Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.1299 (6d6224db99*) started at 2025-11-27T16:02:53.775


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 8.83s


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

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [053f045d] + SimilaritySearch v0.13.7
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.4.0
  [4fba245c] + ArrayInterface v7.22.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.18.1
  [a33af91c] + CompositionsBase v0.1.2
  [187b0558] + ConstructionBase v1.6.0
  [adafc99b] + CpuId v0.3.1
  [9a962f9c] + DataAPI v1.16.0
⌅ [864edb3b] + DataStructures v0.18.22
  [b4f34e82] + Distances v0.10.12
  [ffbed154] + DocStringExtensions v0.9.5
  [615f187c] + IfElse v0.1.1
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.6
  [10f19ff3] + LayoutPointers v0.1.17
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [1914dd2f] + MacroTools v0.5.16
  [d125e4d3] + ManualMemory v0.1.8
  [e1d29d7a] + Missings v1.2.0
  [bac558e1] + OrderedCollections v1.8.1
  [d96e819e] + Parameters v0.12.3
  [f517fe37] + Polyester v0.7.18
  [1d0040c9] + PolyesterWeave v0.2.2
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [92933f4c] + ProgressMeter v1.11.0
  [ae029012] + Requires v1.3.1
  [94e857df] + SIMDTypes v0.1.0
  [431bcebd] + SciMLPublic v1.0.0
  [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.7
  [a2af1166] + SortingAlgorithms v1.2.2
  [aedffcd0] + Static v1.3.1
  [0d7ed370] + StaticArrayInterface v1.8.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
⌅ [2913bbd2] + StatsBase v0.33.21
  [7792a7ef] + StrideArraysCore v0.5.8
  [8290d209] + ThreadingUtilities v0.5.5
  [3a884ed6] + UnPack v1.0.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [d6f4376e] + Markdown v1.11.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v1.0.0
  [9e88b42a] + Serialization v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [f489334b] + StyledStrings v1.13.0
  [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 4.49s


################################################################################
# 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...
   9155.2 ms  ✓ StatsBase
   5529.1 ms  ✓ SearchModels
  11104.2 ms  ✓ SimilaritySearch
  3 dependencies successfully precompiled in 27 seconds. 65 already precompiled.

Precompilation completed after 39.57s


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

     Testing SimilaritySearch
      Status `/tmp/jl_wdhaf5/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_wdhaf5/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 v1.13.0
  [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 v8.17.0+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  15.8s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  3.9s
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}})
 10.563449 seconds (1000 allocations: 78.125 KiB)
 10.551033 seconds (1000 allocations: 78.125 KiB)
  4.342525 seconds (1000 allocations: 78.125 KiB)
  4.250947 seconds (1000 allocations: 78.125 KiB)
  4.033105 seconds (1000 allocations: 78.125 KiB)
  4.039215 seconds (1000 allocations: 78.125 KiB)
  4.031550 seconds (1000 allocations: 78.125 KiB)
  3.984006 seconds (1000 allocations: 78.125 KiB)
 15.259480 seconds (1000 allocations: 78.125 KiB)
 15.142259 seconds (1000 allocations: 78.125 KiB)
 27.121139 seconds (1000 allocations: 78.125 KiB)
 27.771382 seconds (1000 allocations: 78.125 KiB)
 20.572203 seconds (6.23 k allocations: 358.094 KiB)
