Oracle International Corporation

Michael Feng

Gary C Ngai

Mark Charles Ramacher

Graham Wood

Benoit Dageville

Karl Dias

Alex Tsukerman

John Mark Beresniewicz

Leng Leng Tan

Sushil Kumar

Richard Sarwal

Connie Green

Venkateshwaran Venkatatamani

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 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

A self-managing workload repository infrastructure (or 'AWR' for Automatic workload repository) which is useful for a database server to collect and manage useful system performance statistics. The AWR runs automatically to collect performance data about the operation of the database system, and stores the data that it captures into the database. The collection process is done inside the database, and the collection process is highly efficient as data is retrieved directly from the shared memory of the database kernel. The data captured allows both system level and user level analysis to be performed without unduly impacting system performance, e.g., by eliminating or reducing the requirement to repeat the workload in order to diagnose problems. 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. The captured performance data includes one or more of the top N (e.g., 20 or 30) statistics for activities involving a large set of objects, time-based statistics, cumulative statistics, sampled data and metrics and other data types.

Automatic workload repository battery of performance statistics

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San Mateo, CA, United States of America

Michael Feng