延迟调度的主要目的是提高数据本地性(data locality)，减少数据在网络中的传输。对于那些输入数据不在本地的MapTask，调度器将会延迟调度他们，而把slot分配给那些具备本地性的MapTask。
延迟调度的大体思想如下：
若该job找到一个node-local的MapTask，则返回该task；若找不到，则延迟调度。即在nodeLocalityDelay时长内，重新找到一个node-local的MapTask并返回；
否则等待时长超过nodeLocalityDelay之后，寻找一个rack-local的MapTask并返回；若找不到，则延迟调度。即在rackLocalityDelay时长内，重新找到一个rack-local的MapTask并返回；
否则等待超过nodeLocalityDelay + rackLocalityDelay之后，重新寻找一个off-switch的MapTask并返回。
FairScheduler.java中关于延迟调度的主要变量：
1 long nodeLocalityDelay：//node-local已经等待的时间 2 long rackLocalityDelay： //rack-local已经等待的时间 3 boolean skippedAtLastHeartbeat：//该job是否被延迟调度(是否被跳过) 4 timeWaitedForLocalMap：//自从上次MapTask被分配以来等待的时间 5 LocalityLevel lastMapLocalityLevel：//上次分配的MapTask对应的本地级别 6 nodeLocalityDelay = rackLocalityDelay = 7 Math.min(15000 , (long) (1.5 * jobTracker.getNextHeartbeatInterval()));
在fair scheduler中，每个job维护了两个变量用来完成延迟调度：最后一个被调度的MapTask的本地性级别(lastMapLocalityLevel)与自从这个job被跳过以来所等待的时间(timeWaitedForLocalMap)。工作流程如下(具体工作在FairScheduler.java的getAllowedLocalityLevel ()方法中完成)：
1 /** 2 * Get the maximum locality level at which a given job is allowed to 3 * launch tasks, based on how long it has been waiting for local tasks. 4 * This is used to implement the "delay scheduling" feature of the Fair 5 * Scheduler for optimizing data locality. 6 * If the job has no locality information (e.g. it does not use HDFS), this 7 * method returns LocalityLevel.ANY, allowing tasks at any level. 8 * Otherwise, the job can only launch tasks at its current locality level 9 * or lower, unless it has waited at least nodeLocalityDelay or 10 * rackLocalityDelay milliseconds depends on the current level. If it 11 * has waited (nodeLocalityDelay + rackLocalityDelay) milliseconds, 12 * it can go to any level. 13 */ 14 protected LocalityLevel getAllowedLocalityLevel(JobInProgress job, 15 long currentTime) { 16 JobInfo info = infos.get(job); 17 if (info == null) { // Job not in infos (shouldn't happen) 18 LOG.error("getAllowedLocalityLevel called on job " + job 19 + ", which does not have a JobInfo in infos"); 20 return LocalityLevel.ANY; 21 } 22 if (job.nonLocalMaps.size() > 0) { // Job doesn't have locality information 23 return LocalityLevel.ANY; 24 } 25 // Don't wait for locality if the job's pool is starving for maps 26 Pool pool = poolMgr.getPool(job); 27 PoolSchedulable sched = pool.getMapSchedulable(); 28 long minShareTimeout = poolMgr.getMinSharePreemptionTimeout(pool.getName()); 29 long fairShareTimeout = poolMgr.getFairSharePreemptionTimeout(); 30 if (currentTime - sched.getLastTimeAtMinShare() > minShareTimeout || 31 currentTime - sched.getLastTimeAtHalfFairShare() > fairShareTimeout) { 32 eventLog.log("INFO", "No delay scheduling for " 33 + job.getJobID() + " because it is being starved"); 34 return LocalityLevel.ANY; 35 } 36 // In the common case, compute locality level based on time waited 37 switch(info.lastMapLocalityLevel) { 38 case NODE: // Last task launched was node-local 39 if (info.timeWaitedForLocalMap >= 40 nodeLocalityDelay + rackLocalityDelay) 41 return LocalityLevel.ANY; 42 else if (info.timeWaitedForLocalMap >= nodeLocalityDelay) 43 return LocalityLevel.RACK; 44 else 45 return LocalityLevel.NODE; 46 case RACK: // Last task launched was rack-local 47 if (info.timeWaitedForLocalMap >= rackLocalityDelay) 48 return LocalityLevel.ANY; 49 else 50 return LocalityLevel.RACK; 51 default: // Last task was non-local; can launch anywhere 52 return LocalityLevel.ANY; 53 } 54 }
