Book Image

Building Python Microservices with FastAPI

By : Sherwin John C. Tragura
3 (2)
Book Image

Building Python Microservices with FastAPI

3 (2)
By: Sherwin John C. Tragura

Overview of this book

FastAPI is an Asynchronous Server Gateway Interface (ASGI)-based framework that can help build modern, manageable, and fast microservices. Because of its asynchronous core platform, this ASGI-based framework provides the best option when it comes to performance, reliability, and scalability over the WSGI-based Django and Flask. When working with Python, Flask, and Django microservices, you’ll be able to put your knowledge to work with this practical guide to building seamlessly manageable and fast microservices. You’ll begin by understanding the background of FastAPI and learning how to install, configure, and use FastAPI to decompose business units. You’ll explore a unique and asynchronous REST API framework that can provide a better option when it comes to building microservices. After that, this book will guide you on how to apply and translate microservices design patterns in building various microservices applications and RESTful APIs using the FastAPI framework. By the end of this microservices book, you’ll be able to understand, build, deploy, test, and experiment with microservices and their components using the FastAPI framework.
Table of Contents (17 chapters)
1
Part 1: Application-Related Architectural Concepts for FastAPI microservice development
6
Part 2: Data-Centric and Communication-Focused Microservices Concerns and Issues
11
Part 3: Infrastructure-Related Issues, Numerical and Symbolic Computations, and Testing Microservices

Plotting data models

With the help of the numpy and pandas modules, FastAPI services can generate and render different types of graphs and charts using the matplotlib utilities. Like in the previous discussions, we will utilize an io.ByteIO stream and StreamResponse to generate graphical results for the API endpoints. The following API service retrieves survey data from the repository, computes the mean for each data strata, and returns a line graph of the data in PNG format:

from io import BytesIO
import matplotlib.pyplot as plt
from survey.repository.answers import AnswerRepository
from survey.repository.location import LocationRepository
@router.get("/answers/line")
async def plot_answers_mean():
    x = [1, 2, 3, 4, 5, 6, 7]
    repo_loc = LocationRepository()
    repo_answers = AnswerRepository()
    locations = await repo_loc.get_all_location()
    temp = []
 ...