Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1189 (111bc9af8b*) started at 2025-09-22T15:43:29.508


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.98s


################################################################################
# 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 3.9s


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

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 113.73s


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

     Testing SimilaritySearch
      Status `/tmp/jl_CYWTHM/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_CYWTHM/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  11.9s
Precompiling packages...
 106434.2 ms  ✓ JET
  1 dependency successfully precompiled in 107 seconds. 38 already precompiled.
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 20005  20005  3.1s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.8s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.1s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
  9.520828 seconds (1000 allocations: 78.125 KiB)
 10.636028 seconds (1000 allocations: 78.125 KiB)
  4.133407 seconds (1000 allocations: 78.125 KiB)
  3.171748 seconds (1000 allocations: 78.125 KiB)
  4.154716 seconds (1000 allocations: 78.125 KiB)
  4.064610 seconds (1000 allocations: 78.125 KiB)
  3.895699 seconds (1000 allocations: 78.125 KiB)
  4.109173 seconds (1000 allocations: 78.125 KiB)
 15.537688 seconds (1000 allocations: 78.125 KiB)
 15.317955 seconds (1000 allocations: 78.125 KiB)
 28.838174 seconds (1000 allocations: 78.125 KiB)
 28.295931 seconds (1000 allocations: 78.125 KiB)
 21.529630 seconds (6.23 k allocations: 388.641 KiB)
 19.942149 seconds (1000 allocations: 78.125 KiB)
 17.633274 seconds (1.00 k allocations: 78.141 KiB)
 18.093883 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m40.6s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.805080 seconds (1000 allocations: 78.125 KiB)
  3.821029 seconds (1000 allocations: 78.125 KiB)
 28.137238 seconds (1000 allocations: 78.125 KiB)
 26.799096 seconds (1000 allocations: 78.125 KiB)
 30.640709 seconds (1000 allocations: 78.125 KiB)
 29.029219 seconds (1000 allocations: 78.125 KiB)
  4.937499 seconds (1000 allocations: 78.125 KiB)
  5.126472 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m16.1s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  8.237846 seconds (1000 allocations: 78.125 KiB)
  7.876298 seconds (1000 allocations: 78.125 KiB)
  7.994693 seconds (1000 allocations: 78.125 KiB)
  8.305212 seconds (1000 allocations: 78.125 KiB)
  8.186990 seconds (1000 allocations: 78.125 KiB)
  8.001319 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  51.5s
  0.044963 seconds (1.00 k allocations: 78.141 KiB)
  0.045138 seconds (1000 allocations: 78.125 KiB)
  0.040191 seconds (1000 allocations: 78.125 KiB)
  0.038821 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  3.0s
  0.051997 seconds (1000 allocations: 78.125 KiB)
  0.051941 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.371334 seconds (2.47 M allocations: 137.302 MiB, 1.40% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.182958 seconds (606.49 k allocations: 31.876 MiB, 99.84% compilation time)
