Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.1871 (50d44f5be7*) started at 2026-03-08T18:59:47.842


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

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

Set-up completed after 13.71s


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

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [053f045d] + SimilaritySearch v0.14.0
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.43
  [79e6a3ab] + Adapt v4.5.0
  [66dad0bd] + AliasTables v1.1.3
  [4fba245c] + ArrayInterface v7.23.0
  [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
  [31c24e10] + Distributions v0.25.123
  [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 v0.11.35
  [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 v2.11.2
  [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 v0.5.0
  [053f045d] + SimilaritySearch v0.14.0
  [a2af1166] + SortingAlgorithms v1.2.2
  [276daf66] + SpecialFunctions v2.7.1
  [aedffcd0] + Static v1.3.1
  [0d7ed370] + StaticArrayInterface v1.9.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.8.0
⌅ [2913bbd2] + StatsBase v0.33.21
  [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
  [b77e0a4c] + InteractiveUtils 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 v1.3.0+1
  [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
        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

Installation completed after 6.02s


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

Precompiling PkgEval dependencies...

Precompiling package dependencies...
Precompiling packages...
   3255.2 ms  ✓ SearchModels
   9788.5 ms  ✓ SimilaritySearch
  2 dependencies successfully precompiled in 14 seconds. 107 already precompiled.

Precompilation completed after 38.79s


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

     Testing SimilaritySearch
      Status `/tmp/jl_3JvvyL/Project.toml`
  [7d9f7c33] Accessors v0.1.43
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.123
  [f517fe37] Polyester v0.7.19
  [92933f4c] ProgressMeter v1.11.0
  [0e966ebe] SearchModels v0.5.0
  [053f045d] SimilaritySearch v0.14.0
  [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_3JvvyL/Manifest.toml`
  [7d9f7c33] Accessors v0.1.43
  [79e6a3ab] Adapt v4.5.0
  [66dad0bd] AliasTables v1.1.3
  [4c88cf16] Aqua v0.8.14
  [4fba245c] ArrayInterface v7.23.0
  [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
  [31c24e10] Distributions v0.25.123
  [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 v0.11.35
  [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 v2.11.2
  [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 v0.5.0
  [053f045d] SimilaritySearch v0.14.0
  [a2af1166] SortingAlgorithms v1.2.2
  [276daf66] SpecialFunctions v2.7.1
  [aedffcd0] Static v1.3.1
  [0d7ed370] StaticArrayInterface v1.9.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.8.0
⌅ [2913bbd2] StatsBase v0.33.21
  [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 v1.3.0+1
  [deac9b47] LibCURL_jll v8.18.0+0
  [e37daf67] LibGit2_jll v1.9.2+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.12.2
  [4536629a] OpenBLAS_jll v0.3.30+0
  [05823500] OpenLibm_jll v0.8.7+0
  [458c3c95] OpenSSL_jll v3.5.5+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 v1.68.0+1
  [3f19e933] p7zip_jll v17.7.0+0
        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading.
     Testing Running tests...
