Package evaluation to test SimilaritySearch on Julia 1.14.0-DEV.36 (e2f3178d9b*) started at 2025-11-06T17:42:59.445 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.7s ################################################################################ # Installation # Installing SimilaritySearch... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [053f045d] + SimilaritySearch v0.13.5 Updating `~/.julia/environments/v1.14/Manifest.toml` [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.4.0 [4fba245c] + ArrayInterface v7.22.0 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] + CPUSummary v0.2.7 [fb6a15b2] + CloseOpenIntervals v0.1.13 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [187b0558] + ConstructionBase v1.6.0 [adafc99b] + CpuId v0.3.1 [9a962f9c] + DataAPI v1.16.0 ⌅ [864edb3b] + DataStructures v0.18.22 [b4f34e82] + Distances v0.10.12 [ffbed154] + DocStringExtensions v0.9.5 [615f187c] + IfElse v0.1.1 [3587e190] + InverseFunctions v0.1.17 [92d709cd] + IrrationalConstants v0.2.6 [10f19ff3] + LayoutPointers v0.1.17 [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.16 [d125e4d3] + ManualMemory v0.1.8 [e1d29d7a] + Missings v1.2.0 [bac558e1] + OrderedCollections v1.8.1 [d96e819e] + Parameters v0.12.3 [f517fe37] + Polyester v0.7.18 [1d0040c9] + PolyesterWeave v0.2.2 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.0 [92933f4c] + ProgressMeter v1.11.0 [ae029012] + Requires v1.3.1 [94e857df] + SIMDTypes v0.1.0 [431bcebd] + SciMLPublic v1.0.0 [0e966ebe] + SearchModels v0.4.1 [053f045d] + SimilaritySearch v0.13.5 [a2af1166] + SortingAlgorithms v1.2.2 [aedffcd0] + Static v1.3.1 [0d7ed370] + StaticArrayInterface v1.8.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 ⌅ [2913bbd2] + StatsBase v0.33.21 [7792a7ef] + StrideArraysCore v0.5.8 [8290d209] + ThreadingUtilities v0.5.5 [3a884ed6] + UnPack v1.0.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [d6f4376e] + Markdown v1.11.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.0.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.11.0 [fa267f1f] + TOML v1.0.3 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [4536629a] + OpenBLAS_jll v0.3.29+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [8e850b90] + libblastrampoline_jll v5.15.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 4.29s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Error: Failed to use TestEnv.jl; test dependencies will not be precompiled │ exception = │ UndefVarError: `project_rel_path` not defined in `TestEnv` │ Suggestion: this global was defined as `Pkg.Operations.project_rel_path` but not assigned a value. │ Stacktrace: │ [1] get_test_dir(ctx::Pkg.Types.Context, pkgspec::PackageSpec) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:75 │ [2] test_dir_has_project_file │ @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:52 [inlined] │ [3] maybe_gen_project_override! │ @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:83 [inlined] │ [4] activate(pkg::String; allow_reresolve::Bool) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:12 │ [5] activate(pkg::String) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:9 │ [6] top-level scope │ @ /PkgEval.jl/scripts/precompile.jl:24 │ [7] include(mod::Module, _path::String) │ @ Base ./Base.jl:309 │ [8] exec_options(opts::Base.JLOptions) │ @ Base ./client.jl:344 │ [9] _start() │ @ Base ./client.jl:577 └ @ Main /PkgEval.jl/scripts/precompile.jl:26 Precompiling package dependencies... Precompiling packages... 