Apache Mesos: Orchestrating Big Data Workloads
Apache Mesos is a distributed systems kernel that abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to be built and run effectively. It's a foundational technology for many big data processing frameworks, including Apache Spark.
Core Concepts of Apache Mesos
Mesos operates on a two-level scheduling architecture. The Mesos master manages Mesos agents (slaves) and receives resource offers from them. Frameworks (like Spark, Hadoop, or custom applications) then accept or reject these offers to run tasks on the agents. This offers flexibility and allows for custom scheduling policies.
Mesos uses a two-level scheduling model for efficient resource allocation.
The Mesos master acts as a central coordinator, offering resources from agents to registered frameworks. Frameworks then decide which tasks to run on those resources.
The Mesos master is responsible for managing the Mesos agents and coordinating resource offers. It aggregates resource availability from all agents and presents these resources to registered frameworks. Frameworks, such as Apache Spark or Marathon, are responsible for deciding which tasks to run on the offered resources. This two-level approach allows for sophisticated scheduling policies to be implemented at the framework level, while the Mesos master focuses on resource aggregation and fault tolerance.
Mesos Architecture Components
| Component | Role | Key Function |
|---|---|---|
| Mesos Master | Central Coordinator | Manages agents, registers frameworks, offers resources |
| Mesos Agent (Slave) | Resource Provider | Runs tasks, reports resource availability to master |
| Framework | Task Scheduler | Accepts resource offers, launches and manages tasks |
| Executor | Task Runner | Runs actual tasks on agent nodes |
Mesos and Apache Spark Integration
Apache Spark can run natively on Mesos. When Spark is deployed on Mesos, the Spark driver acts as a Mesos framework. It registers with the Mesos master and receives resource offers to launch Spark executors. This allows Spark to leverage Mesos for cluster management, providing dynamic resource allocation and fault tolerance for Spark applications.
Mesos provides a robust platform for deploying and managing distributed applications like Apache Spark, enabling efficient resource utilization and scalability.
Key Benefits of Using Mesos
Mesos offers several advantages for big data environments: 1. Resource Isolation: Ensures that tasks do not interfere with each other. 2. Scalability: Can manage thousands of nodes and tasks. 3. Fault Tolerance: Designed to handle node failures gracefully. 4. Flexibility: Supports various frameworks and custom scheduling policies.
The Mesos master manages agents, registers frameworks, and offers resources to frameworks.
Spark acts as a Mesos framework, with its driver registering with the Mesos master to launch Spark executors.
Learning Resources
The official source for understanding Mesos architecture, installation, and usage.
A blog post discussing the evolution and benefits of Mesos for cluster management.
Detailed guide on how to configure and run Apache Spark applications on a Mesos cluster.
A video explaining the core components and architecture of Apache Mesos.
In-depth explanation of how Mesos handles resource offers to frameworks.
A comparative analysis of Mesos against other popular cluster management systems.
An article providing a conceptual overview of Mesos as a distributed systems kernel.
Wikipedia entry providing a comprehensive overview of Apache Mesos, its history, and features.
Information about different types of frameworks that can run on Mesos, including those for big data.
A presentation offering practical advice and steps for deploying Spark on Mesos.