The final image will be around 42mb (or 25mb) in size, compared to 177mb.
EDIT: as mentioned on HN, alpine-based images can even go around 15mb in size.
If you want to try this, the first step is to install Nix.
Here's the
redis.nix
snippet:Build it with:
nix-build redis.nix
Load it with:
docker load < result
Once loaded, you can see with
docker images
that it takes about 42mb of space.Fundamental differences with classic docker builds
- We do not use any base image, like it's done for most docker images including redis from the hub. It starts from scratch. In fact, we set up some basic shadow-related files with the
shadowSetup
utility, enough to add the redis user and makegosu
work. - The Redis package is not being compiled inside Docker. It's being done by Nix, just like any other package.
- The built image has only one layer, compared to dozens usually spitted by a readable
Dockerfile
. In our case, having multiple layers is useless because caching is handled by Nix, and not by Docker.
A smaller image
We can cut the size down to 25mb by avoid using id from coreutils. As an example we'll always launch redis without the entrypoint:You might ask: but coreutils is still needed for the
chown
, mkdir
and other commands like that!The secret is that those commands are only used at build time and are not required at runtime in the container. Nix is able to detect that automatically for us.
It means we don't need to manually remove packages after the container is built, like with other package managers! See this line in Redis
Dockerfile
for example.Using a different redis version
Let's say we want to build a Docker image with Redis 2.8.23. First we want to write a package (or derivation in Nix land) for it, and then use that inside the image:Note we also added the tag 2.8.23 to the resulting image. And that's it. The beauty is that we reuse the same redis expression from nixpkgs, but we override only the version to build.
A generic build
There's more you can do with Nix. Being a language, it's possible to create a generic function for building Redis images given a specific package:We created a "redisImage" function that takes a "redis" parameter as input, and returns a Docker image as output.
Build it with:
nix-build redis-generic.nix -A redisDocker_3_0_7
nix-build redis-generic.nix -A redisDocker_2_8_23
Building off a base image
One of the selling points of Docker is reusing an existing image to add more stuff on top of it.Nix comes with a completely different set of packages compared to other distros, with its own toolchain and glibc version. This doesn't mean it's not possible to base a new image off an existing Debian image for instance.
By using
dockerTools.pullImage
it's also possible to pull images from the Docker hub.Build it with:
nix-build redis-generic.nix -A redisOnDebian
.Note that we added a couple of things. We pass the base image (
debianImage
), to our generic redisImage
function, and that we only initialize shadow-utils if the base image is null.The result is a Docker image based off latest Debian but running Redis compiled with nixpkgs toolchain and using nixpkgs glibc. It's about 150mb. It has all the layers from the base image, plus the new single layer for Redis.
That said, it's as well possible to use one of the previously defined Redis images as base image. The result of `pullImage` and `buildImage` is a .tar.gz docker image in both cases.
You realize it's possible to build something quite similar to docker-library using only Nix expressions. It might be an interesting project.
Be aware that things like PAM configurations, or other stuff, created to be suitable for Debian may not work with Nix programs that use a different glibc.
Other random details
The code above has been made possible by using nixpkgs commit 3ae4d2afe (2016-04-14) onwards, commit at which I've finally packagedgosu
and since the size of the derivations have been notably reduced.Building the image is done without using any of the Docker commands. The way it works is as follows:
- Create a layer directory with all the produced contents inside. This includes the filesystem as well as the json metadata. This process will use certain build dependencies (like
coreutils
,shadow-utils
,bash
,redis
,gosu
, ...). - Ask Nix what are the runtime dependencies of the layer directory (like
redis
,gosu
). Such dependencies will be always a subset of the build dependencies. - Add such runtime dependencies to the layer directory.
- Pack the layer in a .tar.gz by following the Docker specification.
As for Docker, great care has to be taken in order to use the layer cache correctly, because such caching is solely based on the
RUN
command string. This blog post explains it well.This is not the case for Nix, because every output depends on a set of exact inputs. If any of the inputs change, the output will be rebuilt.
So what is Nix?
Nix is a language and deployment tool, often used as package manager or configuration builder and system provisioning. The operating system NixOS is based on it.The code shown above is Nix. We have used the nixpkgs repository which provides several reusable Nix expressions like redis and dockerTools.
