Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1141 (aecb173ae6*) started at 2025-09-17T03:55:40.968


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.66s


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

Installing SimilaritySearch...
   Resolving package versions...
   Installed SimilaritySearch ─ v0.13.0
    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 v4.3.0
  [4fba245c] + ArrayInterface v7.20.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [da1fd8a2] + CodeTracking v2.0.0
  [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
  [7e506255] + ScopedValues v1.5.0
  [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.0
  [a2af1166] + SortingAlgorithms v1.2.2
  [aedffcd0] + Static v1.2.0
  [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 v1.67.0+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 4.76s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 118.47s


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

     Testing SimilaritySearch
      Status `/tmp/jl_0Chgyk/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_0Chgyk/Manifest.toml`
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.3.0
  [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 v2.0.0
  [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
  [7e506255] ScopedValues v1.5.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.0
  [a2af1166] SortingAlgorithms v1.2.2
  [aedffcd0] Static v1.2.0
  [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 v1.67.0+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  11.8s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 20005  20005  3.8s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.5s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.2s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.233389 seconds (1000 allocations: 78.125 KiB)
 10.083170 seconds (1000 allocations: 78.125 KiB)
  3.862037 seconds (1000 allocations: 78.125 KiB)
  3.801471 seconds (1000 allocations: 78.125 KiB)
  3.768210 seconds (1000 allocations: 78.125 KiB)
  3.805829 seconds (1000 allocations: 78.125 KiB)
  3.761663 seconds (1000 allocations: 78.125 KiB)
  3.822332 seconds (1000 allocations: 78.125 KiB)
 14.781482 seconds (1000 allocations: 78.125 KiB)
 15.060576 seconds (1000 allocations: 78.125 KiB)
 27.741966 seconds (1000 allocations: 78.125 KiB)
 27.162144 seconds (1000 allocations: 78.125 KiB)
 20.145031 seconds (6.23 k allocations: 388.641 KiB)
 20.220977 seconds (1000 allocations: 78.125 KiB)
 16.927141 seconds (1.00 k allocations: 78.141 KiB)
 16.964834 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m33.3s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.635061 seconds (1000 allocations: 78.125 KiB)
  2.571231 seconds (1000 allocations: 78.125 KiB)
 28.771499 seconds (1000 allocations: 78.125 KiB)
 27.832642 seconds (1000 allocations: 78.125 KiB)
 28.390367 seconds (1000 allocations: 78.125 KiB)
 28.456650 seconds (1000 allocations: 78.125 KiB)
  4.640437 seconds (1000 allocations: 78.125 KiB)
  4.467852 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m12.5s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  7.767460 seconds (1000 allocations: 78.125 KiB)
  7.724990 seconds (1000 allocations: 78.125 KiB)
  7.629382 seconds (1000 allocations: 78.125 KiB)
