Abstract: Techniques are described herein for probabilistic monitoring of high-frequency, low-latency database queries. In some embodiments, a probabilistic query monitoring system periodically samples active database sessions. For example, the system may generate sample data every one second or at some other sampling rate for each database session that is currently active. The sample data may include a mapping between query identifiers to sample counter values that are extracted at different sample intervals. The system may then estimate performance metrics for the set of active database based on the counter values sampled across consecutive sample intervals.

Abstract: Techniques are described herein for probabilistic monitoring of high-frequency, low-latency database queries. In some embodiments, a probabilistic query monitoring system periodically samples active database sessions. For example, the system may generate sample data every one second or at some other sampling rate for each database session that is currently active. The sample data may include a mapping between query identifiers to sample counter values that are extracted at different sample intervals. The system may then estimate performance metrics for the set of active database based on the counter values sampled across consecutive sample intervals.

Abstract: Techniques are described for generating a composite display of multiple user interface elements, each of which presents datasets corresponding to the operational parameters of computing resources even though the computer resources may operate in different computing environments. In some cases, the composite display includes a feature that enables a comparison of operational performance data between a target measurement period relative to a selectable reference period. The reference period may be manually selectable from a menu of candidate time periods or identified and/or selected by the system based on a similarity between data characteristics in the target measurement period and a baseline measurement period.

Abstract: Techniques are described herein for probabilistic monitoring of high-frequency, low-latency database queries. In some embodiments, a probabilistic query monitoring system periodically samples active database sessions. For example, the system may generate sample data every one second or at some other sampling rate for each database session that is currently active. The sample data may include a mapping between query identifiers to sample counter values that are extracted at different sample intervals. The system may then estimate performance metrics for the set of active database based on the counter values sampled across consecutive sample intervals.

Abstract: Techniques are described for generating a composite display of multiple user interface elements, each of which presents datasets corresponding to the operational parameters of computing resources even though the computer resources may operate in different computing environments. In some cases, the composite display includes a feature that enables a comparison of operational performance data between a target measurement period relative to a selectable reference period. The reference period may be manually selectable from a menu of candidate time periods or identified and/or selected by the system based on a similarity between data characteristics in the target measurement period and a baseline measurement period.

Abstract: Techniques for managing database workloads using similarity measures based on queries executed are described. Classical techniques from information retrieval are applied to the domain of database workload management. Specifically, the technique of using document term vectors to compute similarity measures are applied using the conceptual mapping of SQL workloads as “documents” composed of SQL queries as “terms.” The techniques include generating two or more sets of workloads with each workload representing a set of queries executed on at least one database. Based on the sets of workloads, workload term vectors are calculated that represent the set of queries executed on the database. Then, based on the calculated workload vectors, a similarity score is generated between the two or more sets of workloads.

Abstract: Techniques for managing database workloads using similarity measures based on queries executed are described. Classical techniques from information retrieval are applied to the domain of database workload management. Specifically, the technique of using document term vectors to compute similarity measures are applied using the conceptual mapping of SQL workloads as “documents” composed of SQL queries as “terms.” The techniques include generating two or more sets of workloads with each workload representing a set of queries executed on at least one database. Based on the sets of workloads, workload term vectors are calculated that represent the set of queries executed on the database. Then, based on the calculated workload vectors, a similarity score is generated between the two or more sets of workloads.

Abstract: A method for replaying a simulation workload is provided. The simulation workload identifies a plurality of workload parts. Each workload part is at least a portion of a respective captured workload. In an embodiment, a first workload part is a portion of a first workload executed relative to a first capture database server and a second workload part is a portion of a second workload executed relative to a second capture database server. A portion of the first workload part and a portion of the second workload part may be replayed concurrently.

Abstract: A method for replaying a simulation workload is provided. The simulation workload identifies a plurality of workload parts. Each workload part is at least a portion of a respective captured workload. In an embodiment, a first workload part is a portion of a first workload executed relative to a first capture database server and a second workload part is a portion of a second workload executed relative to a second capture database server. A portion of the first workload part and a portion of the second workload part may be replayed concurrently.

Abstract: A method and apparatus for diagnosing database performance problems using a plurality of wait classes is provided. A set of statistical data that describes current activity within a database system is periodically retrieved. The set of statistical data may include information about the current activity of each user session connected to the database system. Thereafter, a set of cumulative statistical data that describes activity in the database system over a period of time is updated to reflect the retrieved set of statistical data. The set of cumulative statistical data includes statistics associated with each of a plurality of wait classes. A graphical user interface that displays the set of cumulative statistical data may be presented to a user. The graphical user interface allows the user to quickly ascertain the nature of the database performance problems by providing a view of the set of cumulative statistical data.