1. 若lastMapLocalityLevel为Node：
1）若timeWaitedForLocalMap >= nodeLocalityDelay + rackLocalityDelay，则可以调度off-switch及以下级别的MapTask；
2）若timeWaitedForLocalMap >= nodeLocalityDelay，则可以调度rack-local及以下级别的MapTask；
3）否则调度node-local级别的MapTask。
2. 若lastMapLocalityLevel为Rack：
1）若timeWaitedForLocalMap >= rackLocalityDelay，则调度off-switch及以下级别的MapTask；
2）否则调度rack-local及以下级别的MapTask；
3. 否则调度off-switch及以下级别的MapTask；
延迟调度的具体工作流程如下(具体工作在FairScheduler.java的assignTasks()方法中完成)：
1 @Override 2 public synchronized List<Task> assignTasks(TaskTracker tracker) 3 throws IOException { 4 if (!initialized) // Don't try to assign tasks if we haven't yet started up 5 return null; 6 String trackerName = tracker.getTrackerName(); 7 eventLog.log("HEARTBEAT", trackerName); 8 long currentTime = clock.getTime(); 9 10 // Compute total runnable maps and reduces, and currently running ones 11 int runnableMaps = 0; 12 int runningMaps = 0; 13 int runnableReduces = 0; 14 int runningReduces = 0; 15 for (Pool pool: poolMgr.getPools()) { 16 runnableMaps += pool.getMapSchedulable().getDemand(); 17 runningMaps += pool.getMapSchedulable().getRunningTasks(); 18 runnableReduces += pool.getReduceSchedulable().getDemand(); 19 runningReduces += pool.getReduceSchedulable().getRunningTasks(); 20 } 21 22 ClusterStatus clusterStatus = taskTrackerManager.getClusterStatus(); 23 // Compute total map/reduce slots 24 // In the future we can precompute this if the Scheduler becomes a 25 // listener of tracker join/leave events. 26 int totalMapSlots = getTotalSlots(TaskType.MAP, clusterStatus); 27 int totalReduceSlots = getTotalSlots(TaskType.REDUCE, clusterStatus); 28 29 eventLog.log("RUNNABLE_TASKS", 30 runnableMaps, runningMaps, runnableReduces, runningReduces); 31 32 // Update time waited for local maps for jobs skipped on last heartbeat 33 //备注一 34 updateLocalityWaitTimes(currentTime); 35 36 // Check for JT safe-mode 37 if (taskTrackerManager.isInSafeMode()) { 38 LOG.info("JobTracker is in safe-mode, not scheduling any tasks."); 39 return null; 40 } 41 42 TaskTrackerStatus tts = tracker.getStatus(); 43 44 int mapsAssigned = 0; // loop counter for map in the below while loop 45 int reducesAssigned = 0; // loop counter for reduce in the below while 46 int mapCapacity = maxTasksToAssign(TaskType.MAP, tts); 47 int reduceCapacity = maxTasksToAssign(TaskType.REDUCE, tts); 48 boolean mapRejected = false; // flag used for ending the loop 49 boolean reduceRejected = false; // flag used for ending the loop 50 51 // Keep track of which jobs were visited for map tasks and which had tasks 52 // launched, so that we can later mark skipped jobs for delay scheduling 53 Set<JobInProgress> visitedForMap = new HashSet<JobInProgress>(); 54 Set<JobInProgress> visitedForReduce = new HashSet<JobInProgress>(); 55 Set<JobInProgress> launchedMap = new HashSet<JobInProgress>(); 56 57 ArrayList<Task> tasks = new ArrayList<Task>(); 58 // Scan jobs to assign tasks until neither maps nor reduces can be assigned 59 //备注二 60 while (true) { 61 // Computing the ending conditions for the loop 62 // Reject a task type if one of the following condition happens 63 // 1. number of assigned task reaches per heatbeat limit 64 // 2. number of running tasks reaches runnable