Tag Archives: Continuous integration

Continuous Deployment: Implementation with Ansible and Docker

This article is part of the Continuous Integration, Delivery and Deployment series.

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.

Source code used in this article can be found in the GitHub repo vfarcic/provisioning (directory ansible).
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Continuous Deployment: Implementation

This article is part of the Continuous Integration, Delivery and Deployment series.

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.
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Continuous Deployment: Strategies

This article is part of the Continuous Integration, Delivery and Deployment series.

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.
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Continuous Deployment: Introduction

This article is part of the Continuous Integration, Delivery and Deployment series.

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.
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Continuous Delivery: Code Coverage

This article is part of the Continuous Integration, Delivery and Deployment series.

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.

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.
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Feature Toggles (Feature Switches or Feature Flags) vs Feature Branches

This article is part of the Continuous Integration, Delivery and Deployment series.

Feature Branches

If you are using branches, you are not doing Continuous Integration/Deployment/Delivery!

You might have great Code Coverage with unit tests, you might be doing TDD, you might have functional and integrations tests written in BDD format and you might run all of them on every commit to the repository. However, if you are having branches, integration is delayed until they are merged and that means that there is no continuous integration.
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BDD (Behavior-Driven Development): Missing Piece in the Continuous Integration Puzzle

This article is part of the Continuous Integration, Delivery and Deployment series.

Behavior-Driven Development (BDD) is a process or it can be a tool. In many cases, BDD is both. However, it should not be a goal in itself. The goal of software development is to deliver quality as fast and as cheap as possible. The only real measure of quality is whether it fulfills user needs in a reliable manner. The best way we can take to accomplish that goal is through continuous integration, deployment and delivery. For the sake of this article I will ignore the differences between those three and refer to all of them as continuous integration or CI.
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Continuous Delivery: Unit Tests

This article is part of the Continuous Integration, Delivery and Deployment series.

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

tddBestPracticesAllTestsPassUnit 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.
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Continuous Delivery: Static Analysis

This article is part of the Continuous Integration, Delivery and Deployment series.

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.
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Continuous Delivery: CI Tools Setup

This article is part of the Continuous Integration, Delivery and Deployment series.

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.
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