id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
1284094480,I_kwDOAMm_X85MiboQ,6722,Avoid loading any data for reprs,2448579,closed,0,,,5,2022-06-24T19:04:30Z,2022-10-28T16:23:20Z,2022-10-28T16:23:20Z,MEMBER,,,,"### 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
```Python
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

- [x] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- [x] Complete example — the example is self-contained, including all data and the text of any traceback.
- [x] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result.
- [x] 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

<details>
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


</details>
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