Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.30 (073666df8b*) started at 2025-11-04T17:40:57.742


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.93s


################################################################################
# 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 4.32s


################################################################################
# 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...
   9097.8 ms  ✓ StatsBase
   5598.3 ms  ✓ SearchModels
  14258.9 ms  ✓ SimilaritySearch
  3 dependencies successfully precompiled in 30 seconds. 65 already precompiled.

Precompilation completed after 42.54s


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

     Testing SimilaritySearch
      Status `/tmp/jl_kOiyzF/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_kOiyzF/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  16.4s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  4.0s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.6s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.1s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
  7.534880 seconds (1000 allocations: 78.125 KiB)
 10.332767 seconds (1000 allocations: 78.125 KiB)
  4.105440 seconds (1000 allocations: 78.125 KiB)
  4.230907 seconds (1000 allocations: 78.125 KiB)
  4.155809 seconds (1000 allocations: 78.125 KiB)
  4.103326 seconds (1000 allocations: 78.125 KiB)
  3.707084 seconds (1000 allocations: 78.125 KiB)
  4.006806 seconds (1000 allocations: 78.125 KiB)
 15.075850 seconds (1000 allocations: 78.125 KiB)
 15.335314 seconds (1000 allocations: 78.125 KiB)
 28.058716 seconds (1000 allocations: 78.125 KiB)
 27.670703 seconds (1000 allocations: 78.125 KiB)
 20.887823 seconds (6.23 k allocations: 358.094 KiB)
 21.052266 seconds (1000 allocations: 78.125 KiB)
 17.622674 seconds (1.00 k allocations: 78.141 KiB)
 17.748090 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m37.2s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.043946 seconds (1000 allocations: 78.125 KiB)
  3.247954 seconds (1000 allocations: 78.125 KiB)
 28.849305 seconds (1000 allocations: 78.125 KiB)
 26.693358 seconds (1000 allocations: 78.125 KiB)
 29.424973 seconds (1000 allocations: 78.125 KiB)
 29.479508 seconds (1000 allocations: 78.125 KiB)
  4.217306 seconds (1000 allocations: 78.125 KiB)
  4.158832 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m12.9s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
 10.028355 seconds (1000 allocations: 78.125 KiB)
  9.960361 seconds (1000 allocations: 78.125 KiB)
 10.542114 seconds (1000 allocations: 78.125 KiB)
 10.424025 seconds (1000 allocations: 78.125 KiB)
  9.890230 seconds (1000 allocations: 78.125 KiB)
  9.954722 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total     Time
indexing sets with ExhaustiveSearch | 3000   3000  1m03.7s
  0.044032 seconds (1.00 k allocations: 78.141 KiB)
  0.044279 seconds (1000 allocations: 78.125 KiB)
  0.039847 seconds (1000 allocations: 78.125 KiB)
  0.041396 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.9s
  0.051805 seconds (1000 allocations: 78.125 KiB)
  0.051814 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.645471 seconds (2.44 M allocations: 129.812 MiB, 7.21% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.229237 seconds (615.38 k allocations: 30.730 MiB, 99.86% compilation time)
Test Summary: | Pass  Total  Time
allknn        |    3      3  6.4s
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, 6.0]
Test Summary: | Total  Time
HSP           |     0  2.5s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:04.030
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-04T17:50:04.261
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) 2025-11-04T17:50:05.436
