Cause
It's a race on shared state between threads. current_user is a class attribute, so every thread in a gunicorn worker process reads and writes the same slot. With --threads 4, two requests can interleave like this:
| Thread A (user Alice) | Thread B (user Bob) |
|---|---|
CartView.current_user = Alice |
|
_cart_cache.get(alice.id) → miss |
|
CartView.current_user = Bob |
|
load_cart(CartView.current_user) → loads Bob's cart |
|
_cart_cache[alice.id] = Bob's cart |
Alice is shown Bob's cart. Two details make it worse:
- The bad entry sticks. The wrong cart is stored in
_cart_cacheunder Alice's id. Her later requests to that worker hit the cache and keep showing Bob's cart until the process restarts. So one short race turns into a lasting leak for that user on that worker. - The key and the value come from different sources. The cache key uses the thread-local
request.user.id, but the value is loaded from the sharedCartView.current_user. Nothing checks that the two refer to the same user.
Why only production, and why rarely
-
Staging (
--threads 1): each worker process handles one request at a time, so nothing else can overwritecurrent_userbetween the write and the read. The bug can't happen there. Separate gunicorn workers are separate processes, so they don't share the class attribute. -
Production (
--threads 4): requests share the attribute. It only goes wrong when all three of these line up:- a cache miss (first visit to that worker, or after a restart)
- a second request writes
current_userin the short gap beforeload_cartreads it - the GIL switches threads at exactly that point
That combination fits "once in a few hundred requests."
How to confirm it before changing anything
These checks are cheap. I haven't run any of them; this diagnosis comes from reading the code.
- Log the mismatch. Temporarily log a warning in production or in a load test whenever
CartView.current_user.id != request.user.idjust beforeload_cart. Any hit proves this mechanism. - Reproduce in staging. Run staging with
--threads 4and send concurrent requests from many different users to the cart page. A fresh worker gives the most cache misses. Then check which user's items each response contains. Running the same load with--threads 1should show zero leaks. - Force it in a test. Make
load_cartsleep briefly or wait on athreading.Barrier, then callCartView().get()from two threads with different users. This reproduces it every time. - Check that it persists. Once a user has seen a wrong cart, their repeat requests to the same worker should keep showing that same wrong cart. Restarting the worker should clear it. If that's what happens, the cache is what turns the race into a lasting leak.
If check 1 or 2 fires, the cause is confirmed. Note that any fix will also need to clear the poisoned entries in _cart_cache, either by restarting the workers or by discarding the cache. Holding off on the fix as you asked.
Real run recorded with claude-code / claude-opus-5-5. Output is shown verbatim, unmodified.
Qué hace
Una regla de trabajo breve para fallos poco claros: reunir evidencia y determinar la causa antes de editar el código del producto.
Cómo funciona
- Separa el síntoma observado de la causa sospechada.
- Traza las entradas, los cambios de estado y los límites de propiedad, y ordena las hipótesis según la evidencia y según lo barato que resulte descartar cada una.
- No realiza ninguna edición hasta que un mecanismo explique la evidencia; luego informa la causa y la prueba. No corrige nada a menos que la tarea solicite una corrección.
Útil para
Fallos intermitentes, regresiones de rendimiento y problemas del tipo «funciona en staging, falla en producción».
Conviene saber
Es uno de los patrones de trabajo generales del proyecto de código abierto Caveman. Las instrucciones en sí no llevan la marca Caveman y funcionan en cualquier proyecto.
Archivo de solo instrucciones: sin scripts, sin acceso a la red y sin escritura de archivos. El paquete también contiene LICENSE, NOTICE y agents/openai.yaml (nombre visible y prompt predeterminado para Codex).