Scaling a mobile backend for influencer traffic spikes

Influencer-driven traffic is bursty in a way normal growth curves never prepare you for. Instead of a smooth ramp, you get a near-vertical spike the moment a creator hits publish - and just as sharp a drop a few hours later.

Cache first, fail open

A fail-open Redis layer with per-account epoch invalidation lets reads stay fast without ever blocking a request when the cache is cold or unavailable. If Redis is down, the app degrades to direct database reads rather than erroring out.

def cached(key, ttl, loader):
    try:
        hit = redis.get(key)
        if hit is not None:
            return json.loads(hit)
    except RedisError:
        pass  # fail open - never block the request

    value = loader()
    try:
        redis.set(key, json.dumps(value), ex=ttl)
    except RedisError:
        pass
    return value

Fail-open cache wrapper

Protect the database

Gunicorn workers behind Nginx, tuned pool sizes, and SKIP LOCKED reservations at checkout keep the database from becoming the bottleneck under contention.

A pre-launch stress test that simulates the concurrency profile of a real drop is the only way to find the limits before your users do.