1、提交Job
MapReduce程序框架请参考
http://blog.csdn.net/chengyuqiang/article/details/72804007
@Override
public int run(String[] args) throws Exception {
//读取配置文件
Configuration conf = getConf();
//设置参数
conf.set("fs.defaultFS", "hdfs://192.168.11.81:9000");
//自定义key value 之间的分隔符（默认为tab）
conf.set("mapreduce.input.keyvaluelinerecordreader.key.value.separator", ",");
// Job表示一个MapReduce任务,构造器第二个参数为Job的名称。
Job job = Job.getInstance(conf, "MapReduceTemplate");
job.setJarByClass(MapReduceTemplate.class);//主类
Path in = new Path(args[0]);//输入路径
Path out = new Path(args[1]);//输出路径
FileSystem hdfs = out.getFileSystem(conf);
if (hdfs.isDirectory(out)) {//如果输出路径存在就删除
hdfs.delete(out, true);
}
FileInputFormat.setInputPaths(job, in);//文件输入
FileOutputFormat.setOutputPath(job, out);//文件输出
job.setMapperClass(MapTemplate.class); //设置自定义Mapper
job.setReducerClass(ReduceTemplate.class); //设置自定义Reducer
job.setInputFormatClass(KeyValueTextInputFormat.class);//文件输入格式
job.setOutputFormatClass(TextOutputFormat.class);//文件输出格式
job.setOutputKeyClass(Text.class);//设置作业输出值 Key 的类
job.setOutputValueClass(Text.class);//设置作业输出值 Value 的类
return job.waitForCompletion(true)?0:1;//等待作业完成退出
} 2、进入Job.waitForCompletion()方法
在判断状态state可以提交Job后，执行submit()方法。monitorAndPrintJob()方法会不断的刷新获取job运行的进度信息，并打印。boolean参数verbose为true表明要打印运行进度，为false就只是等待job运行结束，不打印运行日志。
/** * 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 {
//当state为define时，则提交
if (state == JobState.DEFINE) {
submit(); //提交过程在submit()方法中
}
//verbose是waitForCompletion(verbose)传进来的true，表示执行monitorAndPrintJob()
//检测并打印Job相关信息
if (verbose) {
monitorAndPrintJob();
} else {
// get the completion poll interval from the client.
//从客户端获得完成轮询时间间隔
int completionPollIntervalMillis =
Job.getCompletionPollInterval(cluster.getConf());
while (!isComplete()) {
try {
Thread.sleep(completionPollIntervalMillis);
} catch (InterruptedException ie) {
}
}
}
//返回一个boolean值，表示作业是否成功提交
return isSuccessful();
}3、进入submit()方法
submit方法首先是确保当前的Job的状态是处于DEFINE，否则不能提交Job。然后启用新的API，即org.apache.hadoop.mapreduce下的Mapper和Reducer。Connect()方法会产生一个Client实例，用来和ResourceManager通信。其实submit()方法里关键的两处代码,一处是调用connect()方法，另一处是获取一个JobSubmitter类的实例，调用该对象的submitJobInternal方法来提交任务。
/** * Submit the job to the cluster and return immediately. * @throws IOException */
public void submit()
throws IOException, InterruptedException, ClassNotFoundException {
//再次检查作业的状态
ensureState(JobState.DEFINE);
//两套API，这里使用新API
setUseNewAPI();
connect();
//初始化工作，为cluster赋值，Client即是提交器,分为本体提交器和Yarn提交器，由配置文件决定
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);//提交
}
});
state = JobState.RUNNING;
LOG.info("The url to track the job: " + getTrackingURL());
}4、首先看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
private synchronized void connect()
throws IOException, InterruptedException, ClassNotFoundException {
if (cluster == null) {//若cluster空，则构造Cluster实例
//cluster是连接MapReduce集群的工具，提供了远程获取MapReduce集群的方法
cluster =
ugi.doAs(new PrivilegedExceptionAction<Cluster>() {
public Cluster run()
throws IOException, InterruptedException,
ClassNotFoundException {
return new Cluster(getConfiguration());
}
});
}
}5、进入return的Cluster(getConfiguration())构造方法
来到了Cluster类。先来看下Cluster类的成员信息。
package org.apache.hadoop.mapreduce;
import ...
