Robin hood hashing

Celis, Pedro; Larson, Per-Åke; Munro, J. Ian

doi:10.1109/sfcs.1985.48

Cited by 86 publications

(98 citation statements)

References 6 publications

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“…If that too is occupied, its old inhabitant is moved, etc. RH Robin Hood, see Celis, Larson and Munro [4] and [15,. When an item wants a cell that is already occupied by another item, the item (of the two) with the largest current displacement is put in the cell and the other is moved to the next cell, where the same rule applies recursively.…”

Section: Introductionmentioning

confidence: 99%

Individual displacements for linear probing hashing with different insertion policies

Janson

2005

ACM Trans. Algorithms

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Abstract. We study the distribution of the individual displacements in hashing with linear probing for three different versions: First Come, Last Come and Robin Hood. Asymptotic distributions and their moments are found when the the size of the hash table tends to infinity with the proportion of occupied cells converging to some α, 0 < α < 1. (In the case of Last Come, the results are more complicated and less complete than in the other cases.)We also show, using the diagonal Poisson transform studied by Poblete, Viola and Munro, that exact expressions for finite m and n can be obtained from the limits as m, n → ∞.We end with some results, conjectures and questions about the shape of the limit distributions. These have some relevance for computer applications.

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Section: Introductionmentioning

confidence: 99%

Individual displacements for linear probing hashing with different insertion policies

Janson

2005

ACM Trans. Algorithms

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“…For FCFS linear probing the result was proven by Knuth [10]. This is also true for the other variants, in particular for last-comefirst-served (LCFS) [17] and Robin Hood [2,4]. Take for instance LCFS and let x be the item to be removed.…”

Section: Deletions In Linear Probing Hash Tablesmentioning

confidence: 85%

“…For instance, in many practical settings it is highly unlikely to have probe sequences of length ≥ 32 unless the load factor is too close to 1-recall that the maximum length of a probe sequence is O(logn) in FCFS [16,7] and O(loglogn) in RH [5,2] if α < 1. Unless space is a severe restriction and we are willing to deal with bit fiddling, alignment issues, etc., the extra space to store the distance to home locations (or number of probes made) will not occupy more memory than the Boolean flag per slot which would be necessary to know whether the slot is empty or not.…”

Section: Deletions In Linear Probing Hash Tablesmentioning

confidence: 99%

“…When the collisions are resolved in favor of the newcomer, we have LCFS (Last-Come-First-Served), a variant introduced by Poblete and Munro [17]. In the Robin Hood variant, collisions are resolved in favor of the item which is further away from its hash address [4,2], whereas in ordered hashing [1], the smaller item-according to some prespecified total order-remains and the greater continues with the probe sequence. While all the variants have identical expected cost for successful and unsuccessful searches, they exhibit notable differences when it comes to the variance or the length of the longest probe sequence (see for instance [1,4,2,5,7,15,16,17,6]).…”

Section: Introductionmentioning

confidence: 99%

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On Deletions in Open Addressing Hashing

Gómez¹,

Parra²

2018

2018 Proceedings of the Fifteenth Workshop on Analytic Algorithmics and Combinatorics (ANALCO)

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Deletions in open addressing tables have often been seen as problematic. The usual solution is to use a special mark 'deleted' so that probe sequences continue past deleted slots, as if there was an element still sitting there. Such a solution, notwithstanding is wide applicability, may involve performance degradation. In the first part of this paper we review a practical implementation of the often overlooked deletion algorithm for linear probing hash tables, analyze its properties and performance, and provide several strong arguments in favor of the Robin Hood variant. In particular, we show how a small variation can yield substantial improvements for unsuccessful search. In the second part we propose an algorithm for true deletion in open addressing hashing with secondary clustering, like quadratic hashing. As far as we know, this is the first time that such an algorithm appears in the literature. Moreover, for tables built using the Robin Hood variant the deletion algorithm strongly preserves randomness (the resulting table is identical to the table that would result if the item were not inserted at all). Although it involves some extra memory for bookkeeping, the algorithm is comparatively easy and efficient, and it might be of some practical value, besides its theoretical interest.

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“…Robin Hood Hashing [6] tied to equalize the length of probe sequences by leaving the item with the longer probe sequence in current probing position and move forward the other one with shorter probe sequence in case of collision. Cuckoo Hashing [7][8] [9] used two hash tables and two different hash functions respectively; each item, is either in the first hash table or in the other one.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Hash Table Performance with Open Addressing and Closed Addressing: An Empirical Study

Liu¹,

Xu²

2015

IJNDC

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In this paper, we conducted empirical experiments to study the performance of hashing with a large set of data and compared the results of different collision approaches. The experiment results leaned more to closed addressing than to open addressing and deemed linear probing impractical due to its low performance. Moreover, when items are randomly distributed with keys in a large space, different hash algorithms might produce similar performance. Increasing randomness in keys does not help hash table performance either and it seems that the load factor solely determines possibility of collision. These new discoveries might help programmers to design software products using hash tables.

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Robin hood hashing

Cited by 86 publications

References 6 publications

Individual displacements for linear probing hashing with different insertion policies

Individual displacements for linear probing hashing with different insertion policies

On Deletions in Open Addressing Hashing

Comparison of Hash Table Performance with Open Addressing and Closed Addressing: An Empirical Study

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