What is Docker Swarm and what are its top alternatives?
Top Alternatives to Docker Swarm
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. ...
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. ...
Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use. ...
Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers. ...
It is designed for security, consistency, and reliability. Instead of installing packages via yum or apt, it uses Linux containers to manage your services at a higher level of abstraction. A single service's code and all dependencies are packaged within a container that can be run on one or many machines. ...
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. ...
Compose makes it easy to spin up multiple open source databases with just one click. Deploy MongoDB for production, take Redis out for a performance test drive, or spin up RethinkDB in development before rolling it out to production. ...
Docker Cloud is the best way to deploy and manage Dockerized applications. Docker Cloud makes it easy for new Docker users to manage and deploy the full spectrum of applications, from single container apps to distributed microservices stacks, to any cloud or on-premises infrastructure. ...
Docker Swarm alternatives & related posts
- Multi-container descriptor121
- Fast development environment setup109
- Easy linking of containers75
- Simple yaml configuration66
- Easy setup58
- Yml or yaml format15
- Use Standard Docker API11
- Open source7
- Can choose Discovery Backend4
- Go from template to application in minutes4
- Easy configuration2
- Kubernetes integration2
- Quick and easy1
- Tied to single machine8
- Still very volatile, changing syntax often5
related Docker Compose posts
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis 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.
Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.
We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.
- Easy to use103
- Open source and totally free79
- Multi-host docker-compose support63
- Load balancing and health check included58
- Rolling upgrades, green/blue upgrades feature44
- Dns and service discovery out-of-the-box42
- Only requires docker37
- Multitenant and permission management34
- Easy to use and feature rich29
- Cross cloud compatible11
- Does everything needed for a docker infrastructure11
- Simple and powerful8
- Next-gen platform8
- Very Docker-friendly7
- Support Kubernetes and Swarm6
- Application catalogs with stack templates (wizards)6
- Supports Apache Mesos, Docker Swarm, and Kubernetes6
- Rolling and blue/green upgrades deployments6
- High Availability service: keeps your app up 24/76
- Easy to use service catalog5
- Very intuitive UI4
- IaaS-vendor independent, supports hybrid/multi-cloud4
- Awesome support4
- Requires less infrastructure requirements2
- Hosting Rancher can be complicated8
related Rancher posts
- Great configuration204
- Easy to learn151
- Doesn't get in the way of getting s--- done54
- Makes sense34
- Super efficient and flexible29
- Dynamic Inventory11
- Backed by Red Hat8
- Works with AWS7
- Cloud Oriented6
- Easy to maintain6
- Because SSH4
- Multi language4
- Procedural or declarative, or both4
- Simple and powerful4
- Vagrant provisioner3
- Fast as hell2
- Merge hash to get final configuration similar to hiera2
- Debugging is simple2
- Work on windows, but difficult to manage1
- Certified Content1
- Hard to install5
- Backward compatibility3
- Doesn't Run on Windows2
- No immutable infrastructure2
related Ansible posts
Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).
It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear
vagrant up or
vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.
I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as
vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.
We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.
If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.
The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).
Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.
Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.
- Easy scaling20
- Web UI6
- Elastic Distributed System1
- Not for long term1
- Depends on Zookeeper1
related Apache Mesos posts
Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.
- Container management21
related CoreOS posts
- Leading docker container management solution159
- Simple and powerful124
- Open source101
- Backed by google75
- The right abstractions56
- Scale services24
- Replication controller18
- Permission managment9
- Supports autoscaling7
- No cloud platform lock-in4
- Open, powerful, stable3
- Quick cloud setup3
- Promotes modern/good infrascture practice3
- Backed by Red Hat2
- Runs on azure2
- Cloud Agnostic2
- Custom and extensibility2
- Captain of Container Ship2
- A self healing environment with rich metadata2
- Easy setup1
- Everything of CaaS1
- Poor workflow for development13
- Steep learning curve11
- Orchestrates only infrastructure5
- High resource requirements for on-prem clusters2
related Kubernetes posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.
After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...
- Simple to set up42
- One-click mongodb32
- Automated Backups29
- Designed to scale23
- Easy interface21
- Fast and Simple13
- Real-Time Monitoring10
- Fastest MongoDB Available7
- Great Design6
- REST API6
- Easy to set up4
- Free for testing3
- Geospatial support3
- Heroku Add-on2
- Automated Health Checks1
- Email Support1
- Query Logs1
related Compose posts
We went with MongoDB , almost by mistake. I had never used it before, but I knew I wanted the *EAN part of the MEAN stack, so why not go all in. I come from a background of SQL (first MySQL , then PostgreSQL ), so I definitely abused Mongo at first... by trying to turn it into something more relational than it should be. But hey, data is supposed to be relational, so there wasn't really any way to get around that.
There's a lot I love about MongoDB, and a lot I hate. I still don't know if we made the right decision. We've been able to build much quicker, but we also have had some growing pains. We host our databases on MongoDB Atlas , and I can't say enough good things about it. We had tried MongoLab and Compose before it, and with MongoDB Atlas I finally feel like things are in a good place. I don't know if I'd use it for a one-off small project, but for a large product Atlas has given us a ton more control, stability and trust.