A capability stays maintainable only when the team can explain every state, retry, and piece of residual data—not merely show one successful run. WAL permits readers with a writer but still has one writer. Long reads prevent checkpoint progress, growing the WAL and shifting latency to later writes.
Data design covers atomic writes, concurrent access, schema migration, crash recovery, and cleanup. One successful save says nothing about power loss, full storage, or reopening with an older version.
Questions the design must answer
Write the following choices as reviewable rules instead of scattering them across callbacks and UI conditions. Explicit rules make scaling, compatibility, and diagnosis less dependent on guesswork.
- Set a bounded busy timeout, keep transactions database-only, close paged reads promptly, and monitor WAL bytes, checkpoints, fsync, and writer wait.
- Define success, degraded, cancelled, and failed terminal states before UI, storage, and metrics consume the same state.
- Retries need an idempotency key, backoff, and deadline; after the deadline create a new task instead of reviving old callbacks.
The delivery standard for SQLite WAL Concurrency and the Checkpoint Latency Trap is a usable normal path, convergent failures, bounded resources, and a state users can understand. The result is a production capability that can be explained, degraded safely, and rolled back—not a demo that works once.
Edge cases are part of the feature
An abnormal path is more than an error banner. It decides how in-flight work stops, how the peer learns the outcome, what residue remains, and whether the next operation inherits it.
- Calling networks inside a transaction holds the writer, while copying a live db without WAL/SHM can omit recent commits.
- Fixing only the UI leaves queues, locks, or expired credentials for the next operation to inherit and fail again.
- Ideal-size tests miss large files, long sessions, and concurrency that cross hidden limits and cause cascading failure.
Prove that it works with evidence
Build golden cases from known inputs and controlled faults, then align production metrics with those results. Verification extends to production only when signals detect the same degradation early.
- Run concurrent short writes plus one long read, checkpoint near disk-full, and kill the process; verify recovery and bounded p99 write latency.
- Disconnect, change networks, and recover mid-operation; reconcile endpoint state, persistence, and resource counts.
- Before release, record success rate, p50/p95/p99 latency, error classes, and resource high-water marks with explicit rollback thresholds.
The release bar is clear: users understand the current state, failures stop or recover, resources stay bounded, and operators can identify the phase from minimum necessary evidence.