Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.24 (d5fb6bbb43*) started at 2025-11-02T17:32:51.644


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.67s


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

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [053f045d] + SimilaritySearch v0.13.5
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.4.0
  [4fba245c] + ArrayInterface v7.22.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.18.1
  [a33af91c] + CompositionsBase v0.1.2
  [187b0558] + ConstructionBase v1.6.0
  [adafc99b] + CpuId v0.3.1
  [9a962f9c] + DataAPI v1.16.0
⌅ [864edb3b] + DataStructures v0.18.22
  [b4f34e82] + Distances v0.10.12
  [ffbed154] + DocStringExtensions v0.9.5
  [615f187c] + IfElse v0.1.1
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.6
  [10f19ff3] + LayoutPointers v0.1.17
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [1914dd2f] + MacroTools v0.5.16
  [d125e4d3] + ManualMemory v0.1.8
  [e1d29d7a] + Missings v1.2.0
  [bac558e1] + OrderedCollections v1.8.1
  [d96e819e] + Parameters v0.12.3
  [f517fe37] + Polyester v0.7.18
  [1d0040c9] + PolyesterWeave v0.2.2
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [92933f4c] + ProgressMeter v1.11.0
  [ae029012] + Requires v1.3.1
  [94e857df] + SIMDTypes v0.1.0
  [431bcebd] + SciMLPublic v1.0.0
  [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.5
  [a2af1166] + SortingAlgorithms v1.2.2
  [aedffcd0] + Static v1.3.1
  [0d7ed370] + StaticArrayInterface v1.8.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
⌅ [2913bbd2] + StatsBase v0.33.21
  [7792a7ef] + StrideArraysCore v0.5.8
  [8290d209] + ThreadingUtilities v0.5.5
  [3a884ed6] + UnPack v1.0.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [d6f4376e] + Markdown v1.11.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v1.0.0
  [9e88b42a] + Serialization v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [f489334b] + StyledStrings v1.11.0
  [fa267f1f] + TOML v1.0.3
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [8e850b90] + libblastrampoline_jll v5.15.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 4.33s


################################################################################
# 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...
   5449.2 ms  ✓ SearchModels
  13833.8 ms  ✓ SimilaritySearch
  2 dependencies successfully precompiled in 20 seconds. 66 already precompiled.

Precompilation completed after 32.89s


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

     Testing SimilaritySearch
      Status `/tmp/jl_HBL34e/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_HBL34e/Manifest.toml`
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.4.0
  [4c88cf16] Aqua v0.8.14
  [4fba245c] ArrayInterface v7.22.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] CPUSummary v0.2.7
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.1
  [a33af91c] CompositionsBase v0.1.2
  [187b0558] ConstructionBase v1.6.0
  [adafc99b] CpuId v0.3.1
  [9a962f9c] DataAPI v1.16.0
⌅ [864edb3b] DataStructures v0.18.22
  [b4f34e82] Distances v0.10.12
  [ffbed154] DocStringExtensions v0.9.5
  [615f187c] IfElse v0.1.1
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.6
  [10f19ff3] LayoutPointers v0.1.17
  [2ab3a3ac] LogExpFunctions v0.3.29
  [1914dd2f] MacroTools v0.5.16
  [d125e4d3] ManualMemory v0.1.8
  [e1d29d7a] Missings v1.2.0
  [bac558e1] OrderedCollections v1.8.1
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [1d0040c9] PolyesterWeave v0.2.2
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.0
  [92933f4c] ProgressMeter v1.11.0
  [ae029012] Requires v1.3.1
  [94e857df] SIMDTypes v0.1.0
  [431bcebd] SciMLPublic v1.0.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.5
  [a2af1166] SortingAlgorithms v1.2.2
  [aedffcd0] Static v1.3.1
  [0d7ed370] StaticArrayInterface v1.8.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.7.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [8290d209] ThreadingUtilities v0.5.5
