Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.65 (b05afe0f25*) started at 2025-11-11T18:28:18.570


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.69s


################################################################################
# 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 5.76s


################################################################################
# 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...
  10753.1 ms  ✓ StatsBase
   6260.7 ms  ✓ SearchModels
  14935.4 ms  ✓ SimilaritySearch
  3 dependencies successfully precompiled in 33 seconds. 65 already precompiled.

Precompilation completed after 46.22s


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

     Testing SimilaritySearch
      Status `/tmp/jl_RFoYXX/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_RFoYXX/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 v2025.11.4
  [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  17.2s
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  2.4s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.1s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.753412 seconds (1000 allocations: 78.125 KiB)
  9.491759 seconds (1000 allocations: 78.125 KiB)
  3.943225 seconds (1000 allocations: 78.125 KiB)
  3.994760 seconds (1000 allocations: 78.125 KiB)
  4.051516 seconds (1000 allocations: 78.125 KiB)
  3.651513 seconds (1000 allocations: 78.125 KiB)
  3.905706 seconds (1000 allocations: 78.125 KiB)
  3.830427 seconds (1000 allocations: 78.125 KiB)
 15.336318 seconds (1000 allocations: 78.125 KiB)
 13.289963 seconds (1000 allocations: 78.125 KiB)
 28.639464 seconds (1000 allocations: 78.125 KiB)
 28.819642 seconds (1000 allocations: 78.125 KiB)
 21.939525 seconds (6.23 k allocations: 358.172 KiB)
 21.737893 seconds (1000 allocations: 78.125 KiB)
 17.376953 seconds (1.00 k allocations: 78.141 KiB)
 17.803220 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m40.4s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.175106 seconds (1000 allocations: 78.125 KiB)
  3.145708 seconds (1000 allocations: 78.125 KiB)
 29.605162 seconds (1000 allocations: 78.125 KiB)
 29.734665 seconds (1000 allocations: 78.125 KiB)
 28.961912 seconds (1000 allocations: 78.125 KiB)
 29.592802 seconds (1000 allocations: 78.125 KiB)
  4.154605 seconds (1000 allocations: 78.125 KiB)
  4.144374 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m16.3s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
 10.339810 seconds (1000 allocations: 78.125 KiB)
 10.320539 seconds (1000 allocations: 78.125 KiB)
 10.372202 seconds (1000 allocations: 78.125 KiB)
 10.309549 seconds (1000 allocations: 78.125 KiB)
 10.375909 seconds (1000 allocations: 78.125 KiB)
 10.352803 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total     Time
indexing sets with ExhaustiveSearch | 3000   3000  1m05.2s
  0.044190 seconds (1.00 k allocations: 78.141 KiB)
  0.045026 seconds (1000 allocations: 78.125 KiB)
  0.039595 seconds (1000 allocations: 78.125 KiB)
  0.039808 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  3.1s
  0.051650 seconds (1000 allocations: 78.125 KiB)
  0.051619 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.793147 seconds (3.21 M allocations: 180.272 MiB, 7.24% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.230974 seconds (794.50 k allocations: 43.424 MiB, 99.85% compilation time)
Test Summary: | Pass  Total  Time
allknn        |    3      3  6.6s
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  3.1s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:41.328
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-11T18:37:41.581
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) 2025-11-11T18:37:43.072
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: 2025-11-11T18:37:43.477
