JobTracker and TaskTracker

Two types of nodes that control the job of job execution ptrocess: A Jobtracker and TaskTracker These two terms are very important and you will see them countless times.So let us understand them one by one.

JobTracker

It is a node in cluster which client applications submit MapReduce jobs. It coordinates all the jobs run on the system by scheduling tasks to run on TaskTrackers.If a task fails, the JobTracker can reschedule it on a different Taskracker. In other words JobTracker pushes work out to available TaskTracker nodes in the cluster, trying to keep the work as close to the data as possible. With a rack-aware file system( which is the essence of HDFS. For more info Click Here ), the JobTracker knows which node contains the data, and which other machines are nearby.

If the work cannot be hosted on the actual node where the data resides, priority is given to nodes in the same rack.The TaskTracker on each node creates a separate Java Virtual Machine process to prevent the TaskTracker itself from failing if the running job crashes the JVM. A heartbeat is sent from the TaskTracker to the JobTracker every few minutes to check its status.

TaskTracker

A TaskTracker is a node in the cluster that accepts tasks - Map, Reduce and Shuffle operations - from a JobTracker. It runs tasks and send progress reports to the JobTracker,which keeps a record the overall progress of each job. Every TaskTracker is configured with a set of slots, these indicate the number of tasks that it can accept. As explained in JobTracker above, when the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.

The TaskTracker creates a separate JVM processes to do the actual work; this is to ensure that process failure does not take down the task tracker. The TaskTracker monitors these created processes, capturing the output and exit codes. When the process finishes, successfully or not, the tracker notifies the JobTracker. The TaskTrackers also send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated.

JobTracker and TaskTracker: Complete working

The JobTracker is the service within Hadoop that farms out MapReduce tasks to specific nodes in the cluster, ideally the nodes that have the data, or at least are in the same rack.The overall processing of JobTracker and TaskTracker can be explained in the following steps.

1. Client applications submit jobs to the Job tracker. 2.The JobTracker talks to the NameNode to determine the location of the data
2. The JobTracker locates TaskTracker nodes with available slots at or near the data
3. The JobTracker submits the work to the chosen TaskTracker nodes.
4. The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker.
5. A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable.
6. When the work is completed, the JobTracker updates its status.
7. Client applications can poll the JobTracker for information. The JobTracker is a point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted.