Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1222 (39e1473f3b*) started at 2025-09-30T19:15:04.836


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.83s


################################################################################
# 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 v3.4.4
  [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 v3.5.2+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 5.11s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 55.05s


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

     Testing SimilaritySearch
      Status `/tmp/jl_iVjSEl/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_iVjSEl/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 v3.4.4
  [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 v3.5.2+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  14.9s
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}})
 10.647212 seconds (1000 allocations: 78.125 KiB)
  9.903946 seconds (1000 allocations: 78.125 KiB)
  2.647520 seconds (1000 allocations: 78.125 KiB)
  2.958605 seconds (1000 allocations: 78.125 KiB)
  4.132496 seconds (1000 allocations: 78.125 KiB)
  4.085048 seconds (1000 allocations: 78.125 KiB)
  3.555251 seconds (1000 allocations: 78.125 KiB)
  3.970425 seconds (1000 allocations: 78.125 KiB)
 14.753485 seconds (1000 allocations: 78.125 KiB)
 11.748777 seconds (1000 allocations: 78.125 KiB)
 18.712416 seconds (1000 allocations: 78.125 KiB)
 21.889907 seconds (1000 allocations: 78.125 KiB)
 17.239660 seconds (6.23 k allocations: 388.672 KiB)
 21.146678 seconds (1000 allocations: 78.125 KiB)
 17.113583 seconds (1.00 k allocations: 78.141 KiB)
 17.544292 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m13.0s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.595185 seconds (1000 allocations: 78.125 KiB)
  3.719004 seconds (1000 allocations: 78.125 KiB)
 30.532932 seconds (1000 allocations: 78.125 KiB)
 29.877640 seconds (1000 allocations: 78.125 KiB)
 21.894573 seconds (1000 allocations: 78.125 KiB)
 22.215443 seconds (1000 allocations: 78.125 KiB)
  4.345671 seconds (1000 allocations: 78.125 KiB)
  4.796582 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m04.6s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  8.252711 seconds (1000 allocations: 78.125 KiB)
  8.327811 seconds (1000 allocations: 78.125 KiB)
  8.343060 seconds (1000 allocations: 78.125 KiB)
  8.418395 seconds (1000 allocations: 78.125 KiB)
  8.319820 seconds (1000 allocations: 78.125 KiB)
  9.068240 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  53.5s
  0.046889 seconds (1.00 k allocations: 78.141 KiB)
  0.046172 seconds (1000 allocations: 78.125 KiB)
  0.040927 seconds (1000 allocations: 78.125 KiB)
  0.040623 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.9s
  0.052729 seconds (1000 allocations: 78.125 KiB)
  0.052953 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.5s
ExhaustiveSearch allknn: 4.084972 seconds (2.32 M allocations: 128.820 MiB, 1.17% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.207383 seconds (610.02 k allocations: 31.948 MiB, 99.85% compilation time)
Test Summary: | Pass  Total   Time
allknn        |    5      5  58.0s