  7.743986 seconds (1000 allocations: 78.125 KiB)
  7.757148 seconds (1000 allocations: 78.125 KiB)
  7.524736 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  48.9s
  0.043685 seconds (1.00 k allocations: 78.141 KiB)
  0.044219 seconds (1000 allocations: 78.125 KiB)
  0.041514 seconds (1000 allocations: 78.125 KiB)
  0.044633 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.9s
  0.054969 seconds (1000 allocations: 78.125 KiB)
  0.055800 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.3s
ExhaustiveSearch allknn: 3.979109 seconds (2.11 M allocations: 118.053 MiB, 1.69% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.206442 seconds (602.16 k allocations: 31.732 MiB, 99.85% compilation time)
Test Summary: | Pass  Total   Time
allknn        |    5      5  59.0s
quantile(length.(hsp_knns), 0:0.1:1) = [2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0]
Test Summary: | Total  Time
HSP           |     0  3.5s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:48.401
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-17T04:06:48.434
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) 2025-09-17T04:06:49.587
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:49.952
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000009, 0x0000000b, 0x0000000d, 0x00000014, 0x00000024]
D.nn = Int32[1, 2, 3, 4, 5, 3, 1, 1, 9, 3, 11, 11, 13, 3, 4, 3, 3, 9, 2, 20, 9, 3, 9, 1, 1, 3, 11, 20, 11, 3, 11, 5, 2, 13, 13, 36, 9, 3, 9, 20, 3, 20, 13, 9, 5, 9, 4, 4, 3, 3, 3, 11, 3, 3, 36, 9, 5, 4, 3, 2, 2, 3, 5, 5, 3, 3, 9, 3, 3, 5, 9, 9, 11, 1, 3, 2, 4, 2, 2, 2, 2, 36, 11, 20, 5, 5, 3, 1, 3, 36, 2, 20, 3, 20, 20, 2, 20, 1, 9, 5]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.046642184, 0.060022473, 0.007433772, 0.0, 0.053967178, 0.0, 0.011857748, 0.0, 0.022534132, 0.06971395, 0.0076173544, 0.047622323, 0.050546825, 0.0076220036, 0.0, 0.013975441, 0.026183605, 0.054067314, 0.02992791, 0.00290066, 0.08577484, 0.00796473, 0.03973341, 0.032199025, 0.08070773, 0.06583482, 0.012250841, 0.040851533, 0.04689765, 0.013913453, 0.0, 0.019674778, 0.040145338, 0.038927257, 0.0036868453, 0.041314244, 0.049524724, 0.06909126, 0.042008102, 0.044278026, 0.02943778, 0.010204434, 0.030166447, 0.033293247, 0.07615435, 0.04522425, 0.01629132, 0.021968305, 0.022191584, 0.024647295, 0.037662268, 0.04523188, 0.04773575, 0.054207027, 0.040454924, 0.06579518, 0.092035115, 0.043105125, 0.013143063, 0.061578095, 0.04076302, 0.0018385053, 0.029061139, 0.00857532, 0.06594533, 0.034421742, 0.04149443, 0.030424058, 0.06789398, 0.055561483, 0.03199196, 0.04132396, 0.02008897, 0.0665167, 0.030838907, 0.066687286, 0.032631516, 0.058318675, 0.029593587, 0.018066525, 0.08450854, 0.049887, 0.0250749, 0.0616861, 0.03143269, 0.035809875, 0.07078779, 0.031078756, 0.031199038, 0.039103568, 0.003498137, 0.05654806, 0.054879308, 0.040949702, 0.047686636]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  17.8s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.100
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-17T04:06:51.100
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: 2025-09-17T04:06:51.101
[ Info: neardup> range: 33:48, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.101
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.101
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.101
