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Understanding Map Reduce Job Running on a Distributed Hadoop Cluster

Job Tracker:

The Job Tracker is a component of the Hadoop MapReduce framework that runs on the master node (typically the NameNode in HDFS) of the Hadoop cluster.
Its primary function is to accept and manage MapReduce jobs submitted by users.
When a job is submitted, the Job Tracker breaks it down into smaller tasks, known as map and reduce tasks, and schedules these tasks to run on the cluster nodes.
The Job Tracker keeps track of the progress of all tasks, monitors task failures, and reschedules failed tasks.
It also handles job optimization, attempting to schedule tasks on nodes where data is already stored (to minimize data transfer) and ensuring efficient resource allocation.
In Hadoop 2.x and later, the Job Tracker has been replaced by the ResourceManager and the ApplicationMaster in YARN (Yet Another Resource Negotiator) for more advanced resource management and scheduling.
Task Tracker:

Task Trackers run on each of the worker nodes in a Hadoop cluster.
They are responsible for executing the map and reduce tasks assigned to them by the Job Tracker.
The Task Tracker reports task progress and status back to the Job Tracker.
Task Trackers also monitor the health of their respective nodes and report hardware or software failures to the Job Tracker.
The combination of Job Tracker and Task Tracker enables the parallel processing of data in a Hadoop cluster. When a MapReduce job is submitted, the Job Tracker divides it into smaller units of work, assigns these tasks to available Task Trackers, and ensures that they run in parallel across multiple nodes. This parallelism allows Hadoop to process large datasets efficiently and in a distributed manner.

It's worth noting that, as of my last knowledge update in September 2021, Hadoop's ecosystem has been evolving rapidly, with developments like YARN and advancements in cluster resource management. Be sure to check for the latest updates and changes in Hadoop's architecture and components for a more accurate picture of how parallel processing is achieved in Hadoop clusters.