Cloud Data Warehouse Cache for Dashboard Loading
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Solution Overview
Problem
Modern businesses face challenges in efficiently presenting large query responses from cloud-based data warehouses in web applications, leading to computational and network traffic burdens.
Innovation Solution
The method involves determining if query results are stored in a cloud-based data warehouse cache, sending a request for the cached result if available, and providing dashboard visualizations based on the cached data to improve loading speed and reduce computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If query results are retrieved directly from cloud-based data warehouse, then data accuracy is ensured, but dashboard loading time increases and computational burden increases
Solution Approach 1:
The system performs preliminary actions by caching query results in the cloud-based data warehouse before they are needed for dashboard rendering. The cache stores pre-computed results that can be quickly retrieved, eliminating the need to re-execute complex queries each time a dashboard is loaded. This preliminary caching action resolves the contradiction by providing both accurate data (from the cache) and fast loading times (avoiding re-computation).
Solution Approach 2:
The invention creates a copy of the query results and stores it in a cache within the cloud-based data warehouse. Instead of repeatedly querying the original data sources, the system retrieves data from this cached copy. The copying mechanism maintains data accuracy while dramatically reducing loading time, as the copy can be served much faster than generating new queries against the source systems.
2Loss of information
If full query results are retrieved from cloud-based data warehouse, then complete data is available for visualization, but network traffic increases and computational load increases
Solution Approach 1:
The system extracts only the essential query results needed for dashboard visualization and stores them in the cache. Rather than retrieving and processing complete datasets every time, the invention extracts and caches only the necessary information. This extraction approach maintains data completeness for visualization purposes while significantly reducing network traffic and computational load by avoiding unnecessary data transfer and processing.
3Reliability
If dashboard queries are executed against cloud-based data warehouse each time, then up-to-date data is provided, but system performance decreases
Solution Approach 1:
The system implements periodic action by refreshing the cache at scheduled intervals or when data changes are detected. Instead of executing queries on every dashboard load, the cache is periodically updated with fresh data. This periodic refresh mechanism maintains data freshness for reliability while improving system performance, as the majority of dashboard loads can serve from the existing cache without triggering new query executions.
Solution Approach 2:
The system performs preliminary data preparation and caching before dashboard requests are made. By pre-computing and storing query results in the cloud-based data warehouse cache, the system eliminates the need for repeated query executions. This preliminary action ensures data is ready for immediate retrieval, maintaining freshness while preserving system performance during actual dashboard rendering operations.
Data Source
AI summary
Dashboard loading from a cloud-based data warehouse cache, including determining that a result for a first query is stored in a cache of a cloud-based data warehouse; sending, in response to the result being stored in the cache, to the cloud-based data warehouse, a request for the result from the cache; and providing, based on the result for the first query, one or more dashboard visualizations.