Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1216 (bcb9a929e5*) started at 2025-09-28T19:09:42.231


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 10.08s


################################################################################
# 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 4.93s


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

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 117.61s


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

     Testing SimilaritySearch
      Status `/tmp/jl_abjMdt/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_abjMdt/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  11.4s
Precompiling packages...
 108299.2 ms  ✓ JET
  1 dependency successfully precompiled in 109 seconds. 38 already precompiled.
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 20005  20005  4.0s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.7s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.3s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.809247 seconds (1000 allocations: 78.125 KiB)
 10.417497 seconds (1000 allocations: 78.125 KiB)
  3.901812 seconds (1000 allocations: 78.125 KiB)
  3.823198 seconds (1000 allocations: 78.125 KiB)
  3.941030 seconds (1000 allocations: 78.125 KiB)
  4.030791 seconds (1000 allocations: 78.125 KiB)
  3.867484 seconds (1000 allocations: 78.125 KiB)
  3.829851 seconds (1000 allocations: 78.125 KiB)
 15.481794 seconds (1000 allocations: 78.125 KiB)
 15.091436 seconds (1000 allocations: 78.125 KiB)
 27.254012 seconds (1000 allocations: 78.125 KiB)
 28.155960 seconds (1000 allocations: 78.125 KiB)
 20.031986 seconds (6.23 k allocations: 388.703 KiB)
 20.537457 seconds (1000 allocations: 78.125 KiB)
 18.156478 seconds (1.00 k allocations: 78.141 KiB)
 17.761777 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m39.5s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.651290 seconds (1000 allocations: 78.125 KiB)
  3.588987 seconds (1000 allocations: 78.125 KiB)
 29.694448 seconds (1000 allocations: 78.125 KiB)
 30.381222 seconds (1000 allocations: 78.125 KiB)
 29.406316 seconds (1000 allocations: 78.125 KiB)
 30.458985 seconds (1000 allocations: 78.125 KiB)
  5.015486 seconds (1000 allocations: 78.125 KiB)
  4.816427 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m20.9s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  7.965576 seconds (1000 allocations: 78.125 KiB)
  7.990107 seconds (1000 allocations: 78.125 KiB)
  8.128224 seconds (1000 allocations: 78.125 KiB)
  8.318420 seconds (1000 allocations: 78.125 KiB)
  8.465401 seconds (1000 allocations: 78.125 KiB)
  8.478589 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  52.3s
  0.045912 seconds (1.00 k allocations: 78.141 KiB)
  0.045410 seconds (1000 allocations: 78.125 KiB)
  0.043125 seconds (1000 allocations: 78.125 KiB)
  0.042902 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  3.0s
  0.056586 seconds (1000 allocations: 78.125 KiB)
  0.056940 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.123139 seconds (2.33 M allocations: 129.415 MiB, 1.00% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.308348 seconds (610.11 k allocations: 31.952 MiB, 99.85% compilation time)
