Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1244 (c841b5fe7d*) started at 2025-10-02T22:10:59.199


################################################################################
# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.85s


################################################################################
# Installation
#

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.13/Project.toml`
  [053f045d] + SimilaritySearch v0.13.0
    Updating `~/.julia/environments/v1.13/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.4.0
  [4fba245c] + ArrayInterface v7.20.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [da1fd8a2] + CodeTracking v2.0.1
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.18.1
  [807dbc54] + Compiler v0.1.1
  [a33af91c] + CompositionsBase v0.1.2
  [187b0558] + ConstructionBase v1.6.0
  [adafc99b] + CpuId v0.3.1
  [9a962f9c] + DataAPI v1.16.0
⌅ [864edb3b] + DataStructures v0.18.22
  [b4f34e82] + Distances v0.10.12
  [ffbed154] + DocStringExtensions v0.9.5
  [5789e2e9] + FileIO v1.17.0
  [076d061b] + HashArrayMappedTries v0.2.0
  [615f187c] + IfElse v0.1.1
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.4
  [c3a54625] + JET v0.10.7
⌅ [033835bb] + JLD2 v0.5.15
  [aa1ae85d] + JuliaInterpreter v0.10.5
  [70703baa] + JuliaSyntax v1.0.2
  [10f19ff3] + LayoutPointers v0.1.17
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [6f1432cf] + LoweredCodeUtils v3.4.4
  [1914dd2f] + MacroTools v0.5.16
  [d125e4d3] + ManualMemory v0.1.8
  [e1d29d7a] + Missings v1.2.0
  [bac558e1] + OrderedCollections v1.8.1
  [d96e819e] + Parameters v0.12.3
  [f517fe37] + Polyester v0.7.18
  [1d0040c9] + PolyesterWeave v0.2.2
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [92933f4c] + ProgressMeter v1.11.0
  [ae029012] + Requires v1.3.1
  [94e857df] + SIMDTypes v0.1.0
  [431bcebd] + SciMLPublic v1.0.0
  [7e506255] + ScopedValues v1.5.0
  [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.0
  [a2af1166] + SortingAlgorithms v1.2.2
  [aedffcd0] + Static v1.3.0
  [0d7ed370] + StaticArrayInterface v1.8.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
⌅ [2913bbd2] + StatsBase v0.33.21
  [7792a7ef] + StrideArraysCore v0.5.8
  [8290d209] + ThreadingUtilities v0.5.5
  [3bb67fe8] + TranscodingStreams v0.11.3
  [3a884ed6] + UnPack v1.0.2
  [0dad84c5] + ArgTools v1.1.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [f43a241f] + Downloads v1.7.0
  [7b1f6079] + FileWatching v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2 v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [56ddb016] + Logging v1.11.0
  [d6f4376e] + Markdown v1.11.0
  [a63ad114] + Mmap v1.11.0
  [ca575930] + NetworkOptions v1.3.0
  [44cfe95a] + Pkg v1.13.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [f489334b] + StyledStrings v1.11.0
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test v1.11.0
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] + LibCURL_jll v8.16.0+0
  [e37daf67] + LibGit2_jll v1.9.1+0
  [29816b5a] + LibSSH2_jll v1.11.3+1
  [14a3606d] + MozillaCACerts_jll v2025.9.9
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [458c3c95] + OpenSSL_jll v3.5.4+0
  [efcefdf7] + PCRE2_jll v10.46.0+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [83775a58] + Zlib_jll v1.3.1+2
  [3161d3a3] + Zstd_jll v1.5.7+1
  [8e850b90] + libblastrampoline_jll v5.13.1+0
  [8e850ede] + nghttp2_jll v1.67.1+0
  [3f19e933] + p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 4.86s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 111.5s


################################################################################
# Testing
#

     Testing SimilaritySearch
      Status `/tmp/jl_JVvW9K/Project.toml`
  [7d9f7c33] Accessors v0.1.42
