Bloom Filter

A probabilistic data structure for membership testing.

Usage

from redis_kit import BloomFilter

bf = BloomFilter(conn.sync_client, "emails", expected_items=100_000, false_positive_rate=0.01)

bf.add("alice@example.com")
bf.exists("alice@example.com")   # True
bf.exists("unknown@example.com") # False (probably)

Batch Operations

bf.add_many(["a@x.com", "b@x.com", "c@x.com"])
results = bf.exists_many(["a@x.com", "d@x.com"])  # [True, False]

Reset

bf.reset()  # Delete the underlying Redis key, clearing the filter

How It Works

• Uses double hashing technique (two SHA-256-based hashes to derive k offsets), FIPS-compatible
• Pipeline-based SETBIT/GETBIT operations for improved performance
• exists_many uses a single pipeline batch check instead of N independent calls
• Automatically calculates optimal bit array size and hash function count based on expected_items and false_positive_rate