bazel-postgres-sketch VS pronto

I uploaded most of our Workspace setup here: https://github.com/jschaf/bazel-postgres-sketch. The tooling is a bunch of Go to manage the Postgres process. Basically, give it schema files and receive a running database with a tear down command.

We make temp instances of Postgres quickly by:

- avoiding Docker, especially on Mac

- keeping the data dir on tmpfs

- Disable initdb cleanup

- Disable fsync and other data integrity flags

- Use unlogged tables.

- Use sockets instead of TCP localhost.

For a test suite, it was 12x faster to call createdb with the same Postgres cluster for each test than than to create a whole new db cluster. The trick was to create a template database after loading the schema and use that for each createdb call.

Sure, here's sketch of how it works: https://github.com/jschaf/bazel-postgres-sketch

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

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.

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

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.