Package evaluation to test SimilaritySearch on Julia 1.13.0-DEV.1342 (4ff19f0352*) started at 2025-10-19T16:54:05.088


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.3s


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

Installing SimilaritySearch...
   Resolving package versions...
   Installed SimilaritySearch ─ v0.13.5
    Updating `~/.julia/environments/v1.13/Project.toml`
  [053f045d] + SimilaritySearch v0.13.5
    Updating `~/.julia/environments/v1.13/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.4.0
  [4fba245c] + ArrayInterface v7.21.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.18.1
  [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
  [615f187c] + IfElse v0.1.1
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.6
  [10f19ff3] + LayoutPointers v0.1.17
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [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
  [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.5
  [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
  [3a884ed6] + UnPack v1.0.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [d6f4376e] + Markdown v1.11.0
  [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
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [8e850b90] + libblastrampoline_jll v5.15.0+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 5.21s


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

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 38.57s


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

     Testing SimilaritySearch
      Status `/tmp/jl_ApW2oU/Project.toml`
  [7d9f7c33] Accessors v0.1.42
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [92933f4c] ProgressMeter v1.11.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.5
  [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_ApW2oU/Manifest.toml`
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.4.0
  [4c88cf16] Aqua v0.8.14
  [4fba245c] ArrayInterface v7.21.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] CPUSummary v0.2.7
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.1
  [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
  [615f187c] IfElse v0.1.1
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.6
  [10f19ff3] LayoutPointers v0.1.17
  [2ab3a3ac] LogExpFunctions v0.3.29
  [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
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.5
  [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
  [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
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.13.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization v1.11.0
  [6462fe0b] Sockets v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [f489334b] StyledStrings v1.11.0
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test v1.11.0
  [cf7118a7] UUIDs v1.11.0
  [4ec0a83e] Unicode v1.11.0
  [e66e0078] CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] LibCURL_jll v8.16.0+0
  [e37daf67] LibGit2_jll v1.9.1+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.9.9
  [4536629a] OpenBLAS_jll v0.3.29+0
  [458c3c95] OpenSSL_jll v3.5.4+0
  [efcefdf7] PCRE2_jll v10.46.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.1+2
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.15.0+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  18.2s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  4.0s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.7s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.2s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.857055 seconds (1000 allocations: 78.125 KiB)
 10.742593 seconds (1000 allocations: 78.125 KiB)
  4.107681 seconds (1000 allocations: 78.125 KiB)
