What are the major differences between Hadoop 1 and Hadoop 2?

As an experienced tutor registered on UrbanPro.com specializing in Hadoop training, I can provide insights into the major differences between Hadoop 1 and Hadoop 2. Understanding these distinctions is crucial for anyone seeking comprehensive knowledge in the field of Big Data.

Hadoop 1 Overview:

• Architecture:

  • Hadoop 1 follows a simple architecture with a single master node called the JobTracker and multiple slave nodes known as TaskTrackers.

• Resource Management:

  • JobTracker is responsible for both job scheduling and resource management, which can lead to a bottleneck in large-scale deployments.

• Scalability:

  • Hadoop 1 has limitations in terms of scalability, especially when dealing with a growing volume of data and increasing workloads.

• High Availability:

  • High availability is a challenge in Hadoop 1 as there is a single point of failure with the JobTracker.

Hadoop 2 Enhancements:

• YARN (Yet Another Resource Negotiator):

  • Hadoop 2 introduces YARN, a significant enhancement that separates the resource management and job scheduling functions. This allows for better scalability and flexibility.

• Resource Management:

  • With YARN, resource management is decentralized. It includes ResourceManager for resource allocation and NodeManager for managing resources on individual nodes.

• Multiple Applications:

  • Hadoop 2 supports the concurrent execution of multiple applications, making it more versatile and suitable for diverse workloads.

• Scalability Improvements:

  • YARN's distributed architecture addresses scalability issues, enabling Hadoop 2 to handle larger datasets and more complex processing tasks.

• High Availability with ResourceManager:

  • Hadoop 2 provides improved high availability with ResourceManager, which can be configured in a highly available mode, reducing the risk of a single point of failure.

Performance Comparison:

• MapReduce Performance:

  • Hadoop 2 demonstrates better MapReduce performance due to the decoupling of resource management, allowing for more efficient task execution.

• Job Scheduling:

  • Hadoop 2's YARN allows for more dynamic and adaptive job scheduling, optimizing resource utilization and improving overall system efficiency.

Conclusion:

In conclusion, the transition from Hadoop 1 to Hadoop 2 represents a significant evolution in the capabilities of the Hadoop framework. The introduction of YARN addresses key limitations, enhancing scalability, resource management, and high availability. As a tutor offering Hadoop online coaching, it's essential to impart this knowledge to students for a comprehensive understanding of Big Data processing in modern Hadoop environments.