Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.1284 (37b9484954*) started at 2025-11-23T17:25:11.844


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 7.98s


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

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [053f045d] + SimilaritySearch v0.13.7
    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 v0.13.7
  [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 4.17s


################################################################################
# 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/i9lgt/src/julia-1.11/common.jl:75
│     [2] test_dir_has_project_file
│       @ ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/common.jl:52 [inlined]
│     [3] maybe_gen_project_override!
│       @ ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/common.jl:83 [inlined]
│     [4] activate(pkg::String; allow_reresolve::Bool)
│       @ TestEnv ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/activate_set.jl:12
│     [5] activate(pkg::String)
│       @ TestEnv ~/.julia/packages/TestEnv/i9lgt/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...
   5572.4 ms  ✓ SearchModels
  10491.6 ms  ✓ SimilaritySearch
  2 dependencies successfully precompiled in 17 seconds. 66 already precompiled.

Precompilation completed after 28.49s


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

     Testing SimilaritySearch
      Status `/tmp/jl_JASCV4/Project.toml`
  [7d9f7c33] Accessors v0.1.42
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [f517fe37] Polyester v0.7.18
  [92933f4c] ProgressMeter v1.11.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.7
  [10745b16] Statistics v1.11.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [ade2ca70] Dates v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [9a3f8284] Random v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_JASCV4/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 v0.13.7
  [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 v8.17.0+0
  [e37daf67] LibGit2_jll v1.9.1+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.11.4
  [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 v1.68.0+1
  [3f19e933] p7zip_jll v17.7.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
Test Summary:              | Pass  Total   Time
test database abstractions |   56     56  15.0s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  3.3s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.1s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.0s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.959055 seconds (1000 allocations: 78.125 KiB)
 11.269657 seconds (1000 allocations: 78.125 KiB)
  4.095104 seconds (1000 allocations: 78.125 KiB)
  4.070811 seconds (1000 allocations: 78.125 KiB)
  4.058818 seconds (1000 allocations: 78.125 KiB)
  4.079595 seconds (1000 allocations: 78.125 KiB)
  4.114986 seconds (1000 allocations: 78.125 KiB)
  3.926816 seconds (1000 allocations: 78.125 KiB)
 15.458830 seconds (1000 allocations: 78.125 KiB)
 15.294009 seconds (1000 allocations: 78.125 KiB)
 28.649245 seconds (1000 allocations: 78.125 KiB)
 28.916831 seconds (1000 allocations: 78.125 KiB)
 20.636241 seconds (6.23 k allocations: 358.094 KiB)
 20.626561 seconds (1000 allocations: 78.125 KiB)
 16.798709 seconds (1.00 k allocations: 78.141 KiB)
 15.870186 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m39.2s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.205306 seconds (1000 allocations: 78.125 KiB)
  3.151183 seconds (1000 allocations: 78.125 KiB)
 30.676900 seconds (1000 allocations: 78.125 KiB)
