Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.2082 (4fdd12e277*) started at 2026-04-26T19:34:52.585


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

Installing PkgEval dependencies (TestEnv)...
  Activating project at `~/.julia/environments/v1.14`

Set-up completed after 15.12s


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

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [053f045d] + SimilaritySearch v0.14.3
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.44
  [79e6a3ab] + Adapt v4.5.2
  [66dad0bd] + AliasTables v1.1.3
  [4fba245c] + ArrayInterface v7.24.0
  [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 v0.19.4
  [b4f34e82] + Distances v0.10.12
  [31c24e10] + Distributions v0.25.125
  [ffbed154] + DocStringExtensions v0.9.5
  [1a297f60] + FillArrays v1.16.0
  [34004b35] + HypergeometricFunctions v0.3.28
  [615f187c] + IfElse v0.1.1
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.6
  [692b3bcd] + JLLWrappers v1.7.1
  [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
  [90014a1f] + PDMats v0.11.37
  [f517fe37] + Polyester v0.7.19
  [1d0040c9] + PolyesterWeave v0.2.2
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.2
  [92933f4c] + ProgressMeter v1.11.0
  [43287f4e] + PtrArrays v1.4.0
  [1fd47b50] + QuadGK v2.11.3
  [189a3867] + Reexport v1.2.2
  [ae029012] + Requires v1.3.1
  [79098fc4] + Rmath v0.9.0
  [94e857df] + SIMDTypes v0.1.0
  [431bcebd] + SciMLPublic v1.0.1
  [0e966ebe] + SearchModels v0.5.1
  [053f045d] + SimilaritySearch v0.14.3
  [a2af1166] + SortingAlgorithms v1.2.2
  [276daf66] + SpecialFunctions v2.7.2
  [aedffcd0] + Static v1.3.1
  [0d7ed370] + StaticArrayInterface v1.9.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.8.0
  [2913bbd2] + StatsBase v0.34.10
  [4c63d2b9] + StatsFuns v1.5.2
  [7792a7ef] + StrideArraysCore v0.5.8
  [8290d209] + ThreadingUtilities v0.5.5
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.13.0
  [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
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.4.0+0
  [4536629a] + OpenBLAS_jll v0.3.30+0
  [05823500] + OpenLibm_jll v0.8.7+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [8e850b90] + libblastrampoline_jll v5.15.0+0

Installation completed after 6.16s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...
Precompiling project...
   3.5 s  ✓ SearchModels
   9.3 s  ✓ SimilaritySearch
  2 dependencies successfully precompiled in 14 seconds. 108 already precompiled.

Precompilation completed after 42.16s


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

     Testing SimilaritySearch
      Status `/tmp/jl_nUTsab/Project.toml`
  [7d9f7c33] Accessors v0.1.44
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.125
  [f517fe37] Polyester v0.7.19
  [92933f4c] ProgressMeter v1.11.0
  [0e966ebe] SearchModels v0.5.1
  [053f045d] SimilaritySearch v0.14.3
  [10745b16] Statistics v1.11.1
  [2913bbd2] StatsBase v0.34.10
  [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_nUTsab/Manifest.toml`
  [7d9f7c33] Accessors v0.1.44
  [79e6a3ab] Adapt v4.5.2
  [66dad0bd] AliasTables v1.1.3
  [4c88cf16] Aqua v0.8.14
  [4fba245c] ArrayInterface v7.24.0
  [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 v0.19.4
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.125
  [ffbed154] DocStringExtensions v0.9.5
  [1a297f60] FillArrays v1.16.0
  [34004b35] HypergeometricFunctions v0.3.28
  [615f187c] IfElse v0.1.1
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.6
  [692b3bcd] JLLWrappers v1.7.1
  [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
  [90014a1f] PDMats v0.11.37
  [f517fe37] Polyester v0.7.19
  [1d0040c9] PolyesterWeave v0.2.2
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.2
  [92933f4c] ProgressMeter v1.11.0
  [43287f4e] PtrArrays v1.4.0
  [1fd47b50] QuadGK v2.11.3
  [189a3867] Reexport v1.2.2
  [ae029012] Requires v1.3.1
  [79098fc4] Rmath v0.9.0
  [94e857df] SIMDTypes v0.1.0
  [431bcebd] SciMLPublic v1.0.1
  [0e966ebe] SearchModels v0.5.1
  [053f045d] SimilaritySearch v0.14.3
  [a2af1166] SortingAlgorithms v1.2.2
  [276daf66] SpecialFunctions v2.7.2
  [aedffcd0] Static v1.3.1
  [0d7ed370] StaticArrayInterface v1.9.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.8.0
  [2913bbd2] StatsBase v0.34.10
  [4c63d2b9] StatsFuns v1.5.2
  [7792a7ef] StrideArraysCore v0.5.8
  [8290d209] ThreadingUtilities v0.5.5
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [f50d1b31] Rmath_jll v0.5.1+0
  [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 v1.13.0
  [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 v1.14.0
  [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
  [4607b0f0] SuiteSparse
  [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 v1.4.0+0
  [deac9b47] LibCURL_jll v8.19.0+0
  [e37daf67] LibGit2_jll v1.9.2+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2026.3.19
  [4536629a] OpenBLAS_jll v0.3.30+0
  [05823500] OpenLibm_jll v0.8.7+0
  [458c3c95] OpenSSL_jll v3.5.6+0
  [efcefdf7] PCRE2_jll v10.47.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.2+0
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.15.0+0
  [8e850ede] nghttp2_jll v1.69.0+0
  [3f19e933] p7zip_jll v17.8.0+0
     Testing Running tests...
