The need to perform testing and tuning of database instances with production-like workloads (W), configurations (C), data (D), and resources (R) arises routinely. The further W, C, D, and R used in testing and tuning deviate from what is observed on the production database instance, the lower is the trustworthiness of the testing and tuning tasks done. For example, it is common to hear about performance degradation observed after the production database is upgraded from one software version to another. A typical cause of this problem is that the W, C, D, or R used during upgrade testing differed in some way from that on the production database. Performing testing and tuning tasks in principled and automated ways is very important, especially since---spurred by innovations in cloud computing---the number of database instances that a database administrator (DBA) has to manage is growing rapidly.

Occasional corruption of stored data is an unfortunate byproduct of the complexity of modern systems. Hardware errors, software bugs, and mistakes by human administrators can corrupt important sources of data. The dominant practice to deal with data corruption today involves administrators writing ad hoc scripts that run data-integrity tests at the application, database, file-system, and storage levels. This manual approach is tedious, error-prone, and provides no understanding of the potential system unavailability and data loss if a corruption were to occur. We introduce the Amulet system that addresses the problem of verifying the correctness of stored data proactively and continuously. To our knowledge, Amulet is the first system that: (i) gives administrators a declarative language to specify their objectives regarding the detection and repair of data corruption; (ii) contains optimization and execution algorithms to ensure that the administrator's objectives are met robustly and with least cost, e.g., using pay-as-you cloud resources; and (iii) provides timely notification when corruption is detected, allowing proactive repair of corruption before it impacts users and applications. We describe the implementation and a comprehensive evaluation of Amulet for a database software stack deployed on an infrastructure-as-a-service cloud provider.

Table partitioning splits a table into smaller parts that can be accessed, stored, and maintained independent of one another. From their traditional use in improving query performance, partitioning strategies have evolved into a powerful mechanism to improve the overall manageability of database systems. Table partitioning simplifies administrative tasks like data loading, removal, backup, statistics maintenance, and storage provisioning. Query language extensions now enable applications and user queries to specify how their results should be partitioned for further use. However, query optimization techniques have not kept pace with the rapid advances in usage and user control of table partitioning. We address this gap by developing new techniques to generate efficient plans for SQL queries involving multiway joins over partitioned tables. Our techniques are designed for easy incorporation into bottom-up query optimizers that are in wide use today. We have prototyped these techniques in the PostgreSQL optimizer. An extensive evaluation shows that our partition-aware optimization techniques, with low optimization overhead, generate plans that can be an order of magnitude better than plans produced by current optimizers.

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.