ASGI and async-first architecture in FastAPI - Mini Project: Build & Apply

Building an Async API with FastAPI and ASGI

📖 Scenario: You are creating a simple web API that returns user data. The API should be fast and able to handle many requests at the same time without waiting. This is important for real-world apps like chat apps or live dashboards.

🎯 Goal: Build a FastAPI app that uses async functions to handle requests. You will create user data, configure a simple setting, write an async route to return users, and complete the app setup to run with ASGI.

📋 What You'll Learn

Create a dictionary called users with exact user data

Add a variable max_users to limit returned users

Write an async function get_users that returns users up to max_users

Complete the FastAPI app setup with correct import and app instance

💡 Why This Matters

🌍 Real World

Many modern web apps need to handle many users at once without slowing down. Using async functions with FastAPI and ASGI lets servers handle many requests efficiently, like chat apps or live data feeds.

💼 Career

Understanding async-first architecture and ASGI is key for backend developers working with Python web frameworks like FastAPI. It helps build fast, scalable APIs used in startups and large companies.

1

Create the user data dictionary

Create a dictionary called users with these exact entries: 1: 'Alice', 2: 'Bob', 3: 'Charlie', 4: 'Diana'.

FastAPI

# Create the users dictionary with IDs and names
# Your code here

Hint

Use curly braces to create a dictionary with keys as numbers and values as strings.

2

Add a max_users configuration variable

Add a variable called max_users and set it to 3 to limit how many users the API will return.

FastAPI

users = {1: 'Alice', 2: 'Bob', 3: 'Charlie', 4: 'Diana'}
# Add max_users variable below
# Your code here

Hint

Just create a simple variable with the number 3.

3

Write an async function to get limited users

Write an async function called get_users that returns a dictionary of users limited to max_users. Use async def get_users() and return a dictionary with the first max_users items from users.

FastAPI

users = {1: 'Alice', 2: 'Bob', 3: 'Charlie', 4: 'Diana'}
max_users = 3

# Write async function get_users below
# Your code here

Hint

Use dictionary comprehension inside the async function to select only the first max_users users.

4

Complete the FastAPI app setup with async route

Import FastAPI from fastapi, create an app instance called app, and add an async route @app.get('/users') that calls get_users and returns its result.

FastAPI

users = {1: 'Alice', 2: 'Bob', 3: 'Charlie', 4: 'Diana'}
max_users = 3

async def get_users():
    limited_users = {k: users[k] for k in list(users)[:max_users]}
    return limited_users

# Import FastAPI, create app, and add async route below
# Your code here

Hint

Remember to use await when calling the async function inside the route.

Practice

1. What does ASGI enable FastAPI to do compared to traditional synchronous frameworks?

easy

A. Use only synchronous functions for better speed

B. Run only one request at a time to avoid conflicts

C. Handle many requests at the same time using async code

D. Automatically convert Python code to JavaScript

Solution

1. Step 1: Understand ASGI's role

   ASGI is designed to handle multiple requests concurrently using asynchronous programming.

2. Step 2: Compare with synchronous frameworks

   Traditional synchronous frameworks handle one request at a time, blocking others until done.

3. Final Answer:

   Handle many requests at the same time using async code -> Option C

4. Quick Check:

   ASGI = concurrent async handling [OK]

Hint: ASGI means multitasking with async code [OK]

Common Mistakes:

• Thinking ASGI runs requests one by one
• Confusing ASGI with code conversion tools
• Believing async slows down the app

2. Which of the following is the correct way to define an async route handler in FastAPI?

easy

A. async def read_root(): return {"message": "Hello"}

B. def read_root(): return {"message": "Hello"}

C. async read_root(): return {"message": "Hello"}

D. def async read_root(): return {"message": "Hello"}

Solution

1. Step 1: Recall async function syntax

   In Python, async functions start with async def followed by the function name.

2. Step 2: Check each option's syntax

   async def read_root(): return {"message": "Hello"} correctly uses async def. Options C and D have invalid syntax. def read_root(): return {"message": "Hello"} is synchronous.

3. Final Answer:

   async def read_root(): return {"message": "Hello"} -> Option A

4. Quick Check:

   Async function = async def [OK]

Hint: Async functions start with 'async def' [OK]

Common Mistakes:

• Omitting 'def' after 'async'
• Placing 'async' after 'def'
• Using synchronous 'def' for async routes

3. Given this FastAPI route handler, what will be the output when accessed?

from fastapi import FastAPI
import asyncio

app = FastAPI()

@app.get("/wait")
async def wait_and_return():
    await asyncio.sleep(1)
    return {"status": "done"}

medium

A. Error because asyncio.sleep cannot be awaited

B. Immediate response with {"status": "done"}

C. Server crashes due to blocking call

D. {"status": "done"} after 1 second delay

Solution

1. Step 1: Understand async sleep behavior

   The function uses await asyncio.sleep(1), which pauses the function asynchronously for 1 second.

2. Step 2: Predict response timing

   The response will be sent after the 1 second delay, returning the JSON with status done.

3. Final Answer:

   {"status": "done"} after 1 second delay -> Option D

4. Quick Check:

   Await asyncio.sleep = delayed async response [OK]

Hint: Await asyncio.sleep delays response asynchronously [OK]

Common Mistakes:

• Thinking await asyncio.sleep blocks all requests
• Expecting immediate response ignoring await
• Confusing asyncio.sleep with time.sleep

4. Identify the error in this FastAPI async route handler:

from fastapi import FastAPI
import asyncio

app = FastAPI()

@app.get("/error")
def error_route():
    await asyncio.sleep(1)
    return {"message": "done"}

medium

A. Missing async keyword before function definition

B. Cannot use await inside async function

C. FastAPI does not support async routes

D. Return statement must be inside try-except block

Solution

1. Step 1: Check function definition

   The function uses await but is defined without async, which is invalid syntax.

2. Step 2: Understand async function requirements

   To use await, the function must be declared with async def.

3. Final Answer:

   Missing async keyword before function definition -> Option A

4. Quick Check:

   Await requires async def [OK]

Hint: Use async def when using await inside function [OK]

Common Mistakes:

• Forgetting async before def
• Thinking await works in sync functions
• Believing FastAPI disallows async routes

5. You want to create a FastAPI endpoint that fetches data from two slow APIs concurrently to improve speed. Which approach best uses FastAPI's async-first architecture?

hard

A. Use async functions with await on both API calls sequentially

B. Use asyncio.gather to run both async API calls concurrently inside an async route

C. Call both APIs one after another using synchronous requests inside an async function

D. Run synchronous API calls in separate threads without async

Solution

1. Step 1: Understand concurrency with async

   Calling APIs sequentially with await waits for each to finish before starting the next, not concurrent.

2. Step 2: Use asyncio.gather for concurrency

   asyncio.gather runs multiple async tasks at the same time, improving speed.

3. Step 3: Compare options

   Use asyncio.gather to run both async API calls concurrently inside an async route uses asyncio.gather inside async route, best leveraging FastAPI's async-first design.

4. Final Answer:

   Use asyncio.gather to run both async API calls concurrently inside an async route -> Option B

5. Quick Check:

   asyncio.gather = concurrent async calls [OK]

Hint: Use asyncio.gather for concurrent async calls [OK]

Common Mistakes:

• Awaiting calls sequentially losing concurrency
• Using synchronous calls blocking event loop
• Ignoring async and threading differences