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Database management systems traditionally provide user-, table-, index-, and schema-level monitoring. For cloud-deployed applications, the research reported herein provides preliminary evidence that transaction cluster-level monitoring simplifies performance troubleshooting—especially for online transaction processing (OLTP) applications. Specifically, problematic rollbacks, performance drifts, system-wide performance problems, and system bottlenecks can be more easily debugged. The DBSCAN algorithm identifies transaction clusters based on transaction features extracted directly from a DBMS server—a job previously done using SQL log-mining. DBSCAN produces more accurate clusters when inter-transaction distances are computed using the angular cosine distance function (ACD) rather than the usual Euclidean distance function. Choice ofACD also simplifies DBSCAN parameter tuning—a task known to be nontrivial.

DBMS Performance Troubleshooting in Cloud Computing Using Transaction Clustering