Oracle International Corporation

Karl Dias

Benoit Dageville

Leonidas Galanis

Mark Charles Ramacher

Khaled Yagoub

Jonathan D Klein

Gary C Ngai

Venkateshwaran Venkataramani

Graham Wood

Yujun Wang

Leng Leng Tan

Peter Clayton Belknap

Uri Shaft

Jae Young Yoon

Yair Sarig

Marcus Fallen

Ajith Kumar Mysorenagarajarao

Konstantinos Morfonios

Zhongtang Cai

Mohamed Ziauddin

Romain Colle

Efstratios Papadomanolakis

Juan R Loaiza

Alex Tsukerman

John Mark Beresniewicz

Kusumaharanadh Poduri

Sushil Kumar

Richard Sarwal

Hailing Yu

Thierry Cruanes

Tirthankar Lahiri

Andrew Witkowski

Mohamed Zait

Balasubrahmanyam Kuchibhotla

Vishwanath Karra

Shivani Gupta

Michael Feng

Rodney Graham

Bo Gong

Qinghui H Altmar

Mehmet Fidanboylu

Tom Hsu

Balasubrahmanya Kuchibhotla

Supiti Buranawatanachoke

Alok Kumar Srivastava

Jonathan Helfman

Sanjay Kaluskar

Abdul Q Munir

Vasudha Krishnaswamy

Dinesh Das

Sujatha Muthulingam

Gianfranco Putzolu

Jeffrey Fischer

Chao Liang

Shriram Krishnan

Arvind Kumar Maheshwari

Shachi Sanklecha

Anupam Singh

Jonathan Giloni

Prasad Ravuri

Wanli Yang

Gregory Scott Smith

Hsiao-Te Su

Neoklis Polyzotis

Francisco Sanchez

Matthew Stephen McKerley

Cecilia Gervasio Grant

Mike Feng

Mughees Minhas

Hsiao-te Su

Sanjay Chinnappaiah Ediga

Anupama Vale

Prabhaker Gongloor

Amol Achyut Chiplunkar

Nancy Chen

Gayatri Tripathi

Amir Valiani

Connie Green

Venkateshwaran Venkatatamani

Chaitra Jayaram

Haranadh Poduri

Stephen Wexler

Anil Kumar Kothuri

Sumankumar Pramanik

Supratim Choudhury

Leng Tan

Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Applications of automated discovery of template patterns based on received requests

The embodiments disclosed herein relate to predictive rate limiting. A workload for completing a request is predicted based on, for example, characteristics of a ruleset to be applied and characteristics of a target set upon which the ruleset is to be applied. The workload is mapped to a set of tokens or credits. If a requestor has sufficient tokens to cover the workload for the request, the request is processed. The request may be processed in accordance with a set of processing queues. Each processing queue is associated with a maximum per-tenant workload. A request may be added to a processing queue as long as adding the request does not result in exceeding the maximum per-tenant workload. Requests within a processing queue may be processed in a First In First Out (FIFO) order.

Rate limiting compliance assessments with multi-layer fair share scheduling

Techniques for analyzing an execution of a query statement based on a random archive are disclosed. A plurality of query statements that are executed during a particular time period are identified. A random sampling function is executed to randomly select a set of query statements from the plurality of query statements. Execution plans and/or performance metrics associated with each execution of the randomly-selected query statements are stored into a random archive. Responsive to determining that a performance metric for a current execution of a particular query statement does not satisfy a performance criteria, information associated with the particular query statement from the random archive is analyzed. A model plan characteristic associated with an execution of the particular query statement stored in the random archive is determined. An execution plan associated with the model plan characteristic is determined for another execution of the particular query statement.

Database performance analysis based on a random archive

Systems, methods, and other embodiments associated with continuous capture of replayable workload for a database are described. In one embodiment, a method includes capturing workload units being executed on a production database and storing the captured workload units in a circular workload buffer. The circular workload buffer is configured to write newest captured workload units over oldest workload units in the workload buffer when the workload buffer becomes full. The method includes, in response to detecting an event, exporting contents of the workload buffer to offline memory. The exporting includes at least one I/O operation. In one embodiment, an advisor feature guides set up of the continuous capture feature by providing historical workload data and making recommendations regarding an amount of memory that should be allocated to the workload buffer.

Continuous capture of replayable database system workload

Techniques are provided for a fully-automated process for tuning database query language statements that selects database query language statements for tuning, tunes the database query language statements and generates tuning recommendations, tests the tuning recommendations, and determines whether to implement the tuning recommendations based on the test results. The fully-automated tuning process may also automatically implement certain tuning recommendations and monitor the performance of the database query language statements for which tuning recommendations have been implemented.

Fully automated SQL tuning

Techniques for generating and processing composite active reports are provided. An active report is a report that can be displayed and interacted with at a client device even though the client device is not connected to a database from which data for the report originates. A composite active report is an active report that includes multiple reports embedded in the same file. Each report in a composite active report may be separately compressed to allow a client device to decompress a report on demand. A composite active report may include, for each report indicated in the composite active report, executable identification data that is used to retrieve, from a remote source, an executable that is used to generate, based on report data of the report, display data, which is displayed on a computer display of a client device.

Composite active reports

Techniques for automatically recommending parallel execution of a SQL statement. In one set of embodiments, a first determination can be made regarding whether a SQL statement can be executed in parallel. Further, a second determination can be made regarding whether executing the SQL statement in parallel is faster than executing the statement in serial by a predetermined factor. If the first determination and second determination are positive (i.e., the statement can be executed in parallel and parallel execution is faster by the predetermined factor), a recommendation can be provided indicating that the SQL statement should be executed in parallel. In some embodiments, the recommendation can include a report specifying the degree of performance improvement gained from parallel execution, additional system resources consumed by parallel execution, and other statistics pertaining to the recommended parallel execution plan.

Techniques for recommending parallel execution of SQL statements

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 are described for upgrading systems that include a first set of agents for collecting target data from a plurality of targets and sending the target data to a first management server. In an embodiment, a second set of agents is pre-deployed. Each pre-deployed agent is configured to, upon activation, collect target data from a respective target of the plurality of targets and send the target data to a second management server. While the pre-deployed agent remains inactive, an agent from the first set of agents collects and sends the target data to the first management server. When a first batch of one or more pre-deployed agents is activated, each activated agent begins collecting target data and sending the target data to the second management server. For agents not in the first batch, the first set of agents continues to collect and send the target data.

Upgrading enterprise managers

Described herein are methods for determining patterns based on requests received by a server. Based on the determined patterns, insight into the types of requests received by the server can be gained. Additionally, performance statistics and query statistics can be aggregated in a useful way. For example, performance statistics may be summarized for each determined pattern. One technique for determining patterns includes determining a sequence of template identifiers identifying templates that correspond to sub-sequences of requests in a sequence of server requests. A model may be created based on the sequence of template identifiers. Based on the model, template patterns may be determined. Template patterns may further be grouped into pattern clusters.

Automated discovery of template patterns based on received server requests

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.

Filtering workload for database replay

Techniques are provided for automatically recommending alternative execution plans for a SQL statement. Information pertaining to one or more execution plans for a SQL statement can be retrieved from a set of plan sources. The one or more execution plans can include the current execution plan for the statement and one or more historical execution plans. Based on the retrieved information, the performance of the one or more execution plans can be compared to each other. Based on the comparison, a recommendation can be provided to a user indicating that a particular execution plan (e.g., a historical execution plan) should be used in place of the current execution plan to execute the SQL statement.

Techniques for recommending alternative SQL execution plans

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Foster City, CA, United States of America

Karl Dias