Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.1272 (5444ac0564*) started at 2025-11-20T17:26:07.131


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 8.61s


################################################################################
# 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 4.57s


################################################################################
# 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...
   5564.3 ms  ✓ SearchModels
  11563.1 ms  ✓ SimilaritySearch
  2 dependencies successfully precompiled in 18 seconds. 66 already precompiled.

Precompilation completed after 31.55s


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

     Testing SimilaritySearch
      Status `/tmp/jl_ax7bb4/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_ax7bb4/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 v8.16.0+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  16.3s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  3.8s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.5s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.3s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.733825 seconds (1000 allocations: 78.125 KiB)
 10.679723 seconds (1000 allocations: 78.125 KiB)
  3.995776 seconds (1000 allocations: 78.125 KiB)
  3.961574 seconds (1000 allocations: 78.125 KiB)
  4.017456 seconds (1000 allocations: 78.125 KiB)
  4.105584 seconds (1000 allocations: 78.125 KiB)
  4.007425 seconds (1000 allocations: 78.125 KiB)
  4.009312 seconds (1000 allocations: 78.125 KiB)
 15.723725 seconds (1000 allocations: 78.125 KiB)
 15.322983 seconds (1000 allocations: 78.125 KiB)
 28.120462 seconds (1000 allocations: 78.125 KiB)
 27.992124 seconds (1000 allocations: 78.125 KiB)
 19.039833 seconds (6.23 k allocations: 358.094 KiB)
 21.330341 seconds (1000 allocations: 78.125 KiB)
 17.986991 seconds (1.00 k allocations: 78.141 KiB)
 17.961505 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m40.3s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.357522 seconds (1000 allocations: 78.125 KiB)
  3.264523 seconds (1000 allocations: 78.125 KiB)
 29.019411 seconds (1000 allocations: 78.125 KiB)
 30.568826 seconds (1000 allocations: 78.125 KiB)
 30.237813 seconds (1000 allocations: 78.125 KiB)
 28.979958 seconds (1000 allocations: 78.125 KiB)
  4.284292 seconds (1000 allocations: 78.125 KiB)
  4.357058 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m17.9s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
 10.128405 seconds (1000 allocations: 78.125 KiB)
  9.734280 seconds (1000 allocations: 78.125 KiB)
 10.229533 seconds (1000 allocations: 78.125 KiB)
 10.330577 seconds (1000 allocations: 78.125 KiB)
 10.694082 seconds (1000 allocations: 78.125 KiB)
 10.670573 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total     Time
indexing sets with ExhaustiveSearch | 3000   3000  1m04.7s
  0.046060 seconds (1.00 k allocations: 78.141 KiB)
  0.046104 seconds (1000 allocations: 78.125 KiB)
  0.043787 seconds (1000 allocations: 78.125 KiB)
  0.043883 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.9s
  0.057290 seconds (1000 allocations: 78.125 KiB)
  0.056881 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.342852 seconds (2.39 M allocations: 127.005 MiB, 6.81% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.204709 seconds (613.31 k allocations: 30.462 MiB, 2.19% gc time, 99.83% 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  2.9s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:10.309
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-20T17:35:10.532
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) 2025-11-20T17:35:11.889
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: 2025-11-20T17:35:12.274
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000006, 0x00000009, 0x0000000c, 0x00000016, 0x0000001d, 0x00000038, 0x0000005d]
