一、源码下载
下面是hadoop官方源码下载地址，我下载的是hadoop-3.2.4，那就一起来看下吧
Index of /dist/hadoop/core
二、从WordCount进入源码
用idea将源码加载进来后，找到org.apache.hadoop.examples.WordCount类（快捷方法：双击Shift输入WordCount）
/**
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.hadoop.examples;
import java.io.IOException;
import java.util.StringTokenizer;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
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);
}
}
}
public static class IntSumReducer
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);
}
}
public static void main(String[] args) throws Exception {
//构建一个新的Configuration，static代码块中加载了core-default.xml、core-site.xml配置
//如果core-site.xml将某个属性的final设置为true，那么用户将无法进行修改
Configuration conf = new Configuration();
//获取用户命令行中指定的选项，并进行配置
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length < 2) {
System.err.println("Usage: wordcount <in> [<in>...] <out>");
System.exit(2);
}
//根据配置和job名称创建一个新的Job，Job是提交者面对的视图
//此时Cluster是空的，只有在需要时，才会根据conf参数创建Cluster
Job job = Job.getInstance(conf, "word count");
//通过查找示例类位置来设置作业的jar文件，此时Job状态被设置为DEFINE
job.setJarByClass(WordCount.class);
//为作业设置Mapper，该类必须是Mapper的子类，那么设置mapreduce.job.map.class的value为该类
job.setMapperClass(TokenizerMapper.class);
//为作业设置combiner，该类必须是Reducer的子类，那么设置mapreduce.job.combine.class的value为该类
job.setCombinerClass(IntSumReducer.class);
//为作业设置Reducer，该类必须是Reducer的子类，那么设置mapreduce.job.reduce.class的value为该类
job.setReducerClass(IntSumReducer.class);
//设置作业输出的Key类型类，即mapreduce.job.output.key.class
job.setOutputKeyClass(Text.class);
//设置作业输出的Value类型类，即mapreduce.job.output.value.class
job.setOutputValueClass(IntWritable.class);
//设置输入数据的路径，设置mapreduce.input.fileinputformat.inputdir为以逗号为连接符的多个输入路径
for (int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
}
//设置输出数据的路径,即mapreduce.output.fileoutputformat.outputdir
FileOutputFormat.setOutputPath(job,
new Path(otherArgs[otherArgs.length - 1]));
//将Job提交到集群并等待其完成。传参为true表示实时监控作业和打印状态
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
注释已加，下面我们从job.waitForCompletion(true) 进入源码学习
其中涉及的方法有很多，不可能一一来看，我们这里只看主线上的方法以及重要的方法
1、Job.waitForCompletion
/**
* 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 {
//如果此时Job状态是否为DEFINE，就提交
if (state == JobState.DEFINE) {
//将作业提交到集群并立即返回。
submit();
}
//如果传入的参数为true，就实时打印Job状态
if (verbose) {
//随着进度和任务的进行，实时监控作业和打印状态
monitorAndPrintJob();
} else {
// get the completion poll interval from the client.
//从客户端获取完成轮询间隔。可以通过mapreduce.client.completion.pollinterval设置，
//默认5000ms,JobClient轮询MapReduce ApplicationMaster以获取有关作业状态的更新的间隔（以毫秒为单位）。
//测试小数据量时可以设置间隔短些，生产上设置的间隔长一些可以减少客户端-服务器交互
int completionPollIntervalMillis =
Job.getCompletionPollInterval(cluster.getConf());
//检查作业是否已完成。这是一个非阻塞呼叫。
while (!isComplete()) {
try {
Thread.sleep(completionPollIntervalMillis);
} catch (InterruptedException ie) {
}
}
}
return isSuccessful();
}
2、Job.submit
/**
* Submit the job to the cluster and return immediately.
