Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.14 (ec5cf08762*) started at 2025-10-30T16:47:12.483


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.61s


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

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [053f045d] + SimilaritySearch v0.13.5
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.4.0
  [4fba245c] + ArrayInterface v7.22.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.18.1
  [a33af91c] + CompositionsBase v0.1.2
  [187b0558] + ConstructionBase v1.6.0
  [adafc99b] + CpuId v0.3.1
  [9a962f9c] + DataAPI v1.16.0
⌅ [864edb3b] + DataStructures v0.18.22
  [b4f34e82] + Distances v0.10.12
  [ffbed154] + DocStringExtensions v0.9.5
  [615f187c] + IfElse v0.1.1
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.6
  [10f19ff3] + LayoutPointers v0.1.17
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [1914dd2f] + MacroTools v0.5.16
  [d125e4d3] + ManualMemory v0.1.8
  [e1d29d7a] + Missings v1.2.0
  [bac558e1] + OrderedCollections v1.8.1
  [d96e819e] + Parameters v0.12.3
  [f517fe37] + Polyester v0.7.18
  [1d0040c9] + PolyesterWeave v0.2.2
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [92933f4c] + ProgressMeter v1.11.0
  [ae029012] + Requires v1.3.1
  [94e857df] + SIMDTypes v0.1.0
  [431bcebd] + SciMLPublic v1.0.0
  [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.5
  [a2af1166] + SortingAlgorithms v1.2.2
  [aedffcd0] + Static v1.3.1
  [0d7ed370] + StaticArrayInterface v1.8.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
⌅ [2913bbd2] + StatsBase v0.33.21
  [7792a7ef] + StrideArraysCore v0.5.8
  [8290d209] + ThreadingUtilities v0.5.5
  [3a884ed6] + UnPack v1.0.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [d6f4376e] + Markdown v1.11.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v1.0.0
  [9e88b42a] + Serialization v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [f489334b] + StyledStrings v1.11.0
  [fa267f1f] + TOML v1.0.3
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [8e850b90] + libblastrampoline_jll v5.15.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 4.1s


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

Precompiling PkgEval dependencies...

┌ Error: Failed to use TestEnv.jl; test dependencies will not be precompiled
│   exception =
│    UndefVarError: `project_rel_path` not defined in `TestEnv`
│    Suggestion: this global was defined as `Pkg.Operations.project_rel_path` but not assigned a value.
│    Stacktrace:
│     [1] get_test_dir(ctx::Pkg.Types.Context, pkgspec::PackageSpec)
│       @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:75
│     [2] test_dir_has_project_file
│       @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:52 [inlined]
│     [3] maybe_gen_project_override!
│       @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:83 [inlined]
│     [4] activate(pkg::String; allow_reresolve::Bool)
│       @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:12
│     [5] activate(pkg::String)
│       @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:9
│     [6] top-level scope
│       @ /PkgEval.jl/scripts/precompile.jl:24
│     [7] include(mod::Module, _path::String)
│       @ Base ./Base.jl:309
│     [8] exec_options(opts::Base.JLOptions)
│       @ Base ./client.jl:344
│     [9] _start()
│       @ Base ./client.jl:577
└ @ Main /PkgEval.jl/scripts/precompile.jl:26
Precompiling package dependencies...
Precompiling packages...
   5238.2 ms  ✓ SearchModels
  13829.6 ms  ✓ SimilaritySearch
  2 dependencies successfully precompiled in 20 seconds. 66 already precompiled.