Test Summary: | Pass  Total     Time
allknn        |    5      5  1m00.7s
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, 6.0]
Test Summary: | Total  Time
HSP           |     0  3.7s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:03.530
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-28T19:23:03.565
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) 2025-09-28T19:23:04.857
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: 2025-09-28T19:23:05.271
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000012, 0x00000019, 0x00000023, 0x00000024, 0x00000032, 0x00000042, 0x00000048]
D.nn = Int32[1, 2, 3, 4, 5, 5, 5, 3, 4, 5, 4, 5, 5, 4, 3, 4, 1, 18, 5, 5, 5, 4, 18, 4, 25, 18, 3, 4, 4, 25, 4, 4, 25, 25, 35, 36, 36, 18, 3, 5, 4, 3, 5, 5, 18, 5, 25, 3, 5, 50, 3, 5, 4, 36, 3, 18, 1, 18, 25, 4, 3, 4, 25, 36, 18, 66, 18, 18, 4, 36, 2, 72, 1, 3, 5, 36, 5, 36, 4, 25, 18, 18, 4, 25, 1, 3, 2, 18, 3, 5, 4, 18, 2, 50, 18, 50, 72, 25, 3, 66]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.047255397, 0.077272534, 0.09512007, 0.081375, 0.081526875, 0.039637387, 0.08027613, 0.048216045, 0.013382137, 0.04892701, 0.012248516, 0.053136587, 0.0, 0.06835872, 0.07817125, 0.09733343, 0.050530314, 0.034932256, 0.05040127, 0.0, 0.054997504, 0.06548834, 0.06390834, 0.044784606, 0.054944992, 0.010563791, 0.014457345, 0.046855092, 0.023556113, 0.0, 0.0, 0.011050999, 0.05211109, 0.017309546, 0.068778396, 0.04953921, 0.06938344, 0.05668688, 0.053429067, 0.05707687, 0.07048923, 0.042632043, 0.012435555, 0.095694005, 0.0, 0.0113277435, 0.06383246, 0.062116444, 0.03029722, 0.020362496, 0.041288078, 0.018612862, 0.041269183, 0.01111269, 0.039203346, 0.050238192, 0.071223915, 0.03113842, 0.023554087, 0.043256104, 0.0, 0.055597484, 0.016118824, 0.011060119, 0.035261393, 0.07400215, 0.0, 0.044641376, 0.034645677, 0.085290074, 0.010368466, 0.08665329, 0.058307946, 0.030130744, 0.06855625, 0.04698378, 0.06897086, 0.051847816, 0.020430923, 0.07747167, 0.029608488, 0.023649395, 0.0048564672, 0.049880147, 0.02103591, 0.050503135, 0.01252228, 0.005718589, 0.017004728, 0.06275219, 0.009947777, 0.04369974, 0.027504027, 0.017052412, 0.03400457]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  18.9s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.465
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-28T19:23:06.466
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.466
[ Info: neardup> range: 33:48, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.466
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.466
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.466
[ Info: neardup> range: 81:96, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.466
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.466
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.466
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000012, 0x00000019, 0x00000023, 0x00000024, 0x00000032, 0x00000042, 0x00000048]
