Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1200 (a5576b4ddb*) started at 2025-09-25T19:08:20.836


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 10.05s


################################################################################
# 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 v4.18.0
  [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 v3.4.3
  [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 v3.5.2+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 5.28s


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

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 62.34s


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

     Testing SimilaritySearch
      Status `/tmp/jl_sP23XM/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_sP23XM/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 v4.18.0
  [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 v3.4.3
  [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 v3.5.2+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  11.5s
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  2.6s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.2s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.724630 seconds (1000 allocations: 78.125 KiB)
 10.749664 seconds (1000 allocations: 78.125 KiB)
  4.061408 seconds (1000 allocations: 78.125 KiB)
  4.012434 seconds (1000 allocations: 78.125 KiB)
  3.994564 seconds (1000 allocations: 78.125 KiB)
  4.000560 seconds (1000 allocations: 78.125 KiB)
  3.996209 seconds (1000 allocations: 78.125 KiB)
  3.923999 seconds (1000 allocations: 78.125 KiB)
 15.821461 seconds (1000 allocations: 78.125 KiB)
 15.619950 seconds (1000 allocations: 78.125 KiB)
 28.539291 seconds (1000 allocations: 78.125 KiB)
 28.493725 seconds (1000 allocations: 78.125 KiB)
 20.989634 seconds (6.23 k allocations: 388.641 KiB)
 20.815020 seconds (1000 allocations: 78.125 KiB)
 17.655010 seconds (1.00 k allocations: 78.141 KiB)
 17.710599 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m43.1s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.212006 seconds (1000 allocations: 78.125 KiB)
  3.219547 seconds (1000 allocations: 78.125 KiB)
 29.781965 seconds (1000 allocations: 78.125 KiB)
 28.874276 seconds (1000 allocations: 78.125 KiB)
 29.189497 seconds (1000 allocations: 78.125 KiB)
 29.563188 seconds (1000 allocations: 78.125 KiB)
  4.384631 seconds (1000 allocations: 78.125 KiB)
  4.359657 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m16.4s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  8.816629 seconds (1000 allocations: 78.125 KiB)
  8.842275 seconds (1000 allocations: 78.125 KiB)
  8.706337 seconds (1000 allocations: 78.125 KiB)
  8.858612 seconds (1000 allocations: 78.125 KiB)
  8.925865 seconds (1000 allocations: 78.125 KiB)
  8.787247 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  55.9s
  0.046042 seconds (1.00 k allocations: 78.141 KiB)
  0.046619 seconds (1000 allocations: 78.125 KiB)
  0.039209 seconds (1000 allocations: 78.125 KiB)
  0.040513 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.9s
  0.053030 seconds (1000 allocations: 78.125 KiB)
  0.054466 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.5s