Precompilation completed after 32.34s


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

     Testing SimilaritySearch
      Status `/tmp/jl_vJExCu/Project.toml`
  [7d9f7c33] Accessors v0.1.42
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [92933f4c] ProgressMeter v1.11.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.5
  [10745b16] Statistics v1.11.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [ade2ca70] Dates v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [9a3f8284] Random v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_vJExCu/Manifest.toml`
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.4.0
  [4c88cf16] Aqua v0.8.14
  [4fba245c] ArrayInterface v7.22.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] CPUSummary v0.2.7
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.1
  [a33af91c] CompositionsBase v0.1.2
  [187b0558] ConstructionBase v1.6.0
  [adafc99b] CpuId v0.3.1
  [9a962f9c] DataAPI v1.16.0
⌅ [864edb3b] DataStructures v0.18.22
  [b4f34e82] Distances v0.10.12
  [ffbed154] DocStringExtensions v0.9.5
  [615f187c] IfElse v0.1.1
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.6
  [10f19ff3] LayoutPointers v0.1.17
  [2ab3a3ac] LogExpFunctions v0.3.29
  [1914dd2f] MacroTools v0.5.16
  [d125e4d3] ManualMemory v0.1.8
  [e1d29d7a] Missings v1.2.0
  [bac558e1] OrderedCollections v1.8.1
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [1d0040c9] PolyesterWeave v0.2.2
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.0
  [92933f4c] ProgressMeter v1.11.0
  [ae029012] Requires v1.3.1
  [94e857df] SIMDTypes v0.1.0
  [431bcebd] SciMLPublic v1.0.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.5
  [a2af1166] SortingAlgorithms v1.2.2
  [aedffcd0] Static v1.3.1
  [0d7ed370] StaticArrayInterface v1.8.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.7.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [8290d209] ThreadingUtilities v0.5.5
  [3a884ed6] UnPack v1.0.2
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [8ba89e20] Distributed v1.11.0
  [f43a241f] Downloads v1.7.0
  [7b1f6079] FileWatching v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0
  [b27032c2] LibCURL v1.0.0
  [76f85450] LibGit2 v1.11.0
  [8f399da3] Libdl v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.13.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v1.0.0
  [9e88b42a] Serialization v1.11.0
  [6462fe0b] Sockets v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [f489334b] StyledStrings v1.11.0
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test v1.11.0
  [cf7118a7] UUIDs v1.11.0
  [4ec0a83e] Unicode v1.11.0
  [e66e0078] CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] LibCURL_jll v8.16.0+0
  [e37daf67] LibGit2_jll v1.9.1+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.9.9
  [4536629a] OpenBLAS_jll v0.3.29+0
  [458c3c95] OpenSSL_jll v3.5.4+0
  [efcefdf7] PCRE2_jll v10.47.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.1+2
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.15.0+0
  [8e850ede] nghttp2_jll v1.67.1+0
  [3f19e933] p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
Test Summary:              | Pass  Total   Time
test database abstractions |   56     56  16.2s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  3.5s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.0s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  0.8s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.594838 seconds (1000 allocations: 78.125 KiB)
 10.799853 seconds (1000 allocations: 78.125 KiB)
  4.091406 seconds (1000 allocations: 78.125 KiB)
  3.505848 seconds (1000 allocations: 78.125 KiB)
  4.068920 seconds (1000 allocations: 78.125 KiB)
  4.129804 seconds (1000 allocations: 78.125 KiB)
