Package evaluation to test SimilaritySearch on Julia 1.13.0-alpha2.30 (5abf758bb1*) started at 2026-01-09T03:21:25.422


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 8.32s


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

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.13/Project.toml`
  [053f045d] + SimilaritySearch v0.13.7
    Updating `~/.julia/environments/v1.13/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.43
  [79e6a3ab] + Adapt v4.4.0
  [4fba245c] + ArrayInterface v7.22.0
  [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 v1.5.1
  [92933f4c] + ProgressMeter v1.11.0
  [ae029012] + Requires v1.3.1
  [94e857df] + SIMDTypes v0.1.0
  [431bcebd] + SciMLPublic v1.0.1
⌅ [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.7
  [a2af1166] + SortingAlgorithms v1.2.2
  [aedffcd0] + Static v1.3.1
  [0d7ed370] + StaticArrayInterface v1.8.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.8.0
⌅ [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 v1.0.0
  [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 4.28s


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

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 44.6s


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

     Testing SimilaritySearch
      Status `/tmp/jl_ONe59w/Project.toml`
  [7d9f7c33] Accessors v0.1.43
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [f517fe37] Polyester v0.7.18
  [92933f4c] ProgressMeter v1.11.0
⌅ [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.7
  [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_ONe59w/Manifest.toml`
  [7d9f7c33] Accessors v0.1.43
  [79e6a3ab] Adapt v4.4.0
  [4c88cf16] Aqua v0.8.14
  [4fba245c] ArrayInterface v7.22.0
  [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 v1.5.1
  [92933f4c] ProgressMeter v1.11.0
  [ae029012] Requires v1.3.1
  [94e857df] SIMDTypes v0.1.0
  [431bcebd] SciMLPublic v1.0.1
⌅ [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.7
  [a2af1166] SortingAlgorithms v1.2.2
  [aedffcd0] Static v1.3.1
  [0d7ed370] StaticArrayInterface v1.8.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.8.0
⌅ [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 v1.0.0
  [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 v1.0.0
  [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 v2025.12.2
  [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 v17.7.0+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
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  2.7s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  1.6s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  0.8s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
  4.789439 seconds (1000 allocations: 78.125 KiB)
  5.264265 seconds (1000 allocations: 78.125 KiB)
  1.845272 seconds (1000 allocations: 78.125 KiB)
  1.936550 seconds (1000 allocations: 78.125 KiB)
  1.935756 seconds (1000 allocations: 78.125 KiB)
  1.929708 seconds (1000 allocations: 78.125 KiB)
  1.990785 seconds (1000 allocations: 78.125 KiB)
  1.921780 seconds (1000 allocations: 78.125 KiB)
 10.766650 seconds (1000 allocations: 78.125 KiB)
 10.960586 seconds (1000 allocations: 78.125 KiB)
 22.880247 seconds (1000 allocations: 78.125 KiB)
 22.725989 seconds (1000 allocations: 78.125 KiB)
 16.082457 seconds (6.23 k allocations: 358.094 KiB)
 14.299728 seconds (1000 allocations: 78.125 KiB)
 13.057715 seconds (1.00 k allocations: 78.141 KiB)
 13.611735 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  2m35.4s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  2.426087 seconds (1000 allocations: 78.125 KiB)
  2.304721 seconds (1000 allocations: 78.125 KiB)
 15.221485 seconds (1000 allocations: 78.125 KiB)
 14.527859 seconds (1000 allocations: 78.125 KiB)
  9.992160 seconds (1000 allocations: 78.125 KiB)
 10.792963 seconds (1000 allocations: 78.125 KiB)
  1.850988 seconds (1000 allocations: 78.125 KiB)
  1.765888 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  1m01.5s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  6.840772 seconds (1000 allocations: 78.125 KiB)
