Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.1262 (4060c45d70*) started at 2025-11-18T17:23:44.788


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.23s


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

Installing SimilaritySearch...
   Resolving package versions...
   Installed SimilaritySearch ─ v0.13.7
    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 4.95s


################################################################################
# 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...
   5582.7 ms  ✓ SearchModels
  11205.9 ms  ✓ SimilaritySearch
  2 dependencies successfully precompiled in 17 seconds. 66 already precompiled.

Precompilation completed after 30.72s


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

     Testing SimilaritySearch
      Status `/tmp/jl_ucyqlm/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_ucyqlm/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  16.7s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  3.5s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.5s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.2s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.649929 seconds (1000 allocations: 78.125 KiB)
 11.095176 seconds (1000 allocations: 78.125 KiB)
  4.146061 seconds (1000 allocations: 78.125 KiB)
  4.041690 seconds (1000 allocations: 78.125 KiB)
  4.193772 seconds (1000 allocations: 78.125 KiB)
  4.124036 seconds (1000 allocations: 78.125 KiB)
  4.181112 seconds (1000 allocations: 78.125 KiB)
  4.096202 seconds (1000 allocations: 78.125 KiB)
 15.658771 seconds (1000 allocations: 78.125 KiB)
 15.559241 seconds (1000 allocations: 78.125 KiB)
 27.902383 seconds (1000 allocations: 78.125 KiB)
 20.699428 seconds (1000 allocations: 78.125 KiB)
 18.528248 seconds (6.23 k allocations: 358.094 KiB)
 21.384555 seconds (1000 allocations: 78.125 KiB)
 18.624907 seconds (1.00 k allocations: 78.141 KiB)
 18.727201 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m34.9s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.370392 seconds (1000 allocations: 78.125 KiB)
  3.295846 seconds (1000 allocations: 78.125 KiB)
 29.221736 seconds (1000 allocations: 78.125 KiB)
 29.317049 seconds (1000 allocations: 78.125 KiB)
 30.307266 seconds (1000 allocations: 78.125 KiB)
 29.062714 seconds (1000 allocations: 78.125 KiB)
  4.260398 seconds (1000 allocations: 78.125 KiB)
  4.607726 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m17.3s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
 10.375220 seconds (1000 allocations: 78.125 KiB)
 10.334285 seconds (1000 allocations: 78.125 KiB)
 10.139375 seconds (1000 allocations: 78.125 KiB)
 10.357485 seconds (1000 allocations: 78.125 KiB)
  9.894140 seconds (1000 allocations: 78.125 KiB)
 10.178880 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total     Time
indexing sets with ExhaustiveSearch | 3000   3000  1m04.1s
  0.031126 seconds (1.00 k allocations: 78.141 KiB)
  0.030437 seconds (1000 allocations: 78.125 KiB)
  0.027441 seconds (1000 allocations: 78.125 KiB)
  0.026069 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.3s
  0.034987 seconds (1000 allocations: 78.125 KiB)
  0.037157 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.0s
ExhaustiveSearch allknn: 3.912909 seconds (2.39 M allocations: 127.035 MiB, 1.48% gc time, 99.95% compilation time)
ParallelExhaustiveSearch allknn: 1.218515 seconds (613.31 k allocations: 30.464 MiB, 99.84% compilation time)
Test Summary: | Pass  Total  Time
allknn        |    3      3  5.7s
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  3.0s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:40.603
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-18T17:32:40.791
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=3 ep=3 n=3 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-18T17:32:41.881
