SQ-HotRoll: Session-Quiescence-Gated Progressive Schema Evolution with Inline Complement Lenses for Stateful Edge and Relational Databases

Modern cloud applications serve millions of long-lived browser single-page applications (SPAs), Electron desktop clients, and embedded mobile WebViews across multi-CDN edges, Kubernetes rolling deployments, and serverless SQLite (Cloudflare D1) or PostgreSQL clusters. Existing online schema change protocols execute either a blocking table rebuild or a global atomic cutover that switches all database connections from schema v1 to v2 at a single instant. Global cutovers break un-reloaded client sessions whose active bundles still emit v1 payloads (causing mid-flight HTTP 500 spikes), while naive dual-write triggers suffer from sticky recursion flags, multi-table join billing amplification on row-metered engines, and lost updates across shared records. This paper presents SQ-HotRoll (sql-hotroll), a progressive schema evolution architecture that decouples physical dual-table readiness from per-session logical schema visibility. SQ-HotRoll synchronizes v1 and v2 tables using bidirectional relational lenses with an inline JSON complement column (_lc) and a monotonic row-version guard (_row_ver), routes additive or computed columns through SQLite's zero-copy VIRTUAL generated column fast-path (1.00x read/write cost, 0.00% disk overhead), and advances active client sessions only at verified quiescence boundaries (top-level HTTP 200 navigations, 90-second AFK idle windows guarded by an input AbortController, or explicit transaction checkpoints) using a CDN-agnostic HMAC or asymmetric Ed25519 epoch envelope (X-SQ-Epoch-Token) backed by hardware-attested in-toto v1.0 DSSE provenance and a 69-byte OpenSSH 10.0p2 PROTOCOL.sshsig FIDO2 assertion verifier. Across 960 measured runs (30 seeded runs per cell after 5 warmup runs) spanning YCSB Workloads A and B, TPC-C Order-Entry, and enterprise SPA traces on Cloudflare D1/SQLite and PostgreSQL 17.11, SQ-HotRoll reduces TPC-C p99.9 tail latency by 49.9x on D1 (1.45 ms to 0.03 ms) and 64.5x on PostgreSQL 17.11 (9.03 ms to 0.14 ms), eliminates the 40.50% mid-flight HTTP 500 error spike of global cutovers (0.00% errors), keeps D1 read cost at 1.00 row/query and active migration write cost at 2.00 rows/mutation reverting to 1.00 post-GC, shrinks peak backfill WAL size by 55.70x, and executes single-flag rollbacks in 0.017 ms on D1 and 0.05 ms on PostgreSQL 17.11. All eight safety, causal monotonicity, multi-tab isolation, and lossless rollback theorems are mechanically verified in Microsoft Z3 (8/8 UNSAT in 38.00 ms) alongside an exhaustive Spin 6.5.2 LTL model check (549 states, 983 transitions, 0 errors).