Article #1: Uplevelling Your Container Lifecycle Management
This article is the first in a series of three exploring how open-source Buildpacks and their ecosystem offer a compelling alternative to Docker for container image creation. Over the series, we will dive into three key areas where Buildpacks shine: Build process and image creation (this article), dependency and runtime management and optimization of portability, lifecycle and performance.
The shift to Buildpacks can feel like a leap for organizations that are used to Docker workflows. However, Buildpacks simplify and automate many aspects of containerization, helping organizations reduce complexity, improve security and accelerate their shipping pipelines. In this first article, I will explore how Buildpacks streamline the image creation process and solve common challenges Docker users face.
Before We Start
In case you are new to Buildpack, I want to provide some context on how a container image is created. Using the Pack CLI tool, running one command is all you need to provide an OCI production-ready container image. Buildpacks automatically detect the application’s language, dependencies and runtime requirements, then assemble and configure all necessary components to create a container image without requiring a Dockerfile.
It looks just like this:
pack build my-python-app –path ./my-app
That’s it. Again, there is no need to write any configuration files, such as a Dockerfile.
Build Context Automation: Eliminating Toil
In Docker workflows, developers define a ‘build context’ by specifying a directory containing source code, dependencies and configuration files. This can lead to bloated images if unnecessary files are included, such as .env, .DS_Store, .git and artifacts such as
__pycache__/, target/, dist/, bin/ and node_modules/.
With Buildpacks, this concern is eliminated. Buildpacks analyze the source code and automatically determine what should be included and what should not. By default, Buildpacks ignore unnecessary files and build artifacts, ensuring that the resulting container image is lean and optimized. Specifying the proper context is done automatically, and developers no longer need to handle it.
Base Image Selection: Automated and Optimized
When using Docker, developers manually specify a base image in the Dockerfile, such as:
# Dockerfile Example
FROM ubuntu:22.04
RUN apt-get update && apt-get install -y python3
While this approach offers control, it also requires constant vigilance to ensure the base image is secure and up to date. This task becomes even more challenging when you have hundreds of Dockerfiles to maintain.
With Buildpacks, the latest available base images are automatically selected and maintained for you. The community maintains these base images, like Google or Paketo Buildpacks. This means yet another task is off your plate.
Layer Caching: Smarter, Faster Builds
Docker uses a layer caching mechanism that relies heavily on the order of commands in the Dockerfile. In the example below, Docker invalidates all subsequent layers if the application code changes, forcing a complete rebuild.
# Dockerfile with Suboptimal Caching
COPY . /app
RUN pip install -r /app/requirements.txt
To optimize this, developers must order commands in this way:
# Optimized caching
RUN pip install -r /app/requirements.txt
COPY . /app
The reason for this order of commands is that Docker will rebuild all layers below the one that needs to be rebuilt. In the first example, Docker would need to execute the pip install command every time the application code changes and is copied.
Buildpacks handle caching automatically. They separate layers into logical components like dependencies and source code, ensuring that only modified layers are rebuilt. While the example above is a straightforward illustration of the issue, it can get more complicated and is yet another element developers need to remember for an optimized caching flow.
Default Output as Multi-Stage Builds
Multi-stage builds in Dockerfiles streamline the image-building process by enabling the use of multiple FROM statements within a single Dockerfile. This approach allows developers to isolate the build environment from the runtime environment, resulting in smaller, more efficient images by excluding unnecessary build tools and artifacts from the final container. Below is an example of a multi-stage Dockerfile.
# Dockerfile with Multi-Stage Build
FROM maven:3.8.1 AS build
WORKDIR /app
COPY . .
RUN mvn clean package
FROM openjdk:11
WORKDIR /app
COPY –from=build /app/target/my-app.jar /app/
ENTRYPOINT [“java”, “-jar”, “my-app.jar”]
With Buildpacks, this complexity is abstract. The build and runtime stages are inherently separated within the Buildpack lifecycle. Omitting multi-stage can result in much heavier container images. Previously, I found that my container was 50% lighter when using a multi-stage approach.
Only Rebase a Specific Layer
The Buildpack rebase feature allows you to swap out the layers of a container image with a newer version without rebuilding the entire container — which would be required using Docker. This feature is especially valuable for applying urgent OS security patches, significantly reducing downtime for organizations managing thousands of images in production. When benchmarking, I found that simply updating the OS layer of my image was 70% faster than rebuilding the entire image.
It is Time to Improve Your Build and Image Creation Process
By automating base image selection, managing layers intelligently and abstracting multi-stage builds, Buildpacks improves many aspects of building a container image.
In the following article, I will explore how Buildpacks enhance dependency and runtime management, diving into how they handle language runtimes, dependency installation and security patching. Stay tuned!
