Hewlett-Packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Overture Services, Inc.

Alta Vista Company

Micro Focus Lcc

Janet Lynn Wiener

Harumi Kuno

Goetz Graefe

Umeshwar Dayal

Michael Burrows

Stefan Krompass

Parthasarathy Ranganathan

Mehul A Shah

Marcos Kawazoe Aguilera

Jeffrey Clifford Mogul

Dirk Manfred Beyer

Terence Patrick Kelly

Stavros Harizopoulos

Ratnesh Kumar Sharma

Keith Farkas

John Wilkes

Ira Cohen

Marc Alexander Najork

William Kevin Wilkinson

Eric T Anderson

Alvin AuYoung

Raymond Paul Stata

Clark Allan Heydon

Justin Moore

Murali Mallela Krishna

Lyle H Ramshaw

Dimitrios Tsirogiannis

Patrick A Reynolds

Laura Ellen Grit

Raja Sambasivan

Computer-implemented systems and associated operating methods take measurements and landmarks associated with robustness maps and perform tests evaluating the robustness of a database engine's operator implementations and/or query components. The illustrative computer-implemented system comprises logic that receives one or more robustness maps of measured database system performance acquired during database execution in a predetermined range of runtime conditions and uses information from the robustness map or maps to perform regression testing wherein landmarks in the robustness maps are operated upon as a robustness bugs describing conditions under which a predetermined implementation of a database operator or query component degrades in a manner different from a predetermined expected manner.

Database system testing using robustness maps

A data processing system includes a query progress analyzer. The database processing system comprises a receiver that receives database queries including a target query directed to a database and a query analyzer. The query analyzer is coupled to the receiver and analyzes progress status of the target query based on comparison of measured target query resource usage to resource usage predicted from a query plan of the target query and measured system resource usage.

Progress analyzer for database queries

One embodiment is a method to manage queries in a database. The method identifies a query that executes on the database for an elapsed time that is greater than a threshold and then implements a remedial action when the query executes on the database for an execution time that is greater than an estimated execution time.

Managing execution of database queries

Computer-implemented systems and associated operating methods implement a fast join for databases which is adapted for usage with flash storage. A system comprises a processor that performs a join of two tables stored in a storage in pages processed in a column orientation wherein column values for all rows on a page are co-located in mini-pages within the page. The processor reduces input/output operations of the join by accessing only join columns and mini-pages containing join results.

Database join optimized for flash storage

Computer-implemented systems and associated operating methods create and use an operator map to identify and evaluate database query plan robustness landmarks. The computer-implemented system comprises logic that creates a map evaluating performance of an implementation of a database engine operator during execution under actual operating conditions. The logic measures and maps performance for a selected range of runtime conditions including resource availability and data characteristics.

Evaluation of database query plan robustness landmarks using operator maps or query maps

According to one embodiment, a method comprises evaluating messages between nodes of a distributed computing environment. Based on timing relationships of the messages, probability of causal links between pairs of messages determined. Based on the determined probability, at least one causal path comprising a plurality of causal links is inferred. Further, an overall probability that the inferred causal path is accurate is determined. In certain embodiments, the overall probability is output for the corresponding causal path to provide an indication of the confidence of the accuracy of such inferred causal path.

Inferring causal paths in a distributed computing environment

Computer-implemented and associated operating methods evaluate robustness of a query plan by measuring performance with regard to a range of runtime conditions and producing a map of relative performance of the given query plan when compared with alternative plans for a range of conditions. The computer-implemented system comprises logic that evaluates the selected query plan in comparison to multiple alternative query plans in a predetermined range of runtime conditions that include data characteristics. The logic produces a set of performance measurements and analyzes the measured performance to map performance of the selected query plan in comparison to performance of one or more alternative query plans.