 20.255075 seconds (1000 allocations: 78.125 KiB)
 17.089310 seconds (1.00 k allocations: 78.141 KiB)
 17.148618 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m37.7s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.029456 seconds (1000 allocations: 78.125 KiB)
  3.087872 seconds (1000 allocations: 78.125 KiB)
 29.393402 seconds (1000 allocations: 78.125 KiB)
 20.370355 seconds (1000 allocations: 78.125 KiB)
 17.704469 seconds (1000 allocations: 78.125 KiB)
 22.316037 seconds (1000 allocations: 78.125 KiB)
  4.214260 seconds (1000 allocations: 78.125 KiB)
  4.175407 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  1m47.9s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
 10.553986 seconds (1000 allocations: 78.125 KiB)
  9.795988 seconds (1000 allocations: 78.125 KiB)
 10.355672 seconds (1000 allocations: 78.125 KiB)
 10.351878 seconds (1000 allocations: 78.125 KiB)
 10.243086 seconds (1000 allocations: 78.125 KiB)
 10.474990 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total     Time
indexing sets with ExhaustiveSearch | 3000   3000  1m04.5s
  0.044741 seconds (1.00 k allocations: 78.141 KiB)
  0.044665 seconds (1000 allocations: 78.125 KiB)
  0.038720 seconds (1000 allocations: 78.125 KiB)
  0.038785 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.9s
  0.053062 seconds (1000 allocations: 78.125 KiB)
  0.053016 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.261075 seconds (2.38 M allocations: 126.950 MiB, 0.90% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.185072 seconds (613.36 k allocations: 30.471 MiB, 99.85% compilation time)
Test Summary: | Pass  Total  Time
allknn        |    3      3  6.0s
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: 2025-11-27T16:11:33.109
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-27T16:11:33.325
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=2 ep=2 n=2 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-27T16:11:34.479
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: 2025-11-27T16:11:34.823
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000007, 0x0000000c, 0x00000016, 0x00000017, 0x0000001c, 0x00000034, 0x0000003a, 0x00000041, 0x00000048]
D.nn = Int32[1, 2, 1, 4, 4, 1, 7, 4, 1, 2, 1, 12, 1, 7, 2, 1, 7, 1, 4, 4, 2, 22, 23, 7, 12, 1, 12, 28, 2, 4, 7, 1, 4, 4, 28, 7, 2, 2, 7, 1, 23, 4, 4, 22, 4, 22, 7, 7, 28, 4, 4, 52, 28, 22, 4, 4, 22, 58, 12, 52, 22, 4, 2, 28, 65, 1, 4, 4, 7, 28, 58, 72, 4, 7, 7, 4, 1, 58, 12, 7, 4, 4, 22, 1, 4, 2, 7, 2, 52, 52, 1, 1, 1, 1, 4, 22, 2, 1, 7, 7]
D.dist = Float32[0.0, 0.0, 0.068537116, 0.0, 0.056498945, 0.037936747, 0.0, 0.0526585, 0.088062465, 0.031159937, 0.0029659867, 0.0, 0.0062274337, 0.024499, 0.05891776, 0.039084256, 0.067384124, 0.02133739, 0.0034413338, 0.031663537, 0.044787526, 0.0, 0.0, 0.03172934, 0.04098034, 0.07706118, 0.09483421, 0.0, 0.061555147, 0.04262817, 0.03253007, 0.015010059, 0.036628187, 0.015076816, 0.07721478, 0.01657474, 0.076480865, 0.039161444, 0.005243957, 0.033708334, 0.040854454, 0.0527277, 0.0324713, 0.031837523, 0.07317191, 0.028278232, 0.0693776, 0.046002805, 0.061601937, 0.08961564, 0.046278536, 0.0, 0.042916715, 0.03525424, 0.022030413, 0.019114733, 0.0023524165, 0.0, 0.050696194, 0.023105204, 0.027668953, 0.076585054, 0.031717837, 0.058261037, 0.0, 0.044758856, 0.032554507, 0.027039647, 0.057903886, 0.07747364, 0.054675817, 0.0, 0.029405653, 0.041381, 0.02262497, 0.0045672655, 0.05425346, 0.0649243, 0.031852365, 0.029518485, 0.025767505, 0.06589931, 0.03259301, 0.0754807, 0.03740573, 0.018821895, 0.029182732, 0.056081176, 0.039133728, 0.00094366074, 0.015683949, 0.0098744035, 0.044406056, 0.022551298, 0.006912768, 0.030003846, 0.04273045, 0.051394105, 0.051519573, 0.053843558]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  17.2s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.822
