Logs in continuous delivery pipelines are often entirely ignored, right up until something goes wrong. We usually find ourselves wishing we’d put some thought into our logs, once we’re in the midst of trawling through thousands of lines. In order to try to prevent this, we can add DevOps metrics into our logs, which will provide us with greater observability, and give insight into anything going wrong in our pipelines.
Nowadays, software development teams utilize continuous delivery or some variation, to create better, faster, more accurate software releases. Continuous delivery is a DevOps practice that empowers software teams to continuously ship code directly to an environment once automated tests pass. Continuous delivery is facilitated through the deployment pipeline. You can read more about it in a previous post.
GitLab CI/CD is a tool that is built into GitLab. It allows you to create automated tasks that you can use to form a Continuous Integration and Continuous Delivery / Deployment process. You configure GitLab CI/CD by adding a yaml file (called `.gitlab-ci.yml`) to your source repository. This file creates a pipeline, which will then run when a code change is pushed to the repository. Pipelines are made up of a series of stages, and each stage can each contain a number of jobs or scripts.
The standard Command Line Interface for Kubernetes (kubectl) is a very powerful tool for debugging or monitoring purposes. It is very inefficient, but just if you want to get a high-level overview of your Kubernetes cluster or want to work with multiple resources at the same time. A large number of graphical Kubernetes dashboards exist today and chances are that you already used the default Kubernetes dashboard or the one that comes with your cloud provider.
COVID-19 is leading to large-scale migrations away from on-premises environments, according to Codefresh’s second annual State of DevOps survey that revealed this and other surprising insights into the continued evolution of the industry. At the same time, DevOps automation continues to expand in scope and complexity with more and more processes becoming automated, and more involved technologies like Kubernetes continuing to gain strong traction.