How Distributed Cache Works
When a job is executed, the Hadoop system first copies the required files to the cache on each node at the start of the job. These files are then available locally on the nodes where the tasks execute, which significantly speeds up their performance since the files do not have to be fetched from a central server each time they are needed.
Files in the Distributed Cache can be broadly categorized into three types:
- Regular Files: These could be data files or configuration files needed by the job.
- Archive Files: These are compressed files such as tar or zip files, which Hadoop automatically decompresses locally on the nodes.
- JAR Files: Libraries required by the job to process data.
To read more please read this article – Distributed Cache in Hadoop MapReduce
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.