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-27T16:11:35.822
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.822
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.822
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.822
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.822
[ Info: neardup> range: 81:96, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.822
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.822
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.823
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000007, 0x0000000c, 0x00000016, 0x00000017, 0x0000001c, 0x00000034, 0x0000003a, 0x00000041, 0x00000048]
D.nn = Int32[1, 2, 1, 4, 4, 1, 7, 4, 1, 2, 1, 12, 1, 7, 2, 1, 7, 1, 4, 4, 2, 22, 23, 7, 12, 1, 12, 28, 2, 4, 7, 1, 4, 4, 28, 7, 2, 2, 7, 1, 23, 4, 4, 22, 4, 22, 7, 7, 28, 4, 4, 52, 28, 22, 4, 4, 22, 58, 12, 1, 22, 4, 2, 28, 65, 1, 4, 4, 7, 28, 58, 72, 4, 7, 7, 4, 1, 58, 12, 7, 4, 4, 22, 1, 4, 2, 7, 2, 52, 52, 1, 1, 1, 1, 4, 22, 2, 1, 7, 7]
D.dist = Float32[0.0, 0.0, 0.068537116, 0.0, 0.056498945, 0.037936747, 0.0, 0.0526585, 0.088062465, 0.031159937, 0.0029659867, 0.0, 0.0062274337, 0.024499, 0.05891776, 0.039084256, 0.067384124, 0.02133739, 0.0034413338, 0.031663537, 0.044787526, 0.0, 0.0, 0.03172934, 0.04098034, 0.07706118, 0.09483421, 0.0, 0.061555147, 0.04262817, 0.03253007, 0.015010059, 0.036628187, 0.015076816, 0.07721478, 0.01657474, 0.076480865, 0.039161444, 0.005243957, 0.033708334, 0.040854454, 0.0527277, 0.0324713, 0.031837523, 0.07317191, 0.028278232, 0.0693776, 0.046002805, 0.061601937, 0.08961564, 0.046278536, 0.0, 0.042916715, 0.03525424, 0.022030413, 0.019114733, 0.0023524165, 0.0, 0.050696194, 0.08463955, 0.027668953, 0.076585054, 0.031717837, 0.058261037, 0.0, 0.044758856, 0.032554507, 0.027039647, 0.057903886, 0.07747364, 0.054675817, 0.0, 0.029405653, 0.041381, 0.02262497, 0.0045672655, 0.05425346, 0.0649243, 0.031852365, 0.029518485, 0.025767505, 0.06589931, 0.03259301, 0.0754807, 0.03740573, 0.018821895, 0.029182732, 0.056081176, 0.039133728, 0.00094366074, 0.015683949, 0.0098744035, 0.044406056, 0.022551298, 0.006912768, 0.030003846, 0.04273045, 0.051394105, 0.051519573, 0.053843558]
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: 2025-11-27T16:11:35.902
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-27T16:11:35.902
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: 2025-11-27T16:11:35.902
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.902
[ Info: neardup> range: 49:64, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.902
[ Info: neardup> range: 65:80, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.902
[ Info: neardup> range: 81:96, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.902
[ Info: neardup> range: 97:100, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.903
[ Info: neardup> finished current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:35.903
