This paper presents Meta's FBDetect system, which advances the state of the art in performance regression detection by catching regressions as small as 0.005% in noisy production environments. FBDetect monitors around 800,000 time series covering various types of metrics (e.g., throughput, latency, CPU and memory usage) to detect regressions caused by code or configuration changes in hundreds of services running on millions of servers. FBDetect introduces advanced techniques to capture stack traces fleet-wide, measure fine-grained subroutine-level performance differences, filter out deceptive false-positive regressions, deduplicate correlated regressions, and analyze root causes. Beyond these individual techniques, a key strength of FBDetect over prior work is its battle-tested robustness, proven by seven years of production use, and each year catching regressions that would have wasted millions of servers if left undetected.

Multiple teams at Facebook are tasked with monitoring compute and memory utilization metrics that are important for managing the efficiency of the codebase. An efficiency regression is characterized by instances where the CPU utilization or query per second (QPS) patterns of a function or endpoint experience an unexpected increase over its prior baseline. If the code changes responsible for these regressions get propagated to Facebook's fleet of web servers, the impact of the inefficient code will get compounded over billions of executions per day, carrying potential ramifications to Facebook's scaling efforts and the quality of the user experience. With a codebase ingesting in excess of 1,000 diffs across multiple pushes per day, it is important to have a real-time solution for detecting regressions that is not only scalable and high in recall, but also highly precise in order to avoid overrunning the remediation queue with thousands of false positives. This paper describes the end-to-end regression detection system designed and used at Facebook. The main detection algorithm is based on sequential statistics supplemented by signal processing transformations, and the performance of the algorithm was assessed with a mixture of online and offline tests across different use cases. We compare the performance of our algorithm against a simple benchmark as well as a commercial anomaly detection software solution.