Query Path Determination Circuitry for Database Optimization
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Solution Overview
Problem
As data generation and storage increase, existing data search technologies face challenges in efficiently retrieving relevant data from large data sources, leading to decreased user experience due to increased computational costs and slower query execution times.
Innovation Solution
The implementation of query path determination circuitry that evaluates and selects query paths incorporating aggregate projections, which utilize pre-calculated data to reduce query costs and execution time by comparing candidate paths derived from both aggregate and non-aggregate projections, and generating join graphs to identify optimal query paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional data search technologies are used to retrieve data from large data sources, then data retrieval can be performed, but query execution time increases and computational costs increase
Solution Approach 1:
The system pre-calculates and stores aggregate projections of data tables before queries are executed. When a query arrives, the query path determination circuitry can directly utilize these pre-computed aggregate projections instead of calculating them from scratch, significantly reducing query execution time and computational costs for aggregate queries.
2Productivity
If aggregate projections are utilized to reduce query costs, then query execution speed improves, but device complexity increases due to query path determination circuitry
Solution Approach 1:
The query path determination circuitry automatically evaluates multiple candidate query paths and selects the optimal one without requiring manual intervention or complex configuration. The system self-manages the complexity of determining whether to use aggregate projections or base tables, making the complexity transparent to users while delivering performance benefits.
3Quantity of substance
If data storage increases to support larger data sources, then more data can be searched, but search efficiency decreases due to increased computational costs
Solution Approach 1:
The system segments data into base tables and pre-computed aggregate projections. This segmentation allows queries to be answered by combining results from different segments (base tables and aggregate projections) in optimized ways, reducing the computational burden of querying large volumes of data by leveraging pre-processed segments.
Data Source
AI summary
In some examples, a system includes query path determination circuitry to identify an aggregate projection to consider for generating a query path for the query. The aggregate projection may include an aggregated value calculated from a table column of a database table using an aggregate function. The query path determination circuitry may further be to compare candidate query paths for the query derived using the aggregate projection with candidate query paths for the query derived using a non-aggregate projection, determine a selected query path among the candidate query paths derived using the aggregate projection and the non-aggregate projection and use the selected query path as part of the query path for handling the query.