Hadoop 2.0 represents a significant improvement over Hadoop 1.0 in several aspects, addressing limitations in the 1.0 version. I will outline several use cases for using Hadoop 2.0 over Hadoop 1.0, along with the key features making it more suitable:
1. Scalability: Hadoop 1.0 had a scalability cap, limiting it to a few thousand nodes per cluster. Hadoop 2.0 overcomes this limitation through the YARN (Yet Another Resource Negotiator) framework, which allows tens of thousands of nodes in a single cluster. Consequently, Hadoop 2.0 is better suited to handle larger datasets and clusters for expanding organizations.
2. Multi-tenancy: Hadoop 1.0 only supported the MapReduce paradigm for processing data. However, Hadoop 2.0, thanks to YARN, supports various processing frameworks (including MapReduce), making it possible to execute non-MapReduce applications like Apache Spark, Apache Tez, and Apache Storm – allowing organizations to run multiple applications simultaneously on a single platform, enabling cost savings and ease of administration.
3. Improved Resource Management: Hadoop 2.0 introduces YARN, which separates resource management concerns from the MapReduce application. YARN manages resources (CPU, memory) across the cluster in a more efficient manner and allows multiple applications to run on the same shared platform, thus improving cluster utilization and reducing resource contention.
4. High Availability (HA): In Hadoop 1.0, there was a single point of failure, the NameNode, which if down, brought down the entire HDFS cluster. Hadoop 2.0 introduced the High Availability (HA) feature that allows an active and a standby NameNode for automatic failover, making the Hadoop cluster more reliable and resilient against potential hardware or software failure.
5. Data Storage Improvements: Hadoop 2.0 introduced the concept of federation, allowing multiple independent NameNodes to manage separate namespaces. This significantly increases the overall namespace storage capacity and improves cluster performance and scalability.
In conclusion, Hadoop 2.0 is better suited for organizations that require:
- Handling large datasets and clusters with an increased number of nodes.
- Support for multiple processing frameworks, not only MapReduce.
- Improved resource management and cluster utilization.
- High availability for avoiding single points of failure.
- Increased namespace storage capacity and improved performance.
Organizations with use-cases that may leverage these features should prefer using Hadoop 2.0 over Hadoop 1.0.