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [c3a54625] JET v0.10.7
⌅ [033835bb] JLD2 v0.5.15
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [92933f4c] ProgressMeter v1.11.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.0
  [10745b16] Statistics v1.11.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [ade2ca70] Dates v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [9a3f8284] Random v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_JVvW9K/Manifest.toml`
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.4.0
  [4c88cf16] Aqua v0.8.14
  [4fba245c] ArrayInterface v7.20.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] CPUSummary v0.2.7
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [da1fd8a2] CodeTracking v2.0.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.1
  [807dbc54] Compiler v0.1.1
  [a33af91c] CompositionsBase v0.1.2
  [187b0558] ConstructionBase v1.6.0
  [adafc99b] CpuId v0.3.1
  [9a962f9c] DataAPI v1.16.0
⌅ [864edb3b] DataStructures v0.18.22
  [b4f34e82] Distances v0.10.12
  [ffbed154] DocStringExtensions v0.9.5
  [5789e2e9] FileIO v1.17.0
  [076d061b] HashArrayMappedTries v0.2.0
  [615f187c] IfElse v0.1.1
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.4
  [c3a54625] JET v0.10.7
⌅ [033835bb] JLD2 v0.5.15
  [aa1ae85d] JuliaInterpreter v0.10.5
  [70703baa] JuliaSyntax v1.0.2
  [10f19ff3] LayoutPointers v0.1.17
  [2ab3a3ac] LogExpFunctions v0.3.29
  [6f1432cf] LoweredCodeUtils v3.4.4
  [1914dd2f] MacroTools v0.5.16
  [d125e4d3] ManualMemory v0.1.8
  [e1d29d7a] Missings v1.2.0
  [bac558e1] OrderedCollections v1.8.1
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [1d0040c9] PolyesterWeave v0.2.2
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.0
  [92933f4c] ProgressMeter v1.11.0
  [ae029012] Requires v1.3.1
  [94e857df] SIMDTypes v0.1.0
  [431bcebd] SciMLPublic v1.0.0
  [7e506255] ScopedValues v1.5.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.0
  [a2af1166] SortingAlgorithms v1.2.2
  [aedffcd0] Static v1.3.0
  [0d7ed370] StaticArrayInterface v1.8.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.7.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [8290d209] ThreadingUtilities v0.5.5
  [3bb67fe8] TranscodingStreams v0.11.3
  [3a884ed6] UnPack v1.0.2
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [8ba89e20] Distributed v1.11.0
  [f43a241f] Downloads v1.7.0
  [7b1f6079] FileWatching v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2 v1.11.0
  [8f399da3] Libdl v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [a63ad114] Mmap v1.11.0
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.13.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization v1.11.0
  [6462fe0b] Sockets v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [f489334b] StyledStrings v1.11.0
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test v1.11.0
  [cf7118a7] UUIDs v1.11.0
  [4ec0a83e] Unicode v1.11.0
  [e66e0078] CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] LibCURL_jll v8.16.0+0
  [e37daf67] LibGit2_jll v1.9.1+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.9.9
  [4536629a] OpenBLAS_jll v0.3.29+0
  [458c3c95] OpenSSL_jll v3.5.4+0
  [efcefdf7] PCRE2_jll v10.46.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.1+2
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.13.1+0
  [8e850ede] nghttp2_jll v1.67.1+0
  [3f19e933] p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
Test Summary:              | Pass  Total   Time
test database abstractions |   56     56  15.8s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 20005  20005  3.9s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.4s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.1s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.906463 seconds (1000 allocations: 78.125 KiB)
