Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1191 (63a270f4d4*) started at 2025-09-23T09:54:41.765


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 10.02s


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

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.13/Project.toml`
  [053f045d] + SimilaritySearch v0.13.0
    Updating `~/.julia/environments/v1.13/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.3.0
  [4fba245c] + ArrayInterface v7.20.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [da1fd8a2] + CodeTracking v2.0.1
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.18.0
  [807dbc54] + Compiler v0.1.1
  [a33af91c] + CompositionsBase v0.1.2
  [187b0558] + ConstructionBase v1.6.0
  [adafc99b] + CpuId v0.3.1
  [9a962f9c] + DataAPI v1.16.0
⌅ [864edb3b] + DataStructures v0.18.22
  [b4f34e82] + Distances v0.10.12
  [ffbed154] + DocStringExtensions v0.9.5
  [5789e2e9] + FileIO v1.17.0
  [076d061b] + HashArrayMappedTries v0.2.0
  [615f187c] + IfElse v0.1.1
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.4
  [c3a54625] + JET v0.10.7
⌅ [033835bb] + JLD2 v0.5.15
  [aa1ae85d] + JuliaInterpreter v0.10.5
  [70703baa] + JuliaSyntax v1.0.2
  [10f19ff3] + LayoutPointers v0.1.17
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [6f1432cf] + LoweredCodeUtils v3.4.3
  [1914dd2f] + MacroTools v0.5.16
  [d125e4d3] + ManualMemory v0.1.8
  [e1d29d7a] + Missings v1.2.0
  [bac558e1] + OrderedCollections v1.8.1
  [d96e819e] + Parameters v0.12.3
  [f517fe37] + Polyester v0.7.18
  [1d0040c9] + PolyesterWeave v0.2.2
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [92933f4c] + ProgressMeter v1.11.0
  [ae029012] + Requires v1.3.1
  [94e857df] + SIMDTypes v0.1.0
  [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 v1.67.1+0
  [3f19e933] + p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 3.96s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 118.6s


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

     Testing SimilaritySearch
      Status `/tmp/jl_S5gArn/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_S5gArn/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 v2.0.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.0
  [807dbc54] Compiler v0.1.1
  [a33af91c] CompositionsBase v0.1.2
  [187b0558] ConstructionBase v1.6.0
  [adafc99b] CpuId v0.3.1
  [9a962f9c] DataAPI v1.16.0
⌅ [864edb3b] DataStructures v0.18.22
  [b4f34e82] Distances v0.10.12
  [ffbed154] DocStringExtensions v0.9.5
  [5789e2e9] FileIO v1.17.0
  [076d061b] HashArrayMappedTries v0.2.0
  [615f187c] IfElse v0.1.1
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.4
  [c3a54625] JET v0.10.7
⌅ [033835bb] JLD2 v0.5.15
  [aa1ae85d] JuliaInterpreter v0.10.5
  [70703baa] JuliaSyntax v1.0.2
  [10f19ff3] LayoutPointers v0.1.17
  [2ab3a3ac] LogExpFunctions v0.3.29
  [6f1432cf] LoweredCodeUtils v3.4.3
  [1914dd2f] MacroTools v0.5.16
  [d125e4d3] ManualMemory v0.1.8
  [e1d29d7a] Missings v1.2.0
  [bac558e1] OrderedCollections v1.8.1
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [1d0040c9] PolyesterWeave v0.2.2
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.0
  [92933f4c] ProgressMeter v1.11.0