5529.5 ms ✓ SearchModels 2037.8 ms ✓ Polyester 13506.5 ms ✓ SimilaritySearch 3 dependencies successfully precompiled in 22 seconds. 65 already precompiled. Precompilation completed after 34.62s ################################################################################ # Testing # Testing SimilaritySearch Status `/tmp/jl_ijZnZi/Project.toml` [7d9f7c33] Accessors v0.1.42 [4c88cf16] Aqua v0.8.14 [b4f34e82] Distances v0.10.12 [d96e819e] Parameters v0.12.3 [f517fe37] Polyester v0.7.18 [92933f4c] ProgressMeter v1.11.0 [0e966ebe] SearchModels v0.4.1 [053f045d] SimilaritySearch v0.13.5 [10745b16] Statistics v1.11.1 ⌅ [2913bbd2] StatsBase v0.33.21 [7792a7ef] StrideArraysCore v0.5.8 [ade2ca70] Dates v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [9a3f8284] Random v1.11.0 [2f01184e] SparseArrays v1.13.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_ijZnZi/Manifest.toml` [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.4.0 [4c88cf16] Aqua v0.8.14 [4fba245c] ArrayInterface v7.22.0 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] CPUSummary v0.2.7 [fb6a15b2] CloseOpenIntervals v0.1.13 [f70d9fcc] CommonWorldInvalidations v1.0.0 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [187b0558] ConstructionBase v1.6.0 [adafc99b] CpuId v0.3.1 [9a962f9c] DataAPI v1.16.0 ⌅ [864edb3b] DataStructures v0.18.22 [b4f34e82] Distances v0.10.12 [ffbed154] DocStringExtensions v0.9.5 [615f187c] IfElse v0.1.1 [3587e190] InverseFunctions v0.1.17 [92d709cd] IrrationalConstants v0.2.6 [10f19ff3] LayoutPointers v0.1.17 [2ab3a3ac] LogExpFunctions v0.3.29 [1914dd2f] MacroTools v0.5.16 [d125e4d3] ManualMemory v0.1.8 [e1d29d7a] Missings v1.2.0 [bac558e1] OrderedCollections v1.8.1 [d96e819e] Parameters v0.12.3 [f517fe37] Polyester v0.7.18 [1d0040c9] PolyesterWeave v0.2.2 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.0 [92933f4c] ProgressMeter v1.11.0 [ae029012] Requires v1.3.1 [94e857df] SIMDTypes v0.1.0 [431bcebd] SciMLPublic v1.0.0 [0e966ebe] SearchModels v0.4.1 [053f045d] SimilaritySearch v0.13.5 [a2af1166] SortingAlgorithms v1.2.2 [aedffcd0] Static v1.3.1 [0d7ed370] StaticArrayInterface v1.8.0 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.1 ⌅ [2913bbd2] StatsBase v0.33.21 [7792a7ef] StrideArraysCore v0.5.8 [8290d209] ThreadingUtilities v0.5.5 [3a884ed6] UnPack v1.0.2 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.7.0 [7b1f6079] FileWatching v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0 [b27032c2] LibCURL v1.0.0 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.13.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v1.0.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.13.0 [f489334b] StyledStrings v1.11.0 [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] LibCURL_jll v8.16.0+0 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.11.4 [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 v17.6.