Ocient Holdings LLC

Ocient Inc.

International Business Machines Corporation

Oclient Inc.

Jason Arnold

George Kondiles

S Christopher Gladwin

Andrew D Baptist

Joseph Jablonski

Daniel Coombs

Greg R Dhuse

Ellis Mihalko Saupe

Shantan Kethireddy

Sarah Kate Schieferstein

Susmita Saha

Samuel Peter Bove

Henry E Calzaretta

Dylan S Murphy

Anna Veselova

Andrew Park

Max Guthmann

Matthew Ashbeck

Finley Jordan Lau

Gary W Crupi

Benjamin Daniel Rabe

Natalia Marie Jenuwine

Alyssa Catherine Wagenmaker

Pieter Charles Jas Svenson

David Charles Boutcher

Austen Thomas Darre

Darius Russell Kish

A method includes a host computing device receiving a segment group of data. The method further includes the host computing device evaluating availability status of other computing devices in the storage cluster of computing devices. When one of the other computing devices is unavailable, the method further includes the host computing device dividing the segment group of data into a plurality of lines of data blocks. For a line of the data blocks, the method further includes the host computing device generating at least one parity block. The method further includes the host computing device sending a first data segment that includes first positioned data blocks to a first available computing device. The method further includes the host computing device sending a second data segment that includes second positioned data blocks to a second available computing device. The method further includes the host computing device storing a parity segment.

Logical partitioning of memory within a computing device

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 query processing system operates by: determining a query for execution that indicates a join expression and further indicates a threshold maximum number of output rows for the join expression; determining a query operator execution flow for the join expression that includes performance of two join operations based on the threshold maximum number of output rows for the join expression, wherein a union of output of the two join operations is semantically equivalent to an output of the join expression; and executing the query based on: performing the two join operations in parallel upon sets of input rows; and based on determining a set of output rows outputted by the two join operations has reached the threshold maximum number of output rows, finalizing execution of the query before at least one of the two join operations has finished processing its input rows.

Query processing with limit optimization in a database system

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 is operable to determine a query for execution. A disjunction of a plurality of conjunctive normal form (CNF) filtering predicates is determined based on filtering predicates of the query. An IO pipeline is generated based on the disjunction of the plurality of CNF filtering predicates that includes, based on applying at least one type of optimization, at least one shared IO element common to multiple ones of plurality of the CNF filtering predicates. The IO pipeline is executed in accordance with execution of the query to generate a filtered subset of a plurality of rows that each meet at least one of the plurality of CNF filtering predicates.

Query processing in a database system based on applying a disjunction of conjunctive normal form predicates

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 database system is operable to determine a query that includes a join expression for execution. A set of input rows are dispersed for processing via a plurality of parallelized join processes in conjunction with executing the join expression based on identifying a first proper subset of the set of input rows based on a null-handling strategy. First rows in the first proper subset are dispersed for processing across the plurality of parallelized join processes in accordance with the null-handling strategy. second rows in a set difference between the set of input rows and the first proper subset are dispersed across the plurality of parallelized join processes in accordance with a join key-based assignment strategy. The set of input rows are processed via the plurality of parallelized join processes.

Handling null values in processing join operations during query execution

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 computing device is operable to receive a plurality of partition allocation requests based on execution of a corresponding plurality of query operations in conjunction with execution of a corresponding query. Processing a first partition allocation request of the plurality of partition allocation requests corresponding to execution of a first operation of the corresponding plurality of query operations is based on allocating a set of partitions of a plurality of partitions. Content is loaded into the set of partitions for access in executing the first operation. Access to the content via the set of partitions is facilitated in conjunction with execution of the first operation. The set of partitions are released based on determining the first operation has completed access of the content in conjunction with the execution of the first operation.

Allocating partitions for storing a data set for subsequent query execution

A method for execution, by a first intermediate node of a plurality of nodes in a database management system, includes processing a message that includes data that is being sent in accordance with a routing path from a source node to a destination node, is a first size, and indicates a next node of the routing path, wherein the first intermediate node is limited to communication with a subset of nodes of the plurality of nodes, and wherein the subset of nodes includes the next node. The method further includes maintaining a tracking table that indicates a total amount of data sent to each node of the subset of nodes during a first time period. The method further includes resetting the total amount of data sent to each of the subset of nodes to zero based one or more of a command and an initiation of a second time period.

System and method for optimal routing in a large database management system

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 data processing system is operable to determining a query for execution and generate a query operator execution flow for the query that includes a first at least one operator serially before a second at least one operator. The first at least one operator of the query operator execution flow is executed based on generating a request for rows in accordance with an object storage communication protocol indicating filtering parameter data parameter, sending the request to an object storage system, and receiving a response indicating a filtered row set from the object storage system. The second at least one operator of the query operator execution flow is executed based on processing the filtered row set indicated in the response in accordance with the second at least one operator to produce the query resultant.

Query execution via communication with an object storage system via an object storage communication protocol

A database system is operable to determine a query for execution against at least one relational database table and executing the query. Executing the query is based on identifying a plurality of required binomial coefficient values required for generating a set of output values for the query. The plurality of required binomial coefficient values are precomputed and stored in cache memory resources. Generating each output value of the set of output values is based on identifying a subset of the plurality of required binomial coefficient values for generating the each output value, and accessing the subset of the plurality of required binomial coefficient value in the cache memory resources. Each output value is generated as a function of the subset of the plurality of required binomial coefficient values.

Caching precomputed binomial coefficient values for query execution

A database system operates by: determining a first query that indicates a first request to generate a K nearest neighbors (KNN) model; executing the first query to generate KNN model data for the KNN model, wherein the KNN model data is set as a reduced dataset for one of a plurality of training iterations; determining a second query that indicates a second request to apply the KNN model to input data; and executing the second query to generate model output of the KNN model for the input data based on, for each row in the input data, identifying a classification label for the each row from the discrete set of labels based on performing the KNN classification algorithm to classify the each row in the input data by applying the reduced data set.

Dimensionality reduction and model training in a database system implementation of a K nearest neighbors model

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.

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Chicago, IL, United States of America

Jason Arnold