  [ae029012] Requires v1.3.1
  [94e857df] SIMDTypes v0.1.0
  [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 v1.67.1+0
  [3f19e933] p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
Test Summary:              | Pass  Total   Time
test database abstractions |   56     56  11.4s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 20005  20005  3.5s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.9s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.3s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.615925 seconds (1000 allocations: 78.125 KiB)
 10.697351 seconds (1000 allocations: 78.125 KiB)
  4.055499 seconds (1000 allocations: 78.125 KiB)
  4.222818 seconds (1000 allocations: 78.125 KiB)
  4.131606 seconds (1000 allocations: 78.125 KiB)
  3.454846 seconds (1000 allocations: 78.125 KiB)
  4.029510 seconds (1000 allocations: 78.125 KiB)
  3.767683 seconds (1000 allocations: 78.125 KiB)
 15.345852 seconds (1000 allocations: 78.125 KiB)
 15.380082 seconds (1000 allocations: 78.125 KiB)
 27.021394 seconds (1000 allocations: 78.125 KiB)
 26.916551 seconds (1000 allocations: 78.125 KiB)
 20.224485 seconds (6.23 k allocations: 388.641 KiB)
 20.328402 seconds (1000 allocations: 78.125 KiB)
 17.608566 seconds (1.00 k allocations: 78.141 KiB)
 17.664559 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m37.2s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  2.866021 seconds (1000 allocations: 78.125 KiB)
  2.854179 seconds (1000 allocations: 78.125 KiB)
 27.704264 seconds (1000 allocations: 78.125 KiB)
 26.758369 seconds (1000 allocations: 78.125 KiB)
 28.371988 seconds (1000 allocations: 78.125 KiB)
 28.568384 seconds (1000 allocations: 78.125 KiB)
  4.189092 seconds (1000 allocations: 78.125 KiB)
  4.253804 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m09.6s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  8.645108 seconds (1000 allocations: 78.125 KiB)
  8.466207 seconds (1000 allocations: 78.125 KiB)
  8.282006 seconds (1000 allocations: 78.125 KiB)
  8.212082 seconds (1000 allocations: 78.125 KiB)
  7.995576 seconds (1000 allocations: 78.125 KiB)
  8.094738 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  52.7s
  0.044665 seconds (1.00 k allocations: 78.141 KiB)
  0.045080 seconds (1000 allocations: 78.125 KiB)
  0.040050 seconds (1000 allocations: 78.125 KiB)
  0.040179 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.9s
  0.052891 seconds (1000 allocations: 78.125 KiB)
  0.054051 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.5s
ExhaustiveSearch allknn: 4.769861 seconds (2.37 M allocations: 131.685 MiB, 13.03% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.366933 seconds (608.22 k allocations: 32.003 MiB, 99.86% compilation time)
Test Summary: | Pass  Total     Time
allknn        |    5      5  1m07.8s
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  3.3s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:14.241
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-23T10:06:14.278
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) 2025-09-23T10:06:15.508
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:15.915
