Oracle International Corporation

Richard Sarwal

Graham Wood

Leng Leng Tan

Benoit Dageville

Karl Dias

Gary C Ngai

Mark Charles Ramacher

Alex Tsukerman

Sushil Kumar

Tirthankar Lahiri

Jonathan D Klein

Mohamed Ziauddin

Sanjay Kaluskar

Vasudha Krishnaswamy

Sujatha Muthulingam

Venkateshwaran Venkataramani

Gianfranco Putzolu

Leonidas Galanis

Chao Liang

Yujun Wang

Wanli Yang

Vishwanath Karra

Francisco Sanchez

Michael Feng

Rajiv K Maheshwari

Hsiao-te Su

Venkata Ramana Kapuram

Leng Tan

Techniques for improved metric correlation and analysis include, during a modeling phase, a user familiar with the types of system components deployed in an enterprise network and the metrics available for those types of system components specifying dependencies between metrics in a metric dependency model. During a binding phase, the metric dependency model is provided to a modified enterprise management (MEM) system which binds the model to particular enterprise system environment. During a metric correlation and analysis phase, the MEM system provides a user-interface through which a user visually compares metric data for metrics collected from system components deployed in an enterprise system environment bound to the model. The improved metric correlation analysis techniques allow users to more easily identify whether degradation in the performance of one metric is caused by performance degradation of underlying information technology system components as measured by related metrics specified in the metric dependency model.

Metric correlation and analysis

A method and apparatus for capturing database system workload and replaying that workload in a database system is provided. According to one aspect, in order to subject a test database system to the same workload to which the production database system actually would be subjected, a database server in the production system captures and records workload that the database server receives from external entities. This captured workload is processed. Processes external to a database server in the test database system send the processed workload to that database server. As a result, the test database system is subjected to the same workload to which the production database system originally was subjected. The foregoing technique permits a database administrator to determine how the production database system will fare if the difference that is present in the test database system is introduced into the production database system.

Database workload capture and replay architecture

A self-managing workload repository (AWR) infrastructure useful for a database server to collect and manage selected sets of important system performance statistics. Based on a schedule, the AWR runs automatically to collect data about the operation of the database system, and stores the data that it captures into the database. The AWR is advantageously designed to be lightweight and to self manage its use of storage space so as to avoid ending up with a repository of performance data that is larger than the database that it is capturing data about. The AWR is configured to automatically capture snapshots of statistics data on a periodic basis as well as purge stale data on a periodic basis. Both the frequency of the statistics data capture and length of time for which data is kept is adjustable. Manual snapshots and purging may also be performed. The AWR captured data allows for both system level and user level analysis to be automatically performed without unduly impacting system performance, e.g., by eliminating or reducing the requirement to repeat the workload in order to diagnose problems.

Self-managing performance statistics repository for databases

Systems and methods to define and store performance baselines. A baseline may be defined as a pair of snapshots, each snapshot containing the same set of statistics and having a timestamp value associated therewith. The present invention allows for the designation, automatically or manually, of statistics collected over a certain period of time to be stored and used for comparison. Baselines may be used, for example, to manually or automatically compare with current system performance, compare difference-difference values and set thresholds to monitor current system performance.

Database performance baselines

An intelligent database infrastructure wherein the management of all database components is performed by and within the database itself by integrating management of various components with a central management control. Each individual database component, as well as the central management control, is self-managing. A central management control module integrates and interacts with the various database components. The database is configured to automatically tune to varying workloads and configurations, correct or alert about bad conditions, and advise on ways to improve overall system performance.

Self-managing database architecture

Growing community of inventors

Portola Valley, CA, United States of America

Richard Sarwal