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A HashTable is a form of Dictionary, derived from the DictionaryBase class Dictionary collections store pairs of objects, usually referred to as Key and Value pairs In the same way that a real-world dictionary provides us with a way to associate pairs of items (a word and its de nition or translation), so a Dictionary collection allows us to associate pairs of objects Normally, the Key object is a value that would be looked up (like looking up a word in the Oxford English Dictionary) and the Value object is the object that is returned from the look-up In this way, we can build complex structures that allow us to collect pairs of objects, where one is used as a way of accessing the other Typically, the Key of an entry in a Dictionary collection is a string that uniquely identi es the Value object; for example we could use a person s social security number as the key to an object containing other data referring to the person, or a licence number as the key to an object containing a ot of data about a speci c car A HashTable gets its name from the way data is stored and retrieved in it A hashing function, or hash function, is a function that takes some data, jumbles it up and converts it into a number that is seemingly random This type of function, fairly useless on its own, is ideal for distributing a lot of key values evenly across a numeric range To see why this is useful, consider the job done by a local post of ce sorting of ce Mail is arranged into bins, where each bin contains mail for a particular area Since the actual areas (normally a postman s walk ) can be selected by the post of ce, they are organized so that each area (and bin) will be approximately the same size, and will therefore receive a similar amount of mail This is organizationally sensible Now consider a situation where we cannot predetermine how items would be distributed Organizing items into bins by the initial letter of someone s surname, for example, would be more haphazard We call the property or piece of data that we use to identify an item its key and although the aim is to have an even distribution of keys, this is unlikely for real-world data We would not divide the population of a country into 26 groups, where each group is of people with the same initial letter to their surname, and expect each group to be the same size If we used the initial letter of a surname, for example, we would have 26 bins, one for each letter A to Z (see Figure 64) However, each bin would not be likely to have to accommodate a similar amount of mail In the UK, names beginning with certain letters are quite unlikely (X and Z for example) These two bins would be nearly.

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