Where selection for Netcdf raster slower than for in Memory raster with same dimensions

[felixcremer]

When I am doing a Where Selector it seems to be quite slow compared to other selection methods.
I expect that the Where selector is slower because it has to check the function for every element of the axis.
But in my case the selection takes 2 seconds while the selection function takes 30 nanoseconds and when I multiply it with the number of elements I get 10 microseconds.
This selection is much faster when I do it with random data that I generate compared to the selection on a netcdf based Raster.
When I construct a Raster with the same dimensions as ras but a random array the timing is similar to the fully random based Raster.

# Selection on random data 
julia> r = Raster(rand(800,800, 289), (X(1:800), Y(1:800), Ti(DateTime(1995):Month(1):DateTime(2019))))
800×800×289 Raster{Float64,3} with dimensions: 
  X Sampled{Int64} 1:800 ForwardOrdered Regular Points,
  Y Sampled{Int64} 1:800 ForwardOrdered Regular Points,
  Ti Sampled{DateTime} DateTime("1995-01-01T00:00:00"):Month(1):DateTime("2019-01-01T00:00:00") ForwardOrdered Regular Points
extent: Extent(X = (1, 800), Y = (1, 800), Ti = (DateTime("1995-01-01T00:00:00"), DateTime("2019-01-01T00:00:00")))
missingval: missing
parent:


julia> @btime r[Ti=Where(x->year(x)==1995)]
  29.809 ms (19 allocations: 58.60 MiB)
800×800×12 Raster{Float64,3} with dimensions: 
  X Sampled{Int64} 1:800 ForwardOrdered Regular Points,
  Y Sampled{Int64} 1:800 ForwardOrdered Regular Points,
  Ti Sampled{DateTime} DateTime[1995-01-01T00:00:00, …, 1995-12-01T00:00:00] ForwardOrdered Irregular Points
extent: Extent(X = (1, 800), Y = (1, 800), Ti = (DateTime("1995-01-01T00:00:00"), DateTime("1995-12-01T00:00:00")))
missingval: missing
parent:


# results of a NetCDF backed raster
julia> @btime ras[Ti=Where(x->year(x)==1995)]
  1.994 s (643532 allocations: 240.25 MiB)
848×824×12 Raster{Union{Missing, Float32},3} with dimensions: 
  Dim{:rlon} Sampled{Float64} -28.4029998779297:0.0550011811599821:18.18300056457514 ForwardOrdered Regular Points,
  Dim{:rlat} Sampled{Float64} -23.4029998779297:0.0550012159753107:21.863000869751005 ForwardOrdered Regular Points,
  Ti Sampled{DateTime} DateTime[1995-01-16T12:00:00, …, 1995-12-16T12:00:00] ForwardOrdered Irregular Points
extent: Extent(rlon = (-28.4029998779297, 18.18300056457514), rlat = (-23.4029998779297, 21.863000869751005), Ti = (DateTime("1995-01-16T12:00:00"), DateTime("1995-12-16T12:00:00")))
missingval: missing

# Results of a random array with the same dimensions as ras 
ulia> ra = Raster(rand(size(ras)...), dims(ras))
848×824×288 Raster{Float64,3} with dimensions: 
  Dim{:rlon} Sampled{Float64} -28.4029998779297:0.0550011811599821:18.18300056457514 ForwardOrdered Regular Points,
  Dim{:rlat} Sampled{Float64} -23.4029998779297:0.0550012159753107:21.863000869751005 ForwardOrdered Regular Points,
  Ti Sampled{DateTime} DateTime[1995-01-16T12:00:00, …, 2018-12-16T12:00:00] ForwardOrdered Irregular Points
extent: Extent(rlon = (-28.4029998779297, 18.18300056457514), rlat = (-23.4029998779297, 21.863000869751005), Ti = (DateTime("1995-01-16T12:00:00"), DateTime("2018-12-16T12:00:00")))
missingval: missing


julia> @btime ra[Ti=Where(x->year(x)==1995)]
  32.535 ms (19 allocations: 63.97 MiB)
848×824×12 Raster{Float64,3} with dimensions: 
  Dim{:rlon} Sampled{Float64} -28.4029998779297:0.0550011811599821:18.18300056457514 ForwardOrdered Regular Points,
  Dim{:rlat} Sampled{Float64} -23.4029998779297:0.0550012159753107:21.863000869751005 ForwardOrdered Regular Points,
  Ti Sampled{DateTime} DateTime[1995-01-16T12:00:00, …, 1995-12-16T12:00:00] ForwardOrdered Irregular Points
extent: Extent(rlon = (-28.4029998779297, 18.18300056457514), rlat = (-23.4029998779297, 21.863000869751005), Ti = (DateTime("1995-01-16T12:00:00"), DateTime("1995-12-16T12:00:00")))
missingval: missing

[and 11 more slices...]