* @throws IOException
*/
public void submit()
throws IOException, InterruptedException, ClassNotFoundException {
ensureState(JobState.DEFINE);
//默认设置为新API，除非它们被显式设置，或者使用了旧的映射器或reduce属性。
setUseNewAPI();
//采用impersonation(doAs)机制，为符合身份和权限的用户构建Cluster
//Cluster提供一种访问有关 map/reduce 群集的信息的方法。
connect();
//获取JobSubmitter 从字面上看时Job提交者 （参数为文件系统和客户端）
//JobClient可以使用自有方法提交作业以供执行，并了解当前系统状态。
final JobSubmitter submitter =
getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
status = ugi.doAs(new PrivilegedExceptionAction<JobStatus>() {
public JobStatus run() throws IOException, InterruptedException,
ClassNotFoundException {
//用于向系统提交作业的内部方法。
return submitter.submitJobInternal(Job.this, cluster);
}
});
//更改作业状态为RUNNING
state = JobState.RUNNING;
//获取可以显示该作业进度信息的URL。
LOG.info("The url to track the job: " + getTrackingURL());
}
3、JobSubmitter.submitJobInternal
/**
* 用于向系统提交Job的内部方法。
* Job提交过程包括：
* 1、检查Job的输入和输出规格
* 2、计算Job的InputSplit
* 3、如有必要，请为Job的DistributedCache设置必要的记帐信息
* 4、将Job的jar和配置复制到分布式文件系统上的map-reduce系统目录中
* 5、将作业提交到ResourceManager，并可选择监视其状态。
* @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 {
//验证作业输出规格，如果输出目录存在为避免重写则抛出异常
checkSpecs(job);
//根据Job获取Configuration，刚刚是根据配置创建Job，可见他们可以互相得到
Configuration conf = job.getConfiguration();
//加载MapReduce框架存档路径到分布式缓存conf
//如果设置了MapReduce框架存档的路径（此路径通常位于HDFS文件系统中的公共位置），框架存档将自动与作业一起分发
addMRFrameworkToDistributedCache(conf);
//初始化临时目录并返回路径
Path jobStagingArea = JobSubmissionFiles.getStagingDir(cluster, conf);
//configure the command line options correctly on the submitting dfs
//在提交的dfs上正确配置命令行选项
//返回本地主机的地址。通过从系统中检索主机的名称，然后将该名称解析为InetAddress
//注意：解析后的地址可能会被缓存一小段时间
//如果存在安全管理器并被阻挡，那么返回表示环回地址的InetAddress
//会获取系统的所有网卡信息，但是返回的是第一个
InetAddress ip = InetAddress.getLocalHost();
if (ip != null) {
//设置提交端的ip地址
submitHostAddress = ip.getHostAddress();
//设置提交端的hostname
submitHostName = ip.getHostName();
//设置job相关配置mapreduce.job.submithostname
conf.set(MRJobConfig.JOB_SUBMITHOST,submitHostName);
//设置job相关配置mapreduce.job.submithostaddress
conf.set(MRJobConfig.JOB_SUBMITHOSTADDR,submitHostAddress);
}
//作业分配一个唯一的jobId
JobID jobId = submitClient.getNewJobID();
//为job设置jobId
job.setJobID(jobId);
//
Path submitJobDir = new Path(jobStagingArea, jobId.toString());
JobStatus status = null;
try {
//设置mapreduce.job.user.name
conf.set(MRJobConfig.USER_NAME,
UserGroupInformation.getCurrentUser().getShortUserName());
//设置hadoop.http.filter.initializers，默认的过滤类是org.apache.hadoop.http.lib.StaticUserWebFilter
//这里设置的是AmFilterInitializer
//该配置是以逗号分隔的类名列表，必须是FilterInitializer子类
//这些Filter将应用于所有面向用户的jsp和servlet网页
conf.set("hadoop.http.filter.initializers",
"org.apache.hadoop.yarn.server.webproxy.amfilter.AmFilterInitializer");
//设置mapreduce.job.dir
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
//获取dir的委派令牌
TokenCache.obtainTokensForNamenodes(job.getCredentials(),
new Path[] { submitJobDir }, conf);
//获取密钥和令牌并将其存储到TokenCache中
populateTokenCache(conf, job.getCredentials());
// generate a secret to authenticate shuffle transfers
// 生成一个密钥以验证无序传输
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);
}
//设置MapReduce中Shuffle的密钥key，可见Shuffle的传输是有校验的，是有数据完整性保障的
SecretKey shuffleKey = keyGen.generateKey();
TokenCache.setShuffleSecretKey(shuffleKey.getEncoded(),
job.getCredentials());
}
//判断是否加密中间MapReduce溢写文件，默认false（mapreduce.job.encrypted-intermediate-data）
if (CryptoUtils.isEncryptedSpillEnabled(conf)) {
//如果设置了加密，就把最大作业尝试次数设置为1，默认值是2
//该参数是应用程序尝试的最大次数，如果失败ApplicationMaster会进行重试
