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

Apache Mesos vs Kubernetes

OverviewDecisionsComparisonAlternatives

Overview

Apache Mesos
Apache Mesos
Stacks306
Followers418
Votes31
GitHub Stars5.3K
Forks1.7K
Kubernetes
Kubernetes
Stacks61.2K
Followers52.8K
Votes685

Apache Mesos vs Kubernetes: What are the differences?

Introduction

In this article, we will explore and compare the key differences between Apache Mesos and Kubernetes, two popular orchestration platforms used for managing and scheduling containers in a cluster environment.

  1. Architecture: Apache Mesos follows a master-slave architecture, where the cluster consists of one or more masters that manage resources and multiple slave nodes responsible for running tasks. On the other hand, Kubernetes follows a master-worker architecture, where the master controls the cluster state and the worker nodes execute tasks.

  2. Resource Allocation: Mesos uses fine-grained resource allocation, allowing tasks to be allocated CPU and memory resources based on specific requirements. Kubernetes, on the other hand, uses a coarser-grained approach, allocating resources based on predefined resource limits and requests.

  3. Scalability: Mesos was designed to scale up to tens of thousands of nodes, making it suitable for large-scale deployments. Kubernetes, although capable of scaling to a significant number of nodes, is known to be more efficient in managing clusters with hundreds or thousands of nodes.

  4. Service Discovery: Kubernetes provides built-in service discovery and load balancing mechanisms through its DNS-based service discovery and Ingress features. Mesos, on the other hand, relies on external frameworks like Marathon or Chronos for these capabilities.

  5. Networking Models: Kubernetes supports multiple networking models, including overlay networks and host networking. Mesos provides more flexibility in terms of networking choices, allowing users to choose between different networking models like virtual network interfaces or Linux bridges.

  6. Community Support: Kubernetes boasts a larger and more active community compared to Mesos, with a wider range of plugins, tools, and resources available. This strong community support makes it easier for users to find help, contribute to the project, and adopt Kubernetes in various environments.

In Summary, Apache Mesos and Kubernetes differ in their architecture, resource allocation approaches, scalability, service discovery mechanisms, networking models, and community support. These differences enable users to choose the platform that best suits their requirements and infrastructure.

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Advice on Apache Mesos, Kubernetes

Simon
Simon

Senior Fullstack Developer at QUANTUSflow Software GmbH

Apr 27, 2020

DecidedonGitHubGitHubGitHub PagesGitHub PagesMarkdownMarkdown

Our whole DevOps stack consists of the following tools:

  • @{GitHub}|tool:27| (incl. @{GitHub Pages}|tool:683|/@{Markdown}|tool:1147| for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
  • Respectively @{Git}|tool:1046| as revision control system
  • @{SourceTree}|tool:1599| as @{Git}|tool:1046| GUI
  • @{Visual Studio Code}|tool:4202| as IDE
  • @{CircleCI}|tool:190| for continuous integration (automatize development process)
  • @{Prettier}|tool:7035| / @{TSLint}|tool:5561| / @{ESLint}|tool:3337| as code linter
  • @{SonarQube}|tool:2638| as quality gate
  • @{Docker}|tool:586| as container management (incl. @{Docker Compose}|tool:3136| for multi-container application management)
  • @{VirtualBox}|tool:774| for operating system simulation tests
  • @{Kubernetes}|tool:1885| as cluster management for docker containers
  • @{Heroku}|tool:133| for deploying in test environments
  • @{nginx}|tool:1052| as web server (preferably used as facade server in production environment)
  • @{SSLMate}|tool:2752| (using @{OpenSSL}|tool:3091|) for certificate management
  • @{Amazon EC2}|tool:18| (incl. @{Amazon S3}|tool:25|) for deploying in stage (production-like) and production environments
  • @{PostgreSQL}|tool:1028| as preferred database system
  • @{Redis}|tool:1031| as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

  • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
  • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
  • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
  • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
  • Scalability: All-in-one framework for distributed systems.
  • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
12.8M views12.8M
Comments
Anis
Anis

Founder at Odix

Nov 7, 2020

Review

I recommend this : -Spring reactive for back end : the fact it's reactive (async) it consumes half of the resources that a sync platform needs (so less CPU -> less money). -Angular : Web Front end ; it's gives you the possibility to use PWA which is a cheap replacement for a mobile app (but more less popular). -Docker images. -Kubernetes to orchestrate all the containers. -I Use Jenkins / blueocean, ansible for my CI/CD (with Github of course) -AWS of course : u can run a K8S cluster there, make it multi AZ (availability zones) to be highly available, use a load balancer and an auto scaler and ur good to go. -You can store data by taking any managed DB or u can deploy ur own (cheap but risky).

You pay less money, but u need some technical 2 - 3 guys to make that done.

Good luck

115k views115k
Comments
Michael
Michael

CEO at asencis Ltd

Jan 5, 2021

Needs advice

We develop rapidly with docker-compose orchestrated services, however, for production - we utilise the very best ideas that Kubernetes has to offer: SCALE! We can scale when needed, setting a maximum and minimum level of nodes for each application layer - scaling only when the load balancer needs it. This allowed us to reduce our devops costs by 40% whilst also maintaining an SLA of 99.87%.

272k views272k
Comments

Detailed Comparison

Apache Mesos
Apache Mesos
Kubernetes
Kubernetes

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

Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

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
Lightweight, simple and accessible;Built for a multi-cloud world, public, private or hybrid;Highly modular, designed so that all of its components are easily swappable
Statistics
GitHub Stars
5.3K
GitHub Stars
-
GitHub Forks
1.7K
GitHub Forks
-
Stacks
306
Stacks
61.2K
Followers
418
Followers
52.8K
Votes
31
Votes
685
Pros & Cons
Pros
  • 21
    Easy scaling
  • 6
    Web UI
  • 2
    Fault-Tolerant
  • 1
    Elastic Distributed System
  • 1
    High-Available
Cons
  • 1
    Not for long term
  • 1
    Depends on Zookeeper
Pros
  • 166
    Leading docker container management solution
  • 130
    Simple and powerful
  • 108
    Open source
  • 76
    Backed by google
  • 58
    The right abstractions
Cons
  • 16
    Steep learning curve
  • 15
    Poor workflow for development
  • 8
    Orchestrates only infrastructure
  • 4
    High resource requirements for on-prem clusters
  • 2
    Too heavy for simple systems
Integrations
Apache Aurora
Apache Aurora
Vagrant
Vagrant
Docker
Docker
Rackspace Cloud Servers
Rackspace Cloud Servers
Microsoft Azure
Microsoft Azure
Google Compute Engine
Google Compute Engine
Ansible
Ansible
Google Kubernetes Engine
Google Kubernetes Engine

What are some alternatives to Apache Mesos, Kubernetes?

Rancher

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Compose

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

Docker Swarm

Docker Swarm

Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.

Tutum

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

Codefresh

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

Nomad

Nomad

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.

CAST.AI

CAST.AI

It is an AI-driven cloud optimization platform for Kubernetes. Instantly cut your cloud bill, prevent downtime, and 10X the power of DevOps.

k3s

k3s

Certified Kubernetes distribution designed for production workloads in unattended, resource-constrained, remote locations or inside IoT appliances. Supports something as small as a Raspberry Pi or as large as an AWS a1.4xlarge 32GiB server.

Flocker

Flocker

Flocker is a data volume manager and multi-host Docker cluster management tool. With it you can control your data using the same tools you use for your stateless applications. This means that you can run your databases, queues and key-value stores in Docker and move them around as easily as the rest of your app.

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