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, 6.0]
Test Summary: | Total  Time
HSP           |     0  3.5s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:46.480
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-30T19:24:46.693
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=3 ep=3 n=3 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-30T19:24:47.705
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: 2025-09-30T19:24:47.888
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000008, 0x00000009, 0x0000000f, 0x00000010, 0x0000001c, 0x00000027, 0x00000038, 0x0000003c]
D.nn = Int32[1, 2, 3, 1, 3, 2, 3, 8, 9, 3, 3, 8, 3, 8, 15, 16, 16, 3, 8, 3, 3, 3, 1, 15, 9, 15, 3, 28, 3, 15, 28, 28, 15, 28, 8, 9, 9, 28, 39, 8, 8, 9, 9, 3, 15, 2, 39, 9, 28, 28, 8, 9, 16, 3, 8, 56, 3, 9, 15, 60, 9, 3, 3, 3, 60, 9, 1, 60, 9, 8, 3, 9, 39, 2, 8, 9, 28, 8, 28, 9, 2, 3, 28, 8, 15, 3, 28, 15, 8, 16, 3, 2, 3, 9, 3, 8, 3, 3, 15, 28]
D.dist = Float32[0.0, 0.0, 0.0, 0.06551498, 0.08940166, 0.018122554, 0.0053584576, 0.0, 0.0, 0.0073385835, 0.09904832, 0.02546972, 0.063370585, 0.019483387, 0.0, 0.0, 0.06366372, 0.021842241, 0.03839469, 0.044121563, 0.081154585, 0.07499218, 0.08906722, 0.072776675, 0.037261605, 0.012176573, 0.08179206, 0.0, 0.0529415, 0.031312883, 0.043644488, 0.053578675, 0.055766642, 0.0367285, 0.051959455, 0.07025635, 0.09497589, 0.049693227, 0.0, 0.039838493, 0.043881893, 0.01949656, 0.034495473, 0.048812866, 0.043068767, 0.011790633, 0.029364824, 0.019585371, 0.043201923, 0.021640897, 0.038039565, 0.031029165, 0.029323995, 0.047137916, 0.057770252, 0.0, 0.014527917, 0.031179905, 0.03504318, 0.0, 0.0060074925, 0.07524836, 0.028259158, 0.0142092705, 0.052054584, 0.043940723, 0.0009716153, 0.028732419, 0.027468741, 0.060083807, 0.008331895, 0.014938593, 0.043883324, 0.026036143, 0.026256144, 0.03529662, 0.032317758, 0.056301594, 0.010348558, 0.0079215765, 0.055588722, 0.0034270883, 0.017822802, 0.058519244, 0.011506617, 0.04092592, 0.05240172, 0.045682967, 0.005752802, 0.017893732, 0.05850047, 0.027243018, 0.03228271, 0.09048253, 0.054545105, 0.042328954, 0.05407381, 0.042803586, 0.014768481, 0.05071217]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  16.5s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.692
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-30T19:24:48.692
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.692
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.692
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.692
[ Info: neardup> range: 65:80, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.693
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.693
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.693
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.693
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000008, 0x00000009, 0x0000000f, 0x00000010, 0x0000001c, 0x00000027, 0x00000038, 0x0000003c]
D.nn = Int32[1, 2, 3, 1, 3, 2, 3, 8, 9, 3, 3, 8, 3, 8, 15, 16, 16, 3, 8, 3, 3, 3, 1, 15, 9, 15, 3, 28, 3, 15, 28, 1, 15, 28, 8, 9, 9, 28, 39, 8, 8, 9, 9, 3, 15, 2, 3, 9, 28, 28, 8, 9, 16, 3, 8, 56, 3, 9, 15, 60, 9, 3, 3, 3, 60, 9, 1, 60, 9, 8, 3, 9, 39, 2, 8, 9, 28, 8, 28, 9, 2, 3, 28, 8, 15, 3, 28, 15, 8, 16, 3, 2, 3, 9, 3, 8, 3, 3, 15, 28]