conf.setInt(MRJobConfig.MR_AM_MAX_ATTEMPTS, 1);
LOG.warn("Max job attempts set to 1 since encrypted intermediate" +
"data spill is enabled");
}
//上传和配置与传递job相关的文件、libjar、jobjar和归档文件。
//如果启用了共享缓存，则此客户端将使用libjar、文件、归档和jobjar的共享缓存
//1.对于已经成功共享的资源，我们将继续以共享的方式使用它们。
//2.对于不在缓存中并且需要NM上传的资源，我们不会要求NM上传。
copyAndConfigureFiles(job, submitJobDir);
//获取job conf的文件路径
Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);
// Create the splits for the job
//为job创建 splits
LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
//重点看该方法，该方法为job计算分片
int maps = writeSplits(job, submitJobDir);
//设置map个数 mapreduce.job.maps 可见分片数=map个数
conf.setInt(MRJobConfig.NUM_MAPS, maps);
LOG.info("number of splits:" + maps);
//获取最大map数 mapreduce.job.max.map 默认 -1 即无限制
int maxMaps = conf.getInt(MRJobConfig.JOB_MAX_MAP,
MRJobConfig.DEFAULT_JOB_MAX_MAP);
if (maxMaps >= 0 && maxMaps < maps) {
throw new IllegalArgumentException("The number of map tasks " + maps +
" exceeded limit " + maxMaps);
}
// write "queue admins of the queue to which job is being submitted"
// to job file.
//将“作业提交到的队列的队列管理员”写入作业文件
//获取队列名称 mapreduce.job.queuename 默认是default
String queue = conf.get(MRJobConfig.QUEUE_NAME,
JobConf.DEFAULT_QUEUE_NAME);
//获取给定作业队列的管理员。此方法仅供hadoop内部使用。
AccessControlList acl = submitClient.getQueueAdmins(queue);
//设置mapred.queue.default.acl-administer-jobs
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.
//在将job conf复制到HDFS之前删除jobtoken引用，因为任务不需要此设置，
//实际上它们可能会因此而中断，因为引用将指向不同的作业。
TokenCache.cleanUpTokenReferral(conf);
//判断配置中mapreduce.job.token.tracking.ids.enabled（跟踪作业使用的令牌的ID的配置，默认false）
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
//设置预订信息（如果存在）mapreduce.job.reservation.id
ReservationId reservationId = job.getReservationId();
if (reservationId != null) {
conf.set(MRJobConfig.RESERVATION_ID, reservationId.toString());
}
// Write job file to submit dir
//写入作业文件以提交目录(HDFS上)
writeConf(conf, submitJobFile);
//
// Now, actually submit the job (using the submit name)
// 现在，真正提交作业（使用提交名称）
//这里调用了YARNRunner.submitJob() 下面我们看下这个方法
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);
}
}
}
3.1 JobSubmitter.writeSplits
private int writeSplits(org.apache.hadoop.mapreduce.JobContext job,
Path jobSubmitDir) throws IOException,
InterruptedException, ClassNotFoundException {
JobConf jConf = (JobConf)job.getConfiguration();
int maps;
//默认是false，在Job.submit是用setUseNewAPI()方法设置过true
if (jConf.getUseNewMapper()) {
//重点看该方法
maps = writeNewSplits(job, jobSubmitDir);
} else {
maps = writeOldSplits(jConf, jobSubmitDir);
}
return maps;
}
3.2 JobSubmitter.writeNewSplits
private <T extends InputSplit>
int writeNewSplits(JobContext job, Path jobSubmitDir) throws IOException,
InterruptedException, ClassNotFoundException {
Configuration conf = job.getConfiguration();
//获取输入格式化类，可以通过mapreduce.job.inputformat.class设置，
//默认为TextInputFormat.class
InputFormat<?, ?> input =
ReflectionUtils.newInstance(job.getInputFormatClass(), conf);
//重点看这个方法
//按逻辑拆分作业的输入文件集。
//每个InputSplit都被分配给一个单独的Mapper进行处理(分片数量=MapTask数量)
//注意：InputSplit是逻辑上的分割(比如 <输入文件路径，开始，偏移量>)，并没有改变文件对应的块
//InputFormat还创建RecordReader以读取InputSplit。
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
//根据大小将拆分部分按顺序排序，使最大的优先
Arrays.sort(array, new SplitComparator());
JobSplitWriter.createSplitFiles(jobSubmitDir, conf,
jobSubmitDir.getFileSystem(conf), array);
return array.length;
}
3.3 FileInputFormat.getSplits
/**
* Generate the list of files and make them into FileSplits.
