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Cuckoo filter based on fingerprint family
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A filter and cuckoo technology, applied in instruments, special data processing applications, electrical digital data processing, etc., can solve the problems of increased storage space overhead and dynamic changes of data members, achieve fast insertion, and improve storage space efficiency Effect
Pending Publication Date: 2020-12-29
PENG CHENG LAB +1
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However, the existing cuckoo filter has the problem that the storage space overhead of each data member dynamically changes with the number of elements, because the XOR operation of the cuckoo filter requires that the number of storage buckets must be a power of 2 (that is, 2
b
, b is the exponent), resulting in the storage space overhead of each data member increasing by 2 times in the worst case
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example 1
[0101] Example 1: If Figure 4 As shown, when an element x is inserted, formulas (1) and (2) are used to calculate the 4 fingerprints of the data member x, in, as well as The options are 10, 11, 00, 01 respectively, and the remainder of the fingerprint is 110101. After obtaining the 4 fingerprints, use the formula (3) to calculate the 4 candidate bucket index values h of the data member x 0 (x), h 1 (x), h 2 (x) and h 3 (x), where h 0 (x)=1, h 1 (x)=3, h 2 (x)=5, h 3 (x)=8; respectively search for 4 candidate buckets h of data member x 0 (x), h 1 (x), h 2 (x) and h 3 (x), discover bucket h 0 (x), h 2 (x), h 3 (x) both contain free storage locations; finally, randomly select a candidate storage bucket for a free location of the data member x (ie h 0 (x)=1), store the corresponding fingerprint in the candidate bucket.
example 2
[0102] Example 2: If Figure 5 As shown, when element y is inserted, formulas (1) and (2) are used to calculate the 4 fingerprints of data member x as well as And use the formula (3) to calculate the 4 candidate bucket index values h of the data member x 0 (y), h 1 (y), h 2 (y) and h 3 (y), where h 0 (y)=1, h 1 (y)=3,h 2 (y)=6, h 3 (y)=9; respectively search for 4 candidate buckets h of data member x 0 (y), h 1 (y), h 2 (y) and h 3 (y), discover bucket h 0 (y), h 1 (y), h 2 (y) and h 3 Neither (y) contains free memory locations. At this time, randomly select a candidate bucket of data member x (ie h 2 (y)=6), store the corresponding fingerprint In that candidate bucket, remove old fingerprints from that bucket at the same time Calculate old fingerprints using formulas (4) and (5) The other 3 reference fingerprints of and the corresponding three reference bucket index values h 3 (s), h 0 (s), h 1 (s), found old fingerprints The candidate bu...
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Abstract
The invention discloses a cuckoo filter based on a fingerprint family, the cuckoo filter based on the fingerprint family comprises a cuckoohash table, the cuckoo hash table comprises a plurality of storage buckets, each data member corresponds to a plurality of fingerprints, and each fingerprint in the plurality of fingerprints is stored in different storage buckets; and when the cuckoo filter receives a data member management operation, a plurality of fingerprints corresponding to the data members and candidate storage buckets corresponding to the fingerprints are determined based on addition and subtraction operations, and the data member management operation is executed based on the determined fingerprints and the candidate storage buckets corresponding to the fingerprints. According to the invention, the plurality of fingerprints and the plurality of candidate storage buckets are allocated to each data member, and the number of the plurality of fingerprints can be greater than orequal to 2, so that the storage space efficiency can be improved, and quick insertion, deletion and query operations are supported.
Description
technical field [0001] The invention relates to the technical fields of computer information representation and information retrieval, in particular to a cuckoo filter based on a fingerprint family. Background technique [0002] Membership query (Membership Query) is one of the key methods of many network applications and distributed systems (such as: collaborative caching, data packet processing, key-value storage and deduplication), which requires low storage space overhead, fast query and growth Three key requirements such as volume update. At present, Bloom Filter, Standard Bloom Filter, Counting Bloom Filter, and Cuckoo Filter are commonly used in member affiliation query. It is difficult for the Bloom filter and its variants to satisfy the above three key requirements simultaneously. For example, standard Bloom filters support element insertion and query operations, but not element deletion operations. Counting Bloom filter is a kind of Bloom filter that supports de...
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