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.101
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.101
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.101
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000009, 0x0000000b, 0x0000000d, 0x00000014, 0x00000024]
D.nn = Int32[1, 2, 3, 4, 5, 3, 1, 1, 9, 3, 11, 11, 13, 3, 4, 3, 3, 9, 2, 20, 9, 3, 9, 1, 1, 3, 11, 3, 11, 3, 11, 5, 2, 13, 13, 36, 9, 3, 9, 20, 3, 20, 13, 9, 5, 9, 4, 4, 3, 3, 3, 11, 3, 3, 36, 9, 5, 4, 3, 2, 2, 3, 5, 5, 3, 3, 9, 3, 3, 5, 9, 9, 11, 1, 3, 2, 4, 2, 2, 2, 2, 36, 11, 20, 5, 5, 3, 1, 3, 36, 2, 20, 3, 20, 20, 2, 20, 1, 9, 5]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.046642184, 0.060022473, 0.007433772, 0.0, 0.053967178, 0.0, 0.011857748, 0.0, 0.022534132, 0.06971395, 0.0076173544, 0.047622323, 0.050546825, 0.0076220036, 0.0, 0.013975441, 0.026183605, 0.054067314, 0.02992791, 0.00290066, 0.08577484, 0.00796473, 0.090773225, 0.032199025, 0.08070773, 0.06583482, 0.012250841, 0.040851533, 0.04689765, 0.013913453, 0.0, 0.019674778, 0.040145338, 0.038927257, 0.0036868453, 0.041314244, 0.049524724, 0.06909126, 0.042008102, 0.044278026, 0.02943778, 0.010204434, 0.030166447, 0.033293247, 0.07615435, 0.04522425, 0.01629132, 0.021968305, 0.022191584, 0.024647295, 0.037662268, 0.04523188, 0.04773575, 0.054207027, 0.040454924, 0.06579518, 0.092035115, 0.043105125, 0.013143063, 0.061578095, 0.04076302, 0.0018385053, 0.029061139, 0.00857532, 0.06594533, 0.034421742, 0.04149443, 0.030424058, 0.06789398, 0.055561483, 0.03199196, 0.04132396, 0.02008897, 0.0665167, 0.030838907, 0.066687286, 0.032631516, 0.058318675, 0.029593587, 0.018066525, 0.08450854, 0.049887, 0.0250749, 0.0616861, 0.03143269, 0.035809875, 0.07078779, 0.031078756, 0.031199038, 0.039103568, 0.003498137, 0.05654806, 0.054879308, 0.040949702, 0.047686636]
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: 2025-09-17T04:06:51.192
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-17T04:06:51.192
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: 2025-09-17T04:06:51.194
[ Info: neardup> range: 33:48, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.194
[ Info: neardup> range: 49:64, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.194
[ Info: neardup> range: 65:80, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.194
[ Info: neardup> range: 81:96, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.194
[ Info: neardup> range: 97:100, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.194
[ Info: neardup> finished current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:51.194
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000014, 0x00000024]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 14, 9, 2, 20, 9, 14, 9, 8, 1, 10, 11, 14, 11, 6, 11, 5, 2, 10, 13, 36, 9, 14, 9, 20, 3, 20, 10, 9, 7, 9, 4, 4, 3, 3, 6, 11, 16, 6, 36, 9, 10, 15, 14, 2, 7, 3, 14, 5, 6, 16, 9, 3, 3, 5, 9, 9, 11, 1, 14, 7, 4, 2, 14, 7, 10, 36, 11, 20, 5, 14, 14, 1, 10, 36, 7, 20, 3, 20, 20, 2, 20, 1, 9, 7]
