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 →
In the previous article we explored unit tests as the first and fastest set of tests we should run. Now it’s time to see whether our unit tests provide enough code coverage.
Unit tests by themselves do not provide enough confidence unless we know that they cover significant code coverage. Having all tests successful while, for example, covering only 15% of the code cannot provide enough trust.
Mature teams might not need to measure code coverage. They might know from experience that their unit tests are covering as much code as the project they’re working on needs. Teams like that tend to have years of practice with Test-driven development (TDD). However, for the majority of us, tools that measure the coverage are very indeed useful addition to our tool-belt. Continue reading →
In the previous article we explored static analysis as one of the first steps in Continuous Delivery. Our journey will continue with unit tests.
Unit tests are probably the most important part of Continuous Delivery. While unit tests cannot substitute integration and functional tests, they are very easy to write and should be very fast to execute. As any other type of tests, each unit test should be independent of each other. What differentiates unit from other types of tests (integration, functional) is the scope. Each unit test should verify only a single unit of code (method, class, etc). Main benefit of unit tests are that they can find problems early due to their speed of execution. When ease of writing is combined with very short execution time it is no wonder that unit test often represent biggest part of automated tests. Continue reading →
The previous article CI Tools Setup ended with Jenkins up and running waiting for us to use it. Travis, on the other hand, was left aside and soon we’ll see why.
In this article we’ll explore static analysis as the first type of tasks that we should do in our delivery pipeline. Since that is our starting point, let’s go through the brief explanation. Continue reading →
We’ll continue where we left in Introduction to concepts and tools. The goal of this article is to set up a Jenkins server locally through automated and repeatable process with all the artifacts stored in the GIT repository. This will require tools like VirtualBox and Vagrant. It will also require registration to Docker. Unlike the previous article that provided only general information, in this one we’ll have to get our hands dirty and follow the examples.
What do we need in order to have a Jenkins server up and running locally? We need a virtual machine or available server, operating system installed and configured, Jenkins installed and configured. Since everything we do needs to be reliable and repeatable, we’ll also need a Version Control System. Examples in this article will use GitHub as VCS. Continue reading →
This is the first article of the Continuous Integration, Delivery and Deployment series. We’ll start out journey with brief explanation of Continuous Delivery. After short exploration of some of the tools used today, we’ll move towards the flow (from setting up brand new environment and getting the code from the repository to the creation of fully tested and verified distribution). Each section will present different approaches, compare different tools and, finally, provide some hand-on examples. After the flow, we’ll explore changes required in the development life cycle. Finally, we’ll dive into last steps required for the transition from Continuous Integration towards Continuous Delivery and Deployment.