Oracle International Corporation

Other

Motorola Corporation

Yahoo! Inc.

Ipventure, Inc.

Delphix Corporation

June Ray Limited

International Business Machines Corporation

C3 Corporation

Kaiser Foundation Hospitals

Boris Klots

Roger J Bamford

Peter Ham

Juan R Loaiza

William H Waddington

Garret Frederick Swart

Patricia C Grewell

William Havinden Bridge, Jr

Nipun Agarwal

Meichun Hsu

Tirthankar Lahiri

Ajit Mylavarapu

Ashok M Joshi

Shasank Kisan Chavan

Yunrui Li

Rajesh Girish Parekh

Jesse Kamp

John David Bruner

Allison Holloway

Vasudha Krishnaswamy

Teck Hua Lee

Hideaki Kimura

Aurosish Mishra

Pavel Berkhin

Sukhada Pendse

Vadim Draluk

Kartik Kulkarni

Subhadeep Sinha

Krishna Kantikiran Pasupuleti

Satish Kumar

Richard Tao-Hwa Chow

Elena Eneva

Nicolas Eddy Mayoraz

Denis A Petushkov

Gerry Perham

Amit Ganesh

Sam Idicula

Jonathan D Klein

James T Brady

Wei Huang

Sabina Petride

Felix Schmidt

Dinesh Das

Gautam Bhargava

David Brower

Yongyong Xu

Neil MacNaughton

Jeffrey Fischer

Neil J S MacNaughton

Linda Marie Duyanovich

Alexander C Ho

Franklin R Koenig

Anantha Kiran Kandukuri

Umesh Panchaksharaiah

Gong Zhang

Vikas Aggarwal

Dmitri R Latypov

John S Kowtko

Kantikiran Pasupuleti

Radhakrishna Hari

Vijayakrishnan Nagarajan

Ravi Prem Mirchandaney

Larry Stevens

Harish Prabandham

Susan L Griese

Allison L Holloway

Neil J S Macnaughton

In an embodiment, a computer-implemented method includes receiving a query from a client and determining a query plan for the query. The query plan comprises one or more query operators for executing at least a portion of the query on a database. The method also includes sending the one or more query operators to one or more computing nodes for the one or more computing nodes to execute the one or more query operators on one or more data fragments of the database. In this example, each computing node of the one or more computing nodes hosts a respective data fragment of the one or more data fragments. Further, the method includes detecting an error in executing a first query operator by a first computing node on a first data fragment, and sending, in response to detecting the error, the first query operator to a replacement computing node for executing on the first data fragment hosted by the spare computing node.

Fault tolerance in scale-out distributed query processing appliance

A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to 'slices', and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

System and method for an ultra highly available, high performance, persistent memory optimized, scale-out database

A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to 'slices', and storing multiple copies ('duplicas') of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the &#x201c;primary duplica&#x201d;. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (&#x201c;secondary duplicas&#x201d;) of the same slice.

Herein are acceleration techniques for resuming offloaded execution by replacing a failed computer with a hot spare computer. In an embodiment, a distributed system configures a DBMS, a set of participating computers, and a set of spare computers. The DBMS receives a query of a database. From the query, an offload query plan is generated for distributed execution. The DBMS sends the offload query plan and a respective portion of the database to each participating computer. The distributed system detects that a participating computer failed after the offload query plan was sent. Responsively, the DBMS sends the same offload query plan and same respective portion of the database of the failed computer to a replacement computer from the spare computers. Despite the computer failure, the DBMS receives results of successful distributed execution of the offload query plan that include a result from the replacement computer.

High availability and automated recovery in scale-out distributed database system

System and method for rapid fault detection and repair in a shared nothing distributed database

Allocation of resources across multiple consumers allows efficient utilization of shared resources. Observed usages of resources by consumers over time intervals are used to determine a total throughput of resources by the consumers. The total throughput of resources is used to determine allocation of resources for a subsequent time interval. The consumers are associated with priorities used to determine their allocations. Minimum and maximum resource guarantees may be associated with consumers. The resource allocation aims to allocate resources based on the priorities of the consumers while aiming to avoid starvation by any consumer. The resource allocation allows efficient usage of network resources in a database storage system storing multiple virtual databases.

Adaptive resource allocation based upon observed historical usage

A method and apparatus for adaptive data repartitioning and adaptive data replication is provided. A data set stored in a distributed data processing system is partitioned by a first partitioning key. A live workload comprising a plurality of data processing commands is processed. While processing the live workload, statistical properties of the live workload are maintained. Based on the statistical properties of the live workload with respect to the data set, it is determined to replicate and/or repartition the data set by a second partitioning key. The replicated and/or repartitioned data set is partitioned by the second partitioning key.

Adaptive data repartitioning and adaptive data replication

Herein is described a data placement scheme for a distributed query processing systems that achieves load balance amongst the nodes of the system. To identify a node on which to place particular data, a supervisor node performs a placement algorithm over the particular data's identifier, where the placement algorithm utilizes two or more hash functions. The supervisor node runs the placement algorithm until a destination node is identified that is available to store the data, or the supervisor node has run the placement algorithm an established number of times. If no available node is identified using the placement algorithm, then an available destination node is identified for the particular data and information identifying the data and the selected destination node is included in an exception map. Most data may be located by any node in the system based on the node performing the placement algorithm for the required data.

Method for failure-resilient data placement in a distributed query processing system

Order allocation techniques pertaining to stops that a container makes in the process of fulfilling a customer order is disclosed. In one embodiment, this is accomplished by first identifying a pod that stocks the largest number of different items in a customer order. Then, a second pod is identified that stocks the largest number of remaining items in the customer order. The collection of pods defines a container path through the distribution center.

Method and system for order fulfillment in a distribution center

Adaptive resource management using survival minimum resources for low priority consumers

Methods, machine-readable media, apparatuses and systems are provided to collect and analyze data relating to energy consumption and factors affecting energy production. In various embodiments, such analysis can include time series analysis, multivariate correlation analysis, cluster analysis, anomaly detection methods, multi-dimensional density analysis and demand modeling.

Systems and methods for data mining of energy consumption data

Methods and apparatus are described for evaluating a binary classification system operable to classify each of a plurality of events as a first event type or a second event type. At least some of the events of the first event type are independently verifiable with reference to verification data. The binary classification system is susceptible to a first error type in which events of the first event type are classified as the second event type, and a second error type in which events of the second event type are classified as the first event type. Operation of a first configuration of the binary classification system is evaluated with reference to an objective function. The objective function is derived by expressing a number of errors of the second error type in terms of a number of errors of the first error type with reference to the verification data, and by assuming relative proportions of the first and second event types within the plurality of events.

Evaluating performance of click fraud detection systems

Evaluating performance of binary classification systems

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Belmont, CA, United States of America

Boris Klots