* 生成文件列表，并将它们制作成FileSplits。
* @param job the job context
* @throws IOException
*/
public List<InputSplit> getSplits(JobContext job) throws IOException {
StopWatch sw = new StopWatch().start();
//getFormatMinSplitSize() 返回 1
//getMinSplitSize(job)) 获取mapreduce.input.fileinputformat.split.minsize值，默认1
//两者取最大值，因为两者默认值都是1，那么 minSize = 1
long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
//获取mapreduce.input.fileinputformat.split.maxsize的值，默认值Long.MAX_VALUE(2的63次方-1 MAX_VALUE=0x7fffffffffffffffL)
long maxSize = getMaxSplitSize(job);
// generate splits
// 声明分片列表
List<InputSplit> splits = new ArrayList<InputSplit>();
//列出输入目录，仅选择与正则表达式匹配的文件
List<FileStatus> files = listStatus(job);
//获取mapreduce.input.fileinputformat.input.dir.recursive的值 默认false
//获取mapreduce.input.fileinputformat.input.dir.nonrecursive.ignore.subdirs的值 默认false
//两者都为true，才把ignoreDirs 设置为true
boolean ignoreDirs = !getInputDirRecursive(job)
&& job.getConfiguration().getBoolean(INPUT_DIR_NONRECURSIVE_IGNORE_SUBDIRS, false);
//循环输入的每个文件，计算全部的InputSplit
for (FileStatus file: files) {
//忽略目录
if (ignoreDirs && file.isDirectory()) {
continue;
}
//FileStatus接口，表示文件的客户端信息
//Path 为FileSystem中文件或目录的名称
//通过FileStatus获取Path
Path path = file.getPath();
//获取此文件的长度，以字节为单位。
long length = file.getLen();
//如果文件长度不等于0
if (length != 0) {
//BlockLocation 表示块的网络位置、有关包含块副本的主机的信息以及其他块元数据
//（例如，与块相关的文件偏移量、长度、是否已损坏等）。
//如果文件是3个复本，则BlockLocation的偏移量和长度表示文件中的绝对值，而主机是保存副本的3个数据节点。以下是一个示例：
//BlockLocation(offset: 0, length: BLOCK_SIZE,hosts: {"host1:9866", "host2:9866, host3:9866"})
//如果文件是擦除编码的，则每个BlockLocation表示一个逻辑块组。值偏移是文件中块组的偏移，值长度是块组的总长度。BlockLocation的主机是保存块组的所有数据块和奇偶校验块的数据节点。
//假设我们有一个RS_3_2编码文件（3个数据单元和2个奇偶校验单元）。BlockLocation示例如下：
//BlockLocation(offset: 0, length: 3 * BLOCK_SIZE, hosts: {"host1:9866","host2:9866","host3:9866","host4:9866","host5:9866"})
BlockLocation[] blkLocations;
//判断文件是否是LocatedFileStatus的实例
//获取文件的 block 位置列表
if (file instanceof LocatedFileStatus) {
blkLocations = ((LocatedFileStatus) file).getBlockLocations();
} else {
FileSystem fs = path.getFileSystem(job.getConfiguration());
blkLocations = fs.getFileBlockLocations(file, 0, length);
}
//判断文件是可拆分的吗？通常情况下，这是真的，但如果文件是流压缩的，则不会。
if (isSplitable(job, path)) {
//获取该文件的块大小，HDFS允许文件可以指定自己的块大小和副本数
long blockSize = file.getBlockSize();
//计算该文件的分片大小：
//Math.max(minSize, Math.min(maxSize, blockSize));
//minSize 默认 = 1
//maxSize 默认 = Long.MAX_VALUE
//那么默认情况下该文件的分片大小=blockSize（该文件的块大小）
long splitSize = computeSplitSize(blockSize, minSize, maxSize);
//默认剩下的字节长度=文件总的字节长度
long bytesRemaining = length;
//文件剩下的字节长度 / 分片大小（默认该文件块大小） > 1.1
//含义：如果文件剩下的字节长度还有 块大小的1.1倍就继续
// 如果一个文件只有一个块 那么就不走该循环了
while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
//length-bytesRemaining 相当于对于该文件整体的偏移量
//根据偏移量获取对应该文件的第几个块
int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
//添加分片
splits.add(makeSplit(path, length-bytesRemaining, splitSize,
blkLocations[blkIndex].getHosts(),
blkLocations[blkIndex].getCachedHosts()));
bytesRemaining -= splitSize;
}