/** * Provides a way to access information about the map/reduce cluster. */
@InterfaceAudience.Public
@InterfaceStability.Evolving
public class Cluster {
@InterfaceStability.Evolving
public static enum JobTrackerStatus {INITIALIZING, RUNNING};
private ClientProtocolProvider clientProtocolProvider; //客户端通信协议提供者
private ClientProtocol client; //客户端通信协议实例
private UserGroupInformation ugi; //用户组信息
private Configuration conf; //配置信息
private FileSystem fs = null; //文件系统实例
private Path sysDir = null; //系统路径
private Path stagingAreaDir = null; //作业资源存放路径
private Path jobHistoryDir = null; //作业历史路径
private static final Log LOG = LogFactory.getLog(Cluster.class); //日志
//客户端通信协议提供者加载器
private static ServiceLoader<ClientProtocolProvider> frameworkLoader =
ServiceLoader.load(ClientProtocolProvider.class);
static {
ConfigUtil.loadResources();
}
public Cluster(Configuration conf) throws IOException {
this(null, conf);
}
public Cluster(InetSocketAddress jobTrackAddr, Configuration conf)
throws IOException {
this.conf = conf; //设置配置信息
this.ugi = UserGroupInformation.getCurrentUser(); //获取当前用户
initialize(jobTrackAddr, conf); //完成初始化
}
...
}Cluster最重要的两个成员变量是客户端通信协议提供者ClientProtocolProvider实例clientProtocolProvider和客户端通信协议ClientProtocol实例client，而后者是依托前者的create()方法生成的。 Cluster类提供了两个构造函数。
6、进入initialize()方法
private void initialize(InetSocketAddress jobTrackAddr, Configuration conf)
throws IOException {
synchronized (frameworkLoader) {
//依次取出每个ClientProtocolProvider，通过其create()方法构造ClientProtocol实例
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) {
clientProtocol = provider.create(conf);
} else {
clientProtocol = provider.create(jobTrackAddr, conf);
}
//设置成员变量clientProtocolProvider和client，并退出循环
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);
}
}
}
//如果clientProtocolProvider或client空，抛出IOException
if (null == clientProtocolProvider || null == client) {
throw new IOException(
"Cannot initialize Cluster. Please check your configuration for "
+ MRConfig.FRAMEWORK_NAME
+ " and the correspond server addresses.");
}
}7、ClientProtocolProvider实现类LocalClientProtocolProvider
上面create()方法时提到了两种ClientProtocolProvider实现类。
MapReduce中，ClientProtocolProvider抽象类的实现共有YarnClientProtocolProvider、LocalClientProtocolProvider两种，前者为Yarn模式，而后者为Local模式。
我们先看下看下Local模式，LocalClientProtocolProvider的create()方法，代码如下
package org.apache.hadoop.mapred;
import java.io.IOException;
import java.net.InetSocketAddress;
import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.MRConfig;
import org.apache.hadoop.mapreduce.protocol.ClientProtocol;
import org.apache.hadoop.mapreduce.protocol.ClientProtocolProvider;
@InterfaceAudience.Private
public class LocalClientProtocolProvider extends ClientProtocolProvider {
@Override
public ClientProtocol create(Configuration conf) throws IOException {
//两个常量："mapreduce.framework.name","local"
String framework =
conf.get(MRConfig.FRAMEWORK_NAME, MRConfig.LOCAL_FRAMEWORK_NAME);
//若framework是local，则返回LocalJobRunner，并且设置Map任务数1;否则返回null
if (!MRConfig.LOCAL_FRAMEWORK_NAME.equals(framework)) {
return null;
}
conf.setInt(JobContext.NUM_MAPS, 1);
return new LocalJobRunner(conf);
}
@Override
public ClientProtocol create(InetSocketAddress addr, Configuration conf) {
return null; // LocalJobRunner doesn't use a socket
}
@Override
public void close(ClientProtocol clientProtocol) {
// no clean up required
}
}由上可知，MapReduce需要看参数mapreduce.framework.name确定连接模式，但默认是Local模式的。
8、ClientProtocolProvider实现类YarnClientProtocolProvider