Test Summary:              | Pass  Total   Time
test database abstractions |   57     57  17.5s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.2s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  4.1s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.8s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.4s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
SimilaritySearch.Dist.L2: 0.030341 seconds
SimilaritySearch.Dist.L2: 0.030004 seconds
SimilaritySearch.Dist.L1: 0.031169 seconds
SimilaritySearch.Dist.L1: 0.030431 seconds
SimilaritySearch.Dist.LInfty: 0.027732 seconds
SimilaritySearch.Dist.LInfty: 0.027791 seconds
SimilaritySearch.Dist.SqL2: 0.028112 seconds
SimilaritySearch.Dist.SqL2: 0.028515 seconds
SimilaritySearch.Dist.Lp: 0.135666 seconds
SimilaritySearch.Dist.Lp: 0.134253 seconds
SimilaritySearch.Dist.Lp: 0.267910 seconds
SimilaritySearch.Dist.Lp: 0.263594 seconds
SimilaritySearch.Dist.Angle: 0.185065 seconds (5.21 k allocations: 279.094 KiB)
SimilaritySearch.Dist.Angle: 0.177151 seconds
SimilaritySearch.Dist.Cosine: 0.154965 seconds (1 allocation: 16 bytes)
SimilaritySearch.Dist.Cosine: 0.158162 seconds
Test Summary:     | Pass  Total   Time
Searching vectors |  800    800  24.8s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
SimilaritySearch.Dist.Seqs.CommonPrefix: 0.015562 seconds
SimilaritySearch.Dist.Seqs.CommonPrefix: 0.015452 seconds
SimilaritySearch.Dist.Seqs.Levenshtein: 0.257324 seconds
SimilaritySearch.Dist.Seqs.Levenshtein: 0.262516 seconds
SimilaritySearch.Dist.Seqs.LCS: 0.255185 seconds
SimilaritySearch.Dist.Seqs.LCS: 0.250989 seconds
SimilaritySearch.Dist.Seqs.Hamming: 0.026728 seconds
SimilaritySearch.Dist.Seqs.Hamming: 0.025558 seconds
Test Summary:       | Pass  Total   Time
Searching sequences |  400    400  11.3s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
SimilaritySearch.Dist.Sets.Jaccard: 0.068558 seconds
SimilaritySearch.Dist.Sets.Jaccard: 0.067242 seconds
SimilaritySearch.Dist.Sets.Dice: 0.068142 seconds
SimilaritySearch.Dist.Sets.Dice: 0.068333 seconds
SimilaritySearch.Dist.Sets.Intersection: 0.070185 seconds
SimilaritySearch.Dist.Sets.Intersection: 0.068359 seconds
SimilaritySearch.Dist.Sets.RogersTanimoto: 0.071333 seconds
SimilaritySearch.Dist.Sets.RogersTanimoto: 0.069904 seconds
Test Summary:                     | Pass  Total   Time
Searching on sets (ordered lists) |  400    400  10.3s
SimilaritySearch.Dist.NormAngle: 0.003882 seconds (1 allocation: 16 bytes)
SimilaritySearch.Dist.NormAngle: 0.003837 seconds
SimilaritySearch.Dist.NormCosine: 0.002824 seconds
SimilaritySearch.Dist.NormCosine: 0.002833 seconds
Test Summary:                        | Pass  Total  Time
Searching with angle-based distances |  200    200  5.7s
SimilaritySearch.Dist.Bits.Hamming: 0.003828 seconds
SimilaritySearch.Dist.Bits.Hamming: 0.003725 seconds
SimilaritySearch.Dist.Bits.RogersTanimoto: 0.008782 seconds
SimilaritySearch.Dist.Bits.RogersTanimoto: 0.009180 seconds
Test Summary:    | Pass  Total  Time
Binary distances |  200    200  5.2s
quantile(length.(hsp_knns), 0:0.1:1) = [3.0, 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  5.5s
computing farthest point 1, dmax: Inf, imax: 17, n: 30
computing farthest point 2, dmax: 1.2837257, imax: 3, n: 30
computing farthest point 3, dmax: 1.0187205, imax: 25, n: 30
computing farthest point 4, dmax: 0.9147483, imax: 28, n: 30
computing farthest point 5, dmax: 0.8302742, imax: 13, n: 30
computing farthest point 6, dmax: 0.8185073, imax: 6, n: 30
computing farthest point 7, dmax: 0.8043785, imax: 19, n: 30
computing farthest point 8, dmax: 0.68505186, imax: 2, n: 30
