Apache Oozie vs Yarn: What are the differences?

Introduction:

Apache Oozie and Apache Yarn are two popular technologies used in the Apache Hadoop ecosystem. While Apache Oozie is a workflow scheduling system for managing Apache Hadoop jobs, Apache Yarn is a resource management framework that allows multiple data processing engines to run on Hadoop clusters. Understanding the key differences between these two technologies is crucial for making informed decisions when working with big data processing.

1. Integration with Hadoop Components: One of the key differences between Apache Oozie and Yarn is their integration with other components in the Hadoop ecosystem. Oozie acts as a workflow scheduler for managing jobs in Hadoop, while Yarn provides a resource management framework that allows multiple data processing engines such as MapReduce, Spark, and Tez to run on Hadoop clusters.

2. Functionality: Another major difference lies in their functionality. Oozie primarily focuses on coordinating and scheduling workflows, providing advanced scheduling capabilities and supporting complex job dependencies. On the other hand, Yarn focuses on the resource management aspects of a Hadoop cluster, providing a fine-grained control of resources and facilitating efficient utilization of cluster resources among different data processing frameworks.

3. High-Level vs. Low-Level: Oozie is a high-level workflow coordination system that abstracts the underlying details of the data processing frameworks, providing a simplified approach to managing job orchestration. Yarn, on the other hand, is a low-level resource management framework that allows fine-grained control over resources, giving more flexibility but requiring users to explicitly manage resource allocation and scheduling.

4. Granularity of Management: Oozie manages workflows at a higher level, focusing on overall job coordination and dependencies, while Yarn manages resources at a more granular level, allowing fine-grained control over resource allocation and usage.

5. Dependency and Job Scheduling: Oozie provides advanced features for managing dependencies between jobs and scheduling complex workflows, allowing users to define job dependencies and specify conditions for job execution. Yarn, on the other hand, focuses on resource management and doesn't provide built-in features for managing job dependencies and scheduling workflows.

6. User Community and Adoption: Oozie has been widely adopted and has a strong user community, with extensive documentation and tutorials available. Yarn, as a core component of the Hadoop ecosystem, is widely used in many big data processing frameworks and has a large user community as well.

In summary, Apache Oozie is a workflow scheduling system for managing Hadoop jobs and provides advanced features for job coordination and scheduling, while Apache Yarn is a resource management framework that focuses on efficient resource allocation and utilization in a Hadoop cluster.