International Business Machines Corporation

Pure Storage, Inc.

Cleversafe, Inc.

Ocient Holdings LLC

Other

Green Market Square Limited

Andrew D Baptist

Jason K Resch

Greg R Dhuse

Wesley B Leggette

Ilya Volvovski

Manish Motwani

S Christopher Gladwin

Ravi V Khadiwala

Gary W Grube

Ethan S Wozniak

Bart Richard Cilfone

Zachary J Mark

Thomas Franklin Shirley, Jr

Adam Michael Gray

George Kondiles

Jason Arnold

Timothy W Markison

Praveen Viraraghavan

Scott Michael Horan

Franco Vincent Borich

Joseph Jablonski

Daniel J Scholl

John Quigley

Daniel Coombs

Kumar Abhijeet

Yogesh Ramesh Vedpathak

Michael Colin Storm

Niall John McShane

Trevor J Vossberg

Thomas Darrel Cocagne

Joseph M Kaczmarek

Srinivas Palthepu

Ahmad Alnafoosi

Vance T Thornton

Sebastien Vas

Asimuddin Kazi

Harsha Hegde

James L Lester

Brian Frederick Ober

Ilir Iljazi

Dustin M Hendrickson

Jordan Harrison Williams

Ying Zhao Guo

Benjamin Lee Martin

Robert Clark McCammon

Russell Paul Kennedy

Gregory Palinckx

James Sherer

Michael Edward Factor

Ram S Narayanan

Mingyu Li

Randy D Pfeifer

Bart Richard Clifone

Jesse Louis Young

Trent William Johnson

Gregory A Papadopoulos

Andrew George Peake

Patrick Aaron Tamborski

Sanjaya Kumar

Alan M Frazier

Amit H Lamba

Thomas Dubucq

Gregory R Dhuse

Renars W Narubin

Steven Mark Hoffman

Andrew Tsvetkov

Ryan Joseph Kuester

A database system operates by: determining a query request corresponding to a requesting entity; generating potential query cost data based on query cost data for the query request including power consumption and further based on running query cost data for at least one previous query request corresponding to the requesting entity; generating minimum query cost compliance data for the query request based on determining whether the potential query cost data complies with a minimum query cost rule; when the minimum query cost compliance data indicates the potential query cost data complies with the minimum query cost rule: determining a query result for the query request based on execution of the query request against a database system.

Generating query cost data for query requests based on power

A method for execution by one or more computing devices of a storage network includes dispersed storage error encoding a data segment of data into a set of encoded data slices in accordance with an information dispersal algorithm width number and a decode threshold number, where the decode threshold number of encoded data slices of the set of encoded data slices are needed to reconstruct the data segment. The method further includes storing the set of encoded data slices in primary storage slots of a set of storage units deployed across multiple sites of the storage network in accordance with a mapping of the primary storage slots to storage units of the set of storage units, where a number of the primary storage slots is equal to or greater than the information dispersal algorithm width number.

Storing encoded data slices in primary storage slots

A database system operates by: determining a query pricing scheme, wherein the query pricing scheme indicates a record valuation scheme; receiving a query request from a requesting entity that indicates access to a subset of a plurality of records in a database system; generating query cost data based on the subset of the plurality of records by utilizing the query pricing scheme and a power consumption, wherein generating the query cost data includes: calculating plurality of record valuations by calculating a record valuation for each of the plurality of records in the subset of the plurality of records, and aggregating over the plurality of record valuations to generate a query price total indicated in the query cost data; and transmitting the query cost data to the requesting entity.

Power based query processing system and methods for use therewith

A method for execution by a storage network begins by receiving data for storage by the storage network and continues by determining data preparation tasks for the data. The method continues by indexing the data in accordance with the data preparation tasks to generate a data index and processing the data in accordance with the data index to produce indexed data. The method then continues by determining distribution criteria for the data based on the data index and distributing the data and the data index to a set of distributed storage units in accordance with the distribution criteria, Finally, the method establishes criteria for analyzing found data of the data in the storage network.

Storing, processing and analyzing large volumes of data in a storage network

A processing system of a storage network operates by: selecting a queue memory type of a plurality of memory types to store a data object, based on a size parameter associated with the data object; storing the data object in a queue memory device having the queue memory type, when the queue memory type is selected; selecting a main memory type of a plurality of memory types to store the data object, when the queue memory type is not selected; and storing the data object in a main memory device having the main memory type, when the queue memory type is not selected; wherein the data object is dispersed error encoded and stored as a plurality of encoded data slices.

Storing data objects in a storage network with multiple memory types

A distributed storage integrity system in a dispersed storage network includes a scanning agent and a control unit. The scanning agent identifies an encoded data slice that requires rebuilding, wherein the encoded data slice is one of a plurality of encoded data slices generated from a data segment using an error encoding dispersal function. The control unit retrieves at least a number T of encoded data slices needed to reconstruct the data segment based on the error encoding dispersal function. The control unit is operable to reconstruct the data segment from at least the number T of the encoded data slices and generate a rebuilt encoded data slice from the reconstructed data segment. The scanning agent is located in a storage unit and the control unit is located in the storage unit or in a storage integrity processing unit, a dispersed storage processing unit or a dispersed storage managing unit.

