Database ML Object Execution With Parallel Query Planning
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Solution Overview
Problem
Existing database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, leading to inefficiencies in data processing and query execution.
Innovation Solution
Implementing a parallelized database system architecture that includes a parallelized data input, storage, retrieval, and processing sub-systems, along with a query and response system, to optimize data storage and processing through segmentation, restructuring, and efficient query planning, utilizing multiple computing devices and nodes with independent processing core resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database systems process queries sequentially with single CPU, then hardware simplicity is maintained, but processing speed and productivity are limited
Solution Approach 1:
The patent divides the database system into multiple independent processing cores (first processing core, second processing core, etc.) that can execute queries concurrently. Each processing core operates independently on different data segments or queries, enabling parallel processing that significantly improves productivity while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent transitions from sequential single-CPU processing to multi-dimensional parallel processing by introducing multiple processing cores that operate simultaneously. This dimensional shift from one processor to many processors enables concurrent query execution and substantially increases data processing speed without requiring complex coordination mechanisms
2Productivity
If data is stored in traditional formats, then storage simplicity is maintained, but processing efficiency and query execution speed are limited
Solution Approach 1:
The patent segments data into distinct data segments that can be independently accessed and processed by different processing cores. This segmentation allows parallel query execution where each core works on specific data portions simultaneously, improving query execution speed while maintaining storage structure simplicity through straightforward segment organization
3Productivity
If limited co-processing options are used, then system simplicity is maintained, but processing efficiency and throughput are restricted
Solution Approach 1:
The patent implements a universal processing architecture where multiple processing cores can handle various types of queries and data processing tasks independently. Each processing core is designed with similar functionality to process different data segments or execute different queries concurrently, enabling efficient parallel processing without requiring complex specialized co-processing configurations
Data Source
AI summary
A database system includes multiple computing device clusters having computing devices with computing nodes and processing core resources. A set of the processing core resources receives a query regarding a machine learning model stored as an object in memory of the database system, the query includes an object name that identifies the machine learning model as a non-standard data object and specifies a requested machine learning purpose. The system identifies data for use by the model to achieve the requested purpose, obtains a query plan that allocates computing resources for executing the model, and retrieves the model from memory based on the object name. The system executes the machine learning model using the identified data in accordance with the query plan to produce a machine learning result corresponding to the requested purpose.