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000008, 0x0000000f, 0x00000014, 0x0000001e, 0x0000003e, 0x0000004e, 0x00000052]
D.nn = Int32[1, 2, 3, 4, 5, 4, 1, 8, 1, 3, 2, 2, 2, 1, 15, 1, 8, 2, 15, 20, 8, 3, 1, 1, 2, 1, 2, 1, 5, 30, 8, 15, 8, 1, 20, 15, 4, 8, 1, 5, 30, 5, 15, 8, 3, 4, 1, 1, 4, 20, 2, 2, 1, 2, 3, 4, 20, 1, 15, 4, 5, 62, 1, 8, 2, 8, 1, 1, 2, 8, 1, 20, 5, 8, 2, 3, 2, 78, 1, 4, 8, 82, 1, 8, 3, 15, 20, 1, 4, 4, 20, 1, 20, 30, 2, 8, 5, 30, 1, 20]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0898937, 0.048050284, 0.0, 0.0024152398, 0.022375286, 0.07997751, 0.044194758, 0.011560082, 0.0668326, 0.0, 0.029541552, 0.014331996, 0.01736182, 0.042559564, 0.0, 0.0262326, 0.042310894, 0.05123067, 0.0173046, 0.027300656, 0.037683666, 0.065258265, 0.022508562, 0.014117122, 0.0, 0.02555424, 0.042639434, 0.031096876, 0.034015894, 0.0312739, 0.026841164, 0.028106809, 0.08919585, 0.001445055, 0.008046091, 0.019421458, 0.030667484, 0.047724485, 0.020654619, 0.03638214, 0.08475673, 0.052607596, 0.026737452, 0.027225673, 0.008800924, 0.049986422, 0.04317653, 0.02135408, 0.04611373, 0.07798606, 0.045180082, 0.053724647, 0.015040576, 0.0152692795, 0.054640174, 0.020810366, 0.0, 0.05273968, 0.042768955, 0.018627882, 0.0154646635, 0.008313119, 0.07489073, 0.021812737, 0.07399225, 0.04525113, 0.0065770745, 0.036361217, 0.024127483, 0.01780188, 0.015157402, 0.043517113, 0.0, 0.049495578, 0.05154991, 0.019044936, 0.0, 0.028320253, 0.03567177, 0.029804766, 0.052395284, 0.032212436, 0.030791879, 0.0033898354, 0.042599857, 0.07883686, 0.02033788, 0.028176427, 0.051927388, 0.01671505, 0.020623386, 0.052499354, 0.053592086, 0.02488023, 0.04730171]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  19.4s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.723
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-11T18:37:44.724
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.725
[ Info: neardup> range: 33:48, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.725
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.725
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.725
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.725
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.725
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.726
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000008, 0x0000000f, 0x00000014, 0x0000001e, 0x0000003e, 0x0000004e, 0x00000052]
D.nn = Int32[1, 2, 3, 4, 5, 4, 1, 8, 1, 3, 2, 2, 2, 1, 15, 1, 8, 2, 15, 20, 8, 3, 1, 1, 2, 1, 2, 1, 5, 30, 8, 15, 8, 1, 20, 15, 4, 8, 1, 5, 30, 5, 15, 8, 3, 4, 1, 1, 4, 20, 2, 2, 1, 2, 3, 4, 20, 1, 15, 4, 5, 62, 1, 8, 2, 8, 1, 1, 2, 8, 1, 20, 5, 8, 2, 3, 2, 78, 1, 4, 8, 82, 1, 8, 3, 15, 20, 1, 4, 4, 20, 1, 20, 30, 2, 8, 5, 30, 1, 20]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0898937, 0.048050284, 0.0, 0.0024152398, 0.022375286, 0.07997751, 0.044194758, 0.011560082, 0.0668326, 0.0, 0.029541552, 0.014331996, 0.01736182, 0.042559564, 0.0, 0.0262326, 0.042310894, 0.05123067, 0.0173046, 0.027300656, 0.037683666, 0.065258265, 0.022508562, 0.014117122, 0.0, 0.02555424, 0.042639434, 0.031096876, 0.034015894, 0.0312739, 0.026841164, 0.028106809, 0.08919585, 0.001445055, 0.008046091, 0.019421458, 0.030667484, 0.047724485, 0.020654619, 0.03638214, 0.08475673, 0.052607596, 0.026737452, 0.027225673, 0.008800924, 0.049986422, 0.04317653, 0.02135408, 0.04611373, 0.07798606, 0.045180082, 0.053724647, 0.015040576, 0.0152692795, 0.054640174, 0.020810366, 0.0, 0.05273968, 0.042768955, 0.018627882, 0.0154646635, 0.008313119, 0.07489073, 0.021812737, 0.07399225, 0.04525113, 0.0065770745, 0.036361217, 0.024127483, 0.01780188, 0.015157402, 0.043517113, 0.0, 0.049495578, 0.05154991, 0.019044936, 0.0, 0.028320253, 0.03567177, 0.029804766, 0.052395284, 0.032212436, 0.030791879, 0.0033898354, 0.042599857, 0.07883686, 0.02033788, 0.028176427, 0.051927388, 0.01671505, 0.020623386, 0.052499354, 0.053592086, 0.02488023, 0.04730171]