D.nn = Int32[1, 2, 3, 4, 5, 5, 5, 3, 4, 5, 4, 5, 5, 4, 3, 4, 1, 18, 5, 5, 5, 4, 18, 4, 25, 3, 3, 4, 4, 25, 4, 4, 25, 25, 35, 36, 36, 18, 3, 5, 4, 3, 5, 5, 18, 5, 25, 3, 5, 50, 3, 5, 4, 36, 3, 18, 1, 18, 25, 4, 3, 4, 25, 36, 18, 66, 18, 18, 4, 36, 2, 72, 1, 3, 5, 36, 5, 36, 4, 25, 18, 18, 4, 25, 1, 3, 2, 18, 3, 5, 4, 18, 2, 50, 18, 50, 72, 25, 3, 66]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.047255397, 0.077272534, 0.09512007, 0.081375, 0.081526875, 0.039637387, 0.08027613, 0.048216045, 0.013382137, 0.04892701, 0.012248516, 0.053136587, 0.0, 0.06835872, 0.07817125, 0.09733343, 0.050530314, 0.034932256, 0.05040127, 0.0, 0.0726729, 0.06548834, 0.06390834, 0.044784606, 0.054944992, 0.010563791, 0.014457345, 0.046855092, 0.023556113, 0.0, 0.0, 0.011050999, 0.05211109, 0.017309546, 0.068778396, 0.04953921, 0.06938344, 0.05668688, 0.053429067, 0.05707687, 0.07048923, 0.042632043, 0.012435555, 0.095694005, 0.0, 0.0113277435, 0.06383246, 0.062116444, 0.03029722, 0.020362496, 0.041288078, 0.018612862, 0.041269183, 0.01111269, 0.039203346, 0.050238192, 0.071223915, 0.03113842, 0.023554087, 0.043256104, 0.0, 0.055597484, 0.016118824, 0.011060119, 0.035261393, 0.07400215, 0.0, 0.044641376, 0.034645677, 0.085290074, 0.010368466, 0.08665329, 0.058307946, 0.030130744, 0.06855625, 0.04698378, 0.06897086, 0.051847816, 0.020430923, 0.07747167, 0.029608488, 0.023649395, 0.0048564672, 0.049880147, 0.02103591, 0.050503135, 0.01252228, 0.005718589, 0.017004728, 0.06275219, 0.009947777, 0.04369974, 0.027504027, 0.017052412, 0.03400457]
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: 2025-09-28T19:23:06.565
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-28T19:23:06.565
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: 2025-09-28T19:23:06.565
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.565
[ Info: neardup> range: 49:64, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.566
[ Info: neardup> range: 65:80, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.566
[ Info: neardup> range: 81:96, current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.566
[ Info: neardup> range: 97:100, current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.566
[ Info: neardup> finished current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:06.566
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000023, 0x00000026, 0x0000002d, 0x00000032, 0x00000042]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 8, 12, 5, 9, 4, 8, 9, 12, 8, 7, 9, 9, 12, 4, 14, 11, 11, 35, 10, 10, 38, 3, 5, 16, 15, 5, 13, 45, 7, 11, 3, 12, 50, 3, 6, 14, 10, 3, 9, 1, 9, 12, 11, 3, 45, 14, 38, 45, 66, 9, 38, 4, 10, 8, 7, 1, 15, 16, 10, 16, 7, 4, 13, 9, 9, 4, 12, 11, 3, 2, 9, 15, 5, 45, 8, 2, 50, 9, 50, 7, 12, 3, 66]
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, 0.053136587, 0.050709188, 0.018828988, 0.07817125, 0.05408305, 0.050530314, 0.0054421425, 0.008594394, 0.028690875, 0.013697505, 0.0033509731, 0.022907972, 0.010614395, 0.08291024, 0.010563791, 0.012969494, 0.0037800074, 0.010066688, 0.0, 0.01728487, 0.01562798, 0.0, 0.017309546, 0.068778396, 0.014146328, 0.052237093, 0.05668688, 0.007294476, 0.0, 0.03204435, 0.018545628, 0.012435555, 0.09453249, 0.0, 0.0113277435, 0.0020626187, 0.023115277, 0.00699383, 0.020362496, 0.010772347, 0.018612862, 0.0036007762, 0.013972759, 0.020881534, 0.050238192, 0.0641557, 0.009317219, 0.02605158, 0.020269156, 0.0, 0.0017926693, 0.050723612, 0.011060119, 0.005892217, 0.05838728, 0.07744753, 0.044641376, 0.011163652, 0.056581378, 0.03043294, 0.04659748, 0.02210468, 0.030130744, 0.015588701, 0.0007118583, 0.013330162, 0.051847816, 0.061253548, 0.026396155, 0.029608488, 0.023649395, 0.045217276, 0.0035588741, 0.02103591, 0.020115197, 0.03744358, 0.005718589, 0.017004728, 0.001232624, 0.009947777, 0.038030565, 0.020333886, 0.017052412, 0.03400457]
