Combine a Bloom filter, an exact-match cache, and Aurora PostgreSQL into a three-tier pattern that returns most membership answers in sub-millisecond time while avoiding false positives by always consulting the authoritative database when needed.
Use a Bloom filter as a fast-negative gate: BF.EXISTS=0 proves absence and lets you short-circuit without downstream I/O.
Be deliberate about propagation and invalidation: the only functional correctness risk is the window between an Aurora commit and filter propagation, which requires a consistency model.
High-throughput systems often need to answer a binary membership question on a hot path (for example, "is this card blocked for this merchant?"). A relational lookup can be too slow for strict latency budgets. A standalone Bloom filter is fast and memory-efficient but allows false positives, which some applications cannot tolerate. The design goal is to serve most decisions with sub-millisecond latency while guaranteeing no false-positive business outcomes.
Tier 1 — Bloom filter (ElastiCache for Valkey)
Tier 2 — Exact-match cache (ElastiCache for Valkey)
Tier 3 — Aurora PostgreSQL (source of truth)
Why this composition is correctness-safe
Each tier provides a narrow guarantee. The Bloom filter never yields false negatives relative to the data it has been updated with, but it can yield false positives. Those false positives are harmless because Tier 2 and Tier 3 will resolve them: a BF.EXISTS=1 leads to a cache lookup and, if needed, a database query. The database always has the final word, so no false-positive business outcomes occur.
Operational notes and implementation choices
Performance and cost considerations
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