Apache Mesos vs YARN Hadoop: What are the differences?

Key Differences between Apache Mesos and YARN Hadoop

Apache Mesos and YARN Hadoop are two popular resource management platforms used in distributed computing. While they share similarities in their goals and functionalities, there are several key differences that set them apart.

1. Architecture: Apache Mesos is built on a two-level architecture, where the resource management and scheduling are separated from the application framework. In contrast, YARN Hadoop has a three-level architecture, where the resource management functions are further divided into separate components. This architectural difference affects how the platforms handle scalability and fault-tolerance.

2. Scheduling: Mesos offers a fine-grained resource allocation mechanism, enabling multiple frameworks to share resources dynamically. It uses a two-level scheduling approach, with a centralized master node making the resource offers to the frameworks. On the other hand, YARN Hadoop uses a hierarchical resource manager that schedules resources based on applications' resource requests and priorities. It allows for fair scheduling using different schedulers such as Capacity, Fair, and Dominant Resource Fairness.

3. Multi-tenancy: Mesos provides strong multi-tenancy support, allowing multiple frameworks to coexist and share resources efficiently. It offers resource isolation at the task level, ensuring individual frameworks do not interfere with each other. YARN Hadoop also supports multi-tenancy but at a coarser granularity by dividing resources into queues for different applications or organizations.

4. Framework Ecosystem: Mesos has a more extensible framework ecosystem, providing a wider range of frameworks for various applications such as Spark, Marathon, and TensorFlow. It offers flexibility in choosing frameworks and supports frameworks written in different languages. YARN Hadoop, on the other hand, focuses more on integrating with the Hadoop ecosystem, providing native support for MapReduce and other Hadoop-specific applications.

5. Fault-tolerance: Mesos relies on ZooKeeper for master election and offers strong fault-tolerance, ensuring high availability. It uses a master-slave architecture where the master node is replicated for fault-tolerance. YARN Hadoop uses the ResourceManager and NodeManager model, where the ResourceManager handles master election and NodeManagers report to it. It also provides fault-tolerance through redundancy, but it relies on external high-availability mechanisms like ZooKeeper.

6. Containerization: Mesos was designed with containerization in mind, providing built-in support for Docker containers. It offers a seamless integration with container orchestration tools like Kubernetes, allowing scalable and efficient container management. YARN Hadoop supports containerization as well but does not provide native support for Docker. It relies on other containerization frameworks like Docker Swarm or Apache Slider for running applications in containers.

In summary, Apache Mesos and YARN Hadoop differ in their architecture, resource scheduling mechanisms, multi-tenancy support, framework ecosystem, fault-tolerance mechanisms, and containerization capabilities. These differences make them suitable for different use cases and environments.