- 以WordCount为例
public class WordCount
{
public static void main(String[] args)
throws Exception
{
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length < 2) {
System.err.println("Usage: wordcount <in> [<in>...] <out>");
System.exit(2);
}
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
for (int i = 0; i < otherArgs.length - 1; i++) {
FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
}
FileOutputFormat.setOutputPath(job, new Path(otherArgs[(otherArgs.length - 1)]));
//开始提交
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable>
{
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context)
throws IOException, InterruptedException
{
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
this.result.set(sum);
context.write(key, this.result);
}
}
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>
{
private static final IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException
{
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
this.word.set(itr.nextToken());
context.write(this.word, one);
}
}
}
}
2.Job类分析
public boolean waitForCompletion(boolean verbose
) throws IOException, InterruptedException,
ClassNotFoundException {
if (state == JobState.DEFINE) {
submit(); //开始提交
}
if (verbose) {
monitorAndPrintJob(); //提交成功的话，监控打印任务进度
} 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();
}
4.Job submit方法
public void submit()
throws IOException, InterruptedException, ClassNotFoundException {
ensureState(JobState.DEFINE);
setUseNewAPI();
connect(); //根据配置信息向resourcemanager建立联系
//得到提交器
final JobSubmitter submitter =
getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
status = ugi.doAs(new PrivilegedExceptionAction<JobStatus>() {
public JobStatus run() throws IOException, InterruptedException,
ClassNotFoundException {
//提交器提交任务
return submitter.submitJobInternal(Job.this, cluster);
}
});
state = JobState.RUNNING;
LOG.info("The url to track the job: " + getTrackingURL());
}
- submitter.submitJobInternal(Job.this, cluster);
//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);
}
//会使用Rpc与resoucemanager建立联系，得到JobId
JobID jobId = submitClient.getNewJobID();
job.setJobID(jobId);
//任务在yarn上的目录，存放jar包资源文件等
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 {
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");
}
//把任务的jar包复制放到HDFS上的任务目录下
copyAndConfigureFiles(job, submitJobDir);
Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);
// Create the splits for the job
LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
//写分片信息
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.
//设置任务的队列，每个用户会有一个队列
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
//所有的配置文件写到任务目录下
writeConf(conf, submitJobFile);
//
// Now, actually submit the job (using the submit name)
//
printTokens(jobId, job.getCredentials());
//真正开始提交任务
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);
}
}
5 写分片信息 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;
}
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);
//对文件进行切分,得到分片信息
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
//对分片信息进行排序，大的分片先运行
Arrays.sort(array, new SplitComparator());
//将分片元数据信息写到文件中，文件在HDFS的任务目录下
JobSplitWriter.createSplitFiles(jobSubmitDir, conf,
jobSubmitDir.getFileSystem(conf), array);
return array.length;
}
6.org.apache.hadoop.mapred.YARNRunner由这个类来进行提交
@Override
public JobStatus submitJob(JobID jobId, String jobSubmitDir, Credentials ts)
throws IOException, InterruptedException {
addHistoryToken(ts);
// Construct necessary information to start the MR AM
设置ApplicatonMaster的初始化信息,包括jar包，资源，启动命令等，每个应用都有一个ApplicationMaster
ApplicationSubmissionContext appContext =
createApplicationSubmissionContext(conf, jobSubmitDir, ts);
// Submit to ResourceManager
try {
//再交由底层的,使用rpc协议提交到resourcemanager
ApplicationId applicationId =
resMgrDelegate.submitApplication(appContext);
//整个后面，判断appMaster的提交状态
ApplicationReport appMaster = resMgrDelegate
.getApplicationReport(applicationId);
String diagnostics =
(appMaster == null ?