  4.187034 seconds (1000 allocations: 78.125 KiB)
  3.944550 seconds (1000 allocations: 78.125 KiB)
 15.691203 seconds (1000 allocations: 78.125 KiB)
 15.355828 seconds (1000 allocations: 78.125 KiB)
 28.227697 seconds (1000 allocations: 78.125 KiB)
 27.935223 seconds (1000 allocations: 78.125 KiB)
 20.881635 seconds (6.23 k allocations: 358.094 KiB)
 21.013651 seconds (1000 allocations: 78.125 KiB)
 16.518846 seconds (1.00 k allocations: 78.141 KiB)
 16.502792 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m37.4s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.253211 seconds (1000 allocations: 78.125 KiB)
  3.272110 seconds (1000 allocations: 78.125 KiB)
 29.621280 seconds (1000 allocations: 78.125 KiB)
 29.693005 seconds (1000 allocations: 78.125 KiB)
 29.000312 seconds (1000 allocations: 78.125 KiB)
 28.878677 seconds (1000 allocations: 78.125 KiB)
  4.232733 seconds (1000 allocations: 78.125 KiB)
  4.351805 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}})
 10.174029 seconds (1000 allocations: 78.125 KiB)
  9.977807 seconds (1000 allocations: 78.125 KiB)
 10.590868 seconds (1000 allocations: 78.125 KiB)
 10.149102 seconds (1000 allocations: 78.125 KiB)
 10.233983 seconds (1000 allocations: 78.125 KiB)
  9.895278 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total     Time
indexing sets with ExhaustiveSearch | 3000   3000  1m03.8s
  0.028226 seconds (1.00 k allocations: 78.141 KiB)
  0.028304 seconds (1000 allocations: 78.125 KiB)
  0.041032 seconds (1000 allocations: 78.125 KiB)
  0.039896 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.8s
  0.054397 seconds (1000 allocations: 78.125 KiB)
  0.054569 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.354911 seconds (2.44 M allocations: 129.770 MiB, 6.29% gc time, 99.97% compilation time)
ParallelExhaustiveSearch allknn: 1.191240 seconds (615.38 k allocations: 30.730 MiB, 2.28% gc time, 99.86% compilation time)
Test Summary: | Pass  Total  Time
allknn        |    3      3  6.1s
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  2.7s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:07.183
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-30T16:56:07.346
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=10 ep=10 n=9 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-30T16:56:08.409
LOG n.size quantiles:[3.0, 3.0, 3.0, 3.0, 3.0]
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:08.410
D.map = UInt32[0x00000001, 0x00000003, 0x00000004, 0x00000005, 0x00000007, 0x00000009, 0x00000010, 0x00000015, 0x00000016, 0x0000001c]
D.nn = Int32[1, 1, 3, 4, 5, 3, 7, 3, 9, 1, 1, 3, 3, 4, 7, 16, 3, 7, 7, 7, 21, 22, 7, 16, 7, 4, 4, 28, 1, 5, 7, 3, 4, 1, 5, 9, 5, 7, 7, 16, 4, 3, 1, 7, 7, 4, 4, 7, 21, 3, 9, 4, 7, 9, 22, 3, 4, 7, 16, 28, 1, 9, 7, 3, 5, 5, 3, 7, 3, 7, 7, 1, 21, 7, 16, 4, 4, 9, 5, 7, 7, 5, 1, 5, 4, 9, 3, 21, 1, 9, 4, 9, 9, 7, 9, 9, 3, 7, 9, 5]
D.dist = Float32[0.0, 0.060170114, 0.0, 0.0, 0.0, 0.017489314, 0.0, 0.016905606, 0.0, 0.033159614, 0.06529069, 0.008831382, 0.058189094, 0.08436018, 0.027261198, 0.0, 0.03581184, 0.02616632, 0.05553043, 0.019616544, 0.0, 0.0, 0.037756026, 0.07256132, 0.047088683, 0.045084536, 0.098302245, 0.0, 0.08644819, 0.055879414, 0.0027108788, 0.0510332, 0.09711099, 0.030340433, 0.013617098, 0.029112339, 0.006523609, 0.038773417, 0.07077861, 0.041833818, 0.025660574, 0.010362804, 0.039386094, 0.033133805, 0.031401515, 0.08216429, 0.059301257, 0.007020116, 0.050341785, 0.046949625, 0.049456656, 0.049803197, 0.017291367, 0.02014631, 0.055644512, 0.013424814, 0.055890024, 0.05815941, 0.022961438, 0.016482115, 0.006756544, 0.0081576705, 0.039546072, 0.033889115, 0.08608365, 0.03097552, 0.014812708, 0.025043607, 0.09277654, 0.029408932, 0.059645772, 0.027473629, 0.013305843, 0.030764103, 0.031596303, 0.05953473, 0.07824111, 0.026635349, 0.018405497, 0.04510027, 0.02732253, 0.0028438568, 0.058856368, 0.046432495, 0.062657475, 0.071850955, 0.04283762, 0.015443742, 0.017017901, 0.020863831, 0.052734733, 0.06412995, 0.058521032, 0.041229486, 0.031137407, 0.06052226, 0.020651698, 0.012294292, 0.06510353, 0.052442968]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  15.9s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.225