computing farthest point 9, dmax: 0.64749104, imax: 24, n: 30
computing farthest point 10, dmax: 0.63064474, imax: 29, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  2.7s
n = 10
Test Summary: | Pass  Total  Time
AdjList       |   28     28  4.4s
  5.339659 seconds (3 allocations: 62.586 KiB)
SEARCH Exhaustive 1: 0.004396 seconds
SEARCH Exhaustive 2: 0.004720 seconds
SEARCH Exhaustive 3: 0.006101 seconds
typeof(seq) = ExhaustiveSearch{SimilaritySearch.Dist.SqL2, 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{IdDist}}
[ Info: ===================== minrecall Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}() ==============================
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:38:31.064
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add! sp=2 ep=2 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:38:34.775
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
LOG add! sp=293 ep=293 n=292 BeamSearch(bsize=8, Δ=0.9047619, maxvisits=270) mem=1GB max-rss=1GB 2026-04-26T19:38:44.290
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=15102 ep=15102 n=15101 BeamSearch(bsize=8, Δ=0.88435376, maxvisits=572) mem=1GB max-rss=1GB 2026-04-26T19:38:46.290
LOG n.size quantiles:[5.0, 5.0, 5.0, 5.0, 5.0]
LOG add! sp=26828 ep=26828 n=26827 BeamSearch(bsize=6, Δ=0.79999995, maxvisits=646) mem=1GB max-rss=1GB 2026-04-26T19:38:48.290
LOG n.size quantiles:[6.0, 6.0, 6.0, 6.0, 6.0]
LOG add! sp=38106 ep=38106 n=38105 BeamSearch(bsize=4, Δ=0.86, maxvisits=570) mem=1GB max-rss=1GB 2026-04-26T19:38:50.290
LOG n.size quantiles:[8.0, 8.0, 8.0, 8.0, 8.0]
LOG add! sp=49464 ep=49464 n=49463 BeamSearch(bsize=4, Δ=0.86, maxvisits=570) mem=1GB max-rss=1GB 2026-04-26T19:38:52.290
LOG n.size quantiles:[14.0, 14.0, 14.0, 14.0, 14.0]
LOG add! sp=58574 ep=58574 n=58573 BeamSearch(bsize=6, Δ=1.0476191, maxvisits=736) mem=1GB max-rss=1GB 2026-04-26T19:38:54.291
LOG n.size quantiles:[12.0, 12.0, 12.0, 12.0, 12.0]
LOG add! sp=67868 ep=67868 n=67867 BeamSearch(bsize=6, Δ=1.0476191, maxvisits=736) mem=1GB max-rss=1GB 2026-04-26T19:38:56.291
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=76635 ep=76635 n=76634 BeamSearch(bsize=6, Δ=1.0476191, maxvisits=736) mem=1GB max-rss=1GB 2026-04-26T19:38:58.291
LOG n.size quantiles:[7.0, 7.0, 7.0, 7.0, 7.0]
LOG add! sp=85187 ep=85187 n=85186 BeamSearch(bsize=6, Δ=1.0476191, maxvisits=736) mem=1GB max-rss=1GB 2026-04-26T19:39:00.291
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=92468 ep=92468 n=92467 BeamSearch(bsize=6, Δ=0.8952381, maxvisits=734) mem=1GB max-rss=1GB 2026-04-26T19:39:02.291
LOG n.size quantiles:[9.0, 9.0, 9.0, 9.0, 9.0]
(length(graph.adj), length(graph), length(B.db)) = (100000, 100000, 100000)
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 9.0, 10.0, 12.0, 13.0, 15.0, 17.0, 20.0, 23.0, 30.0, 113.0]
[ Info: minrecall: queries per second: 12860.707808602521, recall: 0.903375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=2, Δ=1.075, maxvisits=834))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 9.0, 10.0, 12.0, 13.0, 15.0, 17.0, 20.0, 23.0, 30.0, 113.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8571428, maxvisits=642)), 1000, 8)
[ Info: rebuild: queries per second: 13951.875600166088, recall: 0.895625
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8571428, maxvisits=642))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [4.0, 16.0, 19.0, 21.0, 23.0, 24.0, 26.0, 28.0, 30.0, 34.0, 55.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.79, maxvisits=958)), 1000, 8)
  2.807598 seconds (488.81 k allocations: 29.707 MiB, 1.44% gc time, 96.87% compilation time)