D.map = UInt32[0x00000001, 0x00000002, 0x00000005, 0x00000006, 0x0000000c, 0x00000013, 0x00000017, 0x0000001e, 0x00000029, 0x0000002f, 0x0000004b, 0x0000005e, 0x00000060]
D.nn = Int32[1, 2, 2, 2, 5, 6, 2, 5, 1, 5, 6, 12, 12, 5, 2, 12, 2, 2, 19, 19, 1, 5, 23, 23, 1, 12, 2, 12, 12, 30, 6, 6, 2, 23, 5, 6, 12, 12, 5, 2, 41, 23, 41, 19, 2, 2, 47, 6, 6, 23, 2, 6, 1, 2, 12, 1, 19, 12, 1, 6, 2, 12, 5, 2, 23, 30, 6, 6, 47, 23, 2, 30, 12, 19, 75, 6, 2, 23, 12, 23, 2, 12, 2, 75, 2, 12, 12, 12, 2, 1, 2, 5, 12, 94, 5, 96, 96, 2, 12, 5]
D.dist = Float32[0.0, 0.0, 0.0537768, 0.064314306, 0.0, 0.0, 0.023597956, 0.008505642, 0.08912969, 0.0324651, 0.040052652, 0.0, 0.0777244, 0.09580988, 0.018051267, 0.052820742, 0.05456674, 0.020756781, 0.0, 0.045062482, 0.0161708, 0.06897205, 0.0, 0.008132815, 0.05044198, 0.032088995, 0.02673781, 0.052967727, 0.06692582, 0.0, 0.055306435, 0.030316055, 0.04721862, 0.06781465, 0.03851241, 0.020673215, 0.06858581, 0.050204337, 0.041534066, 0.09962857, 0.0, 0.008868575, 0.009717643, 0.017003775, 0.00022280216, 0.0049410462, 0.0, 0.06915492, 0.044381917, 0.03418857, 0.038993657, 0.047856808, 0.08605689, 0.015575111, 0.093034685, 0.004475236, 0.0071356893, 0.055309713, 0.03693378, 0.0898149, 0.083394825, 0.010862529, 0.02316457, 0.044133127, 0.047288954, 0.02412033, 0.07671708, 0.04164809, 0.028197646, 0.053033113, 0.06071049, 0.0699476, 0.073272884, 0.043835282, 0.0, 0.050641418, 0.039182365, 0.030008495, 0.049741864, 0.0707075, 0.06788242, 0.06711757, 0.022382498, 0.005941689, 0.037234902, 0.030500472, 0.0051078796, 0.068796515, 0.03387338, 0.027435005, 0.028014362, 0.013472438, 0.019180119, 0.0, 0.039285064, 0.0, 0.028549552, 0.007137835, 0.011880875, 0.05229169]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  20.4s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.149
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-23T10:06:17.149
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.149
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.149
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.149
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.149
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.149
[ Info: neardup> range: 97:100, current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.149
[ Info: neardup> finished current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.150
D.map = UInt32[0x00000001, 0x00000002, 0x00000005, 0x00000006, 0x0000000c, 0x00000013, 0x00000017, 0x0000001e, 0x00000029, 0x0000002f, 0x0000004b, 0x0000005e, 0x00000060]
D.nn = Int32[1, 2, 2, 2, 5, 6, 2, 5, 1, 5, 6, 12, 12, 5, 2, 12, 2, 2, 19, 1, 1, 5, 23, 23, 1, 12, 2, 12, 12, 30, 6, 6, 2, 23, 5, 6, 12, 12, 5, 2, 41, 23, 19, 19, 2, 2, 47, 6, 6, 23, 2, 6, 1, 2, 12, 1, 19, 12, 1, 6, 2, 12, 5, 2, 23, 30, 6, 6, 47, 23, 2, 30, 12, 19, 75, 6, 2, 23, 12, 23, 2, 12, 2, 75, 2, 12, 12, 12, 2, 1, 2, 5, 12, 94, 5, 96, 96, 2, 12, 5]