D.nn = Int32[1, 2, 3, 3, 1, 6, 3, 6, 9, 3, 9, 12, 9, 3, 12, 3, 3, 3, 9, 9, 9, 22, 22, 6, 9, 2, 9, 2, 29, 2, 29, 9, 29, 22, 1, 29, 22, 2, 12, 9, 6, 29, 3, 3, 9, 3, 3, 6, 6, 3, 3, 3, 29, 9, 3, 56, 29, 22, 9, 6, 2, 6, 6, 9, 22, 56, 1, 6, 56, 29, 3, 29, 56, 22, 3, 9, 3, 29, 3, 3, 56, 9, 29, 12, 29, 9, 6, 2, 1, 3, 6, 1, 93, 56, 12, 2, 9, 3, 56, 56]
D.dist = Float32[0.0, 0.0, 0.0, 0.01971531, 0.09316558, 0.0, 0.08719748, 0.027813256, 0.0, 0.05545634, 0.043335438, 0.0, 0.03355664, 0.054423094, 0.056361258, 0.07445115, 0.014839113, 0.048422694, 0.06669384, 0.08014727, 0.06447345, 0.0, 0.049396336, 0.07446748, 0.07092047, 0.025934935, 0.026279986, 0.0069827437, 0.0, 0.0460943, 0.032084167, 0.016492367, 0.022872508, 0.092727244, 0.01811105, 0.04451573, 0.03515929, 0.044145703, 0.013322711, 0.048980713, 0.03420955, 0.006090224, 0.08962715, 0.0035902858, 0.067059875, 0.022965193, 0.043827653, 0.020443797, 0.029811561, 0.06235683, 0.047357023, 0.04938227, 0.023320138, 0.026761115, 0.049073815, 0.0, 0.050121903, 0.06651676, 0.041947067, 0.04712385, 0.011205971, 0.022122085, 0.008843303, 0.048182428, 0.04580331, 0.033510923, 0.09584051, 0.06240201, 0.06965667, 0.09923315, 0.047338843, 0.023852646, 0.07120788, 0.07619721, 0.08920467, 0.046682954, 0.011267066, 0.041846633, 0.024071693, 0.045247257, 0.07737899, 0.071370006, 0.06272811, 0.079153836, 0.030891955, 0.020512998, 0.010106564, 0.05896038, 0.049366653, 0.049391568, 0.020451486, 0.020677626, 0.0, 0.06748885, 0.030453622, 0.011168838, 0.050889492, 0.012375832, 0.06176001, 0.09533316]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  17.7s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.339
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-20T17:35:13.340
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: 2025-11-20T17:35:13.340
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.340
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.340
[ Info: neardup> range: 65:80, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.340
[ Info: neardup> range: 81:96, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.340
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.341
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.341
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000006, 0x00000009, 0x0000000c, 0x00000016, 0x0000001d, 0x00000038, 0x0000005d]
D.nn = Int32[1, 2, 3, 3, 1, 6, 3, 6, 9, 3, 9, 12, 9, 3, 12, 3, 3, 3, 9, 9, 9, 22, 22, 6, 9, 2, 9, 2, 29, 2, 12, 9, 29, 22, 1, 29, 22, 2, 12, 9, 6, 29, 3, 3, 9, 3, 3, 6, 6, 3, 3, 3, 29, 9, 3, 56, 29, 22, 9, 6, 2, 6, 6, 9, 22, 56, 1, 6, 56, 29, 3, 29, 56, 22, 3, 9, 3, 29, 3, 3, 56, 9, 29, 12, 29, 9, 6, 2, 1, 3, 6, 1, 93, 56, 12, 2, 9, 3, 56, 56]
D.dist = Float32[0.0, 0.0, 0.0, 0.01971531, 0.09316558, 0.0, 0.08719748, 0.027813256, 0.0, 0.05545634, 0.043335438, 0.0, 0.03355664, 0.054423094, 0.056361258, 0.07445115, 0.014839113, 0.048422694, 0.06669384, 0.08014727, 0.06447345, 0.0, 0.049396336, 0.07446748, 0.07092047, 0.025934935, 0.026279986, 0.0069827437, 0.0, 0.0460943, 0.069058895, 0.016492367, 0.022872508, 0.092727244, 0.01811105, 0.04451573, 0.03515929, 0.044145703, 0.013322711, 0.048980713, 0.03420955, 0.006090224, 0.08962715, 0.0035902858, 0.067059875, 0.022965193, 0.043827653, 0.020443797, 0.029811561, 0.06235683, 0.047357023, 0.04938227, 0.023320138, 0.026761115, 0.049073815, 0.0, 0.050121903, 0.06651676, 0.041947067, 0.04712385, 0.011205971, 0.022122085, 0.008843303, 0.048182428, 0.04580331, 0.033510923, 0.09584051, 0.06240201, 0.06965667, 0.09923315, 0.047338843, 0.023852646, 0.07120788, 0.07619721, 0.08920467, 0.046682954, 0.011267066, 0.041846633, 0.024071693, 0.045247257, 0.07737899, 0.071370006, 0.06272811, 0.079153836, 0.030891955, 0.020512998, 0.010106564, 0.05896038, 0.049366653, 0.049391568, 0.020451486, 0.020677626, 0.0, 0.06748885, 0.030453622, 0.011168838, 0.050889492, 0.012375832, 0.06176001, 0.09533316]
