pronto
jet
pronto | jet | |
---|---|---|
4 | 26 | |
6 | 2,067 | |
- | - | |
0.0 | 8.1 | |
over 3 years ago | 25 days ago | |
Java | Go | |
MIT License | Apache License 2.0 |
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.
jet
-
Open-sourcing SQX, a way to build flexible database models in Go
We are really happy using jet. It lets you write type safe SQL and can read the results into structs- including joins into slice fields.
https://github.com/go-jet/jet
-
The "preferred" way of mapping SQL results in Golang is honestly, subjectively, awful, how to deal with this
Check go-jet https://github.com/go-jet/jet
- Comparing database/sql, GORM, sqlx, and sqlc
-
goscanql - conveniently reading joined SQL data into structs
https://github.com/go-jet/jet does a similar thing.
-
Does Go, has something similar to Laravel eloquent (ORM) ?
Try go-jet, it generates the models based on the schema, provides typed column names.
-
Any mid sized / big open source code base in golang that makes use of SQL DBs?
I have tried doing that, but was unable to get it to work. I posted about it in a discussion here: https://github.com/go-jet/jet/discussions/215
-
Automatic CRUD code generetor?
Jet might be what you're looking for - https://github.com/go-jet/jet
- How to Work with SQL Databases in Go
-
ORM or no ORM (and which ones)?
Use sql builder https://github.com/go-jet/jet.
- GitHub - go-jet/jet: Type safe SQL builder with code generation and automatic query result data mapping
What are some alternatives?
simd-json - Rust port of simdjson
sqlc - Generate type-safe code from SQL
pike - Generate CRUD gRPC backends from single YAML description.
goqu - SQL builder and query library for golang
sqlparser-rs - Extensible SQL Lexer and Parser for Rust
SQLBoiler - Generate a Go ORM tailored to your database schema.
grpc-gateway - gRPC to JSON proxy generator following the gRPC HTTP spec
migrate - Database migrations. CLI and Golang library.
pggen - A database first code generator focused on postgres
pgcapture - A scalable Netflix DBLog implementation for PostgreSQL
sqlite
go-queryset - 100% type-safe ORM for Go (Golang) with code generation and MySQL, PostgreSQL, Sqlite3, SQL Server support. GORM under the hood.