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Label cuckoo filter

A cuckoo and filter technology, which is applied to instruments, calculation models, structured data retrieval, etc., can solve the problems of increased storage space overhead and dynamic changes of data members, and achieve the effect of reducing storage space overhead

Active Publication Date: 2020-08-18
SOUTH UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

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

Method used

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Examples

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example 1

[0107] Example 1: If Figure 4 As shown, when inserting an element x, first, use the formulas (1) and (2) to calculate the tag fingerprint remainder r of the element x x and two candidate bucket indexes h 0 (x)=6 and h 1 (x) = 4; secondly, the two tag fingerprints of element x are calculated using formulas (3), (4) and (5) and Adjust both candidate bucket indexes h at the same time 0 (x) and h 1 (x) is in the range [0,…,4], so the adjusted candidate bucket index is h 0 (x) = 1 and h 1 (x)=4; Next, search two candidate buckets h 0 (x) = 1 and h 1 (x)=4, it is found that the two candidate storage buckets contain free storage locations; finally, randomly select a free candidate storage bucket h 0 (x)=1, store the corresponding tag fingerprint in that bucket.

example 2

[0108] Example 2: If Figure 5 As shown, when inserting an element y, first, use the formulas (1) and (2) to calculate the tag fingerprint remainder r of the element y y and two candidate bucket indexes h 0 (y)=3 and h 1 (y) = 6; secondly, the two tag fingerprints of element y are calculated using formulas (3), (4) and (5) and Adjust both candidate bucket indexes h at the same time 0 (y) and h 1 (y) is in the range [0,…,4], so the adjusted candidate bucket index is h 0 (y)=3 and h 1 (y)=1; Next, search two candidate bucket indexes h 0 (y)=3 and h 1 (y)=1, it is found that the two storage buckets do not contain free storage locations, randomly select a candidate storage bucket h 1 (y) = 1, remove old fingerprints from this bucket store new fingerprint in that bucket; then, based on the old fingerprint and the current bucket index h 1 (a) = 1, assuming tag fingerprint has a label value of 1 and a remainder of r a , using formulas (6), (7) and (8) to calculat...

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Abstract

The invention discloses a label cuckoo filter which comprises a cuckoo hash table, the cuckoo hash table comprises a plurality of storage buckets, each data member corresponds to two label fingerprints, and the two label fingerprints are respectively stored in the two storage buckets; and when the tag cuckoo filter receives a data member management operation, determining two candidate storage buckets and two tag fingerprints corresponding to a preset data member based on the exclusive-OR operation, and executing the data member management operation based on the two candidate storage buckets and the two tag fingerprints obtained through determination. According to the method, two label fingerprints and two storage buckets are configured for each data member, the candidate storage buckets corresponding to the data members are determined by adopting the exclusive-OR operation based on the label fingerprints, and the number of the storage buckets is not required to be the power of 2, so that the storage space overhead of each data member is reduced.

Description

technical field [0001] The invention relates to the technical field of computer information representation and information retrieval, in particular to a label cuckoo filter. 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 delete operation, but its s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/22G06F16/242G06N3/00
CPCG06F16/2255G06F16/2443G06N3/006Y02D10/00
Inventor 黄昆
Owner SOUTH UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA
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