How Does Hadoop Work?
Stage 1
A user/application can submit a job to the Hadoop (a hadoop job client) for required process by specifying the following items:
- The location of the input and output files in the distributed file system.
- The java classes in the form of jar file containing the implementation of map and reduce functions.
- The job configuration by setting different parameters specific to the job.
Stage 2
The Hadoop job client then submits the job (jar/executable etc) and configuration to the JobTracker which then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.
Stage 3
The TaskTrackers on different nodes execute the task as per MapReduce implementation and output of the reduce function is stored into the output files on the file system.
Advantages of Hadoop
- Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
- Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer.
- Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
- Another big advantage of Hadoop is that apart from being open source, it is compatible on all the platforms since it is Java based.
Posts
