Parallelized Database Query Execution via Data Segmentation
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Solution Overview
Problem
Current database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, which hinder efficient execution of complex queries and large-scale data processing.
Innovation Solution
A parallelized database system architecture that divides data into segments, processes queries in parallel across multiple nodes, and utilizes optimized query plans to distribute processing tasks, enabling decentralized execution and improved scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is processed sequentially in traditional database systems, then hardware resource usage is simplified, but processing speed and query execution time deteriorate
Solution Approach 1:
The patent divides data into segments and distributes them across multiple nodes in a parallelized database architecture. Each node independently processes query segments, enabling concurrent execution and significantly improving processing speed while managing complexity through structured data division and node coordination protocols
2Productivity
If complex queries are executed on large-scale data, then data processing capability is improved, but query execution time increases due to hardware and storage limitations
Solution Approach 1:
The system performs preliminary actions by pre-segmenting data and pre-configuring node assignments before query execution. Query plans are generated in advance that map specific data segments to appropriate nodes, eliminating runtime decision-making overhead and reducing query execution time while maintaining high data processing capability
Solution Approach 2:
The patent transitions from sequential single-dimension processing to parallel multi-dimensional processing by distributing data across spatial nodes and temporal threads. This dimensional expansion allows complex queries to be processed simultaneously across multiple data segments, dramatically reducing execution time while scaling processing capability
3Productivity
If traditional co-processing options are used, then system simplicity is maintained, but processing efficiency for large-scale data deteriorates
Solution Approach 1:
The parallelized database nodes are designed with universal functionality to handle multiple query types and data operations. Each node can independently execute various SQL operations, join operations, and data transformations, providing versatile co-processing capabilities that improve efficiency without requiring specialized processing components for different query types
Data Source
AI summary
A database system is operable to execute a request to generate a decision tree model. A training set of rows are determined based on accessing a plurality of rows of a relational database table of a relational database. First query data is generated for execution based on the training set of rows. First query output is generated based on executing the first query data. A first portion of the decision tree model data is built based on the first query output. Additional query data is generated for execution based on the first query output. Additional query output is generated based on executing the additional query data. An additional portion of the decision tree model data is built based on the additional query output. Model output for the decision tree model is generated via processing input data in conjunction with processing the decision tree model data.