Home Backend Development Python Tutorial Scalable Python backend: Building a containerized FastAPI Application with uv, Docker, and pre-commit: a step-by-step guide

Scalable Python backend: Building a containerized FastAPI Application with uv, Docker, and pre-commit: a step-by-step guide

Jan 17, 2025 pm 10:17 PM

In today's containerized world, efficient backend application deployment is crucial. FastAPI, a popular Python framework, excels at creating fast, high-performance APIs. We'll use uv, a package manager, to streamline dependency management.

uv

Assuming you've installed uv and Docker, let's create our app: uv init simple-app. This generates:

<code>simple-app/
├── .python-version
├── README.md
├── hello.py
└── pyproject.toml</code>
Copy after login

pyproject.toml holds project metadata:

[project]
name = "simple-app"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.11"
dependencies = []
Copy after login

Add project dependencies to pyproject.toml:

dependencies = [
    "fastapi[standard]=0.114.2",
    "python-multipart=0.0.7",
    "email-validator=2.1.0",
    "pydantic>2.0",
    "SQLAlchemy>2.0",
    "alembic=1.12.1",
]

[tool.uv]
dev-dependencies = [
    "pytest=7.4.3",
    "mypy=1.8.0",
    "ruff=0.2.2",
    "pre-commit=4.0.0",
]
Copy after login

The [tool.uv] section defines development dependencies excluded during deployment. Run uv sync to:

  1. Create uv.lock.
  2. Create a virtual environment (.venv). uv downloads a Python interpreter if needed.
  3. Install dependencies.

FastAPI

Create the FastAPI application structure:

<code>recipe-app/
├── app/
│   ├── main.py
│   ├── __init__.py
│   └── ...
├── .python-version
├── README.md
└── pyproject.toml</code>
Copy after login

In app/main.py:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Hello(BaseModel):
    message: str

@app.get("/", response_model=Hello)
async def hello() -> Hello:
    return Hello(message="Hi, I am using FastAPI")
Copy after login

Run with: uv run fastapi dev app/main.py. You'll see output similar to:

Scalable Python backend: Building a containerized FastAPI Application with uv, Docker, and pre-commit: a step-by-step guide

Access it at https://www.php.cn/link/c099034308f2a231c24281de338726c1.

Docker

Let's Dockerize. We'll develop within containers. Add a Dockerfile:

FROM python:3.11-slim

ENV PYTHONUNBUFFERED=1

COPY --from=ghcr.io/astral-sh/uv:0.5.11 /uv /uvx /bin/

ENV UV_COMPILE_BYTE=1
ENV UV_LINK_MODE=copy

WORKDIR /app

ENV PATH="/app/.venv/bin:$PATH"

COPY ./pyproject.toml ./uv.lock ./.python-version /app/

RUN --mount=type=cache,target=/root/.cache/uv \
    --mount=type=bind,source=uv.lock,target=uv.lock \
    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
    uv sync --frozen --no-install-project --no-dev

COPY ./app /app/app

RUN --mount=type=cache,target=/root/.cache/uv \
    uv sync --frozen --no-dev

CMD ["fastapi", "dev", "app/main.py", "--host", "0.0.0.0"]
Copy after login

For easier container management, use docker-compose.yaml:

services:
  app:
    build:
      context: .
      dockerfile: Dockerfile
    working_dir: /app
    volumes:
      - ./app:/app/app
    ports:
      - "${APP_PORT:-8000}:8000"
    environment:
      - DATABASE_URL=${DATABASE_URL}
    depends_on:
      - postgres

  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: ${POSTGRES_DB}
      POSTGRES_USER: ${POSTGRES_USER}
      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  postgres_data: {}
Copy after login

Create a .env file with environment variables. Run with: docker compose up --build.

[tool.uv] and Development Tools

The [tool.uv] section in pyproject.toml lists development tools:

  • pytest: Testing framework (out of scope here).
  • mypy: Static type checker. Run manually: uv run mypy app.
  • ruff: Fast linter (replaces multiple tools).
  • pre-commit: Manages pre-commit hooks. Create .pre-commit-config.yaml:
repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.4.0
    hooks:
      - id: check-added-large-files
      - id: check-toml
      - id: check-yaml
        args:
          - --unsafe
      - id: end-of-file-fixer
      - id: trailing-whitespace
  - repo: https://github.com/astral-sh/ruff-pre-commit
    rev: v0.8.6
    hooks:
      - id: ruff
        args: [--fix]
      - id: ruff-format
Copy after login

Add pyproject.toml configurations for mypy and ruff (example provided in the original text). Install a VS Code Ruff extension for real-time linting. This setup ensures consistent code style, type checking, and pre-commit checks for a streamlined workflow.

The above is the detailed content of Scalable Python backend: Building a containerized FastAPI Application with uv, Docker, and pre-commit: a step-by-step guide. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
3 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. Best Graphic Settings
3 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. How to Fix Audio if You Can't Hear Anyone
3 weeks ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

How to solve the permissions problem encountered when viewing Python version in Linux terminal? How to solve the permissions problem encountered when viewing Python version in Linux terminal? Apr 01, 2025 pm 05:09 PM

Solution to permission issues when viewing Python version in Linux terminal When you try to view Python version in Linux terminal, enter python...

How Do I Use Beautiful Soup to Parse HTML? How Do I Use Beautiful Soup to Parse HTML? Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

How to Perform Deep Learning with TensorFlow or PyTorch? How to Perform Deep Learning with TensorFlow or PyTorch? Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Mathematical Modules in Python: Statistics Mathematical Modules in Python: Statistics Mar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

What are some popular Python libraries and their uses? What are some popular Python libraries and their uses? Mar 21, 2025 pm 06:46 PM

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H

How to Create Command-Line Interfaces (CLIs) with Python? How to Create Command-Line Interfaces (CLIs) with Python? Mar 10, 2025 pm 06:48 PM

This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.

How to efficiently copy the entire column of one DataFrame into another DataFrame with different structures in Python? How to efficiently copy the entire column of one DataFrame into another DataFrame with different structures in Python? Apr 01, 2025 pm 11:15 PM

When using Python's pandas library, how to copy whole columns between two DataFrames with different structures is a common problem. Suppose we have two Dats...

Explain the purpose of virtual environments in Python. Explain the purpose of virtual environments in Python. Mar 19, 2025 pm 02:27 PM

The article discusses the role of virtual environments in Python, focusing on managing project dependencies and avoiding conflicts. It details their creation, activation, and benefits in improving project management and reducing dependency issues.

See all articles