  [3a884ed6] UnPack v1.0.2
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [8ba89e20] Distributed v1.11.0
  [f43a241f] Downloads v1.7.0
  [7b1f6079] FileWatching v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0
  [b27032c2] LibCURL v1.0.0
  [76f85450] LibGit2 v1.11.0
  [8f399da3] Libdl v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.13.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v1.0.0
  [9e88b42a] Serialization v1.11.0
  [6462fe0b] Sockets v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [f489334b] StyledStrings v1.11.0
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test v1.11.0
  [cf7118a7] UUIDs v1.11.0
  [4ec0a83e] Unicode v1.11.0
  [e66e0078] CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] LibCURL_jll v8.16.0+0
  [e37daf67] LibGit2_jll v1.9.1+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.9.9
  [4536629a] OpenBLAS_jll v0.3.29+0
  [458c3c95] OpenSSL_jll v3.5.4+0
  [efcefdf7] PCRE2_jll v10.47.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.1+2
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.15.0+0
  [8e850ede] nghttp2_jll v1.67.1+0
  [3f19e933] p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
Test Summary:              | Pass  Total   Time
test database abstractions |   56     56  16.9s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  4.0s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.5s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.2s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.396477 seconds (1000 allocations: 78.125 KiB)
  7.408087 seconds (1000 allocations: 78.125 KiB)
  4.067471 seconds (1000 allocations: 78.125 KiB)
  3.967210 seconds (1000 allocations: 78.125 KiB)
  3.975285 seconds (1000 allocations: 78.125 KiB)
  4.379681 seconds (1000 allocations: 78.125 KiB)
  3.906551 seconds (1000 allocations: 78.125 KiB)
  3.690757 seconds (1000 allocations: 78.125 KiB)
 15.041083 seconds (1000 allocations: 78.125 KiB)
 14.616530 seconds (1000 allocations: 78.125 KiB)
 28.478478 seconds (1000 allocations: 78.125 KiB, 0.28% gc time)
 27.764029 seconds (1000 allocations: 78.125 KiB)
 20.662291 seconds (6.23 k allocations: 358.125 KiB)
 21.168141 seconds (1000 allocations: 78.125 KiB)
 17.666027 seconds (1.00 k allocations: 78.141 KiB)
 17.749728 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m36.2s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.350428 seconds (1000 allocations: 78.125 KiB)
  3.273890 seconds (1000 allocations: 78.125 KiB)
 29.288439 seconds (1000 allocations: 78.125 KiB)
 29.209498 seconds (1000 allocations: 78.125 KiB)
 29.022723 seconds (1000 allocations: 78.125 KiB)
 29.256204 seconds (1000 allocations: 78.125 KiB)
  4.331149 seconds (1000 allocations: 78.125 KiB)
  4.350842 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m16.0s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
 10.553753 seconds (1000 allocations: 78.125 KiB)
 10.576174 seconds (1000 allocations: 78.125 KiB)
 10.131954 seconds (1000 allocations: 78.125 KiB)
 10.320924 seconds (1000 allocations: 78.125 KiB)
 10.409266 seconds (1000 allocations: 78.125 KiB)
 10.382194 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total     Time
indexing sets with ExhaustiveSearch | 3000   3000  1m05.2s
  0.049677 seconds (1.00 k allocations: 78.141 KiB)
  0.054400 seconds (1000 allocations: 78.125 KiB)
  0.044388 seconds (1000 allocations: 78.125 KiB)
  0.044444 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.8s
  0.058464 seconds (1000 allocations: 78.125 KiB)
  0.058695 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.626679 seconds (2.44 M allocations: 129.786 MiB, 6.61% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.217167 seconds (615.38 k allocations: 30.734 MiB, 99.85% compilation time)
Test Summary: | Pass  Total  Time
allknn        |    3      3  6.4s
quantile(length.(hsp_knns), 0:0.1:1) = [2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0]