[felixcremer]

This might be a Rasters issue in the end.
There is a difference between a YAXArrays and a Raster on the same data.

The above timings are from a Raster wrapping a YAXArray and this might contribute to the long timings.

But when I do the selection on a Raster compared to a YAXArray the selection is a factor four roughly slower. And the YAXArrays selection is also a factor two slower than the selection on an in memory based Raster.

urls = Dict(y=> "https://esgf1.dkrz.de/thredds/fileServer/cosmo-rea/reanalysis/EUR-6km/DWD/ECMWF-ERAINT/REA6/r1i1p1f1/COSMO/v1/mon/atmos/tas/v20230314/tas_EUR-6km_ECMWF-ERAINT_REA6_r1i1p1f1_COSMO_v1_mon_$(y)01-$(y)12.nc" for y in 1995:2018)


filenames = Dict(y=> joinpath("data", split(url, "/")[end]) for (y,url) in urls)
mkpath("data/")
#pathdict = Dict(y => download(url, filenames[y]) for (y, url) in urls)
# After download this should be this:
pathdict = [glob("tas_EUR*$y*.nc", "/home/fcremer/Documents/NFDI4Earth/deliverables/data")[1] for y in 1995:2018]

rlist = Raster.(pathdict, lazy=true, key="tas");
r = cat(rlist..., dims=Ti)
julia> dslist  = open_dataset.(pathdict)
taslist = getproperty.(dslist, :tas)
c = cat(taslist..., dims=Ti)

julia> @btime c[Ti=Where(x->year(x)==1995)];
  66.370 ms (26354 allocations: 1.98 MiB)

julia> @btime r[Ti=Where(x->year(x)==1995)];
  232.956 ms (7357 allocations: 232.82 MiB)

[rafaqz]

This looks like the problem:

(643532 allocations: 240.25 MiB)

Which is probably because youre using lazy=true with NCDatasets.jl... maybe try main ?

[rafaqz]

Is this still true? the files dont seem to be there anymore to test it.

[felixcremer]

The data was moved. I try to test it this week.

[felixcremer]

The location of the files have been moved.
The new position should be this:

path = "https://esgf1.dkrz.de/thredds/fileServer/cosmo-rea/reanalysis/EUR-6km/DWD/ECMWF-ERAINT/REA6/r1i1p1f1/COSMO/v1/mon/atmos/tas/v20230918/tas_EUR-6km_ECMWF-ERAINT_REA6_r1i1p1f1_COSMO_v1_mon_199501-199512.nc"

I will try it again in the next days.

[felixcremer]

I tested it again and these are the new timings:

julia> @btime c[Ti=Where(x->year(x)==1995)]; # YAXArrays with NetCDF backend
  71.301 ms (26373 allocations: 1.98 MiB)

julia> @btime r[Ti=Where(x->year(x)==1995)]; # Rasters with NCDatasets backend
  252.776 ms (6246 allocations: 232.31 MiB)

This might also be some difference between the backends.

This is with these package versions:

(Rasters) pkg> st
Project Rasters v0.11.1
Status `~/.julia/dev/Rasters/Project.toml`
  [79e6a3ab] Adapt v4.0.4
  [3da002f7] ColorTypes v0.11.5
  [1fbeeb36] CommonDataModel v0.3.6
  [187b0558] ConstructionBase v1.5.5
⌃ [0703355e] DimensionalData v0.27.2
⌅ [3c3547ce] DiskArrays v0.3.23
  [411431e0] Extents v0.1.2
  [1a297f60] FillArrays v1.11.0
  [4c728ea3] Flatten v0.4.3
  [68eda718] GeoFormatTypes v0.4.2
  [cf35fbd7] GeoInterface v1.3.4
  [e1d29d7a] Missings v1.2.0
  [30363a11] NetCDF v0.11.8
  [6fe1bfb0] OffsetArrays v1.14.0
  [92933f4c] ProgressMeter v1.10.0
  [3cdcf5f2] RecipesBase v1.3.4
  [189a3867] Reexport v1.2.2
  [efcf1570] Setfield v1.1.1
  [c21b50f5] YAXArrays v0.5.6
  [ade2ca70] Dates
  [a63ad114] Mmap

[rafaqz]

Ok this not just the time to load the data?

getindex is not lazy in Rasters like in YAXArrays, it moves data to memory. But view is lazy!

[felixcremer]

Ah yeah right. I forgot that. So this is a useless comparison. I will test the view but most likely this can be closed

[rafaqz]

It is kind of annoying that the lazy syntax is harder to write in julia generally.