tasks 65 // 3. task is rejected by the LoadManager.canAssign 66 if (!mapRejected) { 67 if (mapsAssigned == mapCapacity || 68 runningMaps == runnableMaps || 69 !loadMgr.canAssignMap(tts, runnableMaps, 70 totalMapSlots, mapsAssigned)) { 71 eventLog.log("INFO", "Can't assign another MAP to " + trackerName); 72 mapRejected = true; 73 } 74 } 75 if (!reduceRejected) { 76 if (reducesAssigned == reduceCapacity || 77 runningReduces == runnableReduces || 78 !loadMgr.canAssignReduce(tts, runnableReduces, 79 totalReduceSlots, reducesAssigned)) { 80 eventLog.log("INFO", "Can't assign another REDUCE to " + trackerName); 81 reduceRejected = true; 82 } 83 } 84 // Exit while (true) loop if 85 // 1. neither maps nor reduces can be assigned 86 // 2. assignMultiple is off and we already assigned one task 87 if (mapRejected && reduceRejected || 88 !assignMultiple && tasks.size() > 0) { 89 break; // This is the only exit of the while (true) loop 90 } 91 92 // Determine which task type to assign this time 93 // First try choosing a task type which is not rejected 94 TaskType taskType; 95 if (mapRejected) { 96 taskType = TaskType.REDUCE; 97 } else if (reduceRejected) { 98 taskType = TaskType.MAP; 99 } else { 100 // If both types are available, choose the task type with fewer running 101 // tasks on the task tracker to prevent that task type from starving 102 if (tts.countMapTasks() + mapsAssigned <= 103 tts.countReduceTasks() + reducesAssigned) { 104 taskType = TaskType.MAP; 105 } else { 106 taskType = TaskType.REDUCE; 107 } 108 } 109 110 // Get the map or reduce schedulables and sort them by fair sharing 111 List<PoolSchedulable> scheds = getPoolSchedulables(taskType); 112 //对job进行排序 113 Collections.sort(scheds, new SchedulingAlgorithms.FairShareComparator()); 114 boolean foundTask = false; 115 //备注三 116 for (Schedulable sched: scheds) { // This loop will assign only one task 117 eventLog.log("INFO", "Checking for " + taskType + 118 " task in " + sched.getName()); 119 //备注四 120 Task task = taskType == TaskType.MAP ? 121 sched.assignTask(tts, currentTime, visitedForMap) : 122 sched.assignTask(tts, currentTime, visitedForReduce); 123 if (task != null) { 124 foundTask = true; 125 JobInProgress job = taskTrackerManager.getJob(task.getJobID()); 126 eventLog.log("ASSIGN", trackerName, taskType, 127 job.getJobID(), task.getTaskID()); 128 // Update running task counts, and the job's locality level 129 if (taskType == TaskType.MAP) { 130 launchedMap.add(job); 131 mapsAssigned++; 132 runningMaps++; 133 //备注五 134 updateLastMapLocalityLevel(job, task, tts); 135 } else { 136 reducesAssigned++; 137 runningReduces++; 138 } 139 // Add task to the list of assignments 140 tasks.add(task); 141 break; // This break makes this loop assign only one task 142 } // end if(task != null) 143 } // end for(Schedulable sched: scheds) 144 145 // Reject the task type if we cannot find a task 146 if (!foundTask) { 147 if (taskType == TaskType.MAP) { 148 mapRejected = true; 149 } else { 150 reduceRejected = true; 151 } 152 } 153 } // end while (true) 154 155 // Mark any jobs that were visited for map tasks but did not launch a task 156 // as skipped on this heartbeat 157 for (JobInProgress job: visitedForMap) { 158 if (!launchedMap.contains(job)) { 159 infos.get(job).skippedAtLastHeartbeat = true; 160 } 161 } 162 163 // If no tasks were found, return null 164 return tasks.isEmpty() ? null : tasks; 165 }