Test Summary: | Total  Time
HSP           |     0  3.0s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:52.654
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-02T17:41:52.903
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) 2025-11-02T17:41:54.318
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: 2025-11-02T17:41:54.712
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000007, 0x0000000b, 0x0000000f, 0x00000010, 0x0000001b, 0x00000022]
D.nn = Int32[1, 2, 3, 4, 5, 2, 7, 3, 3, 4, 11, 1, 5, 3, 15, 16, 15, 3, 1, 3, 3, 15, 3, 2, 7, 1, 27, 2, 2, 2, 4, 3, 15, 34, 5, 2, 5, 3, 4, 3, 5, 3, 3, 1, 15, 7, 27, 1, 4, 2, 16, 3, 27, 2, 27, 4, 3, 1, 16, 7, 3, 7, 3, 7, 4, 3, 1, 4, 3, 3, 27, 2, 34, 4, 1, 4, 11, 15, 34, 3, 5, 15, 7, 4, 3, 15, 4, 7, 7, 4, 16, 7, 4, 5, 7, 3, 1, 34, 4, 5]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.053561985, 0.0, 0.002714336, 0.01986289, 0.030650854, 0.0, 0.05722803, 0.0692361, 0.019481957, 0.0, 0.0, 0.04507959, 0.021128058, 0.058585882, 0.027890086, 0.05921024, 0.037650466, 0.07690436, 0.007703483, 0.035946608, 0.09297979, 0.0, 0.0032131672, 0.021472394, 0.036646187, 0.029733121, 0.037808836, 0.016747892, 0.0, 0.028960764, 0.030650973, 0.033764124, 0.039610982, 0.017981589, 0.018723905, 0.08320004, 0.03176844, 0.045191884, 0.026296616, 0.03410119, 0.019215763, 0.026905894, 0.03484255, 0.024704993, 0.024056256, 0.02053374, 0.012937486, 0.056614816, 0.03506291, 0.080666006, 0.0512048, 0.036617994, 0.05828786, 0.02991569, 0.006670296, 0.04995978, 0.062913656, 0.032346487, 0.0333519, 0.05968511, 0.040831685, 0.047052264, 0.045748055, 0.03587997, 0.05368632, 0.02771169, 0.037720203, 0.009595573, 0.028151631, 0.0677976, 0.02441299, 0.04147929, 0.04615611, 0.03478813, 0.054980874, 0.048968732, 0.014716566, 0.03540784, 0.03490734, 0.03927028, 0.005397737, 0.04060018, 0.04309231, 0.010900199, 0.037130237, 0.042345703, 0.058941185, 0.06851792, 0.029159069, 0.018612206, 0.0028924942, 0.071965456, 0.035066485, 0.03819412, 0.09375137]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  18.7s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.864
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-02T17:41:55.864
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.865
[ Info: neardup> range: 33:48, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.865
[ Info: neardup> range: 49:64, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.865
[ Info: neardup> range: 65:80, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.865
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.865
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.865
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.865
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000007, 0x0000000b, 0x0000000f, 0x00000010, 0x0000001b, 0x00000022]
D.nn = Int32[1, 2, 3, 4, 5, 2, 7, 3, 3, 4, 11, 1, 5, 3, 15, 16, 15, 3, 1, 3, 3, 15, 3, 2, 7, 1, 27, 2, 2, 2, 4, 3, 15, 34, 5, 2, 5, 3, 4, 3, 5, 3, 3, 1, 15, 7, 27, 1, 4, 2, 16, 3, 27, 2, 27, 4, 3, 1, 16, 7, 3, 7, 3, 7, 4, 3, 1, 4, 3, 3, 27, 2, 34, 4, 1, 4, 11, 15, 34, 3, 5, 15, 7, 4, 3, 15, 4, 7, 7, 4, 16, 7, 4, 5, 7, 3, 1, 34, 4, 5]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.053561985, 0.0, 0.002714336, 0.01986289, 0.030650854, 0.0, 0.05722803, 0.0692361, 0.019481957, 0.0, 0.0, 0.04507959, 0.021128058, 0.058585882, 0.027890086, 0.05921024, 0.037650466, 0.07690436, 0.007703483, 0.035946608, 0.09297979, 0.0, 0.0032131672, 0.021472394, 0.036646187, 0.029733121, 0.037808836, 0.016747892, 0.0, 0.028960764, 0.030650973, 0.033764124, 0.039610982, 0.017981589, 0.018723905, 0.08320004, 0.03176844, 0.045191884, 0.026296616, 0.03410119, 0.019215763, 0.026905894, 0.03484255, 0.024704993, 0.024056256, 0.02053374, 0.012937486, 0.056614816, 0.03506291, 0.080666006, 0.0512048, 0.036617994, 0.05828786, 0.02991569, 0.006670296, 0.04995978, 0.062913656, 0.032346487, 0.0333519, 0.05968511, 0.040831685, 0.047052264, 0.045748055, 0.03587997, 0.05368632, 0.02771169, 0.037720203, 0.009595573, 0.028151631, 0.0677976, 0.02441299, 0.04147929, 0.04615611, 0.03478813, 0.054980874, 0.048968732, 0.014716566, 0.03540784, 0.03490734, 0.03927028, 0.005397737, 0.04060018, 0.04309231, 0.010900199, 0.037130237, 0.042345703, 0.058941185, 0.06851792, 0.029159069, 0.018612206, 0.0028924942, 0.071965456, 0.035066485, 0.03819412, 0.09375137]