Test Summary: | Pass  Total     Time
allknn        |    5      5  1m00.6s
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  3.1s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:33.768
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-22T15:56:33.803
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) 2025-09-22T15:56:35.036
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: 2025-09-22T15:56:35.445
D.map = UInt32[0x00000001, 0x00000003, 0x00000009, 0x0000000a, 0x0000000e, 0x0000000f, 0x00000011, 0x0000001c, 0x00000022, 0x00000035]
D.nn = Int32[1, 1, 3, 3, 1, 1, 3, 3, 9, 10, 10, 9, 9, 14, 15, 3, 17, 14, 10, 3, 10, 10, 3, 1, 15, 10, 17, 28, 28, 9, 15, 1, 28, 34, 10, 10, 1, 10, 10, 28, 3, 3, 3, 28, 17, 10, 3, 34, 14, 3, 10, 1, 53, 1, 34, 9, 10, 28, 17, 14, 3, 1, 15, 3, 14, 3, 3, 9, 28, 10, 14, 14, 28, 1, 3, 10, 28, 10, 14, 10, 28, 1, 3, 10, 3, 10, 28, 1, 17, 3, 3, 9, 15, 28, 9, 3, 10, 1, 28, 3]
D.dist = Float32[0.0, 0.05937308, 0.0, 0.030883431, 0.055345237, 0.07523131, 0.012868762, 0.052239776, 0.0, 0.0, 0.04785669, 0.04719788, 0.037659287, 0.0, 0.0, 0.042390406, 0.0, 0.095544815, 0.071819186, 0.016668081, 0.039117217, 0.039698243, 0.025649011, 0.011431515, 0.009743154, 0.053505123, 0.09572196, 0.0, 0.019121826, 0.020256042, 0.0691182, 0.03207934, 0.029350162, 0.0, 0.050689697, 0.018202126, 0.0034663677, 0.021851659, 0.061883867, 0.023374677, 0.02054882, 0.015196919, 0.011253238, 0.050070047, 0.05074483, 0.040167987, 0.04029101, 0.052467763, 0.020125568, 0.030769825, 0.029883325, 0.03465396, 0.0, 0.045693636, 0.002806902, 0.046340287, 0.042938113, 0.019025505, 0.01757735, 0.0289253, 0.049609125, 0.066875935, 0.04322231, 0.01658839, 0.057316005, 0.056604147, 0.053130686, 0.05785829, 0.093085706, 0.008665502, 0.024621189, 0.03894323, 0.05583942, 0.046479464, 0.010591328, 0.016099095, 0.00285542, 0.05183381, 0.091807604, 0.024849057, 0.075939536, 0.044534504, 0.025596857, 0.036468863, 0.054348886, 0.040037334, 0.010785639, 0.039699078, 0.035729885, 0.021884024, 0.01020515, 0.013876319, 0.04739064, 0.043410897, 0.06713736, 0.055864215, 0.024037838, 0.021993816, 0.047454953, 0.051793873]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  18.8s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.651
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-22T15:56:36.651
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.652
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.652
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.652
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.652
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.652
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.652
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.653
D.map = UInt32[0x00000001, 0x00000003, 0x00000009, 0x0000000a, 0x0000000e, 0x0000000f, 0x00000011, 0x0000001c, 0x00000022, 0x00000035]
D.nn = Int32[1, 1, 3, 3, 1, 1, 3, 3, 9, 10, 10, 9, 9, 14, 15, 3, 17, 14, 10, 3, 10, 10, 3, 1, 15, 10, 17, 28, 28, 9, 15, 1, 28, 34, 10, 10, 1, 10, 10, 28, 3, 3, 3, 28, 17, 10, 3, 34, 14, 3, 10, 1, 53, 1, 34, 9, 10, 28, 17, 14, 3, 1, 15, 3, 14, 3, 3, 9, 28, 10, 14, 14, 28, 1, 3, 10, 28, 10, 14, 10, 28, 1, 3, 10, 3, 10, 28, 1, 17, 3, 3, 9, 15, 28, 9, 3, 10, 1, 28, 3]
