Canonical Query View Suggestions for Federation Engines
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Solution Overview
Problem
Optimizing data federation systems is complex due to the need for determining efficient views in systems with numerous data tables and queries, requiring significant time and effort from data architects.
Innovation Solution
A federated system that includes a processor, a multidiff statistics engine, and a multidiff console, which canonicalizes queries, performs similarity searches, and suggests views by grouping similar queries into viewable sets, allowing data architects to efficiently create and utilize reusable virtual views for optimized query processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data federation systems utilize a large number of data tables and process a large number of queries, then the system can handle diverse data access requirements, but determining views to optimize performance becomes complex and time-intensive
Solution Approach 1:
The system automatically analyzes query patterns and generates view suggestions without requiring manual analysis by data architects. The multidiff statistics engine autonomously performs similarity searches on canonical queries and identifies optimization opportunities, allowing the system to self-optimize based on observed usage patterns
Solution Approach 2:
The system pre-processes queries into canonical forms and stores them in a query database before actual query execution. By transforming queries to a standardized representation in advance and maintaining a history of canonical queries, the system prepares optimization data proactively, enabling faster view determination when queries are actually executed
2Manufacturing precision
If data architects manually determine views for optimization, then views can be precisely tailored to specific query patterns, but the process is time-intensive and requires significant expertise
Solution Approach 1:
The system replaces the manual mechanical process of view creation with an automated computational system. The multidiff statistics engine uses algorithmic similarity searches and pattern matching to automatically identify query patterns and generate view suggestions, substituting human expert analysis with automated statistical processing
Solution Approach 2:
The system creates canonical copies of queries by transforming them into a standardized form that preserves the essential query pattern while removing syntax variations. These canonical query copies are stored in a database and used for similarity matching, allowing the system to identify patterns across different query formulations without manual analysis
3Productivity
If the system stores and analyzes all query patterns, then it can identify optimization opportunities, but the query database size and processing overhead increase
Solution Approach 1:
The system extracts only the essential pattern information from queries by converting them to canonical forms that retain the core query structure while eliminating redundant syntax details. By storing only these condensed canonical representations rather than full query text, the system maintains pattern analysis capability while reducing database size and processing overhead
Data Source
AI summary
Methods, systems, and computer program products for converting a query to a canonical query; inserting the query and the canonical query into a database; searching one or more viewable sets stored in the database to determine whether the canonical query is similar to one or more canonical queries associated with the one or more viewable sets; and upon determining a similar canonical query, returning a first view suggestion associated with a first viewable set of the one or more viewable sets that includes the similar canonical query.