[ Info: matrixhints: queries per second: 11345.101304380332, recall: 0.899
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.79, maxvisits=958))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 9.0, 10.0, 12.0, 13.0, 15.0, 17.0, 20.0, 23.0, 30.0, 113.0]
[ Info: ===================== minrecall Base.Pairs{Symbol, AdjDict{UInt32}, Nothing, @NamedTuple{adj::AdjDict{UInt32}}}(:adj => AdjDict{UInt32}(Dict{UInt32, Vector{UInt32}}(), ReentrantLock())) ==============================
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:40:34.488
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add! sp=293 ep=293 n=292 BeamSearch(bsize=8, Δ=0.9047619, maxvisits=270) mem=1GB max-rss=1GB 2026-04-26T19:40:37.799
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=20574 ep=20574 n=20573 BeamSearch(bsize=6, Δ=0.8809524, maxvisits=590) mem=1GB max-rss=1GB 2026-04-26T19:40:39.800
LOG n.size quantiles:[12.0, 12.0, 12.0, 12.0, 12.0]
LOG add! sp=38199 ep=38199 n=38198 BeamSearch(bsize=4, Δ=0.86, maxvisits=570) mem=1GB max-rss=1GB 2026-04-26T19:40:41.800
LOG n.size quantiles:[6.0, 6.0, 6.0, 6.0, 6.0]
LOG add! sp=52380 ep=52380 n=52379 BeamSearch(bsize=4, Δ=0.86, maxvisits=570) mem=1GB max-rss=1GB 2026-04-26T19:40:43.800
LOG n.size quantiles:[11.0, 11.0, 11.0, 11.0, 11.0]
LOG add! sp=62291 ep=62291 n=62290 BeamSearch(bsize=6, Δ=1.0476191, maxvisits=736) mem=1GB max-rss=1GB 2026-04-26T19:40:45.800
LOG n.size quantiles:[6.0, 6.0, 6.0, 6.0, 6.0]
LOG add! sp=71797 ep=71797 n=71796 BeamSearch(bsize=6, Δ=1.0476191, maxvisits=736) mem=1GB max-rss=1GB 2026-04-26T19:40:47.800
LOG n.size quantiles:[8.0, 8.0, 8.0, 8.0, 8.0]
LOG add! sp=80702 ep=80702 n=80701 BeamSearch(bsize=6, Δ=1.0476191, maxvisits=736) mem=1GB max-rss=1GB 2026-04-26T19:40:49.800
LOG n.size quantiles:[7.0, 7.0, 7.0, 7.0, 7.0]
LOG add! sp=88720 ep=88720 n=88719 BeamSearch(bsize=6, Δ=0.8952381, maxvisits=734) mem=1GB max-rss=1GB 2026-04-26T19:40:51.800
LOG n.size quantiles:[15.0, 15.0, 15.0, 15.0, 15.0]
LOG add! sp=98828 ep=98828 n=98827 BeamSearch(bsize=6, Δ=0.8952381, maxvisits=734) mem=1GB max-rss=1GB 2026-04-26T19:40:53.800
LOG n.size quantiles:[9.0, 9.0, 9.0, 9.0, 9.0]
(length(graph.adj), length(graph), length(B.db)) = (100000, 100000, 100000)
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 9.0, 10.0, 12.0, 13.0, 15.0, 17.0, 20.0, 23.0, 30.0, 113.0]
[ Info: minrecall: queries per second: 11440.62186827277, recall: 0.903375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=2, Δ=1.075, maxvisits=834))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 9.0, 10.0, 12.0, 13.0, 15.0, 17.0, 20.0, 23.0, 30.0, 113.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8571428, maxvisits=642)), 1000, 8)
[ Info: rebuild: queries per second: 14492.210480343447, recall: 0.895625
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8571428, maxvisits=642))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [4.0, 16.0, 19.0, 21.0, 23.0, 24.0, 26.0, 28.0, 30.0, 34.0, 55.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.79, maxvisits=958)), 1000, 8)
  2.783843 seconds (483.67 k allocations: 29.231 MiB, 3.14% gc time, 96.86% compilation time)
[ Info: matrixhints: queries per second: 11533.224168291055, recall: 0.899
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.79, maxvisits=958))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 9.0, 10.0, 12.0, 13.0, 15.0, 17.0, 20.0, 23.0, 30.0, 113.0]
  1.100639 seconds (3 allocations: 62.586 KiB)
SEARCH Exhaustive 1: 0.001393 seconds
SEARCH Exhaustive 2: 0.001470 seconds
SEARCH Exhaustive 3: 0.001260 seconds
typeof(seq) = ExhaustiveSearch{SimilaritySearch.Dist.SqL2, 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{IdDist}}
