Query Cache Invalidation

Hard problem; patterns.

Overview

Query cache invalidation keeps cached data fresh enough for the workload. Cache hit rate is the easy metric; staleness is the hard one because it depends on the workload’s tolerance for stale data, which varies per query.

• Hard problem; patterns. Cache invalidation is famously hard; the patterns map to consistency need.
• TTL-based. Per-cache freshness window; matches simple cases where staleness has known bound.
• Event-based. Per-update invalidation event; matches consistency-sensitive workloads where stale is unacceptable.
• Tag-based plus stale-while-revalidate. Per-tag invalidation supports cross-cutting changes; SWR serves stale and refreshes in background for user-experience workloads.

The approach

The practical approach: per-cache strategy matched to consistency need, TTL plus event for layered freshness, staleness monitoring per cache, SWR for user-facing pages, documented per-cache strategy. The team’s discipline produces matched caching rather than blanket TTL.

• Per-cache strategy. Per-cache invalidation approach; the right strategy depends on the workload.
• TTL plus event. TTL as backstop, event as primary; the layering produces real freshness with bounded worst case.
• Monitor staleness. Per-cache staleness percentile; the metric that matters for the workload.
• Stale-while-revalidate plus documented invalidation. Per-page SWR strategy supports user experience; per-cache strategy committed for operational reviews.

Why this compounds

Cache invalidation discipline compounds across services. Each correct strategy produces ongoing freshness; the team’s caching expertise grows; new caches inherit the per-workload methodology.

• Better user experience. Right freshness for the workload; users see data current enough to act on.
• Better cost efficiency. Right cache hit rate plus freshness; the caching reduces backend load without serving stale.
• Better operational fit. Strategy matches consistency need; user-facing and analytical workloads get different strategies.
• Institutional knowledge. Each cache teaches consistency patterns; the team’s caching muscle grows.

Cache invalidation discipline is an operational discipline that pays off across years. Nova AI Ops integrates with cache telemetry, surfaces patterns, and supports the team’s caching discipline.