


What is Docker BuildKit, and how does it improve build performance?
Jun 19, 2025 am 12:20 AMDocker BuildKit is a modern image building backend. It can improve construction efficiency and maintainability by 1) parallel processing of independent construction steps, 2) more advanced caching mechanisms (such as remote cache reuse), and 3) structured output improves construction efficiency and maintainability, significantly optimizing the speed and flexibility of Docker image building. Users only need to enable the DOCKER_BUILDKIT environment variable or use the buildx command to activate this function.
Docker BuildKit is a modern, enhanced backend for building Docker images. It replaces the older builder with smarter and more efficient mechanisms under the hood. If you're building container images regularly, especially in CI/CD pipelines or complex projects, BuildKit can make a noticeable difference in both speed and flexibility.
Faster Builds Through Parallel Processing
One of the main ways BuildKit improves performance is by intelligently executing build steps in parallel when possible. Unlike the classic Docker builder, which runs each step strictly one after another, BuildKit analyzes the build graph and identifies independent stages that can be processed concurrently.
- This means if your Dockerfile has multiple
RUN
commands that don't depend on each other, BuildKit can run them at the same time. - In multi-stage builds, it can also optimize how layers are built and reused across stages.
This kind of optimization is especially useful in large or modular projects where different parts of the image can be built independently.
Better Caching Mechanisms
BuildKit uses a more advanced caching system than the legacy builder. It doesn't just rely on layer hashes — it tracks content and usage more precisely.
- Cache is versioned and can be exported/imported between buildings, making it ideal for CI environments.
- With remote caching, you can reuse intermediate build results from previous jobs without rebuilding everything from scratch.
For example, if you're building an app where only the application code changes but the dependencies stay the same, BuildKit can reuse cached layers for installing packages, saving significant time.
Cleaner Output and Debugging Support
While not directly related to performance, BuildKit's structured output makes it easier to understand what's happening during a build. Instead of dumping logs in a messy way, it organizes them into clear sections per step.
- You can enable a plain log view or switch to a more compact progress display.
- This clarity helps spot bottlenecks or failed steps quickly, especially when optimizing build pipelines.
Additionally, because BuildKit supports LLB (Low-Level Builder) descriptions, it opens the door for tools and frontends beyond just Dockerfiles, like custom build definitions or integration with higher-level systems.
How to Enable and Use BuildKit
Using BuildKit is straightforward — you just need to opt in. It's available by default in Docker 18.09 and later.
To enable it globally, you can set:
export DOCKER_BUILDKIT=1
Or for a single build:
docker buildx build --progress=plain .
You can also configure it via the Docker daemon config file for consistent behavior across environments.
That's the core of what BuildKit brings to the table — smarter execution, better caching, and cleaner control. For most users, flipping the switch to use BuildKit is a low-effort, high-impact change. Not complicated, but definitely worth adopting.
The above is the detailed content of What is Docker BuildKit, and how does it improve build performance?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

1. The Origin of .NETCore When talking about .NETCore, we must not mention its predecessor .NET. Java was in the limelight at that time, and Microsoft also favored Java. The Java virtual machine on the Windows platform was developed by Microsoft based on JVM standards. It is said to be the best performance Java virtual machine at that time. However, Microsoft has its own little abacus, trying to bundle Java with the Windows platform and add some Windows-specific features. Sun's dissatisfaction with this led to a breakdown of the relationship between the two parties, and Microsoft then launched .NET. .NET has borrowed many features of Java since its inception and gradually surpassed Java in language features and form development. Java in version 1.6

To develop a complete Python Web application, follow these steps: 1. Choose the appropriate framework, such as Django or Flask. 2. Integrate databases and use ORMs such as SQLAlchemy. 3. Design the front-end and use Vue or React. 4. Perform the test, use pytest or unittest. 5. Deploy applications, use Docker and platforms such as Heroku or AWS. Through these steps, powerful and efficient web applications can be built.

There are three ways to view the process information inside the Docker container: 1. Use the dockertop command to list all processes in the container and display PID, user, command and other information; 2. Use dockerexec to enter the container, and then use the ps or top command to view detailed process information; 3. Use the dockerstats command to display the usage of container resources in real time, and combine dockertop to fully understand the performance of the container.

Deploying a PyTorch application on Ubuntu can be done by following the steps: 1. Install Python and pip First, make sure that Python and pip are already installed on your system. You can install them using the following command: sudoaptupdatesudoaptinstallpython3python3-pip2. Create a virtual environment (optional) To isolate your project environment, it is recommended to create a virtual environment: python3-mvenvmyenvsourcemyenv/bin/activatet

Deploying and tuning Jenkins on Debian is a process involving multiple steps, including installation, configuration, plug-in management, and performance optimization. Here is a detailed guide to help you achieve efficient Jenkins deployment. Installing Jenkins First, make sure your system has a Java environment installed. Jenkins requires a Java runtime environment (JRE) to run properly. sudoaptupdatesudoaptininstallopenjdk-11-jdk Verify that Java installation is successful: java-version Next, add J

There are two ways to compare the differences in different Docker image versions: 1. Use the dockerdiff command to view changes in the container file system; 2. Use the dockerhistory command to view the hierarchy difference in the image building. These methods help to understand and optimize image versioning.

An efficient way to batch stop a Docker container includes using basic commands and tools. 1. Use the dockerstop$(dockerps-q) command and adjust the timeout time, such as dockerstop-t30$(dockerps-q). 2. Use dockerps filtering options, such as dockerstop$(dockerps-q--filter"label=app=web"). 3. Use the DockerCompose command docker-composedown. 4. Write scripts to stop containers in order, such as stopping db, app and web containers.

Through Docker containerization technology, PHP developers can use PhpStorm to improve development efficiency and environmental consistency. The specific steps include: 1. Create a Dockerfile to define the PHP environment; 2. Configure the Docker connection in PhpStorm; 3. Create a DockerCompose file to define the service; 4. Configure the remote PHP interpreter. The advantages are strong environmental consistency, and the disadvantages include long startup time and complex debugging.