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-30T16:56:09.225
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.225
[ Info: neardup> range: 33:48, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.225
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.225
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.225
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.225
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.225
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.226
D.map = UInt32[0x00000001, 0x00000003, 0x00000004, 0x00000005, 0x00000007, 0x00000009, 0x00000010, 0x00000015, 0x00000016, 0x0000001c]
D.nn = Int32[1, 1, 3, 4, 5, 3, 7, 3, 9, 1, 1, 3, 3, 4, 7, 16, 3, 7, 7, 7, 21, 22, 7, 16, 7, 4, 4, 28, 1, 5, 7, 3, 4, 1, 5, 9, 5, 7, 7, 16, 4, 3, 1, 7, 7, 4, 4, 7, 21, 3, 9, 4, 7, 9, 22, 3, 4, 7, 16, 28, 1, 9, 7, 3, 5, 5, 3, 7, 3, 7, 7, 1, 21, 7, 16, 4, 4, 9, 5, 7, 7, 5, 1, 5, 4, 9, 3, 21, 1, 9, 4, 9, 9, 7, 9, 9, 3, 7, 9, 5]
D.dist = Float32[0.0, 0.060170114, 0.0, 0.0, 0.0, 0.017489314, 0.0, 0.016905606, 0.0, 0.033159614, 0.06529069, 0.008831382, 0.058189094, 0.08436018, 0.027261198, 0.0, 0.03581184, 0.02616632, 0.05553043, 0.019616544, 0.0, 0.0, 0.037756026, 0.07256132, 0.047088683, 0.045084536, 0.098302245, 0.0, 0.08644819, 0.055879414, 0.0027108788, 0.0510332, 0.09711099, 0.030340433, 0.013617098, 0.029112339, 0.006523609, 0.038773417, 0.07077861, 0.041833818, 0.025660574, 0.010362804, 0.039386094, 0.033133805, 0.031401515, 0.08216429, 0.059301257, 0.007020116, 0.050341785, 0.046949625, 0.049456656, 0.049803197, 0.017291367, 0.02014631, 0.055644512, 0.013424814, 0.055890024, 0.05815941, 0.022961438, 0.016482115, 0.006756544, 0.0081576705, 0.039546072, 0.033889115, 0.08608365, 0.03097552, 0.014812708, 0.025043607, 0.09277654, 0.029408932, 0.059645772, 0.027473629, 0.013305843, 0.030764103, 0.031596303, 0.05953473, 0.07824111, 0.026635349, 0.018405497, 0.04510027, 0.02732253, 0.0028438568, 0.058856368, 0.046432495, 0.062657475, 0.071850955, 0.04283762, 0.015443742, 0.017017901, 0.020863831, 0.052734733, 0.06412995, 0.058521032, 0.041229486, 0.031137407, 0.06052226, 0.020651698, 0.012294292, 0.06510353, 0.052442968]
Test Summary:       | Pass  Total  Time
neardup small block |    3      3  0.0s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.285
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-30T16:56:09.285
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.285
[ Info: neardup> range: 33:48, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.285
[ Info: neardup> range: 49:64, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.285
[ Info: neardup> range: 65:80, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.285
[ Info: neardup> range: 81:96, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.285
[ Info: neardup> range: 97:100, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.285
[ Info: neardup> finished current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:09.285
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000015, 0x00000016]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 13, 7, 7, 7, 21, 22, 13, 11, 15, 14, 14, 11, 2, 5, 7, 8, 4, 10, 5, 10, 5, 7, 7, 10, 4, 12, 2, 7, 15, 8, 4, 7, 21, 8, 10, 2, 7, 9, 22, 6, 11, 2, 16, 11, 1, 9, 2, 6, 5, 5, 3, 7, 13, 15, 15, 1, 21, 13, 16, 14, 14, 9, 5, 7, 15, 5, 10, 5, 14, 9, 8, 21, 10, 9, 14, 10, 15, 13, 9, 9, 6, 7, 9, 6]
