Data Model Dependency Declarations for Query Optimization
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Solution Overview
Problem
Complex database queries often result in sub-optimal execution plans due to the lack of explicit dependencies and equivalences in data models, leading to increased resource consumption, latency, and unreliable query responses.
Innovation Solution
Defining and storing dependencies and equivalences for data elements in the data model at design time, allowing for optimized query generation and execution by pushing relevant filters and operations deeper into the data model, thereby reducing the complexity of query execution and improving response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex database queries are executed without explicit dependencies and equivalences in the data model, then the system can handle general query requests, but the query execution time increases and resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-defining dependencies and equivalences between data elements in the data model before query execution. This allows the query optimizer to have advance knowledge of data relationships, enabling more efficient query plan generation and execution without requiring complex runtime analysis.
Solution Approach 2:
The patent introduces an intermediary layer (the explicit dependency and equivalence declarations in the data model) that mediates between the query request and the database execution. This intermediary provides structured information about data relationships, allowing the system to generate optimized execution plans that respect these relationships and avoid sub-optimal query strategies.
2Productivity
If complex database queries are executed without explicit dependencies and equivalences in the data model, then the system can process general requests, but resource consumption increases
Solution Approach 1:
The system pre-establishes dependency and equivalence relationships in the data model, creating a ready-to-use optimization framework before queries are executed. This preliminary structuring of information enables the query processor to make informed decisions about execution strategies, reducing the computational resources needed during actual query processing.
Solution Approach 2:
The patent changes the parameters of the data model by adding explicit dependency and equivalence declarations. These parameter changes transform the data model from a simple structural definition to an optimization-aware model that provides guidance to the query optimizer, enabling more resource-efficient query execution plans.
3Productivity
If complex database queries are executed without explicit dependencies and equivalences in the data model, then the system can handle general queries, but the execution plans become unreliable
Solution Approach 1:
The system pre-defines dependency and equivalence relationships that serve as reliable guidance for query execution. These pre-established relationships provide a consistent foundation for generating execution plans, reducing variability and improving the reliability of query results across different scenarios.
Solution Approach 2:
The explicit dependency and equivalence declarations act as an intermediary that ensures reliable translation of query intentions into execution plans. This intermediary layer provides structured constraints and relationships that guide the optimizer toward consistent and reliable execution strategies, reducing the unreliability associated with complex queries.
4Productivity
If the data model includes explicit dependencies and equivalences, then query optimization is improved, but the data model complexity increases
Solution Approach 1:
The patent segments the data model complexity by separating the core data structure from the optimization metadata. Dependencies and equivalences are defined as distinct, modular declarations that can be added to the data model without fundamentally changing its structure. This segmentation allows the model to remain manageable while gaining optimization capabilities.
Solution Approach 2:
The explicit dependency and equivalence declarations serve multiple functions: they define data relationships for optimization, provide constraints for query planning, and enable various optimization strategies simultaneously. This multi-functionality justifies the added complexity by delivering comprehensive query optimization benefits across different query types and scenarios.
Data Source
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AI summary
The disclosure generally describes methods, software, and systems, including a method for defining and using dependencies and equivalences for data elements in the data model in order to improve query efficiency. At design time of a data model, dependencies and equivalences for data elements in the data model are received over a computer network. The dependencies and equivalences are stored in persistent storage with the data model. A request is received over the computer network, the request to be executed to obtain results from a database associated with the data model. An optimized query is generated using the received request and the dependencies and equivalences. The optimized query is optimized relative to the received request.