Abstract: The present disclosure generally relates to a method for intelligent device initiated SAS Phy PM. Using device internal phy characteristics and future phy usage queue, the device determines optimal SAS Phy PM usage based on a predetermined configuration preference of power versus performance. The device achieves optimal SAS Phy PM Usage by implementing a state machine to manage phy PM states and transitions between the PM states. The device state machine includes capabilities to proactively initiate transitions to partial or slumber PM states, start early wake-up from partial or slumber PM states to mask the associated latency impacts of exiting partial or slumber PM states, and selectively reject host requests to enter a partial or slumber PM state.

Abstract: A data storage device may include a volatile memory device and a controller. The volatile memory device may include a plurality of sections. The controller may be configured to cause data to be stored to a section of the volatile memory device. The controller may also be configured to apply data protection to the data in response to determining that the data comprises a first type of data. The controller may also be configured to refrain from applying data protection to the data in response to determining that the data comprises a second type of data.

Abstract: The present disclosure generally relates to a method for intelligent device initiated SAS Phy PM. Using device internal phy characteristics and future phy usage queue, the device determines optimal SAS Phy PM usage based on a predetermined configuration preference of power versus performance. The device achieves optimal SAS Phy PM Usage by implementing a state machine to manage phy PM states and transitions between the PM states. The device state machine includes capabilities to proactively initiate transitions to partial or slumber PM states, start early wake-up from partial or slumber PM states to mask the associated latency impacts of exiting partial or slumber PM states, and selectively reject host requests to enter a partial or slumber PM state.

Abstract: A data storage device may include a volatile memory device and a controller. The volatile memory device may include a plurality of sections. The controller may be configured to cause data to be stored to a section of the volatile memory device. The controller may also be configured to apply data protection to the data in response to determining that the data comprises a first type of data. The controller may also be configured to refrain from applying data protection to the data in response to determining that the data comprises a second type of data.

Abstract: Methods, systems, and computer program products are provided for query expression evaluation for query optimization. Embodiments include receiving a query including a plurality of predicates; creating a truth table representing the possible solutions to the query; identifying a plurality of intersections of the truth table, the plurality of intersections including each predicate of the query; assigning a selectivity factor to each predicate; determining in dependence upon the selectivity factors for each predicate a selectivity rating for each of the intersections of the truth table; selecting one or more of the intersections in dependence upon the selectivity rating; and determining in dependence upon the selected intersections a percentage of rows in a database table returned by the query; and providing the percentage of rows in the database table to an access plan generator for query optimization.

Abstract: A method, apparatus and program product are provided for representing a database query expression including a plurality of predicates related to one another by at least one logical relation. A list of operands is generated from the database query expression, where each operand is representative of a predicate. Each operand is mapped into an operand map. A data structure is generated based upon the generated list of operands, where the data structure includes a plurality of nodes. Each node includes an operand identifier that identifies at least one operand based upon the operand map. Each node defines a first logical relation between those predicates in the database query expression that are represented by operands identified by the operand identifier for such node. The nodes are arranged relative to one another in the data structure to define at least a second logical relation among predicates in the database query expression.

Abstract: A method, apparatus and program product are provided for representing a database query expression including a plurality of predicates related to one another by at least one logical relation. A list of operands is generated from the database query expression, where each operand is representative of a predicate. Each operand is mapped into an operand map. A data structure is generated based upon the generated list of operands, where the data structure includes a plurality of nodes. Each node includes an operand identifier that identifies at least one operand based upon the operand map. Each node defines a first logical relation between those predicates in the database query expression that are represented by operands identified by the operand identifier for such node. The nodes are arranged relative to one another in the data structure to define at least a second logical relation among predicates in the database query expression.

Abstract: Methods, systems, and computer program products are provided for query expression evaluation for query optimization. Embodiments include receiving a query including a plurality of predicates; creating a truth table representing the possible solutions to the query; identifying a plurality of intersections of the truth table, the plurality of intersections including each predicate of the query; assigning a selectivity factor to each predicate; determining in dependence upon the selectivity factors for each predicate a selectivity rating for each of the intersections of the truth table; selecting one or more of the intersections in dependence upon the selectivity rating; and determining in dependence upon the selected intersections a percentage of rows in a database table returned by the query; and providing the percentage of rows in the database table to an access plan generator for query optimization.