 11.152819 seconds (1000 allocations: 78.125 KiB)
  3.618293 seconds (1000 allocations: 78.125 KiB)
  3.872007 seconds (1000 allocations: 78.125 KiB)
  4.228446 seconds (1000 allocations: 78.125 KiB)
  4.194744 seconds (1000 allocations: 78.125 KiB)
  3.787823 seconds (1000 allocations: 78.125 KiB)
  3.796360 seconds (1000 allocations: 78.125 KiB)
 15.687953 seconds (1000 allocations: 78.125 KiB)
 15.666072 seconds (1000 allocations: 78.125 KiB)
 27.714244 seconds (1000 allocations: 78.125 KiB)
 28.468778 seconds (1000 allocations: 78.125 KiB)
 21.183801 seconds (6.23 k allocations: 388.641 KiB)
 19.175066 seconds (1000 allocations: 78.125 KiB)
 17.665375 seconds (1.00 k allocations: 78.141 KiB)
 17.678001 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m40.1s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.865147 seconds (1000 allocations: 78.125 KiB)
  3.809179 seconds (1000 allocations: 78.125 KiB)
 29.757209 seconds (1000 allocations: 78.125 KiB)
 30.079660 seconds (1000 allocations: 78.125 KiB)
 29.693278 seconds (1000 allocations: 78.125 KiB)
 31.601629 seconds (1000 allocations: 78.125 KiB)
  5.037717 seconds (1000 allocations: 78.125 KiB)
  4.889305 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m22.5s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  7.667352 seconds (1000 allocations: 78.125 KiB)
  8.512274 seconds (1000 allocations: 78.125 KiB)
  8.227555 seconds (1000 allocations: 78.125 KiB)
  8.980944 seconds (1000 allocations: 78.125 KiB)
  8.483905 seconds (1000 allocations: 78.125 KiB)
  8.311635 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  53.0s
  0.046746 seconds (1.00 k allocations: 78.141 KiB)
  0.046839 seconds (1000 allocations: 78.125 KiB)
  0.037174 seconds (1000 allocations: 78.125 KiB)
  0.027430 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.5s
  0.048912 seconds (1000 allocations: 78.125 KiB)
  0.049990 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.2s
ExhaustiveSearch allknn: 3.816236 seconds (2.31 M allocations: 128.292 MiB, 1.90% gc time, 99.95% compilation time)
ParallelExhaustiveSearch allknn: 0.870166 seconds (610.12 k allocations: 31.980 MiB, 99.86% compilation time)
Test Summary: | Pass  Total   Time
allknn        |    5      5  52.7s
quantile(length.(hsp_knns), 0:0.1:1) = [2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0]
Test Summary: | Total  Time
HSP           |     0  2.5s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:10.905
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-02T22:22:11.143
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=2 ep=2 n=2 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-02T22:22:12.319
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:12.711
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000005, 0x00000008, 0x0000000c, 0x00000012, 0x00000024, 0x00000039, 0x00000047, 0x00000050, 0x00000053]
D.nn = Int32[1, 2, 1, 4, 5, 4, 5, 8, 8, 8, 1, 12, 8, 12, 1, 12, 8, 18, 8, 2, 2, 1, 1, 5, 2, 12, 1, 2, 8, 8, 8, 4, 18, 8, 8, 36, 8, 4, 4, 12, 18, 8, 12, 36, 8, 8, 5, 36, 8, 1, 8, 8, 5, 36, 1, 12, 57, 8, 4, 8, 1, 57, 36, 8, 18, 36, 8, 8, 12, 18, 71, 8, 36, 57, 18, 57, 5, 36, 2, 80, 36, 8, 83, 8, 8, 8, 1, 12, 5, 8, 1, 8, 57, 4, 8, 8, 4, 8, 80, 5]
