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  1. Stackups
  2. Application & Data
  3. Infrastructure as a Service
  4. Cluster Management
  5. Apache Mesos vs Nomad

Apache Mesos vs Nomad

OverviewComparisonAlternatives

Overview

Apache Mesos
Apache Mesos
Stacks306
Followers418
Votes31
GitHub Stars5.3K
Forks1.7K
Nomad
Nomad
Stacks256
Followers344
Votes32
GitHub Stars15.9K
Forks2.0K

Apache Mesos vs Nomad: What are the differences?

Introduction:

Apache Mesos and Nomad are two popular cluster management systems used for scheduling and executing tasks across a cluster of machines. While they serve similar purposes, there are key differences between the two that make them suitable for different use cases. This article aims to highlight the main differences between Apache Mesos and Nomad.

1. Scalability: Apache Mesos is known for its ability to handle large-scale computing clusters, making it suitable for organizations with big data processing needs. It can efficiently manage and allocate resources across thousands of machines, allowing for elastic scaling and high availability. On the other hand, Nomad is designed for smaller clusters and is more lightweight, making it easier to set up and manage in smaller environments.

2. Language Support: Mesos has a broader range of language support compared to Nomad. It provides native support for several programming languages, including Java, C++, Python, and Go, enabling developers to use their preferred language for writing and executing tasks. Nomad, while still offering support for popular languages, has a more limited language support compared to Mesos.

3. Containerization: Both Mesos and Nomad support containerization, enabling users to run tasks within containers for better isolation and resource management. However, Mesos has a more mature and feature-rich containerization ecosystem, with built-in support for Docker, Kubernetes, and other container technologies. Nomad, on the other hand, has its own lightweight container runtime called "Nomad client," which simplifies the deployment and management of containers.

4. Task Management: Mesos provides more advanced task management capabilities compared to Nomad. It offers advanced features like task grouping, task dependencies, and task health checks, allowing for more fine-grained control and monitoring of task execution. Nomad, while still offering basic task management functionalities, is more focused on simplicity and ease of use, providing a more streamlined task execution experience.

5. Plugin Ecosystem: Mesos has a rich ecosystem of plugins and frameworks, making it easy to integrate with various tools and technologies. It provides a wide range of built-in frameworks like Apache Spark, Apache Hadoop, and Apache Kafka, allowing users to easily deploy and manage these popular technologies. Nomad, while supporting plugins, has a more limited ecosystem compared to Mesos.

6. Community and Adoption: Mesos has a larger and more mature community compared to Nomad, with a significant number of contributors and users. This vibrant community ensures continuous development, improvement, and support for Mesos, making it a reliable choice for organizations. Nomad, while gaining popularity, still has a smaller community and user base, which may impact the availability of resources and support in certain cases.

In summary, Apache Mesos is a highly scalable and feature-rich cluster management system, making it suitable for large-scale environments with diverse requirements. Nomad, on the other hand, is more lightweight and focused on simplicity, making it easier to set up and manage in smaller environments.

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Detailed Comparison

Apache Mesos
Apache Mesos
Nomad
Nomad

Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers.

Nomad is a cluster manager, designed for both long lived services and short lived batch processing workloads. Developers use a declarative job specification to submit work, and Nomad ensures constraints are satisfied and resource utilization is optimized by efficient task packing. Nomad supports all major operating systems and virtualized, containerized, or standalone applications.

Fault-tolerant replicated master using ZooKeeper;Scalability to 10,000s of nodes;Isolation between tasks with Linux Containers;Multi-resource scheduling (memory and CPU aware);Java, Python and C++ APIs for developing new parallel applications;Web UI for viewing cluster state
Handles the scheduling and upgrading of the applications over time; With built-in dry-run execution, Nomad shows what scheduling decisions it will take before it takes them. Operators can approve or deny these changes to create a safe and reproducible workflow; Nomad runs applications and ensures they keep running in failure scenarios. In addition to long-running services, Nomad can schedule batch jobs, distributed cron jobs, and parameterized jobs; Stream logs, send signals, and interact with the file system of scheduled applications. These operator-friendly commands bring the familiar debugging tools to a scheduled world
Statistics
GitHub Stars
5.3K
GitHub Stars
15.9K
GitHub Forks
1.7K
GitHub Forks
2.0K
Stacks
306
Stacks
256
Followers
418
Followers
344
Votes
31
Votes
32
Pros & Cons
Pros
  • 21
    Easy scaling
  • 6
    Web UI
  • 2
    Fault-Tolerant
  • 1
    High-Available
  • 1
    Elastic Distributed System
Cons
  • 1
    Depends on Zookeeper
  • 1
    Not for long term
Pros
  • 7
    Built in Consul integration
  • 6
    Easy setup
  • 4
    Bult-in Vault integration
  • 3
    Built-in federation support
  • 2
    Autoscaling support
Cons
  • 3
    Easy to start with
  • 1
    HCL language for configuration, an unpopular DSL
  • 1
    Small comunity
Integrations
Apache Aurora
Apache Aurora
Consul
Consul
Docker
Docker
Vault
Vault

What are some alternatives to Apache Mesos, Nomad?

DC/OS

DC/OS

Unlike traditional operating systems, DC/OS spans multiple machines within a network, aggregating their resources to maximize utilization by distributed applications.

Mesosphere

Mesosphere

Mesosphere offers a layer of software that organizes your machines, VMs, and cloud instances and lets applications draw from a single pool of intelligently- and dynamically-allocated resources, increasing efficiency and reducing operational complexity.

Gardener

Gardener

Many Open Source tools exist which help in creating and updating single Kubernetes clusters. However, the more clusters you need the harder it becomes to operate, monitor, manage and keep all of them alive and up-to-date. And that is exactly what project Gardener focuses on.

YARN Hadoop

YARN Hadoop

Its fundamental idea is to split up the functionalities of resource management and job scheduling/monitoring into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM).

Atmosly

Atmosly

AI-powered Kubernetes platform for developers & DevOps. Deploy applications without complexity, with intelligent automation and one-click environments.

kops

kops

It helps you create, destroy, upgrade and maintain production-grade, highly available, Kubernetes clusters from the command line. AWS (Amazon Web Services) is currently officially supported, with GCE in beta support , and VMware vSphere in alpha, and other platforms planned.

Apache Aurora

Apache Aurora

Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.

Elastic Apache Mesos

Elastic Apache Mesos

Elastic Apache Mesos is a web service that automates the creation of Apache Mesos clusters on Amazon Elastic Compute Cloud (EC2). It provisions EC2 instances, installs dependencies including Apache ZooKeeper and HDFS, and delivers you a cluster with all the services running.

Peloton

Peloton

A Unified Resource Scheduler to co-schedule mixed types of workloads such as batch, stateless and stateful jobs in a single cluster for better resource utilization. Designed for web-scale companies with millions of containers and tens of thousands of nodes.

Kocho

Kocho

Kocho provides a set of mechanisms to bootstrap AWS nodes that must follow a specific configuration with CoreOS. It sets up fleet meta-data, and patched versions of fleet, etcd, and docker when using Yochu.

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