Performance

Learn essential concepts of performance in FastAPI.

What You'll Learn

This section covers FastAPI performance optimization, including:

• Async/await for concurrent request handling

• Connection pooling for database access

• Response compression and caching headers

• Lazy loading and pagination strategies

• Profiling and bottleneck identification

• Production deployment optimization

Core Concepts

FastAPI's async nature enables handling thousands of concurrent requests efficiently. Performance optimization focuses on eliminating bottlenecks and leveraging FastAPI's strengths.

Key Ideas

Understanding performance helps you:

• Handle concurrent requests efficiently with async

• Reduce database query overhead with pooling

• Minimize response sizes with compression

• Implement smart caching strategies

• Identify and fix performance bottlenecks

• Scale API deployments confidently

Async Request Handling

from fastapi import FastAPI
import asyncio

app = FastAPI()

@app.get("/slow-endpoint")
async def slow_endpoint():
    # Async operations can run concurrently
    await asyncio.sleep(1)  # Simulated I/O
    return {"data": "result"}

# This endpoint handles many concurrent requests
# while the other is waiting for I/O

Connection Pooling Example

from sqlalchemy.pool import QueuePool
from sqlalchemy import create_engine

# Use connection pooling for database efficiency
engine = create_engine(
    "postgresql://user:password@localhost/db",
    poolclass=QueuePool,
    pool_size=20,           # Keep 20 connections ready
    max_overflow=0,         # Don't create extra connections
    pool_pre_ping=True,     # Test connections before use
)

def get_db():
    db = SessionLocal(bind=engine)
    try:
        yield db
    finally:
        db.close()

Response Compression

from fastapi.middleware.gzip import GZIPMiddleware

app = FastAPI()
app.add_middleware(GZIPMiddleware, minimum_size=1000)

@app.get("/large-data")
async def get_large_data():
    return {"items": [{"id": i} for i in range(1000)]}

Pagination Pattern

from pydantic import BaseModel
from typing import List

class Item(BaseModel):
    id: int
    name: str

@app.get("/items")
async def list_items(skip: int = 0, limit: int = 10, db: Session = Depends(get_db)):
    # Only fetch what's needed, not entire dataset
    items = db.query(Item).offset(skip).limit(limit).all()
    return {"items": items, "skip": skip, "limit": limit}

Cache Headers

from fastapi.responses import Response

@app.get("/static-data")
async def get_static_data():
    return Response(
        content={"data": "static"},
        headers={"Cache-Control": "public, max-age=3600"}
    )

Real-World Usage

Performance optimization is critical for:

• **Scalability**: Handle growing user base without infrastructure overhaul

• **User Experience**: Fast responses keep users engaged

• **Cost**: Efficient resource use reduces hosting costs

• **Reliability**: Better performance prevents timeouts and failures

• **Competitive Advantage**: Faster APIs attract and retain users

🔑 Key Takeaways

• ✅ Understand the purpose of performance

• ✅ Know when to apply this pattern

• ✅ Follow best practices consistently

• ✅ Test thoroughly in production scenarios

Next step: Explore the advanced section for production patterns and optimization techniques.