1. Anatomy of a Cache Stampede
Caching is the most effective lever for scaling high-throughput web backends, but naive key expiration policies introduce catastrophic failure modes. When a hot cache key expires under thousands of concurrent requests, every worker thread attempts to recompute the same payload simultaneously, overwhelming the database.
Distributed Mutex with Redlock
Redis distributed mutex locks ensure only one worker thread recomputes an expired key while other requests wait briefly or receive stale-while-revalidate data.
2. Implementation: XFetch Probabilistic Early Expiration
To eliminate latency spikes completely, we implemented the XFetch algorithm in Django service layers. As a key approaches its TTL, requests probabilistically trigger background recomputation before the key actually expires.
import time, math, random
from django.core.cache import cache
def get_user_profile_optimized(user_id: int, beta: float = 1.0):
cache_key = f"user:{user_id}:profile"
cached = cache.get(cache_key)
if cached:
val, delta, expiry = cached
# XFetch: Recompute early if probability threshold is crossed
if -(delta * beta * math.log(random.random())) > (expiry - time.time()):
# Trigger background recomputation asynchronously
pass
return val
# Recompute with Redis distributed lock
with cache.lock(f"lock:{cache_key}", timeout=5):
cached = cache.get(cache_key)
if cached:
return cached[0]
data = db_fetch_user_profile(user_id)
cache.set(cache_key, (data, 0.05, time.time() + 3600), 3600)
return dataXFetch probabilistic algorithm implemented with Django cache and Redis distributed lock.