D.dist = Float32[0.0, 0.0, 0.0537768, 0.064314306, 0.0, 0.0, 0.023597956, 0.008505642, 0.08912969, 0.0324651, 0.040052652, 0.0, 0.0777244, 0.09580988, 0.018051267, 0.052820742, 0.05456674, 0.020756781, 0.0, 0.050653815, 0.0161708, 0.06897205, 0.0, 0.008132815, 0.05044198, 0.032088995, 0.02673781, 0.052967727, 0.06692582, 0.0, 0.055306435, 0.030316055, 0.04721862, 0.06781465, 0.03851241, 0.020673215, 0.06858581, 0.050204337, 0.041534066, 0.09962857, 0.0, 0.008868575, 0.08175546, 0.017003775, 0.00022280216, 0.0049410462, 0.0, 0.06915492, 0.044381917, 0.03418857, 0.038993657, 0.047856808, 0.08605689, 0.015575111, 0.093034685, 0.004475236, 0.0071356893, 0.055309713, 0.03693378, 0.0898149, 0.083394825, 0.010862529, 0.02316457, 0.044133127, 0.047288954, 0.02412033, 0.07671708, 0.04164809, 0.028197646, 0.053033113, 0.06071049, 0.0699476, 0.073272884, 0.043835282, 0.0, 0.050641418, 0.039182365, 0.030008495, 0.049741864, 0.0707075, 0.06788242, 0.06711757, 0.022382498, 0.005941689, 0.037234902, 0.030500472, 0.0051078796, 0.068796515, 0.03387338, 0.027435005, 0.028014362, 0.013472438, 0.019180119, 0.0, 0.039285064, 0.0, 0.028549552, 0.007137835, 0.011880875, 0.05229169]
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: 2025-09-23T10:06:17.251
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-23T10:06:17.251
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: 2025-09-23T10:06:17.251
[ Info: neardup> range: 33:48, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.251
[ Info: neardup> range: 49:64, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.251
[ Info: neardup> range: 65:80, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.251
[ Info: neardup> range: 81:96, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.251
[ Info: neardup> range: 97:100, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.252
[ Info: neardup> finished current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:17.252
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x0000001e, 0x00000029, 0x0000002b, 0x0000005e]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 7, 4, 3, 11, 1, 8, 14, 14, 1, 12, 15, 15, 12, 30, 11, 11, 15, 14, 14, 6, 16, 16, 15, 4, 41, 14, 43, 3, 2, 2, 16, 14, 6, 14, 15, 14, 9, 2, 9, 1, 3, 16, 11, 4, 15, 12, 4, 11, 14, 30, 4, 6, 8, 14, 4, 30, 15, 3, 16, 14, 2, 14, 12, 13, 15, 15, 7, 16, 15, 16, 12, 13, 15, 9, 15, 8, 12, 94, 4, 9, 9, 15, 12, 4]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.011263669, 0.017348528, 0.021471143, 0.012080133, 0.0161708, 0.042287886, 0.016518056, 0.01453644, 0.05044198, 0.032088995, 0.008934259, 0.02714765, 0.06692582, 0.0, 0.006581247, 0.024721503, 0.027533472, 0.034913838, 0.022667289, 0.020673215, 0.0029640198, 0.0016251206, 0.027203083, 0.0073156357, 0.0, 0.015183568, 0.0, 0.05202675, 0.00022280216, 0.0049410462, 0.06862575, 0.041374624, 0.044381917, 0.046879113, 0.020559847, 0.04565555, 0.043119192, 0.015575111, 0.02304089, 0.004475236, 0.026457727, 0.012424886, 0.0019419193, 0.08156073, 0.029082298, 0.010862529, 0.021010995, 0.024520934, 0.02561462, 0.02412033, 0.07133949, 0.04164809, 0.038412035, 0.036872566, 0.01797992, 0.0699476, 0.051149964, 0.012855768, 0.075181484, 0.0446347, 0.039182365, 0.0415262, 0.049741864, 0.07286602, 0.029388487, 0.022672832, 0.018546283, 0.097180784, 0.030460715, 0.021868587, 0.0051078796, 0.0028985143, 0.011468649, 0.022647262, 0.02271539, 0.011386454, 0.019180119, 0.0, 0.022836506, 0.013369918, 0.038278162, 0.0069617033, 0.011880875, 0.023383737]
