当前位置 博文首页 > Simon-Lau：MapReduce——客户端提交任务源码分析
计算向数据移动
MR程序并不会在客户端执行任何的计算操作，它是为计算工作做好准备，例如计算出切片信息，直接影响到Map任务的并行度。
在Driver中提交任务时，会写到这样的语句：
boolean result = job.waitForCompletion(true);
进入到waitForCompletion中：
public boolean waitForCompletion(boolean verbose) throws IOException, InterruptedException,
ClassNotFoundException {
if (state == JobState.DEFINE) {
// 提交任务语句
submit();
}
..............
继续跟进 submit():
public void submit() throws IOException, InterruptedException, ClassNotFoundException {
ensureState(JobState.DEFINE);
setUseNewAPI();
connect();
final JobSubmitter submitter =
getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
status = ugi.doAs(new PrivilegedExceptionAction<JobStatus>() {
public JobStatus run() throws IOException, InterruptedException,
ClassNotFoundException {
// 执行提交任务
return submitter.submitJobInternal(Job.this, cluster);
}
});
..............
}
上面代码可以看出，客户端经过连接集群，获得任务提交器submitter后执行了submitJobInternal(Job.this, cluster)方法，进入看（其实我只想看切片方法）
/**
* Internal method for submitting jobs to the system.
* The job submission process involves:
* 1、Checking the input and output specifications of the job.
* 2、Computing the InputSplits for the job.
* 3、Setup the requisite accounting information for the
* DistributedCache of the job, if necessary.
* 4、Copying the job's jar and configuration to the map-reduce system
* directory on the distributed file-system.
* 5、Submitting the job to the JobTracker and optionally
* monitoring it's status.
*/
..............
// Create the splits for the job
LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
int maps = writeSplits(job, submitJobDir);
conf.setInt(MRJobConfig.NUM_MAPS, maps);
LOG.info("number of splits:" + maps);
..............
从这个方法头上的注释信息可以看到，在真正执行任务之前，客户端做了这么5件事，稍微翻译一下：
DistributedCache 设置必要的记帐信息；JobTracker 并可选择监控它的状态可以看到执行切片的方法时writeSplits(job, submitJobDir)
private int writeSplits(org.apache.hadoop.mapreduce.JobContext job,Path jobSubmitDir) throws IOException,InterruptedException, ClassNotFoundException {
JobConf jConf = (JobConf)job.getConfiguration();
int maps;
if (jConf.getUseNewMapper()) {
maps = writeNewSplits(job, jobSubmitDir);
} else {
maps = writeOldSplits(jConf, jobSubmitDir);
}
return maps;
}
也有新旧API的区分，看新的writeNewSplits(job, jobSubmitDir)
private <T extends InputSplit>
int writeNewSplits(JobContext job, Path jobSubmitDir) throws IOException,
InterruptedException, ClassNotFoundException {
..................
// 只看切片方法
List<InputSplit> splits = input.getSplits(job);
T[] array = (T[]) splits.toArray(new InputSplit[splits.size()]);
..............
// 返回值是数组的长度，也就是切片的个数，也就是mapTask的并行度
return array.length;
}
进入切片方法，方法太长了，删除部分，留下核心业务逻辑。这个得好好说说
public List<InputSplit> getSplits(JobContext job) throws IOException {
// 如果没有指定的话，minSize = 1
long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
// 如果没有指定的话，maxSize = Long.Max
long maxSize = getMaxSplitSize(job);
// generate splits
List<InputSplit> splits = new ArrayList<InputSplit>();
// FileStatus这个概念来自于HDFS，存储客户端提交文件的元数据
List<FileStatus> files = listStatus(job);
for (FileStatus file: files) {
// 获取到文件的路径
Path path = file.getPath();
// 获取到文件的长度
long length = file.getLen();
if (length != 0) {
// 数据块位置数组，用于存储该文件对应的数据块的位置
BlockLocation[] blkLocations;
if (file instanceof LocatedFileStatus) {
blkLocations = ((LocatedFileStatus) file).getBlockLocations();
} else {
FileSystem fs = path.getFileSystem(job.getConfiguration());
blkLocations = fs.getFileBlockLocations(file, 0, length);
}
if (isSplitable(job, path)) { // 没有指定，默认是可分片的
long blockSize = file.getBlockSize();
// 返回默认值：切片大小 = 块大小
long splitSize = computeSplitSize(blockSize, minSize, maxSize);
// 获取整个文件的长度，用于计算切片的偏移量
long bytesRemaining = length;
// SPLIT_SLOP 的大小是1.1
// 这个判断表达式的含义是如果剩余的块体积大大于1.1倍的切片大小，继续切片
while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
// 在这计算了一步块索引
int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
//-----------getBlockIndex() begin--------------------------------------------
protected int getBlockIndex(BlockLocation[] blkLocations, long offset) {
for (int i = 0 ; i < blkLocations.length; i++) {
// is the offset inside this block?
if ((blkLocations[i].getOffset() <= offset) &&
(offset < blkLocations[i].getOffset() + blkLocations[i].getLength())){
// 代码逻辑非常简单，就是返回当前offset是在哪个block里面
return i;
}
}
....................
//-----------getBlockIndex() end----------------------------------------------
// 计算完成之后加入切片集合
// 切片信息包括：路径，偏移量，切片大小，服务器节点【支撑计算向数据移动】
splits.add(makeSplit(path, length-bytesRemaining, splitSize,
blkLocations[blkIndex].getHosts(),
blkLocations[blkIndex].getCachedHosts()));
bytesRemaining -= splitSize;
}
// 计算剩余数据块的切片信息
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 ：不能切片，那就是一片
splits.add(makeSplit(path, 0, length, blkLocations[0].getHosts(),
blkLocations[0].getCachedHosts()));
}
}
......
// 返回切片文件的集合。根据集合中数据的个数，就可以计算出有多少个maptask
return splits;
}