Test Summary:              | Pass  Total   Time
test database abstractions |   57     57  17.1s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.2s
Test Summary: |  Pass  Total  Time
KnnHeap       | 30005  30005  4.2s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  3.1s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.3s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
SimilaritySearch.Dist.L2: 0.031194 seconds
SimilaritySearch.Dist.L2: 0.032298 seconds
SimilaritySearch.Dist.L1: 0.030568 seconds
SimilaritySearch.Dist.L1: 0.028269 seconds
SimilaritySearch.Dist.LInfty: 0.029336 seconds
SimilaritySearch.Dist.LInfty: 0.029393 seconds
SimilaritySearch.Dist.SqL2: 0.028682 seconds
SimilaritySearch.Dist.SqL2: 0.029297 seconds
SimilaritySearch.Dist.Lp: 0.128568 seconds
SimilaritySearch.Dist.Lp: 0.128142 seconds
SimilaritySearch.Dist.Lp: 0.257721 seconds
SimilaritySearch.Dist.Lp: 0.257770 seconds
SimilaritySearch.Dist.Angle: 0.191743 seconds (5.21 k allocations: 279.125 KiB)
SimilaritySearch.Dist.Angle: 0.182758 seconds
SimilaritySearch.Dist.Cosine: 0.162076 seconds (1 allocation: 16 bytes)
SimilaritySearch.Dist.Cosine: 0.163171 seconds
Test Summary:     | Pass  Total   Time
Searching vectors |  800    800  29.5s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
SimilaritySearch.Dist.Seqs.CommonPrefix: 0.017733 seconds
SimilaritySearch.Dist.Seqs.CommonPrefix: 0.017463 seconds
SimilaritySearch.Dist.Seqs.Levenshtein: 0.267889 seconds
SimilaritySearch.Dist.Seqs.Levenshtein: 0.269223 seconds
SimilaritySearch.Dist.Seqs.LCS: 0.260522 seconds
SimilaritySearch.Dist.Seqs.LCS: 0.255776 seconds
SimilaritySearch.Dist.Seqs.Hamming: 0.028458 seconds
SimilaritySearch.Dist.Seqs.Hamming: 0.028571 seconds
Test Summary:       | Pass  Total   Time
Searching sequences |  400    400  13.7s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
SimilaritySearch.Dist.Sets.Jaccard: 0.068702 seconds
SimilaritySearch.Dist.Sets.Jaccard: 0.067483 seconds
SimilaritySearch.Dist.Sets.Dice: 0.069971 seconds
SimilaritySearch.Dist.Sets.Dice: 0.071123 seconds
SimilaritySearch.Dist.Sets.Intersection: 0.071232 seconds
SimilaritySearch.Dist.Sets.Intersection: 0.075872 seconds
SimilaritySearch.Dist.Sets.RogersTanimoto: 0.070238 seconds
SimilaritySearch.Dist.Sets.RogersTanimoto: 0.068627 seconds
Test Summary:                     | Pass  Total   Time
Searching on sets (ordered lists) |  400    400  13.3s
SimilaritySearch.Dist.NormAngle: 0.003924 seconds (1 allocation: 16 bytes)
SimilaritySearch.Dist.NormAngle: 0.003911 seconds
SimilaritySearch.Dist.NormCosine: 0.003054 seconds
SimilaritySearch.Dist.NormCosine: 0.002949 seconds
Test Summary:                        | Pass  Total  Time
Searching with angle-based distances |  200    200  6.6s
SimilaritySearch.Dist.Bits.Hamming: 0.004038 seconds
SimilaritySearch.Dist.Bits.Hamming: 0.004134 seconds
SimilaritySearch.Dist.Bits.RogersTanimoto: 0.008456 seconds
SimilaritySearch.Dist.Bits.RogersTanimoto: 0.008649 seconds
Test Summary:    | Pass  Total  Time
Binary distances |  200    200  6.6s
quantile(length.(hsp_knns), 0:0.1:1) = [2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0]
Test Summary: | Total  Time
HSP           |     0  7.0s
computing farthest point 1, dmax: Inf, imax: 25, n: 30
computing farthest point 2, dmax: 1.30779, imax: 5, n: 30
computing farthest point 3, dmax: 1.0466162, imax: 10, n: 30
computing farthest point 4, dmax: 0.9016312, imax: 13, n: 30
computing farthest point 5, dmax: 0.7730864, imax: 12, n: 30
computing farthest point 6, dmax: 0.70339227, imax: 19, n: 30