D.dist = Float32[0.0, 0.05937308, 0.0, 0.030883431, 0.055345237, 0.07523131, 0.012868762, 0.052239776, 0.0, 0.0, 0.04785669, 0.04719788, 0.037659287, 0.0, 0.0, 0.042390406, 0.0, 0.095544815, 0.071819186, 0.016668081, 0.039117217, 0.039698243, 0.025649011, 0.011431515, 0.009743154, 0.053505123, 0.09572196, 0.0, 0.019121826, 0.020256042, 0.0691182, 0.03207934, 0.029350162, 0.0, 0.050689697, 0.018202126, 0.0034663677, 0.021851659, 0.061883867, 0.023374677, 0.02054882, 0.015196919, 0.011253238, 0.050070047, 0.05074483, 0.040167987, 0.04029101, 0.052467763, 0.020125568, 0.030769825, 0.029883325, 0.03465396, 0.0, 0.045693636, 0.002806902, 0.046340287, 0.042938113, 0.019025505, 0.01757735, 0.0289253, 0.049609125, 0.066875935, 0.04322231, 0.01658839, 0.057316005, 0.056604147, 0.053130686, 0.05785829, 0.093085706, 0.008665502, 0.024621189, 0.03894323, 0.05583942, 0.046479464, 0.010591328, 0.016099095, 0.00285542, 0.05183381, 0.091807604, 0.024849057, 0.075939536, 0.044534504, 0.025596857, 0.036468863, 0.054348886, 0.040037334, 0.010785639, 0.039699078, 0.035729885, 0.021884024, 0.01020515, 0.013876319, 0.04739064, 0.043410897, 0.06713736, 0.055864215, 0.024037838, 0.021993816, 0.047454953, 0.051793873]
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: 2025-09-22T15:56:36.749
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-22T15:56:36.749
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: 2025-09-22T15:56:36.750
[ Info: neardup> range: 33:48, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.750
[ Info: neardup> range: 49:64, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.751
[ Info: neardup> range: 65:80, current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.751
[ Info: neardup> range: 81:96, current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.751
[ Info: neardup> range: 97:100, current elements: 22, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.751
[ Info: neardup> finished current elements: 22, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:36.751
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x0000001b, 0x00000022, 0x0000002d, 0x00000030, 0x00000035, 0x00000051]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 16, 6, 11, 16, 2, 10, 2, 1, 15, 11, 27, 5, 5, 9, 15, 2, 2, 34, 11, 10, 1, 11, 11, 5, 7, 3, 3, 5, 45, 11, 2, 48, 14, 4, 10, 1, 53, 1, 34, 9, 10, 5, 16, 14, 3, 5, 15, 4, 11, 4, 7, 48, 5, 10, 11, 11, 48, 1, 3, 10, 5, 10, 48, 10, 81, 5, 2, 8, 8, 10, 5, 2, 27, 4, 3, 9, 15, 2, 48, 7, 10, 2, 81, 16]
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, 0.053700387, 0.036873996, 0.06525648, 0.008092642, 0.023303926, 0.039698243, 0.020255446, 0.011431515, 0.009743154, 0.041001797, 0.0, 0.03671646, 0.042626202, 0.020256042, 0.0691182, 0.0180642, 0.05137807, 0.0, 0.019725084, 0.018202126, 0.0034663677, 0.011631131, 0.031077206, 0.05708027, 0.004873395, 0.015196919, 0.011253238, 0.022040248, 0.0, 0.01862663, 0.02583015, 0.0, 0.020125568, 0.006239891, 0.029883325, 0.03465396, 0.0, 0.045693636, 0.002806902, 0.046340287, 0.042938113, 0.009659529, 0.03399086, 0.0289253, 0.049609125, 0.038959563, 0.04322231, 0.00809443, 0.01591301, 0.027521014, 0.017679334, 0.039114773, 0.062213242, 0.008665502, 0.001589179, 0.01877886, 0.057801902, 0.046479464, 0.010591328, 0.016099095, 0.042268693, 0.05183381, 0.045548618, 0.024849057, 0.0, 0.024877667, 0.01517719, 0.0020670295, 0.002818048, 0.040037334, 0.020458758, 0.0035774112, 0.053131044, 0.01651311, 0.01020515, 0.013876319, 0.04739064, 0.024717808, 0.037698746, 0.02305168, 0.024037838, 0.018209279, 0.014845312, 0.0156824]