 30.265896 seconds (1000 allocations: 78.125 KiB)
 22.145591 seconds (1000 allocations: 78.125 KiB)
 27.462883 seconds (1000 allocations: 78.125 KiB)
  3.936389 seconds (1000 allocations: 78.125 KiB)
  3.841219 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m08.3s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
 10.695482 seconds (1000 allocations: 78.125 KiB)
 10.672359 seconds (1000 allocations: 78.125 KiB)
 10.573585 seconds (1000 allocations: 78.125 KiB)
 10.632210 seconds (1000 allocations: 78.125 KiB)
 10.117263 seconds (1000 allocations: 78.125 KiB)
 10.268412 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total     Time
indexing sets with ExhaustiveSearch | 3000   3000  1m05.8s
  0.047380 seconds (1.00 k allocations: 78.141 KiB)
  0.046831 seconds (1000 allocations: 78.125 KiB)
  0.037564 seconds (1000 allocations: 78.125 KiB)
  0.036067 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.9s
  0.036108 seconds (1000 allocations: 78.125 KiB)
  0.035276 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.1s
ExhaustiveSearch allknn: 4.280499 seconds (2.39 M allocations: 127.072 MiB, 8.09% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.177553 seconds (613.31 k allocations: 30.467 MiB, 99.85% compilation time)
Test Summary: | Pass  Total  Time
allknn        |    3      3  6.0s
quantile(length.(hsp_knns), 0:0.1:1) = [2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0]
Test Summary: | Total  Time
HSP           |     0  2.9s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:56.751
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-23T17:33:57.019
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=2 ep=2 n=2 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-23T17:33:58.184
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:58.519
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000006, 0x0000000d, 0x00000010, 0x00000011, 0x00000014, 0x0000001a, 0x0000001b, 0x00000024, 0x00000031]
D.nn = Int32[1, 2, 1, 4, 1, 6, 4, 6, 1, 4, 6, 6, 13, 1, 4, 16, 17, 13, 4, 20, 1, 20, 4, 17, 4, 26, 27, 4, 4, 6, 17, 2, 4, 17, 20, 36, 17, 36, 36, 2, 17, 4, 27, 4, 2, 13, 4, 36, 49, 4, 6, 36, 4, 4, 6, 4, 16, 49, 4, 36, 36, 36, 6, 27, 4, 6, 20, 4, 36, 6, 27, 17, 4, 6, 27, 1, 6, 4, 13, 1, 13, 4, 4, 1, 4, 17, 4, 17, 6, 36, 4, 6, 1, 17, 27, 27, 49, 1, 2, 17]
D.dist = Float32[0.0, 0.0, 0.081080854, 0.0, 0.086194575, 0.0, 0.05394584, 0.076340854, 0.018103898, 0.059666514, 0.019830942, 0.060757875, 0.0, 0.08581406, 0.05133778, 0.0, 0.0, 0.05825746, 0.059224963, 0.0, 0.08573091, 0.03869456, 0.069049776, 0.044924736, 0.007734716, 0.0, 0.0, 0.063091755, 0.010263979, 0.054531693, 0.027269185, 0.064193845, 0.01416254, 0.039232314, 0.018701673, 0.0, 0.036077917, 0.07426369, 0.017401278, 0.047839344, 0.025980592, 0.011084318, 0.023905754, 0.051472306, 0.048512816, 0.015696704, 0.021027744, 0.047593653, 0.0, 0.033792436, 0.02268821, 0.0765118, 0.044051886, 0.0046330094, 0.010872364, 0.030077815, 0.019982874, 0.036684394, 0.019765675, 0.072402, 0.04453957, 0.02429992, 0.03803301, 0.0025844574, 0.05461365, 0.032576084, 0.04943049, 0.023347616, 0.01954639, 0.023691118, 0.05536121, 0.02513361, 0.014588952, 0.08934599, 0.0076830983, 0.01154685, 0.03013575, 0.05360818, 0.030675828, 0.04806912, 0.035260677, 0.03106314, 0.0008984804, 0.018288493, 0.0105778575, 0.009136677, 0.03552389, 0.053707182, 0.028689146, 0.020200849, 0.06578857, 0.034470737, 0.01590097, 0.04302299, 0.015785694, 0.017527997, 0.035357594, 0.022104084, 0.02562666, 0.016703427]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  16.8s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.660
