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/5760#issuecomment-912082889,https://api.github.com/repos/pydata/xarray/issues/5760,912082889,IC_kwDOAMm_X842XUfJ,25606497,2021-09-02T21:49:57Z,2021-09-02T21:49:57Z,NONE,"Thank you @TomNicholas - strangely I can't reproduce it anymore on my local machine - it all happened on our slurm. The result is correct according to the input file index. 
In my case I calculated annual and seasonal climate variables on the same input files, but the matrix index i,j were different. One with upper left corner (0,0) and the other one with (0,1167) - as shown in ncview.
Nevertheless here is what I did - you can test it with https://www.unidata.ucar.edu/software/netcdf/examples/sresa1b_ncar_ccsm3-example.nc:

```python

import numpy as np
import xarray as xr

chunks=4

lonrange=256
latrange=128

##creating the chunks - our slurm can't handle dask_jobqueue and dask chunking wasnt possible as well
x=[x.tolist() for x in np.array_split(range(lonrange), chunks)]
xextend = [[sublist[0],sublist[-1]] for sublist in x]

y=[y.tolist() for y in np.array_split(range(latrange), chunks)]
yextend = [[sublist[0],sublist[-1]] for sublist in y]

#concatenating the chunks
allChunks = [[x,y] for x in xextend for y in yextend]

for k in range(0,chunks*chunks):

	inter = str(k)

	tas = xr.open_dataset('~/pathToFile/sresa1b_ncar_ccsm3-example.nc').isel(longitude=slice(min(allChunks[k][0]), max(allChunks[k][0])), latitude=slice(min(allChunks[k][1]), max(allChunks[k][1])))
        ##instead of my climate calculations
	tas.rename({'tas':'test1'}).to_netcdf('~/pathToFile/climateCalculation1_'+inter+'.nc')
	tas.rename({'tas':'test2'}).to_netcdf('~/pathToFile/climateCalculation2_'+inter+'.nc')

	#combining the single data arrays per chunk
        ##combine using nested
	with xr.open_mfdataset('~/pathToFile/climateCalculation*'+inter+'.nc', chunks=-1, parallel=True, engine='h5netcdf', combine='nested') as ds:
		ds.to_netcdf('~/pathToFile/nestedClimateAnnualCalculations_'+inter+'.nc')
        #combine using default coords
        with xr.open_mfdataset('~/pathToFile/climateCalculation*'+inter+'.nc', chunks=-1, parallel=True, engine='h5netcdf') as ds:
            ds.to_netcdf('~/pathToFile/climateAnnualCalculations_'+inter+'.nc')

##combining all chunks to one final file
##nested input
with xr.open_mfdataset('~/pathToFile/climateCalculations/nestedClimateAnnualCalculations_*', chunks=-1, parallel=True, engine='h5netcdf') as ds:    
	ds.to_netcdf('~/pathToFile/climateAnnualCalculationsCombinedNested.nc')

with xr.open_mfdataset('~/pathToFile/climateCalculations/climateAnnualCalculations_*', chunks=-1, parallel=True, engine='h5netcdf') as ds:    
	ds.to_netcdf('~/pathToFile/climateAnnualCalculationsCombined.nc')
        
```

","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,986436135
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