  6.838739 seconds (1000 allocations: 78.125 KiB)
  6.985063 seconds (1000 allocations: 78.125 KiB)
  7.163179 seconds (1000 allocations: 78.125 KiB)
  7.057388 seconds (1000 allocations: 78.125 KiB)
  6.975308 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  43.5s
  0.025847 seconds (1.00 k allocations: 78.141 KiB)
  0.025966 seconds (1000 allocations: 78.125 KiB)
  0.011711 seconds (1000 allocations: 78.125 KiB)
  0.011754 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  1.6s
  0.014475 seconds (1000 allocations: 78.125 KiB)
  0.014233 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  0.7s
ExhaustiveSearch allknn: 2.522195 seconds (2.44 M allocations: 129.905 MiB, 1.21% gc time, 99.97% compilation time)
ParallelExhaustiveSearch allknn: 0.709728 seconds (615.36 k allocations: 30.730 MiB, 99.89% compilation time)
Test Summary: | Pass  Total  Time
allknn        |    3      3  3.6s
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  1.6s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:01.216
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2026-01-09T03:28:01.388
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> finished current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:02.441
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000007, 0x00000008, 0x00000013]
D.nn = Int32[1, 2, 3, 4, 5, 2, 7, 8, 8, 8, 4, 3, 2, 2, 5, 8, 3, 1, 19, 7, 1, 3, 2, 5, 1, 5, 1, 1, 19, 5, 8, 1, 2, 5, 2, 3, 1, 5, 7, 1, 7, 1, 19, 3, 1, 3, 2, 8, 5, 2, 4, 1, 2, 1, 1, 7, 19, 1, 1, 2, 1, 8, 1, 1, 7, 8, 1, 4, 4, 3, 5, 3, 1, 5, 1, 5, 1, 3, 1, 1, 8, 4, 8, 7, 3, 4, 3, 5, 3, 4, 3, 2, 1, 1, 19, 8, 1, 8, 4, 8]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.08601731, 0.0, 0.0, 0.059098184, 0.061806977, 0.03892052, 0.009747922, 0.0412848, 0.048753142, 0.0245471, 0.037176967, 0.03016615, 0.0375005, 0.0, 0.013938487, 0.029972672, 0.015406072, 0.06569445, 0.046500087, 0.041158915, 0.029080153, 0.0680663, 0.03498268, 0.0021332502, 0.02739209, 0.017062008, 0.06625289, 0.059044957, 0.02950722, 0.09038371, 0.054976046, 0.04736072, 0.040539145, 0.019654334, 0.08266759, 0.05220747, 0.0036774278, 0.011205435, 0.01410687, 0.040932596, 0.023863673, 0.019705296, 0.008938789, 0.09827578, 0.07746792, 0.04400736, 0.06230867, 0.05990678, 0.022516727, 0.044146657, 0.062913656, 0.050128818, 0.029530168, 0.028202176, 0.059330523, 0.032075882, 0.07590747, 0.093462825, 0.06810522, 0.028870165, 0.014000773, 0.08534193, 0.0153974295, 0.0478639, 0.024214625, 0.012424767, 0.050777256, 0.035217583, 0.020887375, 0.096226394, 0.026310265, 0.042144775, 0.04175943, 0.007286608, 0.05680877, 0.054876268, 0.047156572, 0.08411151, 0.015690625, 0.04072094, 0.057590365, 0.044710994, 0.024053156, 0.0027099848, 0.0049166083, 0.0070719123, 0.07025015, 0.06487656, 0.06517786, 0.07106584, 0.044121444, 0.0625571, 0.0040683746, 0.017270505, 0.020474732]
Test Summary:        | Pass  Total  Time
neardup single block |    3      3  9.7s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.123
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2026-01-09T03:28:03.123
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: 2026-01-09T03:28:03.123
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.123
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.123
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.123
[ Info: neardup> range: 81:96, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.123
[ Info: neardup> range: 97:100, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.123
[ Info: neardup> finished current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.123
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000007, 0x00000008, 0x00000013]