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: 2025-11-20T17:35:13.422
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-20T17:35:13.423
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: 2025-11-20T17:35:13.423
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.423
[ Info: neardup> range: 49:64, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.423
[ Info: neardup> range: 65:80, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.423
[ Info: neardup> range: 81:96, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.423
[ Info: neardup> range: 97:100, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.424
[ Info: neardup> finished current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:13.424
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000046, 0x00000049, 0x0000005d]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 3, 3, 9, 9, 9, 16, 14, 14, 9, 2, 11, 2, 5, 10, 5, 11, 5, 13, 1, 9, 14, 10, 12, 8, 10, 5, 15, 3, 13, 3, 3, 6, 10, 15, 16, 15, 5, 13, 3, 13, 5, 14, 9, 11, 2, 6, 6, 11, 16, 3, 1, 4, 7, 70, 16, 9, 73, 13, 16, 13, 3, 3, 3, 4, 7, 11, 73, 15, 73, 11, 6, 4, 1, 14, 6, 1, 93, 73, 12, 2, 11, 4, 7, 73]
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, 0.014839113, 0.048422694, 0.06669384, 0.08014727, 0.06447345, 0.076471806, 0.018669665, 0.005096197, 0.07092047, 0.025934935, 0.004616499, 0.0069827437, 0.06514734, 0.035517037, 0.015487969, 0.0083094835, 0.014584243, 0.035756826, 0.01811105, 0.046136796, 0.04262656, 0.005507529, 0.013322711, 0.045637608, 0.010713756, 0.07330078, 0.011515677, 0.0035902858, 0.04107845, 0.022965193, 0.043827653, 0.020443797, 0.026693404, 0.024300635, 0.008451819, 0.0147048235, 0.05829853, 0.000510633, 0.049073815, 0.048083186, 0.041817546, 0.014210403, 0.041947067, 0.016626358, 0.011205971, 0.022122085, 0.008843303, 0.015446067, 0.0053603053, 0.051981688, 0.09584051, 0.050040722, 0.023799002, 0.0, 0.005279422, 0.0790714, 0.0, 0.046309233, 0.0040100813, 0.015247643, 0.011267066, 0.045231283, 0.024071693, 0.008945286, 0.021980822, 0.005914569, 0.0068777204, 0.040317714, 0.05541855, 0.010775566, 0.010106564, 0.015193164, 0.049366653, 0.014305532, 0.020451486, 0.020677626, 0.0, 0.0015048385, 0.030453622, 0.011168838, 0.016650617, 0.006588757, 0.03715849, 0.07807714]
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: 2025-11-20T17:35:20.677
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=6 n=6 2025-11-20T17:35:20.678
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:20.683
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:20.683
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:20.684
[ Info: neardup> range: 65:80, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:20.684
[ Info: neardup> range: 81:96, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:20.684
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:20.684
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-20T17:35:20.684
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000006, 0x00000009, 0x0000000c, 0x00000016, 0x0000001d, 0x00000038, 0x0000005d]
D.nn = Int32[1, 2, 3, 3, 1, 6, 3, 6, 9, 3, 9, 12, 9, 3, 12, 3, 3, 3, 9, 9, 9, 22, 22, 6, 9, 2, 9, 2, 29, 2, 12, 9, 29, 22, 1, 29, 22, 2, 12, 9, 6, 29, 3, 3, 9, 3, 3, 6, 6, 3, 3, 3, 29, 9, 3, 56, 29, 22, 9, 6, 2, 6, 6, 9, 22, 56, 1, 6, 56, 29, 3, 29, 56, 22, 3, 9, 3, 29, 3, 3, 56, 9, 29, 12, 29, 9, 6, 2, 1, 3, 6, 1, 93, 56, 12, 2, 9, 3, 56, 56]
