Dual-Queue Node Dynamic Instantiation for Data Service Workloads
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Solution Overview
Problem
Data server systems face challenges in optimizing data operations as the size and complexity of data repositories grow, with added system resources often failing to achieve desired performance improvements and incurring undesirable costs and overhead.
Innovation Solution
The implementation of a dual-queue system that dynamically instantiates nodes based on resource availability, segregating live and stale data for efficient processing and storage, allowing for scalable and cost-effective data management by routing transactions through either single-tenant or multi-tenant systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional system resources are added to improve data operation performance, then processing capacity increases, but cost and overhead increase undesirably
Solution Approach 1:
The system dynamically instantiates and terminates data queue nodes based on real-time workload conditions. When workload exceeds threshold, additional nodes are instantiated to handle the load; when workload decreases, nodes are terminated to free resources. This dynamic adaptation resolves the contradiction by making resource allocation flexible rather than static, improving performance only when necessary and reducing resource consumption during low-demand periods.
Solution Approach 2:
The system changes operational parameters by transitioning nodes between active and inactive states based on workload metrics. The queue manager monitors system conditions and adjusts the number of active data queue nodes accordingly, changing the system's resource configuration parameter dynamically. This allows the system to optimize between performance and resource usage by adjusting the active node count based on actual needs.
2Productivity
If system resources are increased to handle growing data repository size and complexity, then data operation capability improves, but expense and overhead become undesirable
Solution Approach 1:
The system segments the data queue functionality into multiple independent nodes that can be instantiated and terminated individually. Rather than increasing the complexity of a single monolithic system, the workload is divided across multiple manageable nodes. Each node handles a portion of the data operations, allowing the system to scale capability by adding simple, standardized units rather than complex integrated systems, thus improving capability while controlling overhead.
Solution Approach 2:
The data queue nodes are designed as universal, multi-functional units that can handle various data operations across different workloads. Each node is capable of performing multiple data processing functions, allowing the system to improve overall capability by deploying identical versatile nodes rather than adding specialized complex components. This universality reduces system overhead while maintaining improved data operation capability.
Data Source
AI summary
A computer-implemented method, system, and computer-readable media are disclosed herein. In embodiments, the computer-implemented method may entail receiving, by a data service, live data associated with an entity. The entity may be, for example, a customer of the data service. The method may further include determining that a dual-queue node assigned to the entity is uninstantiated on the data service. As a result, a dual-queue node associated with the entity may be instantiated on the data service. The dual-queue node may be instantiated by initializing a live data queue, of the dual-queue node, in which to place the live data for processing and a stale data queue, of the dual-queue node, in which to store a persistent backup of the live data. The method may then route the live data to the dual-queue node. The dual-queue node may then process the live data. Additional embodiments are described and/or claimed.