备注一：updateLocalityWaitTimes()。首先更新自上次心跳以来，timeWaitedForLocalMap的时间，并将所有job 的skippedAtLastHeartbeat设为false；代码如下：
1 /** 2 * Update locality wait times for jobs that were skipped at last heartbeat. 3 */ 4 private void updateLocalityWaitTimes(long currentTime) { 5 long timeSinceLastHeartbeat = 6 (lastHeartbeatTime == 0 ? 0 : currentTime - lastHeartbeatTime); 7 lastHeartbeatTime = currentTime; 8 for (JobInfo info: infos.values()) { 9 if (info.skippedAtLastHeartbeat) { 10 info.timeWaitedForLocalMap += timeSinceLastHeartbeat; 11 info.skippedAtLastHeartbeat = false; 12 } 13 } 14 }
备注二：在while(true)循环中不断分配MapTask和ReduceTask，直到没有可被分配的为止；在循环中对所有job进行排序；接着在一个for()循环中进行真正的MapTask分配(Schedulable有两个子类，分别代表PoolSchedulable与JobSchedulable。这里的Schedulable可当做job看待)。
备注三、四：在for()循环里，JobSchedulable中的assignTask()方法会被调用，来选择适当的MapTask或者ReduceTask。在选择MapTask时，先会调用FairScheduler.getAllowedLocalityLevel()方法来确定应该调度哪个级别的MapTask(具体的方法分析见上)，然后根据该方法的返回值来选择对应级别的MapTask。assignTask()方法代码如下：
1 @Override 2 public Task assignTask(TaskTrackerStatus tts, long currentTime, 3 Collection<JobInProgress> visited) throws IOException { 4 if (isRunnable()) { 5 visited.add(job); 6 TaskTrackerManager ttm = scheduler.taskTrackerManager; 7 ClusterStatus clusterStatus = ttm.getClusterStatus(); 8 int numTaskTrackers = clusterStatus.getTaskTrackers(); 9 10 // check with the load manager whether it is safe to 11 // launch this task on this taskTracker. 12 LoadManager loadMgr = scheduler.getLoadManager(); 13 if (!loadMgr.canLaunchTask(tts, job, taskType)) { 14 return null; 15 } 16 if (taskType == TaskType.MAP) { 17 //确定应该调度的级别 18 LocalityLevel localityLevel = scheduler.getAllowedLocalityLevel( 19 job, currentTime); 20 scheduler.getEventLog().log( 21 "ALLOWED_LOC_LEVEL", job.getJobID(), localityLevel); 22 switch (localityLevel) { 23 case NODE: 24 return job.obtainNewNodeLocalMapTask(tts, numTaskTrackers, 25 ttm.getNumberOfUniqueHosts()); 26 case RACK: 27 return job.obtainNewNodeOrRackLocalMapTask(tts, numTaskTrackers, 28 ttm.getNumberOfUniqueHosts()); 29 default: 30 return job.obtainNewMapTask(tts, numTaskTrackers, 31 ttm.getNumberOfUniqueHosts()); 32 } 33 } else { 34 return job.obtainNewReduceTask(tts, numTaskTrackers, 35 ttm.getNumberOfUniqueHosts()); 36 } 37 } else { 38 return null; 39 } 40 }
可以看到，在该方法中又会根据相应的级别调用JobInProgress类中的方法来获取该级别的MapTask。
备注五：最后updateLastMapLocalityLevel()方法会更新该job的一些信息：lastMapLocalityLevel设为该job对应的级别；timeWaitedForLocalMap置为0。
1 /** 2 * Update a job's locality level and locality wait variables given that that 3 * it has just launched a map task on a given task tracker. 4 */ 5 private void updateLastMapLocalityLevel(JobInProgress job, 6 Task mapTaskLaunched, TaskTrackerStatus tracker) { 7 JobInfo info = infos.get(job); 8 boolean isNodeGroupAware = conf.getBoolean( 9 "net.topology.nodegroup.aware", false); 10 LocalityLevel localityLevel = LocalityLevel.fromTask( 11 job, mapTaskLaunched, tracker, isNodeGroupAware); 12 info.lastMapLocalityLevel = localityLevel; 13 info.timeWaitedForLocalMap = 0; 14 eventLog.log("ASSIGNED_LOC_LEVEL", job.getJobID(), localityLevel); 15 }
本文基于hadoop1.2.1。如有错误，还请指正
参考文章： 《Hadoop技术内幕 深入理解MapReduce架构设计与实现原理》 董西成
https://issues.apache.org/jira/secure/attachment/12457515/fair_scheduler_design_doc.pdf