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: 2025-11-11T18:37:44.819
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-11T18:37:44.819
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: 2025-11-11T18:37:44.821
[ Info: neardup> range: 33:48, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.821
[ Info: neardup> range: 49:64, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.821
[ Info: neardup> range: 65:80, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.821
[ Info: neardup> range: 81:96, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.821
[ Info: neardup> range: 97:100, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.821
[ Info: neardup> finished current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:44.821
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000014, 0x00000029, 0x0000003e]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 8, 2, 15, 20, 8, 11, 7, 9, 2, 1, 12, 1, 5, 11, 8, 15, 8, 1, 20, 15, 12, 7, 1, 5, 41, 5, 15, 8, 10, 6, 16, 16, 4, 20, 9, 11, 1, 11, 11, 12, 20, 1, 15, 6, 5, 62, 14, 8, 2, 8, 9, 16, 13, 8, 16, 20, 5, 8, 12, 10, 2, 6, 9, 4, 8, 14, 7, 8, 10, 15, 20, 16, 4, 6, 20, 16, 20, 41, 2, 8, 5, 11, 9, 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, 0.014331996, 0.01736182, 0.042559564, 0.0, 0.0262326, 0.029496431, 0.0038431883, 0.008660793, 0.027300656, 0.037683666, 0.0025411844, 0.022508562, 0.014117122, 0.057395577, 0.02555424, 0.042639434, 0.031096876, 0.034015894, 0.0312739, 0.026841164, 0.015338182, 0.07032204, 0.001445055, 0.008046091, 0.0, 0.030667484, 0.047724485, 0.020654619, 0.02659297, 0.007900596, 0.0075120926, 0.0009521246, 0.027225673, 0.008800924, 0.041424274, 0.010091364, 0.02135408, 0.005419433, 0.015136898, 0.01582706, 0.053724647, 0.015040576, 0.0152692795, 0.03847748, 0.020810366, 0.0, 0.01427269, 0.042768955, 0.018627882, 0.0154646635, 0.006454766, 0.012861013, 0.01125139, 0.07399225, 0.027790189, 0.0065770745, 0.036361217, 0.024127483, 0.007374406, 0.012295842, 0.043517113, 0.017630279, 0.039133072, 0.05154991, 0.019044936, 0.011935055, 0.012364268, 0.03567177, 0.015085578, 0.052395284, 0.032212436, 0.026453257, 0.0033898354, 0.03582245, 0.07883686, 0.020267248, 0.028176427, 0.024280667, 0.01671505, 0.020623386, 0.052499354, 0.021844149, 0.01812023, 0.04730171]
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: 2025-11-11T18:37:52.644
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=7 n=7 2025-11-11T18:37:52.644
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:52.650
[ Info: neardup> range: 33:48, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:52.650
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:52.650
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:52.650
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:52.651
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:52.651
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-11T18:37:52.651
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000008, 0x0000000f, 0x00000014, 0x0000001e, 0x0000003e, 0x0000004e, 0x00000052]
D.nn = Int32[1, 2, 3, 4, 5, 4, 1, 8, 1, 3, 2, 2, 2, 1, 15, 1, 8, 2, 15, 20, 8, 3, 1, 1, 2, 1, 2, 1, 5, 30, 8, 15, 8, 1, 20, 15, 4, 8, 1, 5, 30, 5, 15, 8, 3, 4, 1, 1, 4, 20, 2, 2, 1, 2, 3, 4, 20, 1, 15, 4, 5, 62, 1, 8, 2, 8, 1, 1, 2, 8, 1, 20, 5, 8, 2, 3, 2, 78, 1, 4, 8, 82, 1, 8, 3, 15, 20, 1, 4, 4, 20, 1, 20, 30, 2, 8, 5, 30, 1, 20]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0898937, 0.048050284, 0.0, 0.0024152398, 0.022375286, 0.07997751, 0.044194758, 0.011560082, 0.0668326, 0.0, 0.029541552, 0.014331996, 0.01736182, 0.042559564, 0.0, 0.0262326, 0.042310894, 0.05123067, 0.0173046, 0.027300656, 0.037683666, 