computing farthest point 7, dmax: 0.60519505, imax: 7, n: 30
computing farthest point 8, dmax: 0.59418136, imax: 14, n: 30
computing farthest point 9, dmax: 0.57462436, imax: 26, n: 30
computing farthest point 10, dmax: 0.53510135, imax: 20, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  2.6s
n = 10
Test Summary: | Pass  Total  Time
AdjList       |   28     28  4.6s
  4.985274 seconds (3 allocations: 62.586 KiB)
SEARCH Exhaustive 1: 0.007119 seconds
SEARCH Exhaustive 2: 0.007375 seconds
SEARCH Exhaustive 3: 0.008079 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 2026-03-08T19:03:42.049
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 2026-03-08T19:03:46.171
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
LOG add! sp=293 ep=293 n=292 BeamSearch(bsize=16, Δ=1.05, maxvisits=318) mem=1GB max-rss=1GB 2026-03-08T19:03:56.974
LOG n.size quantiles:[5.0, 5.0, 5.0, 5.0, 5.0]
LOG add! sp=12069 ep=12069 n=12068 BeamSearch(bsize=6, Δ=0.8857143, maxvisits=586) mem=1GB max-rss=1GB 2026-03-08T19:03:58.974
LOG n.size quantiles:[13.0, 13.0, 13.0, 13.0, 13.0]
LOG add! sp=22471 ep=22471 n=22470 BeamSearch(bsize=4, Δ=0.7609998, maxvisits=524) mem=1GB max-rss=1GB 2026-03-08T19:04:00.974
LOG n.size quantiles:[6.0, 6.0, 6.0, 6.0, 6.0]
LOG add! sp=32408 ep=32408 n=32407 BeamSearch(bsize=8, Δ=0.9070295, maxvisits=696) mem=1GB max-rss=1GB 2026-03-08T19:04:02.974
LOG n.size quantiles:[5.0, 5.0, 5.0, 5.0, 5.0]
LOG add! sp=42222 ep=42222 n=42221 BeamSearch(bsize=4, Δ=0.7990498, maxvisits=616) mem=1GB max-rss=1GB 2026-03-08T19:04:04.974
LOG n.size quantiles:[9.0, 9.0, 9.0, 9.0, 9.0]
LOG add! sp=52697 ep=52697 n=52696 BeamSearch(bsize=4, Δ=0.7990498, maxvisits=616) mem=1GB max-rss=1GB 2026-03-08T19:04:06.974
LOG n.size quantiles:[12.0, 12.0, 12.0, 12.0, 12.0]
LOG add! sp=60059 ep=60059 n=60058 BeamSearch(bsize=6, Δ=0.99999994, maxvisits=652) mem=1GB max-rss=1GB 2026-03-08T19:04:08.974
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=67298 ep=67298 n=67297 BeamSearch(bsize=6, Δ=0.99999994, maxvisits=652) mem=1GB max-rss=1GB 2026-03-08T19:04:10.974
LOG n.size quantiles:[11.0, 11.0, 11.0, 11.0, 11.0]
LOG add! sp=75843 ep=75843 n=75842 BeamSearch(bsize=6, Δ=0.99999994, maxvisits=652) mem=1GB max-rss=1GB 2026-03-08T19:04:12.975
LOG n.size quantiles:[9.0, 9.0, 9.0, 9.0, 9.0]
LOG add! sp=84141 ep=84141 n=84140 BeamSearch(bsize=6, Δ=0.99999994, maxvisits=652) mem=1GB max-rss=1GB 2026-03-08T19:04:14.975
LOG n.size quantiles:[8.0, 8.0, 8.0, 8.0, 8.0]
LOG add! sp=91073 ep=91073 n=91072 BeamSearch(bsize=4, Δ=1.0857142, maxvisits=668) mem=1GB max-rss=1GB 2026-03-08T19:04:16.975
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=99034 ep=99034 n=99033 BeamSearch(bsize=4, Δ=1.0857142, maxvisits=668) mem=1GB max-rss=1GB 2026-03-08T19:04:18.975
LOG n.size quantiles:[6.0, 6.0, 6.0, 6.0, 6.0]
(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, 19.0, 23.0, 30.0, 108.0]
[ Info: minrecall: queries per second: 9585.62153702048, recall: 0.916
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=6, Δ=0.79, maxvisits=906))
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, 19.0, 23.0, 30.0, 108.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8844052, maxvisits=674)), 1000, 8)
[ Info: rebuild: queries per second: 12403.008935239264, recall: 0.902875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8844052, maxvisits=674))
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, 60.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=2, Δ=1.0761905, maxvisits=864)), 1000, 8)
  2.871327 seconds (491.08 k allocations: 29.964 MiB, 96.97% compilation time)