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Test Summary: | Pass Total Time test database abstractions | 56 56 15.9s 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 2.4s Test Summary: | Pass Total Time XKnn pop ops | 9603 9603 1.2s [ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}}) 10.896666 seconds (1000 allocations: 78.125 KiB) 10.974102 seconds (1000 allocations: 78.125 KiB) 3.770327 seconds (1000 allocations: 78.125 KiB) 4.066020 seconds (1000 allocations: 78.125 KiB) 4.005080 seconds (1000 allocations: 78.125 KiB) 4.011722 seconds (1000 allocations: 78.125 KiB) 3.916579 seconds (1000 allocations: 78.125 KiB) 3.854276 seconds (1000 allocations: 78.125 KiB) 15.683155 seconds (1000 allocations: 78.125 KiB) 15.852242 seconds (1000 allocations: 78.125 KiB) 31.058164 seconds (1000 allocations: 78.125 KiB) 28.087978 seconds (1000 allocations: 78.125 KiB) 22.132912 seconds (6.23 k allocations: 358.094 KiB) 20.202610 seconds (1000 allocations: 78.125 KiB) 17.645360 seconds (1.00 k allocations: 78.141 KiB) 17.747369 seconds (1000 allocations: 78.125 KiB) Test Summary: | Pass Total Time indexing vectors with ExhaustiveSearch | 8000 8000 3m45.2s [ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}}) 3.297765 seconds (1000 allocations: 78.125 KiB) 3.182289 seconds (1000 allocations: 78.125 KiB) 29.685240 seconds (1000 allocations: 78.125 KiB) 29.313631 seconds (1000 allocations: 78.125 KiB) 29.519602 seconds (1000 allocations: 78.125 KiB) 29.088946 seconds (1000 allocations: 78.125 KiB) 4.254704 seconds (1000 allocations: 78.125 KiB) 4.165784 seconds (1000 allocations: 78.125 KiB) Test Summary: | Pass Total Time indexing sequences with ExhaustiveSearch | 4000 4000 2m16.2s [ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}}) 10.216051 seconds (1000 allocations: 78.125 KiB) 10.326600 seconds (1000 allocations: 78.125 KiB) 10.455539 seconds (1000 allocations: 78.125 KiB) 10.519120 seconds (1000 allocations: 78.125 KiB) 10.223400 seconds (1000 allocations: 78.125 KiB) 10.332177 seconds (1000 allocations: 78.125 KiB) Test Summary: | Pass Total Time indexing sets with ExhaustiveSearch | 3000 3000 1m05.2s 0.044697 seconds (1.00 k allocations: 78.141 KiB) 0.045742 seconds (1000 allocations: 78.125 KiB) 0.039276 seconds (1000 allocations: 78.125 KiB) 0.039982 seconds (1000 allocations: 78.125 KiB) Test Summary: | Pass Total Time Normalized Cosine and Normalized Angle distances | 2000 2000 2.9s 0.069579 seconds (1000 allocations: 78.125 KiB) 0.069218 seconds (1000 allocations: 78.125 KiB) Test Summary: | Pass Total Time Binary hamming distance | 1000 1000 1.4s ExhaustiveSearch allknn: 4.289393 seconds (2.44 M allocations: 129.752 MiB, 0.87% gc time, 99.96% compilation time) ParallelExhaustiveSearch allknn: 1.194664 seconds (615.39 k allocations: 30.746 MiB, 2.07% gc time, 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: 2025-11-06T17:52:09.905 LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-06T17:52:10.144 LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0] LOG add_vertex! sp=2 ep=2 n=2 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-06T17:52:11.440 LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0] [ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:11.830 D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000005, 0x00000008, 0x00000011, 0x00000017, 0x00000019, 0x0000003a, 0x0000004f, 0x00000059] D.nn = Int32[1, 2, 3, 3, 5, 2, 1, 8, 5, 3, 1, 5, 5, 5, 8, 1, 17, 2, 1, 5, 5, 2, 23, 5, 25, 1, 1, 17, 5, 3, 2, 23, 1, 5, 17, 17, 1, 5, 23, 3, 2, 5, 8, 5, 8, 2, 25, 5, 5, 2, 