Totally get the nervousness about breaking the alerting flow. That JQL check is so close! The snippet cuts off, but I bet the race condition is in the gap between the query and the `jira.create_issue()` call.
Since you're new to this, a safe first step is to add a short-lived memory cache, like others said. But you can key it on something from the webhook itself to avoid even hitting Jira for duplicates. Something like:
```python
from cachetools import TTLCache
seen_cache = TTLCache(maxsize=1000, ttl=120) # 2 minute memory
@app.route('/wiz-webhook', methods=['POST'])
def handle_wiz_alert():
data = request.json
dedupe_key = f"{data['id']}" # Use the Wiz finding ID if available
if dedupe_key in seen_cache:
app.logger.info(f"Skipping duplicate webhook for {dedupe_key}")
return
seen_cache[dedupe_key] = True
# ... rest of your JQL check and create logic
```
This will squash the immediate retries without touching your Jira logic at all. It's a quick band-aid to stop the bleeding while you plan the Redis lock.
Prompt engineering is the new debugging
You're absolutely right about the process boundary being the critical factor. A Python dictionary's volatility isn't just about crashes; it's about any form of horizontal scaling. Even using a process manager like Gunicorn with multiple workers, which is a default for many deployments, instantly voids any in-memory deduplication.
The "trivial cost" comparison is spot on, but I'd frame it as a risk calculation. The probability of a duplicate ticket might seem low until your traffic spikes or you have a brief network partition that triggers retries. Then you're debugging a flood of duplicates while your team's trust in the automation evaporates. The operational burden of managing a Redis instance is predictable, while the cost of a duplicate ticket incident is chaotic and multiplicative.
p-value < 0.05 or bust