Q12Big Data Analytics
Question
4 marks
What are the key differences between Hadoop 1.x and Hadoop 2.x?
Answer
A critical architectural comparison between Hadoop 1.x and Hadoop 2.x. Details the catastrophic bottleneck of the monolithic JobTracker and how the invention of YARN violently decentralized cluster resource management, enabling real-time processing beyond MapReduce.
Hadoop 1.x was a pioneering architecture, but it contained a catastrophic mathematical flaw that strictly limited its scaling to 4,000 nodes. Hadoop 2.x completely ripped out the core resource management engine and replaced it with a massively decentralized architecture called YARN (Yet Another Resource Negotiator).
- Monolithic JobTracker: In 1.x, exactly ONE massive server (the JobTracker) was mathematically responsible for EVERYTHING. It had to schedule 100,000 MapReduce tasks AND track the exact CPU/RAM usage of 4,000 worker nodes. It violently bottlenecked the entire cluster.
- Single Point of Failure (SPOF): If the JobTracker hardware caught fire, the ENTIRE multi-million dollar cluster instantly died. There was zero High Availability.
- MapReduce Only: The cluster was mathematically hardcoded to ONLY execute batch MapReduce jobs. You could not run real-time streaming (like Spark) or graph processing on the same servers.
Hadoop 2.x violently separated Resource Management from Job Scheduling by creating YARN.
- ResourceManager & ApplicationMaster: The central ResourceManager now ONLY handles CPU/RAM allocation. When a user submits a job, a dedicated, temporary ApplicationMaster is violently booted up just for that specific job to handle task tracking. This mathematically removes the bottleneck, allowing the cluster to scale past 10,000 nodes.
- High Availability (HA): Hadoop 2.x introduced multiple NameNodes. If the active master server dies, Zookeeper mathematically detects it in milliseconds and violently fails over to a Standby Master with zero data loss.
- Multi-Paradigm Processing: YARN acts as a Cloud Operating System. The exact same Hadoop cluster can now simultaneously run MapReduce (batch), Apache Spark (in-memory), and Apache Storm (real-time streaming), violently maximizing hardware ROI.