[ Info: ===================== minrecall Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}() ==============================
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:42:27.292
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add! sp=293 ep=293 n=292 BeamSearch(bsize=4, Δ=1.075, maxvisits=280) mem=1GB max-rss=1GB 2026-04-26T19:42:36.608
LOG n.size quantiles:[9.0, 9.0, 9.0, 9.0, 9.0]
LOG add! sp=15306 ep=15306 n=15305 BeamSearch(bsize=5, Δ=0.7947306, maxvisits=502) mem=1GB max-rss=1GB 2026-04-26T19:42:38.608
LOG n.size quantiles:[5.0, 5.0, 5.0, 5.0, 5.0]
LOG add! sp=28984 ep=28984 n=28983 BeamSearch(bsize=4, Δ=0.803369, maxvisits=592) mem=1GB max-rss=1GB 2026-04-26T19:42:40.608
LOG n.size quantiles:[8.0, 8.0, 8.0, 8.0, 8.0]
LOG add! sp=41370 ep=41370 n=41369 BeamSearch(bsize=8, Δ=0.98, maxvisits=652) mem=1GB max-rss=1GB 2026-04-26T19:42:42.608
LOG n.size quantiles:[11.0, 11.0, 11.0, 11.0, 11.0]
LOG add! sp=51958 ep=51958 n=51957 BeamSearch(bsize=8, Δ=0.98, maxvisits=652) mem=1GB max-rss=1GB 2026-04-26T19:42:44.608
LOG n.size quantiles:[12.0, 12.0, 12.0, 12.0, 12.0]
LOG add! sp=61449 ep=61449 n=61448 BeamSearch(bsize=8, Δ=0.98, maxvisits=720) mem=1GB max-rss=1GB 2026-04-26T19:42:46.608
LOG n.size quantiles:[7.0, 7.0, 7.0, 7.0, 7.0]
LOG add! sp=70730 ep=70730 n=70729 BeamSearch(bsize=8, Δ=0.98, maxvisits=720) mem=1GB max-rss=1GB 2026-04-26T19:42:48.945
LOG n.size quantiles:[8.0, 8.0, 8.0, 8.0, 8.0]
LOG add! sp=79684 ep=79684 n=79683 BeamSearch(bsize=8, Δ=0.98, maxvisits=720) mem=1GB max-rss=1GB 2026-04-26T19:42:50.945
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=88410 ep=88410 n=88409 BeamSearch(bsize=5, Δ=0.84761906, maxvisits=658) mem=1GB max-rss=1GB 2026-04-26T19:42:52.945
LOG n.size quantiles:[8.0, 8.0, 8.0, 8.0, 8.0]
LOG add! sp=98266 ep=98266 n=98265 BeamSearch(bsize=5, Δ=0.84761906, maxvisits=658) mem=1GB max-rss=1GB 2026-04-26T19:42:54.945
LOG n.size quantiles:[9.0, 9.0, 9.0, 9.0, 9.0]
(length(graph.adj), length(graph), length(B.db)) = (100000, 100000, 100000)
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 9.0, 10.0, 12.0, 13.0, 15.0, 17.0, 20.0, 23.0, 30.0, 112.0]
[ Info: minrecall: queries per second: 13984.087115716588, recall: 0.90325
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.81, maxvisits=894))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 9.0, 10.0, 12.0, 13.0, 15.0, 17.0, 20.0, 23.0, 30.0, 112.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=2, Δ=1.0666667, maxvisits=704)), 1000, 8)
[ Info: rebuild: queries per second: 17292.71840980035, recall: 0.904375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=2, Δ=1.0666667, maxvisits=704))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [5.0, 16.0, 19.0, 21.0, 22.0, 24.0, 26.0, 28.0, 30.0, 33.0, 55.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.7990498, maxvisits=868)), 1000, 8)
  2.770572 seconds (427.82 k allocations: 25.928 MiB, 97.22% compilation time)
[ Info: matrixhints: queries per second: 13148.391563834191, recall: 0.899375
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.7990498, maxvisits=868))
quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 9.0, 10.0, 12.0, 13.0, 15.0, 17.0, 20.0, 23.0, 30.0, 112.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   27     27  5m56.6s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:08.673
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:44:08.978
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add! sp=2 ep=2 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:44:11.267
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:11.269
