html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/7522#issuecomment-1450841385,https://api.github.com/repos/pydata/xarray/issues/7522,1450841385,IC_kwDOAMm_X85WehUp,39069044,2023-03-01T21:01:48Z,2023-03-01T21:01:48Z,CONTRIBUTOR,"Yeah that seems to be it. Dask's write neatly packs all the needed metadata at the beginning of the file, since we can scale this up to a many GB file with dozens of variables and still read in ~100ms. While xarray is doing a less well organized write of the metadata and we have to go seeking in the middle of the byte range. `cache_type=""first""` does provide some improvement but still not as good as on the dask-written file.
FWIW, I inspected the actual bytes of the dask and xarray written files and they are identical for a single variable, but diverge when multiple variables are being written. So, the important differences are probably associated with this step:
> It does set up the whole set of variables as a initialisation stage before writing any data - I don't know if xarray does this.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1581046647
https://github.com/pydata/xarray/issues/7522#issuecomment-1449302032,https://api.github.com/repos/pydata/xarray/issues/7522,1449302032,IC_kwDOAMm_X85WYpgQ,39069044,2023-03-01T04:04:25Z,2023-03-01T04:04:25Z,CONTRIBUTOR,"The slow file:
And the fast file:
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1581046647
https://github.com/pydata/xarray/issues/7522#issuecomment-1428872842,https://api.github.com/repos/pydata/xarray/issues/7522,1428872842,IC_kwDOAMm_X85VKt6K,39069044,2023-02-13T23:49:31Z,2023-02-13T23:49:31Z,CONTRIBUTOR,"I did try many loops and different order of operations to make sure this isn't a caching or auth issue. You can see the std dev of the `timeit` calls above is pretty consistent.
For my actual use case, the difference is very apparent, with `open_dataset` taking about 9 seconds on the numpy-saved file and <1 second on the dask-saved one. I can also clearly see when monitoring network traffic that the slow version has to read in hundreds of MB of data to open the dataset, while the fast one only reads the tiny headers.
I also inspected the actual header bytes of these two files and see they are indeed different.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1581046647