I recently spent two days chasing a ghost in a machine-to-machine API integration. The integration worked flawlessly in staging but in production, our webhook endpoint sporadically failed to acknowledge receipts from a critical third-party service, causing duplicate processing and missed events. The logs from their service showed a 200 OK response from our endpoint, but our logs showed the corresponding background job never ran. The issue was intermittent and impossible to reproduce locally—a classic sign of a state or environmental problem.
After exhaustive logging, I discovered our production API gateway (a layer we didn't control) was aggressively caching POST requests. Because our idempotency key header was static for a period (a business logic decision to group events), the gateway served a cached 200 response for subsequent identical POSTs before they even hit our application server. Our app never saw the request, so the job never enqueued. The "healthy" 200 response from the gateway masked a total failure of our business logic. The lesson wasn't just about our code; it was about the entire request pipeline. Always verify caching behavior at every proxy, CDN, or gateway layer, especially for non-GET requests. A successful HTTP status code from a downstream cache can be a catastrophic lie.
The fix involved two parts: First, we added a Cache-Control: no-store header on this specific webhook endpoint to instruct all intermediate caches to bypass storage. Second, we re-archored our idempotency key strategy to be truly unique per event, ensuring every request was distinct and thus uncachable. This hardened our integration against future infrastructure changes. Now, I treat caching headers as a first-class security concern in API design—what you think is a "get" operation might be interpreted as a "cacheable" operation by the network, and that assumption can silently break your system.
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