[ Info: matrixhints: queries per second: 10910.275249224509, recall: 0.904
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=2, Δ=1.0761905, maxvisits=864))
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, 19.0, 23.0, 30.0, 108.0]
[ Info: ===================== minrecall Base.Pairs{Symbol, AdjDict{UInt32}, Nothing, @NamedTuple{adj::AdjDict{UInt32}}}(:adj => AdjDict{UInt32}(Dict{UInt32, Vector{UInt32}}(), UInt32[], Base.Threads.SpinLock(0))) ==============================
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-03-08T19:06:00.413
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add! sp=293 ep=293 n=292 BeamSearch(bsize=16, Δ=1.05, maxvisits=318) mem=1GB max-rss=1GB 2026-03-08T19:06:03.622
LOG n.size quantiles:[5.0, 5.0, 5.0, 5.0, 5.0]
LOG add! sp=16836 ep=16836 n=16835 BeamSearch(bsize=4, Δ=0.7609998, maxvisits=524) mem=1GB max-rss=1GB 2026-03-08T19:06:05.666
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=32326 ep=32326 n=32325 BeamSearch(bsize=8, Δ=0.9070295, maxvisits=696) mem=1GB max-rss=1GB 2026-03-08T19:06:07.666
LOG n.size quantiles:[11.0, 11.0, 11.0, 11.0, 11.0]
LOG add! sp=44993 ep=44993 n=44992 BeamSearch(bsize=4, Δ=0.7990498, maxvisits=616) mem=1GB max-rss=1GB 2026-03-08T19:06:09.666
LOG n.size quantiles:[6.0, 6.0, 6.0, 6.0, 6.0]
LOG add! sp=56817 ep=56817 n=56816 BeamSearch(bsize=6, Δ=0.99999994, maxvisits=652) mem=1GB max-rss=1GB 2026-03-08T19:06:11.680
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=66924 ep=66924 n=66923 BeamSearch(bsize=6, Δ=0.99999994, maxvisits=652) mem=1GB max-rss=1GB 2026-03-08T19:06:13.680
LOG n.size quantiles:[12.0, 12.0, 12.0, 12.0, 12.0]
LOG add! sp=76649 ep=76649 n=76648 BeamSearch(bsize=6, Δ=0.99999994, maxvisits=652) mem=1GB max-rss=1GB 2026-03-08T19:06:15.680
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=85224 ep=85224 n=85223 BeamSearch(bsize=4, Δ=1.0857142, maxvisits=668) mem=1GB max-rss=1GB 2026-03-08T19:06:17.779
LOG n.size quantiles:[9.0, 9.0, 9.0, 9.0, 9.0]
LOG add! sp=94727 ep=94727 n=94726 BeamSearch(bsize=4, Δ=1.0857142, maxvisits=668) mem=1GB max-rss=1GB 2026-03-08T19:06:19.779
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
(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, 19.0, 23.0, 30.0, 108.0]
[ Info: minrecall: queries per second: 9615.988665903407, recall: 0.916
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=6, Δ=0.79, maxvisits=906))
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, 19.0, 23.0, 30.0, 108.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8844052, maxvisits=674)), 1000, 8)
[ Info: rebuild: queries per second: 12126.893534084677, recall: 0.902875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8844052, maxvisits=674))
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, 60.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=2, Δ=1.0761905, maxvisits=864)), 1000, 8)
  2.864924 seconds (485.27 k allocations: 29.154 MiB, 96.98% compilation time)
[ Info: matrixhints: queries per second: 11460.713556403593, recall: 0.904
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=2, Δ=1.0761905, maxvisits=864))
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, 19.0, 23.0, 30.0, 108.0]
  1.056259 seconds (3 allocations: 62.586 KiB)
SEARCH Exhaustive 1: 0.003330 seconds
SEARCH Exhaustive 2: 0.003273 seconds
SEARCH Exhaustive 3: 0.003394 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 2026-03-08T19:08:03.120
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 2026-03-08T19:08:05.154
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