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, 0.0073876977, 0.050546825, 0.0076220036, 0.0, 0.013975441, 0.010186434, 0.054067314, 0.019719899, 0.00290066, 0.027494073, 0.00796473, 0.07977092, 0.032199025, 0.009175837, 0.06583482, 0.012250841, 0.040851533, 0.0068954825, 0.013913453, 0.0, 0.019674778, 0.024498641, 0.038927257, 0.0036868453, 0.041314244, 0.049524724, 0.04931408, 0.042008102, 0.038924813, 0.02943778, 0.010204434, 0.030166447, 0.033293247, 0.07615435, 0.0066208243, 0.01629132, 0.02070725, 0.0136624575, 0.024647295, 0.037662268, 0.031977832, 0.025788665, 0.03673786, 0.040454924, 0.037094653, 0.092035115, 0.027018964, 0.013143063, 0.018400013, 0.015203357, 0.0018385053, 0.029061139, 0.00857532, 0.06594533, 0.034421742, 0.04149443, 0.030424058, 0.06789398, 0.008898735, 0.029719591, 0.04132396, 0.02008897, 0.012799263, 0.016760707, 0.028590202, 0.032631516, 0.058318675, 0.029593587, 0.018066525, 0.07423711, 0.019901812, 0.0250749, 0.017114043, 0.03143269, 0.018692076, 0.07078779, 0.031078756, 0.031199038, 0.039103568, 0.003498137, 0.05654806, 0.054879308, 0.040949702, 0.036013186]
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: 2025-09-17T04:06:58.174
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=8 n=8 2025-09-17T04:06:58.174
[ Info: neardup> range: 17:32, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:58.180
[ Info: neardup> range: 33:48, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:58.180
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:58.180
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:58.180
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:58.180
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:58.180
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-17T04:06:58.181
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000009, 0x0000000b, 0x0000000d, 0x00000014, 0x00000024]
D.nn = Int32[1, 2, 3, 4, 5, 3, 1, 1, 9, 3, 11, 11, 13, 3, 4, 3, 3, 9, 2, 20, 9, 3, 9, 1, 1, 3, 11, 3, 11, 3, 11, 5, 2, 13, 13, 36, 9, 3, 9, 20, 3, 20, 13, 9, 5, 9, 4, 4, 3, 3, 3, 11, 3, 3, 36, 9, 5, 4, 3, 2, 2, 3, 5, 5, 3, 3, 9, 3, 3, 5, 9, 9, 11, 1, 3, 2, 4, 2, 2, 2, 2, 36, 11, 20, 5, 5, 3, 1, 3, 36, 2, 20, 3, 20, 20, 2, 20, 1, 9, 5]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.046642184, 0.060022473, 0.007433772, 0.0, 0.053967178, 0.0, 0.011857748, 0.0, 0.022534132, 0.06971395, 0.0076173544, 0.047622323, 0.050546825, 0.0076220036, 0.0, 0.013975441, 0.026183605, 0.054067314, 0.02992791, 0.00290066, 0.08577484, 0.00796473, 0.090773225, 0.032199025, 0.08070773, 0.06583482, 0.012250841, 0.040851533, 0.04689765, 0.013913453, 0.0, 0.019674778, 0.040145338, 0.038927257, 0.0036868453, 0.041314244, 0.049524724, 0.06909126, 0.042008102, 0.044278026, 0.02943778, 0.010204434, 0.030166447, 0.033293247, 0.07615435, 0.04522425, 0.01629132, 0.021968305, 0.022191584, 0.024647295, 0.037662268, 0.04523188, 0.04773575, 0.054207027, 0.040454924, 0.06579518, 0.092035115, 0.043105125, 0.013143063, 0.061578095, 0.04076302, 0.0018385053, 0.029061139, 0.00857532, 0.06594533, 0.034421742, 0.04149443, 0.030424058, 0.06789398, 0.055561483, 0.03199196, 0.04132396, 0.02008897, 0.0665167, 0.030838907, 0.066687286, 0.032631516, 0.058318675, 0.029593587, 0.018066525, 0.08450854, 0.049887, 0.0250749, 0.0616861, 0.03143269, 0.035809875, 0.07078779, 0.031078756, 0.031199038, 0.039103568, 0.003498137, 0.05654806, 0.054879308, 0.040949702, 0.047686636]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.0s
computing farthest point 1, dmax: Inf, imax: 8, n: 30
computing farthest point 2, dmax: 1.1970415, imax: 20, n: 30
computing farthest point 3, dmax: 0.9530561, imax: 17, n: 30
