Docker

Container Infrastructure with Docker

Docker is a container platform that packages applications and their dependencies into isolated containers, eliminating the "it works on my machine" problem. It provides a completely consistent runtime environment across developer machines, CI/CD pipelines and production servers.

At Detartech we use Docker as the infrastructure layer for all our projects. We separate development and production images with multi-stage Dockerfiles, producing small, secure images by removing unnecessary dependencies. With Docker Compose we spin up databases, caches, message queues and application services in the local development environment with a single command.

Where does Detartech use Docker?

For us Docker sits at the image step of the delivery pipeline. As described on our DevOps and CI/CD service page, the build output becomes a single versioned image; the image that was tested is the image that ships, and it runs in staging and production without being rebuilt. The difference between environments lives in the environment variables the image reads, not in the code. Rollback relies on the same structure: the previous image is kept ready, the deploy command takes a version parameter and going back is a single step.

When is Docker the right choice?

Docker is the right starting point when the application has to behave the same on a developer machine, in CI and in production, when several services such as a database, a cache and a queue need to start together locally, or when fast and safe rollback is a requirement. Once the decision to containerise has been made, it is our default choice.

When is Docker not enough on its own?

Docker is a packaging tool, not an orchestrator. If services have to scale independently, deployments must happen without downtime and a crashed service should come back by itself, a layer such as Kubernetes is needed on top of the containers; for a single-service application with predictable traffic that layer brings more operational load than benefit. If the bottleneck is load rather than the pipeline, adding containers is not the fix: the work moves to backend scaling, meaning architecture, data access and caching.