D.dist = Float32[0.0, 0.0, 0.0, 0.01971531, 0.09316558, 0.0, 0.08719748, 0.027813256, 0.0, 0.05545634, 0.043335438, 0.0, 0.03355664, 0.054423094, 0.056361258, 0.07445115, 0.014839113, 0.048422694, 0.06669384, 0.08014727, 0.06447345, 0.0, 0.049396336, 0.07446748, 0.07092047, 0.025934935, 0.026279986, 0.0069827437, 0.0, 0.0460943, 0.069058895, 0.016492367, 0.022872508, 0.092727244, 0.01811105, 0.04451573, 0.03515929, 0.044145703, 0.013322711, 0.048980713, 0.03420955, 0.006090224, 0.08962715, 0.0035902858, 0.067059875, 0.022965193, 0.043827653, 0.020443797, 0.029811561, 0.06235683, 0.047357023, 0.04938227, 0.023320138, 0.026761115, 0.049073815, 0.0, 0.050121903, 0.06651676, 0.041947067, 0.04712385, 0.011205971, 0.022122085, 0.008843303, 0.048182428, 0.04580331, 0.033510923, 0.09584051, 0.06240201, 0.06965667, 0.09923315, 0.047338843, 0.023852646, 0.07120788, 0.07619721, 0.08920467, 0.046682954, 0.011267066, 0.041846633, 0.024071693, 0.045247257, 0.07737899, 0.071370006, 0.06272811, 0.079153836, 0.030891955, 0.020512998, 0.010106564, 0.05896038, 0.049366653, 0.049391568, 0.020451486, 0.020677626, 0.0, 0.06748885, 0.030453622, 0.011168838, 0.050889492, 0.012375832, 0.06176001, 0.09533316]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.3s
computing farthest point 1, dmax: Inf, imax: 16, n: 30
computing farthest point 2, dmax: 1.367254, imax: 25, n: 30
computing farthest point 3, dmax: 1.0807335, imax: 1, n: 30
computing farthest point 4, dmax: 0.9830192, imax: 6, n: 30
computing farthest point 5, dmax: 0.79902685, imax: 14, n: 30
computing farthest point 6, dmax: 0.745208, imax: 26, n: 30
computing farthest point 7, dmax: 0.69970506, imax: 21, n: 30
computing farthest point 8, dmax: 0.6381752, imax: 15, n: 30
computing farthest point 9, dmax: 0.5956477, imax: 4, n: 30
computing farthest point 10, dmax: 0.53725034, imax: 11, 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
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-20T17:35:28.657
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=0.8809524, maxvisits=118) 2025-11-20T17:35:39.099
LOG n.size quantiles:[4.0, 4.0, 5.0, 5.0, 6.0]
(i, j, d) = (22, 702, -1.1920929f-7)
(i, j, d, :parallel) = (22, 702, -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 => 17.906141109, :exact => 0.957097657)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  19.4s
  5.911338 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.004474 seconds
SEARCH Exhaustive 2: 0.004565 seconds
SEARCH Exhaustive 3: 0.005163 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) 2025-11-20T17:36:07.721
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=4, Δ=1.0, maxvisits=192) 2025-11-20T17:36:12.966
LOG n.size quantiles:[4.0, 4.0, 5.0, 6.0, 6.0]
LOG add_vertex! sp=17530 ep=17534 n=17529 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=404) 2025-11-20T17:36:13.966
LOG n.size quantiles:[5.0, 7.0, 7.0, 8.0, 9.0]
LOG add_vertex! sp=30405 ep=30409 n=30404 BeamSearch
BeamSearch(bsize=8, Δ=1.155, maxvisits=460) 2025-11-20T17:36:14.967
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=41570 ep=41574 n=41569 BeamSearch
BeamSearch(bsize=16, Δ=1.025, maxvisits=444) 2025-11-20T17:36:15.967
LOG n.size quantiles:[6.0, 7.0, 8.0, 8.0, 10.0]
LOG add_vertex! sp=53865 ep=53869 n=53864 BeamSearch
BeamSearch(bsize=16, Δ=1.025, maxvisits=444) 2025-11-20T17:36:16.967
LOG n.size quantiles:[4.0, 5.0, 7.0, 7.0, 10.0]
LOG add_vertex! sp=62840 ep=62844 n=62839 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=562) 2025-11-20T17:36:17.967
LOG n.size quantiles:[6.0, 6.0, 6.0, 7.0, 9.0]
LOG add_vertex! sp=71770 ep=71774 n=71769 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=562) 2025-11-20T17:36:18.967
LOG n.size quantiles:[6.0, 6.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=80790 ep=80794 n=80789 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=562) 2025-11-20T17:36:19.967
LOG n.size quantiles:[6.0, 7.0, 7.0, 8.0, 10.0]
LOG add_vertex! sp=88090 ep=88094 n=88089 BeamSearch
BeamSearch(bsize=14, Δ=1.025, maxvisits=466) 2025-11-20T17:36:20.968
LOG n.size quantiles:[4.0, 5.0, 5.0, 6.0, 8.0]
LOG add_vertex! sp=97155 ep=97159 n=97154 BeamSearch