0.065258265, 0.022508562, 0.014117122, 0.0, 0.02555424, 0.042639434, 0.031096876, 0.034015894, 0.0312739, 0.026841164, 0.028106809, 0.08919585, 0.001445055, 0.008046091, 0.019421458, 0.030667484, 0.047724485, 0.020654619, 0.03638214, 0.08475673, 0.052607596, 0.026737452, 0.027225673, 0.008800924, 0.049986422, 0.04317653, 0.02135408, 0.04611373, 0.07798606, 0.045180082, 0.053724647, 0.015040576, 0.0152692795, 0.054640174, 0.020810366, 0.0, 0.05273968, 0.042768955, 0.018627882, 0.0154646635, 0.008313119, 0.07489073, 0.021812737, 0.07399225, 0.04525113, 0.0065770745, 0.036361217, 0.024127483, 0.01780188, 0.015157402, 0.043517113, 0.0, 0.049495578, 0.05154991, 0.019044936, 0.0, 0.028320253, 0.03567177, 0.029804766, 0.052395284, 0.032212436, 0.030791879, 0.0033898354, 0.042599857, 0.07883686, 0.02033788, 0.028176427, 0.051927388, 0.01671505, 0.020623386, 0.052499354, 0.053592086, 0.02488023, 0.04730171]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.8s
computing farthest point 1, dmax: Inf, imax: 16, n: 30
computing farthest point 2, dmax: 1.1306785, imax: 6, n: 30
computing farthest point 3, dmax: 0.94020563, imax: 21, n: 30
computing farthest point 4, dmax: 0.88386905, imax: 5, n: 30
computing farthest point 5, dmax: 0.84986925, imax: 2, n: 30
computing farthest point 6, dmax: 0.7790864, imax: 20, n: 30
computing farthest point 7, dmax: 0.7485077, imax: 15, n: 30
computing farthest point 8, dmax: 0.62983817, imax: 11, n: 30
computing farthest point 9, dmax: 0.6114187, imax: 7, n: 30
computing farthest point 10, dmax: 0.5808345, imax: 23, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.5s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.7s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-11T18:38:01.119
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=0.88, maxvisits=110) 2025-11-11T18:38:13.511
LOG n.size quantiles:[4.0, 4.0, 4.0, 5.0, 5.0]
(i, j, d) = (1, 492, -1.1920929f-7)
(i, j, d, :parallel) = (1, 492, -1.1920929f-7, :parallel)
[ Info: NOTE: the exact method will be faster on small datasets due to the preprocessing step of the approximation method
[ Info: ("closestpair computation time", :approx => 20.085379445, :exact => 1.032731752)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  21.6s
  6.224373 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.004927 seconds
SEARCH Exhaustive 2: 0.005040 seconds
SEARCH Exhaustive 3: 0.005832 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) 2025-11-11T18:38:43.260
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=14, Δ=1.075, maxvisits=194) 2025-11-11T18:38:49.343
LOG n.size quantiles:[3.0, 4.0, 4.0, 5.0, 6.0]
LOG add_vertex! sp=19170 ep=19174 n=19169 BeamSearch(bsize=6, Δ=0.95, maxvisits=320) 2025-11-11T18:38:50.343
LOG n.size quantiles:[3.0, 7.0, 7.0, 8.0, 9.0]
LOG add_vertex! sp=33250 ep=33254 n=33249 BeamSearch(bsize=6, Δ=1.21275, maxvisits=444) 2025-11-11T18:38:51.344
LOG n.size quantiles:[5.0, 6.0, 6.0, 9.0, 9.0]
LOG add_vertex! sp=44330 ep=44334 n=44329 BeamSearch(bsize=12, Δ=1.1, maxvisits=454) 2025-11-11T18:38:52.344
LOG n.size quantiles:[5.0, 7.0, 7.0, 8.0, 8.0]
LOG add_vertex! sp=55825 ep=55829 n=55824 BeamSearch(bsize=12, Δ=1.1, maxvisits=454) 2025-11-11T18:38:53.344
LOG n.size quantiles:[5.0, 6.0, 8.0, 8.0, 8.0]
LOG add_vertex! sp=65410 ep=65414 n=65409 BeamSearch(bsize=6, Δ=1.025, maxvisits=390) 2025-11-11T18:38:54.345
LOG n.size quantiles:[5.0, 5.0, 6.0, 7.0, 7.0]
LOG add_vertex! sp=76065 ep=76069 n=76064 BeamSearch(bsize=6, Δ=1.025, maxvisits=390) 2025-11-11T18:38:55.345
LOG n.size quantiles:[5.0, 6.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=85225 ep=85229 n=85224 BeamSearch(bsize=12, Δ=1.075, maxvisits=488) 2025-11-11T18:38:56.412
LOG n.size quantiles:[6.0, 7.0, 7.0, 7.0, 7.0]
LOG add_vertex! sp=94950 ep=94954 n=94949 BeamSearch(bsize=12, Δ=1.075, maxvisits=488) 2025-11-11T18:38:57.412