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000006, 0x00000007, 0x00000010, 0x00000012, 0x0000002e, 0x00000041, 0x0000004b]
D.nn = Int32[1, 2, 3, 1, 3, 6, 7, 7, 7, 6, 1, 1, 1, 3, 3, 16, 16, 18, 18, 18, 3, 16, 7, 18, 7, 2, 16, 7, 16, 18, 3, 3, 1, 3, 3, 6, 16, 3, 1, 2, 3, 2, 2, 16, 18, 46, 16, 7, 18, 6, 7, 18, 7, 3, 18, 2, 3, 18, 3, 16, 18, 6, 1, 1, 65, 18, 7, 7, 3, 2, 7, 3, 18, 18, 75, 65, 6, 3, 6, 46, 18, 7, 2, 18, 75, 18, 16, 18, 2, 18, 1, 16, 3, 2, 18, 1, 75, 6, 7, 18]
D.dist = Float32[0.0, 0.0, 0.0, 0.057955682, 0.009292901, 0.0, 0.0, 0.01974678, 0.005447328, 0.014975131, 0.058260083, 0.0671258, 0.08581561, 0.08725244, 0.004221976, 0.0, 0.007659316, 0.0, 0.05223781, 0.017701805, 0.013202846, 0.0054139495, 0.06544542, 0.055753052, 0.075687826, 0.039106548, 0.04916054, 0.050373018, 0.032609463, 0.04941207, 0.033665597, 0.014618039, 0.05114436, 0.028707862, 0.05952871, 0.03911847, 0.005567074, 0.02382505, 0.015275538, 0.031052947, 0.054796934, 0.04470992, 0.027403176, 0.004499376, 0.039195895, 0.0, 0.07291967, 0.02144581, 0.048537314, 0.07048291, 0.014821947, 0.04485947, 0.072051704, 0.015969396, 0.026899159, 0.034060776, 0.09635997, 0.01092577, 0.02821058, 0.013707519, 0.030742884, 0.0437302, 0.09437466, 0.009863496, 0.0, 0.019527853, 0.032243967, 0.034369946, 0.039993584, 0.035613358, 0.045255303, 0.08460152, 0.028727353, 0.032462, 0.0, 0.001067996, 0.07924956, 0.041177034, 0.046289027, 0.021722853, 0.038672626, 0.04358822, 0.022898257, 0.07973832, 0.02625364, 0.01004976, 0.039354324, 0.007999659, 0.01705718, 0.071900785, 0.05267155, 0.024756253, 0.016242921, 0.06328213, 0.042206883, 0.06763309, 0.031844616, 0.041641057, 0.042726934, 0.027870417]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  21.3s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.294
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:44:12.295
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: 2026-04-26T19:44:12.296
[ Info: neardup> range: 33:48, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.296
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.296
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.296
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.296
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.296
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.296
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000006, 0x00000007, 0x00000010, 0x00000012, 0x0000002e, 0x00000041, 0x0000004b]
D.nn = Int32[1, 2, 3, 1, 3, 6, 7, 7, 7, 6, 1, 1, 1, 3, 3, 16, 16, 18, 7, 3, 3, 16, 7, 3, 7, 2, 16, 7, 16, 7, 3, 3, 1, 3, 3, 6, 16, 3, 1, 2, 3, 2, 2, 16, 18, 46, 16, 7, 18, 6, 7, 18, 7, 3, 18, 2, 3, 18, 3, 16, 18, 6, 1, 1, 65, 18, 7, 7, 3, 2, 7, 3, 18, 18, 75, 7, 6, 3, 6, 46, 18, 7, 2, 18, 75, 18, 16, 18, 2, 18, 1, 16, 3, 2, 18, 1, 75, 6, 7, 18]
D.dist = Float32[0.0, 0.0, 0.0, 0.057955682, 0.009292901, 0.0, 0.0, 0.01974678, 0.005447328, 0.014975131, 0.058260083, 0.0671258, 0.08581561, 0.08725244, 0.004221976, 0.0, 0.007659316, 0.0, 0.075473785, 0.048836112, 0.013202846, 0.0054139495, 0.06544542, 0.057643533, 0.075687826, 0.039106548, 0.04916054, 0.050373018, 0.032609463, 0.08090228, 0.033665597, 0.014618039, 0.05114436, 0.028707862, 0.05952871, 0.03911847, 0.005567074, 0.02382505, 0.015275538, 0.031052947, 0.054796934, 0.04470992, 0.027403176, 0.004499376, 0.039195895, 0.0, 0.07291967, 0.02144581, 0.048537314, 0.07048291, 0.014821947, 0.04485947, 0.072051704, 0.015969396, 0.026899159, 0.034060776, 0.09635997, 0.01092577, 0.02821058, 0.013707519, 0.030742884, 0.0437302, 0.09437466, 0.009863496, 0.0, 0.019527853, 0.032243967, 0.034369946, 0.039993584, 0.035613358, 0.045255303, 0.08460152, 0.028727353, 0.032462, 0.0, 0.091842175, 0.07924956, 0.041177034, 0.046289027, 0.021722853, 0.038672626, 0.04358822, 0.022898257, 0.07973832, 0.02625364, 0.01004976, 0.039354324, 0.007999659, 0.01705718, 0.071900785, 0.05267155, 0.024756253, 0.016242921, 0.06328213, 0.042206883, 0.06763309, 0.031844616, 0.041641057, 0.042726934, 0.027870417]
