hadoop2.7.2 MapReduce Job提交源码及切片源码分析
- 首先从
waitForCompletion函数进入
boolean result = job.waitForCompletion(true);/**
* 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
* JobTracker is lost
*/
public boolean waitForCompletion(boolean verbose
) throws IOException, InterruptedException,
ClassNotFoundException {
// 首先判断state，当state为DEFINE时可以提交，进入 submit() 方法
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();
}
- 进入
submit()方法
/**
* Submit the job to the cluster and return immediately.
* @throws IOException
*/
public void submit()
throws IOException, InterruptedException, ClassNotFoundException {
// 确认JobState状态为可提交状态，否则不能提交
ensureState(JobState.DEFINE);
// 设置使用最新的API
setUseNewAPI();
// 进入connect()方法，MapReduce作业提交时连接集群是通过Job类的connect()方法实现的，
// 它实际上是构造集群Cluster实例cluster
connect();
// connect()方法执行完之后，定义提交者submitter
final JobSubmitter submitter =
getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
status = ugi.doAs(new PrivilegedExceptionAction() {
public JobStatus run() throws IOException, InterruptedException,
ClassNotFoundException {
// 这里的核心方法是submitJobInternal(),顾名思义，提交job的内部方法，实现了提交job的所有业务逻辑
// 进入submitJobInternal
return submitter.submitJobInternal(Job.this, cluster);
}
});
// 提交之后state状态改变
state = JobState.RUNNING;
LOG.info("The url to track the job: " + getTrackingURL());
}
- 进入
connect()方法
- MapReduce作业提交时连接集群通过Job的Connect方法实现，它实际上是构造集群Cluster实例cluster
- cluster是连接MapReduce集群的一种工具，提供了获取MapReduce集群信息的方法
- 在Cluster内部，有一个与集群进行通信的客户端通信协议ClientProtocol的实例client，它由ClientProtocolProvider的静态create()方法构造
- 在create内部，Hadoop2.x中提供了两种模式的ClientProtocol，分别为Yarn模式的YARNRunner和Local模式的LocalJobRunner，Cluster实际上是由它们负责与集群进行通信的
private synchronized void connect()
throws IOException, InterruptedException, ClassNotFoundException {
if (cluster == null) {// cluster提供了远程获取MapReduce的方法
cluster =
ugi.doAs(new PrivilegedExceptionAction() {
public Cluster run()
throws IOException, InterruptedException,
ClassNotFoundException {
// 只需关注这个Cluster()构造器，构造集群cluster实例
return new Cluster(getConfiguration());
}
});
}
}
- 进入
Cluster()构造器
// 首先调用一个参数的构造器，间接调用两个参数的构造器
public Cluster(Configuration conf) throws IOException {
this(null, conf);
}
public Cluster(InetSocketAddress jobTrackAddr, Configuration conf)
throws IOException {
this.conf = conf;
this.ugi = UserGroupInformation.getCurrentUser();
// 最重要的initialize方法
initialize(jobTrackAddr, conf);
}
// cluster中要关注的两个成员变量是客户端通讯协议提供者ClientProtocolProvider和客户端通讯协议ClientProtocol实例client
private void initialize(InetSocketAddress jobTrackAddr, Configuration conf)
throws IOException {
synchronized (frameworkLoader) {
for (ClientProtocolProvider provider : frameworkLoader) {
LOG.debug("Trying ClientProtocolProvider : "
+ provider.getClass().getName());
ClientProtocol clientProtocol = null;
try {
// 如果配置文件没有配置YARN信息，则构建LocalRunner,MR任务本地运行
// 如果配置文件有配置YARN信息，则构建YarnRunner,MR任务在YARN集群上运行
if (jobTrackAddr == null) {
// 客户端通讯协议client是调用ClientProtocolProvider的create()方法实现
clientProtocol = provider.create(conf);
} else {
clientProtocol = provider.create(jobTrackAddr, conf);
}
if (clientProtocol != null) {
clientProtocolProvider = provider;
client = clientProtocol;
LOG.debug("Picked " + provider.getClass().getName()
+ " as the ClientProtocolProvider");
break;
}
else {
LOG.debug("Cannot pick " + provider.getClass().getName()
+ " as the ClientProtocolProvider - returned null protocol");
}
}
catch (Exception e) {
LOG.info("Failed to use " + provider.getClass().getName()
+ " due to error: ", e);
}
}
}
if (null == clientProtocolProvider || null == client) {
throw new IOException(
"Cannot initialize Cluster. Please check your configuration for "
+ MRConfig.FRAMEWORK_NAME
+ " and the correspond server addresses.");
}
}
- 进入
submitJobInternal()，job的内部提交方法，用于提交job到集群