Rebuilding data slices in a storage network based on priority

A query processing system is operable to determine a set of query rules that includes a minimum result set size rule. A query is received from a requesting entity. A size of a result set of the query is determined. Compliance data is determined based on determining whether the size of the result set complies with the minimum result set size rule. When the compliance data indicates the size of the result set complies with the minimum result set size rule, a query result for the query is generated based on the result set, and the query result is generated to the requesting entity. When the compliance data indicates the size of the result set does not comply with the minimum result set size rule: transmission of the query result to the requesting entity is foregone.

Enforcement of a minimum result set size rule for queries requested for execution against a database system

A database system is operable to determine a request to implement a K Nearest Neighbors (KNN) algorithm to generate classification data for a set of new records. A query operator execution flow is determined for the request that includes a KNN-join operator. A query resultant that indicates classification data for the set of new records is generated by performing a plurality of operator executions in accordance with the query operator execution flow based on, for each record of the set of new records, generating a plurality of similarity measures by performing a similarity function on the each record and each of a set of previously-classified records; identifying a proper subset of the set of previously-classified records that includes exactly a predefined number of records; and joining the each record with the proper subset of the set of previously-classified records.

Generating classification data via a query processing system

Methods and apparatus for identifying multiple resources of a storage network for data retrieval are disclosed. In various embodiments, a determination is made to retrieve an encoded data slice from the storage network. Based on configuration information for the storage network, one or more configurations are identified, including a current configuration. For the identified configurations, ranked scoring information relating to the encoded data slice is determined for a plurality of resources associated with the identified configurations. Based on the ranked scoring information, a resource is selected for each of the identified configurations. In addition, a retrieval likelihood level for the data slice is determined for each of the selected resources. Based on the likelihood levels for the selected resources, one or more of the selected resources are identified for encoded data slice retrieval and read slice requests are issued to the one or more selected resources.

Identifying multiple resources of a storage network for data retrieval

A user device is operable to determine a query request based on first user input to a graphical user interface displayed by the user device. Query cost data is determined based on the query request. A prompt is displayed via the graphical user interface that indicates the query cost data. Query approval data is determined based on second user input to the graphical user interface in response to the prompt. The query request is transmitted to an analytics system for execution against a database system when the query approval data indicates approval to transmit the query request.

Utilizing query approval data determined based on query cost data for a query request

A method begins by identifying a plurality of encoded data slices requiring rebuilding. The method continues by determining an amount of memory required for rebuilding the plurality of encoded data slices and allocating memory in one or more storage units for the rebuilding the plurality of encoded data slices as reserve memory. The method continues by obtaining a plurality of rebuilt encoded data slices associated with the plurality of encoded data slices requiring rebuilding and storing the plurality of rebuilt encoded data slices in the reserve memory.

Memory allocation for block rebuilding in a storage network

A processing system is operable to encode data to produce a first set of data slices based on a value of a width parameter. The data is stored based on maintaining storage of the first set of data slices across a set of storage units of a storage pool. Storage of the first set of data slices is maintained in the set of storage units of the storage pool after addition of an additional set of storage units added to the storage pool. The value of the width parameter is increased to an increased value to produce an updated width parameter. The data is re-encoded in accordance with the updated width parameter to produce a second set of data slices. The data is re-stored based on maintaining storage of the second set of data slices across the expanded set of storage units of the storage pool.

Re-encoding data in a storage network based on addition of additional storage units

A processing system of a storage network operates by receiving a write request associated with a data object; identifying dispersed storage error encoding parameters; and determining a number of available storage units. When the number of available storage units is determined to exceed a first number, the processing system operates by: determining a first write threshold number in accordance with the dispersed storage error encoding parameters; dispersed error encoding the data object to produce a first write threshold number of encoded data slices; and writing, to available storage units, the first write threshold number of encoded data slices. When the number of available storage units is determined to not exceed the first number, the processing system operates by: determining a second write threshold number in accordance with the dispersed storage error encoding parameters associated, wherein the second write threshold number is less than the first write threshold number; dispersed error encoding the data object to produce a second write threshold number of encoded data slices; and writing, to the available storage units, the second write threshold number of encoded data slices.

Facilitating write requests in a storage network

A node of a database system is operable to receive a plurality of data blocks from a set of source nodes via a set of relay nodes. At least one feedback data block to be sent to at least one of the set of source nodes is generated. Feedback relay node selection data selecting at least one of a plurality of nodes in the database system for facilitating routing of the at least one feedback data block from the node to the at least one of the set of source nodes via a prioritized feedback communication mechanism is generated. The at least one feedback data block is sent to the at least one of the plurality of nodes selected in the feedback relay node selection data. The at least one feedback data block is routed from the node to the at least one of the set of source nodes via the prioritized feedback communication mechanism.

Routing feedback data blocks via a prioritized feedback communication mechanism

A method for execution by one or more computing devices includes selecting a first routing path from a plurality of routing paths to a set of storage units based on routing path performance information, where the first routing path has a performance level greater than a first performance threshold. The method further includes selecting a second routing path from the plurality of routing paths based on the routing path performance information, where the second routing path has a performance level less than or equal to the first performance threshold. The method further includes sending a first subset of encoded data slices to the set of storage units via the first routing path for storage therein. The method further includes sending a second subset of encoded data slices to the set of storage units via the second routing path for storage therein.

Sending encoded data slices via multiple routing paths

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Bryn Mawr, PA, United States of America

Andrew D Baptist