D.dist = Float32[0.0, 0.0, 0.0, 0.06551498, 0.08940166, 0.018122554, 0.0053584576, 0.0, 0.0, 0.0073385835, 0.09904832, 0.02546972, 0.063370585, 0.019483387, 0.0, 0.0, 0.06366372, 0.021842241, 0.03839469, 0.044121563, 0.081154585, 0.07499218, 0.08906722, 0.072776675, 0.037261605, 0.012176573, 0.08179206, 0.0, 0.0529415, 0.031312883, 0.043644488, 0.082150996, 0.055766642, 0.0367285, 0.051959455, 0.07025635, 0.09497589, 0.049693227, 0.0, 0.039838493, 0.043881893, 0.01949656, 0.034495473, 0.048812866, 0.043068767, 0.011790633, 0.053384423, 0.019585371, 0.043201923, 0.021640897, 0.038039565, 0.031029165, 0.029323995, 0.047137916, 0.057770252, 0.0, 0.014527917, 0.031179905, 0.03504318, 0.0, 0.0060074925, 0.07524836, 0.028259158, 0.0142092705, 0.052054584, 0.043940723, 0.0009716153, 0.028732419, 0.027468741, 0.060083807, 0.008331895, 0.014938593, 0.043883324, 0.026036143, 0.026256144, 0.03529662, 0.032317758, 0.056301594, 0.010348558, 0.0079215765, 0.055588722, 0.0034270883, 0.017822802, 0.058519244, 0.011506617, 0.04092592, 0.05240172, 0.045682967, 0.005752802, 0.017893732, 0.05850047, 0.027243018, 0.03228271, 0.09048253, 0.054545105, 0.042328954, 0.05407381, 0.042803586, 0.014768481, 0.05071217]
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: 2025-09-30T19:24:48.757
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-30T19:24:48.757
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: 2025-09-30T19:24:48.758
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.758
[ Info: neardup> range: 49:64, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.758
[ Info: neardup> range: 65:80, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.758
[ Info: neardup> range: 81:96, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.758
[ Info: neardup> range: 97:100, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.758
[ Info: neardup> finished current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:48.758
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000038, 0x0000003c]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 11, 7, 12, 10, 12, 13, 4, 4, 11, 15, 10, 4, 10, 15, 4, 4, 15, 13, 14, 13, 9, 4, 12, 12, 14, 9, 9, 13, 15, 2, 10, 9, 7, 13, 14, 9, 16, 13, 14, 56, 3, 9, 15, 60, 9, 11, 7, 7, 60, 9, 1, 60, 9, 14, 3, 9, 56, 2, 8, 9, 4, 13, 4, 9, 2, 7, 4, 14, 15, 13, 13, 15, 8, 16, 5, 7, 10, 11, 5, 12, 10, 3, 15, 4]
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, 0.059417307, 0.0070694685, 0.003953159, 0.023807764, 0.03339064, 0.00515604, 0.014974475, 0.03615451, 0.020817816, 0.012176573, 0.07361376, 0.01832813, 0.029912055, 0.031312883, 0.068569064, 0.029444575, 0.055766642, 0.021169543, 0.016815484, 0.03510374, 0.09497589, 0.011928797, 0.07239467, 0.0094127655, 0.005139768, 0.01949656, 0.034495473, 0.022826731, 0.043068767, 0.011790633, 0.031237662, 0.019585371, 0.08532506, 0.0072367787, 0.020007968, 0.031029165, 0.029323995, 0.0014289618, 0.052341938, 0.0, 0.014527917, 0.031179905, 0.03504318, 0.0, 0.0060074925, 0.002277553, 0.009191513, 0.0075408816, 0.052054584, 0.043940723, 0.0009716153, 0.028732419, 0.027468741, 0.04031074, 0.008331895, 0.014938593, 0.070103705, 0.026036143, 0.026256144, 0.03529662, 0.039932966, 0.009541512, 0.021613717, 0.0079215765, 0.055588722, 0.002413988, 0.030587435, 0.027465045, 0.011506617, 0.013440192, 0.08175373, 0.045682967, 0.005752802, 0.017893732, 0.00902617, 0.025352895, 0.01123035, 0.02449882, 0.013385057, 0.0076932907, 0.04083836, 0.042803586, 0.014768481, 0.0368793]
