What is Hadoop 2? Why Hadoop 2? Advantages of MapReduce | Limitation of MapReduce

Why Hadoop 1.x failure?

In Hadoop 1.x, NameNode was a single point of failure. NameNode failure makes the Hadoop cluster inaccessible. In this version, Hadoop Admin has more manually worked on the Namendoe using Secondary NameNode.

Hadoop 1 is a built for web-scale batch apps for Single application into HDFS

What is Hadoop 2?

Basically, Hadoop 2 is the second version of the Apache Hadoop framework for storage and large data processing. It supports for running non-batch applications through YARN, and cluster redesigned with the resource manager. After Hadoop 1.x version Apache includes new features to improve systems like Availablity and scalability

Built for web-scale batch applications in Hadoop Distributed File System (HDFS)

Why Hadoop 2 overcomes Hadoop 1.x?

Below four improvements in Hadoop 2.x version over Hadoop 1.x

  • Hadoop Distributed File System Federation (HDFS F): Horizontal scalability of Name Node in Hadoop Cluster
  • Yet Another Resource Neogotiaite (YARN): It is the ability to process TB and PB of data available in the Hadoop Distributed File System.
  • Name Node HA: Name Node High availability is no longer a single point fo failure
  • RM: Resource Manager is split up into two functionalities. One is Job Tracker and another one is NodeManger ( Application Master + Task Tracker)

MapReduce is good for below-built points:

  • Parallel algorithms – Some of the Bit-level algorithms
  • Summing, grouping, filtering, joining operations
  • Offline batch jobs on large file data including video-related data
  • Analyzing an entire large data sets with a proper file system

MapReduce is OK for below bullet points:

  • Iterative jobs like algorithms including datastore point of view
  • Each iteration must read/write data for users in the Hadoop cluster
  • I/O (Input/Output) and computing cost of an iteration is high

MapReduce is not good for the below points:

  • MapReduce Jobs that need to be shared state/coordination
  • The shared state requires scalable state store
  • Low computing jobs in the Hadoop cluster
  • Jobs on small datasets
  • Finding discrete records

MapReduce Limitations:


  • Maximum cluster sizeĀ  around 4,500 nodes within the Hadoop cluster
  • Maximum concurrent task 40K in Hadoop 2.x


  • FailureĀ  kills all queued and running jobs

Lacks support for alternate paradigms and services:

  • Iterative applications implemented using MapReduce are 10X slower
  • Hard partitions of resources into map and reduce slots
  • Low resource utilization in the MapReduce.