Procedural Data Tables for Query Speed and Storage Efficiency
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Solution Overview
Problem
Existing data retrieval systems face inefficiencies due to slow query processing, redundant data tables, and inconsistent data sources, leading to long response times and resource wastage, especially in big data environments.
Innovation Solution
An automated system for pre-aggregating data tables using offline pre-computation and online optimization components, which analyze query patterns to generate optimized query plans and maintain data consistency, reducing the need for manual efforts and improving query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If manual generation of pre-aggregated data tables is performed to accelerate queries, then query response time is improved, but development time and storage space are worsened due to duplicated tables and manual efforts
Solution Approach 1:
The system performs preliminary analysis of query patterns to automatically identify which data tables should be pre-aggregated. This preliminary action eliminates the need for manual identification and generation of pre-aggregated tables, resolving the contradiction by automating the preparatory work while maintaining fast query responses.
Solution Approach 2:
The system enables self-service automation where the query optimization system automatically generates, maintains, and updates pre-aggregated data tables based on observed query patterns. This self-service mechanism eliminates manual intervention while continuously optimizing query performance, addressing both the speed improvement and time reduction requirements.
2Speed
If more pre-aggregated data tables are created to improve query performance, then query speed is improved, but storage space is worsened due to redundant and overlapping data
Solution Approach 1:
The system creates universal pre-aggregated data tables that serve multiple query patterns simultaneously. By identifying common dimensions and metrics across different query types, the system generates multi-functional tables that can satisfy various query requirements without creating separate dedicated tables for each query pattern, thus reducing storage overhead while maintaining query performance.
Solution Approach 2:
The system merges overlapping data from multiple potential pre-aggregated tables into unified tables. By detecting redundant data and consolidating it, the system reduces storage requirements while preserving the query optimization benefits. This merging process eliminates duplicate storage of the same data across multiple tables.
3Speed
If data tables are manually optimized for specific query patterns, then query performance for those patterns is improved, but adaptability is worsened when query patterns change over time
Solution Approach 1:
The system implements dynamic query optimization where pre-aggregated data tables are automatically created, updated, or discarded based on changing query patterns. The system continuously monitors query behavior and adapts its pre-aggregation strategy in real-time, ensuring optimal query performance regardless of how query patterns evolve over time.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor actual query performance and patterns. This feedback loop enables the system to learn from changing query behaviors and automatically adjust which pre-aggregated tables to maintain or create, ensuring continuous optimization adaptability without manual reconfiguration.
4Quantity of substance
If multiple data sources are integrated to provide comprehensive data, then data completeness is improved, but data consistency is worsened due to inconsistencies between different sources
Solution Approach 1:
The system introduces an intermediary layer that mediates between multiple data sources and the query processing system. This intermediary layer standardizes data formats, validates data consistency, and resolves conflicts between different data sources before presenting unified results to users, thereby maintaining data completeness while ensuring consistency.
Data Source
AI summary
Latency, response times, and efficiency improvements for data querying are provided herein, particularly in the context of querying large database systems and data tables from disparate data sources. There are provided systems and methods for automatic query and data retrieval optimization through procedural generation of data tables from query patterns. A service provider may utilize different computing services for query processing and data retrieval for different applications and services used by internal and/or external users. Instead of querying large database systems and numerous data tables, pre-aggregated data tables may instead be used and searched by procedurally generating such tables based on precomputation rules and query patterns. Once patterns have been identified in queries, corresponding data may be aggregated from data sources in a pre-aggregated data table. Query optimization rules may then be used to have these data tables queried in place of their original sources.