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: 2025-11-04T17:50:05.819
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000007, 0x0000000b, 0x00000014, 0x00000016, 0x0000001b, 0x0000001f]
D.nn = Int32[1, 2, 3, 4, 4, 4, 7, 2, 1, 2, 11, 3, 2, 7, 7, 2, 1, 4, 4, 20, 4, 22, 22, 3, 7, 22, 27, 7, 7, 4, 31, 1, 4, 22, 4, 4, 7, 20, 4, 22, 1, 4, 3, 27, 3, 22, 20, 4, 3, 1, 3, 2, 11, 4, 3, 1, 3, 4, 27, 27, 27, 1, 4, 11, 27, 1, 31, 22, 2, 4, 27, 2, 1, 4, 31, 11, 3, 1, 11, 7, 31, 4, 22, 22, 4, 1, 3, 22, 4, 4, 11, 27, 22, 2, 22, 3, 3, 20, 2, 20]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0846836, 0.064311564, 0.0, 0.050911665, 0.022262096, 0.07109201, 0.0, 0.013343513, 0.007708788, 0.05293858, 0.041178584, 0.021222293, 0.08628845, 0.03705585, 0.007064998, 0.0, 0.02014792, 0.0, 0.0748077, 0.025953233, 0.077350974, 0.01762575, 0.0, 0.048745096, 0.030573785, 0.04093474, 0.0, 0.055698693, 0.054688454, 0.03318268, 0.005015373, 0.0291304, 0.0764038, 0.012121439, 0.021558106, 0.042964935, 0.016581714, 0.021529436, 0.04349953, 0.004785478, 0.048832178, 0.043854773, 0.065416336, 0.05290717, 0.03817582, 0.046168268, 0.031242669, 0.0509488, 0.0052105784, 0.053618252, 0.038991034, 0.047520876, 0.047095776, 0.0071045756, 0.025436461, 0.047394216, 0.038426876, 0.020579517, 0.06882018, 0.0085090995, 0.0062422156, 0.012046754, 0.014924169, 0.048265576, 0.036896706, 0.038790524, 0.045065045, 0.0037569404, 0.034623742, 0.012257099, 0.062428117, 0.05308652, 0.019452393, 0.060663044, 0.032737732, 0.019781291, 0.013048053, 0.00884974, 0.049872518, 0.00090545416, 0.03208232, 0.018131793, 0.043639123, 0.0299654, 0.007590711, 0.038488865, 0.08856672, 0.06371045, 0.012636304, 0.013923466, 0.024098754, 0.01815468, 0.01307714, 0.0057596564, 0.056547463, 0.0029533505]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  18.0s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.004
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-04T17:50:07.004
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: 2025-11-04T17:50:07.005
[ Info: neardup> range: 33:48, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.006
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.006
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.006
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.006
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.006
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.006
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000007, 0x0000000b, 0x00000014, 0x00000016, 0x0000001b, 0x0000001f]
D.nn = Int32[1, 2, 3, 4, 4, 4, 7, 2, 1, 2, 11, 3, 2, 7, 7, 2, 1, 4, 4, 20, 4, 22, 22, 3, 7, 2, 27, 7, 7, 4, 31, 1, 4, 22, 4, 4, 7, 20, 4, 22, 1, 4, 3, 27, 3, 22, 20, 4, 3, 1, 3, 2, 11, 4, 3, 1, 3, 4, 27, 27, 27, 1, 4, 11, 27, 1, 31, 22, 2, 4, 27, 2, 1, 4, 31, 11, 3, 1, 11, 7, 31, 4, 22, 22, 4, 1, 3, 22, 4, 4, 11, 27, 22, 2, 22, 3, 3, 20, 2, 20]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0846836, 0.064311564, 0.0, 0.050911665, 0.022262096, 0.07109201, 0.0, 0.013343513, 0.007708788, 0.05293858, 0.041178584, 0.021222293, 0.08628845, 0.03705585, 0.007064998, 0.0, 0.02014792, 0.0, 0.0748077, 0.025953233, 0.077350974, 0.09162587, 0.0, 0.048745096, 0.030573785, 0.04093474, 0.0, 0.055698693, 0.054688454, 0.03318268, 0.005015373, 0.0291304, 0.0764038, 0.012121439, 0.021558106, 0.042964935, 0.016581714, 0.021529436, 0.04349953, 0.004785478, 0.048832178, 0.043854773, 0.065416336, 0.05290717, 0.03817582, 0.046168268, 0.031242669, 0.0509488, 0.0052105784, 0.053618252, 0.038991034, 0.047520876, 0.047095776, 0.0071045756, 0.025436461, 0.047394216, 0.038426876, 0.020579517, 0.06882018, 0.0085090995, 0.0062422156, 0.012046754, 0.014924169, 0.048265576, 0.036896706, 0.038790524, 0.045065045, 0.0037569404, 0.034623742, 0.012257099, 0.062428117, 0.05308652, 0.019452393, 0.060663044, 0.032737732, 0.019781291, 0.013048053, 0.00884974, 0.049872518, 0.00090545416, 0.03208232, 0.018131793, 0.043639123, 0.0299654, 0.007590711, 0.038488865, 0.08856672, 0.06371045, 0.012636304, 0.013923466, 0.024098754, 0.01815468, 0.01307714, 0.0057596564, 0.056547463, 0.0029533505]
