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Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage. Hadoop Architecture. Hadoop framework includes following four modules: Hadoop Common: These are Java libraries and utilities required by other Hadoop modules. These libraries provide filesystem and OS level abstractions and contain the necessary Java files and scripts required to start Hadoop. Hadoop YARN: This is a framework for job scheduling and cluster resource management. Hadoop Distributed File System (HDFS. The reduce task is always performed after the map task. Typically both the input and the output are stored in a file- system. The framework takes care of scheduling tasks, monitoring them and re- executes the failed tasks. The Map. Reduce framework consists of a single master Job. Tracker and one slave Task. Tracker per cluster node. The master is responsible for resource management, tracking resource consumption/availability and scheduling the jobs component tasks on the slaves, monitoring them and re- executing the failed tasks. The slaves Task. Tracker execute the tasks as directed by the master and provide task status information to the master periodically. The Job. Tracker is a single point of failure for the Hadoop Map. Reduce service which means if Job. Tracker goes down, all running jobs are halted. Hadoop Distributed File System. Hadoop can work directly with any mountable distributed file system such as Local FS, HFTP FS, S3 FS, and others, but the most common file system used by Hadoop is the Hadoop Distributed File System (HDFS). The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on large clusters (thousands of computers) of small computer machines in a reliable, fault- tolerant manner. 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