"application report is null" : appMaster.getDiagnostics());
if (appMaster == null
|| appMaster.getYarnApplicationState() == YarnApplicationState.FAILED
|| appMaster.getYarnApplicationState() == YarnApplicationState.KILLED) {
throw new IOException("Failed to run job : " +
diagnostics);
}
return clientCache.getClient(jobId).getJobStatus(jobId);
} catch (YarnException e) {
throw new IOException(e);
}
}
7.ApplicationMaster的初始化信息
public ApplicationSubmissionContext createApplicationSubmissionContext(
Configuration jobConf,
String jobSubmitDir, Credentials ts) throws IOException {
ApplicationId applicationId = resMgrDelegate.getApplicationId();
// Setup resource requirements
Resource capability = recordFactory.newRecordInstance(Resource.class);
capability.setMemory(
conf.getInt(
MRJobConfig.MR_AM_VMEM_MB, MRJobConfig.DEFAULT_MR_AM_VMEM_MB
)
);
capability.setVirtualCores(
conf.getInt(
MRJobConfig.MR_AM_CPU_VCORES, MRJobConfig.DEFAULT_MR_AM_CPU_VCORES
)
);
LOG.debug("AppMaster capability = " + capability);
// Setup LocalResources
Map<String, LocalResource> localResources =
new HashMap<String, LocalResource>();
Path jobConfPath = new Path(jobSubmitDir, MRJobConfig.JOB_CONF_FILE);
URL yarnUrlForJobSubmitDir = ConverterUtils
.getYarnUrlFromPath(defaultFileContext.getDefaultFileSystem()
.resolvePath(
defaultFileContext.makeQualified(new Path(jobSubmitDir))));
LOG.debug("Creating setup context, jobSubmitDir url is "
+ yarnUrlForJobSubmitDir);
localResources.put(MRJobConfig.JOB_CONF_FILE,
createApplicationResource(defaultFileContext,
jobConfPath, LocalResourceType.FILE));
if (jobConf.get(MRJobConfig.JAR) != null) {
Path jobJarPath = new Path(jobConf.get(MRJobConfig.JAR));
LocalResource rc = createApplicationResource(
FileContext.getFileContext(jobJarPath.toUri(), jobConf),
jobJarPath,
LocalResourceType.PATTERN);
String pattern = conf.getPattern(JobContext.JAR_UNPACK_PATTERN,
JobConf.UNPACK_JAR_PATTERN_DEFAULT).pattern();
rc.setPattern(pattern);
localResources.put(MRJobConfig.JOB_JAR, rc);
} else {
// Job jar may be null. For e.g, for pipes, the job jar is the hadoop
// mapreduce jar itself which is already on the classpath.
LOG.info("Job jar is not present. "
+ "Not adding any jar to the list of resources.");
}
// TODO gross hack
for (String s : new String[] {
MRJobConfig.JOB_SPLIT,
MRJobConfig.JOB_SPLIT_METAINFO }) {
localResources.put(
MRJobConfig.JOB_SUBMIT_DIR + "/" + s,
createApplicationResource(defaultFileContext,
new Path(jobSubmitDir, s), LocalResourceType.FILE));
}
// Setup security tokens
DataOutputBuffer dob = new DataOutputBuffer();
ts.writeTokenStorageToStream(dob);
ByteBuffer securityTokens = ByteBuffer.wrap(dob.getData(), 0, dob.getLength());
// Setup the command to run the AM