ExhaustiveSearch allknn: 3.955734 seconds (2.38 M allocations: 132.168 MiB, 1.30% gc time, 99.95% compilation time)
ParallelExhaustiveSearch allknn: 1.236354 seconds (609.25 k allocations: 31.947 MiB, 99.85% compilation time)
Test Summary: | Pass  Total     Time
allknn        |    5      5  1m00.5s
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  3.0s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:00.150
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-25T19:19:00.182
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) 2025-09-25T19:19:01.310
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:01.681
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000006, 0x00000008, 0x0000000c, 0x00000018, 0x0000001b, 0x00000021, 0x0000003a, 0x00000046]
D.nn = Int32[1, 2, 2, 4, 2, 6, 6, 8, 6, 6, 6, 12, 1, 4, 6, 6, 4, 2, 2, 8, 6, 2, 2, 24, 6, 6, 27, 6, 2, 8, 24, 6, 33, 4, 8, 12, 1, 8, 27, 6, 33, 1, 8, 2, 8, 4, 2, 6, 1, 1, 1, 2, 6, 6, 2, 24, 12, 58, 27, 27, 24, 24, 4, 2, 8, 2, 6, 6, 4, 70, 6, 6, 4, 1, 27, 6, 4, 33, 4, 1, 8, 2, 4, 24, 24, 12, 27, 2, 6, 2, 6, 6, 12, 2, 27, 6, 8, 12, 58, 8]
D.dist = Float32[0.0, 0.0, 0.031109452, 0.0, 0.032582104, 0.0, 0.033867657, 0.0, 0.08312827, 0.01701516, 0.028622389, 0.0, 0.0016977191, 0.018123984, 0.06388503, 0.01931113, 0.042026103, 0.01801747, 0.04441297, 0.05953628, 0.031295657, 0.078032374, 0.043633997, 0.0, 0.026322126, 0.0223037, 0.0, 0.035862267, 0.018826842, 0.07589692, 0.07048869, 0.048186243, 0.0, 0.062547326, 0.010661185, 0.0084421635, 0.054733753, 0.04119551, 0.025752902, 0.028045058, 0.07939118, 0.029702365, 0.026079595, 0.09129572, 0.034537494, 0.00848943, 0.0062448382, 0.017022729, 0.05945176, 0.01486814, 0.031322956, 0.046728075, 0.015212297, 0.045185268, 0.016118586, 0.075490475, 0.011354089, 0.0, 0.04872006, 0.005158305, 0.015143454, 0.043468416, 0.033560812, 0.026740074, 0.030632794, 0.070084095, 0.06934309, 0.01354444, 0.033421993, 0.0, 0.04115373, 0.025861382, 0.022083282, 0.036491513, 0.078011036, 0.009643376, 0.06795359, 0.01599574, 0.030219316, 0.058635533, 0.06300771, 0.027754486, 0.02814281, 0.03450954, 0.048686862, 0.049969256, 0.029772282, 0.023666799, 0.0031869411, 0.038808823, 0.05543226, 0.02844292, 0.062332034, 0.07938439, 0.060308933, 0.05444926, 0.028723419, 0.032695055, 0.061097503, 0.010429919]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  18.8s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.339
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-25T19:19:03.339
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: 2025-09-25T19:19:03.340
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.340
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.340
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.340
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.340
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.340
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.340
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000006, 0x00000008, 0x0000000c, 0x00000018, 0x0000001b, 0x00000021, 0x0000003a, 0x00000046]
