Getting Started with Fast-Api 🏎️ and Docker🐳

This page summarizes the projects mentioned and recommended in the original post on dev.to

Scout Monitoring - Free Django app performance insights with Scout Monitoring
Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
www.scoutapm.com
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InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
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  • nvm

    Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions

  • Node.js and NPM installed on your system (if you want to create a Vue frontend). Nvm is recommended.

  • Fast-Api-example

    Simple asynchronous API implemented with Fast-Api framework utilizing Postgres as a Database and SqlAlchemy as ORM . GitHub Actions as CI/CD Pipeline

  • git clone (https://github.com/KenMwaura1/Fast-Api-example)

  • Scout Monitoring

    Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.

    Scout Monitoring logo
  • SQLAlchemy

    The Database Toolkit for Python

  • In the code ,we are using SQLAlchemy as our ORM(Object Relational Mapper) and Databases as our query builder.

  • Newman

    Newman is a command-line collection runner for Postman

  • The application should be running on port 8002. You can test the application by making requests to the endpoints. You can use Postman/Insomnia to make requests to the endpoints. You can also use the frontend to make requests to the endpoints.

  • PostgreSQL

    Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch

  • In this section, we will be creating a database to store the notes. We will be using PostgreSQL as our database. We will be using Docker to run the database. We will be using Docker Compose to run the database and the application.

  • databases

    Async database support for Python. 🗄

  • In the code ,we are using SQLAlchemy as our ORM(Object Relational Mapper) and Databases as our query builder.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo
  • insomnia

    The open-source, cross-platform API client for GraphQL, REST, WebSockets, SSE and gRPC. With Cloud, Local and Git storage.

  • The application should be running on port 8002. You can test the application by making requests to the endpoints. You can use Postman/Insomnia to make requests to the endpoints. You can also use the frontend to make requests to the endpoints.

  • fastapi

    FastAPI framework, high performance, easy to learn, fast to code, ready for production

  • This repository contains code for asynchronous example api using the Fast Api framework ,Uvicorn server and Postgres Database to perform crud operations on notes.

  • Docker Compose

    Define and run multi-container applications with Docker

  • In this section, we will be creating a database to store the notes. We will be using PostgreSQL as our database. We will be using Docker to run the database. We will be using Docker Compose to run the database and the application.

  • Visual Studio Code

    Visual Studio Code

  • Personally am using Visual Studio Code as my editor. I have installed the Thunder Client extension. This allows me to make requests to the endpoints from within the editor. You can install the extension and make requests to the endpoints from within the editor. Examples as shown below.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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