distroless
go
distroless | go | |
---|---|---|
122 | 2,079 | |
17,781 | 119,900 | |
1.4% | 0.9% | |
9.4 | 10.0 | |
6 days ago | 3 days ago | |
Starlark | Go | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
distroless
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Chainguard Images now available on Docker Hub
lots of questions here regarding what this product is. I guess i can provide some information for the context, from a perspective of an outside contributor.
Chainguard Images is a set of hardened container images.
They were built by the original team that brought you Google's Distroless (https://github.com/GoogleContainerTools/distroless)
However, there were few problems with Distroless:
1. distroless were based on Debian - which in turn, limited to Debian's release cadence for fixing CVE.
2. distroless is using bazelbuild, which is not exactly easy to contrib, customize, etc...
3. distroless images are hard to extend.
Chainguard built a new "undistro" OS for container workload, named Wolfi, using their OSS projects like melange (for packaging pkgs) and apko (for building images).
The idea is (from my understanding) is that
1. You don't have to rely on upstream to cut a release. Chainguard will be doing that, with lots of automation & guardrails in placed. This allow them to fix vulnerabilties extremely fast.
- Language focused Docker images, minus the operating system
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Using Alpine can make Python Docker builds 50× slower
> If you have one image based on Ubuntu in your stack, you may as well base them all on Ubuntu, because you only need to download (and store!) the common base image once
This is only true if your infrastructure is static. If your infrastructure is highly elastic, image size has an impact on your time to scale up.
Of course, there are better choices than Alpine to optimize image size. Distroless (https://github.com/GoogleContainerTools/distroless) is a good example.
- Smaller and Safer Clojure Containers: Minimizing the Software Bill of Materials
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Long Term Ownership of an Event-Driven System
The same as our code dependencies, container updates can include security patches and bug fixes and improvements. However, they can also include breaking changes and it is crucial you test them thoroughly before putting them into production. Wherever possible, I recommend using the distroless base image which will drastically reduce both your image size, your risk vector, and therefore your maintenance version going forward.
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Minimizing Nuxt 3 Docker Images
# Use a large Node.js base image to build the application and name it "build" FROM node:18-alpine as build WORKDIR /app # Copy the package.json and package-lock.json files into the working directory before copying the rest of the files # This will cache the dependencies and speed up subsequent builds if the dependencies don't change COPY package*.json /app # You might want to use yarn or pnpm instead RUN npm install COPY . /app RUN npm run build # Instead of using a node:18-alpine image, we are using a distroless image. These are provided by google: https://github.com/GoogleContainerTools/distroless FROM gcr.io/distroless/nodejs:18 as prod WORKDIR /app # Copy the built application from the "build" image into the "prod" image COPY --from=build /app/.output /app/.output # Since this image only contains node.js, we do not need to specify the node command and simply pass the path to the index.mjs file! CMD ["/app/.output/server/index.mjs"]
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Build Your Own Docker with Linux Namespaces, Cgroups, and Chroot
Lots of examples without the entire OS as other comments mention, an example would be Googles distroless[0]
[0]: https://github.com/GoogleContainerTools/distroless
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Reddit temporarily ban subreddit and user advertising rival self-hosted platform (Lemmy)
Docker doesn't do this all the time. Distroless Docker containers are relatively common. https://github.com/GoogleContainerTools/distroless
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Why elixir over Golang
Deployment: https://github.com/GoogleContainerTools/distroless
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Reviews
Or use distroless image as it includes one, among others. https://github.com/GoogleContainerTools/distroless/blob/main/base/README.md
go
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Arena-Based Parsers
The description indicates it is not production ready, and is archived at the same time.
If you pull all stops in each respective language, C# will always end up winning at parsing text as it offers C structs, pointers, zero-cost interop, Rust-style struct generics, cross-platform SIMD API and simply has better compiler. You can win back some performance in Go by writing hot parts in Go's ASM dialect at much greater effort for a specific platform.
For example, Go has to resort to this https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f... in order to efficiently scan memory, while in C# you write the following once and it compiles to all supported ISAs with their respective SIMD instructions for a given vector width: https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (there is a lot of code because C# covers much wider range of scenarios and does not accept sacrificing performance in odd lengths and edge cases, which Go does).
Another example is computing CRC32: you have to write ASM for Go https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f..., in C# you simply write standard vectorized routine once https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (its codegen is competitive with hand-intrinsified C++ code).
There is a lot more of this. Performance and low-level primitives to achieve it have been an area of focus of .NET for a long time, so it is disheartening to see one tenth of effort in Go to receive so much spotlight.
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Go: the future encoding/json/v2 module
A Discussion about including this package in Go as encoding/json/v2 has been started on the Go Github project on 2023-10-05. Please provide your feedback there.
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Evolving the Go Standard Library with math/rand/v2
I like the Principles section. Very measured and practical approach to releasing new stdlib packages. https://go.dev/blog/randv2#principles
The end of the post they mention that an encoding/json/v2 package is in the works: https://github.com/golang/go/discussions/63397
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Microsoft Maintains Go Fork for FIPS 140-2 Support
There used to be the GO FIPS branch :
https://github.com/golang/go/tree/dev.boringcrypto/misc/bori...
But it looks dead.
And it looks like https://github.com/golang-fips/go as well.
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Borgo is a statically typed language that compiles to Go
I'm not sure what exactly you mean by acknowledgement, but here are some counterexamples:
- A proposal for sum types by a Go team member: https://github.com/golang/go/issues/57644
- The community proposal with some comments from the Go team: https://github.com/golang/go/issues/19412
Here are some excerpts from the latest Go survey [1]:
- "The top responses in the closed-form were learning how to write Go effectively (15%) and the verbosity of error handling (13%)."
- "The most common response mentioned Go’s type system, and often asked specifically for enums, option types, or sum types in Go."
I think the problem is not the lack of will on the part of the Go team, but rather that these issues are not easy to fix in a way that fits the language and doesn't cause too many issues with backwards compatibility.
[1]: https://go.dev/blog/survey2024-h1-results
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.
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How to use Retrieval Augmented Generation (RAG) for Go applications
Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!
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From Homemade HTTP Router to New ServeMux
net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements
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Building a Playful File Locker with GoFr
Make sure you have Go installed https://go.dev/.
- Fastest way to get IPv4 address from string
What are some alternatives?
iron-alpine - Hardened alpine linux baseimage for Docker.
v - Simple, fast, safe, compiled language for developing maintainable software. Compiles itself in <1s with zero library dependencies. Supports automatic C => V translation. https://vlang.io
spring-boot-jib - This project is about Containerizing a Spring Boot Application With Jib
TinyGo - Go compiler for small places. Microcontrollers, WebAssembly (WASM/WASI), and command-line tools. Based on LLVM.
jib - 🏗 Build container images for your Java applications.
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
podman - Podman: A tool for managing OCI containers and pods.
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
dockerfiles - Various Dockerfiles I use on the desktop and on servers.
Angular - Deliver web apps with confidence 🚀
docker-alpine - Official Alpine Linux Docker image. Win at minimalism!
golang-developer-roadmap - Roadmap to becoming a Go developer in 2020