JobStatus submitJobInternal(Job job, Cluster cluster)
throws ClassNotFoundException, InterruptedException, IOException {
//validate the jobs output specs
// 检查结果的输出路径是否已经存在，如果存在会报异常
checkSpecs(job);
// conf里边是集群的xml配置文件信息
Configuration conf = job.getConfiguration();
// 添加MR框架到分布式缓存中
addMRFrameworkToDistributedCache(conf);
// 获取提交执行时相关资源的临时存放路径
// 参数未配置时默认是（工作空间根目录下的）/tmp/hadoop-yarn/staging/提交作业用户名/.staging
Path jobStagingArea = JobSubmissionFiles.getStagingDir(cluster, conf);
//configure the command line options correctly on the submitting dfs
InetAddress ip = InetAddress.getLocalHost();
if (ip != null) {//记录提交作业的主机IP、主机名，并且设置配置信息conf
submitHostAddress = ip.getHostAddress();
submitHostName = ip.getHostName();
conf.set(MRJobConfig.JOB_SUBMITHOST,submitHostName);
conf.set(MRJobConfig.JOB_SUBMITHOSTADDR,submitHostAddress);
}
// 获取JobId
JobID jobId = submitClient.getNewJobID();
// 设置jobId
job.setJobID(jobId);
// 提交作业的路径Path(Path parent, String child)，会将两个参数拼接为一个路径
Path submitJobDir = new Path(jobStagingArea, jobId.toString());
// job的状态
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 {
keyGen = KeyGenerator.getInstance(SHUFFLE_KEYGEN_ALGORITHM);
keyGen.init(SHUFFLE_KEY_LENGTH);
} catch (NoSuchAlgorithmException e) {
throw new IOException("Error generating shuffle secret key", e);
}
SecretKey shuffleKey = keyGen.generateKey();
TokenCache.setShuffleSecretKey(shuffleKey.getEncoded(),
job.getCredentials());
}
if (CryptoUtils.isEncryptedSpillEnabled(conf)) {
conf.setInt(MRJobConfig.MR_AM_MAX_ATTEMPTS, 1);
LOG.warn("Max job attempts set to 1 since encrypted intermediate" +
"data spill is enabled");
}
// 拷贝jar包到集群
// 此方法中调用如下方法：rUploader.uploadFiles(job, jobSubmitDir);
// uploadFiles方法将jar包拷贝到集群
copyAndConfigureFiles(job, submitJobDir);
Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);
// Create the splits for the job
LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
// 计算切片，生成切片规划文件
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 trackingIds = new ArrayList();
for (Token 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)
// 开始正式提交job
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);
}
}
}
- 进入
writeSplits(job, submitJobDir)，计算切片，生成切片规划文件
- 内部会调用
writeNewSplits(job, jobSubmitDir)方法 -
writeNewSplits(job, jobSubmitDir)内部定义了一个InputFormat类型的实例input -
InputFormat主要作用：
- 验证job的输入规范
- 对输入的文件进行切分，形成多个InputSplit（切片）文件，每一个InputSplit对应着一个map任务（MapTask）
- 将切片后的数据按照规则形成key，value键值对RecordReader
- input调用getSplits（）方法：
Listsplits = input.getSplits(job);
- 进入FileInputFormat类下的
getSplits(job)方法
/**
* Generate the list of files and make them into FileSplits.
* @param job the job context
* @throws IOException
*/
public List getSplits(JobContext job) throws IOException {
StopWatch sw = new StopWatch().start();
// getFormatMinSplitSize()返回值固定为1，getMinSplitSize(job)返回job大小
long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
// getMaxSplitSize(job)返回Lang类型的最大值
long maxSize = getMaxSplitSize(job);
// generate splits 生成切片
List splits = new ArrayList();
List files = listStatus(job);
// 遍历job下的所有文件
for (FileStatus file: files) {
// 获取文件路径
Path path = file.getPath();
// 获取文件大小
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);
}
// 判断是否可分割
if (isSplitable(job, path)) {
// 获取块大小
// 本地环境块大小默认为32MB，YARN环境在hadoop2.x新版本为128MB，旧版本为64MB
long blockSize = file.getBlockSize();
// 计算切片的逻辑大小，默认等于块大小
// 返回值为：return Math.max(minSize, Math.min(maxSize, blockSize));
// 其中minSize=1， maxSize=Long类型最大值， blockSize为切片大小
long splitSize = computeSplitSize(blockSize, minSize, maxSize);
long bytesRemaining = length;
// 每次切片时就要判断切片剩下的部分是否大于切片大小的SPLIT_SLOP（默认为1.1）倍，
// 否则就不再切分，划为一块
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.now(TimeUnit.MILLISECONDS));
}
return splits;
}