Hash tables and hash codes are an enormous and active area of research that is just touched upon in this chapter. The online Bibliography on Hashing [7] contains nearly 2000 entries.

A variety of different hash table implementations exist. The one
described in Section 5.1 is known as hashing with chaining
(each array entry contains a chain (`List`) of elements). Hashing with
chaining dates back to an internal IBM memorandum authored by H. P. Luhn
and dated January 1953. This memorandum also seems to be one of the
earliest references to linked lists.

An alternative to hashing with chaining is that used by open
addressing schemes, where all data is stored directly in an
array. These schemes include the `LinearHashTable` structure of
Section 5.2. This idea was also proposed, independently, by
a group at IBM in the 1950s. Open addressing schemes must deal with the
problem of collision resolution: the case where two values hash
to the same array location. Different strategies exist for collision
resolution and these provide different performance guarantees and often
require more sophisticated hash functions than the ones described here.

Yet another category of hash table implementations are the so-called perfect hashing methods. These are methods in which operations take time in the worst-case. For static data sets, this can be accomplished by finding perfect hash functions for the data; these are functions that map each piece of data to a unique array location. For data that changes over time, perfect hashing methods include FKS two-level hash tables [25,19] and cuckoo hashing [48].

The hash functions presented in this chapter are probably among the most practical currently known methods that can be proven to work well for any set of data. Other provably good methods date back to the pioneering work of Carter and Wegman who introduced the notion of universal hashing and described several hash functions for different scenarios [11]. Tabulation hashing, described in Section 5.2.3, is due to Carter and Wegman [11], but its analysis, when applied to linear probing (and several other hash table schemes) is due to Ptracu and Thorup [53].

The idea of multiplicative hashing is very old and seems to be part of the hashing folklore [41, Section 6.4]. However, the idea of choosing the multiplier to be a random odd number, and the analysis in Section 5.1.1 is due to Dietzfelbinger et al. [18]. This version of multiplicative hashing is one of the simplest, but its collision probability of is a factor of 2 larger than what one could expect with a random function from . The multiply-add hashing method uses the function

There are a number of methods of obtaining hash codes from fixed-length sequences of -bit integers. One particularly fast method [8] is the function

The method from Section 5.3.3 of using polynomials over prime fields to hash variable-length arrays and strings is due to Dietzfelbinger et al. [17]. It is, unfortunately, not very fast. This is due to its use of the operator which relies on a costly machine instruction. Some variants of this method choose the prime to be one of the form , in which case the operator can be replaced with addition ( ) and bitwise-and ( ) operations [40, Section 3.6]. Another option is to apply one of the fast methods for fixed-length strings to blocks of length for some constant and then apply the prime field method to the resulting sequence of hash codes.

boolean addSlow(T x) { if (2*(q+1) > t.length) resize(); // max 50% occupancy int i = hash(x); while (t[i] != null) { if (t[i] != del && x.equals(t[i])) return false; i = (i == t.length-1) ? 0 : i + 1; // increment i (mod t.length) } t[i] = x; n++; q++; return true; }