Learn The 10 Best Difference Between MapReduce vs Yarn

Difference Between Map Reduce And Yarn

Yarn stands for Yet Another Resource Negotiator, it is the new framework to manage resources (Memory and CPU). It helps us in developing the distributed application of any kind, it provides us necessary daemons and APIs. Another important feature of YARN is, it handles and schedules resource request from the application and help the process to execute the request. YARN is a generic platform to run any distributed application, Map Reduce version 2 is the distributed application which runs on top of YARN, Whereas map reduce is processing unit of Hadoop component, it process data in parallel in the distributed environment. So basically map-reduce work on huge data component, it processes the data and store in HDFS in such a way that retrieval is easier than traditional storage.

Head to Head comparison Between MapReduce vs Yarn (Infographics)

Below is the Top 10 Comparison between the MapReduce vs Yarn

Key Difference Between MapReduce vs Yarn

1. In Hadoop 1  it has two components first one is HDFS (Hadoop Distributed File System) and second is Map Reduce. Whereas in Hadoop 2 it has also two component HDFS and YARN/MRv2 (we usually called YARN as Map reduce version 2).
2. In Map Reduce, when Map-reduce stops working then automatically all his slave node will stop working this is the one scenario where job execution can interrupt and it is called a single point of failure. YARN overcomes this issue because of its architecture, YARN has the concept of Active name node as well as standby name node. When active node stop working for some time passive node starts working as active node and continue the execution.
3. Map reduce has single master and multiple slave architecture, If master-slave goes down then entire slave will stop working this is the single point of failure in HADOOP1, whereas HADOOP2 which is based on YARN architecture it has the concept of multiple master and slave, if one master goes down then another master will resume its process and continue the execution.
4. As we can see in below diagram, the difference in both Ecosystems HADOOP1 and HADOOP2. Component wise YARN Resource Management interacts with Map-reduce and HDFS.

So basically YARN is responsible for resource management means which job will be executed by which system get decide by YARN, whereas map reduce is programming framework which is responsible for how to execute a particular job, so basically map-reduce has two component mapper and reducer for execution of a program.

5. In Map reduce each data node run individually whereas in Yarn each data node runs by a node manager.
6. Map reduce uses Job tracker to create and assign a task to task tracker due to data the management of the resource is not impressive resulting as some of the data nodes will keep idle and is of no use, whereas in YARN 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.

MapReduce vs Yarn Comparison Table

Conclusion –  MapReduce vs Yarn

In Hadoop 1 which is based on Map Reduce have several issues which overcome in Hadoop 2 with Yarn. Like in Hadoop 1 job tracker is responsible for resource management but YARN has the concept of resource manager as well as node manager which will take of resource management. Map reduce has a single point of failure i.e. Job tracker, if job tracker stop working then we have to restart our entire cluster and executes our job again from Initial. In a real scenario, none of the organization don’t want to take this kind of risk, especially in a bank defense sector. Such organization which works on streamline data will not ready to take this kind of risk. For the sake of few minutes, they are going to lose their data and may have some critical business impact. So YARN has a better result over Map-reduce.

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