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-23T17:33:59.661
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: 2025-11-23T17:33:59.661
[ Info: neardup> range: 33:48, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.661
[ Info: neardup> range: 49:64, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.661
[ Info: neardup> range: 65:80, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.661
[ Info: neardup> range: 81:96, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.661
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.661
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.661
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000006, 0x0000000d, 0x00000010, 0x00000011, 0x00000014, 0x0000001a, 0x0000001b, 0x00000024, 0x00000031]
D.nn = Int32[1, 2, 1, 4, 1, 6, 4, 6, 1, 4, 6, 6, 13, 1, 4, 16, 17, 13, 4, 20, 1, 20, 4, 4, 4, 26, 27, 4, 4, 6, 4, 2, 4, 17, 20, 36, 17, 36, 36, 2, 17, 4, 27, 4, 2, 13, 4, 36, 49, 4, 6, 36, 4, 4, 6, 4, 16, 4, 4, 36, 36, 36, 6, 27, 4, 6, 20, 4, 36, 6, 27, 17, 4, 6, 27, 1, 6, 4, 13, 1, 13, 4, 4, 1, 4, 17, 4, 17, 6, 36, 4, 6, 1, 17, 27, 27, 49, 1, 2, 17]
D.dist = Float32[0.0, 0.0, 0.081080854, 0.0, 0.086194575, 0.0, 0.05394584, 0.076340854, 0.018103898, 0.059666514, 0.019830942, 0.060757875, 0.0, 0.08581406, 0.05133778, 0.0, 0.0, 0.05825746, 0.059224963, 0.0, 0.08573091, 0.03869456, 0.069049776, 0.047701776, 0.007734716, 0.0, 0.0, 0.063091755, 0.010263979, 0.054531693, 0.063946426, 0.064193845, 0.01416254, 0.039232314, 0.018701673, 0.0, 0.036077917, 0.07426369, 0.017401278, 0.047839344, 0.025980592, 0.011084318, 0.023905754, 0.051472306, 0.048512816, 0.015696704, 0.021027744, 0.047593653, 0.0, 0.033792436, 0.02268821, 0.0765118, 0.044051886, 0.0046330094, 0.010872364, 0.030077815, 0.019982874, 0.06769407, 0.019765675, 0.072402, 0.04453957, 0.02429992, 0.03803301, 0.0025844574, 0.05461365, 0.032576084, 0.04943049, 0.023347616, 0.01954639, 0.023691118, 0.05536121, 0.02513361, 0.014588952, 0.08934599, 0.0076830983, 0.01154685, 0.03013575, 0.05360818, 0.030675828, 0.04806912, 0.035260677, 0.03106314, 0.0008984804, 0.018288493, 0.0105778575, 0.009136677, 0.03552389, 0.053707182, 0.028689146, 0.020200849, 0.06578857, 0.034470737, 0.01590097, 0.04302299, 0.015785694, 0.017527997, 0.035357594, 0.022104084, 0.02562666, 0.016703427]
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: 2025-11-23T17:33:59.746
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-23T17:33:59.746
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: 2025-11-23T17:33:59.747
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.747
[ Info: neardup> range: 49:64, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.747
[ Info: neardup> range: 65:80, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.747
[ Info: neardup> range: 81:96, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.747
[ Info: neardup> range: 97:100, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.747
[ Info: neardup> finished current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:33:59.747
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000023, 0x00000024, 0x00000031]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 5, 13, 15, 15, 14, 15, 15, 7, 4, 5, 5, 7, 4, 12, 4, 2, 4, 7, 35, 36, 12, 3, 15, 8, 4, 4, 14, 4, 8, 12, 7, 3, 49, 4, 3, 15, 4, 4, 11, 15, 16, 4, 4, 36, 15, 36, 3, 5, 4, 6, 35, 7, 36, 11, 5, 4, 4, 8, 14, 9, 10, 15, 13, 5, 13, 7, 4, 1, 4, 7, 10, 7, 12, 36, 7, 3, 1, 5, 14, 14, 49, 1, 2, 5]