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: 2025-09-23T10:06:25.053
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=5 n=5 2025-09-23T10:06:25.054
[ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:25.063
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:25.063
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:25.063
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:25.063
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:25.064
[ Info: neardup> range: 97:100, current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:25.064
[ Info: neardup> finished current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-23T10:06:25.064
D.map = UInt32[0x00000001, 0x00000002, 0x00000005, 0x00000006, 0x0000000c, 0x00000013, 0x00000017, 0x0000001e, 0x00000029, 0x0000002f, 0x0000004b, 0x0000005e, 0x00000060]
D.nn = Int32[1, 2, 2, 2, 5, 6, 2, 5, 1, 5, 6, 12, 12, 5, 2, 12, 2, 2, 19, 1, 1, 5, 23, 23, 1, 12, 2, 12, 12, 30, 6, 6, 2, 23, 5, 6, 12, 12, 5, 2, 41, 23, 19, 19, 2, 2, 47, 6, 6, 23, 2, 6, 1, 2, 12, 1, 19, 12, 1, 6, 2, 12, 5, 2, 23, 30, 6, 6, 47, 23, 2, 30, 12, 19, 75, 6, 2, 23, 12, 23, 2, 12, 2, 75, 2, 12, 12, 12, 2, 1, 2, 5, 12, 94, 5, 96, 96, 2, 12, 5]
D.dist = Float32[0.0, 0.0, 0.0537768, 0.064314306, 0.0, 0.0, 0.023597956, 0.008505642, 0.08912969, 0.0324651, 0.040052652, 0.0, 0.0777244, 0.09580988, 0.018051267, 0.052820742, 0.05456674, 0.020756781, 0.0, 0.050653815, 0.0161708, 0.06897205, 0.0, 0.008132815, 0.05044198, 0.032088995, 0.02673781, 0.052967727, 0.06692582, 0.0, 0.055306435, 0.030316055, 0.04721862, 0.06781465, 0.03851241, 0.020673215, 0.06858581, 0.050204337, 0.041534066, 0.09962857, 0.0, 0.008868575, 0.08175546, 0.017003775, 0.00022280216, 0.0049410462, 0.0, 0.06915492, 0.044381917, 0.03418857, 0.038993657, 0.047856808, 0.08605689, 0.015575111, 0.093034685, 0.004475236, 0.0071356893, 0.055309713, 0.03693378, 0.0898149, 0.083394825, 0.010862529, 0.02316457, 0.044133127, 0.047288954, 0.02412033, 0.07671708, 0.04164809, 0.028197646, 0.053033113, 0.06071049, 0.0699476, 0.073272884, 0.043835282, 0.0, 0.050641418, 0.039182365, 0.030008495, 0.049741864, 0.0707075, 0.06788242, 0.06711757, 0.022382498, 0.005941689, 0.037234902, 0.030500472, 0.0051078796, 0.068796515, 0.03387338, 0.027435005, 0.028014362, 0.013472438, 0.019180119, 0.0, 0.039285064, 0.0, 0.028549552, 0.007137835, 0.011880875, 0.05229169]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.8s
computing farthest point 1, dmax: Inf, imax: 3, n: 30
computing farthest point 2, dmax: 1.4438654, imax: 13, n: 30
computing farthest point 3, dmax: 1.019382, imax: 14, n: 30
computing farthest point 4, dmax: 1.0025069, imax: 25, n: 30
computing farthest point 5, dmax: 0.7758429, imax: 16, n: 30
computing farthest point 6, dmax: 0.71897554, imax: 4, n: 30
computing farthest point 7, dmax: 0.58582664, imax: 28, n: 30
computing farthest point 8, dmax: 0.54770297, imax: 8, n: 30
computing farthest point 9, dmax: 0.54711086, imax: 6, n: 30
computing farthest point 10, dmax: 0.5064177, imax: 26, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.4s
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) 2025-09-23T10:06:33.338
