A hash table stores data as key-value pairs using a hash function to assign each key a unique value in an array. Efficient hash functions provide uniform distribution and avoid collisions and clusters. Load factor measures efficiency and a perfect hash function would have constant performance.
In computer science, a hash table is a data structure for storing data that consists of a list of values, called keys, which are paired with a corresponding list of values, called an array. For example, a business name might be associated with its address. Typically, each value in the array has a position number called a hash. The hash function is usually a set of instructions or an algorithm that associates each key value with a hash, such as linking the business name to its address, phone number and business category. The purpose of the hash function is to assign each key to a corresponding unique value in the array; this is commonly referred to as hashing. Hash functions must be formatted correctly for a hash table to work properly.
The performance of a hash table on a dataset depends on the efficiency of its hash function. A good hash function typically provides uniform key lookup and uniform distribution of mappings in the corresponding array. A hash collision occurs when two keys are assigned the same corresponding value. When a hash collision occurs, the hash function is usually executed again until a unique matching value is found; this generally results in longer hashing times. While the number of keys in a hashtable is usually fixed, there may sometimes be duplicate keys. Even so, a well-designed hash table has effective hash functions that map each key to a corresponding unique value in the array.
Sometimes, inefficient hash functions in a hash table can also produce a cluster of mappings. If a hash function creates a cluster of mappings for existing keys, this can increase the time it takes to look up matching values. This can slow down hashing for future keys since most hash functions generally look for the next available location in the array. If a large group of values has already been assigned, it would typically take much longer to search for an unassigned value for a new key.
Load factor is another concept related to the efficiency of a hash function; load factor is the amount of already existing hashes in relation to the overall size of the corresponding array in a hash table. It is usually defined by dividing the number of keys already assigned by the size of the corresponding array. As the load factor increases, a good hash function will normally still maintain a constant number of collisions and clusters up to some point. Often this threshold can be used to determine how efficient a hash function is with a given number of keys and when a new hash function might be needed.
Many computer science researchers have strived to produce the perfect hash function, one that produces no collisions or clusters given an increasing load factor. In theory, the key to producing a perfect hash table is to produce a perfect hash function. In general, the researchers believe that a perfect hash function should have constant performance – the number of collisions and clusters – with an increasing load factor. In worst-case scenarios, a perfect hash function would still allow constant hashing without hitting a threshold.
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