The Nix concept is simple: write a Nix expression, build it. This is how the building process works at a high-level:
- Read a Nix expression
- Evaluate it and determine the thing (called derivation) to be built.
- By evaluating the code, Nix is able to determine exactly the build inputs needed for such derivation.
- Build (or fetch from cache) all the needed inputs.
- Build (or fetch from the cache) the final derivation.
/nix/store
), identified by an hash. Each derivation may have dependencies to other paths in the same store. Each derivation is stored in a separate directory from other derivations.Won't go deeper as there's plenty of documentation about how Nix works and how its storage works.
Hope you enjoyed the reading, and that you may give Nix a shot.
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Creating cost-effective Docker images with Nix is a smart approach to optimize your container infrastructure, reducing image size and improving deployment efficiency. This method aligns well with modern DevOps practices. In the realm of data analytics, Glasgow offers high-quality Data Analytics courses that enable professionals to master data processing, analysis, and visualization, ensuring they're well-equipped to excel in data-driven fields. Please also read Data Analytics courses in Glasgow.
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This blog post highlights a fantastic approach to creating lean and efficient Docker images using Nix. The reduction in image size from 177mb to just 42mb (or even 15mb with Alpine) is truly impressive. The use of Nix to handle package compilation outside the Docker environment is a clever move that simplifies the Dockerfile and results in a single-layer image.
The explanation of the fundamental differences in this process is also quite enlightening. It showcases how Nix's caching capabilities can streamline the image creation process. The tip about not needing coreutils at runtime thanks to Nix's intelligence is a great insight into the optimization potential.
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The way you effortlessly explained optimizing images while incorporating Nix principles was truly enlightening. Thanks for sharing your expertise and simplifying what seemed like a complex process.
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The blog post provides an insightful exploration of using Nix for creating lightweight Docker images. The examples and explanations are clear, making it easier for readers to understand the process of reducing image sizes significantly. The comparison with traditional Docker builds and the emphasis on Nix's safer caching add valuable insights. The post also encourages readers to give Nix a try, making it a compelling read for those interested in optimizing Docker images. Great job!
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This blog on creating cost-effective Docker images using Nix is a game-changer for developers looking to optimize their workflows and minimize expenses. The step-by-step guide not only provides a practical solution but also demystifies the process for those unfamiliar with Nix. Digital Marketing Courses In Norwich
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I particularly appreciated the inclusion of real-world examples and the comparison with traditional Docker image creation. This not only clarifies the advantages but also allows readers to appreciate the impact of integrating Nix into their Docker pipelines. Digital marketing for business
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Nix enables lightweight Docker images by composing packages from source, slashing image sizes. Its declarative approach ensures minimal layers, enhancing efficiency and reducing image bloat, perfect for cost-effective deployments. Combining Nix with Docker creates lean, efficient containers that streamline resource usage and deployment processes.
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"This blog is a goldmine for those looking to optimize Docker images using Nix! The detailed exploration of creating efficient and cost-effective Docker images through Nix is incredibly valuable. The step-by-step guide and practical examples demonstrate how to significantly reduce image size while maintaining functionality. The emphasis on cost-efficiency and resource optimization aligns perfectly with the needs of developers working with Docker. A must-read for anyone seeking to streamline their Docker workflows and reduce operational costs!"
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The combination of Nix's declarative package management and Docker's containerization brings a new level of efficiency and reproducibility to our development workflow. Creating lightweight and cheap Docker images is now a breeze, thanks to Nix's ability to define and manage dependencies in a consistent manner.
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The integration of Nix with Docker to create lightweight, efficient images like Redis is impressive. By utilizing Nix's capabilities in package management and system provisioning, the article demonstrates a paradigm shift in Docker image creation, emphasizing minimized sizes and optimal build strategies.
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Exploring Docker images with Nix has been a game-changer for optimizing costs! The lightweight and efficient nature of Nix-built images not only ensures faster deployments but also significantly reduces resource consumption. Embracing this approach has not only streamlined our containerized workflows but has also proven to be a cost-effective solution
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