Multi-Tenant Serverless Database Scaling Across Replica Sets
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Solution Overview
Problem
Conventional database systems often have limitations in hosting data for multiple tenants with fixed performance and volume caps, leading to inefficiencies and increased costs for database tenants.
Innovation Solution
A scalable cloud distributed database system that dynamically allocates resources based on workload demands, allowing migration of tenants between replica sets and throttling operations to accommodate fluctuating usage, with tiered storage and flexible pricing models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional database systems host data for multiple tenants with fixed performance and volume caps, then resource allocation is simplified and system stability is maintained, but resource utilization efficiency decreases and tenant costs increase
Solution Approach 1:
The patent implements dynamic resource allocation where the database system automatically adjusts resource allocation based on actual workload demands. The system monitors usage patterns and dynamically provisions or de-provisions resources for different tenants, transitioning from static fixed caps to flexible dynamic allocation that adapts to changing conditions.
Solution Approach 2:
The system employs automated self-service mechanisms where the database management system itself performs resource allocation, scaling, and optimization without requiring manual intervention. The system automatically detects when tenants need additional resources or when resources can be reallocated, enabling self-managed resource optimization.
2Productivity
If database systems implement strict caps on data volume and operation measures for multiple tenants, then system stability and predictability are maintained, but productivity and scalability are limited
Solution Approach 1:
The system implements dynamic scaling capabilities that allow data processing capacity to adjust in real-time based on actual demand. When tenants experience increased workload, the system automatically provisions additional capacity; when workload decreases, resources are released. This maintains high productivity while ensuring service level consistency through automated control.
Solution Approach 2:
The database system incorporates continuous monitoring and feedback mechanisms that track usage patterns, performance metrics, and resource utilization. This feedback loop enables the system to automatically adjust resource allocation and operational caps to maintain service level agreements while maximizing productivity, preventing both over-provisioning and under-provisioning.
3Reliability
If database systems allocate dedicated resources to each tenant, then service reliability and performance consistency are improved, but resource utilization efficiency and cost-effectiveness deteriorate
Solution Approach 1:
The patent implements multi-tenant architecture where resources are merged and shared across multiple tenants. The system combines resource pools that can be dynamically allocated to different tenants based on demand, allowing efficient utilization of shared infrastructure while maintaining isolation and quality of service guarantees through virtualization and resource management mechanisms.
Solution Approach 2:
The system applies local quality by providing differentiated resource allocation and service levels tailored to each tenant's specific needs and requirements. While resources are shared globally, each tenant receives customized resource guarantees, performance levels, and service quality appropriate to their local requirements, achieving both efficiency through sharing and reliability through customization.
4Reliability
If database systems scale infrastructure to accommodate peak usage, then service availability during high demand is ensured, but cost efficiency during low usage periods deteriorates
Solution Approach 1:
The system implements dynamic scaling that automatically adjusts infrastructure capacity based on real-time usage patterns. During peak demand periods, the system provisions additional resources to ensure service availability; during low-usage periods, resources are de-provisioned or reallocated to other tenants, eliminating the need to maintain permanent over-capacity infrastructure and reducing overall costs.
Solution Approach 2:
The database system employs predictive scaling mechanisms that anticipate upcoming demand surges and proactively provision resources before peak usage occurs. By analyzing historical patterns and predicting future demand, the system prepares infrastructure in advance, ensuring service availability during expected peak periods without requiring permanent over-provisioning of resources.
Data Source
AI summary
According to aspects of the disclosure, there is provided a scalable cloud distributed database system for hosting data of multiple database tenants. In some embodiments, the database may be serverless. The serverless database may be configured to automatically and dynamically match resources to workload demands for tenants of the database. Databases described herein may include replica sets hosting multiple tenants. Tenants may be migrated from one replica set to another replica set based on usage. Usage of tenants may be throttled during migration between replica sets. Tenants with lower usage may be selected for migration between replica sets. During migration, files containing both a document and a history of updates to the document may be migrated. Databases described herein may include multiple storage tiers having different read and/or write speeds.