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: 2025-09-28T19:23:13.902
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=5 n=5 2025-09-28T19:23:13.902
[ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:13.907
[ Info: neardup> range: 33:48, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:13.907
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:13.907
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:13.907
[ Info: neardup> range: 81:96, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:13.908
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:13.908
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-09-28T19:23:13.908
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000012, 0x00000019, 0x00000023, 0x00000024, 0x00000032, 0x00000042, 0x00000048]
D.nn = Int32[1, 2, 3, 4, 5, 5, 5, 3, 4, 5, 4, 5, 5, 4, 3, 4, 1, 18, 5, 5, 5, 4, 18, 4, 25, 3, 3, 4, 4, 25, 4, 4, 25, 25, 35, 36, 36, 18, 3, 5, 4, 3, 5, 5, 18, 5, 25, 3, 5, 50, 3, 5, 4, 36, 3, 18, 1, 18, 25, 4, 3, 4, 25, 36, 18, 66, 18, 18, 4, 36, 2, 72, 1, 3, 5, 36, 5, 36, 4, 25, 18, 18, 4, 25, 1, 3, 2, 18, 3, 5, 4, 18, 2, 50, 18, 50, 72, 25, 3, 66]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.047255397, 0.077272534, 0.09512007, 0.081375, 0.081526875, 0.039637387, 0.08027613, 0.048216045, 0.013382137, 0.04892701, 0.012248516, 0.053136587, 0.0, 0.06835872, 0.07817125, 0.09733343, 0.050530314, 0.034932256, 0.05040127, 0.0, 0.0726729, 0.06548834, 0.06390834, 0.044784606, 0.054944992, 0.010563791, 0.014457345, 0.046855092, 0.023556113, 0.0, 0.0, 0.011050999, 0.05211109, 0.017309546, 0.068778396, 0.04953921, 0.06938344, 0.05668688, 0.053429067, 0.05707687, 0.07048923, 0.042632043, 0.012435555, 0.095694005, 0.0, 0.0113277435, 0.06383246, 0.062116444, 0.03029722, 0.020362496, 0.041288078, 0.018612862, 0.041269183, 0.01111269, 0.039203346, 0.050238192, 0.071223915, 0.03113842, 0.023554087, 0.043256104, 0.0, 0.055597484, 0.016118824, 0.011060119, 0.035261393, 0.07400215, 0.0, 0.044641376, 0.034645677, 0.085290074, 0.010368466, 0.08665329, 0.058307946, 0.030130744, 0.06855625, 0.04698378, 0.06897086, 0.051847816, 0.020430923, 0.07747167, 0.029608488, 0.023649395, 0.0048564672, 0.049880147, 0.02103591, 0.050503135, 0.01252228, 0.005718589, 0.017004728, 0.06275219, 0.009947777, 0.04369974, 0.027504027, 0.017052412, 0.03400457]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.3s
computing farthest point 1, dmax: Inf, imax: 5, n: 30
computing farthest point 2, dmax: 1.0993284, imax: 24, n: 30
computing farthest point 3, dmax: 0.82901263, imax: 3, n: 30
computing farthest point 4, dmax: 0.7738799, imax: 22, n: 30
computing farthest point 5, dmax: 0.7614268, imax: 29, n: 30
computing farthest point 6, dmax: 0.7085302, imax: 2, n: 30