D.nn = Int32[1, 2, 3, 4, 5, 2, 7, 8, 8, 8, 4, 3, 2, 2, 5, 8, 3, 1, 19, 7, 1, 3, 2, 5, 1, 5, 1, 1, 5, 5, 8, 1, 2, 5, 2, 3, 1, 5, 7, 1, 7, 1, 19, 3, 1, 3, 2, 8, 5, 2, 4, 1, 2, 1, 1, 7, 19, 1, 1, 2, 1, 8, 1, 1, 7, 8, 1, 4, 4, 3, 5, 3, 1, 5, 1, 5, 1, 3, 1, 1, 8, 4, 8, 7, 3, 4, 3, 5, 3, 4, 3, 2, 1, 1, 19, 8, 1, 8, 4, 8]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.08601731, 0.0, 0.0, 0.059098184, 0.061806977, 0.03892052, 0.009747922, 0.0412848, 0.048753142, 0.0245471, 0.037176967, 0.03016615, 0.0375005, 0.0, 0.013938487, 0.029972672, 0.015406072, 0.06569445, 0.046500087, 0.041158915, 0.029080153, 0.0680663, 0.03498268, 0.09916204, 0.02739209, 0.017062008, 0.06625289, 0.059044957, 0.02950722, 0.09038371, 0.054976046, 0.04736072, 0.040539145, 0.019654334, 0.08266759, 0.05220747, 0.0036774278, 0.011205435, 0.01410687, 0.040932596, 0.023863673, 0.019705296, 0.008938789, 0.09827578, 0.07746792, 0.04400736, 0.06230867, 0.05990678, 0.022516727, 0.044146657, 0.062913656, 0.050128818, 0.029530168, 0.028202176, 0.059330523, 0.032075882, 0.07590747, 0.093462825, 0.06810522, 0.028870165, 0.014000773, 0.08534193, 0.0153974295, 0.0478639, 0.024214625, 0.012424767, 0.050777256, 0.035217583, 0.020887375, 0.096226394, 0.026310265, 0.042144775, 0.04175943, 0.007286608, 0.05680877, 0.054876268, 0.047156572, 0.08411151, 0.015690625, 0.04072094, 0.057590365, 0.044710994, 0.024053156, 0.0027099848, 0.0049166083, 0.0070719123, 0.07025015, 0.06487656, 0.06517786, 0.07106584, 0.044121444, 0.0625571, 0.0040683746, 0.017270505, 0.020474732]
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: 2026-01-09T03:28:03.177
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2026-01-09T03:28:03.177
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: 2026-01-09T03:28:03.177
[ Info: neardup> range: 33:48, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.177
[ Info: neardup> range: 49:64, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.177
[ Info: neardup> range: 65:80, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.177
[ Info: neardup> range: 81:96, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.177
[ Info: neardup> range: 97:100, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.177
[ Info: neardup> finished current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:03.177
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000013]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 3, 1, 19, 7, 1, 3, 6, 5, 11, 5, 9, 11, 5, 5, 16, 16, 6, 5, 6, 3, 11, 5, 7, 6, 7, 1, 19, 3, 1, 3, 13, 8, 5, 6, 16, 1, 6, 1, 1, 7, 19, 1, 11, 6, 1, 16, 1, 1, 7, 16, 1, 4, 9, 3, 5, 11, 11, 5, 9, 5, 11, 3, 1, 9, 10, 4, 16, 7, 12, 11, 9, 15, 3, 4, 3, 6, 11, 13, 19, 9, 11, 8, 4, 8]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03016615, 0.0375005, 0.0, 0.013938487, 0.029972672, 0.015406072, 0.0136009455, 0.046500087, 0.027832806, 0.029080153, 0.031640768, 0.031048357, 0.09916204, 0.02739209, 0.006670296, 0.01157099, 0.04654348, 0.02950722, 0.02568835, 0.054976046, 0.0064187646, 0.040539145, 0.019654334, 0.02654314, 0.05220747, 0.0036774278, 0.011205435, 0.01410687, 0.040932596, 0.023863673, 0.010211229, 0.008938789, 0.09827578, 0.031328738, 0.022232294, 0.06230867, 0.017152429, 0.022516727, 0.044146657, 0.062913656, 0.050128818, 0.029530168, 0.026262522, 0.012422919, 0.032075882, 0.019268632, 0.093462825, 0.06810522, 0.028870165, 0.010406077, 0.08534193, 0.0153974295, 0.034374356, 0.024214625, 0.012424767, 0.045720458, 0.034214497, 0.020887375, 0.038685977, 0.026310265, 0.0067788363, 0.04175943, 0.007286608, 0.024208367, 0.029322743, 0.047156572, 0.065986514, 0.015690625, 0.031180501, 0.026065111, 0.04195714, 0.00214988, 0.0027099848, 0.0049166083, 0.0070719123, 0.036349893, 0.037856758, 0.063919306, 0.07106584, 0.012620091, 0.034346044, 0.0040683746, 0.017270505, 0.020474732]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  0.0s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:07.161
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=7 n=7 2026-01-09T03:28:07.161
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:07.165
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:07.165
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:07.165
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:07.165
[ Info: neardup> range: 81:96, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:07.165
