issue_comments

A reproducible example would help but indexing with dask arrays is a bit limited.

On https://github.com/pydata/xarray/pull/5873 it's possible it will raise an error and ask you to compute the indexer. Also see https://github.com/dask/dask/issues/4156

EDIT: your slowdown is probably because it's compuing Sn multiple times. You could speed it up by calling compute once and passing a numpy array to isel

I see, they are not the same - the slow one is still a dask array, the other one is not: Sn (r, theta, phi, sampling) float64 dask.array<chunksize=(14, 52, 2, 10), meta=np.ndarray>, Sn (r, theta, phi, sampling) float64 nan nan nan nan ... nan nan nan Otherwise they are the same, so this might be dask related ...

@dschwoerer are you sure that you are actually calculating the same thing in both cases? What exactly do the values of slc[d] look like? I would test thing on smaller inputs to verify. My guess is that you are inadvertently calculating something different, recalling that Xarray's broadcasting rules differ slightly from NumPy's.

I just changed theisel = ds[k].isel(**slc, missing_dims="ignore") to: slcp = [slc[d] if d in slc else slice(None) for d in ds[k].dims] theisel = ds[k].values[tuple(slcp)] And that changed the runtime of my code from (unknown, still running after 3 hours) to around 10 seconds.

ds[k] is a 3 dimensional array slc[d] are 7-d numpy array of integers

I don't know much about indexing but that PR propagates a "new" indexes property as part of #1603 (work towards enabling more flexible indexing), it doesn't change anything about "indexing". I think the dask docs may be more relevant to what you may be asking about: https://docs.dask.org/en/latest/array-slicing.html

Hi, I'd like to understand how isel works exactly in conjunction with dask arrays. As it seems, #3481 propagates the isel operation onto each dask chunk for lazy evaluation. Is this correct?

Yes, align checks index.equals(other) first, which has a shortcut for the same object.

The real mystery here is why time_filter.indexes['time'] and ds.indexes['time'] are not the same object. I guess this is likely due to lazy initialization of indexes, and should be fixed eventually by the explicit indexes refactor.

I think align tries to optimize that case, so maybe something's also possible there?

Can we short-circuit the special case where the index of the array used for slicing is the same object as the index being sliced, so no alignment is needed?

```python

time_filter.time._variable is ds.time._variable True %timeit xr.align(time_filter, ds.a) 477 ms ± 13.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ``` the time spent on that align call could be zero!

Before #3319: ```

%timeit ds.a.values[time_filter] 158 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit ds.a.isel(time=time_filter.values) 2.57 s ± 3.65 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit ds.a.isel(time=time_filter) 3.12 s ± 37.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) After #3319: %timeit ds.a.values[time_filter] 158 ms ± 2.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit ds.a.isel(time=time_filter.values) 665 ms ± 6.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit ds.a.isel(time=time_filter) 1.15 s ± 1.55 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ``` Good job!

https://github.com/pydata/xarray/pull/3319 gives us about a 2x performance boost. It could likely be much faster, but at least this fixes the regression.

Yes, I'm seeing similar numbers, about 10x slower indexing in a DataArray. This seems to have gotten slower over time. It would be good to track this down and add a benchmark!

On master I'm seeing

``` %timeit ds.a.isel(time=time_filter) 3.65 s ± 29.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit ds.a.isel(time=time_filter.values) 2.99 s ± 15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit ds.a.values[time_filter] 227 ms ± 6.59 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ```

Can someone else reproduce?

In an effort to reduce the issue backlog, I'll close this, but please reopen if you disagree

It would be ten files opened via xr.open_mfdataset() concatenated across a time dimension, each one looking like: ``` netcdf convect_gust_20180301_0000 { dimensions: latitude = 3502 ; longitude = 7002 ; variables: double latitude(latitude) ; latitude:_FillValue = NaN ; latitude:_Storage = "contiguous" ; latitude:_Endianness = "little" ; double longitude(longitude) ; longitude:_FillValue = NaN ; longitude:_Storage = "contiguous" ; longitude:_Endianness = "little" ; float gust(latitude, longitude) ; gust:_FillValue = NaNf ; gust:units = "m/s" ; gust:description = "gust winds" ; gust:_Storage = "chunked" ; gust:_ChunkSizes = 701, 1401 ; gust:_DeflateLevel = 8 ; gust:_Shuffle = "true" ; gust:_Endianness = "little" ;

