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/4113#issuecomment-636619598,https://api.github.com/repos/pydata/xarray/issues/4113,636619598,MDEyOklzc3VlQ29tbWVudDYzNjYxOTU5OA==,6815844,2020-06-01T05:24:35Z,2020-06-01T05:24:35Z,MEMBER,"> Reading with chunks load the memory more than reading without chunks, but not loading an amount of memory equals to the size of the array (300MB for a 800MB array in the example below). And by the way, also loading up the memory a bit more when stacking.

I think it depends on the chunk size.
If I use the chunks `chunks=dict(x=128, y=128)`, the memory usage is
```
RAM: 118.14 MB
 da: 800.0 MB
RAM: 119.14 MB
RAM: 125.59 MB
RAM: 943.79 MB
``` 

> When stacking a chunked array, only chunks alongside the first stacking dimension are conserved, and chunks along the second stacking dimension seem to be merged.

I am not sure where `512` comes from in your example (maybe dask does something). If I work with `chunks=dict(x=128, y=128)`, the chunksize after the stacking was `(100, 16384)`, which is reasonable (z=100, px=(128, 128)).

> A workaround could have been to save the data already stacked, but ""MultiIndex cannot yet be serialized to netCDF"".

You can do `reset_index` before saving it into the netCDF, but it requires another computation when creating the MultiIndex after loading.

","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,627735640
https://github.com/pydata/xarray/issues/4113#issuecomment-636418772,https://api.github.com/repos/pydata/xarray/issues/4113,636418772,MDEyOklzc3VlQ29tbWVudDYzNjQxODc3Mg==,6815844,2020-05-31T04:21:29Z,2020-05-31T04:21:29Z,MEMBER,"Thank you for raising an issue.
I confirmed this problem is reproduced.

Since our Lazyarray does not support the reshaping, it loads the data automatically.
This automatic loading happens in many other operations.

For example, if you multiply your array by a scalar,
``` python
mda = da *2
```
It also loads the data into memory.
Maybe we should improve the documentation.

FYI, using dask arrays may solve this problem.
To open the file with dask, you could add `chunks` keywords,
```python
da = xr.open_dataarray(""da.nc"", chunks={'x': 16, 'y': 16}) 
```
Then, the reshape will be a lazy operation too.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,627735640