probabilistic-data-structuresperformancededupcaching
Bloom Filters
Use memory-efficient probabilistic membership checks to avoid expensive negative lookups.
Definition
Bloom filters quickly test possible set membership with false positives but no false negatives.
When To Use
- High-QPS negative lookups where backend misses are expensive.
- Pre-filtering in dedup, cache, and storage read paths.
- Distributed systems where memory efficiency matters.
When Not To Use
- When false positives are unacceptable.
- Small datasets where direct lookup is already cheap.
- Frequently deleted keys without stable counting filter strategy.
Tradeoffs
- Cuts backend miss traffic, but can still send false positive lookups.
- Very memory efficient, with tuning complexity for error rate targets.
- Speeds negative checks, while adding rebuild/rotation operations.
Common Failure Modes
- Filter saturation increases false positives dramatically.
- Outdated filter after shard rebalance misguides routing.
- Incorrect hash function count hurts effectiveness.
Interview Framing
Use this structure when the interviewer asks for this pattern explicitly.
Discuss expected false positive rate, sizing formula inputs, and how you rotate/rebuild filters safely.
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