Description: The YARN Resource Manager (Yet Another Resource Negotiator) is the master daemon responsible for managing resources and scheduling applications in a YARN cluster. Its primary function is to allocate resources efficiently to the various applications running in the cluster, thereby optimizing the use of the available infrastructure. YARN allows multiple applications to run simultaneously, improving scalability and flexibility in data processing. This manager consists of two key components: the ResourceManager, which handles global resource management, and the NodeManager, which operates on each node of the cluster and monitors resource usage on that specific node. YARN is fundamental to the Hadoop ecosystem as it enables the execution of different types of workloads, from batch processing to real-time processing, facilitating the integration of various data analysis tools and frameworks. Its modular architecture and ability to handle multiple processing frameworks make it a versatile and powerful solution for resource management in Big Data environments.
History: YARN was first introduced in 2012 as part of Hadoop version 2.0, aiming to overcome the limitations of the original MapReduce programming model. Before YARN, Hadoop could only run MapReduce jobs, which limited its ability to handle other types of data processing. The introduction of YARN allowed developers to create more complex and diverse applications, significantly expanding the Hadoop ecosystem.
Uses: YARN is primarily used in Big Data environments to manage and schedule resource-intensive applications. It allows for the simultaneous execution of multiple applications, which is crucial for companies that need to process large volumes of data in real-time. Additionally, YARN is compatible with various processing frameworks, making it ideal for environments where tools for data processing are used.
Examples: A practical example of YARN usage is in a data analytics company that uses Apache Spark to perform real-time analysis on large datasets. YARN manages the cluster resources, ensuring that Spark has access to the necessary memory and CPU to execute its tasks efficiently. Another example is the use of YARN in a batch processing environment, where MapReduce jobs are run to process historical data stored in distributed storage systems.
