30 posts tagged with "Docker"

Docker's legacy builder processes Dockerfile instructions sequentially, one layer at a time, even when stages have no dependency on each other. BuildKit changes this fundamentally — it analyzes the build graph, parallelizes independent stages, provides smarter caching, and adds features that were simply impossible before: secret mounts that never leak into image layers, SSH forwarding for private repos, and building images for architectures your machine does not even run.

You have containers running on a single host. Now you need them running across five hosts with load balancing, rolling updates, and automatic restarts. Kubernetes is one answer, but it comes with a steep learning curve and significant operational overhead. Docker Swarm is built into the Docker Engine, requires no additional installation, and can have a production cluster running in under ten minutes.

Containers are ephemeral. When a container is removed, everything inside it — application data, uploaded files, database tables — is gone. Docker offers three storage mechanisms to persist data beyond the container lifecycle, and choosing the wrong one causes problems ranging from poor performance to data loss. Here is when and why to use each.

Your application ran fine for three weeks. Then at 2 AM, the container was killed with exit code 137. No error in the application logs. No exception. Just dead. The culprit: the Linux OOM (Out of Memory) killer. Your container consumed all available memory, and the kernel chose violence. Resource limits prevent this — but most people never set them, and those who do often set them wrong.

Your application logs are the single most important debugging tool you have. In a containerized world, those logs disappear when the container is removed — unless you have a logging strategy. Most teams start with docker logs and stop there. That works for a single container. It falls apart completely at 50 containers across 10 services.

You are building Docker images on your laptop and pushing them to production with docker push. It works until it does not — someone forgets to run tests, pushes a debug build, or tags latest over a stable release. CI/CD pipelines eliminate these human errors by making every build reproducible, tested, and traceable to a specific commit.