Geospatial processing with Clickhouse, take 2

The authors of the article discuss their design decisions and iterations to improve the performance of a GIS system using Clickhouse as the database. They explain how they used the quadtree keys or quadkey system to divide the map into cache-friendly tiles, which reduced the number of read rows from 387M to just 2M. To further optimize this process, they employed sampling, which allowed them to reduce the reads from disk and improve performance. Additionally, they implemented a sampling-based approach for aggregating data, which enabled them to calculate histograms and other widgets more efficiently. By making these optimizations, they were able to significantly improve the system's performance, reducing response times by up to 80% in some cases.

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