Process Flow of Map Reduce and Map Reduce Life Cycle

Map Reduce is heart of Hadoop Process and it is meant for processing huge data. We can see process flow and Life Cycle of Map Reduce.

Phase	Input	Output
Mapper	Key, Value	Key, Value
Sort & Shuffle	Key, Value	Key, List(Values)
Reducer	Key, List(Values)	Key, Value

Map Reduce Life Cycle:

Job Tracker and Task Tracker are two daemons which are primarily responsible for Map Reduce Job Execution. Below are the steps that taken place during the Map Reduce process.

When the client submits input which is in the form of .jar file (driver code, mapper code and reducer code) will be received by Job Tracker always.
By taking mapper business logic from the jar file, Job Tracker will initiate mapper phase on all the available Task Trackers.
Once the assigned Task Trackers are done with mapper phase completely (100%), they will send status to the Job Tracker.
Upon the completion of the mapper task (100%), Job Tracker will initiate Sort & Shuffle phase on the mapper outputs.
Once Sort & Shuffle is completely done (100%), Job Tracker will initiate reducer phase on all the Task Trackers by taking the business logic from the Jar file
Once all the assigned Task Trackers done with reducer processing (100%), they will respond back Job Tracker with their outputs. Job Tracker will consolidate the final output and sends the report the client.

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