[ Info: neardup> range: 97:100, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:07.165
[ Info: neardup> finished current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-01-09T03:28:07.165
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000007, 0x00000008, 0x00000013]
D.nn = Int32[1, 2, 3, 4, 5, 2, 7, 8, 8, 8, 4, 3, 2, 2, 5, 8, 3, 1, 19, 7, 1, 3, 2, 5, 1, 5, 1, 1, 5, 5, 8, 1, 2, 5, 2, 3, 1, 5, 7, 1, 7, 1, 19, 3, 1, 3, 2, 8, 5, 2, 4, 1, 2, 1, 1, 7, 19, 1, 1, 2, 1, 8, 1, 1, 7, 8, 1, 4, 4, 3, 5, 3, 1, 5, 1, 5, 1, 3, 1, 1, 8, 4, 8, 7, 3, 4, 3, 5, 3, 4, 3, 2, 1, 1, 19, 8, 1, 8, 4, 8]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.08601731, 0.0, 0.0, 0.059098184, 0.061806977, 0.03892052, 0.009747922, 0.0412848, 0.048753142, 0.0245471, 0.037176967, 0.03016615, 0.0375005, 0.0, 0.013938487, 0.029972672, 0.015406072, 0.06569445, 0.046500087, 0.041158915, 0.029080153, 0.0680663, 0.03498268, 0.09916204, 0.02739209, 0.017062008, 0.06625289, 0.059044957, 0.02950722, 0.09038371, 0.054976046, 0.04736072, 0.040539145, 0.019654334, 0.08266759, 0.05220747, 0.0036774278, 0.011205435, 0.01410687, 0.040932596, 0.023863673, 0.019705296, 0.008938789, 0.09827578, 0.07746792, 0.04400736, 0.06230867, 0.05990678, 0.022516727, 0.044146657, 0.062913656, 0.050128818, 0.029530168, 0.028202176, 0.059330523, 0.032075882, 0.07590747, 0.093462825, 0.06810522, 0.028870165, 0.014000773, 0.08534193, 0.0153974295, 0.0478639, 0.024214625, 0.012424767, 0.050777256, 0.035217583, 0.020887375, 0.096226394, 0.026310265, 0.042144775, 0.04175943, 0.007286608, 0.05680877, 0.054876268, 0.047156572, 0.08411151, 0.015690625, 0.04072094, 0.057590365, 0.044710994, 0.024053156, 0.0027099848, 0.0049166083, 0.0070719123, 0.07025015, 0.06487656, 0.06517786, 0.07106584, 0.044121444, 0.0625571, 0.0040683746, 0.017270505, 0.020474732]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  4.0s
computing farthest point 1, dmax: Inf, imax: 8, n: 30
computing farthest point 2, dmax: 1.2597182, imax: 13, n: 30
computing farthest point 3, dmax: 1.038058, imax: 28, n: 30
computing farthest point 4, dmax: 0.9963705, imax: 29, n: 30
computing farthest point 5, dmax: 0.90522045, imax: 3, n: 30
computing farthest point 6, dmax: 0.70568836, imax: 15, n: 30
computing farthest point 7, dmax: 0.7053659, imax: 16, n: 30
computing farthest point 8, dmax: 0.691114, imax: 24, n: 30
computing farthest point 9, dmax: 0.6532221, imax: 4, n: 30
computing farthest point 10, dmax: 0.62588334, imax: 1, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  0.8s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.0s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2026-01-09T03:28:11.173
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch
BeamSearch(bsize=2, Δ=0.95238096, maxvisits=106) 2026-01-09T03:28:17.141
LOG n.size quantiles:[2.0, 4.0, 4.0, 5.0, 5.0]
(i, j, d) = (14, 301, -1.1920929f-7)
(i, j, d, :parallel) = (14, 301, -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 => 9.337247567999999, :exact => 0.695189385)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  10.4s
  1.930777 seconds (1.00 k allocations: 140.742 KiB)
SEARCH Exhaustive 1: 0.001598 seconds
SEARCH Exhaustive 2: 0.001598 seconds
SEARCH Exhaustive 3: 0.001549 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
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2026-01-09T03:28:31.244
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch
BeamSearch(bsize=8, Δ=1.1851876, maxvisits=208) 2026-01-09T03:28:35.333
LOG n.size quantiles:[4.0, 4.0, 4.0, 4.0, 5.0]
LOG add_vertex! sp=26880 ep=26884 n=26879 BeamSearch
BeamSearch(bsize=12, Δ=1.0, maxvisits=412) 2026-01-09T03:28:36.333
LOG n.size quantiles:[5.0, 8.0, 8.0, 8.0, 9.0]
LOG add_vertex! sp=46910 ep=46914 n=46909 BeamSearch
BeamSearch(bsize=14, Δ=1.075, maxvisits=366) 2026-01-09T03:28:37.333
LOG n.size quantiles:[5.0, 5.0, 6.0, 6.0, 10.0]
LOG add_vertex! sp=63450 ep=63454 n=63449 BeamSearch
BeamSearch(bsize=8, Δ=1.21275, maxvisits=472) 2026-01-09T03:28:38.333
LOG n.size quantiles:[5.0, 7.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=77385 ep=77389 n=77384 BeamSearch
