Hadoop中split源码分析

目的:通过一个简单的单词统计案例进行split信息获取的源码分析

实现WordCount的demo代码

单词统计的demo代码主要通过三个类来实现,现将代码贴出来

主类WordCount:

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;

public class WordCount {

public static void main(String[] args) throws Exception {
System.getProperty("HADOOP_USER_NAME", "root");

Configuration conf = new Configuration(true);
Job job = Job.getInstance(conf);

job.setJarByClass(WordCount.class);
job.setJobName("myjob");

//设置mapper output的key和value
job.setMapOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapperClass(MyMapper.class);
job.setReducerClass(MyReducer.
4000
class);

Path input = new Path("/temp/wc/input");
FileInputFormat.addInputPath(job, input);

Path output = new Path("/temp/wc/output");
if (output.getFileSystem(conf).exists(output)) {
output.getFileSystem(conf).delete(output);
}
FileOutputFormat.setOutputPath(job, output);

job.waitForCompletion(true);
}
}

实现Mapper的类MyMapper

import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class MyMapper extends Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

/**
* @param key split后的每一行的偏移量
* @param value split后每一行的内容
*/
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}

实现Reducer的类MyReducer

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}

源码分析

在WordCount类的代码里面有一句话

job.waitForCompletion(true);

把鼠标放在这行代码上可以看到提示:



这行代码的作用就是:Submit the job to the cluster and wait for it to finish(把任务提交到集群上等待任务完成).

进入该方法后可以看到如下代码:

/**
* Submit the job to the cluster and wait for it to finish.
* @param verbose print the progress to the user
* @return true if the job succeeded
* @throws IOException thrown if the communication with the
*         <code>JobTracker</code> is lost
*/
public boolean waitForCompletion(boolean verbose
) throws IOException, InterruptedException,
ClassNotFoundException {
if (state == JobState.DEFINE) {
submit();
}
if (verbose) {
monitorAndPrintJob();
} else {
// get the completion poll interval from the client.
int completionPollIntervalMillis =
Job.getCompletionPollInterval(cluster.getConf());
while (!isComplete()) {
try {
Thread.sleep(completionPollIntervalMillis);
} catch (InterruptedException ie) {
}
}
}
return isSuccessful();
}

这块代码里面有一个重要的方法:submit(),点进去看一下job是怎么submit的.进去该方法看到代码如下:

/**
* Submit the job to the cluster and return immediately.
* @throws IOException
*/
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 {
return submitter.submitJobInternal(Job.this, cluster);
}
});
state = JobState.RUNNING;
LOG.info("The url to track the job: " + getTrackingURL());
}

代码里有一个重要的一行:

return submitter.submitJobInternal(Job.this, cluster);

鼠标放上去可以看到submitJobInternal的作用:



这个方法的作用有:

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.

点击进去submitJobInternal(Job.this, cluster)方法,代码如下:

JobStatus submitJobInternal(Job job, Cluster cluster)
throws ClassNotFoundException, InterruptedException, IOException {

//validate the jobs output specs
checkSpecs(job);

Configuration conf = job.getConfiguration();
addMRFrameworkToDistributedCache(conf);

Path jobStagingArea = JobSubmissionFiles.getStagingDir(cluster, conf);
//configure the command line options correctly on the submitting dfs
InetAddress ip = InetAddress.getLocalHost();
if (ip != null) {
submitHostAddress = ip.getHostAddress();
submitHostName = ip.getHostName();
conf.set(MRJobConfig.JOB_SUBMITHOST,submitHostName);
conf.set(MRJobConfig.JOB_SUBMITHOSTADDR,submitHostAddress);
}
JobID jobId = submitClient.getNewJobID();
job.setJobID(jobId);
Path submitJobDir = new Path(jobStagingArea, jobId.toString());
JobStatus status = null;
try {
conf.set(MRJobConfig.USER_NAME,
UserGroupInformation.getCurrentUser().getShortUserName());
conf.set("hadoop.http.filter.initializers",
"org.apache.hadoop.yarn.server.webproxy.amfilter.AmFilterInitializer");
conf.set(MRJobConfig.MAPREDUCE_JOB_DIR, submitJobDir.toString());
LOG.debug("Configuring job " + jobId + " with " + submitJobDir
+ " as the submit dir");
// get delegation token for the dir
TokenCache.obtainTokensForNamenodes(job.getCredentials(),
new Path[] { submitJobDir }, conf);

populateTokenCache(conf, job.getCredentials());