D.dist = Float32[0.0, 0.0, 0.08142978, 0.0, 0.0, 0.0022916794, 0.024792016, 0.0, 0.096435726, 0.0130934715, 0.03049928, 0.0, 0.054882705, 0.087288976, 0.06900817, 0.032981157, 0.020630836, 0.0, 0.026264071, 0.031798482, 0.05363989, 0.03452128, 0.058707297, 0.043281615, 0.02422297, 0.038268507, 0.016498506, 0.042312503, 0.095665574, 0.09912562, 0.023822248, 0.054497123, 0.0984056, 0.06733757, 0.014556527, 0.0, 0.07185364, 0.060597897, 0.043219566, 0.04316467, 0.05663407, 0.0416314, 0.046959817, 0.010854602, 0.009951949, 0.029613435, 0.09107804, 0.028265893, 0.032804012, 0.019040644, 0.017418206, 0.035163283, 0.005345404, 0.00736326, 0.027003944, 0.04964888, 0.0, 0.08659577, 0.056901217, 0.023677468, 0.06300902, 0.006432712, 0.058148682, 0.06313896, 0.04225433, 0.041187167, 0.042863905, 0.0759244, 0.03284937, 0.0069231987, 0.0, 0.08157033, 0.04482943, 0.0066325665, 0.07135689, 0.054245412, 0.030440152, 0.06460458, 0.07274091, 0.0, 0.019934416, 0.012380421, 0.0, 0.04773879, 0.032954812, 0.0042477846, 0.042104244, 0.04188931, 0.0029560328, 0.04548061, 0.02145797, 0.028969944, 0.020332038, 0.04020244, 0.029007792, 0.023854136, 0.09773642, 0.021039128, 0.0017073154, 0.018950403]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  14.8s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.854
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-02T22:22:13.855
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.855
[ Info: neardup> range: 33:48, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.855
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.855
[ Info: neardup> range: 65:80, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.855
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.855
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.855
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.855
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000005, 0x00000008, 0x0000000c, 0x00000012, 0x00000024, 0x00000039, 0x00000047, 0x00000050, 0x00000053]
D.nn = Int32[1, 2, 1, 4, 5, 4, 5, 8, 8, 8, 1, 12, 8, 12, 1, 12, 8, 18, 8, 2, 2, 1, 1, 5, 2, 12, 1, 2, 8, 8, 8, 4, 18, 8, 8, 36, 8, 4, 4, 12, 18, 8, 12, 36, 8, 8, 5, 1, 8, 1, 8, 8, 5, 36, 1, 12, 57, 8, 4, 8, 1, 8, 36, 8, 18, 36, 8, 8, 12, 18, 71, 8, 36, 57, 18, 57, 5, 36, 2, 80, 36, 8, 83, 8, 8, 8, 1, 12, 5, 8, 1, 8, 57, 4, 8, 8, 4, 8, 80, 5]
D.dist = Float32[0.0, 0.0, 0.08142978, 0.0, 0.0, 0.0022916794, 0.024792016, 0.0, 0.096435726, 0.0130934715, 0.03049928, 0.0, 0.054882705, 0.087288976, 0.06900817, 0.032981157, 0.020630836, 0.0, 0.026264071, 0.031798482, 0.05363989, 0.03452128, 0.058707297, 0.043281615, 0.02422297, 0.038268507, 0.016498506, 0.042312503, 0.095665574, 0.09912562, 0.023822248, 0.054497123, 0.0984056, 0.06733757, 0.014556527, 0.0, 0.07185364, 0.060597897, 0.043219566, 0.04316467, 0.05663407, 0.0416314, 0.046959817, 0.010854602, 0.009951949, 0.029613435, 0.09107804, 0.06755346, 0.032804012, 0.019040644, 0.017418206, 0.035163283, 0.005345404, 0.00736326, 0.027003944, 0.04964888, 0.0, 0.08659577, 0.056901217, 0.023677468, 0.06300902, 0.08699745, 0.058148682, 0.06313896, 0.04225433, 0.041187167, 0.042863905, 0.0759244, 0.03284937, 0.0069231987, 0.0, 0.08157033, 0.04482943, 0.0066325665, 0.07135689, 0.054245412, 0.030440152, 0.06460458, 0.07274091, 0.0, 0.019934416, 0.012380421, 0.0, 0.04773879, 0.032954812, 0.0042477846, 0.042104244, 0.04188931, 0.0029560328, 0.04548061, 0.02145797, 0.028969944, 0.020332038, 0.04020244, 0.029007792, 0.023854136, 0.09773642, 0.021039128, 0.0017073154, 0.018950403]
Test Summary:       | Pass  Total  Time
neardup small block |    3      3  0.0s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.945
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-02T22:22:13.945
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.946
[ Info: neardup> range: 33:48, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.946
[ Info: neardup> range: 49:64, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.946
[ Info: neardup> range: 65:80, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.947
