Map Reduce vs YARN (2024)

MapReduce and YARN are both key components of the Hadoop ecosystem, but they serve different purposes and have different functionalities. Here's a breakdown of the main differences between MapReduce and YARN:

Functionality:

• MapReduce: MapReduce is a programming model and processing framework for distributed computing on large datasets. It consists of two main phases: the Map phase, where input data is divided into smaller chunks and processed in parallel, and the Reduce phase, where the intermediate results from the Map phase are aggregated and processed to produce the final output.
• YARN (Yet Another Resource Negotiator): YARN is a resource management and job scheduling framework introduced in Hadoop version 2.x. It decouples the resource management and job scheduling functionalities from the MapReduce framework. YARN allows multiple processing frameworks, not just MapReduce, to run on the same Hadoop cluster simultaneously. It provides a more flexible and scalable platform for running various types of distributed applications beyond MapReduce, such as Apache Spark, Apache Flink, and custom applications.

Resource Management:

• MapReduce: In Hadoop version 1.x, MapReduce handled both job scheduling and resource management. It had a centralized architecture where the JobTracker managed job scheduling and resource allocation to TaskTrackers.
• YARN: YARN separates the resource management and job scheduling responsibilities into two separate daemons: ResourceManager and NodeManager. The ResourceManager is responsible for allocating resources to applications and tracking resource usage, while the NodeManager runs on each node in the cluster and manages resources such as CPU and memory.

Flexibility and Multi-Tenancy:

• MapReduce: MapReduce was primarily designed for batch processing workloads and had limitations in supporting multiple processing frameworks and concurrent applications on the same cluster.
• YARN: YARN provides a more flexible and multi-tenant architecture, allowing multiple applications and frameworks to coexist and share cluster resources efficiently. It enables better utilization of cluster resources by supporting diverse workloads, including batch processing, interactive querying, stream processing, and machine learning.

Compatibility:

• MapReduce: MapReduce is compatible with applications and frameworks that adhere to its programming model. It works well for batch processing tasks but may not be suitable for other types of processing workloads.
• YARN: YARN provides a generic framework for running various types of distributed applications, making it compatible with a wide range of processing frameworks beyond MapReduce. Applications need to be YARN-compatible to run on a YARN-managed cluster.

In summary, MapReduce is a specific programming model and processing framework tailored for batch processing on large datasets, while YARN is a resource management and job scheduling framework that enables multi-tenancy and supports various processing frameworks beyond MapReduce. YARN's flexibility and scalability make it a critical component of the Hadoop ecosystem, allowing organizations to run diverse workloads on the same cluster efficiently.