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x0000003a, 0x00000041, 0x00000048]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 14, 1, 4, 3, 16, 8, 15, 7, 5, 1, 9, 10, 16, 10, 7, 13, 4, 4, 14, 14, 15, 16, 7, 6, 15, 8, 3, 8, 10, 7, 7, 7, 10, 14, 3, 9, 10, 7, 4, 4, 8, 58, 5, 9, 8, 8, 16, 10, 65, 16, 3, 5, 14, 14, 58, 72, 8, 14, 7, 4, 3, 8, 3, 7, 4, 8, 8, 3, 14, 10, 14, 15, 13, 9, 13, 1, 1, 6, 4, 3, 15, 6, 7, 14]
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, 0.022603095, 0.02133739, 0.0034413338, 0.013472617, 0.02567321, 0.060045123, 0.09075606, 0.03172934, 0.03734404, 0.07706118, 0.022111177, 0.090664506, 0.014741778, 0.039862216, 0.03253007, 0.0108116865, 0.036628187, 0.015076816, 0.04995978, 0.002235055, 0.024190485, 0.018690884, 0.005243957, 0.006362021, 0.020263791, 0.032920003, 0.02788943, 0.07669592, 0.018653214, 0.064661026, 0.0693776, 0.046002805, 0.011760354, 0.08112806, 0.020028114, 0.013757765, 0.03578633, 0.062738895, 0.022030413, 0.019114733, 0.048113704, 0.0, 0.033654988, 0.040337205, 0.026768446, 0.01462388, 0.021373987, 0.015923679, 0.0, 0.010419548, 0.030374408, 0.026336074, 0.033451557, 0.06818372, 0.054675817, 0.0, 0.0066153407, 0.04071766, 0.02262497, 0.0045672655, 0.04585594, 0.040795743, 0.022463739, 0.029518485, 0.025767505, 0.021437526, 0.0072894096, 0.06308651, 0.0070471764, 0.010644972, 0.017435431, 0.014872432, 0.06865996, 0.014113307, 0.0055118203, 0.0098744035, 0.044406056, 0.015262425, 0.006912768, 0.08405876, 0.031749666, 0.029009342, 0.051519573, 0.047321558]
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: 2025-11-27T16:11:42.501
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=5 n=5 2025-11-27T16:11:42.501
[ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:42.505
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:42.505
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:42.505
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:42.505
[ Info: neardup> range: 81:96, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:42.505
[ Info: neardup> range: 97:100, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:42.505
[ Info: neardup> finished current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-11-27T16:11:42.505
D.map = UInt32[0x00000001, 0x00000002, 0x00000004, 0x00000007, 0x0000000c, 0x00000016, 0x00000017, 0x0000001c, 0x00000034, 0x0000003a, 0x00000041, 0x00000048]
D.nn = Int32[1, 2, 1, 4, 4, 1, 7, 4, 1, 2, 1, 12, 1, 7, 2, 1, 7, 1, 4, 4, 2, 22, 23, 7, 12, 1, 12, 28, 2, 4, 7, 1, 4, 4, 28, 7, 2, 2, 7, 1, 23, 4, 4, 22, 4, 22, 7, 7, 28, 4, 4, 52, 28, 22, 4, 4, 22, 58, 12, 1, 22, 4, 2, 28, 65, 1, 4, 4, 7, 28, 58, 72, 4, 7, 7, 4, 1, 58, 12, 7, 4, 4, 22, 1, 4, 2, 7, 2, 52, 52, 1, 1, 1, 1, 4, 22, 2, 1, 7, 7]
D.dist = Float32[0.0, 0.0, 0.068537116, 0.0, 0.056498945, 0.037936747, 0.0, 0.0526585, 0.088062465, 0.031159937, 0.0029659867, 0.0, 0.0062274337, 0.024499, 0.05891776, 0.039084256, 0.067384124, 0.02133739, 0.0034413338, 0.031663537, 0.044787526, 0.0, 0.0, 0.03172934, 0.04098034, 0.07706118, 0.09483421, 0.0, 0.061555147, 0.04262817, 0.03253007, 0.015010059, 0.036628187, 0.015076816, 0.07721478, 0.01657474, 0.076480865, 0.039161444, 0.005243957, 0.033708334, 0.040854454, 0.0527277, 0.0324713, 0.031837523, 0.07317191, 0.028278232, 0.0693776, 0.046002805, 0.061601937, 0.08961564, 0.046278536, 0.0, 0.042916715, 0.03525424, 0.022030413, 0.019114733, 0.0023524165, 0.0, 0.050696194, 0.08463955, 0.027668953, 0.076585054, 0.031717837, 0.058261037, 0.0, 0.044758856, 0.032554507, 0.027039647, 0.057903886, 0.07747364, 0.054675817, 0.0, 0.029405653, 0.041381, 0.02262497, 0.0045672655, 0.05425346, 0.0649243, 0.031852365, 0.029518485, 0.025767505, 0.06589931, 0.03259301, 0.0754807, 0.03740573, 0.018821895, 0.029182732, 0.056081176, 0.039133728, 0.00094366074, 0.015683949, 0.0098744035, 0.044406056, 0.022551298, 0.006912768, 0.030003846, 0.04273045, 0.051394105, 0.051519573, 0.053843558]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  6.6s
