Query Routing Server for Multi-Engine Load Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing query routing technologies face inefficiencies in selecting the most suitable query performance engine based on query characteristics and load, leading to suboptimal query processing and inconvenience in managing multiple engines.
Innovation Solution
A query routing method and system that determine the most suitable query performance engine by considering query characteristics and load, allowing user selection and enabling connection with multiple engines, using a query routing server with a distributed file system and metadata shared among engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple query performance engines are deployed to handle different query types, then query processing capability and adaptability are improved, but system complexity and difficulty in managing multiple engines increase
Solution Approach 1:
A query routing server is introduced as an intermediary component between the user and multiple query performance engines. The routing server receives queries, analyzes query characteristics, and automatically routes queries to the most appropriate engine based on predefined rules and engine status. This mediator eliminates the need for users to directly manage or select among multiple engines, thereby maintaining high adaptability while significantly reducing system management complexity.
Solution Approach 2:
The query routing server provides universal functionality by serving as a single entry point for all query types across multiple engines. It handles diverse query routing decisions, engine status monitoring, and load balancing in one centralized component, allowing the system to manage multiple specialized engines through a unified interface that simplifies overall system management.
2Productivity
If automatic query routing based on engine load and characteristics is implemented, then query processing efficiency is improved, but the complexity of the routing decision system increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring routing rules that map different query characteristics to appropriate engine types. Query characteristics such as SQL type, data size, and complexity are analyzed in advance, and routing decisions are made based on pre-established criteria. This preliminary classification and rule-based routing approach enables efficient automatic query distribution without requiring complex real-time decision algorithms, thus improving processing efficiency while keeping the routing system relatively simple.
Solution Approach 2:
The routing system utilizes changes in engine status parameters (such as current load, availability, and performance metrics) to dynamically adjust routing decisions. When engine load or characteristics change, the routing server adapts by selecting different engines based on updated parameters. This parameter-driven approach allows the system to respond to changing conditions efficiently without implementing complex predictive or optimization algorithms, maintaining simplicity while improving query processing efficiency.
3Adaptability or versatility
If user selection of query performance engine is allowed, then user control and flexibility are improved, but the ease of operation and convenience for average users deteriorates
Solution Approach 1:
The system provides dynamic query routing where the engine selection adapts based on real-time engine status and query characteristics. For standard users, the routing is fully automatic and transparent, providing ease of operation. For advanced users who need more control, the system dynamically allows engine selection based on their preferences or requirements. This dynamic approach enables the system to switch between automatic and manual modes, maintaining both user convenience and flexibility as needed.
Solution Approach 2:
The query routing server implements self-service by automatically analyzing query characteristics and selecting the most appropriate engine without requiring user intervention. The system monitors engine status, evaluates query requirements, and makes routing decisions autonomously. This self-service capability provides ease of operation for average users while maintaining flexibility, as the automated system can adapt to different query types and engine conditions without user input.
Data Source
AI summary
Provided is a method, server, and storage medium for query routing. A query may be processed on a query routing server using a same distributed file system and connectable with a plurality of query performance engines sharing metadata. The method may include receiving, through a user input interface, a query and a criterion to select a query performance engine among a plurality of query performance engines, and determining, by a query routing server, one of the plurality of query performance engines based on the received criterion.