// global attributes: :start_date = "03/01/2018 00:00" ; :end_date = "03/01/2018 01:00" ; :interval = "half-open" ; :init_date = "02/28/2018 22:00" ; :history = "Created 2018-09-12 15:53:44.468144" ; :description = "Convective Downscaling, format V2.0" ; :_NCProperties = "version=1|netcdflibversion=4.6.1|hdf5libversion=1.10.1" ; :_SuperblockVersion = 0 ; :_IsNetcdf4 = 1 ; :_Format = "netCDF-4" ; ```

@WeatherGod - are you reading data from netCDF files by chance?

If so, can you share the compression/chunk layout for those (ncdump -h -s file.nc)?

No, it does not make a difference. The example above peaks at around 5GB of memory (a bit much, but manageable). And it peaks similarly if we chunk it like you suggested.

@WeatherGod does adding something like da = da.chunk({'time': 1}) reproduce this with your example?

Yeah, it looks like if da is backed by a dask array, and you do a .isel(win=window.compute()) because otherwise isel barfs on dask indexers, it seems, then the memory usage shoots through the roof. Note that in my case, the dask chunks are (1, 3000, 7000). If I do a window.load() prior to window.isel(), then the memory usage is perfectly reasonable.

Just for posterity, though, here is my simplified (working!) example: ``` import numpy as np import xarray as xr

da = xr.DataArray(np.random.randn(10, 3000, 7000), dims=('time', 'latitude', 'longitude')) window = da.rolling(time=2).construct('win') indexes = window.argmax(dim='win') result = window.isel(win=indexes) ```

Huh, strange... I just tried a simplified version of what I was doing (particularly, no dask arrays), and everything worked fine. I'll have to investigate further.

@WeatherGod do you have a reproducible example? I'm happy to have a look

I am looking into a similar performance issue with isel, but it seems that the issue is that it is creating arrays that are much bigger than needed. For my multidimensional case (time/x/y/window), what should end up only taking a few hundred MB is spiking up to 10's of GB of used RAM. Don't know if this might be a possible source of performance issues.

My measurements: ```

%timeit ds.a.isel(time=time_filter) 1 loop, best of 3: 906 ms per loop %timeit ds.a.isel(time=time_filter.values) 1 loop, best of 3: 447 ms per loop %timeit ds.a.values[time_filter] 10 loops, best of 3: 169 ms per loop ```

Given the size of this gap, I suspect this could be improved with some investigation and profiling, but there is certainly an upper-limit on the possible performance gain.

One simple example is that indexing the dataset needs to index both 'a' and 'time', so it's going to be at least twice as slow as only indexing 'a'. So the second indexing expression ds.a.isel(time=time_filter.values) is only 447/(169*2) = 1.32 times slower than the best case scenario.

Another part of the matrix of possibilities. Takes about half the time if you pass time_filter.values (numpy array) rather than the time_filter DataArray: python %timeit ds.a.isel(time=time_filter.values) 1.3 s ± 67.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

I am sorry @rabernat and @maxim-lian , the variable's name time and the simple example with the greater than filter are misleading. In general, it is about using a boolean mask via isel and that it is very slow. In my code, I am not able to use your workaround since my boolean mask is more complex.

@rabernat that's a good solution where it's a slice

When is a time that it needs to align a bool array? If you try and pass an array of unequal length, it doesn't work anyway:

```python In [12]: ds.a.isel(time=time_filter[:-1])

IndexError: Boolean array size 54999999 is used to index array with shape (55000000,). ```

I don't have experience using isel with boolean indexing. (Although the docs on positional indexing claim it is supported.) My guess is that that the time is being spent aligning the indexer with the array, which is unnecessary since you know they are already aligned. Probably not the most efficient pattern for xarray.

Here's how I would recommend writing the query using label-based selection: python %timeit ds.a.sel(time=slice(50_001, None)) 117 ms ± 5.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)