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@kobeyk can you try setting ref: https://developmentseed.org/titiler/advanced/performance_tuning/ |
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Excuse me, I have a problem,I converted a cog tif with rio-cogeo and put it locally, then read it with rio-tiler, and ran it with py code alone, and found that the reading process took a long time, >600ms, in fact, normal tif is very fast when read with gdal, I would like to know why this piece is slow. Here's how long it takes to run alone:
code:
In addition, I also used java to write a server to call python code, and wrote a front-end test. I found that cog as a image tile service, its performance is really a little too slow, I want to know, as cog data, whether it supports multi-user concurrent access? Is it possible to improve access efficiency by setting some parameters for tuning? In other words, in order to provide high-quality image tile services, it can only be carried out in accordance with the traditional slicing method. Below is a screenshot of the response time of the front-end call:
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