LOG add! sp=293 ep=293 n=292 BeamSearch(bsize=6, Δ=1.0190476, maxvisits=282) mem=1GB max-rss=1GB 2026-03-08T19:08:15.954
LOG n.size quantiles:[5.0, 5.0, 5.0, 5.0, 5.0]
LOG add! sp=15229 ep=15229 n=15228 BeamSearch(bsize=4, Δ=0.96, maxvisits=566) mem=1GB max-rss=1GB 2026-03-08T19:08:17.954
LOG n.size quantiles:[8.0, 8.0, 8.0, 8.0, 8.0]
LOG add! sp=27227 ep=27227 n=27226 BeamSearch(bsize=4, Δ=0.861678, maxvisits=554) mem=1GB max-rss=1GB 2026-03-08T19:08:19.954
LOG n.size quantiles:[6.0, 6.0, 6.0, 6.0, 6.0]
LOG add! sp=38625 ep=38625 n=38624 BeamSearch(bsize=8, Δ=1.1, maxvisits=710) mem=1GB max-rss=1GB 2026-03-08T19:08:21.955
LOG n.size quantiles:[12.0, 12.0, 12.0, 12.0, 12.0]
LOG add! sp=47409 ep=47409 n=47408 BeamSearch(bsize=8, Δ=1.1, maxvisits=710) mem=1GB max-rss=1GB 2026-03-08T19:08:23.955
LOG n.size quantiles:[12.0, 12.0, 12.0, 12.0, 12.0]
LOG add! sp=56027 ep=56027 n=56026 BeamSearch(bsize=8, Δ=1.1, maxvisits=710) mem=1GB max-rss=1GB 2026-03-08T19:08:25.955
LOG n.size quantiles:[11.0, 11.0, 11.0, 11.0, 11.0]
LOG add! sp=65074 ep=65074 n=65073 BeamSearch(bsize=6, Δ=0.9142857, maxvisits=760) mem=1GB max-rss=1GB 2026-03-08T19:08:27.955
LOG n.size quantiles:[10.0, 10.0, 10.0, 10.0, 10.0]
LOG add! sp=74635 ep=74635 n=74634 BeamSearch(bsize=6, Δ=0.9142857, maxvisits=760) mem=1GB max-rss=1GB 2026-03-08T19:08:29.955
LOG n.size quantiles:[11.0, 11.0, 11.0, 11.0, 11.0]
LOG add! sp=84174 ep=84174 n=84173 BeamSearch(bsize=6, Δ=0.9142857, maxvisits=760) mem=1GB max-rss=1GB 2026-03-08T19:08:31.955
LOG n.size quantiles:[7.0, 7.0, 7.0, 7.0, 7.0]
LOG add! sp=92791 ep=92791 n=92790 BeamSearch(bsize=4, Δ=0.8618789, maxvisits=692) mem=1GB max-rss=1GB 2026-03-08T19:08:33.955
LOG n.size quantiles:[7.0, 7.0, 7.0, 7.0, 7.0]
(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, 24.0, 30.0, 106.0]
[ Info: minrecall: queries per second: 12778.76557635683, recall: 0.900875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=3, Δ=0.8979592, maxvisits=824))
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, 24.0, 30.0, 106.0]
[ Info: ===================== rebuild ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.84761906, maxvisits=648)), 1000, 8)
[ Info: rebuild: queries per second: 14256.421209728336, recall: 0.89675
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.84761906, maxvisits=648))
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, 62.0]
[ Info: ===================== matrixhints ==============================
(graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8, maxvisits=878)), 1000, 8)
  3.221537 seconds (429.88 k allocations: 26.179 MiB, 97.43% compilation time)
[ Info: matrixhints: queries per second: 12021.932525172544, recall: 0.899875
graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=4, Δ=0.8, maxvisits=878))
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, 24.0, 30.0, 106.0]
Test Summary:                    | Pass  Total     Time
vector indexing with SearchGraph |   27     27  6m33.9s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:49.702
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-03-08T19:09:49.977
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:51.915
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000005, 0x00000007, 0x0000000a, 0x0000000b, 0x00000011, 0x00000013, 0x00000023, 0x00000054]
D.nn = Int32[1, 2, 3, 1, 5, 1, 7, 5, 1, 10, 11, 3, 1, 2, 3, 5, 17, 3, 19, 2, 2, 11, 2, 1, 2, 1, 19, 19, 2, 3, 1, 3, 2, 3, 35, 3, 35, 3, 17, 2, 35, 35, 7, 11, 7, 3, 17, 5, 19, 10, 3, 11, 10, 17, 2, 19, 17, 35, 17, 19, 1, 17, 17, 19, 3, 17, 10, 19, 3, 19, 19, 19, 3, 19, 2, 19, 2, 19, 19, 35, 1, 2, 2, 84, 3, 19, 3, 7, 19, 19, 2, 2, 10, 7, 3, 1, 2, 35, 35, 10]
