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/5604#issuecomment-884197067,https://api.github.com/repos/pydata/xarray/issues/5604,884197067,IC_kwDOAMm_X840s8bL,25606497,2021-07-21T13:38:22Z,2021-07-21T14:33:53Z,NONE,"Hi there, I have a very similar problem and before I open another issue I rather share my example here:

**Minimal Complete Verifiable Example**:

This little computation uses >500 MB of memory even if the file reveals only a size of 154MB:
```python
with xr.open_dataset(climdata+'tavg_subset.nc', chunks={""latitude"": 300, ""longitude"": 300}) as ds:
    print(ds)

    <xarray.Dataset>
    Dimensions:    (latitude: 168, longitude: 664, time: 731)
    Coordinates:
      * time       (time) datetime64[ns] 1971-01-01 1971-01-02 ... 1972-12-31
      * longitude  (longitude) float64 20.27 20.3 20.33 20.36 ... 40.92 40.95 40.98
      * latitude   (latitude) float64 40.23 40.2 40.17 40.14 ... 35.08 35.05 35.02
    Data variables:
        tavg       (time, latitude, longitude) float32 dask.array<chunksize=(731, 168, 300), meta=np.ndarray>
    

    annualMean = ds.tavg.resample(time=""1Y"").mean('time', keep_attrs=True)
    annualMean.to_netcdf(""outputMean.nc"", format=""NETCDF4_CLASSIC"", engine=""netcdf4"")
``` 

My problem is that the original files are each >120GB in size and I run into out-of-memory error on our HPC (asking for 10 CPUs with 16GB each).

I thought xarray processes everything in chunks for not overusing the memory - but something seems really wrong here!?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,944996552