Multi-tenant Collaborative Learning for Code Optimization
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Solution Overview
Problem
Multi-tenant database systems face challenges in identifying and applying code fixes and optimizations across different tenants, leading to inefficiencies in resource consumption and performance, as tenants often write similar queries or make similar programming mistakes without sharing best practices effectively.
Innovation Solution
A collaborative learning environment is implemented within the multi-tenant system, which collects and analyzes source code from various tenants to identify common issues and optimize queries, providing code fixes and recommendations based on collective learning, thereby improving system performance and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If each tenant writes their own code and queries independently, then tenant-specific customization and control are improved, but system-wide resource consumption increases and best practices are not shared
Solution Approach 1:
The patent merges individual tenant code and query execution into a unified collaborative learning environment. Source code from multiple tenants is collected, analyzed, and processed centrally to identify common patterns and optimizations, then shared back to improve individual tenant systems without requiring full customization from scratch.
Solution Approach 2:
The system implements feedback mechanisms where code fixes and optimizations learned from collective tenant experiences are fed back to improve future code generation. The collaborative learning environment continuously refines its recommendations based on tenant-specific contexts while leveraging shared knowledge to reduce redundant resource consumption.
2Ease of operation
If tenants write similar queries and code independently, then development flexibility is maintained, but compile times and processing overhead increase
Solution Approach 1:
The collaborative learning environment performs preliminary analysis of source code and queries before execution. By pre-processing and analyzing code patterns across multiple tenants, the system identifies optimizations and common issues in advance, reducing compile times and processing overhead during actual execution without compromising development flexibility.
Solution Approach 2:
The system copies and reuses optimized code patterns and query structures from previous tenant implementations. Instead of compiling every query from scratch, the collaborative learning environment retrieves and adapts proven patterns, significantly reducing compile time while maintaining the ability to handle tenant-specific requirements.
3Reliability
If code fixes and optimizations are applied individually to each tenant, then tenant-specific issues are addressed, but system-wide efficiency decreases
Solution Approach 1:
The collaborative learning environment builds universal code fixes and optimizations that can be applied across multiple tenants. By analyzing common issues across the tenant population, the system develops general solutions that improve reliability for all tenants simultaneously, rather than treating each tenant's issues in isolation.
Solution Approach 2:
The system merges individual tenant code issues into a unified analysis framework where common patterns are identified and addressed collectively. This combining approach enables system-wide efficiency improvements while still allowing tenant-specific customizations where needed, balancing reliability with overall productivity.
Data Source
AI summary
Systems, methods, and computer-readable media are provided for a multi-tenant collaborative learning environment, where information from all tenants in a multi-tenant system is collected and used to provide individual tenants with code fixes and/or optimization recommendations based on the collected information. Other embodiments may be described and/or claimed.