[ Info: neardup> range: 81:96, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.947
[ Info: neardup> range: 97:100, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.947
[ Info: neardup> finished current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:13.947
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000012, 0x00000042, 0x00000047, 0x00000049]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 3, 18, 10, 15, 2, 3, 1, 5, 2, 16, 1, 2, 3, 3, 8, 6, 15, 9, 10, 3, 13, 4, 4, 16, 18, 3, 16, 3, 10, 8, 3, 3, 13, 11, 3, 3, 5, 3, 11, 14, 13, 3, 4, 8, 1, 13, 1, 9, 18, 66, 8, 9, 16, 18, 71, 3, 73, 13, 18, 14, 5, 1, 9, 9, 73, 8, 15, 13, 3, 8, 3, 12, 5, 13, 1, 8, 13, 4, 3, 3, 6, 10, 9, 5]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00424242, 0.0, 0.01649183, 0.028944612, 0.05363989, 0.016076744, 0.058707297, 0.043281615, 0.02422297, 0.0037830472, 0.016498506, 0.042312503, 0.033330142, 0.034760714, 0.023822248, 0.04048282, 0.08999026, 0.0117865205, 0.0074260235, 0.061751723, 0.018950284, 0.060597897, 0.043219566, 0.019304454, 0.05663407, 0.0076545477, 0.018060684, 0.047686696, 0.004540205, 0.029613435, 0.06476718, 0.027005315, 0.0182572, 0.0130034685, 0.007766187, 0.020000398, 0.005345404, 0.085002124, 0.008218527, 0.007563174, 0.041771114, 0.054411292, 0.056901217, 0.023677468, 0.06300902, 0.016177356, 0.08149099, 0.021710515, 0.04225433, 0.0, 0.042863905, 0.009834945, 0.018866003, 0.0069231987, 0.0, 0.042253375, 0.0, 0.037620008, 0.07135689, 0.0043228865, 0.030440152, 0.0760169, 0.06631136, 0.024439216, 0.021416962, 0.012380421, 0.06666529, 0.027899921, 0.017024219, 0.0042477846, 0.022298694, 0.04188931, 0.0029560328, 0.03793639, 0.02145797, 0.028969944, 0.04749137, 0.04020244, 0.02162075, 0.0069931746, 0.0791595, 0.0044476986, 0.026407242, 0.018950403]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  0.1s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:21.201
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=6 n=6 2025-10-02T22:22:21.201
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:21.207
[ Info: neardup> range: 33:48, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:21.207
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:21.207
[ Info: neardup> range: 65:80, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:21.207
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:21.207
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:21.207
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-10-02T22:22:21.207
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000005, 0x00000008, 0x0000000c, 0x00000012, 0x00000024, 0x00000039, 0x00000047, 0x00000050, 0x00000053]
D.nn = Int32[1, 2, 1, 4, 5, 4, 5, 8, 8, 8, 1, 12, 8, 12, 1, 12, 8, 18, 8, 2, 2, 1, 1, 5, 2, 12, 1, 2, 8, 8, 8, 4, 18, 8, 8, 36, 8, 4, 4, 12, 18, 8, 12, 36, 8, 8, 5, 1, 8, 1, 8, 8, 5, 36, 1, 12, 57, 8, 4, 8, 1, 8, 36, 8, 18, 36, 8, 8, 12, 18, 71, 8, 36, 57, 18, 57, 5, 36, 2, 80, 36, 8, 83, 8, 8, 8, 1, 12, 5, 8, 1, 8, 57, 4, 8, 8, 4, 8, 80, 5]
D.dist = Float32[0.0, 0.0, 0.08142978, 0.0, 0.0, 0.0022916794, 0.024792016, 0.0, 0.096435726, 0.0130934715, 0.03049928, 0.0, 0.054882705, 0.087288976, 0.06900817, 0.032981157, 0.020630836, 0.0, 0.026264071, 0.031798482, 0.05363989, 0.03452128, 0.058707297, 0.043281615, 0.02422297, 0.038268507, 0.016498506, 0.042312503, 0.095665574, 0.09912562, 0.023822248, 0.054497123, 0.0984056, 0.06733757, 0.014556527, 0.0, 0.07185364, 0.060597897, 0.043219566, 0.04316467, 0.05663407, 0.0416314, 0.046959817, 0.010854602, 0.009951949, 0.029613435, 0.09107804, 0.06755346, 0.032804012, 0.019040644, 0.017418206, 0.035163283, 0.005345404, 0.00736326, 0.027003944, 0.04964888, 0.0, 0.08659577, 0.056901217, 0.023677468, 0.06300902, 0.08699745, 0.058148682, 0.06313896, 0.04225433, 0.041187167, 0.042863905, 0.0759244, 0.03284937, 0.0069231987, 0.0, 0.08157033, 0.04482943, 0.0066325665, 0.07135689, 0.054245412, 0.030440152, 0.06460458, 0.07274091, 0.0, 0.019934416, 0.012380421, 0.0, 0.04773879, 0.032954812, 0.0042477846, 0.042104244, 0.04188931, 0.0029560328, 0.04548061, 0.02145797, 0.028969944, 0.020332038, 0.04020244, 0.029007792, 0.023854136, 0.09773642, 0.021039128, 0.0017073154, 0.018950403]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.3s
