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/6904#issuecomment-1210383450,https://api.github.com/repos/pydata/xarray/issues/6904,1210383450,IC_kwDOAMm_X85IJPxa,12760310,2022-08-10T09:07:00Z,2022-08-10T09:07:00Z,NONE,"This is a minimal working example that I could come up with. You can try to open any netcdf that you have.
I tested on a small one and it didn't reproduce the error, so it is definitely only happening with large datasets when the arrays are not loaded into memory. Unfortunately, as you need a large file, I cannot really attach it here.
```python
import xarray as xr
from tqdm.contrib.concurrent import process_map
import pprint
def main():
global ds
ds = xr.open_dataset('input.nc')
it = range(0, 5)
results = []
for i in it:
results.append(compute(i))
print(""------------Serial results-----------------"")
pprint.pprint(results)
results = process_map(compute, it, max_workers=6, chunksize=1, disable=True)
print(""------------Parallel results-----------------"")
pprint.pprint(results)
def compute(station):
ds_point = ds.isel(lat=0, lon=0)
return station, ds_point.t_2m_max.mean().item(), ds_point.t_2m_min.mean().item(), ds_point.lon.min().item(), ds_point.lat.min().item()
if __name__ == ""__main__"":
main()
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1333650265
https://github.com/pydata/xarray/issues/6904#issuecomment-1210349031,https://api.github.com/repos/pydata/xarray/issues/6904,1210349031,IC_kwDOAMm_X85IJHXn,12760310,2022-08-10T08:38:31Z,2022-08-10T08:38:31Z,NONE,"> Re nearest, does it replicate with exact lookups?
Ok, it seems to fail also with exact lookups o.O
This is extremely weird
I'm using
```python
def compute():
ds_point = ds.isel(lat=0, lon=0)
return ds_point.t_2m_med.mean().item(), ds_point.t_2m_min.mean().item(), ds_point.lon.min().item(),
ds_point.lat.min().item()
```
Result for the serial version
```python
[(
10.469047546386719,
6.5044121742248535,
6.0,
48.0),
(
10.469047546386719,
6.5044121742248535,
6.0,
48.0),
(
10.469047546386719,
6.5044121742248535,
6.0,
48.0),
(
10.469047546386719,
6.5044121742248535,
6.0,
48.0),
(
10.469047546386719,
6.5044121742248535,
6.0,
48.0)]
```
As you would expect all values are the same.
And for the parallel version with EXACTLY the same code
```python
[(
7.968084812164307,
6.948009967803955,
6.0,
48.0),
(
7.825599193572998,
6.995675563812256,
6.0,
48.0),
(
8.894186019897461,
6.849221706390381,
6.0,
48.0),
(
8.901763916015625,
6.69615364074707,
6.0,
48.0),
(
9.164983749389648,
6.484694480895996,
6.0,
48.0)]
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1333650265
https://github.com/pydata/xarray/issues/6904#issuecomment-1210341456,https://api.github.com/repos/pydata/xarray/issues/6904,1210341456,IC_kwDOAMm_X85IJFhQ,12760310,2022-08-10T08:32:13Z,2022-08-10T08:32:13Z,NONE,"> ```python
> , lock=Lock()
> ```
That causes an error
```python
Error 11: Resource temporarily unavailable
```
Here is the complete tracebabk
```python
concurrent.futures.process._RemoteTraceback:
""""""
Traceback (most recent call last):
File ""/var/models/miniconda3/lib/python3.8/concurrent/futures/process.py"", line 239, in _process_worker
r = call_item.fn(*call_item.args, **call_item.kwargs)
File ""/var/models/miniconda3/lib/python3.8/concurrent/futures/process.py"", line 198, in _process_chunk
return [fn(*args) for args in chunk]
File ""/var/models/miniconda3/lib/python3.8/concurrent/futures/process.py"", line 198, in
return [fn(*args) for args in chunk]
File ""test_sel_bug.py"", line 58, in compute_clima
return station, ds_point.t_2m_med.mean().item(), ds_point.t_2m_min.mean().item(), ds_point.lon.min().item(), ds_point.lat.min().item()
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/common.py"", line 58, in wrapped_func
return self.reduce(func, dim, axis, skipna=skipna, **kwargs)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/dataarray.py"", line 2696, in reduce
var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/variable.py"", line 1806, in reduce
data = func(self.data, **kwargs)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/variable.py"", line 339, in data
return self.values
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/variable.py"", line 512, in values
return _as_array_or_item(self._data)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/variable.py"", line 252, in _as_array_or_item
data = np.asarray(data)
File ""/var/models/miniconda3/lib/python3.8/site-packages/numpy/core/_asarray.py"", line 102, in asarray
return array(a, dtype, copy=False, order=order)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/indexing.py"", line 552, in __array__
self._ensure_cached()
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/indexing.py"", line 549, in _ensure_cached
self.array = NumpyIndexingAdapter(np.asarray(self.array))
File ""/var/models/miniconda3/lib/python3.8/site-packages/numpy/core/_asarray.py"", line 102, in asarray
return array(a, dtype, copy=False, order=order)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/indexing.py"", line 522, in __array__
return np.asarray(self.array, dtype=dtype)
File ""/var/models/miniconda3/lib/python3.8/site-packages/numpy/core/_asarray.py"", line 102, in asarray