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, 0.087596714, 0.05825746, 0.02413845, 0.09174329, 0.034566402, 0.04942131, 0.021471381, 0.040100455, 0.007734716, 0.048760295, 0.07509494, 0.030937314, 0.010263979, 0.014725208, 0.063946426, 0.064193845, 0.01416254, 0.029410422, 0.0, 0.0, 0.022599578, 0.087620854, 0.080632925, 0.035534203, 0.04539931, 0.011084318, 0.02282685, 0.051472306, 0.04610604, 0.0074238777, 0.017986596, 0.04989034, 0.0, 0.033792436, 0.00536114, 0.02654022, 0.044051886, 0.0046330094, 0.008594573, 0.013903737, 0.019982874, 0.06769407, 0.019765675, 0.072402, 0.022700429, 0.02429992, 0.002364099, 0.06171286, 0.05461365, 0.032576084, 0.047937274, 0.01989013, 0.01954639, 0.005733967, 0.026280463, 0.043697715, 0.014588952, 0.012025356, 0.052108765, 0.0049660206, 0.021636724, 0.010440409, 0.030675828, 0.016393602, 0.035260677, 0.022140324, 0.0008984804, 0.018288493, 0.0105778575, 0.07283729, 0.019748032, 0.01280278, 0.0067133904, 0.020200849, 0.002861321, 0.018761277, 0.01590097, 0.09583968, 0.032865286, 0.040001273, 0.035357594, 0.022104084, 0.02562666, 0.08522582]
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: 2025-11-23T17:34:07.253
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=6 n=6 2025-11-23T17:34:07.254
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:34:07.263
[ Info: neardup> range: 33:48, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:34:07.264
[ Info: neardup> range: 49:64, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:34:07.264
[ Info: neardup> range: 65:80, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:34:07.264
[ Info: neardup> range: 81:96, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:34:07.264
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:34:07.264
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-23T17:34:07.264
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000006, 0x0000000d, 0x00000010, 0x00000011, 0x00000014, 0x0000001a, 0x0000001b, 0x00000024, 0x00000031]
D.nn = Int32[1, 2, 1, 4, 1, 6, 4, 6, 1, 4, 6, 6, 13, 1, 4, 16, 17, 13, 4, 20, 1, 20, 4, 4, 4, 26, 27, 4, 4, 6, 4, 2, 4, 17, 20, 36, 17, 36, 36, 2, 17, 4, 27, 4, 2, 13, 4, 36, 49, 4, 6, 36, 4, 4, 6, 4, 16, 4, 4, 36, 36, 36, 6, 27, 4, 6, 20, 4, 36, 6, 27, 17, 4, 6, 27, 1, 6, 4, 13, 1, 13, 4, 4, 1, 4, 17, 4, 17, 6, 36, 4, 6, 1, 17, 27, 27, 49, 1, 2, 17]
D.dist = Float32[0.0, 0.0, 0.081080854, 0.0, 0.086194575, 0.0, 0.05394584, 0.076340854, 0.018103898, 0.059666514, 0.019830942, 0.060757875, 0.0, 0.08581406, 0.05133778, 0.0, 0.0, 0.05825746, 0.059224963, 0.0, 0.08573091, 0.03869456, 0.069049776, 0.047701776, 0.007734716, 0.0, 0.0, 0.063091755, 0.010263979, 0.054531693, 0.063946426, 0.064193845, 0.01416254, 0.039232314, 0.018701673, 0.0, 0.036077917, 0.07426369, 0.017401278, 0.047839344, 0.025980592, 0.011084318, 0.023905754, 0.051472306, 0.048512816, 0.015696704, 0.021027744, 0.047593653, 0.0, 0.033792436, 0.02268821, 0.0765118, 0.044051886, 0.0046330094, 0.010872364, 0.030077815, 0.019982874, 0.06769407, 0.019765675, 0.072402, 0.04453957, 0.02429992, 0.03803301, 0.0025844574, 0.05461365, 0.032576084, 0.04943049, 0.023347616, 0.01954639, 0.023691118, 0.05536121, 0.02513361, 0.014588952, 0.08934599, 0.0076830983, 0.01154685, 0.03013575, 0.05360818, 0.030675828, 0.04806912, 0.035260677, 0.03106314, 0.0008984804, 0.018288493, 0.0105778575, 0.009136677, 0.03552389, 0.053707182, 0.028689146, 0.020200849, 0.06578857, 0.034470737, 0.01590097, 0.04302299, 0.015785694, 0.017527997, 0.035357594, 0.022104084, 0.02562666, 0.016703427]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.5s
computing farthest point 1, dmax: Inf, imax: 24, n: 30
computing farthest point 2, dmax: 1.0147146, imax: 16, n: 30
computing farthest point 3, dmax: 0.9047629, imax: 11, n: 30
computing farthest point 4, dmax: 0.8915137, imax: 21, n: 30
