Docker

Docker lets you package an application together with the runtime, libraries and configuration it needs to run. This package is called an image, and a running instance of it is called a container. The team can use the same definition locally, in testing and during deployment.

In practice, this cuts down on situations where a feature works on a developer’s computer but not in a test environment with a different version of PHP, Node.js or a system library. The configuration lives in the repository and is reviewed along with the code.

We can containerise a new application or tidy up an existing set of services. Before we write a Dockerfile, we check the start-up process, persistent data, dependencies, secrets and how updates are carried out. A container is a deployment tool, not a replacement for infrastructure design.

Docker is most valuable when several people work on an application, the project uses many services, or changes regularly go through testing and staging. Every developer can run the required versions of the database, cache and back end without configuring the whole system by hand.

Containers also help when several projects share one infrastructure. Dependencies are kept separate, and a new version of the application can be built as a separate image. If something goes wrong, it is easier to roll back to the previous build, as long as data migrations and the deployment process were also planned with rollback in mind.

For a single simple website, Docker can be an extra layer with no clear benefit. We do not suggest it by default. We look at how often you deploy, the number of environments, the team’s skills and the cost of maintaining the whole process.

A Compose file can describe the application, database, cache, queue and any other services needed. A new person on the project starts the whole set with one command, instead of rebuilding the setup from out-of-date instructions. You still need to prepare seed data and documentation, but everyone starts from the same point.

In CI/CD, the same image can go through tests, a security scan and publication to a registry. The production environment pulls a tested build instead of building code on the server in an ad hoc way. This means you know exactly which version of the application was deployed and which dependencies it uses.

The pipeline should stop a release when important tests fail or the image contains unacceptable issues. Automation does not remove decisions: it records them in a repeatable process that can be reviewed and improved.

First, we list all dependencies: the language version, extensions, scheduled tasks, workers, the database, user files and connections to external services. Then we build the image and a local set of services. We do not move production until we can recreate the application and run its tests.

We keep persistent data outside the container’s writable layer. This covers the database, uploaded files and any other information that must survive replacing an instance. We also agree how schema migrations are run and the order in which services start.

The move can happen in stages. First, Docker tidies up the development environment, then CI, and finally production. This lets the team learn the tool and spot missing assumptions before changing a critical environment.

Docker Compose is good for describing applications made up of a few containers and is convenient locally and for simpler deployments. When you need instances spread automatically across many nodes, self-healing, advanced scaling and controlled rollouts, orchestration comes into the picture.

Kubernetes solves the problems of large environments, but it brings clusters, manifests, network policies, observability and extra operational responsibility. A small system does not get better just because it runs on Kubernetes. Sometimes a few containers on a well-managed machine are simpler and cheaper.

We match the level of infrastructure to the load, the required recovery time and the team’s skills. We also leave room to grow if the number of services or the traffic really does increase.

A production image should contain only what the application needs. Fewer packages mean a smaller attack surface and faster downloads. We pin base image versions deliberately, update them regularly and scan them for known vulnerabilities.

We do not store secrets in the Dockerfile or the repository. Passwords, tokens and keys go into the secrets management tool suited to the environment. We run containers with minimal permissions and, where possible, without the root user and with limited access to the host.

Container isolation is not a complete security boundary. You still need to update the host, restrict the network, control the registry and know who can publish images. Security covers the whole chain, from dependencies to running in production.

A container can be restarted automatically, but that does not mean the application is working properly. You need health checks, metrics, central logs and alerts for the most important processes. A shop can respond to visitors and still fail to pass orders on to the warehouse.

Backups cover persistent data, configuration and the restore procedure. The application image itself can be pulled again from the registry, but the database and user files cannot be recovered without a tested copy. We regularly test the restore scenario in a separate environment.

We prepare updates as new images and deploy them in a controlled way. Before changing dependencies we run tests, and after a release we watch for errors and key metrics. This makes the process repeatable while keeping it under the team’s control.

The cost depends on the number of services, the state of the current application, security requirements and the target infrastructure. For a simple project, the scope may cover a Dockerfile, Compose and a pipeline. For a critical system, it also includes a registry, monitoring, high availability, secrets management and emergency procedures.

Containers can cut the time spent configuring environments and make deployments easier. They do not, however, guarantee a lower server bill. Poorly chosen orchestration can raise operating costs more than the application itself.

In our estimate, we separate containerising the code from building the infrastructure and ongoing support. This lets you choose the stage that solves your current problem, without rolling out the whole ecosystem from day one.

We can set up an environment for a new application, containerise an existing system or tidy up your current images and pipeline. We start with a short review of dependencies, persistent data and how you deploy. Then we propose the target scope and the order of changes.

If the biggest problem is differences between team members’ computers, the first stage may cover development only. If deployments are manual and hard to repeat, we will focus on the production image and CI/CD. We will choose the infrastructure only once availability requirements are agreed.

Let’s talk about containerising your application and find where Docker will add the most value.