//一般到最后一个分片会走这里，或者该文件特别小只有一个块会走这里
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
if (LOG.isDebugEnabled()) {
// Log only if the file is big enough to be splitted
if (length > Math.min(file.getBlockSize(), minSize)) {
LOG.debug("File is not splittable so no parallelization "
+ "is possible: " + file.getPath());
}
}
//制作分片，分片数量=文件数量，分片为该文件对应的副本中第一个副本所在位置（优先取在缓存中的副本）
splits.add(makeSplit(path, 0, length, blkLocations[0].getHosts(),
blkLocations[0].getCachedHosts()));
}
} else {
//Create empty hosts array for zero length files
//如果输入文件的字节大小=0，创建空的分片
splits.add(makeSplit(path, 0, length, new String[0]));
}
}
// Save the number of input files for metrics/loadgen
// 为 job 设置文件数 mapreduce.input.fileinputformat.numinputfiles
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;
}
4、YARNRunner.submitJob
public JobStatus submitJob(JobID jobId, String jobSubmitDir, Credentials ts)
throws IOException, InterruptedException {
//添加Tokens
addHistoryToken(ts);
//构建启动MapReduce ApplicationMaster所需的所有信息
// 1、设置LocalResources（表示运行容器所需的本地资源，NodeManager负责在启动容器之前本地化资源）
// 2、设置安全令牌
// 3、为ApplicationMaster容器设置ContainerLaunchContext(表示NodeManager启动容器所需的所有信息包括：ContainerId、资源情况、分配给谁、安全令牌、环境变量、启动容器的命令、容器失败退出时的重试策略、运行容器所必需的，如二进制文件、jar、共享对象、辅助文件等、)
// 4、设置ApplicationSubmissionContext（表示ResourceManager启动应用程序的ApplicationMaster所需的所有信息。包括：ApplicationId、用户、名称、优先级、执行ApplicationMaster的容器的ContainerLaunchContext、可尝试的最大次数、尝试间隔、NodeManager处理应用程序日志所需的所有信息）
// 5、设置ApplicationMaster资源请求
// 6、为AM容器请求设置标签（如果存在）
// 7、为job容器设置标签
ApplicationSubmissionContext appContext =
createApplicationSubmissionContext(conf, jobSubmitDir, ts);
// Submit to ResourceManager
// 向ResourceManager提交
try {
//最终是用YarnClient来提交到Yarn
ApplicationId applicationId =
resMgrDelegate.submitApplication(appContext);
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);
}
}
5、YarnClientImpl.submitApplication
/**
* 向YARN提交新申请，这是一个阻塞调用-在提交的应用程序成功提交并被ResourceManager接受之前，
* 它不会返回ApplicationId。
* 用户在提交新应用程序时应提供ApplicationId作为参数ApplicationSubmissionContext的一部分
* 这在内部调用ApplicationClientProtocol.submitApplication() 之后在内部调用 ApplicationClientProtocol.getApplicationReport()
*
*/
public ApplicationId
submitApplication(ApplicationSubmissionContext appContext)
throws YarnException, IOException {
//获取applicationId
ApplicationId applicationId = appContext.getApplicationId();
if (applicationId == null) {
throw new ApplicationIdNotProvidedException(
"ApplicationId is not provided in ApplicationSubmissionContext");
}
//构建SubmitApplicationRequest（向ResourceManager提交应用程序的请求信息）
SubmitApplicationRequest request =
Records.newRecord(SubmitApplicationRequest.class);
request.setApplicationSubmissionContext(appContext);
// Automatically add the timeline DT into the CLC
// Only when the security and the timeline service are both enabled
//仅当安全和时间线服务都启用时自动将时间线DT添加到CLC中
if (isSecurityEnabled() && timelineV1ServiceEnabled) {
addTimelineDelegationToken(appContext.getAMContainerSpec());
}
//TODO: YARN-1763:Handle RM failovers during the submitApplication call.
