@Rushikesh Deshmukh Yarn provides the true multi tenancy. It lets to run multiple jobs at the same time. Yarn is the data operating system The overall architecture is different. YARN MapReduce Another link for you Source 1 2 "You say "Differences between MapReduce and YARN". MapReduce and YARN definitely different. MapReduce is Programming Model, YARN is architecture for distribution cluster. Hadoop 2 using YARN for resource management. Besides that, hadoop support programming model which support parallel processing that we known as MapReduce. Before hadoop 2, hadoop already support MapReduce. In short, MapReduce run above YARN Architecture. Sorry, i don't mention in part of straggler problem. "when MRmaster asks resource manger for resources?" when user submit MapReduce Job. After MapReduce job has done, resource will be back to free. "resource manger will give MRmaster all resources it needs or it is according to cluster computing capabilities" I don't get this question point. Obviously, the resources manager will give all resource it needs no matter what cluster computing capabilities. Cluster computing capabilities will influence on processing time." and MRv1 uses the JobTracker to create and assign tasks to data nodes, which can become a resource bottleneck when the cluster scales out far enough (usually around 4,000 clusters). MRv2 (aka YARN, "Yet Another Resource Negotiator") has a Resource Manager for each cluster, and each data node runs a Node Manager. For each job, one slave node will act as the Application Master, monitoring resources/tasks, etc.
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