Hadoop YARN (Yet Another Resource Negotiator)
In Hadoop 2. x and subsequent versions, YARN is a framework for resource management and task scheduling. It enables the use of several data processing engines on top of the same Hadoop cluster by separating the MapReduce functions for resource management and task scheduling. Batch processing, interactive querying, and stream processing are just a few of the workload types that YARN supports and make resource utilization more effective.
Important Features of YARN
By allowing all the internal Hadoop clusters to manage data processing workloads outside of MapReduce, YARN increases the efficacy and possibility of the Hadoop ecosystem.
- Resource Management: YARN efficiently distributes and manages cluster resources, like as CPU and memory, to the cluster’s operating applications. It guarantees optimal use by enabling dynamic resource sharing across many applications.
- Scalability: To manage increasing data quantities and processing requirements, Hadoop clusters may be horizontally expanded by adding more nodes thanks to YARN.
- Cluster Utilization: The cluster can be used more effectively with YARN by running multiple workloads concurrently, including MapReduce, Apache Spark, Apache Hive, Apache HBase, and others.
- Flexibility: YARN is suitable for a range of data processing tasks and workloads outside of MapReduce, including real-time processing and interactive queries, because it supports several processing engines.
Hadoop : Components, Functionality, and Challenges in Big Data
The technical explosion of data from digital media has led to the proliferation of modern Big Data technologies worldwide in the system. An open-source framework called Hadoop has emerged as a leading real-world solution for the distributed storage and processing of big data. Nevertheless, Apache Hadoop was the first to demonstrate this wave of innovation. In the era of big data processing, businesses across various industries need to manage and analyze internal large volumes of data efficiently and strategically.
In this article, we’ll explore the significance and overview of Hadoop and its components step-by-step.