String hashing for linear probing

String hashing for linear probing (2009)

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by Mikkel Thorup

Venue:	In Proc. 20th SODA
Citations:	3 - 2 self

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Datum	Value	Source
TITLE	String hashing for linear probing	INFERENCE
AUTHOR NAME	Mikkel Thorup	SVM HeaderParse 0.2
ABSTRACT	Linear probing is one of the most popular implementations of dynamic hash tables storing all keys in a single array. When we get a key, we first hash it to a location. Next we probe consecutive locations until the key or an empty location is found. At STOC’07, Pagh et al. presented data sets where the standard implementation of 2-universal hashing leads to an expected number of Ω(log n) probes. They also showed that with 5-universal hashing, the expected number of probes is constant. Unfortunately, we do not have 5-universal hashing for, say, variable length strings. When we want to do such complex hashing from a complex domain, the generic standard solution is that we first do collision free hashing (w.h.p.) into a simpler intermediate domain, and second do the complicated hash function on this intermediate domain. Our contribution is that for an expected constant number of linear probes, it is suffices that each key has O(1) expected collisions with the first hash function, as long as the second hash function is 5-universal. This means that the intermediate domain can be n times smaller, and such a smaller intermediate domain typically means that the overall hash function can be made simpler and at least twice as fast. The same doubling of hashing speed for O(1) expected probes follows for most domains bigger than 32-bit integers, e.g., 64-bit integers and fixed length strings. In addition, we study how the overhead from linear probing diminishes as the array gets larger, and what happens if strings are stored directly as intervals of the array. These cases were not considered by Pagh et al. 1	SVM HeaderParse 0.2
YEAR	2009	INFERENCE
VENUE	In Proc. 20th SODA	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	655--664	INFERENCE
CITATIONS	20 found	ParsCit 1.0