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Python / FastAPI Basics Interview Questions

How do you measure and improve the performance of a FastAPI application?

FastAPI is already one of the fastest Python frameworks, but real-world performance depends on database queries, serialisation, and concurrency patterns. These are the key tools and techniques.

# 1. Measure with locust load testing
# pip install locust
# locustfile.py
from locust import HttpUser, task, between

class APIUser(HttpUser):
    wait_time = between(0.1, 1)

    @task(3)
    def list_items(self):
        self.client.get("/items")

    @task(1)
    def create_item(self):
        self.client.post("/items", json={"name": "test", "price": 1.0})

# locust -f locustfile.py --host=http://localhost:8000


# 2. Add request timing middleware
import time
from fastapi import FastAPI, Request

app = FastAPI()

@app.middleware("http")
async def timing_middleware(request: Request, call_next):
    start = time.perf_counter()
    response = await call_next(request)
    elapsed = time.perf_counter() - start
    response.headers["X-Response-Time"] = f"{elapsed:.4f}s"
    if elapsed > 0.5:  # log slow requests
        print(f"SLOW: {request.url.path} took {elapsed:.3f}s")
    return response

Performance tips
AreaTip
DB queriesUse async SQLAlchemy, add indexes, use select_in_loading for relationships
SerialisationPydantic v2 is ~5x faster than v1; avoid model_dump() in hot paths
ConcurrencyUse async def for I/O-bound routes; avoid blocking calls in async context
Connection poolsSet pool size based on worker count × connections-per-worker
Response sizeUse response_model_exclude_unset=True to reduce payload size
CachingCache expensive queries with Redis; use HTTP Cache-Control headers
What is the most impactful performance improvement for a database-heavy FastAPI app?
Why is it important to avoid blocking synchronous calls inside async def route handlers?

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