computing farthest point 7, dmax: 0.702206, imax: 25, n: 30
computing farthest point 8, dmax: 0.63691264, imax: 1, n: 30
computing farthest point 9, dmax: 0.55087245, imax: 18, n: 30
computing farthest point 10, dmax: 0.5261357, imax: 13, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.7s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.8s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-28T19:23:22.683
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=0.9, maxvisits=106) 2025-09-28T19:23:36.097
LOG n.size quantiles:[2.0, 2.0, 3.0, 3.0, 3.0]
(i, j, d) = (77, 814, -1.1920929f-7)
(i, j, d, :parallel) = (77, 814, -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 => 21.292271850000002, :exact => 0.995138626)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  22.9s
  5.981874 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.005133 seconds
SEARCH Exhaustive 2: 0.005031 seconds
SEARCH Exhaustive 3: 0.005110 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) 2025-09-28T19:24:06.246
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=11, Δ=1.1851876, maxvisits=188) 2025-09-28T19:24:12.622
LOG n.size quantiles:[1.0, 2.0, 2.0, 2.0, 2.0]
[ Info: RECALL BAJO!! recall: 0.29375, #objects: 3329, #queries: 32
[ Info: [0.4902254641056061, 0.7147706151008606, 0.23197528719902039, 0.3922519087791443, 0.30752289295196533, 0.2487257719039917, 0.19431030750274658, 0.2590389847755432, 0.3977384865283966, 0.5474326014518738, 0.6066476106643677, 0.49817633628845215, 0.43903765082359314, 0.22133475542068481, 0.6375880241394043, 0.9727159738540649, 0.7950733304023743, 0.7143622636795044, 0.39277714490890503, 0.2601596415042877, 0.3243597745895386, 0.2619592547416687, 0.5046189427375793, 0.3090984523296356, 0.4411986172199249, 0.38150766491889954, 0.35375040769577026, 0.2977833151817322, 0.534985363483429, 0.36521628499031067, 0.3998382091522217, 0.36352530121803284]
(g, r) = (Set(Int32[1811, 2211, 879, 698, 2529, 1793, 629, 1376, 2891, 2134]), Set(Int32[1852, 1481, 799, 2326, 315, 1300, 864, 1682, 2134, 2167]))
(g, r) = (Set(Int32[2787, 273, 915, 2129, 71, 1498, 1764, 766, 1921, 1227]), Set(Int32[2463, 3099, 2538, 3216, 2740, 1687, 3133, 2831, 2687, 2225]))
(g, r) = (Set(Int32[1131, 2516, 338, 1775, 115, 1391, 93, 2639, 1271, 1091]), Set(Int32[1131, 338, 250, 115, 1391, 2783, 93, 2447, 1271, 78]))
(g, r) = (Set(Int32[2430, 1561, 2386, 2567, 115, 268, 1655, 152, 1419, 381]), Set(Int32[2793, 3276, 2402, 2368, 2591, 1710, 1638, 2044, 877, 2328]))
(g, r) = (Set(Int32[746, 76, 2026, 37, 351, 2341, 1055, 1309, 3156, 1112]), Set(Int32[2546, 608, 2461, 76, 986, 37, 2234, 2341, 1055, 515]))
(g, r) = (Set(Int32[938, 225, 1211, 395, 3147, 881, 1901, 2123, 2559, 1580]), Set(Int32[938, 225, 1211, 3147, 555, 1552, 784, 1165, 1580, 2769]))
(g, r) = (Set(Int32[1833, 1263, 1884, 600, 784, 2559, 1580, 709, 992, 254]), Set(Int32[139, 1833, 1263, 175, 1312, 784, 574, 1580, 992, 254]))
(g, r) = (Set(Int32[2953, 1809, 2196, 247, 296, 3135, 379, 901, 1173, 492]), Set(Int32[2953, 1809, 1949, 247, 296, 1142, 379, 901, 1173, 492]))
(g, r) = (Set(Int32[555, 857, 784, 3129, 2533, 1469, 1303, 356, 2202, 2267]), Set(Int32[1093, 988, 1321, 527, 160, 1141, 433, 356, 1332, 1252]))