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, 0.022163332, 0.02616632, 0.05553043, 0.019616544, 0.0, 0.0, 0.014967561, 0.042743325, 0.015998304, 0.02536124, 0.02787602, 0.02453059, 0.04138863, 0.055879414, 0.0027108788, 0.02273494, 0.09711099, 0.020915568, 0.013617098, 0.024853766, 0.006523609, 0.038773417, 0.07077861, 0.018857002, 0.025660574, 0.00913018, 0.0048003793, 0.033133805, 0.02548182, 0.06346148, 0.059301257, 0.007020116, 0.050341785, 0.016955256, 0.033815145, 0.035149872, 0.017291367, 0.02014631, 0.055644512, 0.0025106668, 0.038297474, 0.0059410334, 0.022961438, 0.01738429, 0.006756544, 0.0081576705, 0.012496054, 0.020464718, 0.08608365, 0.03097552, 0.014812708, 0.025043607, 0.030322075, 0.004001677, 0.01822728, 0.027473629, 0.013305843, 0.027285933, 0.031596303, 0.02621746, 0.03175938, 0.026635349, 0.018405497, 0.04510027, 0.017154515, 0.0028438568, 0.004945576, 0.046432495, 0.022352338, 0.071850955, 0.018321991, 0.015443742, 0.016078949, 0.020863831, 0.01246655, 0.04709184, 0.017653644, 0.008406103, 0.031137407, 0.06052226, 0.013451278, 0.012294292, 0.06510353, 0.03814733]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  0.0s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:15.406
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=7 n=7 2025-10-30T16:56:15.407
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:15.412
[ Info: neardup> range: 33:48, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:15.412
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:15.412
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:15.412
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:15.412
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:15.412
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-30T16:56:15.412
D.map = UInt32[0x00000001, 0x00000003, 0x00000004, 0x00000005, 0x00000007, 0x00000009, 0x00000010, 0x00000015, 0x00000016, 0x0000001c]
D.nn = Int32[1, 1, 3, 4, 5, 3, 7, 3, 9, 1, 1, 3, 3, 4, 7, 16, 3, 7, 7, 7, 21, 22, 7, 16, 7, 4, 4, 28, 1, 5, 7, 3, 4, 1, 5, 9, 5, 7, 7, 16, 4, 3, 1, 7, 7, 4, 4, 7, 21, 3, 9, 4, 7, 9, 22, 3, 4, 7, 16, 28, 1, 9, 7, 3, 5, 5, 3, 7, 3, 7, 7, 1, 21, 7, 16, 4, 4, 9, 5, 7, 7, 5, 1, 5, 4, 9, 3, 21, 1, 9, 4, 9, 9, 7, 9, 9, 3, 7, 9, 5]
D.dist = Float32[0.0, 0.060170114, 0.0, 0.0, 0.0, 0.017489314, 0.0, 0.016905606, 0.0, 0.033159614, 0.06529069, 0.008831382, 0.058189094, 0.08436018, 0.027261198, 0.0, 0.03581184, 0.02616632, 0.05553043, 0.019616544, 0.0, 0.0, 0.037756026, 0.07256132, 0.047088683, 0.045084536, 0.098302245, 0.0, 0.08644819, 0.055879414, 0.0027108788, 0.0510332, 0.09711099, 0.030340433, 0.013617098, 0.029112339, 0.006523609, 0.038773417, 0.07077861, 0.041833818, 0.025660574, 0.010362804, 0.039386094, 0.033133805, 0.031401515, 0.08216429, 0.059301257, 0.007020116, 0.050341785, 0.046949625, 0.049456656, 0.049803197, 0.017291367, 0.02014631, 0.055644512, 0.013424814, 0.055890024, 0.05815941, 0.022961438, 0.016482115, 0.006756544, 0.0081576705, 0.039546072, 0.033889115, 0.08608365, 0.03097552, 0.014812708, 0.025043607, 0.09277654, 0.029408932, 0.059645772, 0.027473629, 0.013305843, 0.030764103, 0.031596303, 0.05953473, 0.07824111, 0.026635349, 0.018405497, 0.04510027, 0.02732253, 0.0028438568, 0.058856368, 0.046432495, 0.062657475, 0.071850955, 0.04283762, 0.015443742, 0.017017901, 0.020863831, 0.052734733, 0.06412995, 0.058521032, 0.041229486, 0.031137407, 0.06052226, 0.020651698, 0.012294292, 0.06510353, 0.052442968]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  6.1s
