pronto | go | |
---|---|---|
4 | 2,075 | |
6 | 119,718 | |
- | 0.7% | |
0.0 | 10.0 | |
over 3 years ago | 6 days ago | |
Java | Go | |
MIT License | 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.
pronto
-
Buf raises $93M to deprecate REST/JSON
5. Message streaming (gRPC streams are amazing)
I can think of a whole host of features that can be built off of protos (I've even built ORMs off of protobuffs for simple things [0]). The value prop is there IMO. HTTP + json APIs are a local minima. The biggest concerns "I want to be able to view the data that is being sent back and forth" is a tooling consideration (curl ... isn't showing you the voltages from the physical layer, it is decoded). Buff is building that tooling.
[0] - https://github.com/CaperAi/pronto
-
Parsing Gigabytes of JSON per Second
I've written translation layers for such systems and it's not too bad. See this project from $job - 1: https://github.com/CaperAi/pronto
It allowed us to have a single model for storage in the DB, for sending between services, and syncing to edge devices.
-
gRPC for Microservices Communication
There's no reason you couldn't use gRPC with json as a serialized message format. For example grpc-gateway [0] provides a very effective way of mapping a gRPC concept to HTTP/JSON. The thing is, after moving to gRPC, I've never really felt a desire to move back to JSON. While it may be correct to say "parsing json is fast enough" it's important to note that there's a "for most use cases" after that. Parsing protos is fast enough for even more use cases. You also get streams which are amazing for APIs where you have to sync some large amounts of data (listing large collections from a DB for example) across two services.
With gRPC you also have a standardized middleware API that is implemented for "all" languages. The concepts cleanly map across multiple languages and types are mostly solved for you.
Adding to that you can easily define some conventions for a proto and make amazing libraries for your team. At a previous job I made this: https://github.com/CaperAi/pronto/
Made it super easy to prototype multiple services as if you mock a service backed by memory we could plop it into a DB with zero effort.
I think this "gRPC vs X" method of thinking isn't appropriate here because protos are more like a Object.prototype in JavaScript. They're a template for what you're sending. If you have the Message you want to send you can serialize that to JSON or read from JSON or XML or another propriety format and automatically get a host of cool features (pretty printing, serialization to text/binary, sending over the network, etc).
[0] - https://github.com/grpc-ecosystem/grpc-gateway
-
We Went All in on Sqlc/Pgx for Postgres and Go
I attempted to make something similar to this except the opposite direction at a previous job. It was called Pronto: https://github.com/CaperAi/pronto/
It allowed us to store and query Protos into MongoDB. It wasn't perfect (lots of issues) but the idea was rather than specifying custom models for all of our DB logic in our Java code we could write a proto and automatically and code could import that proto and read/write it into the database. This made building tooling to debug issues very easy and make it very simple to hide a DB behind a gRPC API.
The tool automated the boring stuff. I wish I could have extended this to have you define a service in a .proto and "compile" that into an ORM DAO-like thing automatically so you never need to worry about manually wiring that stuff ever again.
go
-
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.
-
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
-
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.
-
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
-
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.
-
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!
-
From Homemade HTTP Router to New ServeMux
net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements
-
Building a Playful File Locker with GoFr
Make sure you have Go installed https://go.dev/.
- Fastest way to get IPv4 address from string
- We now have crypto/rand back ends that ~never fail
What are some alternatives?
simd-json - Rust port of simdjson
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
pike - Generate CRUD gRPC backends from single YAML description.
TinyGo - Go compiler for small places. Microcontrollers, WebAssembly (WASM/WASI), and command-line tools. Based on LLVM.
sqlparser-rs - Extensible SQL Lexer and Parser for Rust
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
grpc-gateway - gRPC to JSON proxy generator following the gRPC HTTP spec
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).
pggen - A database first code generator focused on postgres
Angular - Deliver web apps with confidence 🚀
sqlite
golang-developer-roadmap - Roadmap to becoming a Go developer in 2020