSplit split,
JobConf job, Reporter reporter)
throws IOException { reporter.setStatus(split.toString()); return new SequenceFileRecordReader<K, V>(job, (FileSplit) split);
}
public RecordReader<LongWritable, Text> getRecordReader(
InputSplit genericSplit, JobConf job,
Reporter reporter)
throws IOException { reporter.setStatus(genericSplit.toString());
return new LineRecordReader(job, (FileSplit) genericSplit);
}
能够看到里面的差别就在于LineRecordReader和SequenceFileRecordReader的不同了，这也就表明2种方式相应于数据的读取方式可能会不一样。继续往里深入看:
/** An {@link RecordReader} for {@link SequenceFile}s. */
public class SequenceFileRecordReader<K, V> implements RecordReader<K, V> {
private SequenceFile.Reader in;
private long start;
private long end;
private boolean more = true;
protected Configuration conf;
public SequenceFileRecordReader(Configuration conf, FileSplit split)
throws IOException {
Path path = split.getPath();
FileSystem fs = path.getFileSystem(conf);
//从文件系统中读取数据输入流
this.in = new SequenceFile.Reader(fs, path, conf);
this.end = split.getStart() + split.getLength();
this.conf = conf;
if (split.getStart() > in.getPosition())
in.sync(split.getStart()); // sync to start
this.start = in.getPosition();
more = start < end;
}
......
/**
* 获取下一个键值对
*/
public synchronized boolean next(K key, V value) throws IOException {
//推断还有无下一条记录
if (!more) return false;
long pos = in.getPosition();
boolean remaining = (in.next(key) != null);
if (remaining) {
getCurrentValue(value);
}
if (pos >= end && in.syncSeen()) {
more = false;
} else {
more = remaining;
}
return more;
}
我们能够看到SequenceFileRecordReader是从输入流in中一个键值。一个键值的读取，另外一个的实现方式例如以下:
/**
* Treats keys as offset in file and value as line.
*/
public class LineRecordReader implements RecordReader<LongWritable, Text> {
private static final Log LOG
= LogFactory.getLog(LineRecordReader.class.getName()); private CompressionCodecFactory compressionCodecs = null;
private long start;
private long pos;
private long end;
private LineReader in;
int maxLineLength; .... /** Read a line. */
public synchronized boolean next(LongWritable key, Text value)
throws IOException { while (pos < end) {
//设置key
key.set(pos); //依据位置一行一行读取，设置value
int newSize = in.readLine(value, maxLineLength,
Math.max((int)Math.min(Integer.MAX_VALUE, end-pos),
maxLineLength));
if (newSize == 0) {
return false;
}
pos += newSize;
if (newSize < maxLineLength) {
return true;
} // line too long. try again
LOG.info("Skipped line of size " + newSize + " at pos " + (pos - newSize));
} return false;
}
实现的方式为通过读的位置，从输入流中逐行读取key-value。
通过这2种方法，就能得到新的key-value。就会用于后面的map操作。
InputFormat的我忽略了一个事实，整个过程非常详细。通常该过程如上所述。
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