Table Discovery Service for Cloud Data Query Optimization
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Solution Overview
Problem
In cloud environments, multiple users often request similar data using the same or similar queries, leading to the generation of duplicate tables, which is inefficient and resource-intensive.
Innovation Solution
A table discovery service that converts query strings into query graphs, identifies matching graphs from a set of previously generated graphs, and allows users to reuse or regenerate tables based on user response, thereby reducing duplicate table generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tables are generated for each query request, then data retrieval accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by converting queries to query graphs and storing them in a graph cache before actual table generation. When a query graph matches a previously stored graph, the system retrieves the pre-generated table instead of regenerating it, thus avoiding redundant computational work while ensuring accurate data retrieval.
Solution Approach 2:
The system creates query graphs as abstract representations (copies) of actual queries and stores them in a cache. Instead of generating tables for every identical query request, the system copies the previously generated table associated with a matching query graph from the cache, significantly reducing computational resource consumption while maintaining data accuracy.
2Productivity
If query graphs are stored and matched, then duplicate table generation is reduced, but system complexity increases
Solution Approach 1:
The system introduces query graphs as an intermediary representation between the original query and the table generation process. The query graph serves as a mediator that can be easily compared and matched against stored graphs, enabling efficient duplicate detection without requiring complex direct query comparison logic.
Solution Approach 2:
The system transforms queries into a different parameter representation (graph structure with nodes and edges) that facilitates easier matching and comparison. By changing the representation parameters from raw query text to structured graphs, the system enables efficient similarity detection while managing complexity through standardized graph operations.
Data Source
AI summary
A method implements a table discovery service. The method includes receiving a query string, converting the query string to a query graph, and identifying a selected graph, of a set of graphs, that matches the query graph. The method further includes transmitting a notification identifying a previously generated table corresponding to the selected graph, receiving a notification response to accept the previously generated table, and transmitting data from the previously generated table in response to the query string.