issue_comments: 344949160

Looks like it has been resolved! Tested with the latest pre-release v0.10.0rc2 on the dataset linked by najascutellatus above. https://marine.rutgers.edu/~michaesm/netcdf/data/

da.set_options(get=da.async.get_sync) %prun -l 10 ds = xr.open_mfdataset('./*.nc')

xarray==0.10.0rc2-1-g8267fdb dask==0.15.4 ``` 194381 function calls (188429 primitive calls) in 0.869 seconds

Ordered by: internal time List reduced from 469 to 10 due to restriction <10>

ncalls tottime percall cumtime percall filename:lineno(function) 50 0.393 0.008 0.393 0.008 {numpy.core.multiarray.arange} 50 0.164 0.003 0.557 0.011 indexing.py:266(index_indexer_1d) 5 0.083 0.017 0.085 0.017 netCDF4.py:185(open_netcdf4_group) 190 0.024 0.000 0.066 0.000 netCDF4.py:256(open_store_variable) 190 0.022 0.000 0.022 0.000 netCDF4_.py:29(init) 50 0.018 0.000 0.021 0.000 {operator.getitem} 5145/3605 0.012 0.000 0.019 0.000 indexing.py:493(shape) 2317/1291 0.009 0.000 0.094 0.000 _abcoll.py:548(update) 26137 0.006 0.000 0.013 0.000 {isinstance} 720 0.005 0.000 0.006 0.000 {method 'getncattr' of 'netCDF4._netCDF4.Variable' objects}

xarray==0.9.1 dask==0.13.0

     241253 function calls (229881 primitive calls) in 98.123 seconds

Ordered by: internal time List reduced from 659 to 10 due to restriction <10>

ncalls tottime percall cumtime percall filename:lineno(function) 30 87.527 2.918 87.527 2.918 {pandas._libs.tslib.array_to_timedelta64} 65 7.055 0.109 7.059 0.109 {operator.getitem} 80 0.799 0.010 0.799 0.010 {numpy.core.multiarray.arange} 7895/4420 0.502 0.000 0.524 0.000 utils.py:412(shape) 68 0.442 0.007 0.442 0.007 {pandas._libs.algos.ensure_object} 80 0.350 0.004 1.150 0.014 indexing.py:318(_index_indexer_1d) 60/30 0.296 0.005 88.407 2.947 timedeltas.py:158(_convert_listlike) 30 0.284 0.009 0.298 0.010 algorithms.py:719(checked_add_with_arr) 123 0.140 0.001 0.140 0.001 {method 'astype' of 'numpy.ndarray' objects} 1049/719 0.096 0.000 96.513 0.134 {numpy.core.multiarray.array} ```