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: 2025-11-02T17:41:55.956
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-02T17:41:55.957
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: 2025-11-02T17:41:55.957
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.957
[ Info: neardup> range: 49:64, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.957
[ Info: neardup> range: 65:80, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.957
[ Info: neardup> range: 81:96, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.957
[ Info: neardup> range: 97:100, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.957
[ Info: neardup> finished current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:41:55.958
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000022]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 15, 8, 12, 8, 14, 15, 9, 2, 7, 1, 9, 2, 2, 2, 4, 9, 15, 34, 5, 6, 5, 14, 4, 8, 5, 14, 14, 1, 15, 7, 9, 1, 4, 2, 16, 14, 13, 2, 13, 4, 9, 12, 16, 7, 3, 7, 9, 7, 4, 14, 12, 4, 3, 14, 12, 2, 34, 4, 12, 4, 11, 15, 34, 12, 5, 15, 7, 10, 12, 15, 10, 7, 7, 4, 16, 14, 4, 5, 7, 3, 1, 34, 4, 13]
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, 0.04507959, 0.018799126, 0.057260573, 0.017084599, 0.030645669, 0.037650466, 0.040114522, 0.007703483, 0.035946608, 0.09297979, 0.06214577, 0.0032131672, 0.021472394, 0.036646187, 0.029733121, 0.004147172, 0.016747892, 0.0, 0.028960764, 0.012842834, 0.033764124, 0.004436195, 0.017981589, 0.0077137947, 0.08320004, 0.025123298, 0.028182387, 0.026296616, 0.03410119, 0.019215763, 0.021321952, 0.03484255, 0.024704993, 0.024056256, 0.02053374, 0.0068866014, 0.014474273, 0.03506291, 0.0073831677, 0.0512048, 0.03071338, 0.0053123236, 0.02991569, 0.006670296, 0.04995978, 0.062913656, 0.024309814, 0.0333519, 0.05968511, 0.03609383, 0.03405404, 0.045748055, 0.03587997, 0.013227284, 0.031936407, 0.037720203, 0.009595573, 0.028151631, 0.013962507, 0.02441299, 0.04147929, 0.04615611, 0.03478813, 0.025474727, 0.048968732, 0.014716566, 0.03540784, 0.022913933, 0.029278874, 0.005397737, 0.0119562745, 0.04309231, 0.010900199, 0.037130237, 0.042345703, 0.02880174, 0.06851792, 0.029159069, 0.018612206, 0.0028924942, 0.071965456, 0.035066485, 0.03819412, 0.016442835]
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: 2025-11-02T17:42:03.490
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=9 n=9 2025-11-02T17:42:03.490
[ Info: neardup> range: 17:32, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:42:03.496
[ Info: neardup> range: 33:48, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:42:03.496
[ Info: neardup> range: 49:64, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:42:03.496
[ Info: neardup> range: 65:80, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:42:03.496
[ Info: neardup> range: 81:96, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:42:03.496
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:42:03.496
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-02T17:42:03.497
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000007, 0x0000000b, 0x0000000f, 0x00000010, 0x0000001b, 0x00000022]
