Introduction to Map Reduce It's a massive parallel processing framework In general without distributed data it's hard to do parallel processing. Map Reduce uses Java by default The aim of Map Reduce is to achieve data locality and Parallelism. Spark is an alternative for Map Reduce(functionality has 80% similarity) Hive, Sqoop, Pig, Oozie are abstract of Map Reduce. Daemons in Map Reduce are JT and TT. In map reduce we also have two functionalities called Mapper and Reducer which are responsible for achieving the processing. The MR has an disadvantage of Resource Pressure. Advantages of Map Reduce Cluster Monitoring Cluster Management Resource Allocating Scheduling Execution Speculative Execution(L) Working Principle of Map Reduce The task for the MR is given in the form of ZAR(java) file to the Job Tracker. The Job Tracker will be sending the request to the Name Node and in return it gives response. Then the Job Tracker will be sending the task information to Task Tracker. The...
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