D.nn = Int32[1, 2, 2, 4, 2, 6, 6, 8, 6, 6, 6, 12, 1, 4, 6, 6, 4, 2, 2, 8, 6, 2, 2, 24, 6, 6, 27, 6, 2, 8, 24, 6, 33, 4, 8, 12, 1, 8, 27, 6, 8, 1, 8, 2, 8, 4, 2, 6, 1, 1, 1, 2, 6, 6, 2, 24, 12, 58, 27, 27, 24, 24, 4, 2, 8, 2, 6, 6, 4, 70, 6, 6, 4, 1, 27, 6, 4, 33, 4, 1, 8, 2, 4, 24, 24, 12, 27, 2, 6, 2, 6, 6, 12, 2, 27, 6, 8, 12, 58, 8]
D.dist = Float32[0.0, 0.0, 0.031109452, 0.0, 0.032582104, 0.0, 0.033867657, 0.0, 0.08312827, 0.01701516, 0.028622389, 0.0, 0.0016977191, 0.018123984, 0.06388503, 0.01931113, 0.042026103, 0.01801747, 0.04441297, 0.05953628, 0.031295657, 0.078032374, 0.043633997, 0.0, 0.026322126, 0.0223037, 0.0, 0.035862267, 0.018826842, 0.07589692, 0.07048869, 0.048186243, 0.0, 0.062547326, 0.010661185, 0.0084421635, 0.054733753, 0.04119551, 0.025752902, 0.028045058, 0.093298554, 0.029702365, 0.026079595, 0.09129572, 0.034537494, 0.00848943, 0.0062448382, 0.017022729, 0.05945176, 0.01486814, 0.031322956, 0.046728075, 0.015212297, 0.045185268, 0.016118586, 0.075490475, 0.011354089, 0.0, 0.04872006, 0.005158305, 0.015143454, 0.043468416, 0.033560812, 0.026740074, 0.030632794, 0.070084095, 0.06934309, 0.01354444, 0.033421993, 0.0, 0.04115373, 0.025861382, 0.022083282, 0.036491513, 0.078011036, 0.009643376, 0.06795359, 0.01599574, 0.030219316, 0.058635533, 0.06300771, 0.027754486, 0.02814281, 0.03450954, 0.048686862, 0.049969256, 0.029772282, 0.023666799, 0.0031869411, 0.038808823, 0.05543226, 0.02844292, 0.062332034, 0.07938439, 0.060308933, 0.05444926, 0.028723419, 0.032695055, 0.061097503, 0.010429919]
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: 2025-09-25T19:19:03.429
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-25T19:19:03.429
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: 2025-09-25T19:19:03.430
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.430
[ Info: neardup> range: 49:64, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.430
[ Info: neardup> range: 65:80, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.430
[ Info: neardup> range: 81:96, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.430
[ Info: neardup> range: 97:100, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.430
[ Info: neardup> finished current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:03.431
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x0000003c, 0x0000003d]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 14, 2, 2, 8, 11, 5, 2, 15, 16, 16, 16, 9, 11, 8, 15, 9, 3, 14, 8, 12, 16, 8, 6, 6, 3, 1, 8, 2, 10, 4, 2, 6, 10, 13, 1, 11, 10, 6, 11, 15, 12, 9, 16, 60, 61, 15, 16, 3, 8, 15, 15, 10, 14, 15, 16, 11, 4, 13, 7, 6, 4, 5, 4, 16, 8, 3, 14, 15, 61, 14, 60, 11, 6, 2, 10, 11, 12, 2, 16, 10, 8, 12, 9, 8]
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, 0.012176037, 0.01801747, 0.04441297, 0.05953628, 0.018351614, 0.034300745, 0.043633997, 0.07009405, 0.02498746, 0.008346021, 0.0962885, 0.015816808, 0.002691865, 0.07589692, 0.053563476, 0.010890543, 0.053097308, 0.056549847, 0.010661185, 0.0084421635, 0.033625007, 0.04119551, 0.07328695, 0.028045058, 0.04812193, 0.029702365, 0.026079595, 0.09129572, 0.025424063, 0.00848943, 0.0062448382, 0.017022729, 0.043849766, 0.011754453, 0.031322956, 0.020500898, 0.0012091398, 0.045185268, 0.0062325597, 0.023417234, 0.011354089, 0.018877208, 0.043164015, 0.0, 0.0, 0.0707047, 0.030279875, 0.018458784, 0.030632794, 0.058089197, 0.0035293698, 0.0077481866, 0.019076765, 0.05510795, 0.013781071, 0.01466167, 0.022083282, 0.03534466, 0.083378136, 0.009643376, 0.06795359, 0.02763766, 0.030219316, 0.039796054, 0.06300771, 0.016279101, 0.018933356, 0.022698045, 0.037792146, 0.043867886, 0.030692697, 0.0029858947, 0.0031869411, 0.038808823, 0.018401086, 0.0027230978, 0.062332034, 0.07938439, 0.028118253, 0.033638954, 0.028723419, 0.032695055, 0.044453025, 0.010429919]
