Federation Query Optimization via Ordered Queues
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Solution Overview
Problem
Data federation systems face challenges in maintaining high quality of service (QoS) due to high performance demands and frequent high-bandwidth interactions, which are not adequately addressed by traditional techniques.
Innovation Solution
A federation engine with a data quality coordinator that generates query vectors, performs similarity measures to estimate processing times, sets priorities, and orders queries in queues to optimize processing order, ensuring efficient retrieval of data results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional query processing methods are used in data federation systems, then the system structure remains simple, but the quality of service deteriorates due to high performance demands and frequent high-bandwidth interactions
Solution Approach 1:
The system performs preliminary actions by generating query vectors and performing similarity measures before actual query execution to estimate processing times. This allows queries to be prioritized and ordered in advance, ensuring high quality of service without adding complex real-time processing requirements during query execution.
Solution Approach 2:
The patent replaces traditional mechanical query scheduling mechanisms with a vector-based similarity measurement system. Instead of using complex rule-based or heuristic scheduling algorithms, the system uses mathematical vector operations to estimate query processing times and determine optimal execution order, simplifying the control mechanism while improving service quality.
2Productivity
If queries are processed in received order, then the system operation is simple, but the productivity decreases due to suboptimal query processing order
Solution Approach 1:
The system changes the parameter used for query ordering from simple arrival time to estimated processing time derived from vector similarity measures. By transforming the ordering criterion from a temporal parameter to a performance-based parameter, the system achieves optimal query processing order without requiring complex manual scheduling operations.
Solution Approach 2:
The system enables self-service by automatically estimating query processing times and determining optimal execution orders without external intervention. The query vectors and similarity measures allow the system to autonomously prioritize queries based on their expected resource consumption and inter-query dependencies, improving productivity while maintaining operational simplicity.
3Loss of time
If no query prioritization is implemented, then the system complexity remains low, but the loss of time increases due to inefficient query processing sequences
Solution Approach 1:
The system performs preliminary similarity measurements and processing time estimations before query execution to establish optimal processing sequences. This advance preparation reduces actual execution latency by ensuring queries are processed in the most efficient order, without requiring complex real-time scheduling during query processing.
Solution Approach 2:
The patent introduces query vectors as an intermediary representation between the incoming query and the execution engine. These vectors serve as a mediator that enables efficient similarity comparison and processing time estimation, reducing overall query processing latency while keeping the query management mechanism relatively simple through mathematical operations.
Data Source
AI summary
Methods, systems, and computer program products for optimization of query processing in a data federation system using priority queuing techniques are provided. Priority queuing techniques may include generating a query vector corresponding to a query, comparing the query vector to historical query vectors to determine similarity, determining an expected processing time for the query based on the determined similarity, and inserting the query into a priority ordered queue at a particular position based on the expected processing time.