Test Summary:       | Pass  Total  Time
neardup small block |    3      3  0.1s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.448
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:44:12.448
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: 2026-04-26T19:44:12.450
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.450
[ Info: neardup> range: 49:64, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.450
[ Info: neardup> range: 65:80, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.450
[ Info: neardup> range: 81:96, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.450
[ Info: neardup> range: 97:100, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.450
[ Info: neardup> finished current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:12.450
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x0000002e]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 16, 8, 9, 15, 15, 16, 9, 11, 9, 2, 4, 8, 11, 8, 15, 3, 11, 5, 15, 10, 16, 5, 11, 2, 5, 2, 2, 16, 11, 46, 16, 9, 9, 10, 8, 8, 9, 15, 15, 2, 15, 46, 15, 16, 8, 10, 4, 1, 9, 11, 7, 8, 14, 2, 9, 15, 15, 4, 14, 9, 11, 15, 10, 46, 46, 4, 2, 13, 14, 46, 4, 8, 2, 11, 13, 16, 15, 2, 8, 12, 8, 6, 7, 46]
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, 0.007659316, 0.06916165, 0.053681374, 0.031594574, 0.011368275, 0.0054139495, 0.045703173, 0.03349179, 0.05199629, 0.039106548, 0.030720651, 0.035224617, 0.014646292, 0.047498345, 0.025187075, 0.014618039, 0.03040719, 0.008892417, 0.04185587, 0.021689177, 0.005567074, 0.008656979, 0.01489675, 0.031052947, 0.04526955, 0.04470992, 0.027403176, 0.004499376, 0.034778595, 0.0, 0.07291967, 0.0054653883, 0.0649513, 0.022588849, 0.0018289685, 0.009707749, 0.06793338, 0.010143518, 0.040174723, 0.034060776, 0.07293147, 0.08485317, 0.027622104, 0.013707519, 0.01096487, 0.025344968, 0.031969607, 0.009863496, 0.08094281, 0.043864727, 0.032243967, 0.013286531, 0.029609084, 0.035613358, 0.021590114, 0.067640066, 0.045846462, 0.06955409, 0.08720964, 0.07200885, 0.02821976, 0.020599425, 0.027398169, 0.021722853, 0.04839009, 0.040418684, 0.022898257, 0.034380496, 0.032379866, 0.08378887, 0.011803746, 0.031479776, 0.01705718, 0.009070516, 0.02158606, 0.024756253, 0.0145561695, 0.06328213, 0.03392154, 0.018519819, 0.058969855, 0.041641057, 0.042726934, 0.06502789]
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: 2026-04-26T19:44:23.621
LOG append_items! ExhaustiveSearch{SimilaritySearch.Dist.Hacks.DistanceWithIdentifiers{SimilaritySearch.Dist.Cosine, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{Int32}}} sp=0 ep=6 n=6 mem=32768 max-rss=687 2026-04-26T19:44:23.622
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:23.628
[ Info: neardup> range: 33:48, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:23.628
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:23.628
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:23.628
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:23.629
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:23.629
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-04-26T19:44:23.629
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000006, 0x00000007, 0x00000010, 0x00000012, 0x0000002e, 0x00000041, 0x0000004b]