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:42.069
D.map = UInt32[0x00000001, 0x00000004, 0x00000007, 0x0000000a, 0x0000000d, 0x0000000f, 0x00000012, 0x00000013, 0x0000001e, 0x00000032, 0x00000037]
D.nn = Int32[1, 1, 1, 4, 4, 1, 7, 1, 4, 10, 1, 1, 13, 4, 15, 1, 4, 18, 19, 18, 7, 7, 4, 1, 7, 19, 7, 13, 1, 30, 15, 7, 4, 1, 4, 30, 1, 30, 4, 1, 1, 4, 4, 4, 15, 4, 7, 4, 7, 50, 13, 19, 7, 4, 55, 4, 30, 30, 4, 30, 4, 7, 30, 13, 1, 30, 7, 7, 13, 30, 19, 7, 1, 10, 4, 55, 55, 15, 1, 13, 50, 7, 55, 7, 55, 13, 15, 1, 19, 50, 13, 1, 13, 7, 18, 4, 4, 30, 4, 30]
D.dist = Float32[0.0, 0.040063262, 0.07742888, 0.0, 0.07549423, 0.028868616, 0.0, 0.040604293, 0.08472246, 0.0, 0.015509725, 0.06639314, 0.0, 0.013369799, 0.0, 0.0075359344, 0.042369068, 0.0, 0.0, 0.031332016, 0.0060090423, 0.068668365, 0.013364613, 0.014957786, 0.060952485, 0.075032055, 0.033481, 0.05853778, 0.028948605, 0.0, 0.054890394, 0.061219335, 0.02163577, 0.02500695, 0.0624941, 0.07830578, 0.037122905, 0.08052361, 0.0071618557, 0.09147012, 0.050545096, 0.01770842, 0.04134965, 0.013329327, 0.0076096654, 0.058571815, 0.04055071, 0.07003999, 0.013471127, 0.0, 0.05005151, 0.011882067, 0.034267306, 0.014174819, 0.0, 0.053958893, 0.013636947, 0.008381605, 0.017186522, 0.013762474, 0.05092901, 0.07211977, 0.080931306, 0.04393363, 0.056096077, 0.008720696, 0.052202046, 0.03990072, 0.05645567, 0.01054728, 0.011655092, 0.018178284, 0.006967902, 0.01924789, 0.03699702, 0.012010574, 0.050738394, 0.036992908, 0.08006394, 0.06071043, 0.019294083, 0.04354602, 0.008892715, 0.021829426, 0.045964837, 0.02689296, 0.024984002, 0.017008781, 0.023403883, 0.002088666, 0.06081921, 0.024986684, 0.015930474, 0.036002755, 0.040948927, 0.0057348013, 0.043910563, 0.03582084, 0.036531568, 0.022585094]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  17.6s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:42.896
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-18T17:32:42.897
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: 2025-11-18T17:32:42.897
[ Info: neardup> range: 33:48, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:42.897
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:42.897
[ Info: neardup> range: 65:80, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:42.897
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:42.897
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:42.897
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:42.897
D.map = UInt32[0x00000001, 0x00000004, 0x00000007, 0x0000000a, 0x0000000d, 0x0000000f, 0x00000012, 0x00000013, 0x0000001e, 0x00000032, 0x00000037]
D.nn = Int32[1, 1, 1, 4, 4, 1, 7, 1, 4, 10, 1, 1, 13, 4, 15, 1, 4, 18, 19, 7, 7, 7, 4, 1, 7, 4, 7, 13, 1, 30, 15, 7, 4, 1, 4, 30, 1, 30, 4, 1, 1, 4, 4, 4, 15, 4, 7, 4, 7, 50, 13, 19, 7, 4, 55, 4, 30, 30, 4, 30, 4, 7, 30, 13, 1, 30, 7, 7, 13, 30, 19, 7, 1, 10, 4, 55, 55, 15, 1, 13, 50, 7, 55, 7, 55, 13, 15, 1, 19, 50, 13, 1, 13, 7, 18, 4, 4, 30, 4, 30]
D.dist = Float32[0.0, 0.040063262, 0.07742888, 0.0, 0.07549423, 0.028868616, 0.0, 0.040604293, 0.08472246, 0.0, 0.015509725, 0.06639314, 0.0, 0.013369799, 0.0, 0.0075359344, 0.042369068, 0.0, 0.0, 0.094371974, 0.0060090423, 0.068668365, 0.013364613, 0.014957786, 0.060952485, 0.08130962, 0.033481, 0.05853778, 0.028948605, 0.0, 0.054890394, 0.061219335, 0.02163577, 0.02500695, 0.0624941, 0.07830578, 0.037122905, 0.08052361, 0.0071618557, 0.09147012, 0.050545096, 0.01770842, 0.04134965, 0.013329327, 0.0076096654, 0.058571815, 0.04055071, 0.07003999, 0.013471127, 0.0, 0.05005151, 0.011882067, 0.034267306, 0.014174819, 0.0, 0.053958893, 0.013636947, 0.008381605, 0.017186522, 0.013762474, 0.05092901, 0.07211977, 0.080931306, 0.04393363, 0.056096077, 0.008720696, 0.052202046, 0.03990072, 0.05645567, 0.01054728, 0.011655092, 0.018178284, 0.006967902, 0.01924789, 0.03699702, 0.012010574, 0.050738394, 0.036992908, 0.08006394, 0.06071043, 0.019294083, 0.04354602, 0.008892715, 0.021829426, 0.045964837, 0.02689296, 0.024984002, 0.017008781, 0.023403883, 0.002088666, 0.06081921, 0.024986684, 0.015930474, 0.036002755, 0.040948927, 0.0057348013, 0.043910563, 0.03582084, 0.036531568, 0.022585094]