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: 2025-09-30T19:24:53.886
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=7 n=7 2025-09-30T19:24:53.886
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:53.890
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:53.890
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:53.890
[ Info: neardup> range: 65:80, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:53.890
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:53.890
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:53.890
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-30T19:24:53.890
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000008, 0x00000009, 0x0000000f, 0x00000010, 0x0000001c, 0x00000027, 0x00000038, 0x0000003c]
D.nn = Int32[1, 2, 3, 1, 3, 2, 3, 8, 9, 3, 3, 8, 3, 8, 15, 16, 16, 3, 8, 3, 3, 3, 1, 15, 9, 15, 3, 28, 3, 15, 28, 1, 15, 28, 8, 9, 9, 28, 39, 8, 8, 9, 9, 3, 15, 2, 3, 9, 28, 28, 8, 9, 16, 3, 8, 56, 3, 9, 15, 60, 9, 3, 3, 3, 60, 9, 1, 60, 9, 8, 3, 9, 39, 2, 8, 9, 28, 8, 28, 9, 2, 3, 28, 8, 15, 3, 28, 15, 8, 16, 3, 2, 3, 9, 3, 8, 3, 3, 15, 28]
D.dist = Float32[0.0, 0.0, 0.0, 0.06551498, 0.08940166, 0.018122554, 0.0053584576, 0.0, 0.0, 0.0073385835, 0.09904832, 0.02546972, 0.063370585, 0.019483387, 0.0, 0.0, 0.06366372, 0.021842241, 0.03839469, 0.044121563, 0.081154585, 0.07499218, 0.08906722, 0.072776675, 0.037261605, 0.012176573, 0.08179206, 0.0, 0.0529415, 0.031312883, 0.043644488, 0.082150996, 0.055766642, 0.0367285, 0.051959455, 0.07025635, 0.09497589, 0.049693227, 0.0, 0.039838493, 0.043881893, 0.01949656, 0.034495473, 0.048812866, 0.043068767, 0.011790633, 0.053384423, 0.019585371, 0.043201923, 0.021640897, 0.038039565, 0.031029165, 0.029323995, 0.047137916, 0.057770252, 0.0, 0.014527917, 0.031179905, 0.03504318, 0.0, 0.0060074925, 0.07524836, 0.028259158, 0.0142092705, 0.052054584, 0.043940723, 0.0009716153, 0.028732419, 0.027468741, 0.060083807, 0.008331895, 0.014938593, 0.043883324, 0.026036143, 0.026256144, 0.03529662, 0.032317758, 0.056301594, 0.010348558, 0.0079215765, 0.055588722, 0.0034270883, 0.017822802, 0.058519244, 0.011506617, 0.04092592, 0.05240172, 0.045682967, 0.005752802, 0.017893732, 0.05850047, 0.027243018, 0.03228271, 0.09048253, 0.054545105, 0.042328954, 0.05407381, 0.042803586, 0.014768481, 0.05071217]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  5.1s
computing farthest point 1, dmax: Inf, imax: 9, n: 30
computing farthest point 2, dmax: 1.1823183, imax: 24, n: 30
computing farthest point 3, dmax: 1.0696388, imax: 8, n: 30
computing farthest point 4, dmax: 1.0557313, imax: 25, n: 30
computing farthest point 5, dmax: 0.83544844, imax: 6, n: 30
computing farthest point 6, dmax: 0.83095723, imax: 27, n: 30
computing farthest point 7, dmax: 0.618066, imax: 30, n: 30