computing farthest point 1, dmax: Inf, imax: 4, n: 30
computing farthest point 2, dmax: 1.2060955, imax: 23, n: 30
computing farthest point 3, dmax: 0.99610645, imax: 1, n: 30
computing farthest point 4, dmax: 0.83015263, imax: 28, n: 30
computing farthest point 5, dmax: 0.73900265, imax: 10, n: 30
computing farthest point 6, dmax: 0.6982282, imax: 12, n: 30
computing farthest point 7, dmax: 0.6847654, imax: 21, n: 30
computing farthest point 8, dmax: 0.636154, imax: 24, n: 30
computing farthest point 9, dmax: 0.6220009, imax: 22, n: 30
computing farthest point 10, dmax: 0.575162, imax: 15, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.4s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.5s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch
BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-27T16:11:50.401
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=0.9047619, maxvisits=104) 2025-11-27T16:12:01.209
LOG n.size quantiles:[4.0, 4.0, 5.0, 5.0, 5.0]
(i, j, d) = (5, 917, -1.1920929f-7)
(i, j, d, :parallel) = (5, 917, -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 => 18.244893859, :exact => 0.953584054)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  19.7s
  5.748674 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.004731 seconds
SEARCH Exhaustive 2: 0.004631 seconds
SEARCH Exhaustive 3: 0.005271 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) 2025-11-27T16:12:29.426
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch
BeamSearch(bsize=4, Δ=1.07625, maxvisits=194) 2025-11-27T16:12:34.890
LOG n.size quantiles:[3.0, 3.0, 4.0, 5.0, 8.0]
LOG add_vertex! sp=18675 ep=18679 n=18674 BeamSearch
BeamSearch(bsize=6, Δ=1.21275, maxvisits=396) 2025-11-27T16:12:35.890
LOG n.size quantiles:[5.0, 6.0, 7.0, 8.0, 8.0]
LOG add_vertex! sp=33490 ep=33494 n=33489 BeamSearch
BeamSearch(bsize=11, Δ=1.155, maxvisits=472) 2025-11-27T16:12:36.890
LOG n.size quantiles:[4.0, 7.0, 7.0, 8.0, 8.0]
LOG add_vertex! sp=44785 ep=44789 n=44784 BeamSearch
BeamSearch(bsize=6, Δ=1.157625, maxvisits=460) 2025-11-27T16:12:37.890
LOG n.size quantiles:[4.0, 6.0, 7.0, 9.0, 10.0]
LOG add_vertex! sp=55460 ep=55464 n=55459 BeamSearch
BeamSearch(bsize=6, Δ=1.157625, maxvisits=460) 2025-11-27T16:12:38.891
LOG n.size quantiles:[6.0, 7.0, 8.0, 9.0, 9.0]
LOG add_vertex! sp=65620 ep=65624 n=65619 BeamSearch
BeamSearch(bsize=14, Δ=1.1, maxvisits=416) 2025-11-27T16:12:39.891
LOG n.size quantiles:[5.0, 6.0, 8.0, 8.0, 8.0]
LOG add_vertex! sp=75870 ep=75874 n=75869 BeamSearch
BeamSearch(bsize=14, Δ=1.1, maxvisits=416) 2025-11-27T16:12:40.891
LOG n.size quantiles:[5.0, 7.0, 8.0, 9.0, 10.0]
LOG add_vertex! sp=85225 ep=85229 n=85224 BeamSearch
BeamSearch(bsize=14, Δ=1.1, maxvisits=494) 2025-11-27T16:12:42.029
LOG n.size quantiles:[4.0, 6.0, 6.0, 8.0, 8.0]
LOG add_vertex! sp=94760 ep=94764 n=94759 BeamSearch
BeamSearch(bsize=14, Δ=1.1, maxvisits=494) 2025-11-27T16:12:43.030
LOG n.size quantiles:[5.0, 5.0, 6.0, 6.0, 6.0]
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, 84.0]
[ Info: minrecall: queries per second: 17976.75278687756, recall: 0.90125