//提交作业
//客户端用于向ResourceManager提交新应用程序的接口
//客户端需要通过SubmitApplicationRequest提供详细信息，如运行ApplicationMaster所需的队列、用于启动Application Master的等效队列等
rmClient.submitApplication(request);
int pollCount = 0;
long startTime = System.currentTimeMillis();
//Job等待状态设置
EnumSet<YarnApplicationState> waitingStates =
EnumSet.of(YarnApplicationState.NEW,
YarnApplicationState.NEW_SAVING,
YarnApplicationState.SUBMITTED);
//Job失败状态设置
EnumSet<YarnApplicationState> failToSubmitStates =
EnumSet.of(YarnApplicationState.FAILED,
YarnApplicationState.KILLED);
while (true) {
try {
//获取应用的报告，包括：
// ApplicationId
// Applications user
// Application queue
// Application name
// 允许ApplicationMaster的主机
// ApplicationMaster的RPC端口
// 跟踪url
// ApplicationMaster的各种状态
// 出现错误时的诊断信息
// 应用的开始时间
// 如果开启了安全性，应用的客户端令牌
ApplicationReport appReport = getApplicationReport(applicationId);
YarnApplicationState state = appReport.getYarnApplicationState();
if (!waitingStates.contains(state)) {
if(failToSubmitStates.contains(state)) {
throw new YarnException("Failed to submit " + applicationId +
" to YARN : " + appReport.getDiagnostics());
}
LOG.info("Submitted application " + applicationId);
break;
}
long elapsedMillis = System.currentTimeMillis() - startTime;
if (enforceAsyncAPITimeout() &&
elapsedMillis >= asyncApiPollTimeoutMillis) {
throw new YarnException("Timed out while waiting for application " +
applicationId + " to be submitted successfully");
}
// Notify the client through the log every 10 poll, in case the client
// is blocked here too long.
//每10次轮询通过日志通知客户端，以防客户端在此处被阻止的时间过长。
if (++pollCount % 10 == 0) {
LOG.info("Application submission is not finished, " +
"submitted application " + applicationId +
" is still in " + state);
}
try {
//通过yarn.client.app-submission.poll-interval 设置，默认值200ms
Thread.sleep(submitPollIntervalMillis);
} catch (InterruptedException ie) {
String msg = "Interrupted while waiting for application "
+ applicationId + " to be successfully submitted.";
LOG.error(msg);
throw new YarnException(msg, ie);
}
} catch (ApplicationNotFoundException ex) {
// FailOver or RM restart happens before RMStateStore saves
// ApplicationState
//故障转移或RM重新启动发生在RMStateStore保存ApplicationState之前
LOG.info("Re-submit application " + applicationId + "with the " +
"same ApplicationSubmissionContext");
rmClient.submitApplication(request);
}
}
return applicationId;
}
三、总结
1、构建Configuration，并加载hadoop默认的配置文件core-default.xml、core-site.xml
2、解析命令行参数，配置用户配置的环境变量
3、设置Job信息，比如：主类、Mapper类、Reduce类、Combiner类、输出格式、输入输出文件等
4、异步提交Job，实时监控作业并打印Job状态
5、根据用户身份和权限构建Cluster，并向集群提交Job
6、检查Job的输入和输出规格
7、计算Job的InputSplit（格式：<输入文件路径，开始，偏移量>，默认分片数量=所有输入文件对应的块的数量，且每个分片对应一个Mapper）
8、如有必要，请为Job的DistributedCache设置必要的记帐信息
9、将Job的jar和配置复制到分布式文件系统上的map-reduce系统目录中
10、将作业提交到ResourceManager，并可选择监视其状态
本文转载自: https://blog.csdn.net/lu070828/article/details/135747480
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版权归原作者 隔着天花板看星星 所有， 如有侵权，请联系我们删除。