  4.194822 seconds (1000 allocations: 78.125 KiB)
  4.094891 seconds (1000 allocations: 78.125 KiB)
  4.068498 seconds (1000 allocations: 78.125 KiB)
  4.141333 seconds (1000 allocations: 78.125 KiB)
  4.130791 seconds (1000 allocations: 78.125 KiB)
 16.242814 seconds (1000 allocations: 78.125 KiB)
 15.914897 seconds (1000 allocations: 78.125 KiB)
 29.091791 seconds (1000 allocations: 78.125 KiB)
 29.127827 seconds (1000 allocations: 78.125 KiB)
 21.708040 seconds (6.23 k allocations: 358.094 KiB)
 21.663903 seconds (1000 allocations: 78.125 KiB)
 18.878121 seconds (1.00 k allocations: 78.141 KiB)
 19.069100 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m50.0s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.139782 seconds (1000 allocations: 78.125 KiB)
  3.117727 seconds (1000 allocations: 78.125 KiB)
 30.204530 seconds (1000 allocations: 78.125 KiB)
 29.618889 seconds (1000 allocations: 78.125 KiB)
 29.386380 seconds (1000 allocations: 78.125 KiB)
 29.591087 seconds (1000 allocations: 78.125 KiB)
  4.380776 seconds (1000 allocations: 78.125 KiB)
  4.276169 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m17.8s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
 10.319108 seconds (1000 allocations: 78.125 KiB)
 10.431320 seconds (1000 allocations: 78.125 KiB)
 10.506735 seconds (1000 allocations: 78.125 KiB)
 10.472300 seconds (1000 allocations: 78.125 KiB)
 10.746610 seconds (1000 allocations: 78.125 KiB)
 10.639459 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total     Time
indexing sets with ExhaustiveSearch | 3000   3000  1m06.4s
  0.045427 seconds (1.00 k allocations: 78.141 KiB)
  0.045832 seconds (1000 allocations: 78.125 KiB)
  0.039356 seconds (1000 allocations: 78.125 KiB)
  0.040044 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  3.0s
  0.052448 seconds (1000 allocations: 78.125 KiB)
  0.051508 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.505595 seconds (2.44 M allocations: 129.808 MiB, 0.82% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.240728 seconds (615.38 k allocations: 30.729 MiB, 99.85% compilation time)
Test Summary: | Pass  Total  Time
allknn        |    3      3  6.3s
quantile(length.(hsp_knns), 0:0.1:1) = [3.0, 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  3.2s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:32.951
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-19T17:03:33.201
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=2 ep=2 n=2 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-19T17:03:34.520
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:34.918
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x0000000b]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 7, 4, 4, 11, 3, 1, 6, 4, 3, 6, 1, 2, 4, 6, 4, 3, 6, 4, 3, 4, 7, 1, 4, 7, 4, 2, 3, 2, 6, 6, 1, 3, 6, 7, 7, 7, 6, 11, 4, 1, 3, 6, 2, 3, 4, 4, 4, 3, 3, 4, 7, 3, 1, 7, 6, 6, 3, 3, 11, 6, 3, 1, 4, 6, 4, 3, 3, 2, 7, 6, 2, 6, 2, 4, 6, 4, 2, 2, 3, 11, 6, 7, 3, 6, 6, 4, 2, 7, 6, 11, 4, 3, 2]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.027052343, 0.051409245, 0.04493946, 0.0, 0.007964134, 0.064433694, 0.015027404, 0.072757065, 0.012048781, 0.002723217, 0.009903669, 0.07758623, 0.07417917, 0.029194593, 0.040563703, 0.09696525, 0.049999297, 0.06721091, 0.07049984, 0.04710436, 0.038327277, 0.07888186, 0.020347953, 0.052623034, 0.001640439, 0.025092065, 0.03707373, 0.0753904, 0.019490004, 0.040794194, 0.069916606, 0.06397247, 0.06232345, 0.056322753, 0.044831753, 0.092784345, 0.049808204, 0.012167454, 0.048691392, 0.089577794, 0.0067103505, 0.0110654235, 0.07853842, 0.04886186, 0.05812353, 0.0751487, 0.06749487, 0.08510274, 0.07998234, 0.023746192, 0.054404497, 0.020418823, 0.04045725, 