What is Spinnaker and what are its top alternatives?
Top Alternatives to Spinnaker
In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project. ...
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. ...
With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel. ...
GitLab offers git repository management, code reviews, issue tracking, activity feeds and wikis. Enterprises install GitLab on-premise and connect it with LDAP and Active Directory servers for secure authentication and authorization. A single GitLab server can handle more than 25,000 users but it is also possible to create a high availability setup with multiple active servers. ...
Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition). ...
Armory.io makes deployments boring (like ‘waiting for your code to compile’ boring), non-events that happen continuously, and always in the background. We do that by simplifying the installation and configuration of Spinnaker - an open source continuous delivery platform from Netflix. ...
Helm is the best way to find, share, and use software built for Kubernetes.
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. ...
Spinnaker alternatives & related posts
- Hosted internally521
- Free open source463
- Great to build, deploy or launch anything async313
- Tons of integrations243
- Rich set of plugins with good documentation208
- Has support for build pipelines108
- Open source and tons of integrations72
- Easy setup63
- It is open-source61
- Workflow plugin54
- Configuration as code11
- Very powerful tool10
- Many Plugins9
- Great flexibility8
- Git and Maven integration is better8
- Continuous Integration7
- Github integration6
- Slack Integration (plugin)6
- 100% free and open source5
- Self-hosted GitLab Integration (plugin)5
- Easy customisation5
- Docker support4
- Pipeline API3
- Excellent docker integration3
- Platform idnependency3
- Fast builds3
- Hosted Externally2
- It`w worked2
- Can be run as a Docker container2
- AWS Integration2
- It's Everywhere2
- NodeJS Support1
- PHP Support1
- Ruby/Rails Support1
- Universal controller1
- Easily extendable with seamless integration1
- Build PR Branch Only1
- Workarounds needed for basic requirements12
- Groovy with cumbersome syntax8
- Plugins compatibility issues6
- Limited abilities with declarative pipelines6
- Lack of support5
- No YAML syntax4
- Too tied to plugins versions2
related Jenkins posts
Releasing new versions of our services is done by Travis CI. Travis first runs our test suite. Once it passes, it publishes a new release binary to GitHub.
Common tasks such as installing dependencies for the Go project, or building a binary are automated using plain old Makefiles. (We know, crazy old school, right?) Our binaries are compressed using UPX.
Travis has come a long way over the past years. I used to prefer Jenkins in some cases since it was easier to debug broken builds. With the addition of the aptly named “debug build” button, Travis is now the clear winner. It’s easy to use and free for open source, with no need to maintain anything.
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.
- 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 complicated7
related Rancher posts
- Infrastructure as code103
- Declarative syntax71
- Cloud agnostic6
- It's like coding your infrastructure in simple English5
- Automates infrastructure deployments3
- Platform agnostic3
- Immutable infrastructure3
- Scales to hundreds of hosts2
- Doesn't have full support to GKE1
related Terraform posts
Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.
Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!
Check Out My Architecture: CLICK ME
Check out the GitHub repo attached
We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.
To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas
To build #Webapps we decided to use Angular 2 with RxJS
#Devops - GitHub , Travis CI , Terraform , Docker , Serverless
- Self hosted489
- Has community edition332
- Easy setup236
- Familiar interface235
- Includes many features, including ci130
- Nice UI106
- Good integration with gitlabci80
- Simple setup52
- Has an official mobile app32
- Free private repository30
- Continuous Integration24
- Open source, great ui (like github)16
- Slack Integration14
- Full CI flow9
- User, group, and project access management is simple8
- Free and unlimited private git repos8
- Intuitive UI7
- All in one (Git, CI, Agile..)7
- Built-in CI6
- Both public and private Repositories4
- Mattermost Chat client3
- Integrated Docker Registry3
- It's fully integrated2
- Unlimited free repos & collaborators2
- I like the its runners and executors feature2
- So easy to use2
- One-click install through DigitalOcean2
- It's powerful source code management tool2
- Build/pipeline definition alongside code2
- Security and Stable2
- Issue system2
- Free private repos2
- Low maintenance cost due omnibus-deployment2
- Powerful Continuous Integration System1
- Powerful software planning and maintaining tools1
- Groups of groups1
- Kubernetes integration with GitLab CI1
- Review Apps feature1
- Built-in Docker Registry1
- Because is the best remote host for git repositories1
- Not Microsoft Owned1
- Full DevOps suite with Git1
- Many private repo1
- Native CI1
- HipChat intergration1
- Kubernetes Integration1
- Published IP list for whitelisting (gl-infra#434)1
- Great for team collaboration1
- It includes everything I need, all packaged with docker1
- Multilingual interface1
- The dashboard with deployed environments1
- Supports Radius/Ldap & Browser Code Edits0
- Slow ui performance26
- Introduce breaking bugs every release6
- Insecure (no published IP list for whitelisting)5
- Built-in Docker Registry0
- Review Apps feature0
related GitLab posts
I have mixed feelings on GitHub as a product and our use of it for the Zulip open source project. On the one hand, I do feel that being on GitHub helps people discover Zulip, because we have enough stars (etc.) that we rank highly among projects on the platform. and there is a definite benefit for lowering barriers to contribution (which is important to us) that GitHub has such a dominant position in terms of what everyone has accounts with.