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=14, Δ=1.025, maxvisits=108) 2025-09-23T10:06:46.747
LOG n.size quantiles:[2.0, 3.0, 3.0, 3.0, 3.0]
(i, j, d) = (3, 840, -1.1920929f-7)
(i, j, d, :parallel) = (3, 840, -1.1920929f-7, :parallel)
[ Info: NOTE: the exact method will be faster on small datasets due to the preprocessing step of the approximation method
[ Info: ("closestpair computation time", :approx => 21.602841577, :exact => 0.921521302)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  23.1s
  6.322941 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.007304 seconds
SEARCH Exhaustive 2: 0.007250 seconds
SEARCH Exhaustive 3: 0.006927 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) 2025-09-23T10:07:16.783
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=11, Δ=1.3370568, maxvisits=196) 2025-09-23T10:07:22.990
LOG n.size quantiles:[2.0, 2.0, 2.0, 2.0, 2.0]
[ Info: RECALL BAJO!! recall: 0.290625, #objects: 3329, #queries: 32
[ Info: [0.8240321278572083, 0.3116094172000885, 0.46866053342819214, 0.565740168094635, 0.20098495483398438, 0.6078275442123413, 0.3659299910068512, 0.5341203808784485, 0.21222205460071564, 0.20545272529125214, 0.35684141516685486, 0.5435475707054138, 0.4202708303928375, 0.3326367139816284, 0.2961446940898895, 0.4289388358592987, 0.2924584150314331, 0.3888933062553406, 0.4229038953781128, 0.29237329959869385, 0.486738920211792, 0.32207342982292175, 0.3267574906349182, 0.2467380315065384, 0.21149569749832153, 0.5568963885307312, 0.7393008470535278, 0.6080117225646973, 0.4952887296676636, 0.345725953578949, 0.2734159827232361, 0.33514130115509033]
(g, r) = (Set(Int32[2793, 3007, 205, 1710, 653, 675, 455, 78, 2998, 720]), Set(Int32[553, 1893, 1869, 1188, 1607, 1848, 2123, 330, 1770, 1988]))
(g, r) = (Set(Int32[717, 311, 1894, 740, 3123, 3156, 2473, 2923, 2032, 188]), Set(Int32[717, 311, 1539, 2053, 1485, 439, 372, 1600, 466, 2634]))
(g, r) = (Set(Int32[1158, 601, 2729, 2492, 1687, 3267, 3049, 754, 2882, 519]), Set(Int32[3147, 1974, 502, 1594, 420, 1657, 762, 2697, 884, 1896]))
(g, r) = (Set(Int32[994, 2599, 2222, 2289, 2251, 385, 1361, 591, 2317, 786]), Set(Int32[635, 1405, 1664, 1651, 2026, 2205, 591, 1983, 2610, 322]))
(g, r) = (Set(Int32[3176, 105, 571, 2451, 2473, 166, 579, 2595, 1762, 1338]), Set(Int32[252, 2048, 105, 1743, 3048, 166, 2595, 1762, 1338, 2746]))
(g, r) = (Set(Int32[440, 1368, 3167, 646, 61, 1929, 1826, 1282, 2119, 2757]), Set(Int32[1918, 2133, 1752, 1807, 619, 1675, 3113, 27, 189, 290]))
(g, r) = (Set(Int32[1233, 2787, 590, 2821, 2615, 846, 1244, 2768, 999, 122]), Set(Int32[491, 1233, 520, 2615, 846, 1244, 1169, 999, 1478, 122]))
(g, r) = (Set(Int32[1860, 2130, 2232, 1222, 542, 3182, 2280, 1638, 836, 1398]), Set(Int32[2732, 2053, 712, 52, 1232, 816, 1470, 766, 288, 1513]))
(g, r) = (Set(Int32[2710, 2756, 2390, 117, 781, 1004, 2628, 2144, 2070, 152]), Set(Int32[2710, 2756, 1401, 1642, 117, 781, 1004, 1166, 187, 152]))
(g, r) = (Set(Int32[1538, 3291, 1379, 184, 3270, 1952, 2098, 2367, 3187, 1038]), Set(Int32[2710, 858, 2571, 3291, 1379, 184, 1952, 2367, 1038, 152]))