0.036427617, 0.020617008, 0.007031858, 0.03136462, 0.02916199, 0.04104024, 0.05946642, 0.054000854, 0.025232792, 0.021074474, 0.0961166, 0.047834158, 0.006665468, 0.020682871, 0.07256144, 0.016091406, 0.049295425, 0.06174189, 0.04030317, 0.022592783, 0.03547162, 0.08250862, 0.06750417, 0.07227415, 0.015051544, 0.030264795, 0.044600308, 0.01813519, 0.06386626, 0.043556094, 0.036177814, 0.06883097, 0.02583474, 0.04973817, 0.0043667555, 0.017842412, 0.03297186, 0.052946985, 0.0019521713, 0.060019016]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  19.2s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.119
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-19T17:03:36.119
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.120
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.120
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.120
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.120
[ Info: neardup> range: 81:96, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.120
[ Info: neardup> range: 97:100, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.120
[ Info: neardup> finished current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.120
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x0000000b]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 7, 4, 4, 11, 3, 1, 6, 4, 3, 6, 1, 2, 4, 6, 4, 3, 6, 4, 3, 4, 7, 1, 4, 7, 4, 2, 3, 2, 6, 6, 1, 3, 6, 7, 7, 7, 6, 11, 4, 1, 3, 6, 2, 3, 4, 4, 4, 3, 3, 4, 7, 3, 1, 7, 6, 6, 3, 3, 11, 6, 3, 1, 4, 6, 4, 3, 3, 2, 7, 6, 2, 6, 2, 4, 6, 4, 2, 2, 3, 11, 6, 7, 3, 6, 6, 4, 2, 7, 6, 11, 4, 3, 2]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.027052343, 0.051409245, 0.04493946, 0.0, 0.007964134, 0.064433694, 0.015027404, 0.072757065, 0.012048781, 0.002723217, 0.009903669, 0.07758623, 0.07417917, 0.029194593, 0.040563703, 0.09696525, 0.049999297, 0.06721091, 0.07049984, 0.04710436, 0.038327277, 0.07888186, 0.020347953, 0.052623034, 0.001640439, 0.025092065, 0.03707373, 0.0753904, 0.019490004, 0.040794194, 0.069916606, 0.06397247, 0.06232345, 0.056322753, 0.044831753, 0.092784345, 0.049808204, 0.012167454, 0.048691392, 0.089577794, 0.0067103505, 0.0110654235, 0.07853842, 0.04886186, 0.05812353, 0.0751487, 0.06749487, 0.08510274, 0.07998234, 0.023746192, 0.054404497, 0.020418823, 0.04045725, 0.036427617, 0.020617008, 0.007031858, 0.03136462, 0.02916199, 0.04104024, 0.05946642, 0.054000854, 0.025232792, 0.021074474, 0.0961166, 0.047834158, 0.006665468, 0.020682871, 0.07256144, 0.016091406, 0.049295425, 0.06174189, 0.04030317, 0.022592783, 0.03547162, 0.08250862, 0.06750417, 0.07227415, 0.015051544, 0.030264795, 0.044600308, 0.01813519, 0.06386626, 0.043556094, 0.036177814, 0.06883097, 0.02583474, 0.04973817, 0.0043667555, 0.017842412, 0.03297186, 0.052946985, 0.0019521713, 0.060019016]
Test Summary:       | Pass  Total  Time
neardup small block |    3      3  0.0s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.217
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-19T17:03:36.217
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.218
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.219
[ Info: neardup> range: 49:64, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.219
[ Info: neardup> range: 65:80, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.219
[ Info: neardup> range: 81:96, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.219
[ Info: neardup> range: 97:100, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.219
[ Info: neardup> finished current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:36.219