再来看Yarn模式，看下YarnClientProtocolProvider的create()方法
package org.apache.hadoop.mapred;
import java.io.IOException;
import java.net.InetSocketAddress;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.MRConfig;
import org.apache.hadoop.mapreduce.protocol.ClientProtocol;
import org.apache.hadoop.mapreduce.protocol.ClientProtocolProvider;
public class YarnClientProtocolProvider extends ClientProtocolProvider {
@Override
public ClientProtocol create(Configuration conf) throws IOException {
//若参数mapreduce.framework.name配置为Yarn，则构造一个YARNRunner实例并返回，否则返回null
if (MRConfig.YARN_FRAMEWORK_NAME.equals(conf.get(MRConfig.FRAMEWORK_NAME))) {
return new YARNRunner(conf);
}
return null;
}
@Override
public ClientProtocol create(InetSocketAddress addr, Configuration conf)
throws IOException {
return create(conf);
}
@Override
public void close(ClientProtocol clientProtocol) throws IOException {
if (clientProtocol instanceof YARNRunner) {
((YARNRunner)clientProtocol).close();
}
}
}到了这里，我们就能够知道一个很重要的信息，Cluster中客户端通信协议ClientProtocol实例，要么是Yarn模式下的YARNRunner，要么就是Local模式下的LocalJobRunner。
9、YARNRunner
以Yarn模式来分析MapReduce集群连接，看下YARNRunner的实现。
最重要的一个变量就是ResourceManager的代理ResourceMgrDelegate类型的resMgrDelegate实例，Yarn模式下整个MapReduce客户端就是由它负责与Yarn集群进行通信，完成诸如作业提交、作业状态查询等过程，通过它获取集群的信息，其内部有一个YarnClient实例YarnClient，负责与Yarn进行通信，还有ApplicationId、ApplicationSubmissionContext等与特定应用程序相关的成员变量。以后有时间还要详细介绍这个对象。另外一个比较重要的变量就是客户端缓存ClientCache实例clientCache。
package org.apache.hadoop.mapred;
import ...
/** * This class enables the current JobClient (0.22 hadoop) to run on YARN. */
@SuppressWarnings("unchecked")
public class YARNRunner implements ClientProtocol {
private static final Log LOG = LogFactory.getLog(YARNRunner.class);
//记录工厂RecordFactory实例
private final RecordFactory recordFactory = RecordFactoryProvider.getRecordFactory(null);
private ResourceMgrDelegate resMgrDelegate; //ResourceManager代理实例对象
private ClientCache clientCache; //客户端缓存实例
private Configuration conf; //配置信息
private final FileContext defaultFileContext; //文件上下文实例
/** * Yarn runner incapsulates the client interface of * yarn * @param conf the configuration object for the client */
//先构造ResourceManager代理ResourceMgrDelegate实例，再调用两个参数的YARNRunner构造器
public YARNRunner(Configuration conf) {
this(conf, new ResourceMgrDelegate(new YarnConfiguration(conf)));
}
/** * Similar to {@link #YARNRunner(Configuration)} but allowing injecting * {@link ResourceMgrDelegate}. Enables mocking and testing. * @param conf the configuration object for the client * @param resMgrDelegate the resourcemanager client handle. */
//先构造客户端缓存ClientCache实例，再调用三个参数的YARNRunner构造器
public YARNRunner(Configuration conf, ResourceMgrDelegate resMgrDelegate) {
this(conf, resMgrDelegate, new ClientCache(conf, resMgrDelegate));
}
/** * Similar to {@link YARNRunner#YARNRunner(Configuration, ResourceMgrDelegate)} * but allowing injecting {@link ClientCache}. Enable mocking and testing. * @param conf the configuration object * @param resMgrDelegate the resource manager delegate * @param clientCache the client cache object. */
public YARNRunner(Configuration conf, ResourceMgrDelegate resMgrDelegate,
ClientCache clientCache) {
this.conf = conf; //成员变量赋值
try {
this.resMgrDelegate = resMgrDelegate;
this.clientCache = clientCache;
//获取文件上下文defaultFileContext实例
this.defaultFileContext = FileContext.getFileContext(this.conf);
} catch (UnsupportedFileSystemException ufe) {
throw new RuntimeException("Error in instantiating YarnClient", ufe);
}
}
...
