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  • sbiner · 2 ✖

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  • slow performance with open_mfdataset · 2 ✖

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461561653 https://github.com/pydata/xarray/issues/1385#issuecomment-461561653 https://api.github.com/repos/pydata/xarray/issues/1385 MDEyOklzc3VlQ29tbWVudDQ2MTU2MTY1Mw== sbiner 16655388 2019-02-07T19:22:58Z 2019-02-07T19:22:58Z NONE

I just tried and it did not help ...

``` In [5]: run test_ouverture_fichier_nc_vs_xr.py timing glob: 0.00s timing netcdf4: 3.36s timing xarray: 44.82s timing xarray tune: 14.47s

In [6]: xr.show_versions()

INSTALLED VERSIONS

commit: None python: 2.7.15 |Anaconda, Inc.| (default, Dec 14 2018, 19:04:19) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 3.10.0-514.2.2.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_CA.UTF-8 LOCALE: None.None libhdf5: 1.10.4 libnetcdf: 4.6.1

xarray: 0.11.3 pandas: 0.24.0 numpy: 1.13.3 scipy: 1.2.0 netCDF4: 1.4.2 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.0.3.4 PseudonetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.2.1 cyordereddict: None dask: 1.0.0 distributed: 1.25.2 matplotlib: 2.2.3 cartopy: None seaborn: None setuptools: 40.5.0 pip: 19.0.1 conda: None pytest: None IPython: 5.8.0 sphinx: 1.8.2 ```

 
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   slow performance with open_mfdataset 224553135
461551320 https://github.com/pydata/xarray/issues/1385#issuecomment-461551320 https://api.github.com/repos/pydata/xarray/issues/1385 MDEyOklzc3VlQ29tbWVudDQ2MTU1MTMyMA== sbiner 16655388 2019-02-07T18:52:53Z 2019-02-07T18:52:53Z NONE

I have the same problem. open_mfdatasset is 10X slower than nc.MFDataset. I used the following code to get some timing on opening 456 local netcdf files located in a nc_local directory (of total size of 532MB) ``` clef = 'nc_local/*.nc' t00 = time.time() l_fichiers_nc = sorted(glob.glob(clef)) print ('timing glob: {:6.2f}s'.format(time.time()-t00))

netcdf4

t00 = time.time() ds1 = nc.MFDataset(l_fichiers_nc)

dates1 = ouralib.netcdf.calcule_dates(ds1)

print ('timing netcdf4: {:6.2f}s'.format(time.time()-t00))

xarray

t00 = time.time() ds2 = xr.open_mfdataset(l_fichiers_nc) print ('timing xarray: {:6.2f}s'.format(time.time()-t00))

xarray tune

t00 = time.time() ds3 = xr.open_mfdataset(l_fichiers_nc, decode_cf=False, concat_dim='time') ds3 = xr.decode_cf(ds3) print ('timing xarray tune: {:6.2f}s'.format(time.time()-t00)) ``` The output I get is :

timing glob: 0.00s timing netcdf4: 3.80s timing xarray: 44.60s timing xarray tune: 15.61s

I made tests on a centOS server using python2.7 and 3.6, and on mac OS as well with python3.6. The timing changes but the ratios are similar between netCDF4 and xarray.

Is there any way of making open_mfdataset go faster?

In case it helps, here are output from xr.show_versions and %prun xr.open_mfdataset(l_fichiers_nc). I do not know anything about the output of %prun but I have noticed that the first two lines of the ouput are different wether I'm using python 2.7 or python 3.6. I made those tests on centOS and macOS with anaconda environments.

for python 2.7: ``` 13996351 function calls (13773659 primitive calls) in 42.133 seconds

Ordered by: internal time

ncalls tottime percall cumtime percall filename:lineno(function) 2664 16.290 0.006 16.290 0.006 {time.sleep} 912 6.330 0.007 6.623 0.007 netCDF4_.py:244(_open_netcdf4_group) ```

