Query Service Class Selection by Text Pattern for Scalable Execution
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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, particularly when handling large volumes of data.
Innovation Solution
A database system architecture that utilizes parallelized data input, storage, retrieval, and query processing systems, enabling distributed execution of queries across multiple computing devices and nodes, with independent processing core resources to manage metadata and configuration, and employing a service class selection module for optimizing query execution based on text patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hardware resources are increased to improve processing speed, then query execution speed is improved, but system cost and complexity increase
Solution Approach 1:
The patent segments query processing into multiple service classes (e.g., interactive, batch, analytical) that can be independently managed and executed. Each service class has dedicated processing resources and configuration parameters, allowing the system to handle different query types efficiently without requiring a complete overhaul of the entire system architecture.
Solution Approach 2:
The system dynamically selects service classes based on query characteristics and current system state. The service class selection module evaluates incoming queries and assigns them to appropriate service classes in real-time, enabling flexible resource allocation that adapts to changing workloads without manual intervention or system reconfiguration.
2Quantity of substance
If data storage capacity is increased to handle larger volumes, then data capacity is improved, but access speed and processing efficiency deteriorate
Solution Approach 1:
The patent segments large datasets into smaller partitions that can be processed in parallel across multiple nodes. Each partition can be independently accessed and processed, maintaining high access speeds even as total data volume increases. This segmentation is applied both to data storage and to query execution plans.
Solution Approach 2:
The system introduces distributed processing across multiple computing nodes as an additional dimension for handling large data volumes. Instead of relying solely on increasing single-node capacity, the patent distributes data and processing across a network of nodes, enabling linear scalability while maintaining access performance through parallel operations.
3Adaptability or versatility
If co-processing options are expanded to improve execution flexibility, then query optimization is improved, but system complexity and configuration difficulty increase
Solution Approach 1:
The patent creates a universal service class framework that can handle multiple query types and workloads through a standardized interface. Each service class is designed to be multi-functional, capable of handling various query patterns (SELECT, JOIN, AGGREGATION) without requiring separate processing paths, thereby reducing configuration complexity while maintaining execution flexibility.
Solution Approach 2:
The service class selection module automatically evaluates incoming queries and selects appropriate service classes without requiring manual configuration or user intervention. The system self-optimizes by learning from query patterns and performance metrics, reducing the burden of configuration management while maintaining high execution flexibility.
4Productivity
If parallel processing is implemented to improve throughput, then processing capacity is improved, but coordination overhead and system complexity increase
Solution Approach 1:
The patent segments query execution into independent parallel tasks that can be assigned to different processing nodes. Each task operates independently with well-defined input and output interfaces, minimizing coordination overhead. The segmentation of query plans into service class-specific operations enables efficient parallel execution without requiring complex inter-task communication.
Data Source
AI summary
A database system is operable to determine query service class text pattern data indicating a plurality of text patterns each corresponding to one of a plurality of service classes. A query expression indicating a query for execution is determined, and the query service class text pattern data to select one service class of the plurality of service classes for the query based on text of the query expression matching a corresponding text pattern of the plurality of text patterns that corresponds to the one service class. The query is executed in accordance with a set of query execution attributes of the one service class based on selecting the one service class for the query.