// generate a secret to authenticate shuffle transfers
if (TokenCache.getShuffleSecretKey(job.getCredentials()) == null) {
KeyGenerator keyGen;
try {

int keyLen = CryptoUtils.isShuffleEncrypted(conf)
? conf.getInt(MRJobConfig.MR_ENCRYPTED_INTERMEDIATE_DATA_KEY_SIZE_BITS,
MRJobConfig.DEFAULT_MR_ENCRYPTED_INTERMEDIATE_DATA_KEY_SIZE_BITS)
: SHUFFLE_KEY_LENGTH;
keyGen = KeyGenerator.getInstance(SHUFFLE_KEYGEN_ALGORITHM);
keyGen.init(keyLen);
} catch (NoSuchAlgorithmException e) {
throw new IOException("Error generating shuffle secret key", e);
}
SecretKey shuffleKey = keyGen.generateKey();
TokenCache.setShuffleSecretKey(shuffleKey.getEncoded(),
job.getCredentials());
}

copyAndConfigureFiles(job, submitJobDir);

Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);

// 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);

// write "queue admins of the queue to which job is being submitted"
// to job file.
String queue = conf.get(MRJobConfig.QUEUE_NAME,
JobConf.DEFAULT_QUEUE_NAME);
AccessControlList acl = submitClient.getQueueAdmins(queue);
conf.set(toFullPropertyName(queue,
QueueACL.ADMINISTER_JOBS.getAclName()), acl.getAclString());

// removing jobtoken referrals before copying the jobconf to HDFS
// as the tasks don't need this setting, actually they may break
// because of it if present as the referral will point to a
// different job.
TokenCache.cleanUpTokenReferral(conf);

if (conf.getBoolean(
MRJobConfig.JOB_TOKEN_TRACKING_IDS_ENABLED,
MRJobConfig.DEFAULT_JOB_TOKEN_TRACKING_IDS_ENABLED)) {
// Add HDFS tracking ids
ArrayList<String> trackingIds = new ArrayList<String>();
for (Token<? extends TokenIdentifier> t :
job.getCredentials().getAllTokens()) {
trackingIds.add(t.decodeIdentifier().getTrackingId());
}
conf.setStrings(MRJobConfig.JOB_TOKEN_TRACKING_IDS,
trackingIds.toArray(new String[trackingIds.size()]));
}

// Set reservation info if it exists
ReservationId reservationId = job.getReservationId();
if (reservationId != null) {
conf.set(MRJobConfig.RESERVATION_ID, reservationId.toString());
}

// Write job file to submit dir
writeConf(conf, submitJobFile);

//
// Now, actually submit the job (using the submit name)
//
printTokens(jobId, job.getCredentials());
status = submitClient.submitJob(
jobId, submitJobDir.toString(), job.getCredentials());
if (status != null) {
return status;
} else {
throw new IOException("Could not launch job");
}
} finally {
if (status == null) {
LOG.info("Cleaning up the staging area " + submitJobDir);
if (jtFs != null && submitJobDir != null)
jtFs.delete(submitJobDir, true);

}
}
}

split取值分析

上面的submitJobInternal方法中有一段代码:

/
ddb5
/ 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);

这里有一行代码:

int maps = writeSplits(job, submitJobDir);

这是计算map的数量的方法,点进去瞧瞧,进去后代码如下:

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;
}

这里代码jConf.getUseNewMapper()是返回true的,代码会执行writeNewSplits(job, jobSubmitDir)这个方法,进入该方法:

@SuppressWarnings("unchecked")
private <T extends InputSplit>
int writeNewSplits(JobContext job, Path jobSubmitDir) throws IOException,
InterruptedException, ClassNotFoundException {
Configuration conf = job.getConfiguration();
InputFormat<?, ?> input =
ReflectionUtils.newInstance(job.getInputFormatClass(), conf);

List<InputSplit> splits = input.getSplits(job);
T[] array = (T[]) splits.toArray(new InputSplit[splits.size()]);

// sort the splits into order based on size, so that the biggest
// go first
Arrays.sort(array, new SplitComparator());
JobSplitWriter.createSplitFiles(jobSubmitDir, conf,
jobSubmitDir.getFileSystem(conf), array);
return array.length;
}

计算splits的代码是:

List<InputSplit> splits = input.getSplits(job);

打开getSplits方法的实现类FileInputFormat并找到该方法:

/**
* Generate the list of files and make them into FileSplits.
* @param job the job context
* @throws IOException
*/
public List<InputSplit> getSplits(JobContext job) throws IOException {
Stopwatch sw = new Stopwatch().start();
long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
long maxSize = getMaxSplitSize(job);

// generate splits
List<InputSplit> splits = new ArrayList<InputSplit>();
List<FileStatus> files = listStatus(job);
for (FileStatus file: files) {
Path path = file.getPath();
long length = file.getLen();
if (length != 0) {
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)) {
long blockSize = file.getBlockSize();
long splitSize = computeSplitSize(blockSize, minSize, maxSize);

long bytesRemaining = length;
while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
splits.add(makeSplit(path, length-bytesRemaining, splitSize,
blkLocations[blkIndex].getHosts(),
blkLocations[blkIndex].getCachedHosts()));
bytesRemaining -= splitSize;
}

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
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());
}
return splits;
}

该方法的顶部的有两行代码:

long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
long maxSize = getMaxSplitSize(job);

getFormatMinSplitSize(),getMinSplitSize(job)和getMaxSplitSize(job)的代码如下:

/**
* Get the lower bound on split size imposed by the format.
* @return the number of bytes of the minimal split for this format
*/
protected long getFormatMinSplitSize() {
return 1;
}

返回固定值1

/**
* Get the minimum split size
* @param job the job
* @return the minimum number of bytes that can be in a split
*/
public static long getMinSplitSize(JobContext job) {
return job.getConfiguration().getLong(SPLIT_MINSIZE, 1L);
}

如果用户自定义了SPLIT_MINSIZE,则getMinSplitSize取SPLIT_MINSIZE的值,否则getMinSplitSize取值1. 如果用户没有定义SPLIT_MINSIZE的值,则minSize=1.

