Oracle International Corporation

Other

John Mark Beresniewicz

Gary C Ngai

Mark Charles Ramacher

Graham Wood

Karl Dias

Mike Feng

Benoit Dageville

Jonathan D Klein

Uri Shaft

Yair Sarig

Marcus Fallen

Amir Najmi

Hailing Yu

Hsiao-Te Su

Jonathan Helfman

Venkateshwaran Venkataramani

Leonidas Galanis

Leng Leng Tan

Yujun Wang

Ajith Kumar Mysorenagarajarao

Konstantinos Morfonios

Kusumaharanadh Poduri

Jonathan Fredericks Soule

Hui Lin

Vipul Manubhai Shah

Hari Krishna Galla

Shravan Kumar Shivanagari

Paul Tangen

Peter Belknap

Michael Feng

Hsiao-te Su

Ameet Kini

Balasubrahmanya Kuchibhotla

Kyle Hailey

Stephen Wexler

Sriram Vrinda

Xiaobin Chen

Naimuddin Qureshi

Nancy Chen

Amir Valiani

Nauman Ahmed Chaudhry

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.

Estimating query execution performance using a sampled counter

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.

Unified user interface for monitoring hybrid deployment of computing systems

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.

Term vector modeling of database workloads

Techniques that improve manageability of systems. Techniques are provided for creating different types of baselines that are more flexible and dynamic in nature. A future-based baseline may be created defining a period of time, wherein at least a portion of the period of time is in the future. A baseline may be created that is a composite of multiple baselines. In general, baselines may be specified having one or more periods of time that are either contiguous or non-contiguous. A template for creating a set of baselines based on a set of time periods may also be created, where the template can be used to create a baseline for each of the set of time periods. A moving window baseline may be created having an associated time window that changes with passage of time, where accordingly the data associated with the baseline may also dynamically change with passage of time.

Creating composite baselines based on a plurality of different baselines

A method for determining event counts for a database system includes capturing samples for the active sessions based on a pre-defined sampling frequency and identifying events from the captured samples. The method further includes determining the wait time for each of the identified events and determining an event count for the active sessions using a harmonic mean. The harmonic mean is a summation of the maximum of either one or the ratio of the sampling frequency to the determined wait time for each of the identified events.

Methods for determining event counts based on time-sampled data

Aspects of the present disclosure describe systems and methods for providing active session history data to users for use in database performance analysis. In various aspects, active session history data obtained from monitoring a database and/or database system over a period of time may be segmented into multiple dimensions. The segmented data may be subsequently provide and/or displayed on one or more interfaces, such as a graphical user interface, to users. The interface may visualize the dimensional ASH data in a variety of ways, such as through icons, graphs, charts, histograms, temporal delineations, treemaps, etc.

Systems and methods for applying an analytical model to performance analysis

Future-based performance baselines

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.

Creation and replay of a simulation workload using captured workloads

Techniques for systematically gathering, organizing, and storing diagnostic data related to multiple monitored systems (e.g., multiple instances of a product or multiple products). A centralized repository is provided that is organized in a hierarchical manner to facilitate proper organization of the diagnostic data related to multiple monitored systems. In one embodiment, a root directory comprising one or more subdirectories is provided for storing diagnostic data collected for each monitored system. Multiple root directories may be provided under a common base directory for storing diagnostic data corresponding to multiple monitored systems. This enables correlation of diagnostic data across multiple monitored systems.

Diagnostic data repository

Particular embodiments generally relate to displaying database time using a treemap. A set of database time values is determined for a set of dimensions. The database time values measure performance of one or more databases by the time the database takes to respond to a request. The set of database time values is correlated to a set of cells in the treemap. A size of the cell is determined based on the database time value associated with it. For example, the database time value is correlated to an area of the cell. A layout of the set of cells is determined and the treemap is displayed using the layout. For example, the largest values of database time may be shown with the largest sized cells. This makes it easier for an administrator to review and analyze the database performance across multiple dimensions and determine problem areas affecting the performance of the one or more databases.

Treemap visualizations of database time

A web application receives a user input with a SQL injection attack string that references a function. The application generates a corresponding statement based on the user input string, which the application sends to a database server. Upon receiving the statement, the database server executes the statement that invokes the referenced function. When invoked, the referenced function stores a value. The presence of the stored value indicates that the database server invoked the function. Storing the value indicative of the function invocation identifies a vulnerability of the web application to SQL injection attacks, since the function reference is introduced solely through user input and function invocation is not intended by the application. This provides proof of SQL injection vulnerability of the application.

Indicating SQL injection attack vulnerability with a stored value

A diagnosability system for automatically collecting, storing, communicating, and analyzing diagnostic data for one or more monitored systems. The diagnosability system comprises several components configured for the collection, storage, communication, and analysis of diagnostic data for a condition detected in monitored system. The diagnosability system enables targeted dumping of diagnostic data so that only diagnostic data that is relevant for diagnosing the condition detected in the monitored system is collected and stored. This in turn enables first failure analysis thereby reducing the time needed to resolve the condition detected in the monitored system.

Diagnosability system

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.

Diagnosing database performance problems using a plurality of wait classes

A computer derives, from a current measurement of an attribute of a system, an instantaneous severity score based on a probability model that is determined from prior measurements. The computer then displays, for several successive moments in time, either the instantaneous severity score or an aggregated severity score obtained from a set of instantaneous severity scores (e.g. by averaging). Each set of scores to be aggregated may be identified by, e.g., a fixed-width time window located at a fixed offset from a moment in time at which the aggregated score is to be displayed. Aggregation suppresses or dampens momentary abnormalities, which would otherwise get displayed. In some embodiments, graphs for multiple categories of metrics are displayed on a single screen, aligned in time, to enable a human operator to form a visual correlation therebetween, e.g. to identify causes of atypical performance in the system.

Graphical display and correlation of severity scores of system metrics

A computer is programmed to fit exponential models to upper percentile subsets of observed measurements for performance metrics collected as attributes of a computer system. The subsets are defined from sets chosen to reduce model bias due to expected variations in system performance, e.g. those resulting from temporal usage patterns induced by end users and/or workload scheduling. Measurement levels corresponding to high cumulative probability, indicative of likely performance anomalies, are extrapolated from the fitted models generated from measurements of lower cumulative probability. These levels are used to establish and to automatically set warning and alert thresholds which signal to (human) administrators when performance anomalies are observed.

Automatic determination of high significance alert thresholds for system performance metrics using an exponentially tailed model

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Half Moon Bay, CA, United States of America

John Mark Beresniewicz