TcpRT: Instrument and Diagnostic Analysis System for Service Quality of Cloud Databases at Massive Scale in Real-time

Abstract

Smooth end-to-end performance of mission-critical database system is essential to the stability of applications deployed on the cloud. It's a challenge for cloud database vendors to detect any performance degradation in real-time and locate the root cause quickly in sophisticated network environment. Cloud databases vendors tend to favor a multi-tier distributed architecture to achieve multi-tenant management, scalability and high-availability, which may further complicate the problem. This paper presents TcpRT, the instrument and diagnosis infrastructure in Alibaba Cloud RDS that achieves real-time anomaly detection. We wrote a Linux kernel module to collect trace data of each SQL query, designed to be efficient with minimal overhead, it adds tracepoints in callbacks of TCP congestion control kernel module, that is totally transparent to database processes. In order to reduce the amount of data significantly before sending it to backend, raw trace data is aggregated. Aggregated trace data is then processed, grouped and analyzed in a distributed streaming computing platform. By utilizing a self-adjustable Cauchy distribution statistical model from historical performance data for each DB instance, anomalous events can be automatically detected in databases, which eliminates manually configuring thresholds by experience. A fault or hiccup occurred in any network component that is shared among multiple DB instances (e.g. hosted on the same physical machine or uplinked to the same pair of TOR switches) may cause large-scale service quality degradations. The ratio of anomalous DB instances vs networks components is being calculated, which helps pinpoint the faulty component. TcpRT has been deployed in production at Alibaba Cloud for the past 3 years, collects over 20 million raw traces per second, and processes over 10 billion locally aggregated results in the backend per day, and managed to have within 1% performance impact on DB system. We present case studies of typical scenarios where TcpRT helps to solve various problems occurred in production system.