Tenant-Specific Event Log Data Structure Rotation
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Solution Overview
Problem
In multi-tenant Software-as-a-Service (SaaS) environments, the uniform data structure for storing event logs from tenants with varying event rates leads to inefficient resource utilization, causing delays in query performance and storage issues, as the system must frequently generate or purge data structures to accommodate differing log volumes.
Innovation Solution
A system that determines the event rate for each tenant and generates data structures based on a calculated rotation interval, allowing for dynamic adjustment of storage capacity to match the tenant's specific needs within a time window, thereby reducing the need for frequent resource reallocation and minimizing delays in event log management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a uniform data structure is used for storing event logs from all tenants, then the system structure is simple and easy to manage, but resource utilization becomes inefficient and query performance deteriorates due to frequent data structure generation and purging
Solution Approach 1:
The patent segments the uniform data structure into tenant-specific data structures, where each tenant has a dedicated data structure sized according to their event rate. This segmentation allows efficient query performance for each tenant while maintaining manageable system complexity through automated provisioning based on event rates.
Solution Approach 2:
The patent implements dynamic data structure sizing where the size of each tenant's data structure is adjusted based on their measured event rate. This dynamic approach optimizes resource utilization and query performance by matching data structure capacity to actual usage patterns, eliminating the need for frequent generation and purging operations.
2Stability of the object's composition
If a fixed-size data structure is used for event log storage, then resource allocation is predictable, but storage overflow occurs frequently for tenants with varying event rates, requiring frequent data structure regeneration
Solution Approach 1:
The patent changes the size parameter of data structures based on each tenant's event rate. By dynamically adjusting the data structure size to match the tenant's actual log volume, the system maintains stable and reliable storage capacity without frequent regeneration, even as event rates vary across different tenants.
3Ease of operation
If event logs are stored in a centralized uniform structure, then data management is simplified, but resource utilization becomes inefficient due to over-provisioning for high-event tenants and under-provisioning for low-event tenants
Solution Approach 1:
The patent applies local quality by creating tenant-specific data structures with sizes tailored to each tenant's event rate. This allows optimal resource utilization for each tenant locally, while the centralized management system automatically provisions these structures based on measured event rates, maintaining ease of operation through automated processes.
Data Source
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AI summary
Some implementations described herein relate to a system that is configured to obtain one or more event logs associated with a tenant of the system. The system may be configured to determine, based on the one or more event logs, an event rate associated with the tenant and thereby determine, based on the event rate, a rotation interval. The system may be configured to cause, based on the rotation interval, a data structure to be generated for storing event logs associated with the tenant that are obtained during a time window. The system may be configured to obtain, within the time window, one or more additional event logs associated with the tenant and to cause, based on obtaining the one or more additional event logs within the time window, the one or more additional event logs to be stored in the data structure.