What Is An Multistage Dockerfile ?

Last Updated : 21 Feb, 2024

Docker has revolutionized the world of software development and software deployment by simplifying the process of creating, distributing, and running applications within containers. This feature of Docker is very helpful for developers, so Among Docker’s sea of features, multistage Dockerfile stands out as a very powerful tool for optimizing the size and efficiency of container images Let’s get familiar with multistage Dockerfiles and add another tool to our journey with DevOps.

What is a multistage Dockerfile?

A multistage Dockerfile is a feature introduced in Docker to address the challenge of creating lean and efficient container images Traditionally, Docker images used to contain all the dependencies, libraries, and tools required to run an application, leading to bloated images that consume unnecessary disk space and hence increase the deployment times Now Multistage builds allow developers to build multiple intermediate images within a single Dockerfile, and each intermediate image serves a specific purpose in the build process.

How does it work?

In a multistage Dockerfile, developers define multiple build stages, each encapsulating a specific set of instructions and dependencies. These stages can be named and referenced within the Dockerfile, enabling seamless communication between them Basically, the first stage of creating a multistage Dockerfile is dedicated to building the application code, while subsequent stages focus on packaging the application and preparing it for runtime. Intermediate images that are generated in earlier stages are discarded just after their purpose is served, resulting in a final production image that contains only the essential components required to run the application.

Code of a Multistage Dockerfile

# Code template to get you started For Multistage Dockerfile
# Build stage with development tools
FROM python:3.9-slim-buster AS build

WORKDIR /app

COPY requirements.txt ./
RUN pip install -r requirements.txt

COPY . .

CMD ["python", "app.py"]

# Final stage with minimal image
FROM alpine AS prod

COPY --from=build /app /app

WORKDIR /app

CMD ["gunicorn", "--bind", "0.0.0.0:8000", "app:app"]

Explanation of the Code

Build stage
- Uses python:3.9-slim-buster base image with Python and tools.
- Copies requirements.txt and installs dependencies using pip.
- Copies application code to /app.
- Sets the default command to run the application with python app.py.
Final stage
- Uses alpine base image as a minimal runtime environment.
- Copies only the application code (/app) from the build stage.
- Sets the final command to run the application using Gunicorn server (gunicorn).

Necessary commands required for Multi-Stage Dockerfile in Python

Command	Description about Command	Stage-Specific?
FROM image:tag	Defines the base image for the current stage.	Yes
AS name	Assigns a name to the current stage.	Yes
WORKDIR path	Sets the working directory for subsequent commands.	No
COPY source destination	Copies files/directories from context/previous stage.	No
RUN command	Executes shell commands.	No
CMD [“command”, “arg1”, …]	Sets the default command for container start.	No
USER user	Sets the user account for container processes.	No
EXPOSE port	Specifies ports container listens on.	No
ENV KEY=VALUE	Defines environment variables accessible in container.	No
LABEL key=value	Adds metadata labels to the image.	No
–from=stage	Specifies source stage for copying files.	Yes

Benefits of Multistage Dockerfiles

Reduced Image Size: By eliminating unnecessary dependencies and intermediate artifacts, multistage builds produce leaner container images, leading to reduced storage requirements and faster deployment times.
Enhanced Security: Slimmer images minimize the risk of security vulnerabilities by decrease the attack surface this is how Multistage ensure a more secure runtime environment for applications.
Improved Build Efficiency: Multistage builds optimize the build process by separating the compilation and packaging stages, allowing for faster builds and more efficient utilization of resources.
Simplified Maintenance: With a modular and streamlined build process, developers can easily update and maintain Dockerfiles, leading to more maintainable and scalable containerized applications.
Better CI/CD Integration: Multistage builds seamlessly integrate with continuous integration and continuous deployment (CI/CD) pipelines, enabling automated and efficient software delivery workflows.

Realtime-use case examples

Stream Processing and Analytics:

An Python application ingesting and analyzing data streams like tweets or stock prices in real-time To handle this efficiently, we require leverage multi-stage builds:

Build stage: This stage installs libraries like Apache Kafka or RabbitMQ for message queuing, which are only needed during development and testing.
Runtime stage: This stage only includes the application code and essential dependencies for processing data streams. By excluding unnecessary libraries, we achieve a much smaller and faster-starting image.

Chatbots and Conversational AI:

Have you ever thought of Building a Python-based chatbot that can interacts with users in real-time using just NLP libraries? Here’s how multi-stage builds can improve responsiveness:

Build stage: This stage installs Natural Language Processing (NLP) libraries and training data used to train your chatbot model.
Runtime stage: This stage includes the application code and the minimal NLP modules required for understanding user input and generating responses. By excluding unnecessary libraries, we ensure faster response times, leading to smooth and realistic conversations with your real-time chatbot.

Best Practices for Multistage Dockerfiles

Identify Build Stages: Analyze the application requirements and identify distinct build stages based on compilation, testing, packaging, and deployment.
Minimize Dependencies: Install only the necessary dependencies and libraries in each build stage to keep the image size to a minimum.
Optimize Layering: Utilize Docker’s layer caching mechanism to optimize layering and maximize build efficiency.
Leverage Official Images: Whenever possible, leverage official Docker images as base images for your build stages to ensure reliability and security.
Test and Iterate: Its great habit to Continuously test and iterate on your multistage Dockerfiles.

Conclusion

Multistage Dockerfiles offer a streamlined approach to container image creation, reducing size, enhancing security, and improving build efficiency. By segmenting the build process into distinct stages and discarding unnecessary artifacts, developers can produce leaner images that accelerate deployment and minimize attack vectors. Adopting best practices and leveraging multistage builds empower organizations to optimize their containerized workflows, driving innovation and agility in software development and deployment pipelines. Embracing multistage Dockerfiles is essential for modernizing containerization practices and maximizing efficiency in the evolving landscape of container technologies.