computing farthest point 1, dmax: Inf, imax: 19, n: 30
computing farthest point 2, dmax: 1.1349858, imax: 4, n: 30
computing farthest point 3, dmax: 0.94138825, imax: 2, n: 30
computing farthest point 4, dmax: 0.88886404, imax: 18, n: 30
computing farthest point 5, dmax: 0.6639395, imax: 23, n: 30
computing farthest point 6, dmax: 0.64642936, imax: 9, n: 30
computing farthest point 7, dmax: 0.64633995, imax: 13, n: 30
computing farthest point 8, dmax: 0.5384667, imax: 15, n: 30
computing farthest point 9, dmax: 0.5369951, imax: 22, n: 30
computing farthest point 10, dmax: 0.49981338, imax: 1, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.7s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.6s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-02T22:22:29.388
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=4, Δ=0.9975, maxvisits=98) 2025-10-02T22:22:42.173
LOG n.size quantiles:[2.0, 3.0, 3.0, 3.0, 3.0]
(i, j, d) = (17, 984, -1.1920929f-7)
(i, j, d, :parallel) = (17, 984, -1.1920929f-7, :parallel)
[ Info: NOTE: the exact method will be faster on small datasets due to the preprocessing step of the approximation method
[ Info: ("closestpair computation time", :approx => 19.986430903, :exact => 0.967521957)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  21.5s
  6.012323 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.003511 seconds
SEARCH Exhaustive 2: 0.003615 seconds
SEARCH Exhaustive 3: 0.003655 seconds
typeof(seq) = ExhaustiveSearch{SqL2Distance, MatrixDatabase{Matrix{Float32}}}
typeof(ectx) = GenericContext{KnnSorted}
typeof(q) = SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}
typeof(res) = KnnSorted{Vector{IdWeight}}
[ Info: ===================== minrecall ==============================
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-02T22:23:11.471
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=12, Δ=1.1851876, maxvisits=186) 2025-10-02T22:23:17.670
LOG n.size quantiles:[2.0, 2.0, 2.0, 3.0, 3.0]
[ Info: RECALL BAJO!! recall: 0.20937500000000003, #objects: 3329, #queries: 32
[ Info: [0.4651564061641693, 0.2272501438856125, 0.2689501941204071, 0.547218382358551, 0.5899376273155212, 0.5542770624160767, 0.2710615396499634, 0.5236983299255371, 0.40576839447021484, 0.7399064898490906, 0.45405545830726624, 0.43947476148605347, 0.3145042061805725, 1.070373296737671, 0.5858100056648254, 0.3052981495857239, 0.2634029984474182, 0.41436249017715454, 0.5879063606262207, 0.2850455641746521, 0.7233147621154785, 0.4269734025001526, 0.437223345041275, 0.467976450920105, 0.5456681251525879, 0.5936722159385681, 0.50531005859375, 0.2823227345943451, 0.3585725426673889, 0.5541151165962219, 0.23108020424842834, 0.7971976399421692]
(g, r) = (Set(Int32[2971, 2433, 1374, 1134, 2004, 2821, 121, 493, 739, 3299]), Set(Int32[2818, 1099, 125, 2469, 789, 2341, 159, 177, 1272, 31]))
(g, r) = (Set(Int32[2633, 2710, 2679, 632, 2273, 543, 780, 766, 2748, 2054]), Set(Int32[2710, 2569, 632, 941, 543, 780, 766, 2748, 867, 2633]))
(g, r) = (Set(Int32[2711, 590, 3194, 1745, 2706, 893, 2290, 3316, 1580, 68]), Set(Int32[3194, 2706, 971, 3184, 2290, 3316, 1580, 2372, 1791, 1803]))