5, 1, 3, 25, 5, 1, 5, 58, 23, 1, 17, 8, 1, 58, 1, 17, 1, 23, 1, 23, 2, 25, 3, 23, 2, 2, 3, 1, 79, 1, 3, 58, 2, 1, 23, 1, 8, 5, 89, 1, 3, 17, 2, 23, 2, 5, 2, 5, 2, 5] D.dist = Float32[0.0, 0.0, 0.0, 0.08749747, 0.0, 0.09750372, 0.07182056, 0.0, 0.032185674, 0.07455981, 0.06529653, 0.031425238, 0.06751478, 0.028095424, 0.09510428, 0.037485957, 0.0, 0.010450184, 0.09315318, 0.016246438, 0.08485049, 0.097251296, 0.0, 0.029271722, 0.0, 0.040860116, 0.029153824, 0.032440066, 0.017914534, 0.09298718, 0.010782778, 0.023046732, 0.0677855, 0.007119417, 0.073761225, 0.017890692, 0.03207624, 0.035852015, 0.00031924248, 0.05677831, 0.060691953, 0.053844273, 0.07022357, 0.020541131, 0.042748034, 0.041828334, 0.023263931, 0.047727108, 0.0071638227, 0.03223622, 0.0029815435, 0.0051786304, 0.03135997, 0.0729022, 0.0058951974, 0.04216516, 0.014367878, 0.0, 0.045616925, 0.03738165, 0.044550657, 0.08661878, 0.025568426, 0.06907338, 0.01801306, 0.050772548, 0.090201676, 0.08215791, 0.050992727, 0.035457432, 0.031131625, 0.026722312, 0.041046917, 0.040685773, 0.04206139, 0.03663957, 0.020404339, 0.05588156, 0.0, 0.07141763, 0.039180517, 0.05930817, 0.045899212, 0.017511368, 0.022804856, 0.062558174, 0.04409653, 0.02205956, 0.0, 0.045084417, 0.019179821, 0.023424327, 0.034873664, 0.027417421, 0.03817445, 0.058187842, 0.09419602, 0.04140812, 0.01846844, 0.027222574] Test Summary: | Pass Total Time neardup single block | 3 3 18.3s [ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:12.936 LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-06T17:52:12.936 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: 2025-11-06T17:52:12.937 [ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:12.937 [ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:12.937 [ Info: neardup> range: 65:80, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:12.937 [ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:12.937 [ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:12.937 [ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:12.937 D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000005, 0x00000008, 0x00000011, 0x00000017, 0x00000019, 0x0000003a, 0x0000004f, 0x00000059] D.nn = Int32[1, 2, 3, 3, 5, 2, 1, 8, 5, 3, 1, 5, 5, 5, 8, 1, 17, 2, 1, 5, 5, 2, 23, 5, 25, 1, 1, 17, 5, 3, 2, 1, 1, 5, 17, 17, 1, 5, 23, 3, 2, 5, 8, 5, 8, 2, 25, 5, 5, 2, 5, 1, 3, 25, 5, 1, 5, 58, 23, 1, 17, 8, 1, 23, 1, 17, 1, 23, 1, 23, 2, 25, 3, 23, 2, 2, 3, 1, 79, 1, 3, 58, 2, 1, 23, 1, 8, 5, 89, 1, 3, 17, 2, 23, 2, 5, 2, 5, 2, 5] D.dist = Float32[0.0, 0.0, 0.0, 0.08749747, 0.0, 0.09750372, 0.07182056, 0.0, 0.032185674, 0.07455981, 0.06529653, 0.031425238, 0.06751478, 0.028095424, 0.09510428, 0.037485957, 0.0, 0.010450184, 0.09315318, 0.016246438, 0.08485049, 0.097251296, 0.0, 0.029271722, 0.0, 0.040860116, 0.029153824, 0.032440066, 0.017914534, 0.09298718, 0.010782778, 0.076649785, 0.0677855, 0.007119417, 0.073761225, 0.017890692, 0.03207624, 0.035852015, 0.00031924248, 0.05677831, 0.060691953, 0.053844273, 0.07022357, 0.020541131, 0.042748034, 0.041828334, 0.023263931, 0.047727108, 0.0071638227, 0.03223622, 0.0029815435, 0.0051786304, 0.03135997, 0.0729022, 0.0058951974, 0.04216516, 0.014367878, 0.0, 0.045616925, 0.03738165, 0.044550657, 0.08661878, 0.025568426, 0.09879953, 0.01801306, 0.050772548, 0.090201676, 