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Apache Mesos

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description Apache Mesos Overview

Apache Mesos is an open-source cluster manager developed at UC Berkeley's AMPLab that provides efficient resource isolation and sharing across distributed applications. Using a two-level scheduling mechanism, Mesos determines which resources to offer to each framework, while the frameworks themselves decide which resources to accept and what tasks to execute. Mesos can manage thousands of nodes and supports frameworks including Apache Spark, Hadoop, Marathon, and Chronos. Companies such as Twitter (now X), Airbnb, and Apple have used Mesos in their production environments to manage large-scale infrastructure.

help Apache Mesos FAQ

What is the difference between Apache Mesos and Kubernetes?

Apache Mesos uses a two-level scheduling model where the master offers resources to frameworks and each framework decides which tasks to run, while Kubernetes uses a centralized scheduler designed specifically for container orchestration. Mesos can run multiple diverse frameworks on the same cluster simultaneously, whereas Kubernetes is purpose-built for containerized workloads.

What frameworks can run on top of Apache Mesos?

Mesos supports running frameworks such as Marathon for long-running services, Chronos for distributed scheduled jobs, and data-processing frameworks like Hadoop and Spark. The two-level scheduling architecture allows these frameworks to coexist and share cluster resources dynamically.

Is Apache Mesos still actively maintained?

Apache Mesos remains an open-source project under the Apache Software Foundation, but community activity and adoption have declined significantly as Kubernetes has become the dominant container orchestration platform. Companies that were early Mesos adopters, including Twitter and Uber, have largely transitioned to Kubernetes-based infrastructure.

How does Mesos two-level scheduling work?

In the first level, the Mesos master determines which resources to offer to each registered framework based on fairness policies. In the second level, each framework's own scheduler decides which specific tasks to accept or decline from those offers, giving frameworks autonomy over their resource allocation.

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