computing farthest point 8, dmax: 0.6121258, imax: 1, n: 30
computing farthest point 9, dmax: 0.56891215, imax: 20, n: 30
computing farthest point 10, dmax: 0.54890794, imax: 18, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.1s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.2s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-30T19:25:00.124
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=6, Δ=0.975, maxvisits=98) 2025-09-30T19:25:09.701
LOG n.size quantiles:[2.0, 3.0, 3.0, 3.0, 3.0]
(i, j, d) = (14, 738, -1.1920929f-7)
(i, j, d, :parallel) = (14, 738, -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 => 15.212041638999999, :exact => 0.654588191)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  16.3s
  6.261035 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.005301 seconds
SEARCH Exhaustive 2: 0.005020 seconds
SEARCH Exhaustive 3: 0.005270 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) 2025-09-30T19:25:36.597
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=1.075, maxvisits=178) 2025-09-30T19:25:42.583
LOG n.size quantiles:[1.0, 1.0, 2.0, 2.0, 2.0]
[ Info: RECALL BAJO!! recall: 0.296875, #objects: 3329, #queries: 32
[ Info: [0.497763067483902, 0.426325261592865, 0.36176344752311707, 0.8538959622383118, 0.21955694258213043, 0.47162994742393494, 0.2514890730381012, 0.27432844042778015, 0.36106202006340027, 0.21415717899799347, 0.5276371240615845, 0.5418058633804321, 0.6357148885726929, 0.3205510377883911, 0.9720523953437805, 0.36720263957977295, 0.2488892674446106, 0.2542620301246643, 0.5702129006385803, 0.3113189935684204, 0.2787700891494751, 0.5605990290641785, 0.2556672990322113, 0.21204164624214172, 0.3228973150253296, 0.2316412776708603, 0.30367404222488403, 1.0691759586334229, 0.7747352719306946, 0.18109016120433807, 0.20944693684577942, 0.28054478764533997]
(g, r) = (Set(Int32[1476, 2616, 861, 1520, 2120, 2857, 435, 1092, 3017, 437]), Set(Int32[337, 668, 634, 1768, 678, 1498, 759, 2643, 1466, 380]))
(g, r) = (Set(Int32[1860, 1916, 646, 950, 978, 2531, 1983, 232, 878, 2326]), Set(Int32[1372, 2620, 1720, 891, 1225, 1411, 2991, 1977, 200, 846]))
(g, r) = (Set(Int32[1861, 3201, 43, 1279, 2479, 1174, 2702, 1387, 898, 1431]), Set(Int32[1217, 1861, 1337, 43, 1279, 2479, 1174, 1387, 2228, 1431]))
(g, r) = (Set(Int32[583, 1423, 1450, 3036, 1064, 1334, 1146, 2770, 209, 1353]), Set(Int32[745, 691, 827, 631, 2593, 748, 758, 1498, 1611, 87]))
(g, r) = (Set(Int32[1131, 3024, 2829, 515, 2525, 597, 2509, 3275, 2769, 3041]), Set(Int32[1131, 608, 3252, 1119, 761, 536, 1308, 3275, 2542, 413]))
(g, r) = (Set(Int32[1317, 1682, 1242, 1076, 3303, 2228, 2586, 1750, 66, 1827]), Set(Int32[1317, 601, 1077, 1777, 2284, 2584, 1847, 1767, 2199, 66]))
(g, r) = (Set(Int32[2361, 202, 2742, 1201, 452, 1595, 1631, 2472, 728, 2530]), Set(Int32[2361, 202, 2651, 483, 452, 1595, 37, 245, 3085, 2530]))
(g, r) = (Set(Int32[474, 580, 745, 1779, 2774, 2197, 37, 2918, 2347, 1660]), Set(Int32[745, 173, 1190, 235, 2774, 2377, 1446, 1218, 2347, 401]))
(g, r) = (Set(Int32[1975, 2435, 3143, 2198, 2635, 501, 2365, 1603, 1851, 1636]), Set(Int32[2325, 2738, 2435, 768, 1945, 359, 521, 2365, 2725, 1873]))