computing farthest point 5, dmax: 0.8777866, imax: 20, n: 30
computing farthest point 6, dmax: 0.7565419, imax: 26, n: 30
computing farthest point 7, dmax: 0.69497234, imax: 23, n: 30
computing farthest point 8, dmax: 0.6497841, imax: 30, n: 30
computing farthest point 9, dmax: 0.64882267, imax: 2, n: 30
computing farthest point 10, dmax: 0.6351491, imax: 1, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.5s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.5s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-23T17:34:15.560
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch
BeamSearch(bsize=2, Δ=1.075, maxvisits=118) 2025-11-23T17:34:26.751
LOG n.size quantiles:[3.0, 4.0, 4.0, 4.0, 5.0]
(i, j, d) = (2, 855, -1.1920929f-7)
(i, j, d, :parallel) = (2, 855, -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 => 18.736226929, :exact => 0.908937873)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  20.5s
  5.947618 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.004770 seconds
SEARCH Exhaustive 2: 0.004670 seconds
SEARCH Exhaustive 3: 0.005591 seconds
typeof(seq) = ExhaustiveSearch{SqL2Distance, MatrixDatabase{Matrix{Float32}}}
typeof(ectx) = GenericContext{KnnSorted}
typeof(q) = SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}
typeof(res) = KnnSorted{Vector{IdWeight}}
[ Info: ===================== minrecall ==============================
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-23T17:34:54.831
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch
BeamSearch(bsize=12, Δ=1.1, maxvisits=210) 2025-11-23T17:35:00.274
LOG n.size quantiles:[2.0, 2.0, 3.0, 5.0, 7.0]
LOG add_vertex! sp=16965 ep=16969 n=16964 BeamSearch
BeamSearch(bsize=12, Δ=1.21275, maxvisits=410) 2025-11-23T17:35:01.274
LOG n.size quantiles:[6.0, 7.0, 7.0, 7.0, 10.0]
LOG add_vertex! sp=30615 ep=30619 n=30614 BeamSearch
BeamSearch(bsize=4, Δ=1.155, maxvisits=548) 2025-11-23T17:35:02.274
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=42905 ep=42909 n=42904 BeamSearch
BeamSearch(bsize=4, Δ=1.05, maxvisits=434) 2025-11-23T17:35:03.275
LOG n.size quantiles:[4.0, 5.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=54630 ep=54634 n=54629 BeamSearch
BeamSearch(bsize=4, Δ=1.05, maxvisits=434) 2025-11-23T17:35:04.275
LOG n.size quantiles:[6.0, 6.0, 8.0, 8.0, 9.0]
LOG add_vertex! sp=62825 ep=62829 n=62824 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=496) 2025-11-23T17:35:05.275
LOG n.size quantiles:[5.0, 6.0, 6.0, 7.0, 7.0]
LOG add_vertex! sp=72130 ep=72134 n=72129 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=496) 2025-11-23T17:35:06.275
LOG n.size quantiles:[3.0, 5.0, 5.0, 6.0, 7.0]
LOG add_vertex! sp=81105 ep=81109 n=81104 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=496) 2025-11-23T17:35:07.276
LOG n.size quantiles:[4.0, 5.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=88700 ep=88704 n=88699 BeamSearch
BeamSearch(bsize=12, Δ=1.075, maxvisits=480) 2025-11-23T17:35:08.276
LOG n.size quantiles:[6.0, 8.0, 8.0, 9.0, 10.0]
LOG add_vertex! sp=97885 ep=97889 n=97884 BeamSearch
BeamSearch(bsize=12, Δ=1.075, maxvisits=480) 2025-11-23T17:35:09.276
LOG n.size quantiles:[5.0, 6.0, 7.0, 8.0, 9.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 83.0]
[ Info: minrecall: queries per second: 13946.560823427266, recall: 0.900125
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=8, Δ=1.1287501, maxvisits=708))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 83.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=4, Δ=1.155, maxvisits=552)), 1000, 8)
[ Info: rebuild: queries per second: 15248.060324742211, recall: 0.89975
