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 internally520
- Free open source464
- Great to build, deploy or launch anything async314
- Tons of integrations243
- Rich set of plugins with good documentation210
- Has support for build pipelines110
- Open source and tons of integrations72
- Easy setup65
- It is open-source62
- Workflow plugin54
- Configuration as code11
- Very powerful tool10
- Many Plugins9
- Continuous Integration9
- Great flexibility8
- Git and Maven integration is better8
- 100% free and open source7
- Github integration6
- Slack Integration (plugin)6
- Easy customisation5
- Self-hosted GitLab Integration (plugin)5
- Docker support4
- Pipeline API4
- Platform idnependency3
- Excellent docker integration3
- Fast builds3
- Hosted Externally3
- AWS Integration2
- It's Everywhere2
- Can be run as a Docker container2
- It`w worked2
- Easily extendable with seamless integration1
- Build PR Branch Only1
- NodeJS Support1
- PHP Support1
- Ruby/Rails Support1
- Universal controller1
- Loose Coupling1
- Workarounds needed for basic requirements12
- Groovy with cumbersome syntax9
- Plugins compatibility issues7
- Lack of support6
- Limited abilities with declarative pipelines6
- No YAML syntax4
- Too tied to plugins versions3
related Jenkins 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.
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.
- 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 complicated10
related Rancher posts
- Infrastructure as code114
- Declarative syntax73
- Cloud agnostic7
- It's like coding your infrastructure in simple English6
- Platform agnostic5
- Immutable infrastructure5
- Automates infrastructure deployments4
- 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 hosted497
- Has community edition335
- Easy setup239
- Familiar interface238
- Includes many features, including ci131
- Nice UI108
- Good integration with gitlabci81
- Simple setup54
- Has an official mobile app33
- Free private repository32
- Continuous Integration29
- Open source, great ui (like github)19
- Slack Integration15
- Full CI flow11
- Free and unlimited private git repos10
- User, group, and project access management is simple8
- Built-in CI7
- Intuitive UI7
- All in one (Git, CI, Agile..)7
- Both public and private Repositories4
- Integrated Docker Registry3
- Mattermost Chat client3
- Issue system3
- One-click install through DigitalOcean2
- Because is the best remote host for git repositories2
- Full DevOps suite with Git2
- Free private repos2
- Great for team collaboration2
- Unlimited free repos & collaborators2
- It's fully integrated2
- I like the its runners and executors feature2
- So easy to use2
- It's powerful source code management tool2
- Build/pipeline definition alongside code2
- Security and Stable2
- Low maintenance cost due omnibus-deployment2
- Multilingual interface1
- Kubernetes integration with GitLab CI1
- Review Apps feature1
- Powerful software planning and maintaining tools1
- Groups of groups1
- Built-in Docker Registry1
- Not Microsoft Owned1
- Many private repo1
- Published IP list for whitelisting (gl-infra#434)1
- The dashboard with deployed environments1
- HipChat intergration1
- Kubernetes Integration1
- Native CI1
- Powerful Continuous Integration System1
- It includes everything I need, all packaged with docker1
- Supports Radius/Ldap & Browser Code Edits0
- Slow ui performance27
- Introduce breaking bugs every release7
- Insecure (no published IP list for whitelisting)5
- Built-in Docker Registry1
- 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.
- Online service, no need to install anything1
- Autosinchronize the changes to deploy1
- Open Source1
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 code6
- Open source4
- 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.
Kubernetes powers our #backend services as it is very easy in terms of #devops (the managed version). We deploy everything using @helm charts as it provides us to manage deployments the same way we manage our code on GitHub . On every commit a CircleCI job is triggered to run the tests, build Docker images and deploy them to the registry. Finally on every master commit CircleCI also deploys the relevant service using Helm chart to our Kubernetes cluster
- Leading docker container management solution161
- Simple and powerful126
- Open source102
- Backed by google75
- The right abstractions56
- Scale services24
- Replication controller19
- Permission managment9
- Supports autoscaling7
- No cloud platform lock-in4
- Open, powerful, stable3
- Quick cloud setup3
- Promotes modern/good infrascture practice3
- Backed by Red Hat2
- Cloud Agnostic2
- Runs on azure2
- Custom and extensibility2
- Captain of Container Ship2
- A self healing environment with rich metadata2
- Easy setup1
- Everything of CaaS1
- Poor workflow for development14
- Steep learning curve12
- Orchestrates only infrastructure6
- High resource requirements for on-prem clusters3
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...