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: 2025-11-18T17:32:43.012
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-18T17:32:43.012
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: 2025-11-18T17:32:43.012
[ Info: neardup> range: 33:48, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:43.013
[ Info: neardup> range: 49:64, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:43.013
[ Info: neardup> range: 65:80, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:43.013
[ Info: neardup> range: 81:96, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:43.013
[ Info: neardup> range: 97:100, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:43.013
[ Info: neardup> finished current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:43.013
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000013, 0x00000032, 0x00000037, 0x0000003f]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 4, 12, 19, 12, 7, 9, 4, 1, 7, 9, 8, 3, 8, 9, 15, 7, 4, 11, 3, 9, 11, 19, 4, 8, 3, 14, 14, 3, 15, 9, 3, 2, 7, 50, 8, 19, 8, 4, 55, 2, 9, 9, 14, 9, 2, 6, 63, 13, 2, 9, 8, 11, 13, 9, 19, 7, 16, 10, 2, 55, 55, 15, 8, 3, 50, 7, 55, 7, 55, 5, 15, 16, 19, 50, 13, 6, 13, 8, 12, 4, 5, 9, 2, 9]
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, 0.042369068, 0.024053812, 0.0, 0.01570332, 0.0060090423, 0.017847478, 0.013364613, 0.014957786, 0.060952485, 0.05778253, 0.027538657, 0.018461943, 0.0022178292, 0.027041554, 0.054890394, 0.061219335, 0.02163577, 0.019433022, 0.0074717402, 0.07370514, 0.014698803, 0.08242953, 0.0071618557, 0.011171639, 0.011129618, 0.0065173507, 0.02592063, 0.009134829, 0.0076096654, 0.010235846, 0.030409217, 0.021342576, 0.013471127, 0.0, 0.042702436, 0.011882067, 0.009498775, 0.014174819, 0.0, 0.020659983, 0.034813285, 0.051303446, 0.007697463, 0.03284365, 0.010312557, 0.06123644, 0.0, 0.04393363, 0.015789628, 0.03336793, 0.028536856, 0.031626225, 0.05645567, 0.028424263, 0.011655092, 0.018178284, 0.0056898594, 0.01924789, 0.030220509, 0.012010574, 0.050738394, 0.036992908, 0.011741936, 0.013105512, 0.019294083, 0.04354602, 0.008892715, 0.021829426, 0.045964837, 0.022063017, 0.024984002, 0.005113423, 0.023403883, 0.002088666, 0.06081921, 0.01728028, 0.015930474, 0.011069894, 0.075656354, 0.0057348013, 0.022148728, 0.0070443153, 0.025087833, 0.040935576]
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: 2025-11-18T17:32:49.761
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=6 n=6 2025-11-18T17:32:49.762
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:49.767
[ Info: neardup> range: 33:48, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:49.767
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:49.767
[ Info: neardup> range: 65:80, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:49.767
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:49.767
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:49.767
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-18T17:32:49.768
D.map = UInt32[0x00000001, 0x00000004, 0x00000007, 0x0000000a, 0x0000000d, 0x0000000f, 0x00000012, 0x00000013, 0x0000001e, 0x00000032, 0x00000037]
D.nn = Int32[1, 1, 1, 4, 4, 1, 7, 1, 4, 10, 1, 1, 13, 4, 15, 1, 4, 18, 19, 7, 7, 7, 4, 1, 7, 4, 7, 13, 1, 30, 15, 7, 4, 1, 4, 30, 1, 30, 4, 1, 1, 4, 4, 4, 15, 4, 7, 4, 7, 50, 13, 19, 7, 4, 55, 4, 30, 30, 4, 30, 4, 7, 30, 13, 1, 30, 7, 7, 13, 30, 19, 7, 1, 10, 4, 55, 55, 15, 1, 13, 50, 7, 55, 7, 55, 13, 15, 1, 19, 50, 13, 1, 13, 7, 18, 4, 4, 30, 4, 30]