LOG n.size quantiles:[5.0, 6.0, 8.0, 8.0, 9.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 14.0, 17.0, 22.0, 91.0]
[ Info: minrecall: queries per second: 13612.526617253923, recall: 0.900375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.16, maxvisits=728))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 14.0, 17.0, 22.0, 91.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=9, Δ=1.17, maxvisits=674)), 1000, 8)
[ Info: rebuild: queries per second: 15924.84467025945, recall: 0.896
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=9, Δ=1.17, maxvisits=674))
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=9, Δ=1.155, maxvisits=728)), 1000, 8)
  2.047825 seconds (849.28 k allocations: 46.153 MiB, 18.00% gc time, 96.39% compilation time)
[ Info: matrixhints: queries per second: 13869.365445463493, recall: 0.89925
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=9, Δ=1.155, maxvisits=728))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 14.0, 17.0, 22.0, 91.0]
  2.185498 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.002000 seconds
SEARCH Exhaustive 2: 0.001996 seconds
SEARCH Exhaustive 3: 0.002186 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) 2025-11-11T18:40:09.110
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=14, Δ=1.075, maxvisits=194) 2025-11-11T18:40:14.472
LOG n.size quantiles:[3.0, 4.0, 4.0, 5.0, 6.0]
LOG add_vertex! sp=25255 ep=25259 n=25254 BeamSearch(bsize=6, Δ=1.21275, maxvisits=444) 2025-11-11T18:40:15.499
LOG n.size quantiles:[4.0, 5.0, 5.0, 6.0, 8.0]
LOG add_vertex! sp=39245 ep=39249 n=39244 BeamSearch(bsize=12, Δ=1.1, maxvisits=454) 2025-11-11T18:40:16.499
LOG n.size quantiles:[5.0, 5.0, 5.0, 8.0, 8.0]
LOG add_vertex! sp=51235 ep=51239 n=51234 BeamSearch(bsize=12, Δ=1.1, maxvisits=454) 2025-11-11T18:40:17.499
LOG n.size quantiles:[5.0, 6.0, 6.0, 7.0, 10.0]
LOG add_vertex! sp=61375 ep=61379 n=61374 BeamSearch(bsize=6, Δ=1.025, maxvisits=390) 2025-11-11T18:40:18.500
LOG n.size quantiles:[7.0, 7.0, 7.0, 8.0, 8.0]
LOG add_vertex! sp=72000 ep=72004 n=71999 BeamSearch(bsize=6, Δ=1.025, maxvisits=390) 2025-11-11T18:40:19.500
LOG n.size quantiles:[6.0, 6.0, 8.0, 8.0, 10.0]
LOG add_vertex! sp=81840 ep=81844 n=81839 BeamSearch(bsize=6, Δ=1.025, maxvisits=390) 2025-11-11T18:40:20.500
LOG n.size quantiles:[6.0, 6.0, 6.0, 8.0, 8.0]
LOG add_vertex! sp=90380 ep=90384 n=90379 BeamSearch(bsize=12, Δ=1.075, maxvisits=488) 2025-11-11T18:40:21.501
LOG n.size quantiles:[4.0, 5.0, 6.0, 8.0, 9.0]
LOG add_vertex! sp=99510 ep=99514 n=99509 BeamSearch(bsize=12, Δ=1.075, maxvisits=488) 2025-11-11T18:40:22.501
LOG n.size quantiles:[5.0, 6.0, 6.0, 8.0, 9.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 14.0, 17.0, 22.0, 91.0]
[ Info: minrecall: queries per second: 11319.98316401544, recall: 0.900375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.16, maxvisits=728))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 14.0, 17.0, 22.0, 91.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=9, Δ=1.17, maxvisits=674)), 1000, 8)
[ Info: rebuild: queries per second: 14462.290798805687, recall: 0.896
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=9, Δ=1.17, maxvisits=674))
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=9, Δ=1.155, maxvisits=728)), 1000, 8)
  1.704864 seconds (794.04 k allocations: 42.994 MiB, 95.19% compilation time)
[ Info: matrixhints: queries per second: 12144.958532982282, recall: 0.89925
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=9, Δ=1.155, maxvisits=728))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 14.0, 17.0, 22.0, 91.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  2m46.9s
     Testing SimilaritySearch tests passed 

Testing completed after 701.08s

PkgEval succeeded after 776.29s