DIADS: a problem diagnosis tool for databases and storage area networks

Many enterprise environments have databases running on network-attached storage infrastructure (referred to as Storage Area Networks or SANs). Both the database and the SAN are complex subsystems that are managed by separate teams of administrators. As often as not, database administrators have limited understanding of SAN configuration and behavior, and limited visibility into the SAN's run-time performance; and vice versa for the SAN administrators. Diagnosing the cause of performance problems is a challenging exercise in these environments. We propose to remedy the situation through a novel tool, called Diads, for database and SAN problem diagnosis. This demonstration proposal summarizes the technical innovations in Diads: (i) a powerful abstraction called Annotated Plan Graphs (APGs) that ties together the execution path of queries in the database and the SAN using low-overhead monitoring data, and (ii) a diagnosis workflow that combines domain-specific knowledge with machine-learning techniques. The scenarios presented in the demonstration are also described.