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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
850552195 https://github.com/pydata/xarray/issues/5376#issuecomment-850552195 https://api.github.com/repos/pydata/xarray/issues/5376 MDEyOklzc3VlQ29tbWVudDg1MDU1MjE5NQ== d-v-b 3805136 2021-05-28T17:04:27Z 2021-05-28T17:04:27Z NONE

Are there cases in practice where on-demand downsampling computation would be preferred over pre-computing and storing all pyramid levels for the full dataset? I admit it's probably a very naive question since most workflows on the client side would likely start by loading the top level (lowest resolution) dataset at full extent, which would require pre-computing the whole thing?

I'm not sure when dynamic downsampling would be preferred over loading previously downsampled images from disk. In my usage, the application consuming the multiresolution images is an interactive data visualization tool and the goal is to minimize latency / maximize responsiveness of the visualization, and this would be difficult if the multiresolution images were generated dynamically from the full image -- under a dynamic scheme the lowest resolution image, i.e. the one that should be fastest to load, would instead require the most I/O and compute to generate....

Are there cases where it makes sense to pre-compute all the the pyramid levels in-memory (could be, e.g., chunked dask arrays persisted on a distributed cluster) without the need to store them?

Although I do not do this today, I can think of a lot of uses for this functionality -- an data processing pipeline could expose intermediate data over http via xpublish, but this would require a good caching layer to prevent re-computing the same region of the data repeatedly. 

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   Multi-scale datasets and custom indexes 902009258

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