But even ignoring how one might feel about their new corporate owner (MicroSoft), in a lot of ways GitHub is a bad product for open source projects. Years after the "Dear GitHub" letter, there are still basic gaps in its issue tracker:
- You can't give someone permission to label/categorize issues without full write access to a project (including ability to merge things to master, post releases, etc.).
- You can't let anyone with a GitHub account self-assign issues to themselves.
- Many more similar issues.
It's embarrassing, because I've talked to GitHub product managers at various open source events about these things for 3 years, and they always agree the thing is important, but then nothing ever improves in the Issues product. Maybe the new management at MicroSoft will fix their product management situation, but if not, I imagine we'll eventually do the migration to GitLab.
We have a custom bot project, http://github.com/zulip/zulipbot, to deal with some of these issues where possible, and every other large project we talk to does the same thing, more or less.
We use GitLab CI because of the great native integration as a part of the GitLab framework and the linting-capabilities it offers. The visualization of complex pipelines and the embedding within the project overview made Gitlab CI even more convenient. We use it for all projects, all deployments and as a part of GitLab Pages.
While we initially used the Shell-executor, we quickly switched to the Docker-executor and use it exclusively now.
We formerly used Jenkins but preferred to handle everything within GitLab . Aside from the unification of our infrastructure another motivation was the "configuration-in-file"-approach, that Gitlab CI offered, while Jenkins support of this concept was very limited and users had to resort to using the webinterface. Since the file is included within the repository, it is also version controlled, which was a huge plus for us.
related Argo posts
related Armory posts
LaunchDarkly is almost a five year old company, and our methodology for deploying was state of the art... for 2014. We recently undertook a project to modernize the way we #deploy our software, moving from Ansible-based deploy scripts that executed on our local machines, to using Spinnaker (along with Terraform and Packer) as the basis of our deployment system. We've been using Armory's enterprise Spinnaker offering to make this project a reality.
- Infrastructure as code4
- Open source3
- Easy setup2
- Testability and reproducibility1
related Helm posts
We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).
We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .
Read the blog post to go more in depth.
Our backend consists of two major pools of machines. One pool hosts the systems that run our site, manage jobs, and send notifications. These services are deployed within Docker containers orchestrated in Kubernetes. Due to Kubernetes’ ecosystem and toolchain, it was an obvious choice for our fairly statically-defined processes: the rate of change of job types or how many we may need in our internal stack is relatively low.
The other pool of machines is for running our users’ jobs. Because we cannot dynamically predict demand, what types of jobs our users need to have run, nor the resources required for each of those jobs, we found that Nomad excelled over Kubernetes in this area.
We’re also using Helm to make it easier to deploy new services into Kubernetes. We create a chart (i.e. package) for each service. This lets us easily roll back new software and gives us an audit trail of what was installed or upgraded.
- Leading docker container management solution152
- Simple and powerful121
- Open source96
- Backed by google73
- The right abstractions56
- Scale services24
- Replication controller17
- Permission managment9
- Supports autoscaling5
- No cloud platform lock-in3
- Open, powerful, stable3
- Promotes modern/good infrascture practice3
- Cloud Agnostic2
- Backed by Red Hat2
- Custom and extensibility2
- Quick cloud setup2
- Captain of Container Ship2
- A self healing environment with rich metadata2
- Everything of CaaS1
- Easy setup1
- Runs on azure1
- 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...