Real-Time Data Stream Rule Evaluation for Multi-Tenant Metrics
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Solution Overview
Problem
Cloud platforms struggle to generate real-time metrics and insights from data streams, particularly in multi-tenant systems, limiting timely user engagement and insights generation.
Innovation Solution
A system that ingests real-time data streams into definitions for data metrics, evaluates rules, and triggers actions based on calculated insights, supporting applications across various industries for real-time analytics and alert generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time data stream processing is implemented in multi-tenant systems, then real-time metrics and insights generation is enabled, but system complexity and computational overhead increase
Solution Approach 1:
The patent segments the multi-tenant database system into isolated tenant contexts, where each tenant's data stream processing occurs in separate computational spaces. The system divides the overall processing workload by tenant identifier, allowing parallel processing of multiple tenants' data streams independently, thereby enabling real-time metrics generation while managing system complexity through structured partitioning
Solution Approach 2:
The patent introduces a new dimensional layer for real-time stream processing that operates alongside the traditional batch processing architecture. By adding this temporal dimension for real-time analytics, the system enables insights generation without completely redesigning the existing multi-tenant structure, thus balancing productivity improvement with acceptable complexity increase
2Loss of time
If real-time data stream evaluation is performed, then timely insights and user engagement are achieved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-compiling and optimizing rule evaluation logic before real-time data stream processing begins. Query plans are pre-generated and cached, and data transformation pipelines are prepared in advance, allowing the system to execute real-time evaluations with reduced computational overhead during actual operations
Solution Approach 2:
The patent dynamically adjusts processing parameters based on data stream characteristics and system load conditions. By changing parameters such as batch size, evaluation frequency, and caching strategies, the system optimizes the balance between insights generation speed and computational resource consumption, achieving timely results without excessive resource usage
Data Source
AI summary
Methods, systems, apparatuses, and computer program products are described. A computing device may receive a user input indicating a data stream, a data metric configured for a tenant of a multi-tenant database system, a rule associated with the data metric, a trigger based on the data metric, or some combination thereof. The computing device may receive, from the data stream, a real-time data stream including information corresponding to the data metric configured for the tenant, where the real-time data stream may be associated with a first user profile stored at the multi-tenant database system. The computing device may evaluate the rule, the trigger, or both based on ingesting the data stream and may perform the action based on the evaluation. Performing the action may involve sending a message to a user device associated with the first user profile in response to at least a portion of the data stream.