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: 2025-09-22T15:56:44.541
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=6 n=6 2025-09-22T15:56:44.541
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:44.546
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:44.547
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:44.547
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:44.547
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:44.547
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:44.547
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-22T15:56:44.547
D.map = UInt32[0x00000001, 0x00000003, 0x00000009, 0x0000000a, 0x0000000e, 0x0000000f, 0x00000011, 0x0000001c, 0x00000022, 0x00000035]
D.nn = Int32[1, 1, 3, 3, 1, 1, 3, 3, 9, 10, 10, 9, 9, 14, 15, 3, 17, 14, 10, 3, 10, 10, 3, 1, 15, 10, 17, 28, 28, 9, 15, 1, 28, 34, 10, 10, 1, 10, 10, 28, 3, 3, 3, 28, 17, 10, 3, 34, 14, 3, 10, 1, 53, 1, 34, 9, 10, 28, 17, 14, 3, 1, 15, 3, 14, 3, 3, 9, 28, 10, 14, 14, 28, 1, 3, 10, 28, 10, 14, 10, 28, 1, 3, 10, 3, 10, 28, 1, 17, 3, 3, 9, 15, 28, 9, 3, 10, 1, 28, 3]
D.dist = Float32[0.0, 0.05937308, 0.0, 0.030883431, 0.055345237, 0.07523131, 0.012868762, 0.052239776, 0.0, 0.0, 0.04785669, 0.04719788, 0.037659287, 0.0, 0.0, 0.042390406, 0.0, 0.095544815, 0.071819186, 0.016668081, 0.039117217, 0.039698243, 0.025649011, 0.011431515, 0.009743154, 0.053505123, 0.09572196, 0.0, 0.019121826, 0.020256042, 0.0691182, 0.03207934, 0.029350162, 0.0, 0.050689697, 0.018202126, 0.0034663677, 0.021851659, 0.061883867, 0.023374677, 0.02054882, 0.015196919, 0.011253238, 0.050070047, 0.05074483, 0.040167987, 0.04029101, 0.052467763, 0.020125568, 0.030769825, 0.029883325, 0.03465396, 0.0, 0.045693636, 0.002806902, 0.046340287, 0.042938113, 0.019025505, 0.01757735, 0.0289253, 0.049609125, 0.066875935, 0.04322231, 0.01658839, 0.057316005, 0.056604147, 0.053130686, 0.05785829, 0.093085706, 0.008665502, 0.024621189, 0.03894323, 0.05583942, 0.046479464, 0.010591328, 0.016099095, 0.00285542, 0.05183381, 0.091807604, 0.024849057, 0.075939536, 0.044534504, 0.025596857, 0.036468863, 0.054348886, 0.040037334, 0.010785639, 0.039699078, 0.035729885, 0.021884024, 0.01020515, 0.013876319, 0.04739064, 0.043410897, 0.06713736, 0.055864215, 0.024037838, 0.021993816, 0.047454953, 0.051793873]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.8s
computing farthest point 1, dmax: Inf, imax: 30, n: 30
computing farthest point 2, dmax: 1.343215, imax: 14, n: 30
computing farthest point 3, dmax: 1.0362372, imax: 27, n: 30
computing farthest point 4, dmax: 0.9329236, imax: 5, n: 30
computing farthest point 5, dmax: 0.7330865, imax: 4, n: 30
computing farthest point 6, dmax: 0.7004715, imax: 25, n: 30
computing farthest point 7, dmax: 0.68802315, imax: 16, n: 30
computing farthest point 8, dmax: 0.6315278, imax: 10, n: 30