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: 2025-11-04T17:50:07.093
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-04T17:50:07.093
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: 2025-11-04T17:50:07.095
[ Info: neardup> range: 33:48, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.095
[ Info: neardup> range: 49:64, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.095
[ Info: neardup> range: 65:80, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.095
[ Info: neardup> range: 81:96, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.095
[ Info: neardup> range: 97:100, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.095
[ Info: neardup> finished current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:07.095
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000014, 0x00000016]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 4, 4, 20, 4, 22, 16, 3, 14, 16, 10, 7, 14, 5, 15, 9, 8, 22, 4, 4, 7, 20, 4, 22, 1, 4, 3, 10, 12, 22, 5, 14, 12, 9, 3, 8, 11, 5, 12, 9, 12, 4, 20, 12, 14, 1, 6, 11, 14, 1, 15, 22, 15, 14, 10, 2, 9, 4, 16, 11, 3, 9, 11, 7, 15, 4, 16, 22, 4, 1, 12, 22, 4, 4, 11, 8, 22, 13, 22, 12, 3, 20, 16, 20]
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, 0.08628845, 0.03705585, 0.007064998, 0.0, 0.02014792, 0.0, 0.06696361, 0.025953233, 0.003099382, 0.052339733, 0.031104803, 0.048745096, 0.013644338, 0.016877115, 0.048303127, 0.020715296, 0.010724068, 0.03318268, 0.005015373, 0.0291304, 0.0764038, 0.012121439, 0.021558106, 0.042964935, 0.016581714, 0.021529436, 0.04349953, 0.012658954, 0.033397138, 0.043854773, 0.06346905, 0.014031172, 0.009847641, 0.024466038, 0.031242669, 0.00011152029, 0.0052105784, 0.023617983, 0.01619029, 0.01141566, 0.03163904, 0.0071045756, 0.033037484, 0.042549253, 0.028594553, 0.020579517, 0.051719368, 0.0085090995, 0.0326131, 0.012046754, 0.021548271, 0.048265576, 0.022839308, 0.015030146, 0.022835672, 0.0037569404, 0.023419976, 0.012257099, 0.035861194, 0.05308652, 0.019452393, 0.025415778, 0.032737732, 0.019781291, 0.011421621, 0.00884974, 0.0292027, 0.00090545416, 0.03208232, 0.018131793, 0.039400756, 0.0299654, 0.007590711, 0.038488865, 0.08856672, 0.0043281913, 0.012636304, 0.013050497, 0.024098754, 0.0024688244, 0.01307714, 0.0057596564, 0.036886454, 0.0029533505]
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: 2025-11-04T17:50:13.578
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=6 n=6 2025-11-04T17:50:13.578
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:13.584
[ Info: neardup> range: 33:48, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:13.584
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:13.584
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:13.584
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:13.584
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:13.584
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-04T17:50:13.584
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000007, 0x0000000b, 0x00000014, 0x00000016, 0x0000001b, 0x0000001f]
