flyctl VS PostgreSQL

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

Every time I buy a new computer, the first thing I install is the servers for MySql and Postgres, the two most common databases. This way, I can start the databases with a simple command like this:

Data Handling: Utilizes Windmill for data pipelines, with a primary database powered by PostgreSQL. Auxiliary data storage is handled by MongoDB, with Redis for caching to optimize performance

PostgreSQL

The LMS Moodle Operator serves as a meta-operator, orchestrating the deployment and management of Moodle instances in Kubernetes. It handles the entire stack required to run Moodle, including components like Postgres, Keydb, NFS-Ganesha, and Moodle itself. Each of these components has its own Kubernetes Operator, ensuring seamless integration and management.

Another key feature of txtai is being able to quickly move from prototyping to production. This article will demonstrate how txtai can integrate with Postgres, a powerful, production-ready and open source object-relational database system. After txtai persists content to Postgres, we'll show it can be directly queried with SQL from any Postgres client

SQL (Structured Query Language) databases are relational databases. They organize data into tables with rows and columns, and they use SQL for querying and managing data. Examples include MySQL, PostgreSQL, and SQLite.

Node.js, MySQL and PostgreSQL servers installed on your machine

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).