(g, r) = (Set(Int32[1210, 2302, 2048, 1509, 1640, 477, 801, 966, 1521, 2803]), Set(Int32[2302, 2048, 1509, 801, 29, 1313, 1521, 1579, 2803, 387]))
(g, r) = (Set(Int32[417, 2484, 2137, 2737, 2368, 511, 1136, 142, 2938, 1692]), Set(Int32[417, 2325, 1945, 1269, 1077, 1170, 1548, 1136, 382, 2725]))
(g, r) = (Set(Int32[2902, 2545, 2480, 3251, 3249, 1199, 251, 1519, 2483, 2305]), Set(Int32[721, 2027, 826, 899, 2584, 42, 1033, 2509, 727, 751]))
(g, r) = (Set(Int32[2546, 311, 2246, 2490, 1985, 2534, 832, 3053, 866, 1693]), Set(Int32[421, 464, 2239, 1281, 160, 1498, 490, 2508, 2950, 2192]))
(g, r) = (Set(Int32[3253, 611, 1814, 1678, 3187, 3190, 511, 697, 567, 2668]), Set(Int32[3253, 2927, 3167, 2851, 2915, 3187, 2937, 1546, 2668, 2825]))
(g, r) = (Set(Int32[277, 334, 663, 199, 2020, 362, 3057, 1359, 1060, 221]), Set(Int32[1372, 2227, 3113, 3170, 2593, 2670, 814, 1821, 3189, 1363]))
(g, r) = (Set(Int32[2578, 2606, 1178, 1688, 2964, 2890, 438, 3222, 2349, 401]), Set(Int32[2578, 2606, 3293, 1362, 2750, 37, 1558, 1160, 1055, 2745]))
(g, r) = (Set(Int32[1669, 1786, 740, 1533, 2264, 1065, 3094, 1554, 2880, 1024]), Set(Int32[740, 2494, 3171, 1707, 1789, 653, 2477, 482, 2078, 1386]))
(g, r) = (Set(Int32[1589, 1755, 1782, 2160, 2023, 805, 1063, 1957, 2294, 2087]), Set(Int32[1782, 1755, 2160, 2023, 805, 23, 2046, 2294, 2904, 1953]))
(g, r) = (Set(Int32[2135, 2051, 3031, 1813, 2945, 735, 517, 541, 1339, 3257]), Set(Int32[691, 2714, 2956, 134, 1029, 208, 166, 1512, 505, 2260]))
(g, r) = (Set(Int32[2625, 3231, 2161, 1978, 2232, 2343, 321, 785, 2312, 3064]), Set(Int32[2625, 1838, 197, 1978, 698, 1728, 976, 2748, 732, 3231]))
(g, r) = (Set(Int32[910, 1674, 3084, 2165, 1559, 2703, 373, 950, 1760, 1271]), Set(Int32[254, 1372, 20, 2703, 373, 950, 2201, 1760, 1271, 2385]))
(g, r) = (Set(Int32[797, 611, 984, 3143, 2368, 3156, 1906, 142, 1803, 1091]), Set(Int32[417, 2325, 768, 1945, 1269, 1352, 1136, 2725, 741, 401]))
(g, r) = (Set(Int32[2541, 1480, 3217, 706, 2665, 1111, 569, 2459, 1273, 2860]), Set(Int32[1172, 2166, 76, 884, 317, 1111, 569, 236, 1273, 87]))
(g, r) = (Set(Int32[1185, 661, 670, 698, 2467, 1890, 1633, 32, 1011, 1735]), Set(Int32[3255, 670, 698, 2467, 1890, 2356, 1633, 2998, 2033, 1735]))
(g, r) = (Set(Int32[2656, 614, 1776, 468, 50, 2118, 729, 1854, 2444, 2699]), Set(Int32[614, 1776, 506, 50, 2351, 2615, 729, 2444, 324, 2699]))
(g, r) = (Set(Int32[394, 3281, 2016, 2499, 849, 64, 1090, 2967, 1117, 1759]), Set(Int32[394, 2016, 2499, 1132, 849, 64, 1090, 2967, 1117, 1759]))
(g, r) = (Set(Int32[2410, 2788, 3089, 2825, 1809, 2520, 1035, 406, 288, 922]), Set(Int32[2410, 2788, 2896, 1809, 2209, 693, 2117, 2248, 2044, 2551]))
(g, r) = (Set(Int32[967, 858, 110, 1917, 2401, 3173, 920, 480, 539, 2804]), Set(Int32[417, 827, 1269, 1077, 2915, 1548, 1170, 1136, 65, 2279]))
(g, r) = (Set(Int32[797, 2484, 1678, 611, 1950, 2137, 2368, 1442, 511, 142]), Set(Int32[417, 2325, 421, 1269, 1077, 1972, 2915, 1170, 1136, 139]))
(g, r) = (Set(Int32[2899, 1078, 397, 2845, 702, 2840, 2478, 1816, 3164, 390]), Set(Int32[2899, 2845, 397, 2840, 2455, 1053, 2478, 1816, 469, 390]))
(g, r) = (Set(Int32[2411, 1015, 749, 2140, 216, 2615, 2331, 1026, 2645, 324]), Set(Int32[1748, 506, 614, 216, 2615, 2887, 898, 2645, 324, 1524]))