(g, r) = (Set(Int32[2683, 1561, 2871, 77, 1405, 2533, 3176, 152, 1264, 390]), Set(Int32[2546, 3033, 3223, 2730, 1869, 1798, 2884, 705, 1921, 2881]))
(g, r) = (Set(Int32[2489, 3084, 2414, 1948, 1565, 204, 1290, 2050, 709, 1421]), Set(Int32[2625, 2869, 3084, 2245, 2511, 2832, 571, 2314, 3298, 2642]))
(g, r) = (Set(Int32[1186, 440, 237, 587, 3023, 787, 621, 1877, 2011, 3165]), Set(Int32[440, 237, 587, 2842, 621, 431, 382, 301, 2776, 19]))
(g, r) = (Set(Int32[57, 3240, 2178, 2229, 2369, 1502, 1516, 177, 732, 1111]), Set(Int32[57, 1429, 2469, 464, 522, 125, 2178, 1516, 222, 177]))
(g, r) = (Set(Int32[1895, 481, 2439, 364, 950, 2664, 2562, 728, 1942, 2802]), Set(Int32[1996, 3033, 54, 1761, 2866, 329, 627, 406, 2338, 481]))
(g, r) = (Set(Int32[1786, 838, 2560, 871, 2454, 3211, 1422, 2471, 1463, 2021]), Set(Int32[1829, 1284, 2592, 871, 789, 3211, 1422, 1463, 159, 2341]))
(g, r) = (Set(Int32[281, 2900, 1461, 701, 3125, 2252, 2182, 1577, 1173, 67]), Set(Int32[3142, 2541, 2900, 3261, 1583, 679, 3239, 2249, 2349, 2428]))
(g, r) = (Set(Int32[2654, 3280, 1148, 172, 1926, 3172, 1960, 1629, 2046, 2991]), Set(Int32[3280, 2083, 1099, 1926, 125, 1016, 1629, 2370, 493, 1896]))
(g, r) = (Set(Int32[417, 419, 1458, 468, 1982, 158, 2779, 735, 1466, 786]), Set(Int32[3033, 1099, 468, 155, 1009, 291, 26, 3131, 101, 2835]))
(g, r) = (Set(Int32[1050, 1448, 1450, 1042, 3024, 678, 570, 1854, 3082, 2210]), Set(Int32[25, 1050, 719, 126, 1450, 678, 570, 1966, 100, 381]))
(g, r) = (Set(Int32[222, 1508, 378, 1579, 2365, 870, 1636, 159, 2745, 2589]), Set(Int32[2410, 994, 125, 692, 1583, 2300, 2659, 2993, 2527, 1829]))
(g, r) = (Set(Int32[1532, 847, 2230, 14, 1387, 676, 1277, 901, 1049, 10]), Set(Int32[1702, 2030, 2142, 600, 2029, 3113, 432, 2070, 1603, 1139]))
(g, r) = (Set(Int32[2569, 1530, 2795, 2750, 678, 2210, 3082, 381, 2891, 2619]), Set(Int32[1210, 2569, 2633, 847, 2795, 2750, 2659, 164, 2891, 2619]))
(g, r) = (Set(Int32[825, 1943, 737, 2091, 1095, 2356, 1221, 2507, 3241, 2985]), Set(Int32[825, 1856, 2091, 2605, 2356, 2507, 1740, 2238, 1655, 54]))
(g, r) = (Set(Int32[652, 2264, 1698, 2864, 2234, 517, 3213, 368, 2211, 2396]), Set(Int32[1782, 3310, 2841, 2303, 1100, 28, 2234, 3288, 3213, 1305]))
(g, r) = (Set(Int32[3174, 1612, 2606, 3069, 2196, 1098, 420, 120, 2125, 1495]), Set(Int32[2739, 2412, 2363, 2570, 2888, 2343, 2467, 1754, 1798, 2780]))
(g, r) = (Set(Int32[2711, 2191, 2265, 767, 590, 2970, 893, 733, 1580, 2632]), Set(Int32[1930, 2191, 2265, 767, 1389, 733, 2430, 1569, 742, 1194]))
(g, r) = (Set(Int32[3095, 991, 1540, 182, 1213, 3001, 2195, 947, 120, 312]), Set(Int32[2654, 2412, 2468, 2401, 3001, 2343, 2937, 2888, 2363, 2780]))
(g, r) = (Set(Int32[2625, 52, 2079, 2265, 2832, 2556, 2642, 3298, 916, 579]), Set(Int32[491, 52, 328, 485, 40, 916, 484, 245, 1271, 207]))
(g, r) = (Set(Int32[175, 2592, 2057, 1243, 2649, 2298, 164, 1286, 1728, 1215]), Set(Int32[1274, 224, 2057, 2413, 2028, 2342, 39, 1463, 1728, 2291]))
(g, r) = (Set(Int32[3324, 2030, 2137, 2635, 2830, 2070, 1387, 1164, 3085, 1206]), Set(Int32[44, 2897, 1604, 2854, 26, 1333, 90, 2440, 539, 733]))
(g, r) = (Set(Int32[2082, 2080, 3201, 2208, 1836, 1087, 293, 2146, 2000, 2563]), Set(Int32[1829, 2057, 2413, 1515, 2703, 3079, 3048, 1169, 1728, 2757]))
(g, r) = (Set(Int32[2359, 2066, 2635, 3182, 1719, 2070, 2426, 1139, 1359, 821]), Set(Int32[1699, 1099, 778, 1176, 1981, 3193, 1437, 1333, 1296, 3121]))
(g, r) = (Set(Int32[2105, 57, 1504, 1151, 2582, 507, 2369, 1502, 45, 2532]), Set(Int32[2951, 2300, 1583, 2993, 3187, 316, 1659, 2527, 3108, 2800]))
(g, r) = (Set(Int32[1252, 72, 3058, 1687, 654, 975, 2838, 2937, 3144, 1794]), Set(Int32[1252, 458, 72, 1030, 1687, 1493, 975, 2201, 333, 1794]))
