hash means to grind up, and thatís essentially what hashing is all
about. The heart of a hashing algorithm is a hash function that takes
your nice, neat data and grinds it into some random-looking integer.
The idea behind hashing is that some data either has no inherent
ordering (such as images) or is expensive to compare (such as
images). If the data has no inherent ordering, you canít perform
If the data is expensive to compare, the number of comparisons used
even by a binary search might be too many. So instead of looking at
the data themselves, youíll condense (hash) the data to an integer (its
hash value) and keep all the data with the same hash value in the
same place. This task is carried out by using the hash value as an
index into an array.
search for an item, you simply hash it and look at all the data whose
hash values match that of the data youíre looking for. This technique
greatly lessens the number of items you have to look at. If the
parameters are set up with care and enough storage is available for
the hash table, the number of comparisons needed to find an item can
be made arbitrarily close to one.
One aspect that affects the efficiency of a hashing implementation is
the hash function itself. It should ideally distribute data randomly
throughout the entire hash table, to reduce the likelihood of collisions.
Collisions occur when two different keys have the same hash value.
There are two ways to resolve this problem. In open addressing, the
collision is resolved by the choosing of another position in the hash
table for the element inserted later. When the hash table is searched, if
the entry is not found at its hashed position in the table, the search
continues checking until either the element is found or an empty
position in the table is found.
The second method of resolving a hash collision is called chaining. In
this method, a bucket or linked list holds all the elements whose keys
hash to the same value. When the hash table is searched, the list must
be searched linearly.