D.nn = Int32[1, 2, 3, 4, 4, 4, 7, 2, 1, 2, 11, 3, 2, 7, 7, 2, 1, 4, 4, 20, 4, 22, 22, 3, 7, 2, 27, 7, 7, 4, 31, 1, 4, 22, 4, 4, 7, 20, 4, 22, 1, 4, 3, 27, 3, 22, 20, 4, 3, 1, 3, 2, 11, 4, 3, 1, 3, 4, 27, 27, 27, 1, 4, 11, 27, 1, 31, 22, 2, 4, 27, 2, 1, 4, 31, 11, 3, 1, 11, 7, 31, 4, 22, 22, 4, 1, 3, 22, 4, 4, 11, 27, 22, 2, 22, 3, 3, 20, 2, 20]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0846836, 0.064311564, 0.0, 0.050911665, 0.022262096, 0.07109201, 0.0, 0.013343513, 0.007708788, 0.05293858, 0.041178584, 0.021222293, 0.08628845, 0.03705585, 0.007064998, 0.0, 0.02014792, 0.0, 0.0748077, 0.025953233, 0.077350974, 0.09162587, 0.0, 0.048745096, 0.030573785, 0.04093474, 0.0, 0.055698693, 0.054688454, 0.03318268, 0.005015373, 0.0291304, 0.0764038, 0.012121439, 0.021558106, 0.042964935, 0.016581714, 0.021529436, 0.04349953, 0.004785478, 0.048832178, 0.043854773, 0.065416336, 0.05290717, 0.03817582, 0.046168268, 0.031242669, 0.0509488, 0.0052105784, 0.053618252, 0.038991034, 0.047520876, 0.047095776, 0.0071045756, 0.025436461, 0.047394216, 0.038426876, 0.020579517, 0.06882018, 0.0085090995, 0.0062422156, 0.012046754, 0.014924169, 0.048265576, 0.036896706, 0.038790524, 0.045065045, 0.0037569404, 0.034623742, 0.012257099, 0.062428117, 0.05308652, 0.019452393, 0.060663044, 0.032737732, 0.019781291, 0.013048053, 0.00884974, 0.049872518, 0.00090545416, 0.03208232, 0.018131793, 0.043639123, 0.0299654, 0.007590711, 0.038488865, 0.08856672, 0.06371045, 0.012636304, 0.013923466, 0.024098754, 0.01815468, 0.01307714, 0.0057596564, 0.056547463, 0.0029533505]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  6.5s
computing farthest point 1, dmax: Inf, imax: 13, n: 30
computing farthest point 2, dmax: 1.1588234, imax: 11, n: 30
computing farthest point 3, dmax: 0.99229395, imax: 17, n: 30
computing farthest point 4, dmax: 0.9148205, imax: 3, n: 30
computing farthest point 5, dmax: 0.806263, imax: 28, n: 30
computing farthest point 6, dmax: 0.7922077, imax: 20, n: 30
computing farthest point 7, dmax: 0.62236005, imax: 22, n: 30
computing farthest point 8, dmax: 0.5899791, imax: 14, n: 30
computing farthest point 9, dmax: 0.55837816, imax: 4, n: 30
computing farthest point 10, dmax: 0.5570344, imax: 15, 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(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-04T17:50:21.387
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=0.92857146, maxvisits=110) 2025-11-04T17:50:32.153
LOG n.size quantiles:[3.0, 4.0, 4.0, 5.0, 5.0]
(i, j, d) = (9, 813, -1.1920929f-7)
(i, j, d, :parallel) = (9, 813, -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 => 17.925532753, :exact => 0.947302075)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  19.4s
  5.956658 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.005193 seconds
SEARCH Exhaustive 2: 0.004752 seconds
SEARCH Exhaustive 3: 0.005564 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) 2025-11-04T17:51:00.182
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=10, Δ=1.1, maxvisits=214) 2025-11-04T17:51:05.948
LOG n.size quantiles:[3.0, 3.0, 4.0, 4.0, 5.0]
LOG add_vertex! sp=18000 ep=18004 n=17999 BeamSearch(bsize=14, Δ=1.244447, maxvisits=428) 2025-11-04T17:51:06.948
LOG n.size quantiles:[3.0, 7.0, 8.0, 9.0, 9.0]
LOG add_vertex! sp=32395 ep=32399 n=32394 BeamSearch(bsize=12, Δ=0.95, maxvisits=358) 2025-11-04T17:51:07.948
LOG n.size quantiles:[4.0, 5.0, 6.0, 6.0, 9.0]
LOG add_vertex! sp=44355 ep=44359 n=44354 BeamSearch(bsize=7, Δ=1.1851876, maxvisits=524) 2025-11-04T17:51:08.948
LOG n.size quantiles:[5.0, 6.0, 6.0, 7.0, 10.0]
LOG add_vertex! sp=55685 ep=55689 n=55684 BeamSearch(bsize=7, Δ=1.1851876, maxvisits=524) 2025-11-04T17:51:09.948
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=65755 ep=65759 n=65754 BeamSearch(bsize=16, Δ=1.075, maxvisits=430) 2025-11-04T17:51:10.948
LOG n.size quantiles:[5.0, 6.0, 7.0, 7.0, 10.0]
LOG add_vertex! sp=76340 ep=76344 n=76339 BeamSearch(bsize=16, Δ=1.075, maxvisits=430) 2025-11-04T17:51:11.949
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=85225 ep=85229 n=85224 BeamSearch(bsize=6, Δ=1.155, maxvisits=418) 2025-11-04T17:51:13.127
