Using dynamic data structures for storing data objects
AssigneeRed Hat, Inc.
InventorsGabriel Zvi BenHanokh, Uri Lublin
Life of this patent
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Abstract
A technique for dynamic data structure usage for storing data objects is described. In one example of the present disclosure, a system can receive a data object and properties associated with the data object. The system can determine, based on at least one of the properties and pre-defined rules for data objects and corresponding object types, an object type of the data object and a first data structure for storing the data object that is different from a second data structure currently storing data objects in the memory. The system can output a command for causing the first data structure to store the data object in the memory.
Claims17
A system comprising:
- a processor;
- and
- a memory device including instructions that are executable by the processor for causing the processor to:
- receive a data object and properties associated with the data object;
- determine, by a machine learning model and based on at least one of the properties, a first data structure from a plurality of data structures for storing the data object that is different from a second data structure from the plurality of data structures currently storing data objects in the memory device, each of the plurality of data structures being a different type of storage organization in the memory device, the machine learning model being configured to recommend the first data structure from the plurality of data structures for data objects by:
- generating training data by performing operations in a computing system or based on logged information about data objects in the computing system, the training data including consisting of historical information about access types and numbers of items associated with the data objects, and further including labels indicating a data structure from the plurality of data structures for storing the data objects;
- training the machine learning model to recommend the first data structure from the plurality of data structures based on the first data structure being associated with a lowest resource usage of the plurality of data structures by minimizing one or more loss functions using the training data;
- and
- subsequent to training the machine learning model, configuring the machine learning model to receive the properties including an access type and a number of items associated with the data object and to output a recommendation of the first data structure from the plurality of data structures;
- output a command for causing the first data structure to store the data object in the memory device;
- and
- generate the first data structure in the memory device based on the command.
A method comprising:
- receiving, at a management node of a distributed computing system, a data object and properties associated with the data object;
- determining, by the management node and based on the properties, an object type of the data object;
- determining, by the management node and by a machine learning model and based on at least one of the properties, a first data structure from a plurality of data structures for storing the data object that is different from a second data structure from the plurality of data structures currently storing data objects in a memory device, each of the plurality of data structures being a different type of storage organization in the memory device, the machine learning model being configured to recommend the first data structure from the plurality of data structures for data objects by:
- generating training data by performing operations in a computing system or based on logged information about data objects in the computing system, the training data consisting of historical information about access types and numbers of items associated with the data objects, object types, and further including labels indicating a data structure from the plurality of data structures for storing the data objects;
- training the machine learning model to recommend the first data structure from the plurality of data structures based on the first data structure being associated with a lowest resource usage of the plurality of data structures by minimizing one or more loss functions using the training data;
- and
- subsequent to training the machine learning model, configuring the machine learning model to receive the properties including an access type and a number of items associated with the data object and to output a recommendation of the first data structure from the plurality of data structures;
- outputting, by the management node, a command for causing the first data structure to be generated in the memory device;
- and
- automatically generating, by the management node, the first data structure in the memory device based on the command.
A non-transitory computer-readable medium comprising program code executable by a processor for causing the processor to:
- generate, by a memory device, a first data structure from a plurality of data structures based on a first command associated with a first plurality of data objects of a first object type;
- determine, by the memory device and by a machine learning model and based on at least one property of a second plurality of data objects, a second data structure from the plurality of data structures for storing the second plurality of data objects of a second object type that is different than the first object type, each of the data structures being a different type of storage organization in the memory device, the machine learning model being configured to recommend the second data structure from the plurality of data structures for data objects by:
- generating training data by performing operations in a computing system or based on logged information about data objects in the computing system, the training data consisting of historical information about access types and numbers of items associated with the data objects, and further including labels indicating a data structure from the plurality of data structures for storing the data objects;
- training the machine learning model to recommend the second data structure from the plurality of data structures based on the second data structure being associated with a lowest resource usage of the plurality of data structures by minimizing one or more loss functions using the training data;
- and
- subsequent to training the machine learning model, configuring the machine learning model to receive the properties including an access type and a number of items associated with the second plurality of data objects and to output a recommendation of the second data structure from the plurality of data structures;
- receive, by the memory device, a second command indicating the second data structure is to be generated for storing the second plurality of data objects;
- in response to receiving the second command, generate the second data structure in the memory device;
- and
- store, in the memory device, the second plurality of data objects in the second data structure.
Dependencies are inferred from the claim text.