0.08215791, 0.050992727, 0.035457432, 0.031131625, 0.026722312, 0.041046917, 0.040685773, 0.04206139, 0.03663957, 0.020404339, 0.05588156, 0.0, 0.07141763, 0.039180517, 0.05930817, 0.045899212, 0.017511368, 0.022804856, 0.062558174, 0.04409653, 0.02205956, 0.0, 0.045084417, 0.019179821, 0.023424327, 0.034873664, 0.027417421, 0.03817445, 0.058187842, 0.09419602, 0.04140812, 0.01846844, 0.027222574] 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: 2025-11-06T17:52:13.025 LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-06T17:52:13.026 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: 2025-11-06T17:52:13.026 [ Info: neardup> range: 33:48, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:13.026 [ Info: neardup> range: 49:64, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:13.026 [ Info: neardup> range: 65:80, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:13.026 [ Info: neardup> range: 81:96, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:13.027 [ Info: neardup> range: 97:100, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:13.027 [ Info: neardup> finished current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:13.027 D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000011, 0x00000019] D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 2, 4, 5, 14, 2, 4, 14, 25, 1, 7, 16, 9, 10, 2, 11, 15, 5, 9, 17, 1, 5, 4, 10, 7, 13, 7, 16, 12, 10, 25, 16, 5, 2, 5, 1, 10, 25, 5, 11, 5, 12, 11, 1, 17, 7, 1, 15, 1, 4, 6, 15, 9, 14, 2, 25, 10, 14, 9, 10, 3, 16, 10, 1, 10, 13, 6, 1, 11, 1, 7, 5, 13, 16, 3, 17, 9, 14, 9, 9, 2, 14, 2, 12] 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, 0.0, 0.010450184, 0.029215276, 0.016246438, 0.017259538, 0.097251296, 0.0095402, 0.009695947, 0.0, 0.040860116, 0.013533115, 0.0842706, 0.0030823946, 0.017684579, 0.010782778, 0.021454036, 0.060388207, 0.007119417, 0.0297364, 0.017890692, 0.03207624, 0.035852015, 0.0077706575, 0.002377689, 0.02100259, 0.039355457, 0.03648317, 0.016864598, 0.032794416, 0.025660455, 0.023263931, 0.018833935, 0.0071638227, 0.03223622, 0.0029815435, 0.0051786304, 0.02802682, 0.0729022, 0.0058951974, 0.009793103, 0.014367878, 0.02747482, 0.032412827, 0.03738165, 0.044550657, 0.064537704, 0.025568426, 0.007989943, 0.01801306, 0.019439042, 0.0004966259, 0.015883088, 0.023942888, 0.0074905753, 0.031131625, 0.026722312, 0.0056572556, 0.02241838, 0.019264579, 0.031621933, 0.020404339, 0.031637907, 0.069354415, 0.07141763, 0.03863144, 0.018823087, 0.03724259, 0.017511368, 0.01812023, 0.062558174, 0.029996395, 0.02205956, 0.06379682, 0.030952454, 0.019179821, 0.023424327, 0.02599305, 0.016905665, 0.005215585, 0.022722185, 0.09419602, 0.005312681, 0.01846844, 0.026891947] 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: 2025-11-06T17:52:20.492 LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=5 n=5 2025-11-06T17:52:20.493 [ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:20.498 [ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:20.499 [ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:20.499 [ Info: neardup> range: 65:80, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:20.499 [ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:20.499 [ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:20.499 [ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-11-06T17:52:20.499 