computing farthest point 9, dmax: 0.5080383, imax: 17, n: 30
computing farthest point 10, dmax: 0.48804542, imax: 19, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.7s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.7s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-22T15:56:53.188
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=0.9975, maxvisits=108) 2025-09-22T15:57:05.759
LOG n.size quantiles:[2.0, 2.0, 2.0, 2.0, 2.0]
(i, j, d) = (14, 925, -1.1920929f-7)
(i, j, d, :parallel) = (14, 925, -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 => 20.171229441, :exact => 0.923185797)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  21.7s
  5.683232 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.005086 seconds
SEARCH Exhaustive 2: 0.005249 seconds
SEARCH Exhaustive 3: 0.005233 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) 2025-09-22T15:57:35.096
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=1.2155062, maxvisits=204) 2025-09-22T15:57:41.072
LOG n.size quantiles:[1.0, 2.0, 2.0, 3.0, 3.0]
[ Info: RECALL BAJO!! recall: 0.2812499999999999, #objects: 4989, #queries: 32
[ Info: [0.36992719769477844, 0.4079945683479309, 0.581941545009613, 0.3841719329357147, 0.24301670491695404, 0.2514806389808655, 0.45915457606315613, 0.5157809257507324, 0.24263282120227814, 0.4885421395301819, 0.31074395775794983, 0.2113962471485138, 0.35496702790260315, 0.43372923135757446, 0.16880731284618378, 0.2858105003833771, 0.33514106273651123, 0.8702280521392822, 0.27365314960479736, 0.26104429364204407, 0.41989246010780334, 0.2573622167110443, 0.3369719684123993, 0.602289080619812, 0.3121987581253052, 0.30769965052604675, 0.2658522427082062, 0.2404552549123764, 0.431386262178421, 0.6986950635910034, 0.3677053153514862, 0.1639879047870636]
(g, r) = (Set(Int32[4760, 1237, 1598, 2526, 3181, 1509, 790, 4256, 1823, 3425]), Set(Int32[1509, 1476, 1435, 790, 4256, 733, 1797, 1364, 438, 1227]))
(g, r) = (Set(Int32[1103, 633, 1012, 970, 2413, 2479, 4169, 3649, 405, 3585]), Set(Int32[1103, 932, 4327, 3922, 4375, 3649, 3619, 3585, 4381, 2610]))
(g, r) = (Set(Int32[3604, 1210, 2353, 52, 2549, 2117, 1358, 3892, 4173, 3641]), Set(Int32[1210, 450, 607, 533, 2331, 1686, 1791, 3275, 1228, 112]))
(g, r) = (Set(Int32[3390, 2348, 3956, 1122, 2436, 1878, 1279, 3647, 2566, 4955]), Set(Int32[3497, 2658, 1775, 2640, 817, 485, 1279, 489, 2566, 2134]))
(g, r) = (Set(Int32[2624, 4878, 4672, 3964, 4288, 3706, 926, 564, 4598, 2237]), Set(Int32[2624, 806, 4672, 4288, 3706, 926, 564, 4598, 2237, 66]))
(g, r) = (Set(Int32[3932, 636, 2351, 2676, 2570, 944, 4809, 3399, 2607, 1883]), Set(Int32[636, 422, 2351, 2676, 944, 4809, 3399, 2607, 4720, 2452]))
(g, r) = (Set(Int32[3361, 2961, 3229, 2442, 4952, 4223, 4244, 3568, 1036, 4222]), Set(Int32[1015, 724, 1880, 4811, 1684, 2365, 1275, 165, 1409, 2010]))
(g, r) = (Set(Int32[394, 4497, 2968, 1714, 3626, 2810, 3981, 2985, 739, 4630]), Set(Int32[774, 994, 1260, 585, 1392, 1098, 359, 1118, 2852, 2021]))
(g, r) = (Set(Int32[2732, 1260, 2431, 3285, 85, 2481, 4098, 4109, 3408, 1297]), Set(Int32[1260, 3130, 85, 2481, 3408, 4121, 4109, 2688, 166, 582]))