(g, r) = (Set(Int32[1716, 253, 2840, 2400, 699, 1502, 274, 2440, 3085, 650]), Set(Int32[253, 443, 193, 699, 2859, 74, 101, 647, 615, 221]))
(g, r) = (Set(Int32[3283, 366, 3192, 2137, 1744, 3214, 1882, 2749, 180, 2959]), Set(Int32[747, 940, 192, 298, 593, 1418, 1441, 1315, 164, 3188]))
(g, r) = (Set(Int32[416, 339, 2788, 1367, 624, 1280, 1349, 326, 1879, 2237]), Set(Int32[149, 2142, 4, 1367, 1879, 480, 765, 1024, 45, 2347]))
(g, r) = (Set(Int32[1888, 1237, 737, 1574, 1549, 1004, 2473, 2918, 2022, 618]), Set(Int32[1453, 25, 48, 580, 510, 515, 1464, 1024, 10, 582]))
(g, r) = (Set(Int32[667, 2325, 2278, 947, 1447, 433, 436, 278, 1128, 22]), Set(Int32[667, 2325, 548, 2278, 1447, 433, 436, 278, 1128, 22]))
(g, r) = (Set(Int32[803, 2766, 446, 1065, 2307, 1325, 1443, 2688, 1247, 151]), Set(Int32[2766, 1312, 1263, 2234, 1525, 515, 1229, 1463, 189, 992]))
(g, r) = (Set(Int32[1969, 1776, 2543, 1745, 2919, 2531, 2036, 1900, 647, 559]), Set(Int32[221, 499, 743, 909, 2070, 372, 647, 493, 101, 980]))
(g, r) = (Set(Int32[660, 52, 2401, 2753, 2141, 1754, 1298, 999, 913, 2610]), Set(Int32[1621, 443, 397, 656, 1236, 725, 473, 123, 3044, 1852]))
(g, r) = (Set(Int32[1289, 1530, 3303, 352, 2149, 59, 2995, 2862, 1434, 1285]), Set(Int32[1860, 2983, 2864, 1234, 2964, 1275, 1704, 1871, 1432, 2644]))
(g, r) = (Set(Int32[1194, 1158, 3149, 2626, 818, 625, 2255, 2214, 343, 1513]), Set(Int32[3, 1233, 1158, 545, 681, 756, 2255, 1513, 936, 1194]))
(g, r) = (Set(Int32[1594, 2620, 2484, 3328, 632, 1701, 1186, 2261, 220, 423]), Set(Int32[2620, 2484, 3328, 632, 1235, 2535, 1368, 2748, 2261, 815]))
(g, r) = (Set(Int32[2082, 445, 583, 3069, 3226, 2556, 715, 2781, 807, 1794]), Set(Int32[2082, 445, 80, 440, 3069, 2556, 3047, 2781, 807, 1794]))
(g, r) = (Set(Int32[2789, 175, 2540, 2673, 3043, 2118, 884, 2450, 874, 2996]), Set(Int32[24, 175, 380, 322, 2334, 884, 2450, 1255, 874, 413]))
(g, r) = (Set(Int32[799, 879, 698, 2907, 1793, 2529, 2097, 629, 2211, 2971]), Set(Int32[2326, 3090, 799, 77, 1481, 315, 2943, 1682, 2134, 2167]))
(g, r) = (Set(Int32[309, 3032, 563, 3204, 1763, 763, 43, 3176, 1954, 686]), Set(Int32[309, 2682, 563, 3204, 43, 351, 3176, 1954, 686, 2746]))
(g, r) = (Set(Int32[2683, 3281, 2873, 2828, 2370, 1411, 567, 3241, 2034, 990]), Set(Int32[1194, 2327, 2271, 3117, 1732, 2399, 2615, 2836, 1215, 946]))
(g, r) = (Set(Int32[2980, 2058, 1968, 1644, 1677, 170, 499, 2634, 2170, 1308]), Set(Int32[980, 2491, 499, 255, 1151, 194, 808, 493, 647, 221]))
(g, r) = (Set(Int32[2189, 2438, 2899, 1533, 2409, 771, 1056, 319, 1692, 2134]), Set(Int32[2438, 771, 3009, 794, 496, 792, 1855, 962, 1961, 854]))
(g, r) = (Set(Int32[553, 1291, 1299, 1553, 270, 162, 138, 710, 2146, 2530]), Set(Int32[553, 1621, 2001, 2876, 162, 2988, 710, 293, 2279, 877]))
(g, r) = (Set(Int32[413, 1748, 557, 2759, 2934, 2945, 884, 2210, 1964, 2157]), Set(Int32[1046, 994, 527, 743, 3063, 1236, 816, 43, 1470, 2687]))
(g, r) = (Set(Int32[1324, 396, 2288, 1982, 2038, 944, 11, 1840, 3300, 1489]), Set(Int32[788, 1675, 914, 468, 1324, 396, 944, 11, 3300, 1489]))
(g, r) = (Set(Int32[2091, 1461, 2500, 2937, 2339, 1277, 2362, 2554, 1681, 2858]), Set(Int32[658, 1100, 1107, 2964, 1357, 93, 3044, 466, 220, 2870]))