D.nn = Int32[1, 2, 3, 4, 5, 2, 7, 3, 3, 4, 11, 1, 5, 3, 15, 16, 15, 3, 1, 3, 3, 15, 3, 2, 7, 1, 27, 2, 2, 2, 4, 3, 15, 34, 5, 2, 5, 3, 4, 3, 5, 3, 3, 1, 15, 7, 27, 1, 4, 2, 16, 3, 27, 2, 27, 4, 3, 1, 16, 7, 3, 7, 3, 7, 4, 3, 1, 4, 3, 3, 27, 2, 34, 4, 1, 4, 11, 15, 34, 3, 5, 15, 7, 4, 3, 15, 4, 7, 7, 4, 16, 7, 4, 5, 7, 3, 1, 34, 4, 5]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.053561985, 0.0, 0.002714336, 0.01986289, 0.030650854, 0.0, 0.05722803, 0.0692361, 0.019481957, 0.0, 0.0, 0.04507959, 0.021128058, 0.058585882, 0.027890086, 0.05921024, 0.037650466, 0.07690436, 0.007703483, 0.035946608, 0.09297979, 0.0, 0.0032131672, 0.021472394, 0.036646187, 0.029733121, 0.037808836, 0.016747892, 0.0, 0.028960764, 0.030650973, 0.033764124, 0.039610982, 0.017981589, 0.018723905, 0.08320004, 0.03176844, 0.045191884, 0.026296616, 0.03410119, 0.019215763, 0.026905894, 0.03484255, 0.024704993, 0.024056256, 0.02053374, 0.012937486, 0.056614816, 0.03506291, 0.080666006, 0.0512048, 0.036617994, 0.05828786, 0.02991569, 0.006670296, 0.04995978, 0.062913656, 0.032346487, 0.0333519, 0.05968511, 0.040831685, 0.047052264, 0.045748055, 0.03587997, 0.05368632, 0.02771169, 0.037720203, 0.009595573, 0.028151631, 0.0677976, 0.02441299, 0.04147929, 0.04615611, 0.03478813, 0.054980874, 0.048968732, 0.014716566, 0.03540784, 0.03490734, 0.03927028, 0.005397737, 0.04060018, 0.04309231, 0.010900199, 0.037130237, 0.042345703, 0.058941185, 0.06851792, 0.029159069, 0.018612206, 0.0028924942, 0.071965456, 0.035066485, 0.03819412, 0.09375137]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.5s
computing farthest point 1, dmax: Inf, imax: 20, n: 30
computing farthest point 2, dmax: 1.4070638, imax: 11, n: 30
computing farthest point 3, dmax: 1.0087007, imax: 16, n: 30
computing farthest point 4, dmax: 0.928118, imax: 17, n: 30
computing farthest point 5, dmax: 0.91342807, imax: 7, n: 30
computing farthest point 6, dmax: 0.8755682, imax: 5, n: 30
computing farthest point 7, dmax: 0.8274414, imax: 1, n: 30
computing farthest point 8, dmax: 0.6363975, imax: 30, n: 30
computing farthest point 9, dmax: 0.6333265, imax: 29, n: 30
computing farthest point 10, dmax: 0.5358737, imax: 14, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.4s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.2s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-02T17:42:10.335
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=0.9, maxvisits=108) 2025-11-02T17:42:21.412
LOG n.size quantiles:[3.0, 4.0, 4.0, 5.0, 5.0]
(i, j, d) = (8, 155, -1.1920929f-7)
(i, j, d, :parallel) = (8, 155, -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 => 17.87340614, :exact => 0.968728717)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  19.3s
  6.019289 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.004439 seconds
SEARCH Exhaustive 2: 0.004405 seconds
SEARCH Exhaustive 3: 0.004922 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) 2025-11-02T17:42:49.779
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=1.3066694, maxvisits=210) 2025-11-02T17:42:55.385
LOG n.size quantiles:[2.0, 3.0, 3.0, 4.0, 4.0]
LOG add_vertex! sp=18265 ep=18269 n=18264 BeamSearch(bsize=16, Δ=1.075, maxvisits=310) 2025-11-02T17:42:56.385
LOG n.size quantiles:[4.0, 5.0, 6.0, 7.0, 9.0]
LOG add_vertex! sp=32800 ep=32804 n=32799 BeamSearch(bsize=4, Δ=1.1287501, maxvisits=428) 2025-11-02T17:42:57.385
LOG n.size quantiles:[5.0, 7.0, 7.0, 9.0, 10.0]
LOG add_vertex! sp=43915 ep=43919 n=43914 BeamSearch(bsize=4, Δ=1.1, maxvisits=500) 2025-11-02T17:42:58.385
LOG n.size quantiles:[4.0, 6.0, 6.0, 6.0, 7.0]
LOG add_vertex! sp=54820 ep=54824 n=54819 BeamSearch(bsize=4, Δ=1.1, maxvisits=500) 2025-11-02T17:42:59.385
LOG n.size quantiles:[5.0, 5.0, 6.0, 8.0, 9.0]
LOG add_vertex! sp=63535 ep=63539 n=63534 BeamSearch(bsize=9, Δ=1.21275, maxvisits=472) 2025-11-02T17:43:00.385
LOG n.size quantiles:[4.0, 7.0, 7.0, 8.0, 8.0]
LOG add_vertex! sp=73025 ep=73029 n=73024 BeamSearch(bsize=9, Δ=1.21275, maxvisits=472) 2025-11-02T17:43:01.386
LOG n.size quantiles:[4.0, 6.0, 6.0, 8.0, 10.0]
LOG add_vertex! sp=82030 ep=82034 n=82029 BeamSearch(bsize=9, Δ=1.21275, maxvisits=472) 2025-11-02T17:43:02.386
LOG n.size quantiles:[4.0, 5.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=89715 ep=89719 n=89714 BeamSearch(bsize=12, Δ=1.025, maxvisits=456) 2025-11-02T17:43:03.386