(g, r) = (Set(Int32[1782, 2325, 3280, 2407, 124, 418, 1857, 539, 1692, 1874]), Set(Int32[2325, 3280, 2568, 1950, 384, 656, 309, 2127, 2758, 2066]))
(g, r) = (Set(Int32[1675, 1752, 3309, 1367, 3306, 3179, 653, 2855, 2078, 2347]), Set(Int32[1696, 553, 988, 1869, 1188, 1608, 3102, 1607, 1848, 2123]))
(g, r) = (Set(Int32[258, 1448, 1640, 1967, 842, 3269, 2008, 789, 1241, 2542]), Set(Int32[1780, 1355, 1967, 978, 1251, 353, 2008, 1174, 931, 1217]))
(g, r) = (Set(Int32[1591, 1369, 1022, 1940, 137, 2808, 2672, 2586, 518, 8]), Set(Int32[3147, 2788, 1022, 1158, 137, 2586, 42, 518, 8, 46]))
(g, r) = (Set(Int32[2758, 534, 600, 442, 3126, 124, 2886, 2521, 2940, 1271]), Set(Int32[534, 2741, 3126, 2108, 3069, 2886, 489, 539, 2102, 2521]))
(g, r) = (Set(Int32[534, 600, 1110, 3126, 427, 163, 1348, 2521, 2940, 1049]), Set(Int32[534, 2741, 3126, 2108, 2026, 2886, 489, 539, 2102, 2521]))
(g, r) = (Set(Int32[310, 283, 3122, 20, 2193, 1027, 2640, 1789, 2782, 2101]), Set(Int32[310, 283, 20, 82, 681, 2640, 1789, 71, 2782, 493]))
(g, r) = (Set(Int32[1124, 1752, 3179, 646, 575, 653, 2810, 233, 833, 2213]), Set(Int32[1267, 1893, 1869, 1188, 3102, 2123, 2088, 1848, 290, 2316]))
(g, r) = (Set(Int32[687, 749, 556, 96, 1742, 1498, 1512, 2441, 3120, 3195]), Set(Int32[1239, 2402, 749, 556, 96, 1742, 1498, 920, 3098, 313]))
(g, r) = (Set(Int32[2741, 442, 2108, 1348, 3157, 2333, 2482, 2508, 539, 1840]), Set(Int32[391, 534, 2741, 3126, 2108, 434, 539, 2508, 2102, 2521]))
(g, r) = (Set(Int32[308, 1916, 713, 2150, 2284, 406, 1338, 1411, 3139, 854]), Set(Int32[3305, 527, 1997, 796, 72, 420, 731, 313, 2609, 1138]))
(g, r) = (Set(Int32[819, 2860, 2787, 3002, 520, 2615, 846, 2454, 1667, 122]), Set(Int32[2860, 2788, 520, 2615, 704, 846, 1244, 1667, 1011, 122]))
(g, r) = (Set(Int32[712, 1077, 2232, 1821, 3320, 456, 2374, 2170, 2725, 1351]), Set(Int32[2081, 911, 712, 1077, 3125, 1821, 456, 1554, 1470, 89]))
(g, r) = (Set(Int32[310, 3169, 2137, 23, 2876, 2640, 2368, 1789, 2970, 2392]), Set(Int32[310, 3169, 20, 2240, 283, 792, 2640, 2368, 1789, 2782]))
(g, r) = (Set(Int32[82, 1266, 285, 2601, 2798, 563, 3025, 713, 1916, 430]), Set(Int32[82, 1266, 285, 2601, 393, 563, 3025, 713, 430, 3209]))
(g, r) = (Set(Int32[221, 2519, 2333, 442, 124, 1484, 2034, 2119, 2417, 3256]), Set(Int32[3220, 534, 3069, 2108, 2638, 2886, 539, 2508, 2521, 3256]))
(g, r) = (Set(Int32[416, 2680, 929, 1133, 500, 690, 489, 2237, 1173, 889]), Set(Int32[2111, 1833, 1947, 2035, 167, 2197, 2207, 189, 1667, 2192]))
(g, r) = (Set(Int32[1258, 1337, 1236, 2661, 3128, 2887, 2590, 3241, 455, 2689]), Set(Int32[1852, 1895, 1284, 1619, 882, 2259, 2203, 127, 2347, 2068]))
(g, r) = (Set(Int32[422, 2140, 794, 1632, 3203, 1598, 155, 867, 2849, 2260]), Set(Int32[2024, 1632, 2795, 1688, 1849, 3026, 2747, 2041, 1613, 2260]))
(g, r) = (Set(Int32[661, 1291, 637, 1632, 3023, 2726, 864, 2644, 1581, 1739]), Set(Int32[1613, 2719, 1955, 1632, 813, 1739, 1938, 1581, 2045, 2699]))
(g, r) = (Set(Int32[2498, 1078, 3042, 2556, 1471, 325, 2362, 2576, 1624, 505]), Set(Int32[638, 1730, 3042, 3140, 2523, 1991, 1771, 2362, 2576, 1161]))
(g, r) = (Set(Int32[1456, 1023, 1075, 1678, 1650, 3285, 701, 510, 2143, 1963]), Set(Int32[2110, 1678, 2548, 510, 701, 1676, 121, 2172, 572, 1963]))