D.nn = Int32[1, 2, 3, 1, 3, 6, 7, 7, 7, 6, 1, 1, 1, 3, 3, 16, 16, 18, 7, 3, 3, 16, 7, 3, 7, 2, 16, 7, 16, 7, 3, 3, 1, 3, 3, 6, 16, 3, 1, 2, 3, 2, 2, 16, 18, 46, 16, 7, 18, 6, 7, 18, 7, 3, 18, 2, 3, 18, 3, 16, 18, 6, 1, 1, 65, 18, 7, 7, 3, 2, 7, 3, 18, 18, 75, 7, 6, 3, 6, 46, 18, 7, 2, 18, 75, 18, 16, 18, 2, 18, 1, 16, 3, 2, 18, 1, 75, 6, 7, 18]
D.dist = Float32[0.0, 0.0, 0.0, 0.057955682, 0.009292901, 0.0, 0.0, 0.01974678, 0.005447328, 0.014975131, 0.058260083, 0.0671258, 0.08581561, 0.08725244, 0.004221976, 0.0, 0.007659316, 0.0, 0.075473785, 0.048836112, 0.013202846, 0.0054139495, 0.06544542, 0.057643533, 0.075687826, 0.039106548, 0.04916054, 0.050373018, 0.032609463, 0.08090228, 0.033665597, 0.014618039, 0.05114436, 0.028707862, 0.05952871, 0.03911847, 0.005567074, 0.02382505, 0.015275538, 0.031052947, 0.054796934, 0.04470992, 0.027403176, 0.004499376, 0.039195895, 0.0, 0.07291967, 0.02144581, 0.048537314, 0.07048291, 0.014821947, 0.04485947, 0.072051704, 0.015969396, 0.026899159, 0.034060776, 0.09635997, 0.01092577, 0.02821058, 0.013707519, 0.030742884, 0.0437302, 0.09437466, 0.009863496, 0.0, 0.019527853, 0.032243967, 0.034369946, 0.039993584, 0.035613358, 0.045255303, 0.08460152, 0.028727353, 0.032462, 0.0, 0.091842175, 0.07924956, 0.041177034, 0.046289027, 0.021722853, 0.038672626, 0.04358822, 0.022898257, 0.07973832, 0.02625364, 0.01004976, 0.039354324, 0.007999659, 0.01705718, 0.071900785, 0.05267155, 0.024756253, 0.016242921, 0.06328213, 0.042206883, 0.06763309, 0.031844616, 0.041641057, 0.042726934, 0.027870417]
Test Summary:                               | Pass  Total   Time
neardup small block with filterblocks=false |    3      3  11.2s
ExhaustiveSearch allknn: 1.608736 seconds (470.95 k allocations: 28.075 MiB, 99.95% compilation time)
[ Info: All KNN quartile 1-th:
[ Info: 1 => [0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: All KNN quartile 2-th:
[ Info: 2 => [0.08390872925519943, 0.1803918555378914, 0.2201504111289978, 0.2582293376326561, 0.3645845353603363]
[ Info: All KNN quartile 3-th:
[ Info: 3 => [0.13688021898269653, 0.22638052701950073, 0.27136509120464325, 0.3191608637571335, 0.4115774929523468]
[ Info: All KNN quartile 4-th:
[ Info: 4 => [0.19392453134059906, 0.2806035652756691, 0.3148263692855835, 0.3502243161201477, 0.4877392053604126]
[ Info: All KNN quartile 5-th:
[ Info: 5 => [0.21065770089626312, 0.3054675981402397, 0.3442008048295975, 0.3814430981874466, 0.5376037359237671]
[ Info: All KNN quartile 6-th:
[ Info: 6 => [0.23910404741764069, 0.33685435354709625, 0.37096239626407623, 0.4138626679778099, 0.5534763932228088]
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:44:32.520
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
SearchGraph allknn: 4.612655 seconds (598.36 k allocations: 37.739 MiB, 99.99% compilation time)
recall = 0.9633333333333333
recall > 0.8 = true
recall = 0.9633333333333333
quantile(neighbors_length.(Ref(G.adj), 1:length(G)), 0:0.25:1) = [2.0, 4.0, 6.0, 8.0, 18.0]
Test Summary: | Pass  Total   Time
allknn        |    3      3  24.6s
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-04-26T19:44:54.916
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add! sp=293 ep=293 n=292 BeamSearch(bsize=4, Δ=0.88543355, maxvisits=158) mem=1GB max-rss=1GB 2026-04-26T19:45:05.460
LOG n.size quantiles:[2.0, 2.0, 2.0, 2.0, 2.0]
(i, j, d) = (20, 119, -1.1920929f-7)
(i, j, d, :parallel) = (20, 119, -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 => 19.107433562, :exact => 1.116287482)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  20.7s
     Testing SimilaritySearch tests passed 

Testing completed after 549.67s

PkgEval succeeded after 628.72s