}YARNRunner一共提供了三个构造函数，而我们之前说的WordCount作业提交时，其内部调用的是YARNRunner带有一个参数的构造函数，它会先构造ResourceManager代理ResourceMgrDelegate实例，然后再调用两个参数的构造函数，继而构造客户端缓存ClientCache实例，然后再调用三个参数的构造函数，而最终的构造函数只是进行简单的类成员变量赋值，然后通过FileContext的静态getFileContext()方法获取文件山下文FileContext实例defaultFileContext。
10、connect()方法总结
MapReduce作业提交时连接集群是通过Job的connect()方法实现的，它实际上是构造集群Cluster实例cluster。Cluster为连接MapReduce集群的一种工具，提供了一种获取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实例client，它由ClientProtocolProvider的静态create()方法构造，而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，而Yarn模式下，ClientProtocol实例YARNRunner对象内部有一个ResourceManager代理ResourceMgrDelegate实例resMgrDelegate，Yarn模式下整个MapReduce客户端就是由它负责与Yarn集群进行通信，完成诸如作业提交、作业状态查询等过程，通过它获取集群的信息。
11、submitJobInternal()方法
再次回到（3、进入submit()方法）submit()方法，上面已经介绍了connect()方法，下面开始介绍另一个重要的的方法submitJobInternal()。
该方法隶属于JobSubmitter类，顾名思义，该类是MapReduce中作业提交者，而实际上JobSubmitter除了构造方法外，对外提供的唯一一个非private成员变量或方法就是submitJobInternal()方法，它是提交Job的内部方法，实现了提交Job的所有业务逻辑。
JobSubmitter的类一共有四个类成员变量，分别为：
1. 文件系统FileSystem实例jtFs：用于操作作业运行需要的各种文件等；
2. 客户端通信协议ClientProtocol实例submitClient：用于与集群交互，完成作业提交、作业状态查询等，上文已经介绍过了。
3. 提交作业的主机名submitHostName；
4. 提交作业的主机地址submitHostAddress。
@InterfaceAudience.Private
@InterfaceStability.Unstable
class JobSubmitter {
protected static final Log LOG = LogFactory.getLog(JobSubmitter.class);
private static final String SHUFFLE_KEYGEN_ALGORITHM = "HmacSHA1";
private static final int SHUFFLE_KEY_LENGTH = 64;
private FileSystem jtFs; //文件系统FileSystem实例
private ClientProtocol submitClient; //客户端通信协议ClientProtocol实例
private String submitHostName; //提交作业的主机名
private String submitHostAddress; //提交作业的主机
//构造器
JobSubmitter(FileSystem submitFs, ClientProtocol submitClient)
throws IOException {
this.submitClient = submitClient;
this.jtFs = submitFs;
}
...