(g, r) = (Set(Int32[1396, 1097, 2163, 2363, 3068, 2343, 3215, 546, 2777, 1522]), Set(Int32[1753, 3033, 576, 2456, 636, 1503, 2909, 1844, 1273, 2823]))
(g, r) = (Set(Int32[531, 2081, 745, 1707, 1763, 1712, 1170, 1465, 1682, 1874]), Set(Int32[3062, 280, 3146, 1030, 2585, 316, 2527, 2403, 333, 458]))
(g, r) = (Set(Int32[1590, 2054, 2145, 2273, 2679, 487, 864, 2199, 1341, 1555]), Set(Int32[2145, 2682, 2273, 543, 487, 864, 98, 2199, 1341, 1555]))
(g, r) = (Set(Int32[1172, 27, 2286, 3320, 1113, 3110, 233, 3275, 764, 718]), Set(Int32[558, 2900, 2933, 3265, 2833, 3319, 2923, 2577, 2486, 3132]))
collect(Int32, IdView(p)) = Int32[31, 789, 2341, 177, 159, 125, 2818, 1272, 2469, 1099]
collect(Int32, IdView(p)) = Int32[543, 766, 2710, 780, 632, 2633, 2748, 867, 941, 2569]
collect(Int32, IdView(p)) = Int32[1580, 2290, 3316, 3194, 2706, 2372, 3184, 1791, 971, 1803]
collect(Int32, IdView(p)) = Int32[2546, 1798, 3223, 2730, 705, 2884, 3033, 1869, 1921, 2881]
collect(Int32, IdView(p)) = Int32[3084, 2869, 3298, 2625, 571, 2832, 2314, 2642, 2245, 2511]
collect(Int32, IdView(p)) = Int32[587, 621, 440, 237, 431, 2842, 301, 382, 2776, 19]
collect(Int32, IdView(p)) = Int32[57, 2178, 1516, 177, 2469, 464, 522, 1429, 222, 125]
collect(Int32, IdView(p)) = Int32[481, 3033, 2866, 1996, 54, 406, 329, 1761, 627, 2338]
collect(Int32, IdView(p)) = Int32[1463, 3211, 1422, 871, 789, 1284, 159, 2341, 2592, 1829]
collect(Int32, IdView(p)) = Int32[2900, 2349, 3261, 2541, 2249, 3239, 2428, 1583, 679, 3142]
collect(Int32, IdView(p)) = Int32[1926, 3280, 1629, 1896, 2370, 493, 2083, 1099, 125, 1016]
collect(Int32, IdView(p)) = Int32[468, 3033, 3131, 1009, 26, 101, 2835, 155, 291, 1099]
collect(Int32, IdView(p)) = Int32[678, 570, 1450, 1050, 25, 100, 381, 1966, 126, 719]
collect(Int32, IdView(p)) = Int32[2527, 125, 692, 1583, 994, 2300, 2659, 2993, 2410, 1829]
collect(Int32, IdView(p)) = Int32[2070, 2142, 1702, 3113, 432, 1603, 1139, 600, 2030, 2029]
collect(Int32, IdView(p)) = Int32[2795, 2750, 2569, 2891, 2619, 2659, 847, 1210, 164, 2633]
collect(Int32, IdView(p)) = Int32[2507, 2356, 2091, 825, 1655, 54, 2605, 1856, 1740, 2238]
collect(Int32, IdView(p)) = Int32[2234, 3213, 3310, 2841, 3288, 1782, 1305, 2303, 1100, 28]
collect(Int32, IdView(p)) = Int32[2888, 2343, 2780, 2739, 2467, 1754, 2412, 2363, 2570, 1798]
collect(Int32, IdView(p)) = Int32[767, 2191, 733, 2265, 2430, 1569, 1194, 1930, 1389, 742]
collect(Int32, IdView(p)) = Int32[3001, 2468, 2937, 2654, 2343, 2888, 2412, 2401, 2780, 2363]
collect(Int32, IdView(p)) = Int32[52, 916, 245, 328, 1271, 207, 40, 485, 484, 491]
collect(Int32, IdView(p)) = Int32[1728, 2057, 2291, 1274, 2413, 39, 2342, 2028, 224, 1463]
collect(Int32, IdView(p)) = Int32[1333, 2897, 90, 2440, 539, 733, 2854, 26, 44, 1604]
collect(Int32, IdView(p)) = Int32[2057, 2413, 3048, 1515, 2757, 1829, 3079, 1169, 2703, 1728]
collect(Int32, IdView(p)) = Int32[1699, 1981, 1333, 3193, 1437, 1099, 1296, 778, 1176, 3121]
collect(Int32, IdView(p)) = Int32[2951, 3187, 2300, 2993, 2527, 316, 3108, 1659, 2800, 1583]
collect(Int32, IdView(p)) = Int32[975, 1794, 72, 1687, 1252, 1493, 2201, 333, 458, 1030]
collect(Int32, IdView(p)) = Int32[1503, 1844, 1273, 2909, 576, 1753, 3033, 2823, 2456, 636]
collect(Int32, IdView(p)) = Int32[458, 2585, 3062, 333, 280, 1030, 3146, 316, 2403, 2527]
collect(Int32, IdView(p)) = Int32[2273, 1555, 1341, 864, 487, 2199, 2145, 543, 98, 2682]
collect(Int32, IdView(p)) = Int32[2923, 3132, 3265, 2577, 3319, 2833, 2486, 558, 2900, 2933]
quantile(neighbors_length.(Ref(index.adj), 1:length(index)), 0:0.1:1.0) = [1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 6.0, 8.0, 30.0]
     Testing SimilaritySearch tests passed 

Testing completed after 605.97s

PkgEval succeeded after 747.84s