Hadoop Distributed Computing Explained

Hadoop is an open-source distributed computing framework used primarily for storing and processing large-scale datasets, allowing for efficient distributed computing tasks.

The core components of Hadoop are HDFS (Hadoop Distributed File System) and YARN (Yet Another Resource Negotiator). HDFS is responsible for storing data by distributing it across multiple nodes in a cluster to achieve high reliability and throughput. YARN manages resources and schedules tasks by assigning them to different nodes in the cluster for parallel processing.

In Hadoop, users can achieve distributed computing tasks by writing MapReduce programs. These programs consist of two parts: the Map function and the Reduce function. The Map function processes input data according to specified rules and outputs intermediate results, while the Reduce function combines and processes intermediate results with the same key to obtain the final calculation result.

Hadoop also supports other computing models like Spark and Hive, allowing users to choose the appropriate computing model for their distributed computing tasks.

In general, Hadoop achieves efficient distributed computing tasks by storing and parallel processing large-scale datasets. Users can utilize various tools and interfaces provided by Hadoop to carry out complex data processing and analysis tasks.