Bulkhead Pattern: Preventing One Slow Database From Taking Down Your Entire Service

Your payment service is humming along at 3,000 requests per second. Latency is steady at 15ms. Then the fraud detection API starts responding slowly—what usually takes 50ms now takes 30 seconds before timing out. Within two minutes, your entire service is unresponsive. Health checks fail. The load balancer pulls your instances. Customers see error pages. And here's the frustrating part: your core payment logic is perfectly healthy. The database is fine. The checkout flow works. But none of that matters because every thread in your pool is blocked, waiting on a dependency that's never going to respond in time. This is the cascading failure pattern, and it's devastatingly effective at turning a single degraded dependency into a full service outage. The root cause isn't the slow API—it's that your service treats all operations as equally trustworthy, sharing the same thread pools and connection resources. One bad actor exhausts the shared resource, and suddenly unrelated functionality sta