BeamSearch(bsize=8, Δ=1.21275, maxvisits=472) 2026-01-09T03:28:39.334
LOG n.size quantiles:[6.0, 7.0, 8.0, 9.0, 9.0]
LOG add_vertex! sp=88525 ep=88529 n=88524 BeamSearch
BeamSearch(bsize=12, Δ=1.4039096, maxvisits=574) 2026-01-09T03:28:40.334
LOG n.size quantiles:[4.0, 5.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=99280 ep=99284 n=99279 BeamSearch
BeamSearch(bsize=12, Δ=1.4039096, maxvisits=574) 2026-01-09T03:28:41.334
LOG n.size quantiles:[5.0, 7.0, 8.0, 9.0, 11.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 118.0]
[ Info: minrecall: queries per second: 19925.529925944178, recall: 0.901875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=8, Δ=1.05, maxvisits=794))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 118.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=11, Δ=1.155, maxvisits=588)), 1000, 8)
[ Info: rebuild: queries per second: 22771.5421578327, recall: 0.9095
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=11, Δ=1.155, maxvisits=588))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [3.0, 10.0, 11.0, 12.0, 14.0, 14.0, 15.0, 16.0, 18.0, 19.0, 31.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=10, Δ=1.05, maxvisits=794)), 1000, 8)
  1.157127 seconds (611.67 k allocations: 31.306 MiB, 95.81% compilation time)
[ Info: matrixhints: queries per second: 21201.05233543372, recall: 0.901875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=10, Δ=1.05, maxvisits=794))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 118.0]
  1.937044 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.000904 seconds
SEARCH Exhaustive 2: 0.000901 seconds
SEARCH Exhaustive 3: 0.000953 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
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2026-01-09T03:29:21.579
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch
BeamSearch(bsize=8, Δ=1.1851876, maxvisits=208) 2026-01-09T03:29:25.612
LOG n.size quantiles:[4.0, 4.0, 4.0, 4.0, 5.0]
LOG add_vertex! sp=29225 ep=29229 n=29224 BeamSearch
BeamSearch(bsize=12, Δ=1.0, maxvisits=412) 2026-01-09T03:29:26.612
LOG n.size quantiles:[4.0, 4.0, 5.0, 5.0, 6.0]
LOG add_vertex! sp=50180 ep=50184 n=50179 BeamSearch
BeamSearch(bsize=14, Δ=1.075, maxvisits=366) 2026-01-09T03:29:27.612
LOG n.size quantiles:[6.0, 7.0, 8.0, 8.0, 10.0]
LOG add_vertex! sp=65280 ep=65284 n=65279 BeamSearch
BeamSearch(bsize=8, Δ=1.21275, maxvisits=472) 2026-01-09T03:29:28.612
LOG n.size quantiles:[5.0, 7.0, 7.0, 8.0, 8.0]
LOG add_vertex! sp=79320 ep=79324 n=79319 BeamSearch
BeamSearch(bsize=8, Δ=1.21275, maxvisits=472) 2026-01-09T03:29:29.612
LOG n.size quantiles:[5.0, 6.0, 8.0, 9.0, 11.0]
LOG add_vertex! sp=90515 ep=90519 n=90514 BeamSearch
BeamSearch(bsize=12, Δ=1.4039096, maxvisits=574) 2026-01-09T03:29:30.612
LOG n.size quantiles:[4.0, 6.0, 6.0, 7.0, 10.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 118.0]
[ Info: minrecall: queries per second: 21784.918070299802, recall: 0.901875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=8, Δ=1.05, maxvisits=794))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 118.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=11, Δ=1.155, maxvisits=588)), 1000, 8)
[ Info: rebuild: queries per second: 23011.53262076895, recall: 0.9095
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=11, Δ=1.155, maxvisits=588))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [3.0, 10.0, 11.0, 12.0, 14.0, 14.0, 15.0, 16.0, 18.0, 19.0, 31.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=10, Δ=1.05, maxvisits=794)), 1000, 8)
  1.315749 seconds (567.38 k allocations: 29.094 MiB, 96.35% compilation time)
[ Info: matrixhints: queries per second: 22323.17854917555, recall: 0.901875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=10, Δ=1.05, maxvisits=794))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 118.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  1m37.7s
     Testing SimilaritySearch tests passed 

Testing completed after 441.89s

PkgEval succeeded after 524.47s