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 6, 1, 15, 10, 6, 16, 15, 8, 10, 15, 10, 8, 8, 4, 7, 4, 2, 15, 2, 6, 6, 9, 15, 6, 7, 8, 8, 6, 11, 10, 10, 3, 6, 15, 12, 16, 10, 10, 15, 3, 4, 7, 16, 8, 8, 6, 14, 3, 9, 11, 6, 15, 1, 4, 8, 10, 3, 3, 10, 8, 14, 2, 6, 2, 9, 15, 10, 10, 2, 12, 11, 6, 8, 9, 6, 6, 15, 10, 7, 6, 11, 4, 3, 2]
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, 0.002723217, 0.009903669, 0.013418496, 0.017216504, 0.029194593, 0.040142357, 0.057116687, 0.036943972, 0.03306824, 0.037260354, 0.010915399, 0.028603375, 0.052781165, 0.020347953, 0.052623034, 0.001640439, 0.025092065, 0.017028034, 0.0753904, 0.019490004, 0.040794194, 0.024525464, 0.01137352, 0.06232345, 0.056322753, 0.0026529431, 0.044255137, 0.049808204, 0.012167454, 0.036873877, 0.051051676, 0.0067103505, 0.0110654235, 0.017852068, 0.02219379, 0.042093694, 0.0045673847, 0.015490174, 0.00115484, 0.07998234, 0.023746192, 0.054404497, 0.0124902725, 0.026524305, 0.0011472702, 0.020617008, 0.006833732, 0.03136462, 0.008255899, 0.04104024, 0.05946642, 0.014926314, 0.025232792, 0.021074474, 0.052078784, 0.0073671937, 0.006665468, 0.020682871, 0.013912499, 0.006493211, 0.027208805, 0.06174189, 0.04030317, 0.022592783, 0.025708914, 0.015423, 0.0039097667, 0.04276365, 0.015051544, 0.01826775, 0.044600308, 0.01813519, 0.03308016, 0.0180763, 0.036177814, 0.06883097, 0.021524549, 0.03557688, 0.0043667555, 0.017842412, 0.03297186, 0.052946985, 0.0019521713, 0.060019016]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  0.1s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:44.109
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=8 n=8 2025-10-19T17:03:44.110
[ Info: neardup> range: 17:32, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:44.115
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:44.116
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:44.116
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:44.116
[ Info: neardup> range: 81:96, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:44.116
[ Info: neardup> range: 97:100, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:44.116
[ Info: neardup> finished current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-19T17:03:44.116
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x0000000b]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 7, 4, 4, 11, 3, 1, 6, 4, 3, 6, 1, 2, 4, 6, 4, 3, 6, 4, 3, 4, 7, 1, 4, 7, 4, 2, 3, 2, 6, 6, 1, 3, 6, 7, 7, 7, 6, 11, 4, 1, 3, 6, 2, 3, 4, 4, 4, 3, 3, 4, 7, 3, 1, 7, 6, 6, 3, 3, 11, 6, 3, 1, 4, 6, 4, 3, 3, 2, 7, 6, 2, 6, 2, 4, 6, 4, 2, 2, 3, 11, 6, 7, 3, 6, 6, 4, 2, 7, 6, 11, 4, 3, 2]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.027052343, 0.051409245, 0.04493946, 0.0, 0.007964134, 0.064433694, 0.015027404, 0.072757065, 0.012048781, 0.002723217, 0.009903669, 0.07758623, 0.07417917, 0.029194593, 0.040563703, 0.09696525, 0.049999297, 0.06721091, 0.07049984, 0.04710436, 0.038327277, 0.07888186, 0.020347953, 0.052623034, 0.001640439, 0.025092065, 0.03707373, 0.0753904, 0.019490004, 0.040794194, 0.069916606, 0.06397247, 0.06232345, 0.056322753, 0.044831753, 0.092784345, 0.049808204, 0.012167454, 0.048691392, 0.089577794, 0.0067103505, 0.0110654235, 0.07853842, 0.04886186, 0.05812353, 0.0751487, 0.06749487, 0.08510274, 0.07998234, 0.023746192, 0.054404497, 0.020418823, 0.04045725, 0.036427617, 0.020617008, 0.007031858, 0.03136462, 0.02916199, 0.04104024, 0.05946642, 0.054000854, 0.025232792, 0.021074474, 0.0961166, 0.047834158, 0.006665468, 0.020682871, 0.07256144, 0.016091406, 0.049295425, 0.06174189, 0.04030317, 0.022592783, 0.03547162, 0.08250862, 0.06750417, 0.07227415, 0.015051544, 0.030264795, 0.044600308, 0.01813519, 0.06386626, 0.043556094, 0.036177814, 0.06883097, 0.02583474, 0.04973817, 0.0043667555, 0.017842412, 0.03297186, 0.052946985, 0.0019521713, 0.060019016]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.9s
