DevOps Life Cycle
DevOps is not a tool or a term, it is the process or a methodology of using various tools to solve the problems between Developers and Operations teams.
Previously in traditional IT environment, the two teams worked for a common goal that is development and operation through which the software is made and released. The development team worked on the software, developing it and making sure that the code worked perfectly. After hours of hard work and a lot of trial and error, the team release the code and sent to operation team for execution.
The operations team will check the application performance and report back any error or bug back to the development team. Again, the development team will check and upgrade the code and send back to operation team, this process keeps rotating until the entire software is free from error before its final release.
Now the process seems so simple, but there are numerous problems arises between the teams.
For instance, let us say, the development team developed a code using an i7 processor, 8GB RAM, OS as Ubuntu, and php 5.6 scripting language, whereas the Operations team ran the same code using i5 processor, 16GB RAM, OS as Centos and php 7.0 programming language. When the operations team ran the same code, it wouldn’t work. The reason for this could be the difference in the system environment or any missing software library. The operations team flagged this code as faulty, even though the problem could exist in their own system. This resulted in a lot of back and forth between the Developers and the Operations team.
To bridge this gap and break the wall of confusion and mismanagement between Development(‘Dev’) team and Operations (‘Ops’) team, collaborated effort of DevOps came into existence.
The DevOps consist of infinite sign suggesting that it is a continuous process of improving consistency and constant activity. The DevOps approach makes the company to adopt faster updates and make the development changes.
The DevOps culture is implemented in several stages with several tools. Let’s, understand the entire process under various phases of DevOps Life Cycle.
Planning stage: The first stage under DevOps is the planning stage. Here, the development team puts down the plan keeping in mind the application and objectives that are to be delivered to the customer.
Coding stage: The second stage is the preparation of coding. Once the plan is made the development team then prepares the coding to suit the application. The development team works on the same code and the different versions of the code are stored in a repository, just like a code bank. Here with the help of tool like git the codes are merged when required. This process is called version control.
Built stage: Under this sage the codes are made executable. For such execution special tools are involved. The tools like Maven and Gradle are used. Gradle is based on developing domain-specific language projects. It uses a Groovy-based Domain-specific language (DSL) for creating project structure. whereas Maven is based on developing pure Java language-based software. Maven uses Extensible Markup Language (XML) for creating project structure.
Continuous Test stage: After the code is made executable, the code is then passes through rigorous testing stage for any error or bug. The most famous tool to automate this testing is Selenium. Selenium is a free automated testing framework use to validate web applications across different web browsers and platforms. To create a selenium test script various languages can be used like java, c# , python etc. Selenium Software is not just a single tool but a suite of software, each piece catering to different Selenium QA testing needs of an organization. Once the codes are free from errors and bugs the development team sends the code to operation team.
Continuous deployment stage: At this stage, the code is deployed to run in production on a public server. Code must be deployed in a way that doesn’t affect already functioning features and can be available for a large number of users. Frequent deployment allows for a “fail fast” approach, meaning that the new features are tested and verified early. There are various automated tools that help engineers deploy a product increment. The most popular are Chef, Puppet, Azure Resource Manager, and Google Cloud Deployment Manager. After Deployment the code is made run under various software container like Docker, ansible, Kubernetes etc. These are an open source containerization platform that enables developers to package applications into containers—standardized executable components combining application source code with the operating system (OS) libraries and dependencies required to run that code in any environment.
Continuous monitoring stage: The final stage of the DevOps lifecycle is oriented to the assessment of the whole cycle. The goal of monitoring is detecting the problematic areas of a process with tool like Naglos and analyzing the feedback from the team and users to report existing inaccuracies and improve the product’s functioning.
Continuous integration and continuous delivery (CI/CD): After passing through automated testing the code then integrated in a single, shared repository on a server. Developer’s continuous integration merge their changes back to the main branch as often as possible. These changes are validated by creating a build and running automated tests against the main build, avoiding integration challenges that can happen when waiting for release day to merge changes into the release branch.
Continuous delivery is an approach that merges development, testing, and deployment operations into a streamlined process as it heavily relies on automation. This stage enables the automatic delivery of code updates into a production environment.
Infrastructure as a code: Infrastructure as a code (IaC) is an infrastructure management approach that makes continuous delivery and DevOps possible. It entails using scripts to automatically set the deployment environment (networks, virtual machines, etc.) to the needed configuration regardless of its initial state.
Without IaC, engineers would have to treat each target environment individually, which becomes a tedious task as you may have many different environments for development, testing, and production use.
Having the environment configured as code, you
- Can test it the way you test the source code itself and
- Use a virtual machine that behaves like a production environment to test early.
Once the need to scale arises, the script can automatically set the needed number of environments to be consistent with each other.
Containerization: Virtual machines emulate hardware behavior to share computing resources of a physical machine, which enables running multiple application environments or operating systems (Linux and Windows Server) on a single physical server or distributing an application across multiple physical machines.
Containers, on the other hand, are more lightweight and packaged with all runtime components (files, libraries, etc.) but they don’t include whole operating systems, only the minimum required resources. Containers are used within DevOps to instantly deploy applications across various environments and are well combined with the IaC approach described above. A container can be tested as a unit before deployment. Currently, Docker provides the most popular container toolset.
Microservices: The microservice architectural approach entails building one application as a set of independent services that communicate with each other but are configured individually. Building an application this way, you can isolate any arising problems ensuring that a failure in one service doesn’t break the rest of the application functions. With the high rate of deployment, microservices allow for keeping the whole system stable, while fixing the problems in isolation.
Cloud infrastructure: Today most organizations use hybrid cloud as a combination of public and private ones. But the shift towards fully public clouds (i.e. managed by an external provider such as AWS or Microsoft Azure) continues. While cloud infrastructure isn’t a must for DevOps adoption, it provides flexibility, toolsets, and scalability to applications. With the recent introduction of serverless architectures on clouds, DevOps-driven teams can dramatically reduce their effort by basically eliminating server-management operations.