Hadoop 2.0

Hadoop 2.0, also known as Hadoop 2.x, was a significant evolution of the Apache Hadoop ecosystem compared to the earlier Hadoop 1.x versions. It introduced several key improvements and features, addressing limitations found in the initial Hadoop releases. Here are some of the notable changes and features introduced in Hadoop 2.0:

1. YARN (Yet Another Resource Negotiator):

   • The most significant change in Hadoop 2.0 was the introduction of YARN, a resource management and job scheduling framework.
   • YARN decouples the resource management and job scheduling aspects of Hadoop, making it more flexible and accommodating diverse processing frameworks beyond MapReduce.
   • With YARN, Hadoop clusters can run multiple applications concurrently, such as MapReduce, Apache Spark, Apache Flink, and more, each with its resource requirements.

2. Resource Management:

   • YARN provides a more robust and flexible resource management system compared to the fixed slots model used in Hadoop 1.x.
   • It allows better utilization of cluster resources by dynamically allocating containers (CPU and memory) to applications based on their requirements.

3. High Availability for HDFS:

   • Hadoop 2.0 introduced High Availability (HA) for the Hadoop Distributed File System (HDFS). This ensures that the NameNode, a critical component, is fault-tolerant and can failover to another node seamlessly.

4. HDFS Federation:

   • HDFS Federation was introduced to improve the scalability of HDFS. It allows multiple independent namespaces (namespaces are divided into separate directories) within a single HDFS cluster.
   • Each namespace has its own namespace ID and block pool, allowing for more efficient storage management.

5. Compatibility with Hadoop 1.x:

   • Hadoop 2.0 maintained backward compatibility with Hadoop 1.x, enabling a smooth transition for organizations already using Hadoop 1.x.

6. Improved Scalability:

   • Hadoop 2.0 was designed to scale efficiently by addressing the limitations in Hadoop 1.x that could hinder cluster growth.

7. Additional Ecosystem Projects:

   • With YARN’s flexibility, the Hadoop ecosystem expanded to include various processing frameworks like Apache Spark, Apache Tez, and Apache Flink, which could run alongside MapReduce.

8. Resource Scheduling:

   • YARN introduced advanced resource scheduling capabilities, allowing different applications to share cluster resources effectively.

9. Security Enhancements:

   • Hadoop 2.0 also included improvements in security, with features such as Hadoop Secure Mode, which enhances cluster security through user authentication and authorization.

10. Stability and Performance:

    • Overall, Hadoop 2.0 aimed to improve cluster stability, performance, and support for more diverse workloads compared to its predecessor.