computing farthest point 1, dmax: Inf, imax: 24, n: 30
computing farthest point 2, dmax: 1.2849602, imax: 12, n: 30
computing farthest point 3, dmax: 1.0403671, imax: 9, n: 30
computing farthest point 4, dmax: 0.9161875, imax: 28, n: 30
computing farthest point 5, dmax: 0.89253056, imax: 21, n: 30
computing farthest point 6, dmax: 0.8128775, imax: 5, n: 30
computing farthest point 7, dmax: 0.6257361, imax: 25, n: 30
computing farthest point 8, dmax: 0.61337876, imax: 2, n: 30
computing farthest point 9, dmax: 0.6003752, imax: 22, n: 30
computing farthest point 10, dmax: 0.59044534, imax: 4, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.4s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.5s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-30T16:56:22.859
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=2, Δ=0.9, maxvisits=102) 2025-10-30T16:56:33.097
LOG n.size quantiles:[3.0, 4.0, 4.0, 5.0, 5.0]
(i, j, d) = (1, 264, -1.1920929f-7)
(i, j, d, :parallel) = (1, 264, -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 => 17.064734498, :exact => 0.874553598)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  18.5s
  6.357689 seconds (1.00 k allocations: 140.742 KiB)
SEARCH Exhaustive 1: 0.005754 seconds
SEARCH Exhaustive 2: 0.007625 seconds
SEARCH Exhaustive 3: 0.009276 seconds
typeof(seq) = ExhaustiveSearch{SqL2Distance, MatrixDatabase{Matrix{Float32}}}
typeof(ectx) = GenericContext{KnnSorted}
typeof(q) = SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}
typeof(res) = KnnSorted{Vector{IdWeight}}
[ Info: ===================== minrecall ==============================
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-30T16:57:01.779
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=16, Δ=1.244447, maxvisits=234) 2025-10-30T16:57:07.350
LOG n.size quantiles:[3.0, 3.0, 4.0, 4.0, 5.0]
LOG add_vertex! sp=16630 ep=16634 n=16629 BeamSearch(bsize=10, Δ=1.075, maxvisits=380) 2025-10-30T16:57:08.350
LOG n.size quantiles:[5.0, 6.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=30040 ep=30044 n=30039 BeamSearch(bsize=10, Δ=1.2733874, maxvisits=476) 2025-10-30T16:57:09.350
LOG n.size quantiles:[5.0, 5.0, 6.0, 8.0, 8.0]
LOG add_vertex! sp=40475 ep=40479 n=40474 BeamSearch(bsize=10, Δ=1.05, maxvisits=420) 2025-10-30T16:57:10.350
LOG n.size quantiles:[4.0, 5.0, 6.0, 6.0, 8.0]
LOG add_vertex! sp=51995 ep=51999 n=51994 BeamSearch(bsize=10, Δ=1.05, maxvisits=420) 2025-10-30T16:57:11.351
LOG n.size quantiles:[5.0, 5.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=60860 ep=60864 n=60859 BeamSearch(bsize=14, Δ=1.155, maxvisits=560) 2025-10-30T16:57:12.351
LOG n.size quantiles:[6.0, 6.0, 6.0, 7.0, 7.0]
LOG add_vertex! sp=70345 ep=70349 n=70344 BeamSearch(bsize=14, Δ=1.155, maxvisits=560) 2025-10-30T16:57:13.351
LOG n.size quantiles:[6.0, 8.0, 8.0, 9.0, 9.0]
LOG add_vertex! sp=78970 ep=78974 n=78969 BeamSearch(bsize=14, Δ=1.155, maxvisits=560) 2025-10-30T16:57:14.351
LOG n.size quantiles:[3.0, 6.0, 6.0, 8.0, 8.0]
LOG add_vertex! sp=86405 ep=86409 n=86404 BeamSearch(bsize=6, Δ=1.0, maxvisits=444) 2025-10-30T16:57:15.352
LOG n.size quantiles:[7.0, 7.0, 8.0, 8.0, 8.0]
LOG add_vertex! sp=96185 ep=96189 n=96184 BeamSearch(bsize=6, Δ=1.0, maxvisits=444) 2025-10-30T16:57:16.352
LOG n.size quantiles:[4.0, 4.0, 6.0, 6.0, 7.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 93.0]
[ Info: minrecall: queries per second: 13514.607466198953, recall: 0.9005
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=11, Δ=1.09, maxvisits=730))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 93.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=6, Δ=1.1287501, maxvisits=644)), 1000, 8)
[ Info: rebuild: queries per second: 17561.47534937853, recall: 0.9005