(g, r) = (Set(Int32[4906, 91, 2809, 4477, 4947, 1300, 1433, 1024, 594, 355]), Set(Int32[4659, 1155, 4477, 4947, 4918, 4671, 1300, 2508, 1774, 4719]))
(g, r) = (Set(Int32[2080, 2055, 642, 4054, 4419, 1993, 4288, 926, 88, 66]), Set(Int32[53, 2080, 474, 167, 226, 115, 1385, 242, 1061, 34]))
(g, r) = (Set(Int32[1345, 1130, 280, 3087, 4252, 2628, 4090, 4948, 3857, 3233]), Set(Int32[280, 2648, 211, 1904, 4252, 2628, 847, 4090, 1410, 221]))
(g, r) = (Set(Int32[1999, 4683, 1324, 509, 1574, 1957, 4916, 3944, 3698, 4129]), Set(Int32[3957, 258, 4683, 4441, 1071, 1574, 1820, 3993, 266, 1545]))
(g, r) = (Set(Int32[683, 3691, 4179, 937, 2079, 2434, 2915, 4594, 3617, 3936]), Set(Int32[1567, 3493, 2079, 4012, 4680, 3853, 2059, 913, 3617, 1829]))
(g, r) = (Set(Int32[394, 2945, 784, 976, 2367, 1174, 1830, 3040, 1543, 4834]), Set(Int32[394, 3112, 2945, 3247, 1325, 784, 2367, 1174, 1830, 66]))
(g, r) = (Set(Int32[4721, 276, 3007, 1144, 3249, 3927, 1335, 2713, 2947, 2563]), Set(Int32[3661, 3768, 3764, 1407, 1751, 2251, 405, 399, 2947, 380]))
(g, r) = (Set(Int32[252, 1761, 1291, 3551, 1528, 2423, 4486, 2984, 992, 4733]), Set(Int32[3741, 2166, 3551, 533, 1506, 418, 648, 4486, 1111, 3698]))
(g, r) = (Set(Int32[3529, 3607, 2344, 817, 4939, 3871, 657, 4810, 620, 3467]), Set(Int32[4907, 4520, 2920, 2737, 4892, 4920, 2195, 4118, 2542, 448]))
(g, r) = (Set(Int32[1318, 4123, 4877, 714, 842, 480, 675, 1104, 3018, 980]), Set(Int32[1159, 174, 389, 714, 2253, 3350, 129, 1933, 2657, 980]))
(g, r) = (Set(Int32[1618, 1856, 4580, 47, 3243, 3048, 4561, 4055, 2255, 1516]), Set(Int32[1856, 4580, 47, 3243, 3048, 4561, 4055, 2255, 41, 31]))
(g, r) = (Set(Int32[2194, 4519, 2651, 2536, 2966, 985, 4399, 2459, 958, 1829]), Set(Int32[2682, 3778, 2536, 2319, 1548, 2367, 1844, 2510, 3023, 2260]))
(g, r) = (Set(Int32[4492, 3502, 3443, 2756, 4107, 486, 2695, 3686, 1977, 3969]), Set(Int32[3443, 2578, 2212, 3339, 1606, 2314, 2154, 1933, 1259, 3567]))
(g, r) = (Set(Int32[1401, 4202, 1832, 2986, 3375, 2719, 2508, 1352, 1079, 355]), Set(Int32[4659, 3492, 3802, 4202, 1417, 4477, 4947, 4918, 1300, 2508]))
(g, r) = (Set(Int32[3947, 2212, 185, 293, 842, 3372, 3931, 2636, 4236, 1726]), Set(Int32[627, 114, 1068, 2932, 2242, 2490, 1084, 3207, 739, 1159]))
(g, r) = (Set(Int32[2317, 1538, 1644, 1448, 4638, 930, 755, 1203, 3207, 4949]), Set(Int32[254, 1538, 1644, 930, 510, 577, 891, 1203, 3207, 809]))
(g, r) = (Set(Int32[4905, 2538, 3995, 3702, 4589, 564, 4598, 4744, 200, 3165]), Set(Int32[2624, 1567, 4672, 3702, 3706, 564, 4598, 1174, 66, 4834]))
(g, r) = (Set(Int32[3032, 3965, 1404, 1846, 2065, 293, 3947, 727, 4104, 2429]), Set(Int32[3032, 1760, 2476, 3204, 2371, 2065, 293, 727, 2350, 2429]))
(g, r) = (Set(Int32[638, 1397, 3530, 1326, 3885, 317, 3929, 2560, 2931, 1351]), Set(Int32[638, 3530, 20, 1955, 384, 2921, 317, 2560, 2799, 32]))
(g, r) = (Set(Int32[824, 1083, 2715, 3791, 213, 1198, 3918, 2644, 1477, 3992]), Set(Int32[1260, 79, 85, 495, 4109, 2688, 166, 2645, 730, 3992]))
(g, r) = (Set(Int32[4508, 2004, 1435, 3457, 3701, 2806, 3016, 357, 664, 2689]), Set(Int32[2382, 2920, 3917, 4920, 2195, 3650, 2365, 700, 2101, 753]))
(g, r) = (Set(Int32[3690, 4629, 412, 3581, 3792, 971, 2386, 4554, 3622, 2632]), Set(Int32[3882, 4629, 2222, 3478, 4791, 4068, 3648, 3888, 3919, 2687]))