Query plan analysis of alternative plans using robustness mapping

A method of managing the execution of a workload of transactions of different transaction types on a computer system. Each transaction type may have a different resource requirement. The method may include intermittently, during execution of the workload, determining the performance of each transaction type. A determination may be made of whether if there is an overloaded transaction type in which performance is degraded with an increase in the number of transactions of the transaction type. If there is an overloaded transaction type, the number of transactions of at least one transaction type may be changed.

Determining multiprogramming levels

Computer-implemented systems and associated operating methods use atomic query maps to identify and evaluate database query plan robustness landmarks. The computer-implemented system comprises logic that evaluates robustness of a selected atomic query by measuring performance of atomic query execution in a predetermined range of runtime conditions that include data characteristics. The logic produces a set of measurements that can be displayed as one or more performance maps and analyzes the measured performance to identify landmarks indicative of database atomic query performance degradation greater than a predetermined amount.

Evaluation of set of representative query performance using robustness mapping

A method of performing a multi-way join of a plurality of database relations includes executing a plurality of pipelined two-way joins with the database relations. Each two-way join has two sequential phases. In the first phase, missing attributes of the input relations that are required to evaluate a joining criterion specific to said two-way join are fetched from a non-volatile memory device, and the input relations are joined according to the criterion. In the second phase, any additional missing attributes of the input relations are fetched from the non-volatile memory device as assigned by an optimization process executed prior to commencing the multi-way join.

Fetching optimization in multi-way pipelined database joins

According to at least one embodiment, a method comprises identifying at least one causal path that includes a node of a distributed computing environment that is of interest. The method further comprises analyzing the identified at least one causal path to determine at least one time interval when the node is active in such causal path, and correlating consumption of a resource by the node to the node's activity in the at least one causal path.

System and method for ascribing resource consumption to activity in a causal path of a node of a distributed computing system

Computer-implemented systems and associated operating methods use performance maps created by evaluating robustness of a database operator, query plan, or query to analyze health of a currently-executing query. The computer-implemented system comprises logic that receives one or more robustness maps of measured database system performance acquired during database execution in a predetermined range of runtime conditions. The logic analyzes state of a currently-executing query by locating the query's performance on the robustness maps.

Workload management using robustness mapping

An embodiment of a method of dispatching tasks in a multi-processor computing environment begins with a step of a dispatcher within the multi-processor computing environment receiving job properties for a plurality of jobs to be processed in the multi-processor computing environment. Each job comprises at least one task. In a second step, the dispatcher employs an initial dispatching rule to assign an initial subset of tasks to processors within the multi-processor computing environment. The method continues with a third step of the dispatcher monitoring system status of the multi-processor computing environment. In a fourth step, the dispatcher selects a second dispatching rule from a plurality of dispatching rules in response to a change in the system status. In a fifth step, the dispatcher employs the second dispatching rule to assign a second subset of tasks to the processors.

Method of dispatching tasks in multi-processor computing environment with dispatching rules and monitoring of system status

Provided are, inter alia, systems, methods and techniques for controlling workflow by identifying an individual job that is part of an overall deliverables package that covers multiple jobs. An aggregate utility function is obtained, specifying utility based on an aggregate metric across the overall deliverables package, and a current operating point is determined on the aggregate utility function. An effective utility is calculated for the individual job based on the aggregate utility function and the current operating point, and the execution of work is controlled based on the effective utility for the individual job.

Workflow control using an aggregate utility function

An embodiment of a method of dividing past computing instances into predictable and unpredictable sets begins with a first step of a computing entity storing a training data set comprising past computing instances. Each past computing instance comprises attributes and a past computing value. In a second step, the computing entity separates the training data set into a predictable set of past computing instances and an unpredictable set of past computing instances. According to an embodiment, a method of predicting a computing value begins with the first and second steps. The method of predicting the computing value continues with a third step of the computing entity forming a predictor from the predictable set of past computing instances. In a fourth step, the computing entity applies the predictor to a pending computing instance that meets a predictability test to determine a predicted value for the pending computing instance.

Growing community of inventors

Palo Alto, CA, United States of America

Janet Lynn Wiener