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: 2025-09-25T19:19:23.781
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=6 n=6 2025-09-25T19:19:23.782
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:23.787
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:23.787
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:23.787
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:23.787
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:23.787
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:23.787
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-25T19:19:23.787
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000006, 0x00000008, 0x0000000c, 0x00000018, 0x0000001b, 0x00000021, 0x0000003a, 0x00000046]
D.nn = Int32[1, 2, 2, 4, 2, 6, 6, 8, 6, 6, 6, 12, 1, 4, 6, 6, 4, 2, 2, 8, 6, 2, 2, 24, 6, 6, 27, 6, 2, 8, 24, 6, 33, 4, 8, 12, 1, 8, 27, 6, 8, 1, 8, 2, 8, 4, 2, 6, 1, 1, 1, 2, 6, 6, 2, 24, 12, 58, 27, 27, 24, 24, 4, 2, 8, 2, 6, 6, 4, 70, 6, 6, 4, 1, 27, 6, 4, 33, 4, 1, 8, 2, 4, 24, 24, 12, 27, 2, 6, 2, 6, 6, 12, 2, 27, 6, 8, 12, 58, 8]
D.dist = Float32[0.0, 0.0, 0.031109452, 0.0, 0.032582104, 0.0, 0.033867657, 0.0, 0.08312827, 0.01701516, 0.028622389, 0.0, 0.0016977191, 0.018123984, 0.06388503, 0.01931113, 0.042026103, 0.01801747, 0.04441297, 0.05953628, 0.031295657, 0.078032374, 0.043633997, 0.0, 0.026322126, 0.0223037, 0.0, 0.035862267, 0.018826842, 0.07589692, 0.07048869, 0.048186243, 0.0, 0.062547326, 0.010661185, 0.0084421635, 0.054733753, 0.04119551, 0.025752902, 0.028045058, 0.093298554, 0.029702365, 0.026079595, 0.09129572, 0.034537494, 0.00848943, 0.0062448382, 0.017022729, 0.05945176, 0.01486814, 0.031322956, 0.046728075, 0.015212297, 0.045185268, 0.016118586, 0.075490475, 0.011354089, 0.0, 0.04872006, 0.005158305, 0.015143454, 0.043468416, 0.033560812, 0.026740074, 0.030632794, 0.070084095, 0.06934309, 0.01354444, 0.033421993, 0.0, 0.04115373, 0.025861382, 0.022083282, 0.036491513, 0.078011036, 0.009643376, 0.06795359, 0.01599574, 0.030219316, 0.058635533, 0.06300771, 0.027754486, 0.02814281, 0.03450954, 0.048686862, 0.049969256, 0.029772282, 0.023666799, 0.0031869411, 0.038808823, 0.05543226, 0.02844292, 0.062332034, 0.07938439, 0.060308933, 0.05444926, 0.028723419, 0.032695055, 0.061097503, 0.010429919]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.0s
computing farthest point 1, dmax: Inf, imax: 11, n: 30
computing farthest point 2, dmax: 0.9864722, imax: 25, n: 30
computing farthest point 3, dmax: 0.9360805, imax: 12, n: 30
computing farthest point 4, dmax: 0.87938976, imax: 4, n: 30
computing farthest point 5, dmax: 0.86530745, imax: 20, n: 30
computing farthest point 6, dmax: 0.78154033, imax: 29, n: 30
computing farthest point 7, dmax: 0.7051258, imax: 27, n: 30
