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/6033#issuecomment-1020190813,https://api.github.com/repos/pydata/xarray/issues/6033,1020190813,IC_kwDOAMm_X848zuBd,6042212,2022-01-24T15:00:53Z,2022-01-24T15:00:53Z,CONTRIBUTOR,"It would be interesting to turn on s3fs logging to see the access pattern, if you are interested. 
```python
fsspec.utils.setup_logging(logger_name=""s3fs"")
```
Particularly, I am interested in whether xarray is loading chunk-by chunk serially versus concurrently. It would be good to know your chunksize versus total array size.

The dask version is interesting:
```
xr.open_zarr(lookup(f""{path_forecast}/surface""), chunks={}) # uses dask
```
where the dask partition size will be the same as the underlying chunk size. If you find a lot of latency (small chunks), you can sometimes get an order of magnitude download performance increase by specifying the chunksize along some dimension(s) to be a multiple of the on-disk size. I wouldn't normally recommend Dask just for loading the data into memory, but feel free to experiment.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1064837571