computing farthest point 1, dmax: Inf, imax: 26, n: 30
computing farthest point 2, dmax: 0.9410006, imax: 10, n: 30
computing farthest point 3, dmax: 0.9209322, imax: 14, n: 30
computing farthest point 4, dmax: 0.8381724, imax: 18, n: 30
computing farthest point 5, dmax: 0.7841315, imax: 5, n: 30
computing farthest point 6, dmax: 0.7123731, imax: 3, n: 30
computing farthest point 7, dmax: 0.64729017, imax: 2, n: 30
computing farthest point 8, dmax: 0.61410004, imax: 29, n: 30
computing farthest point 9, dmax: 0.5650224, imax: 25, n: 30
computing farthest point 10, dmax: 0.5462676, imax: 21, 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) 2025-10-19T17:03:52.888
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=2, Δ=0.99, maxvisits=106) 2025-10-19T17:04:05.131
LOG n.size quantiles:[3.0, 4.0, 4.0, 4.0, 4.0]
(i, j, d) = (16, 653, -1.1920929f-7)
(i, j, d, :parallel) = (16, 653, -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 => 20.306064299, :exact => 1.016155839)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  21.9s
  6.140154 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.005002 seconds
SEARCH Exhaustive 2: 0.005428 seconds
SEARCH Exhaustive 3: 0.005968 seconds
typeof(seq) = ExhaustiveSearch{SqL2Distance, MatrixDatabase{Matrix{Float32}}}
typeof(ectx) = GenericContext{KnnSorted}
typeof(q) = SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}
typeof(res) = KnnSorted{Vector{IdWeight}}
[ Info: ===================== minrecall ==============================
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-19T17:04:34.770
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=1.05, maxvisits=202) 2025-10-19T17:04:40.610
LOG n.size quantiles:[3.0, 3.0, 3.0, 4.0, 5.0]
LOG add_vertex! sp=17510 ep=17514 n=17509 BeamSearch(bsize=4, Δ=1.21275, maxvisits=402) 2025-10-19T17:04:41.610
LOG n.size quantiles:[5.0, 5.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=31795 ep=31799 n=31794 BeamSearch(bsize=4, Δ=0.975, maxvisits=388) 2025-10-19T17:04:42.611
LOG n.size quantiles:[5.0, 6.0, 6.0, 7.0, 7.0]
LOG add_vertex! sp=43405 ep=43409 n=43404 BeamSearch(bsize=8, Δ=1.1287501, maxvisits=452) 2025-10-19T17:04:43.611
LOG n.size quantiles:[4.0, 5.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=54435 ep=54439 n=54434 BeamSearch(bsize=8, Δ=1.1287501, maxvisits=452) 2025-10-19T17:04:45.039
LOG n.size quantiles:[6.0, 7.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=63090 ep=63094 n=63089 BeamSearch(bsize=10, Δ=1.155, maxvisits=500) 2025-10-19T17:04:46.039
LOG n.size quantiles:[5.0, 5.0, 5.0, 8.0, 9.0]
LOG add_vertex! sp=74100 ep=74104 n=74099 BeamSearch(bsize=10, Δ=1.155, maxvisits=500) 2025-10-19T17:04:47.039
LOG n.size quantiles:[5.0, 5.0, 5.0, 7.0, 9.0]
LOG add_vertex! sp=85225 ep=85229 n=85224 BeamSearch(bsize=6, Δ=1.05, maxvisits=452) 2025-10-19T17:04:48.138
LOG n.size quantiles:[3.0, 4.0, 6.0, 7.0, 9.0]
LOG add_vertex! sp=95925 ep=95929 n=95924 BeamSearch(bsize=6, Δ=1.05, maxvisits=452) 2025-10-19T17:04:49.138
LOG n.size quantiles:[5.0, 5.0, 5.0, 6.0, 10.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 17.0, 23.0, 95.0]
[ Info: minrecall: queries per second: 19253.760071617828, recall: 0.905375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=10, Δ=1.1287501, maxvisits=716))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 17.0, 23.0, 95.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=16, Δ=1.1287501, maxvisits=576)), 1000, 8)
[ Info: rebuild: queries per second: 14047.021279973007, recall: 0.898
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=16, Δ=1.1287501, maxvisits=576))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 18.0, 19.0, 30.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.12, maxvisits=740)), 1000, 8)