return array(a, dtype, copy=False, order=order)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/indexing.py"", line 423, in __array__
return np.asarray(array[self.key], dtype=None)
File ""/var/models/miniconda3/lib/python3.8/site-packages/numpy/core/_asarray.py"", line 102, in asarray
return array(a, dtype, copy=False, order=order)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/coding/variables.py"", line 70, in __array__
return self.func(self.array)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/coding/variables.py"", line 137, in _apply_mask
data = np.asarray(data, dtype=dtype)
File ""/var/models/miniconda3/lib/python3.8/site-packages/numpy/core/_asarray.py"", line 102, in asarray
return array(a, dtype, copy=False, order=order)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/indexing.py"", line 423, in __array__
return np.asarray(array[self.key], dtype=None)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/backends/netCDF4_.py"", line 93, in __getitem__
return indexing.explicit_indexing_adapter(
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/core/indexing.py"", line 712, in explicit_indexing_adapter
result = raw_indexing_method(raw_key.tuple)
File ""/var/models/miniconda3/lib/python3.8/site-packages/xarray/backends/netCDF4_.py"", line 106, in _getitem
array = getitem(original_array, key)
File ""src/netCDF4/_netCDF4.pyx"", line 4420, in netCDF4._netCDF4.Variable.__getitem__
File ""src/netCDF4/_netCDF4.pyx"", line 5363, in netCDF4._netCDF4.Variable._get
File ""src/netCDF4/_netCDF4.pyx"", line 1950, in netCDF4._netCDF4._ensure_nc_success
RuntimeError: Resource temporarily unavailable
""""""
```
I think we may be heading the right direction","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1333650265
https://github.com/pydata/xarray/issues/6904#issuecomment-1210285626,https://api.github.com/repos/pydata/xarray/issues/6904,1210285626,IC_kwDOAMm_X85II346,12760310,2022-08-10T07:41:20Z,2022-08-10T07:41:20Z,NONE,"> > Will that work in the same way if I still use `process_map`, which uses `concurrent.futures` under the hood?
>
> Yes it should, as long as you're using multi-processing under the covers.
>
> If you do multi-threading, then you would want to use `threading.Lock()`. But I believe we already apply a thread lock by default.
mmm ok I'll try and let you know.
BTW is there any advantage or difference in terms of cpu and memory consumption in opening the file only one or let it open by every process? I'm asking because I thought opening in every process was just plain stupid but it seems to perform exactly the same, so maybe I'm just creating a problem where there is none","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1333650265
https://github.com/pydata/xarray/issues/6904#issuecomment-1210238864,https://api.github.com/repos/pydata/xarray/issues/6904,1210238864,IC_kwDOAMm_X85IIseQ,12760310,2022-08-10T06:51:18Z,2022-08-10T06:51:18Z,NONE,"> Can you try explicitly passing in a multiprocessing lock into the `open_dataset()` constructor? Something like:
>
> ```python
> from multiprocessing import Lock
> ds = xarray.open_dataset(file, lock=Lock())
> ```
>
> (We automatically select appropriate locks if using Dask, but I'm not sure how we would do that more generally...)
ok that's a good shot.
Will that work in the same way if I still use `process_map`, which uses `concurrent.futures` under the hood?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1333650265
https://github.com/pydata/xarray/issues/6904#issuecomment-1210220238,https://api.github.com/repos/pydata/xarray/issues/6904,1210220238,IC_kwDOAMm_X85IIn7O,12760310,2022-08-10T06:30:06Z,2022-08-10T06:30:06Z,NONE,"> Re nearest, does it replicate with exact lookups?
I haven't tried yet because it doesn't really match my use case.
One idea that I had was to provide the list of points before starting the loop, creating an iterator with the slices from the xarray and then pass this to the loop.
But I would end up using more data than necessary because I don't process all cases.
another thing that I've noticed is that if the list of iterators is smaller than the chunksize everything's good, probably because it reverts to the serial case as only 1 worker is processing ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1333650265
https://github.com/pydata/xarray/issues/6904#issuecomment-1210174583,https://api.github.com/repos/pydata/xarray/issues/6904,1210174583,IC_kwDOAMm_X85IIcx3,12760310,2022-08-10T05:23:13Z,2022-08-10T05:24:24Z,NONE,"> That sounds quite unfriendly!
>
> A couple of questions to reduce the size of the example, without providing any answers yet unfortunately:
>
> * Is `process_map` from `tqdm`? Do you get the same behavior from the standard `multiprocessing`?
Yep, and yep (believe me, I've tried anything in desperation 😄)
> * What if we remove `method=nearest`?
Which method should I use then? I need the closest point
> * Is the file a single netCDF file?
Yep
I can try to make a minimal example, however, in order to reproduce the issue, I think it's necessary to open a large dataset.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1333650265