Modular Database Partition Redistribution Plan Engine
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Solution Overview
Problem
Conventional plan generation algorithms in database systems lack the flexibility to account for various considerations and their relative importance, limiting their ability to effectively distribute table partitions across multiple hosts for improved scalability and availability.
Innovation Solution
A plan generation engine with modular architecture that allows users to specify and implement custom algorithms through plug-in modules, enabling flexible redistribution plans based on user-defined parameters and metadata, which can be executed to optimize the placement of database table partitions across hosts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional plan generation algorithms are used, then the database system can distribute table partitions across hosts, but the system lacks flexibility to account for multiple considerations and their relative importance
Solution Approach 1:
The plan generation algorithm is divided into multiple independent modules, each responsible for evaluating a specific consideration (e.g., data locality, load balancing, network bandwidth). These modules can be independently configured, enabled, or disabled based on user needs, providing flexibility without requiring complete algorithm redesign.
Solution Approach 2:
The system allows dynamic configuration of algorithm parameters and module weights at runtime. Users can adjust the relative importance of different considerations and reconfigure the plan generation behavior without system downtime, enabling adaptive response to changing database workloads and hardware conditions.
2Adaptability or versatility
If fixed algorithm parameters are used, then the system is easier to implement, but cannot account for user-specific requirements and multiple factors
Solution Approach 1:
The plan generation system is designed as a universal framework that can handle multiple distribution scenarios and considerations through a common modular architecture. The same core infrastructure supports various customization levels, from simple default configurations to complex user-defined parameter sets, serving multiple functions without requiring separate implementations.
Solution Approach 2:
The system includes automated configuration capabilities that allow it to self-adjust based on detected workload patterns and system conditions. Default parameter settings are automatically optimized based on initial system state, reducing the burden on users while still enabling advanced customization when needed.
3Productivity
If conventional algorithms are used, then implementation is straightforward, but the system cannot optimize for multiple competing objectives simultaneously
Solution Approach 1:
Multiple optimization objectives (data locality, load balancing, network efficiency, storage capacity) are merged into a unified plan generation process through modular evaluation functions. Each module contributes its assessment to a composite scoring system that ranks distribution plans according to user-defined priority weights, enabling simultaneous optimization of multiple competing goals.
Solution Approach 2:
A modular evaluation framework acts as an intermediary layer between individual optimization criteria and the final distribution plan. This framework translates multiple competing objectives into a unified decision-making process, allowing complex multi-objective optimization without requiring direct integration of all factors into a single monolithic algorithm.
Data Source
AI summary
A system includes instantiation of a plan generation environment exposing one or more software hooks, each of the one or more software hooks associated with a respective one of a plurality of steps for generating a database table partition redistribution plan, identification, for of the one or more software hooks, of an associated one or more software modules for performing the step associated with the at least one software hook, and execution of the plan generation environment and the identified one or more software modules to generate a database table partition redistribution plan.