computing farthest point 4, dmax: 0.939318, imax: 4, n: 30
computing farthest point 5, dmax: 0.92628074, imax: 26, n: 30
computing farthest point 6, dmax: 0.7541333, imax: 23, n: 30
computing farthest point 7, dmax: 0.72014624, imax: 12, n: 30
computing farthest point 8, dmax: 0.69694394, imax: 19, n: 30
computing farthest point 9, dmax: 0.683448, imax: 27, n: 30
computing farthest point 10, dmax: 0.6657723, imax: 6, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.5s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.6s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-17T04:07:06.322
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=8, Δ=1.1, maxvisits=120) 2025-09-17T04:07:19.044
LOG n.size quantiles:[2.0, 2.0, 3.0, 3.0, 3.0]
(i, j, d) = (47, 597, -1.1920929f-7)
(i, j, d, :parallel) = (47, 597, -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 => 20.164696964, :exact => 0.991151058)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  21.7s
  5.688976 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.004582 seconds
SEARCH Exhaustive 2: 0.004950 seconds
SEARCH Exhaustive 3: 0.004884 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) 2025-09-17T04:07:47.421
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=12, Δ=1.4039096, maxvisits=230) 2025-09-17T04:07:53.379
LOG n.size quantiles:[2.0, 2.0, 2.0, 2.0, 3.0]
[ Info: RECALL BAJO!! recall: 0.2874999999999999, #objects: 3329, #queries: 32
[ Info: [0.21269133687019348, 0.507358729839325, 0.2938789427280426, 0.41932615637779236, 0.2874469459056854, 0.2936452329158783, 0.31472286581993103, 0.36826539039611816, 0.4710187613964081, 0.21229946613311768, 0.3920653164386749, 0.2214323729276657, 0.42577338218688965, 0.2955031991004944, 0.3218400180339813, 0.4702775180339813, 0.38693967461586, 0.40259087085723877, 0.4546622931957245, 0.3956230878829956, 0.29241305589675903, 0.41287216544151306, 0.383625864982605, 0.36088111996650696, 0.3227400481700897, 0.2924555838108063, 0.42175713181495667, 0.23497851192951202, 0.3908969461917877, 0.27213308215141296, 0.29509851336479187, 0.29016727209091187]
(g, r) = (Set(Int32[1802, 137, 2047, 1712, 1498, 1174, 2916, 1241, 480, 316]), Set(Int32[2299, 137, 3247, 2196, 1712, 1498, 1929, 269, 2700, 1534]))
(g, r) = (Set(Int32[1258, 2159, 896, 2314, 1551, 565, 2965, 2226, 2119, 1422]), Set(Int32[2841, 255, 1804, 2616, 2888, 2965, 3106, 2226, 1422, 2551]))
(g, r) = (Set(Int32[1999, 29, 2759, 223, 182, 843, 1439, 1490, 1803, 1648]), Set(Int32[611, 29, 2759, 223, 182, 2047, 117, 843, 1490, 900]))
(g, r) = (Set(Int32[1889, 580, 2897, 3248, 3307, 2829, 1687, 1117, 1477, 2926]), Set(Int32[388, 1099, 193, 1200, 2038, 1595, 1683, 1117, 1477, 946]))
(g, r) = (Set(Int32[1781, 1752, 586, 930, 2284, 1712, 1033, 2916, 1841, 1273]), Set(Int32[1752, 1457, 2062, 3021, 2284, 3101, 1033, 2916, 1841, 2427]))
(g, r) = (Set(Int32[2625, 79, 1405, 1095, 1645, 268, 780, 3041, 981, 3233]), Set(Int32[2625, 79, 1405, 1645, 1133, 2371, 268, 780, 733, 1636]))
(g, r) = (Set(Int32[1919, 1866, 2245, 998, 391, 973, 712, 1362, 2065, 3077]), Set(Int32[1919, 1866, 391, 2005, 415, 1856, 2065, 2698, 2044, 1817]))
(g, r) = (Set(Int32[339, 930, 359, 2693, 2909, 2639, 2126, 2723, 785, 3054]), Set(Int32[1317, 2707, 2728, 1392, 2345, 785, 1443, 28, 124, 301]))