(g, r) = (Set(Int32[3056, 607, 2671, 1329, 3288, 433, 2848, 599, 1493, 491]), Set(Int32[252, 3056, 607, 248, 599, 1493, 3288, 433, 2848, 491]))
collect(Int32, IdView(p)) = Int32[634, 1466, 668, 1768, 759, 1498, 380, 678, 2643, 337]
collect(Int32, IdView(p)) = Int32[200, 1372, 1977, 1225, 2991, 2620, 1720, 891, 1411, 846]
collect(Int32, IdView(p)) = Int32[1174, 43, 1279, 1431, 2479, 1387, 1861, 1337, 2228, 1217]
collect(Int32, IdView(p)) = Int32[631, 691, 2593, 1611, 827, 748, 758, 745, 87, 1498]
collect(Int32, IdView(p)) = Int32[3275, 1131, 1308, 1119, 536, 413, 608, 3252, 761, 2542]
collect(Int32, IdView(p)) = Int32[1317, 66, 2284, 1077, 1847, 2584, 2199, 601, 1767, 1777]
collect(Int32, IdView(p)) = Int32[2530, 2361, 202, 452, 1595, 2651, 37, 483, 245, 3085]
collect(Int32, IdView(p)) = Int32[2774, 2347, 745, 401, 173, 1190, 2377, 1218, 235, 1446]
collect(Int32, IdView(p)) = Int32[2435, 2365, 2325, 768, 1873, 2738, 2725, 1945, 359, 521]
collect(Int32, IdView(p)) = Int32[1521, 1509, 2048, 2302, 2803, 801, 1313, 1579, 29, 387]
collect(Int32, IdView(p)) = Int32[417, 1136, 1170, 1269, 1548, 1077, 1945, 2325, 2725, 382]
collect(Int32, IdView(p)) = Int32[899, 1033, 826, 721, 751, 2027, 727, 2584, 42, 2509]
collect(Int32, IdView(p)) = Int32[1498, 2192, 2508, 2950, 2239, 464, 490, 1281, 421, 160]
collect(Int32, IdView(p)) = Int32[3253, 2668, 3187, 3167, 2851, 1546, 2927, 2937, 2825, 2915]
collect(Int32, IdView(p)) = Int32[3113, 3170, 2227, 3189, 2670, 2593, 1363, 814, 1372, 1821]
collect(Int32, IdView(p)) = Int32[2578, 2606, 1160, 1362, 3293, 2750, 1558, 37, 1055, 2745]
collect(Int32, IdView(p)) = Int32[740, 653, 2494, 1707, 1386, 2078, 3171, 2477, 1789, 482]
collect(Int32, IdView(p)) = Int32[1755, 2294, 2023, 805, 1782, 2160, 1953, 23, 2904, 2046]
collect(Int32, IdView(p)) = Int32[166, 208, 2956, 134, 2714, 1029, 1512, 691, 2260, 505]
collect(Int32, IdView(p)) = Int32[2625, 3231, 1978, 1838, 976, 732, 2748, 197, 698, 1728]
collect(Int32, IdView(p)) = Int32[950, 1760, 373, 2703, 1271, 2385, 2201, 20, 254, 1372]
collect(Int32, IdView(p)) = Int32[417, 741, 1136, 768, 1945, 1269, 2325, 2725, 401, 1352]
collect(Int32, IdView(p)) = Int32[1111, 1273, 569, 87, 2166, 884, 76, 317, 236, 1172]
collect(Int32, IdView(p)) = Int32[670, 698, 2467, 1735, 1633, 1890, 2998, 3255, 2356, 2033]
collect(Int32, IdView(p)) = Int32[2699, 50, 1776, 2444, 614, 729, 506, 2615, 324, 2351]
collect(Int32, IdView(p)) = Int32[849, 1117, 394, 2016, 1090, 2967, 2499, 1759, 64, 1132]
collect(Int32, IdView(p)) = Int32[2410, 2788, 1809, 2248, 2117, 2209, 2896, 2551, 2044, 693]
collect(Int32, IdView(p)) = Int32[417, 1136, 1548, 2915, 1170, 1269, 1077, 65, 827, 2279]
collect(Int32, IdView(p)) = Int32[417, 1269, 1136, 1170, 1077, 2915, 1972, 139, 421, 2325]
collect(Int32, IdView(p)) = Int32[2899, 2478, 390, 1816, 397, 2845, 2840, 2455, 1053, 469]
collect(Int32, IdView(p)) = Int32[324, 216, 2615, 2645, 2887, 1524, 506, 898, 1748, 614]
collect(Int32, IdView(p)) = Int32[433, 3288, 3056, 491, 607, 2848, 599, 1493, 252, 248]
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, 5.0, 6.0, 8.0, 32.0]
     Testing SimilaritySearch tests passed 

Testing completed after 561.94s

PkgEval succeeded after 648.12s