A software development life cycle is divided into several stages, out of which deployment is considered the last and most crucial phase. When the entire software is developed and tested with positive results, the codes need to be pushed into the production environment from the development or offshore environment. The process of transferring code from one environment to the other is known as deployment. DevOps considers deployment to be one of the major phases of the entire SDLC, which is why many new innovative methods have been introduced.
Why are Traditional Deployment Procedures Ineffective?
Until the introduction of the DevOps process, deployment was done traditionally. Here, professionals gathered all codes, libraries, configurations, and other documents and uploaded them to the repository. Once all codes are committed, class files are given. These files are uploaded into the deployment software, which pushes the code further into the production environment.
If there is any issue in the class files or the deployment commands have glitches, issues will arise, and the server won’t be up till they are resolved. This was one of the main reasons professionals needed to integrate new deployment processes. Apart from this, DevOps professionals encountered a couple of more problems like:
- If the code needs to be released into multiple environments simultaneously, it increases the load and introduces discrepancies.
- Often deployment processes get stuck with system exceptions or null pointer errors due to sudden changes in codes that are yet to be locked.
- While giving incremental class files for immediate code deployment, servers need to be shut down till the process is completed.
Top Innovative Deployment Practices for DevOps
With so many issues in the traditional deployment processes, it has become imperative for DevOps professionals to adopt innovative approaches. These can resolve the pain points of deployment processes and reduce server downtime. Over the years, several such approaches have been adopted, out of which the top ones are:
Brief Introduction of Deployment Strategies
Canary deployment is considered a staged release of codes into the target environment. For example, first, 25% of the developed software is made available to 10 users out of 100. The other 90 users will use the old, stable version of the software. Once the deployed codes are tested, and positive feedback, another 25% of codes are released into the same target environment. The process continues in the staged process, where a particular section of codes is rolled into production.
In this deployment technology, two application versions are maintained. When version A is deployed, its corresponding server is bounced, but the server on which version B operates is up. A toggle is done between two servers until both versions of the entire software are deployed ultimately. In this methodology, server downtime is significant, and thus, one must consider it when uptime isn’t a performance metric.
In this deployment strategy, two environments with identical features are used. One environment is used for staging or testing purposes and is marked as blue. Another environment is the production or live one and is marked as green. Codes are deployed in both environments. However, testing and QA are done in the blue environment mainly. Once the changes and updates are adopted and all bugs are fixed, traffic from the green environment is shifted to the blue one, and it is then considered the live version.
A/B Testing Deployment
A/B testing deployment is done by routing codes to a specific group of users under specific conditions. Each user has to perform on different versions of the modified codes. Once the results are gathered, statistical analysis is performed to check which version is closer to the expected results. Only that particular software version will be rolled out into the deployment server.
In this technique, service requests are processed from the live environment and routed to the environment where new features have been deployed. This way, one can test the changes and their impacts in real-life scenarios without causing any impact on live customers.