In The History of Failed Initiatives I mentioned that microservices as a concept existed for a long time. And yet, most of those who tried organizing their architecture around microservices failed or, to use different words, realized that benefits are shadowed by the increase in complexity (especially in operations). The spark that was born long ago became a star only recently. In order to understand why microservices became a hot topic not long ago, we need to understand better why they failed in the past. The explanation is simple. We neither had the tools nor understood the logic we had to apply. Do we have the tools today? Are we capable of putting in place the right procedures and have the mindset microservices require. I’d say yes, we have and we do. When I say we I mean people with the will and resources to make this happen. As in most other cases, once something new is proven and adopted by some, years need to pass until that something is applied by many. The good news is that I think that we are getting there and, slowly, the more conservative part of the industry (in other words, most of it) is starting to at least consider microservices as an option. Even Gartner had positive words for it. And when Gartner says jump, enterprise jumps. Continue reading →
In the previous article we switched from manual to automatic deployment with Jenkins and Ansible. In the quest for zero-downtime we employed Consul to check health of our services and, if one of them fails, initiate deployment through Jenkins.
In this article we’ll explore how to scale individual services.
In the previous article we manually deployed the first version of our service together with a separate instance of the Mongo DB container. Both are (probably) running on different servers. Docker Swarm decided where to run our containers and Consul stored information about service IPs and ports as well as other useful information. That data was used to link one service with another as well as to provide information nginx needed to create proxy.
We’ll continue where we left and deploy a second version of our service. Since we’re practicing blue/green deployment, the first version was called blue and the next one will be green. This time there will be some additional complications. Deploying the second time is a bit more complicated since there are additional things to consider, especially since our goal is to have no downtime.
The previous article showed how scaling across the server farm looks like. We’ll continue where we left and explore details behind the presented implementation. Orchestration has been done through Ansible. Besides details behind tasks in Ansible playbooks, we’ll see how the same result could be accomplished using manual commands in case you might prefer a different orchestration/deployment framework.
Now it’s time to extend what we did in previous articles and scale services across any number of servers. We’ll treat all servers as one server farm and deploy containers not to predefined locations but to those that have the least number of containers running. Instead of thinking about each server as an individual place where we deploy, we’ll treat all of them as one unit.
This article tries to provide one possible way to set up the Continuous Integration, Delivery or Deployment pipeline. We’ll use Jenkins, Docker, Ansible and Vagrant to set up two servers. One will be used as a Jenkins server and the other one as an imitation of production servers. First one will checkout, test and build applications while perform deployment and post-deployment tests. Continue reading →
The previous article described several ways to implement Continuous Deployment. Specifically, it described, among other things, how to implement it using Docker to deploy applications as containers and nginx for reverse proxy necessary for successful utilization of blue-green deployment technique. All that was running on top of CoreOS, operating system specifically designed for running Docker containers.
In this article we’ll try to do the same process using Ansible (an open-source platform for configuring and managing computers). Instead of CoreOS, we’ll be using Ubuntu.
The previous article described several Continuous Deployment strategies. In this one we will attempt to provide one possible solution for reliable, fast and automatic continuous deployment with ability to test new releases before they become available to general users. If something goes wrong we should be able to rollback back easily. On top of that, we’ll try to accomplish zero-downtime. No matter how many times we deploy our applications, there should never be a single moment when they are not operational. Continue reading →
Previous article provided introduction to continuous deployment. In this one we’ll continue where we left and explore different strategies to deploy software. The article is in no way an exhaustive list of ways to deploy applications but tries to provide few common ways that are in use today. Continue reading →
Continuous deployment is the ultimate culmination of software craftsmanship. Our skills need to be on such a high level that we have a confidence to continuously and automatically deploy our software to production. It is the natural evolution of continuous integration and delivery. We usually start with continuous integration with software being built and tests executed on every commit to VCS. As we get better with the process we proceed towards continuous delivery with process and, especially tests, so well done that we have the confidence that any version of the software that passed all validation can be deployed to production. We can release the software any time we want with a click of a button. Continuous deployment is accomplished when we get rid of that button and deploy every “green” build to production. Continue reading →