Lightweight DBMS Microservices Query Parsing
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Solution Overview
Problem
Modern database-management systems (DBMSs) are inefficient as they activate and maintain all functions, even though only a subset is required to process specific tasks, leading to resource wastage and inefficiency.
Innovation Solution
Implementing a lightweight DBMS based on a dynamic microservices architecture where each DBMS feature or function is executed as a distinct, independently executable microservice, selectively activated and run only when necessary, utilizing cognitive computing and machine-learning for intelligent feature selection and preloading based on query type, time, location, and user classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all DBMS functions are activated and maintained in modern database-management systems, then the system can perform an entire class of tasks, but resource wastage and inefficiency occur because only a subset of functions is required for specific tasks
Solution Approach 1:
The DBMS is divided into independent microservices, each representing a specific database function or feature. This segmentation allows the system to activate only the microservices needed for processing a particular query, rather than maintaining all functions simultaneously. Each microservice can be independently deployed, scaled, and managed, enabling fine-grained control over resource allocation based on actual query requirements.
Solution Approach 2:
The system dynamically configures and activates microservices based on the specific requirements of incoming queries. The Parser analyzes each query and determines which bind-time and runtime features are needed, then selectively activates corresponding microservices. This dynamic approach allows the DBMS to adapt its functional composition in real-time, activating only the necessary subset of functions for each task while keeping other functions dormant to conserve resources.
2Loss of energy
If a lightweight DBMS activates only necessary microservices, then resource efficiency improves, but system complexity increases due to dynamic microservice architecture and feature selection mechanisms
Solution Approach 1:
The Parser serves as an intermediary component that bridges the gap between incoming queries and the microservice activation mechanism. It analyzes query requirements and translates them into specific microservice activation requests. Additionally, the Parser maintains a feature registry that maps database features to corresponding microservices, acting as a lookup table that simplifies the activation process. This intermediary layer abstracts the complexity of microservice management from the query processing logic.
Solution Approach 2:
The system changes the operational parameters of microservices dynamically based on query requirements. Instead of maintaining all microservices in a ready-to-execute state, the system transitions microservices between dormant and active states as needed. The Parser determines which microservices should be activated by analyzing query parameters and feature requirements, then adjusts the system state accordingly. This parameter-based control enables efficient resource utilization while managing complexity through standardized activation protocols.
3Speed
If microservices are preloaded and activated on-demand, then query processing speed improves, but memory consumption increases due to preloading mechanisms
Solution Approach 1:
The system performs preliminary actions by preloading microservices into memory before they are needed for query processing. The Parser identifies which microservices will be required based on query analysis and pre-activates them or ensures they are loaded into memory. This preliminary loading reduces the activation overhead during actual query execution, improving query processing speed. The system balances this by only preloading microservices that are likely to be needed, rather than loading all possible microservices simultaneously.
Solution Approach 2:
The system applies partial preloading by loading only the subset of microservices that are needed for a specific query or group of queries, rather than loading all microservices. The Parser determines the minimal necessary set of microservices based on query requirements and preloads only those. This partial action approach provides the speed benefits of preloading while avoiding the excessive memory consumption that would result from loading the complete set of all possible microservices.
Data Source
AI summary
A lightweight database-management system (DBMS) is based on a dynamic microservices architecture that implements each granular DBMS feature or function as a distinct, independently executable microservice. The DBMS's Parser front-end responds to each incoming query by selecting the first bind-time database feature needed to process the query. The Parser forwards its selection through a Channel-Binding subsystem to an Event Services Activation subsystem that activates a corresponding microservice to perform the selected feature. The first feature then selects the next required bind-time feature for activation, and this process continues sequentially until all required bind-time microservices have been identified, activated, and run. Runtime query-processing features are then sequentially selected in a similar manner. However, each selected runtime microservice is preloaded but not run. Only when all runtime functions have been identified does the Parser send an asynchronous message to the Channel-Binding subsystem directing the DBMS engine to run the preloaded runtime microservices.