Indirect Algebraic Partitioning for Database Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current database systems face limitations in data partitioning and optimization due to predetermined schemas that do not capture the original structure of data, restricting the flexibility and extent of partitioning and optimization methods.
Innovation Solution
The system identifies patterns in query statements and algebraic relations to perform direct and indirect partitioning, using algebraic relations to optimize data storage and access by automatically evaluating conditions for partitioning and composing new data sets and relations, allowing for flexible partitioning based on patterns and relationships between data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data is stored using predetermined schemas, then data storage structure is simplified, but partitioning flexibility and optimization capability are limited
Solution Approach 1:
The patent segments data storage into multiple partitions based on algebraic relations and query patterns. Instead of treating data as a single monolithic structure, the system divides it into smaller, manageable partitions that can be independently optimized and accessed, thereby achieving both structural simplicity and partitioning flexibility.
Solution Approach 2:
The patent implements dynamic partitioning where partition structures are not fixed but adapt based on query patterns and algebraic relations. The system can dynamically create, modify, or eliminate partitions based on runtime conditions, allowing the storage structure to remain simple while providing flexible optimization capabilities.
2Productivity
If comprehensive data partitioning is performed, then query performance is improved by reducing data retrieval amount, but system complexity increases
Solution Approach 1:
The patent performs preliminary analysis of query patterns and algebraic relations to identify optimal partitioning strategies before actual queries are executed. By pre-computing partition structures based on historical query patterns, the system reduces query performance overhead while avoiding the complexity of real-time partitioning decisions.
Solution Approach 2:
The patent introduces an intermediary layer (algebraic cache and optimizer) that mediates between the simple storage structure and complex query requirements. This intermediary handles the complexity of partitioning logic, allowing the underlying storage to remain simple while still achieving sophisticated query optimization.
3Loss of time
If algebraic relations are composed and stored for optimization, then query response time is reduced, but storage space requirements increase
Solution Approach 1:
The patent changes the parameters of data representation by storing algebraic relations in a compressed and optimized format. Instead of storing raw data relationships, the system uses algebraic expressions that can represent complex relationships more compactly, reducing the storage overhead while maintaining fast query response capabilities.
Data Source
AI summary
Systems and methods for storing and accessing data. Example embodiments may perform optimization based on patterns of requests received by the system and relations between data sets identified by the system. Example embodiments may identify restrictions on a data set based on a different data set. Conditions for automatically algebraically partitioning the data set based on a constituent of a different data set may be evaluated, including evaluation of the relationship between the data sets and identification of a pattern of statements restricting the data set using the same logical structure. If the conditions are met, component data sets and a partition data set may be algebraically defined based on ranges applied to constituent(s) of the other data set. The component data sets may also be realized in storage to physically partition the data set.