issues: 429511994

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id	node_id	number	title	user	state	locked	assignee	milestone	comments	created_at	updated_at	closed_at	author_association	active_lock_reason	draft	pull_request	body	reactions	performed_via_github_app	state_reason	repo	type
429511994	MDU6SXNzdWU0Mjk1MTE5OTQ=	2867	Very slow coordinate assignment with dask array	14314623	closed	0			7	2019-04-04T22:36:57Z	2019-12-19T17:28:10Z	2019-12-17T16:21:23Z	CONTRIBUTOR				I am trying to reconstruct vertical cell depth from a z-star ocean model. This involves a few operations involving both dimensions and coordinates of a dataset like this: <xarray.Dataset> Dimensions: (nv: 2, run: 2, st_edges_ocean: 51, st_ocean: 50, st_ocean_sub02: 10, sw_edges_ocean: 51, sw_ocean: 50, time: 240, xt_ocean: 360, xu_ocean: 360, yt_ocean: 200, yu_ocean: 200) Coordinates: area_e (yt_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> area_n (yu_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> area_t (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> area_u (yu_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> dxt (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> dxtn (yu_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> dxu (yu_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> dyt (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> dyte (yt_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> dyu (yu_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> geolat_c (yu_ocean, xu_ocean) float32 dask.array<shape=(200, 360), chunksize=(200, 360)> geolat_e (yt_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> geolat_n (yu_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> geolat_t (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> geolat_u (yu_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> geolon_c (yu_ocean, xu_ocean) float32 dask.array<shape=(200, 360), chunksize=(200, 360)> geolon_e (yt_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> geolon_n (yu_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> geolon_t (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> geolon_u (yu_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> ht (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> kmt (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> * nv (nv) float64 1.0 2.0 * run (run) object 'control' 'forced' * st_edges_ocean (st_edges_ocean) float64 0.0 ... 5.5e+03 * st_ocean (st_ocean) float64 5.034 ... 5.395e+03 * st_ocean_sub02 (st_ocean_sub02) float64 5.034 ... 98.62 * sw_edges_ocean (sw_edges_ocean) float64 5.034 ... 5.5e+03 * sw_ocean (sw_ocean) float64 10.07 ... 5.5e+03 * time (time) object 2181-01-16 12:00:00 ... 2200-12-16 12:00:00 tmask (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> tmask_region (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> umask (yu_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> umask_region (yu_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> wet_t (yt_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 360)> * xt_ocean (xt_ocean) float64 -279.5 ... 79.5 * xu_ocean (xu_ocean) float64 -279.0 ... 80.0 * yt_ocean (yt_ocean) float64 -81.5 -80.5 ... 89.5 * yu_ocean (yu_ocean) float64 -81.0 -80.0 ... 90.0 dst (st_ocean, yt_ocean, xt_ocean) float64 dask.array<shape=(50, 200, 360), chunksize=(50, 200, 360)> dswt (sw_ocean, yt_ocean, xt_ocean) float64 dask.array<shape=(50, 200, 360), chunksize=(50, 200, 360)> dxte (yt_ocean, xu_ocean) float64 dask.array<shape=(200, 360), chunksize=(200, 359)> dytn (yu_ocean, xt_ocean) float64 dask.array<shape=(200, 360), chunksize=(199, 360)> The problematic step is when I assign the calculated dask.arrays to the original dataset. This happens in a function like this. `python def add_vertical_spacing(ds): grid = Grid(ds) ds.coords['dst'] = calculate_ds(ds, dim='st') ds.coords['dswt'] = calculate_ds(ds, dim='sw') ds.coords['dzt'] = calculate_dz(ds['eta_t'], ds['ht'], ds['dst']) ds.coords['dzu'] = grid.min(grid.min(ds['dzt'], 'X'),'Y') return ds` This takes very long compared to a version where I assign the values as data variables: `python def add_vertical_spacing(ds): grid = Grid(ds) ds.coords['dst'] = calculate_ds(ds, dim='st') ds.coords['dswt'] = calculate_ds(ds, dim='sw') ds['dzt'] = calculate_dz(ds['eta_t'], ds['ht'], ds['dst']) ds['dzu'] = grid.min(grid.min(ds['dzt'], 'X'),'Y') return ds` I am not able to reproduce this problem in a smaller example yet and realize that my example is quite complex (e.g. has functions that are not shown). But I suspect that something triggers the computation of the array, when assigning a coordinate. I have profiled my more complex code involving this function and it seems like there is a substantial increase in calls to `{method 'acquire' of '_thread.lock' objects}`. Profile output of the first version (assigning coordinates) 27662983 function calls (26798524 primitive calls) in 71.940 seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 268632 46.914 0.000 46.914 0.000 {method 'acquire' of '_thread.lock' objects} 438 4.296 0.010 4.296 0.010 {method 'read' of '_io.BufferedReader' objects} 76883 1.909 0.000 1.939 0.000 local.py:240(release_data) 144 1.489 0.010 4.519 0.031 rechunk.py:514(_compute_rechunk) ... For the second version (assigning data variables) 12928834 function calls (12489174 primitive calls) in 16.554 seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 438 3.841 0.009 3.841 0.009 {method 'read' of '_io.BufferedReader' objects} 9492 3.675 0.000 3.675 0.000 {method 'acquire' of '_thread.lock' objects} 144 1.673 0.012 4.712 0.033 rechunk.py:514(_compute_rechunk) ... Does anyone have a feel for why this could happen or how I could refine my testing to get to the bottom of this? Output of `xr.show_versions()` INSTALLED VERSIONS ------------------ commit: None python: 3.6.7 \| packaged by conda-forge \| (default, Feb 28 2019, 09:07:38) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 2.6.32-696.30.1.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US LOCALE: en_US.ISO8859-1 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.12.0 pandas: 0.24.2 numpy: 1.16.2 scipy: 1.2.1 netCDF4: 1.5.0.1 pydap: None h5netcdf: None h5py: None Nio: None zarr: 2.3.1 cftime: 1.0.3.4 nc_time_axis: 1.2.0 PseudonetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 1.1.5 distributed: 1.26.1 matplotlib: 3.0.3 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 40.8.0 pip: 19.0.3 conda: None pytest: 4.4.0 IPython: 7.1.1 sphinx: None	{ "url": "https://api.github.com/repos/pydata/xarray/issues/2867/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }		completed	13221727	issue

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