Use this probabilistic structure when bounded memory and fast approximate membership, cardinality, or similarity queries are preferable to retaining every original item.

Inserted items

Required list input.

Items to remove

Required list input.

Query

Required string input.

Counters

Required integer input.

Hash functions

Required integer input.

Build a deterministic counting Bloom filter, apply requested removals, and test a query. Positive membership and multiplicity remain probabilistic because hash collisions can overestimate both. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

Counting Bloom filter result

The resulting counting bloom filter result returned as an object.

• Probabilistic structures trade exactness for space. Error rates depend on capacity, hash quality, parameter choice, and merge compatibility; some structures permit false positives, and most cannot reconstruct the inserted values.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Choose parameters from the expected cardinality and acceptable error rate, monitor saturation, and retain an exact store when results must be verified. Merge only structures created with compatible parameters and hashing.

• Bloom Filter Calculator

  Build a deterministic Bloom filter, test an item, and estimate its false-positive probability.

• HyperLogLog Cardinality Estimator

  Estimate the number of distinct strings with a fixed-size HyperLogLog sketch. The estimate is probabilistic and may differ from the exact count.

• Locality-Sensitive Hash Candidate Checker

  Use banded MinHash signatures to flag a pair of string sets as a similarity candidate. A matching band is not proof of semantic similarity.