D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000005, 0x00000008, 0x00000011, 0x00000017, 0x00000019, 0x0000003a, 0x0000004f, 0x00000059] D.nn = Int32[1, 2, 3, 3, 5, 2, 1, 8, 5, 3, 1, 5, 5, 5, 8, 1, 17, 2, 1, 5, 5, 2, 23, 5, 25, 1, 1, 17, 5, 3, 2, 1, 1, 5, 17, 17, 1, 5, 23, 3, 2, 5, 8, 5, 8, 2, 25, 5, 5, 2, 5, 1, 3, 25, 5, 1, 5, 58, 23, 1, 17, 8, 1, 23, 1, 17, 1, 23, 1, 23, 2, 25, 3, 23, 2, 2, 3, 1, 79, 1, 3, 58, 2, 1, 23, 1, 8, 5, 89, 1, 3, 17, 2, 23, 2, 5, 2, 5, 2, 5] D.dist = Float32[0.0, 0.0, 0.0, 0.08749747, 0.0, 0.09750372, 0.07182056, 0.0, 0.032185674, 0.07455981, 0.06529653, 0.031425238, 0.06751478, 0.028095424, 0.09510428, 0.037485957, 0.0, 0.010450184, 0.09315318, 0.016246438, 0.08485049, 0.097251296, 0.0, 0.029271722, 0.0, 0.040860116, 0.029153824, 0.032440066, 0.017914534, 0.09298718, 0.010782778, 0.076649785, 0.0677855, 0.007119417, 0.073761225, 0.017890692, 0.03207624, 0.035852015, 0.00031924248, 0.05677831, 0.060691953, 0.053844273, 0.07022357, 0.020541131, 0.042748034, 0.041828334, 0.023263931, 0.047727108, 0.0071638227, 0.03223622, 0.0029815435, 0.0051786304, 0.03135997, 0.0729022, 0.0058951974, 0.04216516, 0.014367878, 0.0, 0.045616925, 0.03738165, 0.044550657, 0.08661878, 0.025568426, 0.09879953, 0.01801306, 0.050772548, 0.090201676, 0.08215791, 0.050992727, 0.035457432, 0.031131625, 0.026722312, 0.041046917, 0.040685773, 0.04206139, 0.03663957, 0.020404339, 0.05588156, 0.0, 0.07141763, 0.039180517, 0.05930817, 0.045899212, 0.017511368, 0.022804856, 0.062558174, 0.04409653, 0.02205956, 0.0, 0.045084417, 0.019179821, 0.023424327, 0.034873664, 0.027417421, 0.03817445, 0.058187842, 0.09419602, 0.04140812, 0.01846844, 0.027222574] Test Summary: | Pass Total Time neardup small block with filterblocks=false | 3 3 7.5s computing farthest point 1, dmax: Inf, imax: 22, n: 30 computing farthest point 2, dmax: 1.5389057, imax: 19, n: 30 computing farthest point 3, dmax: 1.0185469, imax: 8, n: 30 computing farthest point 4, dmax: 0.88714474, imax: 10, n: 30 computing farthest point 5, dmax: 0.8533297, imax: 25, n: 30 computing farthest point 6, dmax: 0.7605698, imax: 4, n: 30 computing farthest point 7, dmax: 0.7262467, imax: 12, n: 30 computing farthest point 8, dmax: 0.68935126, imax: 26, n: 30 computing farthest point 9, dmax: 0.6037235, imax: 17, n: 30 computing farthest point 10, dmax: 0.56692976, imax: 6, n: 30 Test Summary: | Pass Total Time farthest first traversal | 3 3 1.5s Test Summary: | Pass Total Time AdjacencyList | 15 15 1.6s LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-06T17:52:28.759 LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0] LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=2, Δ=0.97619045, maxvisits=106) 2025-11-06T17:52:40.178 LOG n.size quantiles:[4.0, 4.0, 4.0, 5.0, 5.0] (i, j, d) = (11, 12, -1.1920929f-7) (i, j, d, :parallel) = (11, 12, -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 => 19.092661792, :exact => 0.934386735) Test Summary: | Pass Total Time closestpair | 4 4 20.6s 5.952669 seconds (1.00 k allocations: 140.711 KiB) SEARCH Exhaustive 1: 0.004419 seconds SEARCH Exhaustive 2: 0.004505 seconds SEARCH Exhaustive 3: 0.005334 seconds