(g, r) = (Set(Int32[1646, 2788, 1129, 1420, 724, 3057, 348, 2419, 777, 343]), Set(Int32[3119, 1480, 2980, 3272, 645, 2887, 3298, 2946, 3052, 2972]))
(g, r) = (Set(Int32[2847, 1093, 1864, 2010, 1238, 50, 2028, 2214, 130, 3163]), Set(Int32[2847, 2879, 771, 50, 1992, 2028, 2956, 142, 130, 1864]))
(g, r) = (Set(Int32[2461, 3010, 1443, 2337, 1929, 1879, 2248, 629, 2862, 2225]), Set(Int32[2211, 3010, 137, 792, 1929, 1712, 1498, 2700, 1534, 2825]))
(g, r) = (Set(Int32[531, 661, 2979, 2776, 486, 2422, 3106, 1501, 1735, 277]), Set(Int32[531, 661, 960, 2979, 693, 2047, 486, 1501, 277, 1025]))
(g, r) = (Set(Int32[283, 143, 38, 1199, 2503, 1502, 297, 841, 130, 2768]), Set(Int32[1477, 2080, 1749, 1099, 193, 1404, 1595, 1271, 2010, 1117]))
(g, r) = (Set(Int32[1133, 941, 362, 3228, 249, 2803, 2067, 3325, 200, 2600]), Set(Int32[335, 1133, 362, 249, 3184, 2747, 1298, 3325, 2938, 617]))
(g, r) = (Set(Int32[553, 2269, 1729, 793, 205, 1714, 2890, 1308, 2323, 718]), Set(Int32[2760, 2269, 144, 793, 205, 1714, 14, 1661, 3183, 256]))
(g, r) = (Set(Int32[1616, 2494, 408, 1246, 1787, 242, 1038, 2350, 1445, 1930]), Set(Int32[1852, 933, 1209, 2239, 892, 2285, 1038, 2350, 875, 1342]))
(g, r) = (Set(Int32[3254, 2386, 1472, 2691, 1607, 2121, 2861, 1218, 1941, 1794]), Set(Int32[2139, 1155, 442, 292, 1523, 812, 42, 1306, 2181, 2835]))
(g, r) = (Set(Int32[1376, 3088, 3004, 291, 3271, 264, 2346, 931, 1014, 2223]), Set(Int32[2353, 3088, 3004, 2889, 3271, 2370, 264, 2308, 931, 1014]))
(g, r) = (Set(Int32[2111, 719, 1643, 126, 1188, 16, 2479, 865, 1387, 924]), Set(Int32[1238, 719, 497, 664, 1110, 1349, 1387, 1005, 924, 1069]))
(g, r) = (Set(Int32[3121, 2215, 2786, 373, 163, 3155, 1441, 1581, 3289, 3062]), Set(Int32[2732, 2819, 2681, 373, 3191, 2556, 704, 2918, 3120, 2935]))
(g, r) = (Set(Int32[83, 2929, 1486, 3080, 1212, 353, 1352, 987, 2317, 1419]), Set(Int32[2929, 178, 3080, 2815, 1356, 3292, 1352, 2228, 3166, 732]))
(g, r) = (Set(Int32[2812, 2128, 837, 3296, 271, 1231, 2968, 2688, 1297, 2077]), Set(Int32[2568, 1561, 136, 193, 2661, 271, 1799, 1683, 1161, 1194]))
(g, r) = (Set(Int32[2191, 1457, 778, 2519, 2832, 677, 3019, 1411, 1354, 2192]), Set(Int32[881, 2191, 797, 750, 1804, 1503, 261, 1181, 249, 1411]))
(g, r) = (Set(Int32[988, 1427, 965, 1801, 3043, 3055, 2129, 2524, 1298, 1513]), Set(Int32[1103, 1594, 1427, 2958, 1133, 249, 121, 187, 11, 358]))
(g, r) = (Set(Int32[1194, 2488, 1396, 2672, 2885, 2964, 1740, 1713, 1681, 652]), Set(Int32[149, 235, 566, 2865, 1713, 1740, 574, 820, 2009, 652]))
(g, r) = (Set(Int32[2354, 1697, 2376, 2014, 1956, 654, 39, 1795, 493, 482]), Set(Int32[2354, 1697, 1124, 2376, 1956, 654, 39, 493, 482, 400]))
(g, r) = (Set(Int32[3311, 2789, 1265, 397, 2845, 3293, 3237, 3073, 704, 807]), Set(Int32[2004, 136, 2038, 1459, 3293, 3237, 2364, 515, 3232, 1691]))
(g, r) = (Set(Int32[2359, 2897, 283, 771, 193, 3252, 2080, 417, 102, 2926]), Set(Int32[388, 2080, 1702, 193, 136, 271, 951, 946, 1117, 3232]))
(g, r) = (Set(Int32[1252, 1701, 1491, 3182, 299, 2364, 1171, 1683, 2959, 868]), Set(Int32[1491, 2128, 3036, 1597, 1709, 2887, 3298, 728, 3272, 1871]))
(g, r) = (Set(Int32[2547, 2380, 2325, 1401, 2691, 812, 2720, 1500, 2947, 2690]), Set(Int32[2547, 263, 771, 1651, 2356, 1844, 44, 2690, 2947, 75]))
(g, r) = (Set(Int32[1707, 701, 214, 2436, 2612, 1527, 1732, 2634, 245, 1079]), Set(Int32[2732, 3141, 2240, 3191, 373, 2556, 2612, 2318, 2859, 2609]))
