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

George Kondiles

Jason Arnold

Adam Michael Gray

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

Bart Richard Clifone

Jesse Louis Young

Randy D Pfeifer

Trent William Johnson

Gregory A Papadopoulos

Patrick Aaron Tamborski

Andrew George Peake

Sanjaya Kumar

Alan M Frazier

Thomas Dubucq

Amit H Lamba

Gregory R Dhuse

Renars W Narubin

Steven Mark Hoffman

Anthony J Baldocchi

Ryan Joseph Kuester

Andrew Tsvetkov

A method includes determining rebuilding performance parameter values for a storage unit of a set of storage units, where data is dispersed storage error encoded into a plurality of sets of encoded data slices in accordance with error encoding parameters, and a set of encoded data slices are stored or are to be stored in the set of storage units. The method includes updating a rate of internal rebuilding of encoded data slices by the storage unit based on the rebuilding performance parameter values. During internally rebuilding, the method includes correcting detected storage errors for one or more first encoded data slices within the storage unit. The method includes receiving a rebuilt encoded data slice from another computing device of the storage network and when the received rebuilt encoded data slice is not included in the one or more first encoded data slices, storing the received rebuilt encoded data slice.

Dispersed rebuilding of encoded data slices in a storage network

A database system operates by: receiving a query request from a requesting entity; determining the maximum query cost ruleset for the requesting entity by identifying one of the plurality of maximum query cost rulesets that corresponds to one of the corresponding plurality of end users associated with the requesting entity, wherein the maximum query cost ruleset for the requesting entity indicates a set of query function-based query cost rules corresponding to each of a set of query function types; generating query cost data by utilizing the query pricing scheme based on the query request and a power consumption; and generating maximum query cost compliance data based on determining whether the query cost data complies with the maximum query cost ruleset.

End user configuration of cost rules in a database system with query costs based on power and methods for use therewith

A database system operates by: determining a query pricing scheme; receiving a query request from a requesting entity that indicates at least one query function for execution against a database system; determining a power consumption value based on the at least one query function; generating query cost data based on the at least one query function by utilizing the query pricing scheme based on the power consumption value; and transmitting the query cost data to the requesting entity.

Generating query cost data based on power and at least one query function of a query request

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

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Bryn Mawr, PA, United States of America

Andrew D Baptist