Start by acknowledging that Redis is a cache and the database is the source of truth, so inconsistency is inevitable. Then systematically walk through common patterns like Cache-Aside, Write-Through, Write-Behind, and Double-Delete, discussing their trade-offs in terms of consistency, performance, and complexity. Conclude with how you'd choose based on business requirements and mention monitoring and reconciliation.
Pro tip: Emphasize that perfect consistency is often unnecessary; instead, focus on bounding staleness and having a reconciliation process. Mention that at a financial firm like Fidelity, auditability and correctness are paramount, so you'd lean towards patterns that prioritize database integrity.
Ask about consistency needs (strong vs eventual), read/write ratios, latency requirements, and tolerance for stale data. This shows you tailor solutions to context.
Describe the standard pattern: read from cache, on miss read from DB and populate cache; write to DB and invalidate cache. Discuss its simplicity and common pitfalls like race conditions.
Compare Write-Through (write to cache and DB synchronously) vs Write-Behind (write to cache, async to DB). Highlight consistency, performance, and durability implications.
Cover deletion vs update, double-delete with delay, and using versioning or timestamps to avoid stale writes. Mention the read-repair approach.
Suggest periodic reconciliation jobs, TTLs, and monitoring for cache hit ratio and inconsistency metrics. Emphasize that eventual consistency is often acceptable if bounded.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.