D.dist = Float32[0.0, 0.0, 0.0, 0.038680673, 0.0, 0.04912132, 0.0, 0.049103975, 0.068342924, 0.0, 0.0, 0.018182874, 0.030316472, 0.068686664, 0.07006621, 0.053487122, 0.0, 0.056295037, 0.0, 0.061135054, 0.014110744, 0.0590294, 0.02850914, 0.050929844, 0.027941942, 0.071786284, 0.03546554, 0.026352704, 0.080783546, 0.021574795, 0.05047196, 0.083619475, 0.00823015, 0.01232779, 0.0, 0.021940053, 0.045595527, 0.024729013, 0.021896005, 0.0850485, 0.022850215, 0.09798932, 0.061558604, 0.026698112, 0.0335117, 0.0031980276, 0.003975868, 0.03878033, 0.033243716, 0.056764483, 0.02645725, 0.042345107, 0.022079349, 0.045208573, 0.07697207, 0.07531929, 0.030238748, 0.022692561, 0.01693052, 0.03461474, 0.06224686, 0.044424236, 0.00096058846, 0.047002435, 0.028352022, 0.0032704473, 0.053204656, 0.0031651258, 0.047233343, 0.03134507, 0.0433681, 0.036144733, 0.056630194, 0.061284304, 0.03544885, 0.051301956, 0.07986152, 0.04098183, 0.009044945, 0.0070642233, 0.04974234, 0.033565223, 0.09332955, 0.0, 0.045983434, 0.06618047, 0.022877514, 0.03454417, 0.030526161, 0.039442122, 0.078347445, 0.011498272, 0.015883744, 0.019885004, 0.03876251, 0.04405588, 0.004507005, 0.017556548, 0.01964128, 0.010528743]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  22.2s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:52.942
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-03-08T19:09:52.943
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:52.943
[ Info: neardup> range: 33:48, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:52.943
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:52.943
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:52.943
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:52.943
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:52.944
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:52.944
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000005, 0x00000007, 0x0000000a, 0x0000000b, 0x00000011, 0x00000013, 0x00000023, 0x00000054]
D.nn = Int32[1, 2, 3, 1, 5, 1, 7, 5, 1, 10, 11, 3, 1, 2, 3, 5, 17, 3, 19, 2, 2, 11, 2, 1, 2, 1, 11, 19, 2, 3, 1, 3, 2, 3, 35, 3, 1, 3, 17, 2, 1, 35, 7, 11, 7, 3, 17, 5, 19, 10, 3, 11, 10, 17, 2, 19, 17, 35, 17, 19, 1, 17, 17, 19, 3, 17, 10, 19, 3, 19, 19, 19, 3, 19, 2, 19, 2, 19, 19, 35, 1, 2, 2, 84, 3, 19, 3, 7, 19, 19, 2, 2, 10, 7, 3, 1, 2, 35, 35, 10]
D.dist = Float32[0.0, 0.0, 0.0, 0.038680673, 0.0, 0.04912132, 0.0, 0.049103975, 0.068342924, 0.0, 0.0, 0.018182874, 0.030316472, 0.068686664, 0.07006621, 0.053487122, 0.0, 0.056295037, 0.0, 0.061135054, 0.014110744, 0.0590294, 0.02850914, 0.050929844, 0.027941942, 0.071786284, 0.09638822, 0.026352704, 0.080783546, 0.021574795, 0.05047196, 0.083619475, 0.00823015, 0.01232779, 0.0, 0.021940053, 0.058038473, 0.024729013, 0.021896005, 0.0850485, 0.06991905, 0.09798932, 0.061558604, 0.026698112, 0.0335117, 0.0031980276, 0.003975868, 0.03878033, 0.033243716, 0.056764483, 0.02645725, 0.042345107, 0.022079349, 0.045208573, 0.07697207, 0.07531929, 0.030238748, 0.022692561, 0.01693052, 0.03461474, 0.06224686, 0.044424236, 0.00096058846, 0.047002435, 0.028352022, 0.0032704473, 0.053204656, 0.0031651258, 0.047233343, 0.03134507, 0.0433681, 0.036144733, 0.056630194, 0.061284304, 0.03544885, 0.051301956, 0.07986152, 0.04098183, 0.009044945, 0.0070642233, 0.04974234, 0.033565223, 0.09332955, 0.0, 0.045983434, 0.06618047, 0.022877514, 0.03454417, 0.030526161, 0.039442122, 0.078347445, 0.011498272, 0.015883744, 0.019885004, 0.03876251, 0.04405588, 0.004507005, 0.017556548, 0.01964128, 0.010528743]
