docs(changelog): cut 0.58.0

CHANGELOG.md

@@ -8,6 +8,8 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 ## [Development]
 <!-- Do Not Erase This Section - Used for tracking unreleased changes -->
 
+## [0.58.0 - 2026-07-22]
+
 ### Performance
 - **GFQL seeded fast paths use resident indexes for PROPERTY-seeded lookups (#1658 x #1755)**: the resident-index acceleration previously required the seed filter to hit the node BINDING column, so the common LDBC/user pattern — graph bound on a synthetic key while `MATCH (m {id: $x})` filters the `id` property — never engaged it and paid O(N)/O(E) scans. The gates are now decoupled: the seed row falls back to the property scan when needed, but the CSR adjacency frontier gather and node-id candidate gathers engage whenever valid indexes are resident (binding-column values are the index key domain regardless of how the seed rows were found). Same decline contract otherwise (stale/absent index, non-numeric ids). Pinned: `test_property_seeded_engages_adjacency_index` (rebound-key graph, engagement + parity, pandas+polars).
 - **GFQL seeded fast path covers single-alias property RETURNs (LDBC IS5 shape) (#1755)**: `MATCH (m {id})-[:T]->(p) RETURN p.a AS x, p.b` — which lowers to `rows(source=p)+select(items)` rather than a whole-row projection — now takes the seeded typed-hop fast path: the deduped destination rows are renamed/selected directly (value-identical to the rows-pivot + select pipeline; row order may differ). Dtype parity with the full pandas path is exact: non-id properties ride the rows-pivot there, which upcasts int/float to float64 and bool to object, so the lean tail applies the same casts (the id property keeps its dtype); dtype classes not verified against the pivot (datetimes, pandas extension dtypes, categoricals) decline. `res._edges` is the same empty edges frame the full path yields (never None). Conservative declines keep full-path semantics for everything else: cross-alias refs (`RETURN m.x, p.y`), mixed whole-row+property, DISTINCT/ORDER BY/LIMIT (extra lowered ops), expr items, properties absent from the node frame (the full path's null/error semantics must apply), and requested-vs-actual engine mismatches (the full path converts to the requested engine). Differential fast-vs-full sweep across all of the above on pandas + polars: exact parity incl. dtypes and edges, engagement pinned both ways. Measured on LDBC SNB SF1 (dgx, harness row-validated): `message-creator` (IS5) 116.3→38.3 ms on polars (the residue is the seed scan, targeted separately by resident-index coverage).