Dynamic Consistency Model Management in Multi-Tenant Databases
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Solution Overview
Problem
Conventional distributed database systems face challenges in dynamically managing consistency model configurations, making it difficult to expand functionality across new resources and requiring reconfiguration and intervention from storage engineers, which limits efficient and accurate data management across multiple users and increasing information volumes.
Innovation Solution
A multi-tenant distributed database system that allows customers to customize and configure consistency models for data reads and writes, leveraging local data centers for accurate reads without high latency, and dynamically managing resources to support varying consistency models based on service requirements, enabling self-service usage and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional open source frameworks are used to manage consistency models, then the system can support eventually consistent and strongly consistent applications, but it becomes difficult to expand functionality across new resources and requires reconfiguration and intervention from storage engineers
Solution Approach 1:
The patent implements a self-service portal that enables customers to independently configure and manage consistency model parameters without requiring storage engineer intervention. The portal provides automated service configuration, allowing users to dynamically adjust consistency settings, replication factors, and other parameters based on their specific needs, thereby eliminating the need for manual reconfiguration and expert involvement.
Solution Approach 2:
The system introduces dynamic configuration capabilities that allow consistency model parameters to be adjusted in real-time without system downtime or manual intervention. The self-service portal enables customers to dynamically modify service configurations, replication settings, and consistency levels as their requirements change, making the system adaptable to new resources and changing demands without rigid reconfiguration constraints.
2Adaptability or versatility
If different consistency models are supported, then the system can meet varying service requirements, but different resource requirements of the different consistency models create management complexity
Solution Approach 1:
The patent creates a universal resource pool that can dynamically serve multiple consistency model requirements (eventual consistency, strong consistency, and intermediate levels). Instead of maintaining separate resource allocations for each consistency model, the system provides a unified infrastructure that automatically adapts resource allocation based on the specific consistency requirements of different services and applications, simplifying resource management while supporting diverse consistency needs.
Solution Approach 2:
The system enables dynamic parameter adjustment for consistency models through the self-service portal, allowing customers to modify consistency levels, replication factors, and other parameters without manual intervention. This automated parameter management simplifies the complexity of supporting different consistency models by providing centralized, on-demand configuration capabilities that adapt resource allocation to match specific service requirements.
3Reliability
If data is stored in a distributed system with multiple resources, then reliability and availability are improved, but data must propagate among multiple computer resources before achieving replica convergence
Solution Approach 1:
The patent implements dynamic consistency models that allow the system to adjust the replication factor and convergence requirements based on service-specific needs. For services requiring high availability, the system can configure lower replication factors with eventual consistency, reducing convergence time. For services requiring strong consistency, the system dynamically adjusts to higher replication factors with synchronized convergence, thereby optimizing the balance between reliability and convergence time for each workload.
Solution Approach 2:
The system enables parameter changes for data propagation and convergence through the self-service portal, allowing customers to configure replication factors, consistency levels, and propagation priorities dynamically. This automated parameter adjustment optimizes the trade-off between data availability and replica convergence time, enabling the system to adapt propagation settings to match specific service requirements without manual intervention or storage engineer involvement.
Data Source
AI summary
Embodiments are provided for enabling a dynamic management of a multi-tenant distributed database. According to certain aspects, a management module supports an interface that enables a customer to configure one or more consistency models for a service to be supported by the distributed database. The management module may determine computing resources within the distributed database that are needed to support the service according to the configured consistency model(s), and may instantiate the computing resources for testing and development of the service by the customer.