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
984555353,MDU6SXNzdWU5ODQ1NTUzNTM=,5754,Variable.stack constructs extremely large chunks,2448579,closed,0,,,6,2021-09-01T03:08:02Z,2023-03-22T14:51:44Z,2021-12-14T17:31:45Z,MEMBER,,,,"**Minimal Complete Verifiable Example**:
Here's a small array with too-small chunk sizes just as an example
```python
# Put your MCVE code here
import dask.array
import xarray as xr
var = xr.Variable((""x"", ""y"", ""z""), dask.array.random.random((4, 18483, 1000), chunks=(1, 183, -1)))
```
![]()
Now stack two dimensions, this is a 100x increase in chunk size (in my actual code, 85MB chunks become 8.5GB chunks =) )
```
var.stack(new=(""x"", ""y""))
```
![]()
But calling `reshape` on the dask array preserves the original chunk size
```
var.data.reshape((4*18483, -1))
```
![]()
## Solution
Ah, found it , we transpose then reshape in `Variable_stack_once`.
https://github.com/pydata/xarray/blob/f915515d610b4471888fa44dfb00dbae3fd22349/xarray/core/variable.py#L1521-L1527
Writing those steps with pure dask yields the same 100x increase in chunksize
``` python
var.data.transpose([2, 0, 1]).reshape((-1, 4*18483))
```
![]()
**Anything else we need to know?**:
**Environment**:
Output of xr.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.8.6 | packaged by conda-forge | (default, Jan 25 2021, 23:21:18)
[GCC 9.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-1127.18.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.10.6
libnetcdf: 4.7.4
xarray: 0.19.0
pandas: 1.3.1
numpy: 1.21.1
scipy: 1.5.3
netCDF4: 1.5.6
pydap: installed
h5netcdf: 0.11.0
h5py: 3.3.0
Nio: None
zarr: 2.8.3
cftime: 1.5.0
nc_time_axis: 1.3.1
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: 3.0.4
bottleneck: 1.3.2
dask: 2021.07.2
distributed: 2021.07.2
matplotlib: 3.4.2
cartopy: 0.19.0.post1
seaborn: 0.11.1
numbagg: None
pint: 0.17
setuptools: 49.6.0.post20210108
pip: 21.2.2
conda: 4.10.3
pytest: 6.2.4
IPython: 7.26.0
sphinx: 4.1.2