# Hash tables explained

A hash table is an **unordered** collection of **key-value** pairs, where each key is **unique**.

A hash table offers a combination of efficient lookup, insert and delete operations.

Neither arrays nor linked lists can achieve this:

- a lookup in an unsorted array takes linear worst-case time;
- in a sorted array, a lookup using binary search is very fast, but insertions become inefficient;
- in a linked list an insertion can be efficient, but lookups take linear time.

## Basic idea

Hash tables combine the best properties of arrays and linked lists.

Let’s start with a somewhat simplistic example: a data structure that can store 1000 elements with random integer keys.

To distribute the data evenly, we use several short lists. All elements with keys that end with 000 belong to one list, those with keys that end with 001 belong to another one, and so on. There is a total of 1000 such lists. This structure can be represented as an array of lists:

vartable =newLinkedList[1000]

where `LinkedList`

denotes a linked list of key-value pairs.

Inserting a new element `(key, value)`

is a two-step procedure:

- we extract the three last digits of the key,
`hash = key % 1000`

, - and then insert the key and its value into the list located at
`table[hash]`

```
hash = key % 1000
table[hash].AddFirst(key, value)
```

This is a constant time operation.

A lookup is implemented by

```
value = table[key%1000].Find(key)
```

Since the numbers are random, there will be roughly the same number of elements in each list.
Since there are 1000 lists and at most 1000 elements, there will likely be very few elements
in the list `table[key%1000]`

and therefore the `Find`

operation will be fast.
The expected time complexity is *O*(1).

## Complete data structure

We want to generalize this basic idea to more complicated keys that aren’t evenly distributed.
The number of elements in each list must remain small,
and the elements must be evenly distributed over the lists.
To achieve this we just need to change the **hash function**,
the function which selects the list where a key belongs.

The hash function in the example above is `hash = key % 1000`

.
It takes a key (a positive integer) as input and produces a number in the interval `0..999`

.

In general, a hash function is a function from `E`

to `0..size-1`

,
where `E`

is the set of all possible keys, and `size`

is the number of entry points in the hash table.
We want this function to be uniform: it should map the expected inputs as evenly as possible over its output range.

## Typical hash function

Java’s implementation of hash functions for strings is a good example.
The `hashCode`

method in the `String`

class computes the value
s[0]·31^{n-1} + s[1]·31^{n-2} + … + s[n-1]
using int arithmetic, where `s[i]`

is the i:th character of the string, and n is the length of the string.

This method can be used as a hash function like this:

```
hash = Math.abs(s.hashCode() % size)
```

where `size`

is the number of entry points in the hash table.

Note that this function depends on all characters in the string, and that the value changes when we change the order of the characters. Two properties that should hold for a good hash function.

## Resizing

The efficiency of a hash table depends on the fact that the table size is proportional to the number of elements. If the number of elements is not known in advance, the table must be resized when the lists become too long:

- a new larger table is allocated,
- each element is removed from the old table,
- and inserted into the new table.

If the table size is increased by a constant factor for each resizing, i.e. by doubling its size,
this strategy gives **amortized constant time** performance for lookups, insertions and deletions.

For more on amortized time complexity, see the article Amortized time complexity.

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