BeamSearch(bsize=14, Δ=1.025, maxvisits=466) 2025-11-20T17:36:21.968
LOG n.size quantiles:[5.0, 6.0, 6.0, 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, 80.0]
[ Info: minrecall: queries per second: 12128.60336560981, recall: 0.900125
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=11, Δ=1.12, maxvisits=642))
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, 80.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=7, Δ=1.155, maxvisits=546)), 1000, 8)
[ Info: rebuild: queries per second: 16194.430330548565, recall: 0.901
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=7, Δ=1.155, maxvisits=546))
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, 31.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=12, Δ=1.1287501, maxvisits=744)), 1000, 8)
  1.687856 seconds (610.14 k allocations: 31.092 MiB, 94.54% compilation time)
[ Info: matrixhints: queries per second: 12441.643404733353, recall: 0.90175
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=12, Δ=1.1287501, maxvisits=744))
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, 80.0]
  1.924961 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.002045 seconds
SEARCH Exhaustive 2: 0.002002 seconds
SEARCH Exhaustive 3: 0.002089 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) 2025-11-20T17:37:30.039
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=4, Δ=1.0, maxvisits=192) 2025-11-20T17:37:35.673
LOG n.size quantiles:[4.0, 4.0, 5.0, 6.0, 6.0]
LOG add_vertex! sp=22850 ep=22854 n=22849 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=404) 2025-11-20T17:37:36.673
LOG n.size quantiles:[5.0, 6.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=37880 ep=37884 n=37879 BeamSearch
BeamSearch(bsize=16, Δ=1.025, maxvisits=444) 2025-11-20T17:37:37.685
LOG n.size quantiles:[5.0, 6.0, 6.0, 6.0, 7.0]
LOG add_vertex! sp=51440 ep=51444 n=51439 BeamSearch
BeamSearch(bsize=16, Δ=1.025, maxvisits=444) 2025-11-20T17:37:38.685
LOG n.size quantiles:[4.0, 6.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=62570 ep=62574 n=62569 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=562) 2025-11-20T17:37:39.686
LOG n.size quantiles:[5.0, 5.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=73145 ep=73149 n=73144 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=562) 2025-11-20T17:37:40.686
LOG n.size quantiles:[6.0, 7.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=82960 ep=82964 n=82959 BeamSearch
BeamSearch(bsize=4, Δ=1.1851876, maxvisits=562) 2025-11-20T17:37:41.686
LOG n.size quantiles:[5.0, 6.0, 8.0, 9.0, 12.0]
LOG add_vertex! sp=92880 ep=92884 n=92879 BeamSearch
BeamSearch(bsize=14, Δ=1.025, maxvisits=466) 2025-11-20T17:37:42.686
LOG n.size quantiles:[3.0, 5.0, 5.0, 5.0, 8.0]
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, 80.0]
[ Info: minrecall: queries per second: 14068.956881812672, recall: 0.900125
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=11, Δ=1.12, maxvisits=642))
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, 80.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=7, Δ=1.155, maxvisits=546)), 1000, 8)
[ Info: rebuild: queries per second: 16569.22636028709, recall: 0.901
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=7, Δ=1.155, maxvisits=546))
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, 31.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=12, Δ=1.1287501, maxvisits=744)), 1000, 8)
  1.521566 seconds (566.06 k allocations: 28.906 MiB, 95.69% compilation time)
[ Info: matrixhints: queries per second: 14491.782463737503, recall: 0.90175
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=12, Δ=1.1287501, maxvisits=744))
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, 80.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  2m35.2s
     Testing SimilaritySearch tests passed 

Testing completed after 682.73s

PkgEval succeeded after 742.56s