Table of Contents
Why You Need Dockerfiles
Up to this point, you have worked with images created by others. To truly benefit from Docker in real projects, you must be able to create your own images. A Dockerfile is the standard way to describe how an image should be built, step by step, in a format that both humans and Docker can understand.
A Dockerfile turns a manual setup process into a repeatable recipe. Instead of logging into a machine, installing packages by hand, copying files, and tweaking configuration, you express all those steps as clear instructions. Docker then reads the Dockerfile and produces a new image, always in the same way, no matter which machine you build it on.
In practice, this means that your application environment becomes part of your codebase. It is version controlled, reviewed, and tested just like any other part of the project. Anyone who has the Dockerfile and your source code can build an environment that is identical to yours, which dramatically reduces the familiar “works on my machine” problem.
A Dockerfile is the single source of truth for how your custom image is built. If you install or change things manually inside a container without updating the Dockerfile, those changes are not reproducible and will be lost in the next build.
From Manual Setup to a Reproducible Recipe
Before Dockerfiles, setting up an application environment usually meant following a written guide. You might install a programming language, system libraries, and dependencies in a particular order. If someone missed a step or used a different version of a package, their environment could behave differently.
With Dockerfiles, you write these steps once in a structured, machine readable way. For example, instead of instructing someone to “install Python, then copy the app, then run the dependency installer,” your Dockerfile declares explicit build steps that always run in the same sequence. Docker also reuses results of previous steps when possible, which makes rebuilds faster.
This approach is particularly helpful for beginners, because it makes environment creation more transparent. You can open a Dockerfile and see exactly what will be present in the final image. Over time, you learn which steps belong in a Dockerfile and which steps should remain in your application code or configuration.
How Dockerfiles Fit into the Docker Workflow
Dockerfiles sit between your source code and the images you run as containers. You edit code as usual, then revise the Dockerfile when the environment needs to change, for example when you add a new system dependency or change how the application starts.
You then use the Docker build process to create an image from the Dockerfile. The build process reads the instructions from top to bottom, creating image layers along the way. Each successful build produces a new image version that you can tag, store in a registry, and run as containers.
In typical workflows, the Dockerfile is stored at the root of your project. It travels with the application through development, testing, and deployment. Continuous integration systems can build the image from the Dockerfile and run tests inside containers, which helps keep environments consistent across all stages.
In a normal workflow, you never edit images directly. You only change the Dockerfile and rebuild. This keeps your images consistent, traceable, and aligned with your source code.
The Role of Dockerfiles in Collaboration
When you share a project with others, the Dockerfile defines a common reference environment. Team members do not need to install all tools locally in the same way. Instead, they can pull or build the same image using the shared Dockerfile.
This clarity is important for debugging. If something goes wrong in a container, you can look at the Dockerfile to understand what was installed and configured. You can also review changes to the Dockerfile over time, which helps you see when and why a dependency was added or updated.
For open source projects, Dockerfiles make it easier for contributors to get started. They can build the image and run the application quickly, without having to understand every detail of the environment. Over time, they can learn by reading the Dockerfile and observing how small changes affect the build.
Structure and Behavior at a High Level
Although you will explore specific instructions in later sections, it is useful to recognize some common patterns now. A Dockerfile usually starts by declaring which base image to build on. Then it adds or configures software, copies application files, and describes how the container should start.
These instructions are processed during the build phase, not when you run the container. The final image that results from the build contains everything needed at runtime. When a container starts from that image, it simply follows the startup instructions that were recorded in the Dockerfile.
This separation between build time and runtime is important. Some actions only make sense while building an image, such as compiling code or resolving dependencies. Other actions happen only when the container actually runs, such as reading environment variables or connecting to external services. Dockerfiles help you keep these concerns distinct.
Thinking in Terms of Images and Versions
Once you begin using Dockerfiles, you will naturally start thinking of your application images as versioned artifacts. Every change to the Dockerfile, even a small one, can produce a new version of the image. You can tag those images, test them, and decide which one should be used in a given environment.
This is different from traditional servers where you gradually modify the same machine over time. With Dockerfiles, you treat images as immutable. To change something, you create a new image from an updated Dockerfile, then deploy that image. Older images remain available as snapshots of previous states, which helps with rollback and reproducibility.
Treat images built from Dockerfiles as immutable artifacts. To change behavior, update the Dockerfile and build a new image instead of modifying running containers.
How This Chapter Connects to Later Topics
Learning to work with Dockerfiles is central to everything that comes next. When you optimize images, manage data, or use Docker Compose, you will often adjust Dockerfiles to achieve the desired behavior. A good understanding of Dockerfiles gives you more control over performance, security, and portability.
Later sections in this chapter will explore the structure of a Dockerfile in detail, the most common instructions, how to run the build process, and how to apply best practices. You will also learn about multi stage builds, which allow you to keep development tools out of your final images while still benefiting from them during the build.
For now, the key idea is that Dockerfiles capture how an image is created in a clear, repeatable format. Once you rely on Dockerfiles instead of manual setup, your environments become easier to share, easier to reason about, and much simpler to automate.