Converting Any Python Web Framework to FastAPI: A Comprehensive Guide
Tags: fastapi, python, flask, django
Published: 2023-08-19 18:58:51

---
title: Converting Any Python Web Framework to FastAPI: A Comprehensive Guide
published: true
description: 
tags: fastapi, python, flask, django
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# published_at: 2023-07-25 08:55 +0000
---
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Migrating from one web framework to another can be a daunting task, but with the rise of FastAPI, a modern and high-performance framework for building APIs with Python, the process has become smoother and more rewarding. FastAPI not only offers robust features but also embraces asynchronous programming, resulting in significantly improved performance for web applications. In this guide, we will explore the process of porting code from Django to FastAPI, leveraging FastAPI's features and performance advantages.

## Leveraging Asynchronous Capabilities

FastAPI's core strength lies in its asynchronous capabilities. By harnessing the power of asynchronous programming, you can achieve enhanced performance and responsiveness in your web applications. Here's how to migrate your codebase to FastAPI and make the most of its asynchronous features.

## Approach
A decision must be reached concerning which functions to convert to asynchronous and which to keep synchronous. Typically, we can categorize functions based on whether they are I/O-bound or CPU-bound. During the code migration process, we will exclusively employ asynchronous calls for I/O operations. This approach is driven by the fact that,

- I/O operations are typically slower compared to CPU-bound operations.

- In synchronous programming, I/O calls can block the execution, leading to idle CPU resources.

- By making I/O calls asynchronous, the program can handle multiple I/O operations concurrently, improving overall performance.

- Asynchronous I/O allows the program to proceed with other tasks while waiting for I/O responses, maximizing resource utilization.

## Implementation
### 1. Creating Async Clients and Dependency Injection

#### Asynchronous HTTP Requests with HTTPX/AIOHTTP

In FastAPI, the traditional `requests` library can be replaced with `httpx` or `aiohttp` for making asynchronous HTTP requests. This change allows your application to perform multiple requests concurrently, significantly improving response times and efficiency.

#### Asynchronous Elasticsearch Operations

FastAPI provides support for `AsyncElasticsearch`, which allows you to interact with Elasticsearch asynchronously. This enhancement enables better scalability for search operations by leveraging non-blocking I/O.

#### Asynchronous Database Interactions with SQLAlchemy

Incorporate `sqlalchemy`'s `async_sessionmaker` to enable asynchronous database interactions with SQL databases. This modification boosts the efficiency of database operations by allowing multiple queries to be processed concurrently.

#### Optimizing MongoDB Queries with Motor

Integrate `motor` to execute asynchronous MongoDB queries. By embracing non-blocking I/O, your application's database operations become more optimized and responsive.

#### Asynchronous Redis Calls with aioredis

FastAPI facilitates asynchronous calls to Redis using the `aioredis` library. This change enhances the efficiency of interactions with Redis, resulting in improved performance.

You can write more clients to make asynchronous calls

### 2. Refactoring Synchronous I/O Calls to Async/Await

#### Understanding Async/Await

Async/await is a fundamental concept in Python's asynchronous programming paradigm. When a function is defined with the `async` keyword, it becomes a coroutine, which can be paused and resumed during execution. The `await` keyword is used within an async function to pause its execution until an awaited asynchronous operation is complete. This non-blocking behavior allows the event loop to continue processing other tasks, improving the overall responsiveness of your code.

#### Identifying Synchronous I/O Calls

Begin the migration process by identifying synchronous I/O calls in your Django codebase. These may include caching mechanisms, database queries, and external API requests.

#### Rewriting Functions as Asynchronous

To make the codebase asynchronous, refactor the identified functions by adding the `async` keyword before the `def` keyword. This modification indicates that these functions are coroutines.

#### Using Await for Asynchronous I/O

Replace synchronous I/O calls with their asynchronous counterparts. Precede these calls with the `await` keyword, which ensures that the coroutine waits for the asynchronous operation to complete before moving forward.

### 3. Harnessing the Power of AsyncMixin for Awaitable Constructors

#### The Need for Awaitable Constructors

In Python, constructors (usually defined as `__init__` methods) are synchronous and cannot await asynchronous tasks. However, there are scenarios where asynchronous operations are desired during object initialization. This is where the `AsyncMixin` class comes into play.

#### Introducing AsyncMixin

The `AsyncMixin` class provides a solution to the limitation of synchronous constructors. It introduces an async constructor named `__ainit__`, allowing you to perform asynchronous operations during object creation. Additionally, the mixin overrides the `__await__` method, enabling proper await behavior during object instantiation. You can copy the AsyncMixin class code for [this](https://dev.to/akarshan/asynchronous-python-magic-how-to-create-awaitable-constructors-with-asyncmixin-18j5) link and inherit it for you classes.

Here's a simplified example of using `AsyncMixin`:

```python
class AsyncExample(AsyncMixin):
    async def __ainit__(self, param1, param2):
        # Perform asynchronous operations here
        await self.async_operation1(param1)
        await self.async_operation2(param2)
    
    # ... (other methods here) ...

# Asynchronously create an instance of AsyncExample
async def main():
    obj = await AsyncExample("Parameter 1", "Parameter 2")
```

### 4. Writing the ASGI Entry Point with Uvicorn

Once you've successfully converted your Python web framework to FastAPI and embraced its asynchronous capabilities, it's time to deploy your application. FastAPI is compatible with ASGI (Asynchronous Server Gateway Interface), which is the interface used by asynchronous web servers to communicate with your application. One of the popular ASGI servers is Uvicorn. Let's explore how to write the `asgi.py` file to launch your FastAPI application using Uvicorn.

#### Setting Up the ASGI Entry Point

1. **Install Uvicorn**: If you haven't already, install Uvicorn using your preferred package manager, such as pip:

   ```bash
   pip install uvicorn
   ```

2. **Create the `asgi.py` File**: In the root directory of your FastAPI project, create a file named `asgi.py`. This file will serve as the entry point for the ASGI server.

3. **Import FastAPI App**: Import your FastAPI app from the main module of your application. This is the module where you defined your FastAPI app instance.

   ```python
   from your_app_module import app  # Replace with the actual import path
   ```

4. **Create the Uvicorn Application Instance**: Create an instance of the Uvicorn application, specifying the imported FastAPI app and any additional settings:

   ```python
   import uvicorn

   if __name__ == "__main__":
       uvicorn.run(app, host="0.0.0.0", port=8000)
   ```

   In this example, Uvicorn is instructed to run the FastAPI app on all available network interfaces (`0.0.0.0`) and listen on port `8000`.

#### Running the ASGI Server

With the `asgi.py` file in place, you're ready to launch your FastAPI application using Uvicorn. Open a terminal window and navigate to the directory containing the `asgi.py` file. Then, run the following command:

```bash
uvicorn asgi:app
```

- `asgi:app` specifies the name of the module (`asgi`) and the FastAPI app instance (`app`) to run.

After running the command, Uvicorn will start the ASGI server, and your FastAPI application will be accessible at the specified host and port.


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