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: 2026-03-08T19:09:53.085
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-03-08T19:09:53.086
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: 2026-03-08T19:09:53.087
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:53.087
[ Info: neardup> range: 49:64, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:53.087
[ Info: neardup> range: 65:80, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:53.087
[ Info: neardup> range: 81:96, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:53.088
[ Info: neardup> range: 97:100, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:53.088
[ Info: neardup> finished current elements: 19, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:09:53.088
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x0000002a, 0x00000054, 0x00000059]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 8, 9, 14, 2, 2, 4, 2, 13, 2, 9, 14, 14, 9, 12, 1, 15, 2, 3, 14, 3, 9, 3, 6, 2, 9, 42, 13, 11, 16, 3, 16, 16, 14, 10, 12, 11, 10, 9, 2, 3, 6, 4, 6, 14, 6, 16, 8, 14, 12, 16, 10, 14, 12, 14, 14, 10, 12, 14, 2, 14, 2, 10, 14, 42, 9, 2, 2, 84, 12, 3, 12, 6, 89, 14, 9, 2, 10, 7, 3, 13, 2, 42, 42, 10]
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, 0.026927948, 0.029011607, 0.0567621, 0.061135054, 0.014110744, 0.024709404, 0.02850914, 0.038441896, 0.027941942, 0.008160174, 0.08188152, 0.014446378, 0.0044326186, 0.008859813, 0.05047196, 0.03898275, 0.00823015, 0.01232779, 0.07339895, 0.021940053, 0.032185316, 0.024729013, 0.018252134, 0.0850485, 0.054130375, 0.0, 0.02468592, 0.026698112, 0.017603695, 0.0031980276, 0.013990104, 0.008783221, 0.060465872, 0.056764483, 0.005559981, 0.042345107, 0.022079349, 0.014376342, 0.07697207, 0.08166194, 0.0072392225, 0.044522703, 0.032262504, 0.047675073, 0.02552563, 0.0067219734, 0.02739358, 0.027412593, 0.0165416, 0.018837512, 0.053204656, 0.050328016, 0.018252969, 0.038366437, 0.04386568, 0.043213546, 0.019683719, 0.0021506548, 0.03544885, 0.034222722, 0.07986152, 0.0415951, 0.08001596, 0.08172798, 0.002595663, 0.033565223, 0.09332955, 0.0, 0.023334324, 0.0823431, 0.010474265, 0.02168256, 0.0, 0.0020049214, 0.022567034, 0.011498272, 0.015883744, 0.019885004, 0.03876251, 0.0151636, 0.004507005, 0.05247605, 0.045863688, 0.010528743]
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: 2026-03-08T19:10:04.520
LOG append_items! ExhaustiveSearch{SimilaritySearch.Dist.Hacks.DistanceWithIdentifiers{SimilaritySearch.Dist.Cosine, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{Int32}}} sp=0 ep=7 n=7 mem=32768 max-rss=700 2026-03-08T19:10:04.521
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:10:04.527
[ Info: neardup> range: 33:48, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:10:04.527
[ Info: neardup> range: 49:64, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:10:04.527
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:10:04.527
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:10:04.527
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:10:04.527
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2026-03-08T19:10:04.527
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000005, 0x00000007, 0x0000000a, 0x0000000b, 0x00000011, 0x00000013, 0x00000023, 0x00000054]