(g, r) = (Set(Int32[3065, 2679, 1665, 1001, 1361, 372, 1094, 546, 1951, 3144]), Set(Int32[3065, 1040, 1665, 1001, 1361, 372, 1094, 218, 1951, 3144]))
collect(Int32, IdView(p)) = Int32[2134, 799, 315, 2167, 1852, 1682, 1300, 1481, 864, 2326]
collect(Int32, IdView(p)) = Int32[2687, 3133, 2831, 3099, 2538, 1687, 2225, 3216, 2463, 2740]
collect(Int32, IdView(p)) = Int32[1131, 115, 1271, 1391, 338, 93, 2447, 2783, 78, 250]
collect(Int32, IdView(p)) = Int32[1638, 2793, 2368, 2402, 2328, 2044, 877, 3276, 2591, 1710]
collect(Int32, IdView(p)) = Int32[2341, 1055, 76, 37, 986, 608, 2234, 2546, 515, 2461]
collect(Int32, IdView(p)) = Int32[225, 1580, 1211, 938, 3147, 784, 1552, 1165, 555, 2769]
collect(Int32, IdView(p)) = Int32[992, 254, 784, 1263, 1833, 1580, 175, 1312, 574, 139]
collect(Int32, IdView(p)) = Int32[1173, 1809, 247, 296, 2953, 379, 901, 492, 1949, 1142]
collect(Int32, IdView(p)) = Int32[356, 1332, 1252, 1141, 1321, 1093, 160, 527, 988, 433]
collect(Int32, IdView(p)) = Int32[699, 253, 74, 2859, 101, 193, 647, 221, 443, 615]
collect(Int32, IdView(p)) = Int32[3188, 298, 593, 940, 192, 1418, 1441, 747, 1315, 164]
collect(Int32, IdView(p)) = Int32[1367, 1879, 2142, 1024, 4, 45, 2347, 149, 480, 765]
collect(Int32, IdView(p)) = Int32[1453, 1024, 582, 48, 510, 25, 515, 10, 1464, 580]
collect(Int32, IdView(p)) = Int32[436, 1128, 433, 278, 667, 1447, 22, 2325, 2278, 548]
collect(Int32, IdView(p)) = Int32[2766, 1229, 2234, 1312, 1525, 515, 1463, 189, 992, 1263]
collect(Int32, IdView(p)) = Int32[647, 743, 980, 221, 499, 2070, 493, 909, 101, 372]
collect(Int32, IdView(p)) = Int32[443, 725, 656, 1236, 397, 1852, 123, 3044, 1621, 473]
collect(Int32, IdView(p)) = Int32[1704, 1275, 2864, 1871, 1860, 1432, 2644, 2983, 1234, 2964]
collect(Int32, IdView(p)) = Int32[1513, 1194, 1158, 2255, 936, 756, 545, 1233, 681, 3]
collect(Int32, IdView(p)) = Int32[2620, 2484, 3328, 632, 2261, 2748, 815, 1235, 2535, 1368]
collect(Int32, IdView(p)) = Int32[2082, 807, 2556, 1794, 3069, 2781, 445, 80, 440, 3047]
collect(Int32, IdView(p)) = Int32[884, 2450, 874, 175, 413, 24, 1255, 380, 322, 2334]
collect(Int32, IdView(p)) = Int32[799, 315, 2134, 2167, 2326, 3090, 77, 1682, 2943, 1481]
collect(Int32, IdView(p)) = Int32[3176, 309, 563, 1954, 3204, 686, 43, 351, 2682, 2746]
collect(Int32, IdView(p)) = Int32[2271, 3117, 2615, 2327, 2836, 1215, 946, 1732, 1194, 2399]
collect(Int32, IdView(p)) = Int32[499, 493, 2491, 255, 1151, 221, 647, 808, 980, 194]
collect(Int32, IdView(p)) = Int32[2438, 771, 3009, 794, 496, 792, 1855, 854, 962, 1961]
collect(Int32, IdView(p)) = Int32[710, 162, 553, 1621, 2876, 2988, 293, 2001, 2279, 877]
collect(Int32, IdView(p)) = Int32[816, 1046, 743, 1236, 994, 43, 1470, 527, 2687, 3063]
collect(Int32, IdView(p)) = Int32[1324, 3300, 944, 11, 396, 1489, 914, 788, 468, 1675]
collect(Int32, IdView(p)) = Int32[658, 1100, 1357, 3044, 93, 466, 220, 1107, 2964, 2870]
collect(Int32, IdView(p)) = Int32[1951, 1094, 1361, 372, 3065, 1665, 3144, 1001, 218, 1040]
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, 29.0]
     Testing SimilaritySearch tests passed 

Testing completed after 732.71s

PkgEval succeeded after 879.53s