List<String> vargs = new ArrayList<String>(8);
vargs.add(MRApps.crossPlatformifyMREnv(jobConf, Environment.JAVA_HOME)
+ "/bin/java");
Path amTmpDir =
new Path(MRApps.crossPlatformifyMREnv(conf, Environment.PWD),
YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR);
vargs.add("-Djava.io.tmpdir=" + amTmpDir);
MRApps.addLog4jSystemProperties(null, vargs, conf);
// Check for Java Lib Path usage in MAP and REDUCE configs
warnForJavaLibPath(conf.get(MRJobConfig.MAP_JAVA_OPTS,""), "map",
MRJobConfig.MAP_JAVA_OPTS, MRJobConfig.MAP_ENV);
warnForJavaLibPath(conf.get(MRJobConfig.MAPRED_MAP_ADMIN_JAVA_OPTS,""), "map",
MRJobConfig.MAPRED_MAP_ADMIN_JAVA_OPTS, MRJobConfig.MAPRED_ADMIN_USER_ENV);
warnForJavaLibPath(conf.get(MRJobConfig.REDUCE_JAVA_OPTS,""), "reduce",
MRJobConfig.REDUCE_JAVA_OPTS, MRJobConfig.REDUCE_ENV);
warnForJavaLibPath(conf.get(MRJobConfig.MAPRED_REDUCE_ADMIN_JAVA_OPTS,""), "reduce",
MRJobConfig.MAPRED_REDUCE_ADMIN_JAVA_OPTS, MRJobConfig.MAPRED_ADMIN_USER_ENV);
// Add AM admin command opts before user command opts
// so that it can be overridden by user
String mrAppMasterAdminOptions = conf.get(MRJobConfig.MR_AM_ADMIN_COMMAND_OPTS,
MRJobConfig.DEFAULT_MR_AM_ADMIN_COMMAND_OPTS);
warnForJavaLibPath(mrAppMasterAdminOptions, "app master",
MRJobConfig.MR_AM_ADMIN_COMMAND_OPTS, MRJobConfig.MR_AM_ADMIN_USER_ENV);
vargs.add(mrAppMasterAdminOptions);
// Add AM user command opts
String mrAppMasterUserOptions = conf.get(MRJobConfig.MR_AM_COMMAND_OPTS,
MRJobConfig.DEFAULT_MR_AM_COMMAND_OPTS);
warnForJavaLibPath(mrAppMasterUserOptions, "app master",
MRJobConfig.MR_AM_COMMAND_OPTS, MRJobConfig.MR_AM_ENV);
vargs.add(mrAppMasterUserOptions);
if (jobConf.getBoolean(MRJobConfig.MR_AM_PROFILE,
MRJobConfig.DEFAULT_MR_AM_PROFILE)) {
final String profileParams = jobConf.get(MRJobConfig.MR_AM_PROFILE_PARAMS,
MRJobConfig.DEFAULT_TASK_PROFILE_PARAMS);
if (profileParams != null) {
vargs.add(String.format(profileParams,
ApplicationConstants.LOG_DIR_EXPANSION_VAR + Path.SEPARATOR
+ TaskLog.LogName.PROFILE));
}
}
vargs.add(MRJobConfig.APPLICATION_MASTER_CLASS);
vargs.add("1>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR +
Path.SEPARATOR + ApplicationConstants.STDOUT);
vargs.add("2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR +
Path.SEPARATOR + ApplicationConstants.STDERR);
Vector<String> vargsFinal = new Vector<String>(8);
// Final command
StringBuilder mergedCommand = new StringBuilder();
for (CharSequence str : vargs) {
mergedCommand.append(str).append(" ");
}
vargsFinal.add(mergedCommand.toString());
LOG.debug("Command to launch container for ApplicationMaster is : "
+ mergedCommand);
// Setup the CLASSPATH in environment
// i.e. add { Hadoop jars, job jar, CWD } to classpath.
