Docker is often introduced as a way to “package an application.” That is true, but it undersells the real benefit: containers make the assumptions around an application visible. The PHP version, extensions, operating system packages, web-server configuration, queue worker command, and startup sequence can all live beside the code instead of being scattered across wiki pages and long-lived servers.

That visibility is what makes Docker valuable for backend teams. A container will not automatically create a good architecture, fast database queries, or safe deployments. It does make it much harder to ignore the environment your software actually needs.

Think of a container as a runnable contract

A Docker image is an immutable template. A container is a running instance of that template. The image describes the filesystem and default process; configuration supplied at runtime provides environment-specific values such as database credentials, log levels, and service URLs.

This distinction matters. Application code and its runtime dependencies belong in the image. Secrets and deployment-specific settings do not. When those concerns are mixed, a supposedly portable image becomes tied to one environment and is harder to promote from development to staging to production.

For a PHP API, a useful contract might say: this service runs PHP-FPM, includes the required PHP extensions, contains the application code, listens through its process manager, and expects a database URL and cache endpoint at runtime. That contract is concrete enough for a developer laptop and a production platform to honor in different ways.

Start with a small, explicit PHP image

The first Dockerfile should be boring. Boring is good: it gives reviewers a clear view of what enters the runtime and why.

FROM php:8.3-fpm

WORKDIR /var/www/app

RUN docker-php-ext-install pdo_mysql

COPY --from=composer:2 /usr/bin/composer /usr/bin/composer

COPY composer.json composer.lock ./
RUN composer install --no-dev --prefer-dist --no-interaction --no-scripts

COPY . .

RUN composer dump-autoload --optimize --no-dev

CMD ["php-fpm"]

This example assumes an application that uses MySQL through PDO. If the application needs another extension, add it deliberately and verify its build requirements. Avoid installing a broad collection of packages “just in case.” Every dependency increases image size, patching work, and the number of ways a build can fail.

The order of instructions is intentional. Composer manifests are copied before the rest of the source so Docker can reuse the dependency-installation layer when only application code changes. That makes local iteration and continuous integration noticeably less wasteful without obscuring the build.

In a production image, development-only dependencies are excluded. That is not merely an optimization: tools intended for local debugging should not silently become production runtime requirements. If a deployment needs build assets, generated code, or cache warming, make those steps explicit and make sure they can run without relying on local files that were never copied into the image.

Compose services, but keep their responsibilities separate

Most applications are not one process. A typical backend needs an HTTP entry point, a database, a cache, and perhaps a worker. Docker Compose is useful in development because it records how those services connect without pretending they are a single machine.

services:
  app:
    build: .
    environment:
      DATABASE_URL: mysql://app:secret@db:3306/app
    depends_on:
      db:
        condition: service_healthy

  db:
    image: mysql:8
    environment:
      MYSQL_DATABASE: app
      MYSQL_USER: app
      MYSQL_PASSWORD: secret
      MYSQL_ROOT_PASSWORD: root-secret
    healthcheck:
      test: ["CMD", "mysqladmin", "ping", "-h", "localhost"]
      interval: 5s
      timeout: 3s
      retries: 10
    volumes:
      - mysql-data:/var/lib/mysql

volumes:
  mysql-data:

The application connects to db, not localhost, because each service has its own network namespace. This is one of the most common early surprises. Inside a container, localhost means that container itself.

A health check is also more meaningful than a simple start-order rule. A database process can exist before it can accept connections. Even so, application code should still handle transient connection failures: dependency health checks improve startup behavior, but they do not eliminate network interruptions, database restarts, or maintenance events.

Make startup safe to repeat

Container orchestration expects processes to stop and start. Deployments replace instances. Platforms reschedule them. A worker may be restarted after a crash. That means startup tasks need careful boundaries.

Schema migrations are a classic example. Running them automatically in every web container can create races when several replicas start together. A safer approach is to run migrations as one controlled deployment step, using the same built image and the same runtime configuration as the application. Migrations themselves should be designed to tolerate retries where practical, and large data changes should be planned separately from a request-serving rollout.

The same principle applies to queues. Run a queue worker as its own service with a clear command, resource limit, retry policy, and graceful shutdown behavior. Do not hide a worker behind a background shell command in the web container. Docker can supervise one primary process well; combining unrelated long-running processes makes logs, signals, scaling, and failures ambiguous.

Use containers to improve operations, not just onboarding

A container image becomes most valuable when the artifact tested in continuous integration is the artifact deployed. Build it once, tag it immutably, run tests against it where appropriate, then promote that exact image. Rebuilding from the same Git revision at each environment introduces avoidable variables: package repositories change, base images are updated, and a build may not be as reproducible as it appears.

Observability should follow the same pragmatic approach. Write application logs to standard output and standard error so the runtime can collect them. Keep configuration in environment variables or an approved configuration mechanism. Send termination signals to the application process and allow it time to finish active work. These choices are simple, but they make failures diagnosable when they occur outside a developer machine.

• Use a .dockerignore file to exclude dependency directories, test artifacts, local configuration, and other files the image does not need.
• Run the application as a non-root user when the base image and deployment allow it.
• Pin important base-image versions and review updates as part of normal maintenance.
• Keep persistent data in managed storage or named volumes, never in the writable container filesystem.
• Document the few commands developers actually need, such as build, test, migration, and shell access.

The pragmatic measure of success

Docker is not a badge of engineering maturity. It is useful when it reduces uncertainty: a new developer can start the stack reliably, a CI job runs against the expected runtime, and production receives an artifact whose contents are understood.

Keep the Dockerfile readable, the image focused, services separate, and deployment steps explicit. When containers reveal a fragile dependency or an unclear startup sequence, treat that as useful feedback about the system. The strongest container strategy is not the most elaborate one; it is the one that makes reliable delivery feel routine.