What is Hadoop, and why should I care?

Hadoop is an open-source processing framework that manages data processing and storage for big data applications running on pooled systems.

Apache Hadoop is a collection of open-source utility software that makes it easy to use a network of multiple computers to solve problems involving large amounts of data and computation. It provides a software framework for distributed storage and big data processing using the MapReduce programming model.

Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packets of code to nodes to process the data in parallel. This allows the data set to be processed faster and more efficiently than if conventional supercomputing architecture were used.

Hadoop MapReduce Counter provides a way to measure the progress or the number of operations that occur within MapReduce programs. Basically, MapReduce framework provides a number of built-in counters to measure basic I/O operations, such as FILE_BYTES_READ/WRITTEN and Map/Combine/Reduce input/output records. These counters are very useful especially when you evaluate some MapReduce programs. Besides, the MapReduce Counter allows users to employ your own counters. Since MapReduce Counters are automatically aggregated over Map and Reduce phases, it is one of the easiest way to investigate internal behaviours of MapReduce programs. In this post, Iā€™m going to introduce how to use your own MapReduce Counter.

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