Dynamic Aggregator for Pre-aggregated Dataset Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for generating performance reports from large multidimensional datasets face inefficiencies due to high latency and resource costs, as they often rely on manual analysis and heuristic projections to anticipate demand for specific performance metrics, leading to suboptimal pre-aggregated datasets that do not adequately address real-time query needs.
Innovation Solution
A dynamic aggregator system that monitors user queries and calculates benefit scores for pre-aggregated datasets based on latency and resource usage, dynamically generating and updating these datasets to optimize performance and resource allocation, ensuring that pre-aggregated datasets are maintained only when their benefits outweigh the costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-aggregated datasets are generated based on manual analysis and heuristic projections, then resource usage is reduced, but query response time increases due to high latency
Solution Approach 1:
The system automatically monitors query patterns, calculates benefit scores, and generates pre-aggregated datasets without manual intervention. The dynamic aggregator self-adjusts by observing actual query workloads and autonomously deciding when and what to pre-aggregate, eliminating the need for manual analysis while optimizing both response time and resource usage.
Solution Approach 2:
The system implements a feedback mechanism where query patterns are continuously monitored, benefit scores are calculated based on observed performance metrics, and pre-aggregated datasets are dynamically generated or discarded based on whether the benefit score exceeds thresholds. This closed-loop feedback ensures resources are allocated efficiently based on actual demand rather than projections.
2Loss of time
If pre-aggregated datasets are maintained for all possible query types, then query response time improves, but resource costs increase significantly
Solution Approach 1:
Instead of uniformly pre-aggregating data for all possible query types, the system applies pre-aggregation selectively based on local query patterns. The benefit score calculation identifies specific query types that would benefit most from pre-aggregation, and only those datasets are maintained. This localized approach ensures resources are concentrated on high-value pre-aggregated datasets rather than spreading resources thin across all possibilities.
Solution Approach 2:
The system dynamically changes the state of datasets by generating pre-aggregated versions only when the benefit score exceeds a threshold, and discarding them when it falls below. This parameter-based decision mechanism (using benefit score thresholds) allows flexible adaptation of data storage strategies based on changing query patterns, optimizing the balance between response time and resource consumption.
3Adaptability or versatility
If manual analysis is used to anticipate demand for performance metrics, then resource allocation is simplified, but adaptability to changing query patterns deteriorates
Solution Approach 1:
The system replaces manual analysis with automated self-service mechanisms. The dynamic aggregator continuously monitors query patterns, automatically calculates benefit scores, and makes real-time decisions about pre-aggregation. This self-service approach provides superior adaptability to changing query patterns compared to static manual analysis, while the automation actually reduces operational complexity despite increasing computational capabilities.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for dynamically generating and configuring pre-aggregated datasets optimized for responding to particular types of data requests made against a large sub-optimal multidimensional dataset are disclosed. A dynamic aggregator monitors the query types and response latencies associated with queries made against the large multidimensional dataset. The dynamic aggregator defines pre-aggregated datasets based on the types of queries received from users and calculates a respective benefit score for each pre-aggregated dataset. The benefit score of each pre-aggregated dataset can be based on the recorded latencies and query count for the pre-aggregated dataset. The dynamic aggregator can decide whether to generate and/or maintain particular pre-aggregated datasets based on the current values of the benefit scores associated with the particular pre-aggregated datasets.