Created 02-18-2016 01:19 PM
Hi,
What are advantages of YARN over MapReduce, why YARN was required instead of MapReduce?
Created on 02-18-2016 01:24 PM - edited 08-18-2019 06:18 AM
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Register/LoginCreated 02-18-2016 01:22 PM
@Rushikesh Deshmukh not the same thing, I suggest you read Arun's book for best explanation http://www.amazon.com/Apache-Hadoop-YARN-MapReduce-Processing/dp/B0108CTDB6%3FSubscriptionId%3DAKIAI...
Created 02-18-2016 01:25 PM
@Artem Ervits, thanks for suggestion and quick reply.
Created on 02-18-2016 01:24 PM - edited 08-18-2019 06:18 AM
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Register/LoginCreated 07-31-2017 06:56 PM
@Neeraj SabharwalCan reducers communicate with each other?
Created 11-17-2017 11:24 AM
Nope, reducers don't communicate with each other and neither the mappers do. All of them runs in a separate JVM containers and don't have information of each other. AppMaster is the demon which takes care and manage these JVM based containers (Mapper/Reducer).
Created 02-18-2016 01:31 PM
Yarn is a work scheduler that can run different types of workloads.
- Spark
- MapReduce2
- Storm
- Tez
...
While MapReduce is a core feature and most likely the majority of the workloads its not the only one anymore. Hive/Pig uses Tez and Spark and Storm are big as well. This is the biggest advantage.
Other advantages include better scalability ( local nodemanagers instead of a single bottleneck ) lots of convenience features etc. pp.
Created 02-20-2016 12:58 PM
@Benjamin Leonhardi, thanks for sharing this useful information.
Created 07-11-2016 11:09 AM
YARN has many advantages over MapReduce (MRv1).
1) Scalability - Decreasing the load on the Resource Manager(RM) by delegating the work of handling the tasks running on slaves to application Master, RM can now handle more requests than Job tracker facilitating addition of more nodes.
2) Unlike MPv1 which is strongly coupled with the MapReduce , YARN supports many kinds of code running on them like MR2,Tez, Storm, Spark etc
3) Optimized resource allocation - There are no fixed number of slots separately allocated for Mapper and Reducers in YARN, which is the case in MRv1. So the available capacity of the nodes can be used to any task which needs resources.
4) When Resource manager fails , the jobs running on the cluster need not be restarted again after the recovery of Resource Manager.
5) Failover mechanism is implemented by ZK which is already part of Resource manager which says, we don't need to run another deamon.
Created 02-01-2017 10:14 AM
This is YARN framework which is responsible for doing Cluster Resource Management.
Cluster resource management means managing the resources of the Hadoop Clusters. And by resources we mean Memory, CPU etc. YARN took over this task of cluster management from MapReduce and MapReduce is streamlined to perform Data Processing only in which it is best.
YARN has central resource manager component which manages resources and allocates the resources to the application. Multiple applications can run on Hadoop via YARN and all application could share common resource management.
Advantage of YARN:
Few Important Notes about YARN:
Central Resource Manager and node specific Node Manager together constitutes YARN.