computing farthest point 8, dmax: 0.67206794, imax: 24, n: 30
computing farthest point 9, dmax: 0.6329538, imax: 19, n: 30
computing farthest point 10, dmax: 0.58801377, imax: 28, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.6s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.7s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-25T19:19:32.329
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=10, Δ=0.945, maxvisits=104) 2025-09-25T19:19:44.906
LOG n.size quantiles:[2.0, 2.0, 3.0, 3.0, 3.0]
(i, j, d) = (20, 296, -1.1920929f-7)
(i, j, d, :parallel) = (20, 296, -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 => 20.21497265, :exact => 0.952751411)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  21.7s
  5.978455 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.006146 seconds
SEARCH Exhaustive 2: 0.006064 seconds
SEARCH Exhaustive 3: 0.005955 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) 2025-09-25T19:20:14.425
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=14, Δ=1.2733874, maxvisits=176) 2025-09-25T19:20:20.610
LOG n.size quantiles:[1.0, 2.0, 2.0, 3.0, 3.0]
[ Info: RECALL BAJO!! recall: 0.24375000000000002, #objects: 3329, #queries: 32
[ Info: [0.2757997214794159, 0.7048017382621765, 0.9243682026863098, 0.2826167941093445, 0.43643587827682495, 0.3128206729888916, 0.2882051169872284, 0.2508426904678345, 0.5479796528816223, 0.4852791428565979, 0.6377338171005249, 0.6746434569358826, 0.33533647656440735, 0.2057737112045288, 0.25019946694374084, 0.2950471341609955, 0.6181925535202026, 0.322414755821228, 0.41916728019714355, 0.3273598849773407, 0.3775077760219574, 0.6876472234725952, 0.4912562072277069, 0.3295917212963104, 0.40539708733558655, 0.1969679594039917, 0.676850438117981, 0.4275595545768738, 0.6200215220451355, 0.4217706024646759, 0.28675082325935364, 0.5262860655784607]
(g, r) = (Set(Int32[24, 2199, 2357, 2522, 2276, 2754, 1441, 2032, 2668, 424]), Set(Int32[2276, 1732, 1902, 1733, 1714, 1901, 1952, 165, 2668, 1749]))
(g, r) = (Set(Int32[2760, 3009, 1039, 1583, 620, 2698, 459, 1094, 2335, 2495]), Set(Int32[339, 2382, 2522, 1473, 2975, 2715, 3240, 2885, 1168, 2824]))
(g, r) = (Set(Int32[224, 2648, 1054, 1583, 2722, 459, 128, 1094, 2848, 1886]), Set(Int32[1473, 2975, 2715, 3240, 2885, 1168, 2824, 1173, 3289, 1147]))
(g, r) = (Set(Int32[1079, 2873, 2771, 566, 2956, 1743, 1582, 3139, 2078, 2530]), Set(Int32[1766, 420, 1743, 1986, 1582, 438, 640, 14, 351, 1440]))
(g, r) = (Set(Int32[109, 3291, 551, 2579, 451, 3073, 138, 579, 1164, 602]), Set(Int32[551, 3149, 409, 105, 1461, 602, 579, 561, 3110, 403]))
(g, r) = (Set(Int32[2053, 2083, 3169, 590, 3128, 457, 864, 2449, 3074, 2054]), Set(Int32[2053, 2958, 384, 1156, 457, 2451, 2069, 888, 992, 221]))
(g, r) = (Set(Int32[53, 2142, 2221, 2786, 284, 871, 2315, 3047, 1409, 2417]), Set(Int32[1322, 2812, 2899, 3045, 1020, 2692, 2315, 2224, 3047, 2553]))
(g, r) = (Set(Int32[3032, 2180, 2209, 2779, 2070, 1174, 2906, 1135, 2690, 75]), Set(Int32[908, 3258, 1479, 1666, 118, 2070, 1600, 2180, 969, 887]))
(g, r) = (Set(Int32[2652, 3056, 2704, 3080, 1944, 488, 2778, 1323, 1694, 1332]), Set(Int32[2652, 1944, 3137, 894, 2778, 271, 2339, 3185, 295, 355]))