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=15, Δ=1.1, maxvisits=732))
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, 84.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=13, Δ=1.155, maxvisits=580)), 1000, 8)
[ Info: rebuild: queries per second: 15377.232893389677, recall: 0.90575
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=13, Δ=1.155, maxvisits=580))
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=1.1287501, maxvisits=732)), 1000, 8)
  1.821054 seconds (610.14 k allocations: 31.090 MiB, 3.97% gc time, 95.83% compilation time)
[ Info: matrixhints: queries per second: 13708.341088256124, recall: 0.90625
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=16, Δ=1.1287501, maxvisits=732))
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, 84.0]
  1.460611 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.001298 seconds
SEARCH Exhaustive 2: 0.001290 seconds
SEARCH Exhaustive 3: 0.001442 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) 2025-11-27T16:13:48.840
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch
BeamSearch(bsize=4, Δ=1.07625, maxvisits=194) 2025-11-27T16:13:54.219
LOG n.size quantiles:[3.0, 3.0, 4.0, 5.0, 8.0]
LOG add_vertex! sp=22730 ep=22734 n=22729 BeamSearch
BeamSearch(bsize=6, Δ=1.21275, maxvisits=396) 2025-11-27T16:13:55.219
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=37880 ep=37884 n=37879 BeamSearch
BeamSearch(bsize=6, Δ=1.157625, maxvisits=460) 2025-11-27T16:13:56.235
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 8.0]
LOG add_vertex! sp=51460 ep=51464 n=51459 BeamSearch
BeamSearch(bsize=6, Δ=1.157625, maxvisits=460) 2025-11-27T16:13:57.235
LOG n.size quantiles:[3.0, 5.0, 6.0, 8.0, 8.0]
LOG add_vertex! sp=62770 ep=62774 n=62769 BeamSearch
BeamSearch(bsize=14, Δ=1.1, maxvisits=416) 2025-11-27T16:13:58.235
LOG n.size quantiles:[3.0, 6.0, 7.0, 7.0, 9.0]
LOG add_vertex! sp=74100 ep=74104 n=74099 BeamSearch
BeamSearch(bsize=14, Δ=1.1, maxvisits=416) 2025-11-27T16:13:59.235
LOG n.size quantiles:[6.0, 6.0, 7.0, 7.0, 10.0]
LOG add_vertex! sp=85190 ep=85194 n=85189 BeamSearch
BeamSearch(bsize=14, Δ=1.1, maxvisits=416) 2025-11-27T16:14:00.235
LOG n.size quantiles:[5.0, 5.0, 5.0, 7.0, 8.0]
LOG add_vertex! sp=94565 ep=94569 n=94564 BeamSearch
BeamSearch(bsize=14, Δ=1.1, maxvisits=494) 2025-11-27T16:14:01.236
LOG n.size quantiles:[4.0, 6.0, 7.0, 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, 84.0]
[ Info: minrecall: queries per second: 14631.929782539528, recall: 0.90125
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=15, Δ=1.1, maxvisits=732))
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, 84.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=13, Δ=1.155, maxvisits=580)), 1000, 8)
[ Info: rebuild: queries per second: 16081.693200307926, recall: 0.90575
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=13, Δ=1.155, maxvisits=580))
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=1.1287501, maxvisits=732)), 1000, 8)
  1.497901 seconds (566.06 k allocations: 28.906 MiB, 95.59% compilation time)
[ Info: matrixhints: queries per second: 15657.572644091162, recall: 0.90625
graph.algo = Base.RefValue{BeamSearch}(BeamSearch
BeamSearch(bsize=16, Δ=1.1287501, maxvisits=732))
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, 84.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   18     18  2m33.2s
     Testing SimilaritySearch tests passed 

Testing completed after 646.8s

PkgEval succeeded after 719.46s