Avoid loading any data for reprs

What happened?

For “small” datasets, we load in to memory when displaying the repr. For cloud backed datasets with large number of “small” variables, this can use a lot of time sequentially loading O(100) variables just for a repr.

https://github.com/pydata/xarray/blob/6c8db5ed005e000b35ad8b6ea9080105e608e976/xarray/core/formatting.py#L548-L549

What did you expect to happen?

Fast reprs!

Minimal Complete Verifiable Example

This dataset has 48 “small” variables

import xarray as xr

dc1 = xr.open_dataset('s3://its-live-data/datacubes/v02/N40E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y4750000.zarr', engine= 'zarr', storage_options = {'anon':True})
dc1._repr_html_()

On 2022.03.0 this repr takes 36.4s If I comment the array.size condition I get 6μs.

MVCE confirmation

• Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• Complete example — the example is self-contained, including all data and the text of any traceback.
• Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS ------------------ commit: None python: 3.10.4 | packaged by conda-forge | (main, Mar 24 2022, 17:43:32) [Clang 12.0.1 ] python-bits: 64 OS: Darwin OS-release: 21.5.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: None libnetcdf: None

xarray: 2022.3.0 pandas: 1.4.2 numpy: 1.22.4 scipy: 1.8.1 netCDF4: None pydap: None h5netcdf: None h5py: None Nio: None zarr: 2.11.3 cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: 1.2.10 cfgrib: None iris: None bottleneck: None dask: 2022.05.2 distributed: None matplotlib: 3.5.2 cartopy: 0.20.2 seaborn: 0.11.2 numbagg: None fsspec: 2022.5.0 cupy: None pint: None sparse: None setuptools: 62.3.2 pip: 22.1.2 conda: None pytest: None IPython: 8.4.0 sphinx: 4.5.0

[dcherian]

I think the best thing to do is to not load anything unless asked to. So delete the array.size < 1e5 condition.

[Illviljan]

Is the print still slow if somewhere just before the load the array was masked to only show a few start and end elements, array[[0, 1, -2, -1]]?