/**
* Get the maximum split size.
* @param context the job to look at.
* @return the maximum number of bytes a split can include
*/
public static long getMaxSplitSize(JobContext context) {
return context.getConfiguration().getLong(SPLIT_MAXSIZE,
Long.MAX_VALUE);
}

如果用户定义了SPLIT_MAXSIZE,则maxSize取值SPLIT_MAXSIZE,如果用户没有定义该值,则maxSize取值Long.MAX_VALUE.

getSplits方法里面有一段判断逻辑:

if (isSplitable(job, path)) {
long blockSize = file.getBlockSize();
long splitSize = computeSplitSize(blockSize, minSize, maxSize);

long bytesRemaining = length;
while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
splits.add(makeSplit(path, length-bytesRemaining, splitSize,
blkLocations[blkIndex].getHosts(),
blkLocations[blkIndex].getCachedHosts()));
bytesRemaining -= splitSize;
}

long splitSize = computeSplitSize(blockSize, minSize, maxSize);这一行代码是计算split的大小的,进去瞧瞧:

protected long computeSplitSize(long blockSize, long minSize,
long maxSize) {
return Math.max(minSize, Math.min(maxSize, blockSize));
}

split的大小结论:

用户没有定义SPLIT_MAXSIZE和SPLIT_MINSIZE的值

用户没有自定义SPLIT_MAXSIZE和SPLIT_MINSIZE的值,则split切片的大小值和block的大小值是相等的.

用户自定义了SPLIT_MAXSIZE和SPLIT_MINSIZE的值:

1.如果SPLIT_MAXSIZE的值小于blockSize,则split的值取SPLIT_MAXSIZE;

2.如果SPLIT_MAXSIZE的值大于blockSize,而SPLIT_MINSIZE小于blockSize,则split的值取SPLIT_MINSIZE;

3.如果SPLIT_MINSIZE的值大于blockSize,则split取值SPLIT_MINSIZE;

4.如果SPLIT_MAXSIZE的值小于blockSize,则split取值SPLIT_MAXSIZE

再来看一下getSplits()方法里面的代码块:

if (isSplitable(job, path)) {
long blockSize = file.getBlockSize();
long splitSize = computeSplitSize(blockSize, minSize, maxSize);

long bytesRemaining = length;
while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
splits.add(makeSplit(path, length-bytesRemaining, splitSize,
blkLocations[blkIndex].getHosts(),
blkLocations[blkIndex].getCachedHosts()));
bytesRemaining -= splitSize;
}

if (bytesRemaining != 0) {
int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
splits.add(makeSplit(path, length-bytesRemaining, bytesRemaining,
blkLocations[blkIndex].getHosts(),
blkLocations[blkIndex].getCachedHosts()));
}

这段代码里面有一个重要的操作:

splits.add(makeSplit(path, length-bytesRemaining, splitSize,
blkLocations[blkIndex].getHosts(),
blkLocations[blkIndex].getCachedHosts()));
bytesRemaining -= splitSize;

进去看一下makeSplit()方法:

/**
* A factory that makes the split for this class. It can be overridden
* by sub-classes to make sub-types
*/
protected FileSplit makeSplit(Path file, long start, long length,
String[] hosts, String[] inMemoryHosts) {
return new FileSplit(file, start, length, hosts, inMemoryHosts);
}

再接着进去FileSplit()瞅瞅:

/** Constructs a split with host and cached-blocks information
*
* @param file the file name
* @param start the position of the first byte in the file to process
* @param length the number of bytes in the file to process
* @param hosts the list of hosts containing the block
* @param inMemoryHosts the list of hosts containing the block in memory
*/
public FileSplit(Path file, long start, long length, String[] hosts,
String[] inMemoryHosts) {
this(file, start, length, hosts);
hostInfos = new SplitLocationInfo[hosts.length];
for (int i = 0; i < hosts.length; i++) {
// because N will be tiny, scanning is probably faster than a HashSet
boolean inMemory = false;
for (String inMemoryHost : inMemoryHosts) {
if (inMemoryHost.equals(hosts[i])) {
inMemory = true;
break;
}
}
hostInfos[i] = new SplitLocationInfo(hosts[i], inMemory);
}
}

可以看到getSplits()方法返回的是一个包含splits的所需信息的List.