LOG n.size quantiles:[6.0, 7.0, 7.0, 8.0, 9.0]
LOG add_vertex! sp=93710 ep=93714 n=93709 BeamSearch(bsize=6, Δ=1.155, maxvisits=418) 2025-11-04T17:51:14.128
LOG n.size quantiles:[6.0, 7.0, 8.0, 9.0, 9.0]
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, 85.0]
[ Info: minrecall: queries per second: 10347.08585553467, recall: 0.900875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=8, Δ=1.14, maxvisits=814))
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, 85.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=10, Δ=1.19, maxvisits=648)), 1000, 8)
[ Info: rebuild: queries per second: 14433.257226767977, recall: 0.900875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=10, Δ=1.19, maxvisits=648))
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=7, Δ=1.157625, maxvisits=814)), 1000, 8)
  1.608365 seconds (611.69 k allocations: 31.309 MiB, 95.25% compilation time)
[ Info: matrixhints: queries per second: 13341.712818430828, recall: 0.90325
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=7, Δ=1.157625, maxvisits=814))
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, 85.0]
  2.186699 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.002004 seconds
SEARCH Exhaustive 2: 0.001991 seconds
SEARCH Exhaustive 3: 0.002002 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) 2025-11-04T17:52:24.175
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=10, Δ=1.1, maxvisits=214) 2025-11-04T17:52:29.696
LOG n.size quantiles:[3.0, 3.0, 4.0, 4.0, 5.0]
LOG add_vertex! sp=22000 ep=22004 n=21999 BeamSearch(bsize=14, Δ=1.244447, maxvisits=428) 2025-11-04T17:52:30.696
LOG n.size quantiles:[3.0, 5.0, 6.0, 7.0, 7.0]
LOG add_vertex! sp=37880 ep=37884 n=37879 BeamSearch(bsize=7, Δ=1.1851876, maxvisits=524) 2025-11-04T17:52:31.731
LOG n.size quantiles:[7.0, 7.0, 8.0, 9.0, 9.0]
LOG add_vertex! sp=51030 ep=51034 n=51029 BeamSearch(bsize=7, Δ=1.1851876, maxvisits=524) 2025-11-04T17:52:32.731
LOG n.size quantiles:[4.0, 5.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=62220 ep=62224 n=62219 BeamSearch(bsize=16, Δ=1.075, maxvisits=430) 2025-11-04T17:52:33.731
LOG n.size quantiles:[5.0, 7.0, 7.0, 8.0, 10.0]
LOG add_vertex! sp=74000 ep=74004 n=73999 BeamSearch(bsize=16, Δ=1.075, maxvisits=430) 2025-11-04T17:52:34.732
LOG n.size quantiles:[5.0, 5.0, 7.0, 9.0, 9.0]
LOG add_vertex! sp=85200 ep=85204 n=85199 BeamSearch(bsize=16, Δ=1.075, maxvisits=430) 2025-11-04T17:52:35.732
LOG n.size quantiles:[3.0, 8.0, 8.0, 8.0, 9.0]
LOG add_vertex! sp=94075 ep=94079 n=94074 BeamSearch(bsize=6, Δ=1.155, maxvisits=418) 2025-11-04T17:52:36.732
LOG n.size quantiles:[7.0, 7.0, 9.0, 9.0, 9.0]
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, 85.0]
[ Info: minrecall: queries per second: 13862.192039794249, recall: 0.900875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=8, Δ=1.14, maxvisits=814))
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, 85.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=10, Δ=1.19, maxvisits=648)), 1000, 8)
[ Info: rebuild: queries per second: 16116.708434996859, recall: 0.900875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=10, Δ=1.19, maxvisits=648))
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=7, Δ=1.157625, maxvisits=814)), 1000, 8)
  1.577760 seconds (567.40 k allocations: 29.094 MiB, 5.50% gc time, 96.53% compilation time)
[ Info: matrixhints: queries per second: 15293.794480185108, recall: 0.90325
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=7, Δ=1.157625, maxvisits=814))
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, 85.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  2m41.0s
     Testing SimilaritySearch tests passed 

Testing completed after 678.74s

PkgEval succeeded after 748.36s