typeof(seq) = ExhaustiveSearch{SqL2Distance, MatrixDatabase{Matrix{Float32}}} typeof(ectx) = GenericContext{KnnSorted} typeof(q) = SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true} typeof(res) = KnnSorted{Vector{IdWeight}} [ Info: ===================== minrecall ============================== LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-06T17:53:08.934 LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0] LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=5, Δ=1.3066694, maxvisits=216) 2025-11-06T17:53:14.711 LOG n.size quantiles:[3.0, 4.0, 4.0, 5.0, 6.0] LOG add_vertex! sp=17130 ep=17134 n=17129 BeamSearch(bsize=16, Δ=1.2733874, maxvisits=440) 2025-11-06T17:53:15.712 LOG n.size quantiles:[4.0, 5.0, 5.0, 6.0, 7.0] LOG add_vertex! sp=30120 ep=30124 n=30119 BeamSearch(bsize=4, Δ=1.155, maxvisits=434) 2025-11-06T17:53:16.712 LOG n.size quantiles:[5.0, 6.0, 6.0, 7.0, 8.0] LOG add_vertex! sp=40990 ep=40994 n=40989 BeamSearch(bsize=8, Δ=1.155, maxvisits=450) 2025-11-06T17:53:17.712 LOG n.size quantiles:[4.0, 5.0, 6.0, 6.0, 9.0] LOG add_vertex! sp=51700 ep=51704 n=51699 BeamSearch(bsize=8, Δ=1.155, maxvisits=450) 2025-11-06T17:53:18.712 LOG n.size quantiles:[5.0, 5.0, 6.0, 6.0, 9.0] LOG add_vertex! sp=59805 ep=59809 n=59804 BeamSearch(bsize=13, Δ=1.21275, maxvisits=580) 2025-11-06T17:53:19.712 LOG n.size quantiles:[4.0, 4.0, 6.0, 6.0, 9.0] LOG add_vertex! sp=69235 ep=69239 n=69234 BeamSearch(bsize=13, Δ=1.21275, maxvisits=580) 2025-11-06T17:53:20.713 LOG n.size quantiles:[6.0, 7.0, 8.0, 8.0, 9.0] LOG add_vertex! sp=78260 ep=78264 n=78259 BeamSearch(bsize=13, Δ=1.21275, maxvisits=580) 2025-11-06T17:53:21.713 LOG n.size quantiles:[3.0, 8.0, 8.0, 8.0, 9.0] LOG add_vertex! sp=85225 ep=85229 n=85224 BeamSearch(bsize=4, Δ=1.244447, maxvisits=546) 2025-11-06T17:53:22.731 LOG n.size quantiles:[4.0, 7.0, 8.0, 8.0, 10.0] LOG add_vertex! sp=93475 ep=93479 n=93474 BeamSearch(bsize=4, Δ=1.244447, maxvisits=546) 2025-11-06T17:53:23.731 LOG n.size quantiles:[5.0, 6.0, 6.0, 8.0, 8.0] quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 87.0] [ Info: minrecall: queries per second: 12979.094896327857, recall: 0.902375 graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=16, Δ=1.075, maxvisits=700)) quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 87.0] [ Info: ===================== rebuild ============================== (graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=8, Δ=1.155, maxvisits=576)), 1000, 8) [ Info: rebuild: queries per second: 15180.373271109185, recall: 0.90075 graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=8, Δ=1.155, maxvisits=576)) quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 10.0, 11.0, 12.0, 14.0, 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(bsize=11, Δ=1.08, maxvisits=700)), 1000, 8) 1.712767 seconds (611.69 k allocations: 31.305 MiB, 95.58% compilation time) [ Info: matrixhints: queries per second: 13525.506433846713, recall: 0.9035 graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=11, Δ=1.08, maxvisits=700)) quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 87.0] 2.166812 seconds (1.00 k allocations: 140.711 KiB) SEARCH Exhaustive 1: 0.002185 seconds SEARCH Exhaustive 2: 0.002119 seconds SEARCH Exhaustive 3: 0.002247 seconds typeof(seq) = ExhaustiveSearch{SqL2Distance, StrideMatrixDatabase{StrideArraysCore.StrideArray{Float32, 2, (1, 