🚀 Lesson 4 — AsyncIO, Concurrency & High Performance in FastAPI
This is one of the most important lessons because FastAPI’s biggest advantage is speed, powered by:
✔ async / await
✔ ASGI (not WSGI)
✔ Event loop
✔ Concurrency without threading
✔ High-throughput performance
By the end of this lesson, you will think asynchronously, just like modern backend engineers.
🎯 What You Will Learn Today
✔ Sync vs Async (super simple explanation)
✔ Event Loop (visual understanding)
✔ Async routes
✔ Concurrent tasks
✔ Making multiple API calls at the same time
✔ Long-running tasks
✔ Practical examples used in industry
✔ Performance best practices
Let’s begin 🔥
🧠 1. Sync vs Async — Super Simple Example
🐌 Sync (Traditional Python / Flask)
- One request → handled at a time
- If a 5-second task runs → server gets stuck
- Slow for real-time systems
⚡ Async (FastAPI / Node.js style)
- Can handle thousands of requests concurrently
- Non-blocking
- Perfect for data IO, DB calls, ML inference
🔄 2. Understanding the Event Loop (Simple Visual)
Think of a chef:
- Sync: One chef → cooks one dish → waits → next
- Async: Chef moves between dishes while others simmer
So async = do something else while waiting.
🚀 3. Your First Async Endpoint
from fastapi import FastAPI
import asyncio
app = FastAPI()
@app.get("/slow")
async def slow_api():
await asyncio.sleep(3)
return {"msg": "Completed after 3 seconds"}
Why powerful?
- FastAPI handles OTHER requests during the 3-sec wait
- Sync frameworks would block
🏎 4. Running Multiple Tasks Concurrently
@app.get("/multi")
async def multi_task():
task1 = asyncio.create_task(asyncio.sleep(3))
task2 = asyncio.create_task(asyncio.sleep(2))
await task1
await task2
return {"msg": "Both tasks done!"}
Total time = 3 seconds, not 5 seconds.
That’s concurrency.
🌐 5. Concurrent API Calls (SUPER USEFUL)
Example: fetch 3 external APIs at same time.
import httpx
@app.get("/weather")
async def get_weather():
async with httpx.AsyncClient() as client:
t1 = client.get("https://api1.com/data")
t2 = client.get("https://api2.com/data")
t3 = client.get("https://api3.com/data")
res = await asyncio.gather(t1, t2, t3)
return [r.json() for r in res]
Industry use:
- Flights (multi-source)
- Stock market data
- AI model inference on multiple models
- Microservices architecture
🧪 6. Async Database Calls (Industry Standard)
Using async SQLAlchemy or Tortoise ORM:
@app.get("/users")
async def get_users():
users = await User.all() # async DB query
return users
This handles high traffic smoothly.
🤖 7. Async + AI Model Inference
Example: TensorFlow or PyTorch model served asynchronously.
@app.post("/predict")
async def predict(text: str):
loop = asyncio.get_event_loop()
result = await loop.run_in_executor(None, model.predict, text)
return {"prediction": result}
Why?
- ML prediction is CPU-bound
- Move it to a thread executor
- Don’t block the main event loop
Used in:
- Chatbots
- Recommendation systems
- LLM inference
🔥 8. Background Tasks
For sending emails, logs, notifications, async jobs.
from fastapi import BackgroundTasks
def send_email(to):
print(f"Sending email to {to}")
@app.post("/register")
async def register(user: str, bg: BackgroundTasks):
bg.add_task(send_email, user)
return {"msg": "User registered"}
FastAPI returns instantly → email happens in background.
💎 9. Performance Best Practices (Used by FAANG Companies)
✔ Use async whenever possible
DB, external API calls, long operations.
✔ Use httpx.AsyncClient (not requests)
requests is blocking → slows down server.
✔ Use caching (Redis)
Avoid recomputing expensive results.
✔ Use connection pooling
Especially with PostgreSQL or MongoDB.
✔ Use Gunicorn + Uvicorn workers
More CPU cores → more parallelism.
✔ Use async ORMs (SQLModel, Tortoise, Prisma)
✔ Don’t block the event loop
Don’t run heavy CPU work directly inside async routes.
🏭 10. Industry Project Example — Microservice with AsyncIO
Use case:
Search for a product →
We hit:
- Pricing service
- Inventory service
- Recommendation service
All async in parallel:
@app.get("/product/{id}")
async def product_info(id: int):
async with httpx.AsyncClient() as client:
price = client.get(f"http://pricing/{id}")
stock = client.get(f"http://inventory/{id}")
recs = client.get(f"http://reco/{id}")
price, stock, recs = await asyncio.gather(price, stock, recs)
return {
"price": price.json(),
"stock": stock.json(),
"reco": recs.json()
}
Total time = slowest API
Not the sum of all three → ⚡ extremely fast.
📌 Lesson 4 Summary
You learned:
✔ Why async is powerful
✔ How the event loop works
✔ Async endpoints
✔ Concurrent tasks
✔ Parallel API calls
✔ Async DB operations
✔ ML model async serving
✔ Background tasks
✔ Performance best practices
You now think like a modern backend engineer.
This makes you ready for:
- Microservices
- AI model serving
- High-traffic systems
- System design interviews
🚀 Ready for Lesson 5 — Authentication (JWT, OAuth2), Users Login System, Password Hashing?
Shall I continue with Lesson 5?