flyctl VS bolt

To begin with, you could go with a Platform-as-a-Service (PaaS) provider like Heroku, or Fly for a more seamless experience. You can also do a bit of DevOps: set up a Docker installation on a VPS and deploy your app there.

To begin visit fly.io to create an account. Next install flyctl a command line tool for creating and deploying fly apps. macOS

For frontend deployment, I used Netlify (for the generous free package) and the recommended fly.io for server + database (also cheap package).

Create an account on Fly.io.

You can learn more about fly.io and tigris, we will need to create an account on both platforms for this project regardless. Anyway with the theory out of the way let's get started in the next section as we create our accounts and start building the app.

Fly.io is a platform that helps you run your apps and databases closer to your users all around the world. It takes your app code, packages it up neatly, and puts it on virtual machines that can be quickly started or stopped. This makes your app faster for users and more reliable. Fly.io is easy to use, works well for small projects or personal apps. It's a great way to make sure your app runs smoothly for people no matter where they are.

In this post, we'll start from scratch, running FerretDB locally via Docker, trying out the connection with mongosh and the MongoDB Node.js client, and finally deploy FerretDB to Fly.io for a production ready set up.

2- fly.io

Fly is a cloud platform that focuses on global edge computing. Fly specializes in high-performance hosting and provides a global network of edge locations. Fly is known for its scalability and performance optimizations.

But videototextai.com is built using NextJS + Firebase auth + Firestore and a backend deployed at fly.io . Fly makes it really easy to deploy docker containers and that is IMO the fastest way to develop, you can setup a local setup

This crate started out as just a way for me to learn how boltdb works, while learning Rust at the same time. But somehow people started finding and using it and seem to like the simple API, so I figured I might as well share it in case someone else finds it useful too. If you want to know more about my motivations and the history of this crate, you can read the release notes on version 0.8.0!

Some example of embeddable database could be genji, badger and boltdb

Designing Data Intensive applications- specifically chapter 3 and 4 which deal with strategies and algorithms for storing and encoding data to be stored on disk and their pros and cons.

Once you read that, I'll suggest reading the source of a simple embedded key-value database, I wouldn't bother with RDBMs as they are complex beasts and contain way more than you need. BoltDB is a good project to read the source of https://github.com/boltdb/bolt, the whole thing is <10k lines of code and is a full blown production grade system with ACID semantics so packs a lot in those 10k and isn't just merely a toy.

Bolt db and Bolt db's author post to go with it.

https://github.com/boltdb/bolt is a ACID B+ tree key-value store

For a single server, SQLite, or boltdb[0]

I've never had to scale horizontally. I develop in Go and you can get very far along with just vertical scaling (aka beefier hardware).

Therefore I can't give concrete examples of a distributed db-as-a-library.

But all that you need is to extend the functions that fetch data to not just fetch from disk but from "peers" as well. For this to work you need servers (instances) to know about each other, and as you add more they also get added to their peers - sort of like a bittorrent network. I don't think it's difficult to do.

SQLite might not be suited for being distributed (although RQlite[1] claims to have done it).

Making a distributed data storage based on boltdb[0] is probably more feasible.

Whatever the case, there's no reason why a data storage engine can't be a library, even if it's distributed.

[0]: https://github.com/boltdb/bolt

[1]: https://github.com/rqlite/rqlite