Hadoop has evolved drastically from its initial version to the current one. Hadoop 1.0 and Hadoop 2.0 differ significantly in terms of their architecture, features, and components. Here are the major differences between Hadoop 1.0 and Hadoop 2.0:
1. Hadoop Core Components:
Hadoop 1.0:
- MapReduce: A programming model for processing large-scale data sets.
- HDFS (Hadoop Distributed File System): A distributed filesystem for storing data across multiple nodes.
Hadoop 2.0:
- YARN (Yet Another Resource Negotiator): A resource management layer that separates resource management from the MapReduce programming model.
- HDFS (Hadoop Distributed File System): Similar to Hadoop 1.0 but with added features like HDFS Federation and HDFS snapshots.
2. Cluster Resource Management:
Hadoop 1.0:
- JobTracker: A single point of failure, responsible for managing resources and scheduling tasks across the cluster.
- TaskTracker: Execute tasks and communicate with the JobTracker.
Hadoop 2.0:
- ResourceManager: Takes the role of JobTracker, focusing on resource management.
- ApplicationMaster: Manages the application’s lifecycle and negotiates resources with the ResourceManager.
- NodeManager: Executes tasks on nodes and monitors their resource usage, replacing the TaskTracker.
3. Scalability:
Hadoop 1.0:
- Limited to around 4,000 nodes per cluster.
- JobTracker bottleneck.
Hadoop 2.0:
- Improved scalability to tens of thousands of nodes per cluster.
- Decoupling of resource management and task scheduling allows for better scalability and performance.
4. High Availability:
Hadoop 1.0:
- Single point of failure: JobTracker.
- NameNode is a potential single point of failure; clients must rely on a secondary NameNode for passive standby.
Hadoop 2.0:
- ResourceManager and ApplicationMaster eliminate the Jobtracker single point of failure.
- Multiple NameNodes supported through HDFS Federation.
- Active-standby NameNode high availability with Quorum Journal Manager.
5. Data Processing Frameworks:
Hadoop 1.0:
- Supports only MapReduce for data processing.
Hadoop 2.0:
- Supports multiple data processing frameworks such as MapReduce, Spark, Tez, and others, making it more versatile in handling different types of workloads.
In summary, Hadoop 2.0 introduced YARN, which significantly improved scalability, resource management, high availability, and support for various data processing frameworks. This new architecture overcame many limitations and bottlenecks present in Hadoop 1.0.