Understanding Distributed Cache in Hadoop
Apache Hadoop is primarily known for its two core components: the Hadoop Distributed File System (HDFS) and the MapReduce programming model. While these components handle data storage and processing respectively, Distributed Cache complements these processes by enhancing the efficiency and speed of data access across the nodes in a Hadoop cluster.
Distributed Cache is a facility provided by the Hadoop framework to cache files (text, archives, or jars) needed by applications. Once a file is cached for a particular job, Hadoop makes this file available on each data node where the map/reduce tasks are running, thereby reducing the need to access the file system repeatedly.
What is the importance of Distributed Cache in Apache Hadoop?
In the world of big data, Apache Hadoop has emerged as a cornerstone technology, providing robust frameworks for the storage and processing of vast amounts of data. Among its many features, the Distributed Cache is a critical yet often underrated component. This article delves into the essence of Distributed Cache, its operational mechanisms, key benefits, and practical applications within the Hadoop ecosystem.