NoSQL Database Performance Diagnosis through System Call-level Introspection

Since its emergence, NoSQL databases have firmly established themselves as an indispensable software component of modern cloud-native applications. However, it also becomes increasingly challenging to perform critical management tasks such as troubleshooting unexpected performance problems. This is due to the ever-increasing diversity and specialization of NoSQL databases that make it difficult to observe the internal activities. To address these challenges, we have designed and built a technique for introspecting NoSQL databases. Our technique traces system call sequences of key operations under controlled workloads and filters scaling patterns from constant components. Novel algorithms are developed to uncover repeating patterns of system calls from massive amounts of traces and filter out background noise with high efficiency. The evaluation shows that our technique can greatly enhance the visibility into the NoSQL databases enabling us to diagnose performance problems or gain insights into internal activities.