D.nn = Int32[1, 2, 3, 1, 5, 1, 7, 5, 1, 10, 11, 3, 1, 2, 3, 5, 17, 3, 19, 2, 2, 11, 2, 1, 2, 1, 11, 19, 2, 3, 1, 3, 2, 3, 35, 3, 1, 3, 17, 2, 1, 35, 7, 11, 7, 3, 17, 5, 19, 10, 3, 11, 10, 17, 2, 19, 17, 35, 17, 19, 1, 17, 17, 19, 3, 17, 10, 19, 3, 19, 19, 19, 3, 19, 2, 19, 2, 19, 19, 35, 1, 2, 2, 84, 3, 19, 3, 7, 19, 19, 2, 2, 10, 7, 3, 1, 2, 35, 35, 10]
D.dist = Float32[0.0, 0.0, 0.0, 0.038680673, 0.0, 0.04912132, 0.0, 0.049103975, 0.068342924, 0.0, 0.0, 0.018182874, 0.030316472, 0.068686664, 0.07006621, 0.053487122, 0.0, 0.056295037, 0.0, 0.061135054, 0.014110744, 0.0590294, 0.02850914, 0.050929844, 0.027941942, 0.071786284, 0.09638822, 0.026352704, 0.080783546, 0.021574795, 0.05047196, 0.083619475, 0.00823015, 0.01232779, 0.0, 0.021940053, 0.058038473, 0.024729013, 0.021896005, 0.0850485, 0.06991905, 0.09798932, 0.061558604, 0.026698112, 0.0335117, 0.0031980276, 0.003975868, 0.03878033, 0.033243716, 0.056764483, 0.02645725, 0.042345107, 0.022079349, 0.045208573, 0.07697207, 0.07531929, 0.030238748, 0.022692561, 0.01693052, 0.03461474, 0.06224686, 0.044424236, 0.00096058846, 0.047002435, 0.028352022, 0.0032704473, 0.053204656, 0.0031651258, 0.047233343, 0.03134507, 0.0433681, 0.036144733, 0.056630194, 0.061284304, 0.03544885, 0.051301956, 0.07986152, 0.04098183, 0.009044945, 0.0070642233, 0.04974234, 0.033565223, 0.09332955, 0.0, 0.045983434, 0.06618047, 0.022877514, 0.03454417, 0.030526161, 0.039442122, 0.078347445, 0.011498272, 0.015883744, 0.019885004, 0.03876251, 0.04405588, 0.004507005, 0.017556548, 0.01964128, 0.010528743]
Test Summary:                               | Pass  Total   Time
neardup small block with filterblocks=false |    3      3  11.4s
ExhaustiveSearch allknn: 1.858180 seconds (470.15 k allocations: 27.934 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 => [0.10633589327335358, 0.17409083247184753, 0.2171652764081955, 0.264688603579998, 0.39723578095436096]
[ Info: All KNN quartile 3-th:
[ Info: 3 => [0.15205641090869904, 0.23669620603322983, 0.28059738874435425, 0.31182286888360977, 0.4518436789512634]
[ Info: All KNN quartile 4-th:
[ Info: 4 => [0.22355112433433533, 0.2861882522702217, 0.3249624967575073, 0.36843814700841904, 0.47395533323287964]
[ Info: All KNN quartile 5-th:
[ Info: 5 => [0.2314453274011612, 0.31259000301361084, 0.35352811217308044, 0.3970614895224571, 0.4799823462963104]
[ Info: All KNN quartile 6-th:
[ Info: 6 => [0.260816365480423, 0.337670162320137, 0.3785184174776077, 0.4163152128458023, 0.5285049080848694]
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-03-08T19:10:15.108
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
SearchGraph allknn: 5.422549 seconds (607.62 k allocations: 38.336 MiB, 99.99% compilation time)
recall = 0.9716666666666662
recall > 0.8 = true
recall = 0.9716666666666662
quantile(neighbors_length.(Ref(G.adj), 1:length(G)), 0:0.25:1) = [2.0, 4.0, 6.0, 8.0, 17.0]
Test Summary: | Pass  Total   Time
allknn        |    3      3  29.8s
LOG add! sp=1 ep=1 n=0 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) mem=1GB max-rss=1GB 2026-03-08T19:10:40.830
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=0.92862546, maxvisits=146) mem=1GB max-rss=1GB 2026-03-08T19:10:52.558
LOG n.size quantiles:[2.0, 2.0, 2.0, 2.0, 2.0]
(i, j, d) = (7, 918, -1.1920929f-7)
(i, j, d, :parallel) = (5, 655, -1.1920929f-7, :parallel)
[ Info: NOTE: the exact method will be faster on small datasets due to the preprocessing step of the approximation method
[ Info: ("closestpair computation time", :approx => 20.246288271999997, :exact => 1.075830524)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  21.8s
     Testing SimilaritySearch tests passed 

Testing completed after 607.15s

PkgEval succeeded after 678.08s