Geospatial Query Caching for Low-Latency Entity Search
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Solution Overview
Problem
Existing geospatial database queries are computationally expensive and inefficient, leading to long wait times and resource consumption due to iterative searches with varying parameters to locate a desired number of entities, often violating service level agreements.
Innovation Solution
Implementing a self-adaptive query system that adjusts search regions in real-time to efficiently locate a specific number of entities by iteratively adjusting the search radius until the entity threshold is met, combined with query caching for geospatial cells to store parameters for future queries in similar locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative searches are performed to find the desired number of entities, then query completeness is improved, but query latency increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and caching query results for common geospatial queries. When a similar query is received, the cached result is returned immediately without executing the full iterative search, thus reducing query latency while maintaining query completeness.
Solution Approach 2:
The system creates copies of query results by caching previously computed geospatial query results. These cached copies are then reused for identical or similar queries, eliminating the need to re-execute the iterative search and significantly reducing query latency.
2Reliability
If search regions are expanded to ensure sufficient entities are returned, then query reliability is improved, but computational cost increases
Solution Approach 1:
The system performs preliminary computation by pre-determining optimal search regions and caching the results. When a new query arrives, the system checks if a cached result exists for the given query parameters, and if so, returns the cached result without performing the full computational search, thus reducing computational cost while maintaining reliability.
Solution Approach 2:
The system stores copies of previously computed search results in a cache. When a query with similar parameters is received, the system retrieves and returns the cached copy instead of performing the full computational search again, significantly reducing computational cost while ensuring the entity threshold is satisfied.
3Productivity
If query results are cached for geospatial cells, then query throughput is improved, but cache management complexity increases
Solution Approach 1:
The system segments the geospatial data into discrete cells or grid regions. Each cell can be independently cached and managed. This segmentation simplifies cache management by allowing the system to handle only the relevant cells for each query rather than managing the entire geospatial database, thus improving query throughput while controlling complexity.
Solution Approach 2:
The system applies local quality by caching query results at the cell level rather than globally. Each cell's cache is independently managed and only accessed when needed, allowing the system to optimize for local query patterns. This approach improves query throughput for frequently accessed cells while keeping overall cache management complexity manageable through localized operations.
Data Source
AI summary
Techniques are disclosed relating to improving the efficiency of geolocation queries via geospatial cells and query parameter caching. In various embodiments, a system receives a request from a user device to access geolocation information of entities and determine a first geographic location of the device. The system identifies that the first location is within a geospatial cell stored in a database cache and determines whether query parameters corresponding to the cell are stored in the cache. The system may retrieve the cached query parameters corresponding to the geospatial cell and execute a geolocation query on a database storing location information for different entities based on the cached query parameters. Results of the geolocation query may be different from query results of one or more other queries executed, based on the cached query parameters, for other user computing devices located within the given geospatial cell based on the cached query parameters.