(g, r) = (Set(Int32[3010, 227, 2763, 6, 837, 1960, 1876, 1031, 2338, 2699]), Set(Int32[2396, 2297, 3192, 2929, 2502, 3135, 1443, 1216, 2736, 2021]))
(g, r) = (Set(Int32[2258, 668, 85, 1991, 1307, 1193, 2669, 869, 2634, 2293]), Set(Int32[408, 5, 352, 2287, 372, 1652, 41, 1228, 1271, 1728]))
(g, r) = (Set(Int32[1345, 2432, 2271, 3173, 698, 192, 2717, 1722, 3055, 1149]), Set(Int32[2652, 398, 3137, 442, 2933, 2778, 3184, 839, 3185, 355]))
(g, r) = (Set(Int32[1351, 1693, 1099, 1672, 1517, 1010, 1259, 1531, 2619, 1283]), Set(Int32[1693, 1487, 1099, 474, 1378, 1001, 1010, 222, 1283, 1351]))
(g, r) = (Set(Int32[2460, 1402, 3314, 2401, 996, 2458, 2694, 1256, 2063, 582]), Set(Int32[2460, 1402, 3314, 2401, 996, 1256, 1213, 2420, 2063, 582]))
(g, r) = (Set(Int32[1753, 2111, 799, 3165, 644, 2775, 1254, 2805, 1864, 1164]), Set(Int32[1753, 2111, 799, 2624, 691, 860, 644, 2775, 2805, 672]))
(g, r) = (Set(Int32[2192, 1835, 1586, 1168, 2975, 2990, 2300, 986, 3182, 3256]), Set(Int32[336, 1835, 1586, 575, 850, 1578, 182, 3194, 1490, 652]))
(g, r) = (Set(Int32[2574, 2329, 2569, 136, 521, 462, 1772, 3052, 875, 254]), Set(Int32[831, 1730, 1406, 1751, 2183, 430, 846, 1089, 493, 798]))
(g, r) = (Set(Int32[1181, 658, 3205, 2708, 2149, 64, 2936, 3320, 1466, 1557]), Set(Int32[2411, 3205, 1246, 589, 463, 735, 1579, 2286, 2013, 526]))
(g, r) = (Set(Int32[3311, 1316, 1608, 2706, 1086, 1335, 1062, 766, 2641, 2772]), Set(Int32[2080, 850, 182, 556, 1578, 565, 511, 1490, 131, 751]))
(g, r) = (Set(Int32[988, 2619, 2668, 3148, 867, 2749, 1259, 1531, 1672, 1749]), Set(Int32[2167, 3027, 3033, 3009, 3148, 1733, 2788, 2749, 1749, 2668]))
(g, r) = (Set(Int32[1214, 3087, 2548, 2932, 500, 214, 570, 1413, 3195, 232]), Set(Int32[2380, 3285, 500, 1121, 2773, 2283, 3195, 2758, 2804, 1184]))
(g, r) = (Set(Int32[2138, 3143, 1999, 1125, 1379, 1505, 42, 3156, 1357, 77]), Set(Int32[174, 475, 2165, 421, 28, 1358, 706, 407, 1218, 662]))
(g, r) = (Set(Int32[988, 3033, 2668, 3042, 1733, 3148, 2900, 1822, 324, 1672]), Set(Int32[339, 1693, 1099, 1098, 1001, 1089, 1010, 1952, 1264, 1126]))
(g, r) = (Set(Int32[1289, 25, 2784, 2648, 2975, 2715, 2885, 1123, 19, 2192]), Set(Int32[2382, 2522, 1473, 2975, 2715, 1168, 2885, 2300, 2824, 3240]))
(g, r) = (Set(Int32[1975, 1640, 2295, 2514, 1224, 1412, 37, 187, 1495, 2077]), Set(Int32[883, 1640, 2295, 1224, 2558, 37, 187, 1495, 811, 2077]))
(g, r) = (Set(Int32[58, 876, 2086, 1994, 2185, 2028, 616, 2838, 697, 392]), Set(Int32[1452, 58, 876, 1678, 2185, 616, 2639, 697, 392, 671]))
(g, r) = (Set(Int32[202, 2189, 2016, 3111, 135, 378, 2097, 3180, 1365, 3006]), Set(Int32[202, 802, 2076, 2651, 396, 378, 2885, 867, 733, 344]))
(g, r) = (Set(Int32[652, 990, 639, 2951, 1417, 1494, 1301, 466, 981, 3299]), Set(Int32[719, 1231, 3104, 3194, 1301, 1197, 1770, 3162, 2935, 990]))
(g, r) = (Set(Int32[609, 108, 412, 2001, 136, 2594, 2989, 846, 487, 2394]), Set(Int32[2896, 2461, 1937, 2443, 2978, 2228, 3051, 2501, 3166, 2745]))
(g, r) = (Set(Int32[908, 963, 3248, 3258, 1405, 2504, 678, 1600, 2663, 887]), Set(Int32[908, 3258, 3248, 862, 678, 2070, 1600, 2663, 969, 887]))
(g, r) = (Set(Int32[636, 286, 771, 17, 2094, 2479, 2884, 165, 2270, 1658]), Set(Int32[286, 771, 393, 17, 1902, 2183, 2479, 480, 165, 7]))