(g, r) = (Set(Int32[2569, 2386, 2330, 3205, 442, 2593, 385, 597, 177, 1794]), Set(Int32[2139, 442, 292, 812, 2231, 456, 484, 177, 2181, 810]))
collect(Int32, IdView(p)) = Int32[1712, 1498, 137, 1929, 269, 3247, 2299, 2196, 1534, 2700]
collect(Int32, IdView(p)) = Int32[2226, 2965, 1422, 3106, 2888, 2551, 2841, 2616, 1804, 255]
collect(Int32, IdView(p)) = Int32[223, 1490, 843, 182, 29, 2759, 900, 611, 2047, 117]
collect(Int32, IdView(p)) = Int32[1477, 1117, 1200, 1595, 193, 1683, 2038, 388, 946, 1099]
collect(Int32, IdView(p)) = Int32[2284, 1752, 2916, 1841, 1033, 3101, 2062, 1457, 2427, 3021]
collect(Int32, IdView(p)) = Int32[1405, 2625, 268, 780, 79, 1645, 733, 2371, 1636, 1133]
collect(Int32, IdView(p)) = Int32[1919, 2065, 1866, 391, 2044, 2005, 415, 1856, 1817, 2698]
collect(Int32, IdView(p)) = Int32[785, 2728, 1392, 1317, 1443, 301, 2707, 2345, 28, 124]
collect(Int32, IdView(p)) = Int32[3272, 3298, 2887, 2972, 645, 2946, 3119, 1480, 2980, 3052]
collect(Int32, IdView(p)) = Int32[2847, 130, 50, 2028, 1864, 142, 2879, 1992, 771, 2956]
collect(Int32, IdView(p)) = Int32[3010, 1929, 1534, 1712, 2700, 2825, 2211, 1498, 137, 792]
collect(Int32, IdView(p)) = Int32[486, 1501, 531, 277, 2979, 661, 960, 1025, 2047, 693]
collect(Int32, IdView(p)) = Int32[2010, 193, 1404, 1271, 2080, 1117, 1477, 1749, 1595, 1099]
collect(Int32, IdView(p)) = Int32[249, 362, 1133, 3325, 3184, 617, 1298, 335, 2747, 2938]
collect(Int32, IdView(p)) = Int32[1714, 793, 2269, 205, 3183, 144, 2760, 14, 1661, 256]
collect(Int32, IdView(p)) = Int32[2350, 1038, 875, 2239, 933, 1342, 892, 1209, 2285, 1852]
collect(Int32, IdView(p)) = Int32[2139, 2181, 1155, 442, 1523, 812, 292, 2835, 42, 1306]
collect(Int32, IdView(p)) = Int32[3088, 264, 3004, 931, 1014, 3271, 2353, 2308, 2889, 2370]
collect(Int32, IdView(p)) = Int32[719, 1387, 924, 1110, 1069, 1238, 1349, 497, 664, 1005]
collect(Int32, IdView(p)) = Int32[373, 3191, 2918, 2935, 704, 2681, 2732, 3120, 2819, 2556]
collect(Int32, IdView(p)) = Int32[2929, 3080, 1352, 178, 1356, 3292, 2815, 732, 2228, 3166]
collect(Int32, IdView(p)) = Int32[271, 1161, 2568, 136, 193, 2661, 1799, 1561, 1194, 1683]
collect(Int32, IdView(p)) = Int32[2191, 1411, 797, 750, 1503, 1804, 261, 881, 1181, 249]
collect(Int32, IdView(p)) = Int32[1427, 11, 1594, 1103, 358, 1133, 187, 121, 249, 2958]
collect(Int32, IdView(p)) = Int32[652, 1740, 1713, 820, 566, 2865, 574, 2009, 149, 235]
collect(Int32, IdView(p)) = Int32[654, 39, 1956, 493, 2354, 2376, 482, 1697, 1124, 400]
collect(Int32, IdView(p)) = Int32[3293, 3237, 2038, 1691, 2004, 136, 1459, 2364, 3232, 515]
collect(Int32, IdView(p)) = Int32[193, 2080, 946, 1117, 3232, 136, 1702, 951, 388, 271]
collect(Int32, IdView(p)) = Int32[1491, 2887, 3036, 728, 2128, 1871, 1597, 1709, 3298, 3272]
collect(Int32, IdView(p)) = Int32[2547, 2947, 2690, 75, 1651, 2356, 771, 1844, 263, 44]
collect(Int32, IdView(p)) = Int32[2612, 2318, 3191, 3141, 2859, 2240, 2609, 2732, 373, 2556]
collect(Int32, IdView(p)) = Int32[442, 177, 812, 2231, 2181, 292, 810, 456, 2139, 484]
quantile(neighbors_length.(Ref(index.adj), 1:length(index)), 0:0.1:1.0) = [1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 6.0, 8.0, 33.0]
     Testing SimilaritySearch tests passed 

Testing completed after 595.27s

PkgEval succeeded after 741.81s