Map<String, String> environment = new HashMap<String, String>();
MRApps.setClasspath(environment, conf);
// Shell
environment.put(Environment.SHELL.name(),
conf.get(MRJobConfig.MAPRED_ADMIN_USER_SHELL,
MRJobConfig.DEFAULT_SHELL));
// Add the container working directory in front of LD_LIBRARY_PATH
MRApps.addToEnvironment(environment, Environment.LD_LIBRARY_PATH.name(),
MRApps.crossPlatformifyMREnv(conf, Environment.PWD), conf);
// Setup the environment variables for Admin first
MRApps.setEnvFromInputString(environment,
conf.get(MRJobConfig.MR_AM_ADMIN_USER_ENV,
MRJobConfig.DEFAULT_MR_AM_ADMIN_USER_ENV), conf);
// Setup the environment variables (LD_LIBRARY_PATH, etc)
MRApps.setEnvFromInputString(environment,
conf.get(MRJobConfig.MR_AM_ENV), conf);
// Parse distributed cache
MRApps.setupDistributedCache(jobConf, localResources);
Map<ApplicationAccessType, String> acls
= new HashMap<ApplicationAccessType, String>(2);
acls.put(ApplicationAccessType.VIEW_APP, jobConf.get(
MRJobConfig.JOB_ACL_VIEW_JOB, MRJobConfig.DEFAULT_JOB_ACL_VIEW_JOB));
acls.put(ApplicationAccessType.MODIFY_APP, jobConf.get(
MRJobConfig.JOB_ACL_MODIFY_JOB,
MRJobConfig.DEFAULT_JOB_ACL_MODIFY_JOB));
// Setup ContainerLaunchContext for AM container
ContainerLaunchContext amContainer =
ContainerLaunchContext.newInstance(localResources, environment,
vargsFinal, null, securityTokens, acls);
Collection<String> tagsFromConf =
jobConf.getTrimmedStringCollection(MRJobConfig.JOB_TAGS);
// Set up the ApplicationSubmissionContext
ApplicationSubmissionContext appContext =
recordFactory.newRecordInstance(ApplicationSubmissionContext.class);
appContext.setApplicationId(applicationId); // ApplicationId
appContext.setQueue( // Queue name
jobConf.get(JobContext.QUEUE_NAME,
YarnConfiguration.DEFAULT_QUEUE_NAME));
// add reservationID if present
ReservationId reservationID = null;
try {
reservationID =
ReservationId.parseReservationId(jobConf
.get(JobContext.RESERVATION_ID));
} catch (NumberFormatException e) {
// throw exception as reservationid as is invalid
String errMsg =
"Invalid reservationId: " + jobConf.get(JobContext.RESERVATION_ID)
+ " specified for the app: " + applicationId;
LOG.warn(errMsg);
throw new IOException(errMsg);
}
if (reservationID != null) {
appContext.setReservationID(reservationID);
LOG.info("SUBMITTING ApplicationSubmissionContext app:" + applicationId
+ " to queue:" + appContext.getQueue() + " with reservationId:"
+ appContext.getReservationID());
}
appContext.setApplicationName( // Job name
jobConf.get(JobContext.JOB_NAME,
YarnConfiguration.DEFAULT_APPLICATION_NAME));
appContext.setCancelTokensWhenComplete(
conf.getBoolean(MRJobConfig.JOB_CANCEL_DELEGATION_TOKEN, true));
appContext.setAMContainerSpec(amContainer); // AM Container
appContext.setMaxAppAttempts(
conf.getInt(MRJobConfig.MR_AM_MAX_ATTEMPTS,
MRJobConfig.DEFAULT_MR_AM_MAX_ATTEMPTS));
appContext.setResource(capability);
// set labels for the AM container request if present
String amNodelabelExpression = conf.get(MRJobConfig.AM_NODE_LABEL_EXP);
if (null != amNodelabelExpression
&& amNodelabelExpression.trim().length() != 0) {
ResourceRequest amResourceRequest =
recordFactory.newRecordInstance(ResourceRequest.class);
amResourceRequest.setPriority(AM_CONTAINER_PRIORITY);
amResourceRequest.setResourceName(ResourceRequest.ANY);
amResourceRequest.setCapability(capability);
amResourceRequest.setNumContainers(1);
amResourceRequest.setNodeLabelExpression(amNodelabelExpression.trim());
appContext.setAMContainerResourceRequest(amResourceRequest);
}
// set labels for the Job containers
appContext.setNodeLabelExpression(jobConf
.get(JobContext.JOB_NODE_LABEL_EXP));
appContext.setApplicationType(MRJobConfig.MR_APPLICATION_TYPE);
if (tagsFromConf != null && !tagsFromConf.isEmpty()) {
appContext.setApplicationTags(new HashSet<String>(tagsFromConf));
}
return appContext;
8.ResourceManager收到请求后,通知nodemanager，运行AppMaster，调度器会给nodemanager分配Container，让nodemanager执行AppMaster，
AppMaster启动之后，先向resourcemanager注册自己，resourcemanager,才能知道任务的运行信息，然后向resourcemanager请求分配Container,Container由调度器分配，会得到Container的内存，cpu以及在哪个节点上。
resourcemanager.scheduler.SchedulerNode: Assigned container container_1515076284174_0005_01_000004 of capacity <memory:1024, vCores:1> on host centos-3:8041, which has 1 containers, <memory:1024, vCores:1> used and <memory:36, vCores:0> available after allocation
AppMaster得到Container信息后指定nodemanager启动Container去运行任务，AppMaster会监控Container任务的运行情况，同时向ResourceManger报告任务信息.
