layout: doc_page title: “Bloom Filter”

Bloom Filter

Make sure to include druid-bloom-filter as an extension.

BloomFilter is a probabilistic data structure for set membership check. Following are some characterstics of BloomFilter

BloomFilters are highly space efficient when compared to using a HashSet.
Because of the probabilistic nature of bloom filter false positive (element not present in bloom filter but test() says true) are possible
false negatives are not possible (if element is present then test() will never say false).
The false positive probability is configurable (default: 5%) depending on which storage requirement may increase or decrease.
Lower the false positive probability greater is the space requirement.
Bloom filters are sensitive to number of elements that will be inserted in the bloom filter.
During the creation of bloom filter expected number of entries must be specified.If the number of insertions exceed the specified initial number of entries then false positive probability will increase accordingly.

Internally, this implementation of bloom filter uses Murmur3 fast non-cryptographic hash algorithm.

{
  "type" : "bloom",
  "dimension" : <dimension_name>,
  "bloomKFilter" : <serialized_bytes_for_BloomKFilter>,
  "extractionFn" : <extraction_fn>
}

Property	Description	required?
`type`	Filter Type. Should always be `bloom`	yes
`dimension`	The dimension to filter over.	yes
`bloomKFilter`	Base64 encoded Binary representation of `org.apache.hive.common.util.BloomKFilter`	yes
`extractionFn`	Extraction function to apply to the dimension values	no

Serialized BloomKFilter format:

1 byte for the number of hash functions.
1 big endian int(That is how OutputStream works) for the number of longs in the bitset
big endian longs in the BloomKFilter bitset

Note: org.apache.hive.common.util.BloomKFilter provides a serialize method which can be used to serialize bloom filters to outputStream.