Multi-tenant Database Resource Segmentation for OLTP Isolation
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Solution Overview
Problem
Conventional database systems become inefficient when a large number of users concurrently access the system, leading to excessive resource consumption and potential interference with online transaction processing (OLTP) operations.
Innovation Solution
A multi-tenant on-demand database system that dynamically adjusts computational resources by monitoring resource utilization and varying the number of enqueue/dequeue processes and job handlers to efficiently process computational jobs, storing data in a queue table and allocating resources accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional database systems are used to handle concurrent user access, then data access functionality is provided, but system efficiency deteriorates and resource consumption increases
Solution Approach 1:
The patent segments computational jobs into batch jobs and online transaction processing (OLTP) operations, allowing them to be handled by separate resource pools. Batch jobs are processed during off-peak hours using dedicated computational resources, while OLTP operations maintain their own resources, preventing resource contention and improving overall system efficiency.
Solution Approach 2:
The system dynamically allocates computational resources based on workload demands. Resource allocation is adjusted in real-time to match the actual processing needs of batch jobs and OLTP operations, ensuring optimal resource utilization and preventing excessive resource consumption during low-demand periods.
2Productivity
If computational resources are increased to handle large data processing, then data processing capability is improved, but interference with OLTP operations increases
Solution Approach 1:
The patent creates separate resource pools for batch computational jobs and OLTP operations. This segmentation ensures that increased computational resources allocated for batch processing do not interfere with OLTP operations, as each pool operates independently with dedicated resources.
Solution Approach 2:
The system introduces a resource management intermediary that mediates between batch job resource requirements and OLTP operation needs. This intermediary monitors resource usage and dynamically adjusts allocation to prevent batch processing from interfering with OLTP operations, ensuring continuous reliable service.
3Adaptability or versatility
If fixed computational resources are allocated, then resource management is simplified, but adaptability to varying workload demands is reduced
Solution Approach 1:
The system implements dynamic resource allocation that automatically adjusts computational resource levels based on real-time workload demands. This dynamic approach enhances adaptability to varying workloads while the automated nature of the adjustment minimizes the operational complexity for users.
Solution Approach 2:
The resource management system incorporates feedback mechanisms that continuously monitor workload conditions and automatically adjust resource allocation accordingly. This feedback-driven approach enables the system to adapt to changing demands without requiring complex manual intervention, balancing adaptability with manageable complexity.
Data Source
AI summary
In accordance with embodiments, there are provided techniques for utilizing computational resources of a multi-tenant on-demand database system. These techniques for utilizing computational resources of a multi-tenant on-demand database system may enable embodiments to provide great flexibility to a tenant of the architecture to perform desired functions on content of the database without unduly consuming the resources of the system.