  1.709992 seconds (612.31 k allocations: 31.314 MiB, 95.97% compilation time)
[ Info: matrixhints: queries per second: 14001.541401689828, recall: 0.90325
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.12, maxvisits=740))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 17.0, 23.0, 95.0]
  2.247825 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.002286 seconds
SEARCH Exhaustive 2: 0.002609 seconds
SEARCH Exhaustive 3: 0.002639 seconds
typeof(seq) = ExhaustiveSearch{SqL2Distance, StrideMatrixDatabase{StrideArraysCore.StrideArray{Float32, 2, (1, 2), Tuple{Int64, Int64}, Tuple{Nothing, Nothing}, Tuple{Static.StaticInt{1}, Static.StaticInt{1}}, Matrix{Float32}}}}
typeof(ectx) = GenericContext{KnnSorted}
typeof(q) = StrideArraysCore.StrideArray{Float32, 1, (1,), Tuple{Int64}, Tuple{Nothing}, Tuple{Static.StaticInt{1}}, Matrix{Float32}}
typeof(res) = KnnSorted{Vector{IdWeight}}
[ Info: ===================== minrecall ==============================
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-19T17:05:56.593
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=1.05, maxvisits=202) 2025-10-19T17:06:02.167
LOG n.size quantiles:[3.0, 3.0, 3.0, 4.0, 5.0]
LOG add_vertex! sp=20365 ep=20369 n=20364 BeamSearch(bsize=4, Δ=1.21275, maxvisits=402) 2025-10-19T17:06:03.167
LOG n.size quantiles:[3.0, 4.0, 7.0, 8.0, 9.0]
LOG add_vertex! sp=37225 ep=37229 n=37224 BeamSearch(bsize=4, Δ=0.975, maxvisits=388) 2025-10-19T17:06:04.168
LOG n.size quantiles:[4.0, 5.0, 8.0, 9.0, 11.0]
LOG add_vertex! sp=50730 ep=50734 n=50729 BeamSearch(bsize=8, Δ=1.1287501, maxvisits=452) 2025-10-19T17:06:05.168
LOG n.size quantiles:[6.0, 6.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=62140 ep=62144 n=62139 BeamSearch(bsize=10, Δ=1.155, maxvisits=500) 2025-10-19T17:06:06.168
LOG n.size quantiles:[4.0, 6.0, 6.0, 7.0, 7.0]
LOG add_vertex! sp=73110 ep=73114 n=73109 BeamSearch(bsize=10, Δ=1.155, maxvisits=500) 2025-10-19T17:06:07.169
LOG n.size quantiles:[4.0, 5.0, 6.0, 7.0, 7.0]
LOG add_vertex! sp=83305 ep=83309 n=83304 BeamSearch(bsize=10, Δ=1.155, maxvisits=500) 2025-10-19T17:06:08.169
LOG n.size quantiles:[5.0, 6.0, 6.0, 7.0, 10.0]
LOG add_vertex! sp=92940 ep=92944 n=92939 BeamSearch(bsize=6, Δ=1.05, maxvisits=452) 2025-10-19T17:06:09.169
LOG n.size quantiles:[5.0, 5.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=99815 ep=99819 n=99814 BeamSearch(bsize=6, Δ=1.05, maxvisits=452) 2025-10-19T17:06:10.169
LOG n.size quantiles:[5.0, 6.0, 8.0, 9.0, 10.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 17.0, 23.0, 95.0]
[ Info: minrecall: queries per second: 14454.196530241214, recall: 0.905375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=10, Δ=1.1287501, maxvisits=716))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 17.0, 23.0, 95.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=16, Δ=1.1287501, maxvisits=576)), 1000, 8)
[ Info: rebuild: queries per second: 16460.975529097315, recall: 0.898
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=16, Δ=1.1287501, maxvisits=576))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 18.0, 19.0, 30.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.12, maxvisits=740)), 1000, 8)
  1.644923 seconds (567.92 k allocations: 29.100 MiB, 95.79% compilation time)
[ Info: matrixhints: queries per second: 14729.488090487135, recall: 0.90325
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.12, maxvisits=740))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 17.0, 23.0, 95.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  2m39.8s
     Testing SimilaritySearch tests passed 

Testing completed after 706.2s

PkgEval succeeded after 774.24s