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Developer Productivity

Investigate First

Julius BrusseeApache-2.0Skill language: English
License verifiedNo scripts
DemoReal run · claude-opus-5-5 · October 2026
Our checkout page sometimes shows another user's cart, maybe once in a few hundred requests, only in production. I don't know why yet and I don't want a fix until we know the cause. Here is the relevant code: ```python # cart/views.py (Django, gunicorn with 4 threads per worker) _cart_cache = {} class CartView(View): current_user = None def get(self, request): self.__class__.current_user = request.user cart = _cart_cache.get(request.user.id) if cart is None: cart = load_cart(CartView.current_user) _cart_cache[request.user.id] = cart return render(request, "cart.html", {"cart": cart}) ``` Staging runs gunicorn with `--threads 1`. Production uses `--threads 4`. What is causing this?
Skill: investigate-first

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:

  1. The bad entry sticks. The wrong cart is stored in _cart_cache under 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.
  2. 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 shared CartView.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 overwrite current_user between 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_user in the short gap before load_cart reads 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.

  1. Log the mismatch. Temporarily log a warning in production or in a load test whenever CartView.current_user.id != request.user.id just before load_cart. Any hit proves this mechanism.
  2. Reproduce in staging. Run staging with --threads 4 and 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 1 should show zero leaks.
  3. Force it in a test. Make load_cart sleep briefly or wait on a threading.Barrier, then call CartView().get() from two threads with different users. This reproduces it every time.
  4. 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.

What it does

A short working rule for unclear failures: gather evidence and pin down the cause before editing product code.

How it works

  1. Separates the observed symptom from the suspected cause.
  2. Traces inputs, state changes and ownership boundaries, and ranks hypotheses by evidence and by how cheaply each can be ruled out.
  3. Makes no edits until one mechanism explains the evidence, then reports the cause and the proof. It does not fix anything unless the task asks for a fix.

Good for

Intermittent bugs, performance regressions and "works on staging, fails in production" problems.

Worth knowing

One of the general work patterns from the open-source Caveman project. The instructions themselves carry no Caveman branding and work in any project.

Notes & risks

Pure instruction file: no scripts, no network access and no file writes. The package also contains LICENSE, NOTICE and agents/openai.yaml (display name and default prompt for Codex).