参考 FileInputFormat类中split切分算法和host选择算法介绍 以及 Hadoop2.6.0的FileInputFormat的任务切分原理分析（即如何控制FileInputFormat的map任务数量） 以及 Hadoop中FileInputFormat计算InputSplit的getSplits方法的流程 以及 hadoop作业分片处理以及任务本地性分析（源码分析第一篇）
分析前先介绍一下:
( 这里要注意下, Block 的 hosts 和 Split 的 hosts 不一样, Split 的 hosts 是通过 Split 的 hosts 按一定方法生成的, 如果一个 Block 对应一个 Split (一般情况下是这样的), 这时它们两个 hosts 是一样的. 如果不是一对一( Split > block), 则 Split 需要按一定方法选择 hosts .
Split 和 MapTask 是一一对应的, 一个 Split 对应一个 MapTask. 所以本地性是跟 Split 的 hosts 相关的.
BlocksMap存储 Block 与 BlockInfo 的映射关系, Block 中主要包含3项: long blockId; // 数据块的唯一标识,即数据块的ID号. long numBytes; // 数据块包含的文件数据大小. long generationStamp; // 数据块的版本号,或数据块的时间戳. BlockInfo( 在 Hadoop-2.7.3 中是 BlockInfoContiguous) 包含所以副本所在主机名. )
开始分析: ( 这里是 hadoop-2.7.3-src )
以WordCount开始: org.apache.hadoop.examples.WordCount.main() 内部调用 org.apache.hadoop.mapreduce.Job.waitForCompletion(boolean)
// 该段代码在 org.apache.hadoop.examples.WordCount public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); //指定作业执行规范 , Configuration：map/reduce的j配置类，向hadoop框架描述map-reduce执行的工作 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"); //指定job名称，及运行对象 job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); //为job设置Mapper类 job.setCombinerClass(IntSumReducer.class); //为job设置Combiner类 job.setReducerClass(IntSumReducer.class); //为job设置Reducer类 job.setOutputKeyClass(Text.class); //为job的输出数据设置Key类 job.setOutputValueClass(IntWritable.class); //为job输出设置value类 for (int i = 0; i < otherArgs.length - 1; ++i) { FileInputFormat.addInputPath(job, new Path(otherArgs[i])); //为job设置输入路径, org.apache.hadoop.mapreduce.lib.input.FileInputFormat } FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1])); //为job设置输出路径 System.exit(job.waitForCompletion(true) ? 0 : 1); //运行job, 调用 Job.waitForCompletion() }
在 Job.waitForCompletion() 函数内部会调用 Job 本类的方法 submit(), 在 submit() 内部接着调用 org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(Job , Cluster)
// 该段代码在 org.apache.hadoop.mapreduce.Job 中 // ...... /** * 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(); // 调用本类的 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(); } // ...... /** * 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()); }
在 JobSubmitter.submitJobInternal(Job , Cluster) 函数内部调用本类的 writeSplits(Job ,Path ) 为job创建分片; 接着 writeSplits(Job ,Path ) 方法内部会调用本类的 (1) writeNewSplits(JobContext , Path ) { Hadoop2.0 会调用这个,新版的API}和 (2) writeOldSplits(JobConf , Path ) { 这个是旧版的 API }; 在 JobSubmitter.writeNewSplits(JobContext , Path ) 方法内部会调用抽象类 org.apache.hadoop.mapreduce.InputFormat.getSplites(JobContext ), 计算job的输入文件的逻辑分片集合; 而在 JobSubmitter.writeOldSplits(JobContext , Path ) 方法内部会调用抽象类 org.apache.hadoop.mapred.InputFormat.getSplites(JobContext , int ), 计算job的输入文件的逻辑分片集合.
// 该段代码在 org.apache.hadoop.mapreduce.JobSubmitter 中 // ...... /** * 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 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 { 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"); } 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); // 为job创建分片 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()); // 提交 job 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); } } } // ...... 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; } // ...... @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; } // ...... //method to write splits for old api mapper. private int writeOldSplits(JobConf job, Path jobSubmitDir) throws IOException { org.apache.hadoop.mapred.InputSplit[] splits = job.getInputFormat().getSplits(job, job.getNumMapTasks()); // sort the splits into order based on size, so that the biggest // go first Arrays.sort(splits, new Comparator<org.apache.hadoop.mapred.InputSplit>() { public int compare(org.apache.hadoop.mapred.InputSplit a, org.apache.hadoop.mapred.InputSplit b) { try { long left = a.getLength(); long right = b.getLength(); if (left == right) { return 0; } else if (left < right) { return 1; } else { return -1; } } catch (IOException ie) { throw new RuntimeException("Problem getting input split size", ie); } } }); JobSplitWriter.createSplitFiles(jobSubmitDir, job, jobSubmitDir.getFileSystem(job), splits); return splits.length; }
(1) 在抽象类 org.apache.hadoop.mapreduce.InputFormat.getSplites(JobContext ) 方法,这里实际调用的是实现类 org.apache.hadoop.mapreduce.lib.input.FileInputFormat.getSplites(JobContext ), ( 这里 FileInputFormat 有两个相同的, 分别是 org.apache.hadoop.mapreduce.lib.input.FileInputFormat 和 org.apache.hadoop.mapred.FileInputFormat , 我们选择org.apache.hadoop.mapreduce.lib.input.FileInputFormat 有以下几点原因: 首先, org.apache.hadoop.mapred.FileInputFormat 类是抽象类 InputFormat 的实现类; 其次, WordCount中的FileInputFormat 就是 org.apache.hadoop.mapreduce.lib.input.FileInputFormat.)
我们介绍一个概念, 即新旧 MapReduce API , 从0.20.0版本开始, Hadoop 同时提供了新旧两套 MapReduce API. 新 API 在旧 API 基础上进行了封装,使得其在扩展性和易用性方面更好. 旧版 API 放在 org.apache.hadoop.mapred 包中, 而新版 API 则放在 org.apache.hadoop.mapreduce 包及其子包中.
// 该段代码是在 org.apache.hadoop.mapreduce.InputFormat 中 /** * Logically split the set of input files for the job. * * <p>Each {@link InputSplit} is then assigned to an individual {@link Mapper} * for processing.</p> * * <p><i>Note</i>: The split is a <i>logical</i> split of the inputs and the * input files are not physically split into chunks. For e.g. a split could * be <i><input-file-path, start, offset></i> tuple. The InputFormat * also creates the {@link RecordReader} to read the {@link InputSplit}. * * @param context job configuration. * @return an array of {@link InputSplit}s for the job. */ public abstract List<InputSplit> getSplits(JobContext context ) throws IOException, InterruptedException;
在 org.apache.hadoop.mapreduce.lib.input.FileInputFormat.getSplites(JobContext ) 方法中,
(2) 在抽象类 org.apache.hadoop.mapred.InputFormat.getSplites(JobContext ,int ) 方法,这里实际调用的是实现类 org.apache.hadoop.mapred.FileInputFormat.getSplites(JobContext ,int ) , 这里 org.apache.hadoop.mapred.FileInputFormat 类是抽象类 InputFormat 的实现类. 具体参考 FileInputFormat类中split切分算法和host选择算法介绍 .