D.dist = Float32[0.0, 0.040063262, 0.07742888, 0.0, 0.07549423, 0.028868616, 0.0, 0.040604293, 0.08472246, 0.0, 0.015509725, 0.06639314, 0.0, 0.013369799, 0.0, 0.0075359344, 0.042369068, 0.0, 0.0, 0.094371974, 0.0060090423, 0.068668365, 0.013364613, 0.014957786, 0.060952485, 0.08130962, 0.033481, 0.05853778, 0.028948605, 0.0, 0.054890394, 0.061219335, 0.02163577, 0.02500695, 0.0624941, 0.07830578, 0.037122905, 0.08052361, 0.0071618557, 0.09147012, 0.050545096, 0.01770842, 0.04134965, 0.013329327, 0.0076096654, 0.058571815, 0.04055071, 0.07003999, 0.013471127, 0.0, 0.05005151, 0.011882067, 0.034267306, 0.014174819, 0.0, 0.053958893, 0.013636947, 0.008381605, 0.017186522, 0.013762474, 0.05092901, 0.07211977, 0.080931306, 0.04393363, 0.056096077, 0.008720696, 0.052202046, 0.03990072, 0.05645567, 0.01054728, 0.011655092, 0.018178284, 0.006967902, 0.01924789, 0.03699702, 0.012010574, 0.050738394, 0.036992908, 0.08006394, 0.06071043, 0.019294083, 0.04354602, 0.008892715, 0.021829426, 0.045964837, 0.02689296, 0.024984002, 0.017008781, 0.023403883, 0.002088666, 0.06081921, 0.024986684, 0.015930474, 0.036002755, 0.040948927, 0.0057348013, 0.043910563, 0.03582084, 0.036531568, 0.022585094]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  6.8s
computing farthest point 1, dmax: Inf, imax: 9, n: 30
computing farthest point 2, dmax: 1.267673, imax: 15, n: 30
computing farthest point 3, dmax: 0.8875702, imax: 6, n: 30
computing farthest point 4, dmax: 0.88642067, imax: 5, n: 30
computing farthest point 5, dmax: 0.84671754, imax: 19, n: 30
computing farthest point 6, dmax: 0.79528856, imax: 27, n: 30
computing farthest point 7, dmax: 0.6260431, imax: 2, n: 30
computing farthest point 8, dmax: 0.5917165, imax: 10, n: 30
computing farthest point 9, dmax: 0.5830548, imax: 21, n: 30
computing farthest point 10, dmax: 0.5590373, imax: 20, 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
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-18T17:32:58.201
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=0.9, maxvisits=112) 2025-11-18T17:33:10.312
LOG n.size quantiles:[3.0, 4.0, 4.0, 5.0, 5.0]
(i, j, d) = (24, 501, -1.1920929f-7)
(i, j, d, :parallel) = (24, 501, -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 => 19.529921003, :exact => 0.833055573)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  20.9s
  6.062735 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.004662 seconds
SEARCH Exhaustive 2: 0.004637 seconds
SEARCH Exhaustive 3: 0.005491 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) 2025-11-18T17:33:38.508
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=10, Δ=1.21275, maxvisits=208) 2025-11-18T17:33:43.976
LOG n.size quantiles:[2.0, 2.0, 3.0, 4.0, 5.0]
LOG add_vertex! sp=18035 ep=18039 n=18034 BeamSearch
BeamSearch(bsize=10, Δ=1.155, maxvisits=360) 2025-11-18T17:33:44.976
LOG n.size quantiles:[5.0, 6.0, 7.0, 7.0, 7.0]
LOG add_vertex! sp=31560 ep=31564 n=31559 BeamSearch
BeamSearch(bsize=7, Δ=1.21275, maxvisits=472) 2025-11-18T17:33:45.977
LOG n.size quantiles:[5.0, 6.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=42155 ep=42159 n=42154 BeamSearch
BeamSearch(bsize=4, Δ=1.155, maxvisits=472) 2025-11-18T17:33:46.977
LOG n.size quantiles:[4.0, 5.0, 6.0, 7.0, 8.0]
LOG add_vertex! sp=53720 ep=53724 n=53719 BeamSearch
BeamSearch(bsize=4, Δ=1.155, maxvisits=472) 2025-11-18T17:33:47.977
LOG n.size quantiles:[4.0, 4.0, 6.0, 7.0, 7.0]
LOG add_vertex! sp=62850 ep=62854 n=62849 BeamSearch
BeamSearch(bsize=10, Δ=1.075, maxvisits=400) 2025-11-18T17:33:48.977
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=74245 ep=74249 n=74244 BeamSearch
BeamSearch(bsize=10, Δ=1.075, maxvisits=400) 2025-11-18T17:33:49.977
LOG n.size quantiles:[5.0, 6.0, 7.0, 8.0, 9.0]
LOG add_vertex! sp=84765 ep=84769 n=84764 BeamSearch
BeamSearch(bsize=10, Δ=1.075, maxvisits=400) 2025-11-18T17:33:50.977
LOG n.size quantiles:[6.0, 7.0, 7.0, 7.0, 11.0]
LOG add_vertex! sp=95745 ep=95749 n=95744 BeamSearch
BeamSearch(bsize=7, Δ=1.244447, maxvisits=656) 2025-11-18T17:33:51.978