(g, r) = (Set(Int32[1453, 1431, 2787, 2351, 1199, 2614, 1636, 2294, 188, 3268]), Set(Int32[1453, 82, 1229, 1792, 2720, 400, 1000, 1964, 641, 1431]))
collect(Int32, IdView(p)) = Int32[2668, 2276, 1952, 1732, 1749, 1902, 1714, 1733, 165, 1901]
collect(Int32, IdView(p)) = Int32[2885, 2715, 3240, 2975, 1473, 2522, 339, 1168, 2824, 2382]
collect(Int32, IdView(p)) = Int32[2885, 2715, 3240, 3289, 2975, 1168, 1473, 2824, 1147, 1173]
collect(Int32, IdView(p)) = Int32[1743, 1582, 438, 640, 420, 1986, 14, 351, 1766, 1440]
collect(Int32, IdView(p)) = Int32[579, 602, 551, 561, 3149, 409, 105, 3110, 403, 1461]
collect(Int32, IdView(p)) = Int32[457, 2053, 888, 221, 2451, 2069, 1156, 2958, 992, 384]
collect(Int32, IdView(p)) = Int32[3047, 2315, 3045, 2812, 1322, 1020, 2553, 2692, 2899, 2224]
collect(Int32, IdView(p)) = Int32[2070, 2180, 1600, 3258, 969, 1666, 1479, 908, 887, 118]
collect(Int32, IdView(p)) = Int32[2778, 2652, 1944, 3137, 3185, 2339, 355, 271, 295, 894]
collect(Int32, IdView(p)) = Int32[2502, 2021, 1443, 2736, 3192, 2297, 1216, 2929, 2396, 3135]
collect(Int32, IdView(p)) = Int32[1228, 1271, 2287, 1728, 372, 408, 352, 1652, 41, 5]
collect(Int32, IdView(p)) = Int32[3184, 839, 2778, 2652, 442, 3185, 3137, 398, 2933, 355]
collect(Int32, IdView(p)) = Int32[1283, 1010, 1351, 1099, 1693, 1001, 1487, 474, 222, 1378]
collect(Int32, IdView(p)) = Int32[2460, 2401, 2063, 996, 3314, 582, 1256, 1402, 2420, 1213]
collect(Int32, IdView(p)) = Int32[799, 644, 1753, 2775, 2805, 2111, 2624, 860, 672, 691]
collect(Int32, IdView(p)) = Int32[1586, 1835, 575, 1490, 1578, 850, 182, 3194, 652, 336]
collect(Int32, IdView(p)) = Int32[493, 846, 1089, 831, 798, 1751, 2183, 430, 1406, 1730]
collect(Int32, IdView(p)) = Int32[3205, 463, 2411, 735, 1579, 589, 2286, 2013, 526, 1246]
collect(Int32, IdView(p)) = Int32[1490, 511, 850, 1578, 565, 182, 131, 751, 556, 2080]
collect(Int32, IdView(p)) = Int32[3148, 1749, 2749, 2668, 1733, 3009, 2167, 3027, 3033, 2788]
collect(Int32, IdView(p)) = Int32[500, 3195, 2380, 1184, 2804, 1121, 2773, 2758, 2283, 3285]
collect(Int32, IdView(p)) = Int32[475, 1218, 662, 2165, 407, 706, 421, 1358, 28, 174]
collect(Int32, IdView(p)) = Int32[1264, 1098, 1010, 1089, 1099, 339, 1126, 1693, 1952, 1001]
collect(Int32, IdView(p)) = Int32[2885, 2715, 2975, 1168, 2382, 2522, 2300, 2824, 3240, 1473]
collect(Int32, IdView(p)) = Int32[1640, 2077, 1224, 37, 187, 2295, 1495, 2558, 811, 883]
collect(Int32, IdView(p)) = Int32[58, 392, 876, 616, 697, 2185, 1452, 671, 1678, 2639]
collect(Int32, IdView(p)) = Int32[202, 378, 802, 2076, 733, 867, 2651, 344, 2885, 396]
collect(Int32, IdView(p)) = Int32[990, 1301, 1197, 1770, 1231, 3104, 2935, 3162, 719, 3194]
collect(Int32, IdView(p)) = Int32[2745, 1937, 2228, 2896, 2978, 2461, 3166, 3051, 2501, 2443]
collect(Int32, IdView(p)) = Int32[3248, 1600, 908, 678, 3258, 2663, 887, 2070, 862, 969]
collect(Int32, IdView(p)) = Int32[286, 2479, 165, 771, 17, 7, 480, 1902, 393, 2183]
collect(Int32, IdView(p)) = Int32[1453, 1431, 1964, 2720, 400, 641, 1792, 1000, 82, 1229]
quantile(neighbors_length.(Ref(index.adj), 1:length(index)), 0:0.1:1.0) = [1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 5.0, 6.0, 8.0, 31.0]
     Testing SimilaritySearch tests passed 

Testing completed after 623.57s

PkgEval succeeded after 728.62s