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
928533488,MDU6SXNzdWU5Mjg1MzM0ODg=,5521,Memory inefficiency when using sortby,58827984,open,0,,,0,2021-06-23T18:31:18Z,2021-06-23T18:31:18Z,,NONE,,,,"**What happened**:

High memory usage seen when sorting after loading from disk.  Loading from disk took about 150MB, where after the sort I saw a usage of about 1.5 GB.  I believe this is due to the reindexing that requires the data to be loaded into memory during sort.  So I guess I am not surprised, but I wanted to submit this as a possible issue just to make sure that my reasoning is good.  For my use case, I will have to abandon sortby and ensure data is sorted prior to writing to disk.

I am afraid my MVCE relies on data on disk that I have.  If this is an actual issue that needs more looking into, I can provide an example that anyone can run.  Otherwise I can close.

**Minimal Complete Verifiable Example**:

```python
import xarray as xr
from psutil import virtual_memory
startmem = virtual_memory().used
data = xr.open_zarr(r""D:\falkor\FK181005_processed\em302_105_10_06_2018\attitude.zarr"", synchronizer=None, mask_and_scale=False, decode_coords=False, decode_times=False, decode_cf=False, concat_characters=False)
afterload_mem = virtual_memory().used - startmem
ans = data.sortby('time')
aftersort_mem = virtual_memory().used - startmem
print('Without sort: {}'.format(afterload_mem))
print('With sort: {}'.format(aftersort_mem))

Out: Without sort: 149241856
Out: With sort: 1657593856
```

**Environment**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

INSTALLED VERSIONS
------------------
commit: None
python: 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 15:50:08) [MSC v.1916 64 bit (AMD64)]
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
LOCALE: English_United States.1252
libhdf5: 1.10.6
libnetcdf: 4.7.4
xarray: 0.17.0
pandas: 1.2.3
numpy: 1.20.3
scipy: 1.6.0
netCDF4: 1.5.6
pydap: None
h5netcdf: None
h5py: 2.10.0
Nio: None
zarr: 2.6.1
cftime: 1.4.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.1
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2021.03.0
distributed: 2021.03.0
matplotlib: 3.3.4
cartopy: 0.18.0
seaborn: 0.11.1
numbagg: None
pint: None
setuptools: 49.6.0.post20210108
pip: 21.0.1
conda: None
pytest: 6.2.2
IPython: 7.21.0
sphinx: 3.5.2

</details>
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