2), Tuple{Int64, Int64}, Tuple{Nothing, Nothing}, Tuple{Static.StaticInt{1}, Static.StaticInt{1}}, Matrix{Float32}}}} typeof(ectx) = GenericContext{KnnSorted} typeof(q) = StrideArraysCore.StrideArray{Float32, 1, (1,), Tuple{Int64}, Tuple{Nothing}, Tuple{Static.StaticInt{1}}, Matrix{Float32}} typeof(res) = KnnSorted{Vector{IdWeight}} [ Info: ===================== minrecall ============================== LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-11-06T17:54:35.356 LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0] LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=5, Δ=1.3066694, maxvisits=216) 2025-11-06T17:54:41.200 LOG n.size quantiles:[3.0, 4.0, 4.0, 5.0, 6.0] LOG add_vertex! sp=20835 ep=20839 n=20834 BeamSearch(bsize=16, Δ=1.2733874, maxvisits=440) 2025-11-06T17:54:42.200 LOG n.size quantiles:[2.0, 7.0, 8.0, 8.0, 9.0] LOG add_vertex! sp=35250 ep=35254 n=35249 BeamSearch(bsize=4, Δ=1.155, maxvisits=434) 2025-11-06T17:54:43.200 LOG n.size quantiles:[4.0, 7.0, 7.0, 9.0, 10.0] LOG add_vertex! sp=47475 ep=47479 n=47474 BeamSearch(bsize=8, Δ=1.155, maxvisits=450) 2025-11-06T17:54:44.200 LOG n.size quantiles:[3.0, 4.0, 7.0, 8.0, 9.0] LOG add_vertex! sp=58115 ep=58119 n=58114 BeamSearch(bsize=13, Δ=1.21275, maxvisits=580) 2025-11-06T17:54:45.200 LOG n.size quantiles:[5.0, 6.0, 7.0, 8.0, 8.0] LOG add_vertex! sp=68805 ep=68809 n=68804 BeamSearch(bsize=13, Δ=1.21275, maxvisits=580) 2025-11-06T17:54:46.201 LOG n.size quantiles:[4.0, 6.0, 7.0, 9.0, 10.0] LOG add_vertex! sp=78450 ep=78454 n=78449 BeamSearch(bsize=13, Δ=1.21275, maxvisits=580) 2025-11-06T17:54:47.201 LOG n.size quantiles:[6.0, 7.0, 8.0, 8.0, 10.0] LOG add_vertex! sp=86755 ep=86759 n=86754 BeamSearch(bsize=4, Δ=1.244447, maxvisits=546) 2025-11-06T17:54:48.201 LOG n.size quantiles:[4.0, 8.0, 8.0, 9.0, 10.0] LOG add_vertex! sp=95845 ep=95849 n=95844 BeamSearch(bsize=4, Δ=1.244447, maxvisits=546) 2025-11-06T17:54:49.201 LOG n.size quantiles:[6.0, 7.0, 9.0, 9.0, 11.0] quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 87.0] [ Info: minrecall: queries per second: 14556.783428324838, recall: 0.902375 graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=16, Δ=1.075, maxvisits=700)) quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 87.0] [ Info: ===================== rebuild ============================== (graph.algo, length(B.queries), B.ksearch) = (Base.RefValue{BeamSearch}(BeamSearch(bsize=8, Δ=1.155, maxvisits=576)), 1000, 8) [ Info: rebuild: queries per second: 16939.12429368722, recall: 0.90075 graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=8, Δ=1.155, maxvisits=576)) quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [2.0, 10.0, 11.0, 12.0, 14.0, 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(bsize=11, Δ=1.08, maxvisits=700)), 1000, 8) 1.647281 seconds (567.40 k allocations: 29.100 MiB, 2.25% gc time, 95.11% compilation time) [ Info: matrixhints: queries per second: 14319.466495627321, recall: 0.9035 graph.algo = Base.RefValue{BeamSearch}(BeamSearch(bsize=11, Δ=1.08, maxvisits=700)) quantile(neighbors_length.(Ref(graph.adj), 1:length(graph)), 0:0.1:1.0) = [1.0, 7.0, 8.0, 9.0, 10.0, 11.0, 13.0, 15.0, 18.0, 23.0, 87.0] Test Summary: | Pass Total Time vector indexing with SearchGraph | 18 18 2m44.1s Testing SimilaritySearch tests passed Testing completed after 696.49s PkgEval succeeded after 758.57s