for python 3.6: ``` 9663408 function calls (9499759 primitive calls) in 31.934 seconds

Ordered by: internal time

ncalls tottime percall cumtime percall filename:lineno(function) 5472 15.140 0.003 15.140 0.003 {method 'acquire' of 'thread.lock' objects} 912 5.661 0.006 5.718 0.006 netCDF4.py:244(_open_netcdf4_group) longer output of %prun with python3.6: 9663408 function calls (9499759 primitive calls) in 31.934 seconds

Ordered by: internal time

ncalls tottime percall cumtime percall filename:lineno(function) 5472 15.140 0.003 15.140 0.003 {method 'acquire' of 'thread.lock' objects} 912 5.661 0.006 5.718 0.006 netCDF4.py:244(open_netcdf4_group) 4104 0.564 0.000 0.757 0.000 {built-in method _operator.getitem} 133152/129960 0.477 0.000 0.660 0.000 indexing.py:496(shape) 1554550/1554153 0.414 0.000 0.711 0.000 {built-in method builtins.isinstance} 912 0.260 0.000 0.260 0.000 {method 'close' of 'netCDF4._netCDF4.Dataset' objects} 6384 0.244 0.000 0.953 0.000 netCDF4.py:361(open_store_variable) 910 0.241 0.000 0.595 0.001 duck_array_ops.py:141(array_equiv) 20990 0.235 0.000 0.343 0.000 {pandas.libs.lib.is_scalar} 37483/36567 0.228 0.000 0.230 0.000 {built-in method builtins.iter} 93986 0.219 0.000 1.607 0.000 variable.py:239(__init__) 93982 0.194 0.000 0.194 0.000 variable.py:706(attrs) 33744 0.189 0.000 0.189 0.000 {method 'getncattr' of 'netCDF4._netCDF4.Variable' objects} 15511 0.175 0.000 0.638 0.000 core.py:1776(normalize_chunks) 5930 0.162 0.000 0.350 0.000 missing.py:183(_isna_ndarraylike) 297391/296926 0.159 0.000 0.380 0.000 {built-in method builtins.getattr} 134230 0.155 0.000 0.269 0.000 abc.py:180(__instancecheck__) 6384 0.142 0.000 0.199 0.000 netCDF4.py:34(init) 93986 0.126 0.000 0.671 0.000 variable.py:414(_parse_dimensions) 156545 0.119 0.000 0.811 0.000 utils.py:450(ndim) 12768 0.119 0.000 0.203 0.000 core.py:747(blockdims_from_blockshape) 6384 0.117 0.000 2.526 0.000 conventions.py:245(decode_cf_variable) 741183/696380 0.116 0.000 0.134 0.000 {built-in method builtins.len} 41957/23717 0.110 0.000 4.395 0.000 {built-in method numpy.core.multiarray.array} 93978 0.110 0.000 0.110 0.000 variable.py:718(encoding) 219940 0.109 0.000 0.109 0.000 _weakrefset.py:70(contains) 99458 0.100 0.000 0.440 0.000 variable.py:137(as_compatible_data) 53882 0.085 0.000 0.095 0.000 core.py:891(shape) 140604 0.084 0.000 0.628 0.000 variable.py:272(shape) 3192 0.084 0.000 0.170 0.000 utils.py:88(_StartCountStride) 10494 0.081 0.000 0.081 0.000 {method 'reduce' of 'numpy.ufunc' objects} 44688 0.077 0.000 0.157 0.000 variables.py:102(unpack_for_decoding) ```

output of xr.show_versions() ``` xr.show_versions()

INSTALLED VERSIONS

commit: None python: 3.6.8.final.0 python-bits: 64 OS: Linux OS-release: 3.10.0-514.2.2.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_CA.UTF-8 LOCALE: en_CA.UTF-8

xarray: 0.11.0 pandas: 0.24.1 numpy: 1.15.4 scipy: None netCDF4: 1.4.2 h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.0.3.4 PseudonetCDF: None rasterio: None iris: None bottleneck: None cyordereddict: None dask: 1.1.1 distributed: 1.25.3 matplotlib: 3.0.2 cartopy: None seaborn: None setuptools: 40.7.3 pip: 19.0.1 conda: None pytest: None IPython: 7.2.0 sphinx: None ```

 
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   slow performance with open_mfdataset 224553135

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