(g, r) = (Set(Int32[110, 1891, 712, 4195, 1854, 1380, 1575, 2675, 132, 4813]), Set(Int32[110, 1891, 712, 1654, 4195, 1854, 1575, 3377, 3597, 132]))
collect(Int32, IdView(p)) = Int32[790, 4256, 1509, 733, 1797, 1364, 1476, 1227, 1435, 438]
collect(Int32, IdView(p)) = Int32[1103, 3649, 3585, 3619, 3922, 4327, 4381, 2610, 4375, 932]
collect(Int32, IdView(p)) = Int32[1210, 607, 3275, 1791, 1228, 533, 2331, 112, 1686, 450]
collect(Int32, IdView(p)) = Int32[2566, 1279, 2134, 2658, 3497, 485, 2640, 489, 1775, 817]
collect(Int32, IdView(p)) = Int32[4672, 2624, 3706, 926, 4288, 4598, 2237, 564, 806, 66]
collect(Int32, IdView(p)) = Int32[2351, 2676, 944, 636, 3399, 4809, 2607, 4720, 2452, 422]
collect(Int32, IdView(p)) = Int32[1409, 1015, 1275, 2010, 1684, 724, 2365, 1880, 4811, 165]
collect(Int32, IdView(p)) = Int32[1098, 994, 2021, 774, 1118, 1392, 2852, 359, 1260, 585]
collect(Int32, IdView(p)) = Int32[85, 4109, 2481, 1260, 3408, 166, 4121, 582, 2688, 3130]
collect(Int32, IdView(p)) = Int32[4947, 1300, 4477, 4659, 4671, 4719, 2508, 4918, 1155, 1774]
collect(Int32, IdView(p)) = Int32[2080, 1385, 53, 34, 167, 1061, 474, 242, 226, 115]
collect(Int32, IdView(p)) = Int32[4252, 2628, 4090, 280, 1904, 211, 2648, 1410, 221, 847]
collect(Int32, IdView(p)) = Int32[4683, 1574, 3993, 266, 1820, 4441, 1071, 1545, 3957, 258]
collect(Int32, IdView(p)) = Int32[2079, 3617, 1567, 4012, 1829, 4680, 3853, 2059, 913, 3493]
collect(Int32, IdView(p)) = Int32[1174, 394, 784, 2945, 2367, 1830, 66, 3247, 1325, 3112]
collect(Int32, IdView(p)) = Int32[2947, 405, 1751, 380, 3764, 2251, 399, 3661, 3768, 1407]
collect(Int32, IdView(p)) = Int32[4486, 3551, 533, 1111, 418, 3698, 648, 1506, 3741, 2166]
collect(Int32, IdView(p)) = Int32[4118, 4907, 4920, 2920, 2737, 2195, 448, 4520, 4892, 2542]
collect(Int32, IdView(p)) = Int32[980, 714, 2657, 1159, 2253, 174, 389, 3350, 129, 1933]
collect(Int32, IdView(p)) = Int32[3048, 1856, 3243, 4580, 4561, 2255, 4055, 47, 31, 41]
collect(Int32, IdView(p)) = Int32[2536, 2260, 2367, 2682, 3778, 2319, 1844, 2510, 1548, 3023]
collect(Int32, IdView(p)) = Int32[3443, 2154, 1933, 1259, 2578, 3339, 3567, 2212, 1606, 2314]
collect(Int32, IdView(p)) = Int32[2508, 4202, 4659, 3492, 4477, 1300, 3802, 4947, 1417, 4918]
collect(Int32, IdView(p)) = Int32[1084, 1159, 2242, 2932, 2490, 3207, 627, 114, 1068, 739]
collect(Int32, IdView(p)) = Int32[1538, 1203, 3207, 930, 1644, 510, 809, 254, 577, 891]
collect(Int32, IdView(p)) = Int32[564, 4598, 3702, 2624, 66, 1567, 3706, 4672, 4834, 1174]
collect(Int32, IdView(p)) = Int32[3032, 727, 2065, 2429, 293, 2350, 1760, 2371, 3204, 2476]
collect(Int32, IdView(p)) = Int32[317, 638, 2560, 3530, 32, 2921, 20, 1955, 2799, 384]
collect(Int32, IdView(p)) = Int32[3992, 2688, 166, 2645, 85, 1260, 4109, 495, 79, 730]
collect(Int32, IdView(p)) = Int32[4920, 2195, 2920, 753, 700, 2101, 3650, 2382, 3917, 2365]
collect(Int32, IdView(p)) = Int32[4629, 3648, 3478, 4791, 3882, 2687, 2222, 3888, 3919, 4068]
collect(Int32, IdView(p)) = Int32[4195, 1854, 110, 1575, 712, 132, 1891, 3377, 1654, 3597]
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, 41.0]
     Testing SimilaritySearch tests passed 

Testing completed after 719.15s

PkgEval succeeded after 858.91s