} 接下里是最重要的代码——JobSubmitter唯一的对外核心功能方法submitJobInternal()，它被用于提交作业至集群
/** * Internal method for submitting jobs to the system. * * <p>The job submission process involves: * <ol> * <li> * Checking the input and output specifications of the job. * </li> * <li> * Computing the {@link InputSplit}s for the job. * </li> * <li> * Setup the requisite accounting information for the * {@link DistributedCache} of the job, if necessary. * </li> * <li> * Copying the job's jar and configuration to the map-reduce system * directory on the distributed file-system. * </li> * <li> * Submitting the job to the <code>JobTracker</code> and optionally * monitoring it's status. * </li> * </ol></p> * @param job the configuration to submit * @param cluster the handle to the Cluster * @throws ClassNotFoundException * @throws InterruptedException * @throws IOException */
JobStatus submitJobInternal(Job job, Cluster cluster)
throws ClassNotFoundException, InterruptedException, IOException {
//validate the jobs output specs 验证作业输出规格
//检查作业输出路径是否配置并且是否存在。正确情况是已经配置且不存在
//输出路径的配置参数为mapreduce.output.fileoutputformat.outputdir
checkSpecs(job);
//获取配置信息
Configuration conf = job.getConfiguration();
//添加应用框架路径到分布式缓存中
addMRFrameworkToDistributedCache(conf);
//通过静态方法getStagingDir()获取作业执行时相关资源的存放路径
//参数未配置时默认是/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
//在提交dfs上正确配置命令行选项
InetAddress ip = InetAddress.getLocalHost();//获取当前主机IP
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);
}
JobID jobId = submitClient.getNewJobID();//生成作业ID,即是jobID
job.setJobID(jobId);//将jobID设置入job
//构造提交作业路径,jobStagingArea后接/jobID
Path submitJobDir = new Path(jobStagingArea, jobId.toString());
JobStatus status = null;
try {//设置一些作业参数
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 获得路径的授权令牌
TokenCache.obtainTokensForNamenodes(job.getCredentials(),
new Path[] { submitJobDir }, conf);
//获取秘钥和令牌，并将它们存储到令牌缓存TokenCache中
populateTokenCache(conf, job.getCredentials());
// generate a secret to authenticate shuffle transfers 生成一个秘密来验证洗牌转移
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");
}
//复制并配置相关文件
copyAndConfigureFiles(job, submitJobDir);
//获取配置文件路径
Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);
// Create the splits for the job 创建作业的拆分
LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
//调用writeSplits()方法，写分片数据文件job.splits和分片元数据文件job.splitmetainfo,计算map任务数
int maps = writeSplits(job, submitJobDir);
//设置map数
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.获取作业队列名queue，取参数mapreduce.job.queuename,默认值为default
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);//清除缓存的令牌
//根据参数确定是否需要追踪令牌ID
if (conf.getBoolean(
MRJobConfig.JOB_TOKEN_TRACKING_IDS_ENABLED,
MRJobConfig.DEFAULT_JOB_TOKEN_TRACKING_IDS_ENABLED)) {
// Add HDFS tracking ids 通过job获取令牌id,并且缓存到trackingIds列表中
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 设置保留信息，如果它存在
ReservationId reservationId = job.getReservationId();
if (reservationId != null) {
conf.set(MRJobConfig.RESERVATION_ID, reservationId.toString());
}
// Write job file to submit dir 写作业文件提交目录
writeConf(conf, submitJobFile);
//
// Now, actually submit the job (using the submit name)
// 现在，实际提交作业（使用提交名称）
printTokens(jobId, job.getCredentials());
//通过客户端通信协议ClientProtocol实例submitClient的submitJob()方法提交作业
//并获取作业状态实例status。由上下文可知，此处的submitClient是YARNRunner或LocalJobRunner
status = submitClient.submitJob(jobId, submitJobDir.toString(), job.getCredentials());
if (status != null) {//作业状态不空，直接返回，否则抛出IOException
return status;
} else {
throw new IOException("Could not launch job");
}
} finally {//抛出无法加载作业的IOException前，调用文件系统FileSystem实例jtFs的delete()方法，
//删除作业提交的相关资源目录或者文件submitJobDir
if (status == null) {
LOG.info("Cleaning up the staging area " + submitJobDir);
if (jtFs != null && submitJobDir != null)
jtFs.delete(submitJobDir, true);
}
}
}至此，MapReduce的Job提交的大体过程就分析完毕！