每个nodemanager会向resoucemanager发送心跳信息，resourcemanger会根据所有节点的心跳知道整个集群的资源，这样调度器才能合理的分配Container。
每个AppMaster，任务完成后会向resourcemanage取消注册。这样一个任务就完成了。
9.分析resourcemanager任务日志,分配Container信息全部在resourcemanager节点的日志信息中，这里只截了第一个Container信息。
resourcemanager.ClientRMService: Allocated new applicationId: 5
resourcemanager.ClientRMService: Application with id 5 submitted by user root
resourcemanager.rmapp.RMAppImpl: Storing application with id application_1515076284174_0005
resourcemanager.rmapp.RMAppImpl: application_1515076284174_0005 State change from NEW to NEW_SAVING
resourcemanager.RMAuditLogger: USER=root IP=172.31.109.168 OPERATION=Submit Application Request TARGET=ClientRMService RESULT=SUCCESS APPID=application_1515076284174_0005
resourcemanager.recovery.RMStateStore: Storing info for app: application_1515076284174_0005
resourcemanager.rmapp.RMAppImpl: application_1515076284174_0005 State change from NEW_SAVING to SUBMITTED
resourcemanager.scheduler.fair.FairScheduler: Accepted application application_1515076284174_0005 from user: root, in queue: default, currently num of applications: 1
resourcemanager.rmapp.RMAppImpl: application_1515076284174_0005 State change from SUBMITTED to ACCEPTED
resourcemanager.ApplicationMasterService: Registering app attempt : appattempt_1515076284174_0005_000001
resourcemanager.rmapp.attempt.RMAppAttemptImpl: appattempt_1515076284174_0005_000001 State change from NEW to SUBMITTED
resourcemanager.scheduler.fair.FairScheduler: Added Application Attempt appattempt_1515076284174_0005_000001 to scheduler from user: root
resourcemanager.rmapp.attempt.RMAppAttemptImpl: appattempt_1515076284174_0005_000001 State change from SUBMITTED to SCHEDULED
resourcemanager.rmcontainer.RMContainerImpl: container_1515076284174_0005_01_000001 Container Transitioned from NEW to ALLOCATED
resourcemanager.RMAuditLogger: USER=root OPERATION=AM Allocated Container TARGET=SchedulerApp RESULT=SUCCESS APPID=application_1515076284174_0005 CONTAINERID=container_1515076284174_0005_01_000001
//可以看到container编号的尾号是00001证明这是第一个Container容器，在机器Centos-2上运行。
**resourcemanager.scheduler.SchedulerNode: Assigned container container_1515076284174_0005_01_000001 of capacity <memory:1024, vCores:1> on host centos-2:8041, which has 1 containers, <memory:1024, vCores:1> used and <memory:36, vCores:0> available after allocation**
10.我运行程序的时候有6个map,默认一个reduce，再加上第一个AppMaster运行的Container一共分配了8个Container，第一个Container在Centos-2上分配，其它的7个Container都分配在了Centos-3上了，可以从resourcemanage日志中看出来。可以在Centos-3上验证你的想法
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