What is Apache Mesos and how it is helpful in software design

Apache Mesos is an open-source cluster management platform designed to abstract and efficiently manage resources (CPU, memory, storage) across a cluster of machines.

It enables the deployment and operation of distributed systems, such as containerized applications, big data frameworks, and other distributed workloads.

Key Features

1. Resource Abstraction:

   • Mesos provides a unified interface to manage hardware resources across a cluster.

   • It allows frameworks to dynamically request and allocate resources.

2. Scalability:

   • Built for large-scale environments, Mesos can manage thousands of nodes.

3. Fault Tolerance:

   • Achieved through leader election, replicated state, and failover mechanisms.

4. Multi-Tenancy:

   • Supports multiple distributed frameworks running simultaneously on shared resources.

5. Extensibility:

   • Developers can build custom frameworks for specific use cases.

6. Framework Agnostic:

   • Supports popular distributed systems like Apache Hadoop, Apache Spark, Kubernetes, and custom frameworks.

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Core Components

1. Master Node:

   • The central coordinator that manages resource allocation across the cluster.

   • Runs the Mesos master process.

   • Maintains the state of the cluster, including resource availability and framework registrations.

2. Agent Node (formerly "Slave"):

   • Executes tasks on behalf of frameworks.

   • Reports available resources to the master.

   • Runs the Mesos agent process.

3. Framework:

   • Applications or services that request resources and run tasks.

   • Comprises two parts:

     • Scheduler: Requests resources from the master.

     • Executor: Runs tasks on agent nodes.

4. ZooKeeper:

   • Ensures high availability of the Mesos master by facilitating leader election and state synchronization.

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How Apache Mesos Works

1. Resource Offers:

   • The Mesos master makes resource offers to registered frameworks based on available cluster resources.

   • Offers are decoupled from task execution, allowing frameworks to make decisions about resource allocation.

2. Task Scheduling:

   • Framework schedulers accept or decline resource offers.

   • Accepted offers result in tasks being launched on agent nodes.

3. Isolation:

   • Tasks on agent nodes are isolated using containerization technologies like Docker or Linux cgroups.

4. Fault Tolerance:

   • If a master node fails, ZooKeeper elects a new leader.

   • Frameworks and agents reconnect to the new master seamlessly.

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Use Cases

1. Big Data Frameworks:

   • Run Apache Spark, Apache Hadoop, or Apache Kafka clusters.

2. Container Orchestration:

   • Deploy containerized applications using Mesos with Marathon or Kubernetes.

3. Batch Processing:

   • Schedule and manage batch jobs on shared resources.

4. Multi-Cloud and Hybrid Cloud Environments:

   • Abstract cloud and on-premise resources for seamless operations.

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Advantages

• Efficiency: Optimizes resource utilization in large clusters.

• Flexibility: Supports a wide range of distributed frameworks.

• High Availability: Leader election ensures minimal downtime.

• Resource Fairness: Fair sharing of resources among frameworks.

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Limitations

• Complexity: Requires expertise to configure and manage.

• Evolving Ecosystem: Competes with modern orchestration platforms like Kubernetes.

• Limited Community Support: Adoption has declined with the rise of Kubernetes.

  

  Apache Mesos excels in environments where multiple distributed frameworks or resource types need management but has seen reduced adoption with the rise of Kubernetes for container orchestration.

  

  source:-wikipedia