Apache Hadoop Architecture Explained: Components, Differences, and Use Cases

What is Apache Hadoop?

Apache Hadoop is an open-source framework designed to store and process large datasets across clusters of computers. It uses a distributed file system called HDFS (Hadoop Distributed File System) and a processing engine called MapReduce. Hadoop is widely used in big data applications due to its scalability and fault tolerance.

Components of Hadoop 1.0 Architecture

Hadoop 1.0 consists of the following key components:

1. Name Node

• The Name Node is the master node in HDFS.
• It manages the file system metadata, including file locations, block information, and replication details.
• It communicates directly with clients and ensures data integrity.

2. Secondary Name Node

• The Secondary Name Node acts as a helper to the Name Node.
• It creates checkpoints of the file system metadata to prevent data loss.
• It compacts the fsimage and editlog files for efficient storage.

3. Data Node

• Data Nodes are slave nodes that store actual data in HDFS.
• They send heartbeat signals to the Name Node every 3 seconds to confirm their status.
• If a Data Node fails, the Name Node replicates its data to other nodes.

4. Job Tracker

• The Job Tracker manages MapReduce jobs.
• It communicates with the Name Node to locate data for processing.
• It assigns tasks to Task Trackers and monitors their progress.

5. Task Tracker

• Task Trackers are slave nodes that execute tasks assigned by the Job Tracker.
• They apply the MapReduce code to the data and return results to the Job Tracker.

Components of Hadoop 2.0 Architecture

Hadoop 2.0 introduced YARN (Yet Another Resource Negotiator) for resource management. Its components include:

1. Name Node

• Similar to Hadoop 1.0, the Name Node manages HDFS metadata.

2. Secondary Name Node

• Performs the same checkpointing functions as in Hadoop 1.0.

3. Data Node

• Stores data blocks and communicates with the Name Node.

4. Resource Manager

• The Resource Manager is the central authority in YARN.
• It allocates resources (CPU, memory) to applications.

5. Node Manager

• Node Managers run on each node and manage resources for individual containers.
• They report resource usage to the Resource Manager.

Differences Between Hadoop 1.0 and 2.0

Use Cases of Hadoop

Hadoop is used in various industries for:

• Data Warehousing: Storing and analyzing large datasets.
• Log Processing: Analyzing server logs for insights.
• Machine Learning: Training models on big data.
• Recommendation Systems: Powering personalized recommendations.

Conclusion

Apache Hadoop is a versatile framework for handling big data. Its architecture, consisting of components like the Name Node, Data Node, and YARN, enables efficient data storage and processing. Whether you’re working with Hadoop 1.0 or 2.0, understanding its architecture is key to leveraging its full potential.

Ready to dive deeper into Hadoop? Download Hadoop from the official Apache website or explore distributions like Cloudera’s CDH. For more tutorials, visit W3Schools or Hadoop Documentation.