collect(Int32, IdView(p)) = Int32[1848, 1869, 2123, 1893, 1188, 1988, 553, 1607, 330, 1770]
collect(Int32, IdView(p)) = Int32[311, 717, 1539, 1485, 1600, 466, 439, 372, 2634, 2053]
collect(Int32, IdView(p)) = Int32[1896, 1657, 2697, 3147, 1974, 420, 502, 1594, 762, 884]
collect(Int32, IdView(p)) = Int32[591, 1664, 2026, 1651, 322, 2205, 635, 1405, 1983, 2610]
collect(Int32, IdView(p)) = Int32[105, 166, 2595, 1762, 1338, 252, 2048, 1743, 2746, 3048]
collect(Int32, IdView(p)) = Int32[2133, 1918, 189, 1807, 3113, 1752, 619, 1675, 27, 290]
collect(Int32, IdView(p)) = Int32[846, 2615, 122, 1244, 1233, 999, 491, 1478, 520, 1169]
collect(Int32, IdView(p)) = Int32[2732, 1470, 2053, 766, 712, 288, 1513, 52, 816, 1232]
collect(Int32, IdView(p)) = Int32[117, 781, 1004, 152, 2756, 2710, 1642, 1401, 1166, 187]
collect(Int32, IdView(p)) = Int32[1379, 3291, 184, 1952, 2367, 1038, 2710, 152, 858, 2571]
collect(Int32, IdView(p)) = Int32[2325, 3280, 2568, 2758, 1950, 2066, 384, 2127, 656, 309]
collect(Int32, IdView(p)) = Int32[1869, 1848, 1696, 3102, 1188, 553, 988, 2123, 1607, 1608]
collect(Int32, IdView(p)) = Int32[1967, 2008, 1251, 1780, 1217, 978, 1355, 931, 353, 1174]
collect(Int32, IdView(p)) = Int32[137, 2586, 518, 1022, 8, 1158, 2788, 3147, 46, 42]
collect(Int32, IdView(p)) = Int32[534, 3126, 2521, 2886, 2102, 2108, 539, 2741, 3069, 489]
collect(Int32, IdView(p)) = Int32[2521, 3126, 534, 489, 2102, 2108, 2741, 2026, 2886, 539]
collect(Int32, IdView(p)) = Int32[1789, 2640, 2782, 283, 20, 310, 493, 71, 82, 681]
collect(Int32, IdView(p)) = Int32[290, 2123, 1893, 2088, 2316, 1188, 1869, 1848, 3102, 1267]
collect(Int32, IdView(p)) = Int32[556, 1498, 96, 749, 1742, 1239, 3098, 313, 2402, 920]
collect(Int32, IdView(p)) = Int32[2108, 2741, 2508, 539, 2102, 3126, 2521, 434, 391, 534]
collect(Int32, IdView(p)) = Int32[3305, 527, 1997, 313, 2609, 731, 796, 420, 72, 1138]
collect(Int32, IdView(p)) = Int32[2615, 520, 846, 1667, 122, 2860, 1244, 1011, 2788, 704]
collect(Int32, IdView(p)) = Int32[712, 1821, 1077, 456, 3125, 1554, 911, 2081, 89, 1470]
collect(Int32, IdView(p)) = Int32[2640, 3169, 2368, 1789, 310, 2240, 792, 20, 283, 2782]
collect(Int32, IdView(p)) = Int32[285, 563, 82, 2601, 713, 3025, 1266, 430, 393, 3209]
collect(Int32, IdView(p)) = Int32[3256, 2508, 539, 2886, 2521, 2108, 2638, 534, 3220, 3069]
collect(Int32, IdView(p)) = Int32[189, 2111, 2207, 1833, 2035, 167, 1947, 2197, 1667, 2192]
collect(Int32, IdView(p)) = Int32[2203, 882, 1284, 2347, 2068, 2259, 1852, 1895, 1619, 127]
collect(Int32, IdView(p)) = Int32[1632, 2260, 2747, 2795, 2041, 1688, 2024, 3026, 1613, 1849]
collect(Int32, IdView(p)) = Int32[1581, 1632, 1739, 2045, 1938, 1955, 2699, 1613, 813, 2719]
collect(Int32, IdView(p)) = Int32[2362, 2576, 3042, 2523, 1161, 1730, 3140, 1771, 638, 1991]
collect(Int32, IdView(p)) = Int32[1963, 1678, 701, 510, 2172, 572, 121, 2110, 2548, 1676]
quantile(neighbors_length.(Ref(index.adj), 1:length(index)), 0:0.1:1.0) = [1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 4.0, 5.0, 6.0, 8.0, 24.0]
     Testing SimilaritySearch tests passed 

Testing completed after 620.17s

PkgEval succeeded after 770.54s