YARN (Yet Another Resource Negotiator) is a significant improvement over classic MapReduce in Hadoop. It was introduced in Hadoop 2.x as a resource management layer to allow multiple data processing engines, like Spark, Flink, and Tez, to run in parallel on the same cluster. YARN decouples resource management and scheduling from the MapReduce framework, thereby offering several benefits.
1. Improved Resource Management: In classic MapReduce, there were two dedicated types of nodes: "job tracker" for coordinating jobs and "task trackers" for executing tasks. These nodes had a fixed number of slots for allocating tasks, which could lead to under-utilization of resources. YARN, on the other hand, has a more flexible global ResourceManager and per-application ApplicationMasters that dynamically allocate resources based on demand. This results in better cluster utilization and higher throughput.
2. Support for Multiple Processing Frameworks: Classic MapReduce only allowed MapReduce jobs to be run on the same cluster. YARN allows multiple processing frameworks to coexist happily on the same cluster, reducing the need for separate clusters and improving overall efficiency. You can run Spark, Flink, Tez, and other applications alongside MapReduce on YARN.
3. Scalability: YARN is designed to handle thousands of nodes and tens of thousands of jobs, whereas classic MapReduce faced limitations in its scalability. YARN’s ResourceManager and per-application ApplicationMasters architecture reduce the centralized bottleneck and aid in scaling your Hadoop clusters.
4. Improved Scheduling: In classic MapReduce, the scheduling and allocation of resources were relatively simple and less effective when comparing it to YARN. YARN’s scheduling is more advanced, with features like Capacity Scheduler, Fair Scheduler, and Dominant Resource Fairness, which improve the way resources are allocated and managed, leading to better overall performance.
5. Enhanced Data Processing & Analytics: YARN enables you to execute real-time data processing, interactive querying, and machine learning workloads using a variety of processing engines. The classic MapReduce was mainly designed for batch processing jobs and could not efficiently handle real-time or interactive processing workloads.
6. Security & Multi-tenancy: YARN supports better security and multi-tenancy features by isolating applications and providing authenticated access to resources.
In conclusion, YARN significantly enhances the capabilities of the Hadoop ecosystem compared to the classic MapReduce model. It offers improved resource management, supports multiple data processing frameworks, enhances scalability, improves scheduling, and enables a wide range of data processing and analytics workloads, making it a key component of the Hadoop ecosystem.