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=4, Δ=1.155, maxvisits=552))
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, 29.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=14, Δ=1.155, maxvisits=738)), 1000, 8)
  1.514507 seconds (610.33 k allocations: 31.098 MiB, 94.61% compilation time)
[ Info: matrixhints: queries per second: 12365.375528973764, recall: 0.904
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=14, Δ=1.155, maxvisits=738))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 83.0]
  2.263414 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.001988 seconds
SEARCH Exhaustive 2: 0.001871 seconds
SEARCH Exhaustive 3: 0.001927 seconds
typeof(seq) = ExhaustiveSearch{SqL2Distance, StrideMatrixDatabase{StrideArraysCore.StrideArray{Float32, 2, (1, 2), Tuple{Int64, Int64}, Tuple{Nothing, Nothing}, Tuple{Static.StaticInt{1}, Static.StaticInt{1}}, Matrix{Float32}}}}
typeof(ectx) = GenericContext{KnnSorted}
typeof(q) = StrideArraysCore.StrideArray{Float32, 1, (1,), Tuple{Int64}, Tuple{Nothing}, Tuple{Static.StaticInt{1}}, Matrix{Float32}}
typeof(res) = KnnSorted{Vector{IdWeight}}
[ Info: ===================== minrecall ==============================
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-23T17:36:15.446
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch
BeamSearch(bsize=12, Δ=1.1, maxvisits=210) 2025-11-23T17:36:20.956
LOG n.size quantiles:[2.0, 2.0, 3.0, 5.0, 7.0]
LOG add_vertex! sp=21165 ep=21169 n=21164 BeamSearch
BeamSearch(bsize=12, Δ=1.21275, maxvisits=410) 2025-11-23T17:36:21.956
LOG n.size quantiles:[5.0, 5.0, 6.0, 7.0, 7.0]
LOG add_vertex! sp=37880 ep=37884 n=37879 BeamSearch
BeamSearch(bsize=4, Δ=1.05, maxvisits=434) 2025-11-23T17:36:22.977
LOG n.size quantiles:[6.0, 7.0, 8.0, 8.0, 8.0]
LOG add_vertex! sp=56820 ep=56824 n=56819 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=496) 2025-11-23T17:36:24.036
LOG n.size quantiles:[6.0, 8.0, 9.0, 9.0, 9.0]
LOG add_vertex! sp=71340 ep=71344 n=71339 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=496) 2025-11-23T17:36:25.036
LOG n.size quantiles:[4.0, 9.0, 9.0, 10.0, 11.0]
LOG add_vertex! sp=81550 ep=81554 n=81549 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=496) 2025-11-23T17:36:26.036
LOG n.size quantiles:[4.0, 7.0, 8.0, 8.0, 10.0]
LOG add_vertex! sp=92965 ep=92969 n=92964 BeamSearch
BeamSearch(bsize=12, Δ=1.075, maxvisits=480) 2025-11-23T17:36:27.037
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 9.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 83.0]
[ Info: minrecall: queries per second: 14673.52550291014, recall: 0.900125
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=8, Δ=1.1287501, maxvisits=708))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 83.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=4, Δ=1.155, maxvisits=552)), 1000, 8)
[ Info: rebuild: queries per second: 15662.112354105075, recall: 0.89975
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=4, Δ=1.155, maxvisits=552))
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, 29.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=14, Δ=1.155, maxvisits=738)), 1000, 8)
  1.667634 seconds (566.26 k allocations: 29.919 MiB, 4.83% gc time, 96.17% compilation time)
[ Info: matrixhints: queries per second: 16203.592566563022, recall: 0.904
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=14, Δ=1.155, maxvisits=738))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 83.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  2m34.0s
     Testing SimilaritySearch tests passed 

Testing completed after 667.41s

PkgEval succeeded after 720.63s