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=6, Δ=1.1287501, maxvisits=644))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 18.0, 19.0, 30.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.1025, maxvisits=730)), 1000, 8)
  1.690542 seconds (611.69 k allocations: 31.309 MiB, 1.96% gc time, 95.79% compilation time)
[ Info: matrixhints: queries per second: 13752.046098178718, recall: 0.901875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.1025, maxvisits=730))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 93.0]
  2.190609 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.001987 seconds
SEARCH Exhaustive 2: 0.002005 seconds
SEARCH Exhaustive 3: 0.002029 seconds
typeof(seq) = ExhaustiveSearch{SqL2Distance, StrideMatrixDatabase{StrideArraysCore.StrideArray{Float32, 2, (1, 2), Tuple{Int64, Int64}, Tuple{Nothing, Nothing}, Tuple{Static.StaticInt{1}, Static.StaticInt{1}}, Matrix{Float32}}}}
typeof(ectx) = GenericContext{KnnSorted}
typeof(q) = StrideArraysCore.StrideArray{Float32, 1, (1,), Tuple{Int64}, Tuple{Nothing}, Tuple{Static.StaticInt{1}}, Matrix{Float32}}
typeof(res) = KnnSorted{Vector{IdWeight}}
[ Info: ===================== minrecall ==============================
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-30T16:58:24.106
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=16, Δ=1.244447, maxvisits=234) 2025-10-30T16:58:29.429
LOG n.size quantiles:[3.0, 3.0, 4.0, 4.0, 5.0]
LOG add_vertex! sp=28225 ep=28229 n=28224 BeamSearch(bsize=10, Δ=1.2733874, maxvisits=476) 2025-10-30T16:58:30.429
LOG n.size quantiles:[6.0, 8.0, 9.0, 9.0, 10.0]
LOG add_vertex! sp=42270 ep=42274 n=42269 BeamSearch(bsize=10, Δ=1.05, maxvisits=420) 2025-10-30T16:58:31.430
LOG n.size quantiles:[5.0, 5.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=56820 ep=56824 n=56819 BeamSearch(bsize=14, Δ=1.155, maxvisits=560) 2025-10-30T16:58:32.478
LOG n.size quantiles:[5.0, 7.0, 7.0, 8.0, 8.0]
LOG add_vertex! sp=68195 ep=68199 n=68194 BeamSearch(bsize=14, Δ=1.155, maxvisits=560) 2025-10-30T16:58:33.478
LOG n.size quantiles:[6.0, 7.0, 7.0, 8.0, 9.0]
LOG add_vertex! sp=78700 ep=78704 n=78699 BeamSearch(bsize=14, Δ=1.155, maxvisits=560) 2025-10-30T16:58:34.478
LOG n.size quantiles:[4.0, 5.0, 5.0, 6.0, 7.0]
LOG add_vertex! sp=88545 ep=88549 n=88544 BeamSearch(bsize=6, Δ=1.0, maxvisits=444) 2025-10-30T16:58:35.478
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=99905 ep=99909 n=99904 BeamSearch(bsize=6, Δ=1.0, maxvisits=444) 2025-10-30T16:58:36.479
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 10.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 93.0]
[ Info: minrecall: queries per second: 15406.04471878707, recall: 0.9005
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=11, Δ=1.09, maxvisits=730))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 93.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=6, Δ=1.1287501, maxvisits=644)), 1000, 8)
[ Info: rebuild: queries per second: 17259.563152862393, recall: 0.9005
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=6, Δ=1.1287501, maxvisits=644))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 18.0, 19.0, 30.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.1025, maxvisits=730)), 1000, 8)
  1.593596 seconds (567.40 k allocations: 29.095 MiB, 96.33% compilation time)
[ Info: matrixhints: queries per second: 17192.755825455843, recall: 0.901875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.1025, maxvisits=730))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 93.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  2m35.9s
     Testing SimilaritySearch tests passed 

Testing completed after 671.39s

PkgEval succeeded after 741.72s