LOG n.size quantiles:[6.0, 9.0, 9.0, 9.0, 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, 100.0]
[ Info: minrecall: queries per second: 14066.0238906352, recall: 0.90025
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=6, Δ=1.14, maxvisits=724))
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, 100.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=13, Δ=1.155, maxvisits=578)), 1000, 8)
[ Info: rebuild: queries per second: 16572.95791451466, recall: 0.901375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=13, Δ=1.155, maxvisits=578))
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
BeamSearch(bsize=16, Δ=1.16, maxvisits=902)), 1000, 8)
  1.726156 seconds (610.34 k allocations: 31.097 MiB, 3.46% gc time, 96.13% compilation time)
[ Info: matrixhints: queries per second: 16055.263501392874, recall: 0.90325
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=16, Δ=1.16, maxvisits=902))
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, 100.0]
  2.169854 seconds (1.00 k allocations: 140.742 KiB)
SEARCH Exhaustive 1: 0.001969 seconds
SEARCH Exhaustive 2: 0.001890 seconds
SEARCH Exhaustive 3: 0.001980 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) 2025-11-18T17:34:59.181
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=10, Δ=1.21275, maxvisits=208) 2025-11-18T17:35:04.470
LOG n.size quantiles:[2.0, 2.0, 3.0, 4.0, 5.0]
LOG add_vertex! sp=21930 ep=21934 n=21929 BeamSearch
BeamSearch(bsize=10, Δ=1.155, maxvisits=360) 2025-11-18T17:35:05.470
LOG n.size quantiles:[4.0, 5.0, 8.0, 8.0, 9.0]
LOG add_vertex! sp=37300 ep=37304 n=37299 BeamSearch
BeamSearch(bsize=7, Δ=1.21275, maxvisits=472) 2025-11-18T17:35:06.470
LOG n.size quantiles:[4.0, 6.0, 6.0, 8.0, 10.0]
LOG add_vertex! sp=49525 ep=49529 n=49524 BeamSearch
BeamSearch(bsize=4, Δ=1.155, maxvisits=472) 2025-11-18T17:35:07.470
LOG n.size quantiles:[5.0, 6.0, 7.0, 8.0, 10.0]
LOG add_vertex! sp=61340 ep=61344 n=61339 BeamSearch
BeamSearch(bsize=10, Δ=1.075, maxvisits=400) 2025-11-18T17:35:08.470
LOG n.size quantiles:[4.0, 5.0, 6.0, 6.0, 8.0]
LOG add_vertex! sp=73525 ep=73529 n=73524 BeamSearch
BeamSearch(bsize=10, Δ=1.075, maxvisits=400) 2025-11-18T17:35:09.471
LOG n.size quantiles:[5.0, 5.0, 5.0, 5.0, 6.0]
LOG add_vertex! sp=83240 ep=83244 n=83239 BeamSearch
BeamSearch(bsize=10, Δ=1.075, maxvisits=400) 2025-11-18T17:35:10.471
LOG n.size quantiles:[4.0, 7.0, 8.0, 8.0, 8.0]
LOG add_vertex! sp=90760 ep=90764 n=90759 BeamSearch
BeamSearch(bsize=7, Δ=1.244447, maxvisits=656) 2025-11-18T17:35:11.471
LOG n.size quantiles:[4.0, 5.0, 7.0, 9.0, 10.0]
LOG add_vertex! sp=99610 ep=99614 n=99609 BeamSearch
BeamSearch(bsize=7, Δ=1.244447, maxvisits=656) 2025-11-18T17:35:12.471
LOG n.size quantiles:[5.0, 7.0, 7.0, 8.0, 9.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 100.0]
[ Info: minrecall: queries per second: 14988.010266247464, recall: 0.90025
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=6, Δ=1.14, maxvisits=724))
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, 100.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=13, Δ=1.155, maxvisits=578)), 1000, 8)
[ Info: rebuild: queries per second: 17071.921067085208, recall: 0.901375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=13, Δ=1.155, maxvisits=578))
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
BeamSearch(bsize=16, Δ=1.16, maxvisits=902)), 1000, 8)
  1.639662 seconds (566.26 k allocations: 28.918 MiB, 4.31% gc time, 95.93% compilation time)
[ Info: matrixhints: queries per second: 15247.570688423852, recall: 0.90325
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=16, Δ=1.16, maxvisits=902))
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, 100.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  2m36.3s
     Testing SimilaritySearch tests passed 

Testing completed after 676.75s

PkgEval succeeded after 734.36s