Does FastAPI Work With Redis?
FastAPI and Redis integrate seamlessly for caching, session management, and real-time features with minimal setup required.
Quick Facts
How FastAPI Works With Redis
FastAPI and Redis work together naturally because FastAPI is framework-agnostic about data stores. Redis isn't built into FastAPI, but the community has created excellent libraries like redis-py and aioredis that provide async support matching FastAPI's async nature. You typically instantiate a Redis connection at application startup using FastAPI's lifespan events or startup/shutdown handlers, then inject it into route handlers. This pattern keeps your code clean and testable. Redis excels as a session store, cache layer, rate limiting backend, or Pub/Sub broker within FastAPI applications. The async variants (aioredis/redis[asyncio]) are crucial—using synchronous Redis drivers blocks your event loop and defeats FastAPI's async advantages. Most developers use dependency injection to pass Redis clients to routes, making testing straightforward with mock Redis instances.
Best Use Cases
Quick Setup
pip install fastapi redis uvicornfrom contextlib import asynccontextmanager
from fastapi import FastAPI, Depends
import redis.asyncio as redis
redis_client = None
@asynccontextmanager
async def lifespan(app: FastAPI):
global redis_client
redis_client = await redis.from_url("redis://localhost:6379")
yield
await redis_client.close()
app = FastAPI(lifespan=lifespan)
async def get_redis():
return redis_client
@app.get("/cache/{key}")
async def get_cached(key: str, r = Depends(get_redis)):
value = await r.get(key)
if value:
return {"cached": value.decode()}
return {"cached": None}
@app.post("/cache/{key}/{value}")
async def set_cached(key: str, value: str, r = Depends(get_redis)):
await r.set(key, value, ex=3600)
return {"set": True}Known Issues & Gotchas
Using synchronous redis-py client blocks the event loop
Fix: Use aioredis or redis[asyncio] package with async/await syntax throughout
Redis connection exhaustion under high load if not using connection pooling
Fix: Use ConnectionPool or let aioredis handle pooling automatically via the client
Data loss on Redis restart if not configured with persistence
Fix: Enable RDB snapshots or AOF rewriting in redis.conf for non-cache use cases
Serialization overhead when storing complex Python objects
Fix: Use JSON serialization or pickle carefully; consider storing primitive types
Alternatives
- •FastAPI + Memcached: Similar caching use cases but simpler protocol, less feature-rich
- •FastAPI + PostgreSQL with pgbouncer: For persistence-first approaches with connection pooling
- •FastAPI + RabbitMQ: Better for complex message routing and job queues vs Redis Pub/Sub
Resources
Related Compatibility Guides
Explore more compatibility guides