Hadoop Distributed Computing

Hadoop is a powerful framework for distributed computing, designed to process and analyze large volumes of data across a cluster of commodity hardware. It is widely used in the field of big data analytics and is known for its scalability, fault tolerance, and ability to handle data-intensive workloads. Here’s an overview of Hadoop’s distributed computing model:

1. Distributed Storage:

• Hadoop Distributed File System (HDFS) is the primary storage component of Hadoop. It is designed to store large files across a distributed cluster of machines. Data is divided into blocks, typically 128 MB or 256 MB in size, and replicated across multiple nodes for fault tolerance.
• HDFS provides a reliable and fault-tolerant storage infrastructure that can handle petabytes of data.

2. Distributed Processing:

• Hadoop employs the MapReduce programming model for distributed processing. It allows you to write programs that process data in parallel across the nodes of the cluster.
• The MapReduce model consists of two main phases: the Map phase and the Reduce phase. In the Map phase, data is filtered, transformed, and sorted in parallel. In the Reduce phase, the processed data is aggregated and further analyzed.
• Hadoop automatically manages the distribution of tasks, data shuffling, and fault recovery during the processing.

3. Master-Slave Architecture:

• Hadoop follows a master-slave architecture. The key components include the NameNode (master) and DataNodes (slaves) for HDFS, and the ResourceManager (master) and NodeManagers (slaves) for resource management and job execution.
• The master nodes are responsible for coordination, metadata management, and job scheduling, while the slave nodes perform data storage and execution of tasks.

4. Data Locality:

• Hadoop strives to optimize data locality, which means that processing tasks are scheduled on nodes where the data is stored. This minimizes data transfer over the network and improves overall performance.

5. Fault Tolerance:

• Hadoop provides built-in fault tolerance. If a node or task fails during processing, Hadoop automatically reroutes the task to another available node. Data replication in HDFS also ensures data recovery in case of node failures.

6. Scalability:

• Hadoop clusters can scale horizontally by adding more commodity hardware nodes to accommodate increasing data volumes and processing requirements.

7. Ecosystem:

• Hadoop has a rich ecosystem of tools and libraries, including Hive for SQL-like querying, Pig for data processing, HBase for NoSQL databases, Spark for in-memory processing, and more.

8. Batch Processing and Beyond:

• While Hadoop is known for batch processing using MapReduce, it has evolved to support various processing models, including real-time stream processing, interactive querying, and machine learning through technologies like Apache Spark and Apache Flink.