[ViralBShah]

A whole view of a sparse vector, view(x, :), or of a sparse column, view(A, :, j), should behave like the vector it views. Two things stopped it. view(x, :) of a SparseVector{Tv,Int32} is not a SparseVectorView, so it misses every O(nnz) method on that alias: sum, maximum, norm and dot visit all n elements, rmul! and lmul! take the dense fallback, kron is dense and findnz throws. And for any index type, SparseVector(v) of a whole view, which sparse, convert, vcat, hcat and kron all go through, reads it as a dense vector: O(n), stored zeros dropped, Int indices.

using SparseArrays
x = sparsevec(Int32[2, 4, 5], [1.0, 0.0, 3.0], 6); v = view(x, :)
A = sparse(Int32[1, 3], Int32[1, 1], [1.0, 0.0], 4, 2); c = view(A, :, 1)

Before

SparseVector(v) = sparsevec([2, 5], [1.0, 3.0], 6)
sparse(v) = sparsevec([2, 5], [1.0, 3.0], 6)
vcat(v, v) = sparsevec([2, 5, 8, 11], [1.0, 3.0, 1.0, 3.0], 12)
typeof(kron(v, x)) = Vector{Float64}
typeof(SparseVector(c)) = SparseVector{Float64, Int64}
findnz(v): MethodError: no method matching findnz(::SubArray{Float64, 1, SparseVector{Float64, Int32}, Tuple{Base.Slice{Base.OneTo{Int32}}}, false})

After

SparseVector(v) = sparsevec(Int32[2, 4, 5], [1.0, 0.0, 3.0], 6)
sparse(v) = sparsevec(Int32[2, 4, 5], [1.0, 0.0, 3.0], 6)
vcat(v, v) = sparsevec(Int32[2, 4, 5, 8, 10, 11], [1.0, 0.0, 3.0, 1.0, 0.0, 3.0], 12)
kron(v, x) = sparsevec(Int32[8, 10, 11, 20, 22, 23, 26, 28, 29], [1.0, 0.0, 3.0, 0.0, 0.0, 0.0, 3.0, 0.0, 9.0], 36)
typeof(SparseVector(c)) = SparseVector{Float64, Int32}
findnz(v) = (Int32[2, 4, 5], [1.0, 0.0, 3.0])

For v32 = view(sparsevec(Int32[1, n], [1.0, 2.0], n), :) and v64 likewise, with n = 10^7, the minimum of three runs and the @allocated of a warm call:

f(v)	v32, main	v32, branch	v64, main	v64, branch
sum	0.020 s, 0 B	2.0e-8 s, 0 B	3.0e-8 s, 0 B	3.0e-8 s, 0 B
maximum	0.047 s, 0 B	2.9e-8 s, 0 B	3.0e-8 s, 0 B	2.0e-8 s, 0 B
dot(v, v)	0.035 s, 0 B	3.0e-8 s, 0 B	2.1e-8 s, 0 B	2.0e-8 s, 0 B
count(!iszero, v)	0.021 s, 0 B	2.0e-8 s, 0 B	0.021 s, 0 B	2.0e-8 s, 0 B
findnz	MethodError	3.0e-8 s, 144 B	3.0e-8 s, 160 B	3.0e-8 s, 160 B
SparseVector	0.027 s, 19 MiB	5.0e-8 s, 144 B	0.023 s, 19 MiB	4.0e-8 s, 160 B
vcat(v, v)	0.057 s, 38 MiB	3.1e-7 s, 880 B	0.052 s, 38 MiB	2.6e-7 s, 960 B

A SparseVector stores its length as Ti, so axes(x32) is (OneTo{Int32}(n),) and view(x32, :) slices a Slice{OneTo{Int32}}, while SparseVectorView named Slice{OneTo{Int}} only. A matrix stores its size as Int, so SparseColumnView already matched for every index type. The fix widens SparseVectorView to <:Tuple{Base.Slice{<:Base.OneTo}}; no method on the alias assumes Int axes, since they read the parent through nonzeros, nonzeroinds and length. The SparseVector copy constructors now take SparseVectorOrView rather than AbstractCompressedVector, so a whole view or column view is copied from its stored entries with the parent's index type, and sparsevec of one copies it through them. count on a SparseVector already counted f over the stored values plus f of one zero times the number of unstored entries; that method (_simple_count) now also covers the whole and range views, and takes the zero from _densezero.

This touches dispatch.

Potential breakage, in each case towards what the parent already gives:

• For a whole view or column view of any index type, SparseVector(v), sparse(v), convert(SparseVector, v) and sparse vcat, hcat, hvcat and kron that take one keep its stored zeros, so nnz of the result can be larger than before.
• They also keep the parent's index type instead of Int.
• For a whole view v of a SparseVector with a non-Int index type: nonzeros(v) is the parent's Vector rather than a SubArray of it; nonzeroinds(v) is the parent's own index vector rather than a fresh copy, so writing to it changes the parent; findfirst, findlast, findnext and findprev return Ti rather than Int; diff(v) keeps a stored zero; kron(v, x) and kron(v, A) are sparse rather than dense; and A * v with an Int-indexed A is a SparseVector{Tv,Int} rather than a SparseVector{Tv,Ti}.

length keeps returning Ti, as #176 (JuliaLang/julia#45937) decided. Range views such as view(x, 2:4) still convert through the dense path, and spdiagm(0 => view(x, :)) still stores every diagonal entry; both are left for separate PRs.

🤖 Generated with Claude Code

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 94.68%. Comparing base (e4a42b4) to head (f3c058b).
⚠️ Report is 13 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1119      +/-   ##
==========================================
- Coverage   94.73%   94.68%   -0.05%     
==========================================
  Files          16       16              
  Lines       10325    10433     +108     
==========================================
+ Hits         9781     9879      +98     
- Misses        544      554      +10     

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