LOG n.size quantiles:[5.0, 5.0, 8.0, 8.0, 8.0]
LOG add_vertex! sp=99165 ep=99169 n=99164 BeamSearch(bsize=12, Δ=1.025, maxvisits=456) 2025-11-02T17:43:04.387
LOG n.size quantiles:[5.0, 5.0, 6.0, 8.0, 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, 81.0]
[ Info: minrecall: queries per second: 12744.753268650056, recall: 0.9035
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=1.1851876, maxvisits=800))
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, 81.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.155, maxvisits=558)), 1000, 8)
[ Info: rebuild: queries per second: 15278.978641836615, recall: 0.906625
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.155, maxvisits=558))
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(bsize=11, Δ=1.16, maxvisits=838)), 1000, 8)
  1.668543 seconds (611.70 k allocations: 31.306 MiB, 2.58% gc time, 95.83% compilation time)
[ Info: matrixhints: queries per second: 13977.59829889274, recall: 0.8995
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=11, Δ=1.16, maxvisits=838))
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, 81.0]
  2.289851 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.002124 seconds
SEARCH Exhaustive 2: 0.002137 seconds
SEARCH Exhaustive 3: 0.002382 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) 2025-11-02T17:44:13.633
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=1.3066694, maxvisits=210) 2025-11-02T17:44:18.967
LOG n.size quantiles:[2.0, 3.0, 3.0, 4.0, 4.0]
LOG add_vertex! sp=21440 ep=21444 n=21439 BeamSearch(bsize=16, Δ=1.075, maxvisits=310) 2025-11-02T17:44:19.967
LOG n.size quantiles:[5.0, 6.0, 6.0, 6.0, 7.0]
LOG add_vertex! sp=36475 ep=36479 n=36474 BeamSearch(bsize=4, Δ=1.1287501, maxvisits=428) 2025-11-02T17:44:20.968
LOG n.size quantiles:[8.0, 8.0, 8.0, 9.0, 9.0]
LOG add_vertex! sp=48320 ep=48324 n=48319 BeamSearch(bsize=4, Δ=1.1, maxvisits=500) 2025-11-02T17:44:21.968
LOG n.size quantiles:[4.0, 5.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=59210 ep=59214 n=59209 BeamSearch(bsize=9, Δ=1.21275, maxvisits=472) 2025-11-02T17:44:22.968
LOG n.size quantiles:[6.0, 7.0, 7.0, 8.0, 8.0]
LOG add_vertex! sp=69945 ep=69949 n=69944 BeamSearch(bsize=9, Δ=1.21275, maxvisits=472) 2025-11-02T17:44:23.968
LOG n.size quantiles:[4.0, 5.0, 5.0, 9.0, 10.0]
LOG add_vertex! sp=80060 ep=80064 n=80059 BeamSearch(bsize=9, Δ=1.21275, maxvisits=472) 2025-11-02T17:44:24.968
LOG n.size quantiles:[5.0, 6.0, 8.0, 9.0, 10.0]
LOG add_vertex! sp=89045 ep=89049 n=89044 BeamSearch(bsize=12, Δ=1.025, maxvisits=456) 2025-11-02T17:44:25.969
LOG n.size quantiles:[3.0, 8.0, 8.0, 9.0, 10.0]
LOG add_vertex! sp=99515 ep=99519 n=99514 BeamSearch(bsize=12, Δ=1.025, maxvisits=456) 2025-11-02T17:44:26.969
LOG n.size quantiles:[6.0, 7.0, 7.0, 7.0, 9.0]
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 6.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 81.0]
[ Info: minrecall: queries per second: 13380.936102725018, recall: 0.9035
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=1.1851876, maxvisits=800))
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, 81.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.155, maxvisits=558)), 1000, 8)
[ Info: rebuild: queries per second: 16979.129050821182, recall: 0.906625
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=14, Δ=1.155, maxvisits=558))
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(bsize=11, Δ=1.16, maxvisits=838)), 1000, 8)
  1.630066 seconds (567.40 k allocations: 29.095 MiB, 95.65% compilation time)
[ Info: matrixhints: queries per second: 14004.982412543088, recall: 0.8995
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=11, Δ=1.16, maxvisits=838))
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, 81.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  2m41.3s
     Testing SimilaritySearch tests passed 

Testing completed after 685.32s

PkgEval succeeded after 748.05s