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## PowerPoint Slideshow about ' Hashing' - zoltin

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Motivating Applications

- Large collection of datasets
- Datasets are dynamic (insert, delete)
- Goal: efficient searching/insertion/deletion
- Hashing is ONLY applicable for exact-match searching

Direct Address Tables

- If the keys domain is U Create an array T of size U
- For each key K add the object to T[K]
- Supports insertion/deletion/searching in O(1)

Direct Address Tables

Alg.: DIRECT-ADDRESS-SEARCH(T, k)

return T[k]

Alg.: DIRECT-ADDRESS-INSERT(T, x)

T[key[x]] ← x

Alg.: DIRECT-ADDRESS-DELETE(T, x)

T[key[x]] ← NIL

- Running time for these operations: O(1)

Solution is to use hashing tables

Drawbacks

>> If U is large, e.g., the domain of integers, then T is large (sometimes infeasible)

>> Limited to integer values and does not support duplication

Hashing

- A data structure that maps values from a certain domain or range to another domain or range

Hash function

3

15

Domain: String values

20

55

Domain: Integer values

Hashing

- A data structure that maps values from a certain domain or range to another domain or range

Hash function

Range

0

…..

10000

Student IDs

950000

…..

960000

Domain: numbers [0 … 10,000]

Domain: numbers [950,000 … 960,000]

Hash Tables

- When K is much smaller than U, a hash tablerequires much less space than a direct-address table
- Can reduce storage requirements to |K|
- Can still get O(1) search time, but on the average case, not the worst case

Hash Tables: Main Idea

- Use a hash function h to compute the slot for each key k
- Store the element in slot h(k)
- Maintain a hash table of size m T [0…m-1]
- A hash function h transforms a key into an index in a hash table T[0…m-1]:
h : U → {0, 1, . . . , m - 1}

- We say that k hashes to slot h(k)

Hash Tables: Main Idea

Hash Table (of size m)

0

U

(universe of keys)

h(k1)

h(k4)

k1

K

(actual

keys)

h(k2) = h(k5)

k4

k2

k3

k5

h(k3)

m - 1

>> m is much smaller that U (m <<U)

>> m can be even smaller than |K|

Example

- Back to the example of 100 students, each with 9-digit SSN

- All what we need is a hash table of size 100

What About Collisions

0

U

(universe of keys)

h(k1)

h(k4)

k1

K

(actual

keys)

h(k2) = h(k5)

k4

k2

Collisions!

k3

k5

h(k3)

m - 1

- Collision means two or more keys will go to the same slot

Handling Collisions

- Many ways to handle it
- Chaining
- Open addressing
- Linear probing
- Quadratic probing
- Double hashing

Chaining: Main Idea

- Put all elements that hash to the same slot into a linked list (Chain)
- Slot j contains a pointer to the head of the list of all elements that hash to j

Chaining - Discussion

- Choosing the size of the hash table
- Small enough not to waste space
- Large enough such that lists remain short
- Typically 10% -20% of the total number of elements

- How should we keep the lists: ordered or not?
- Usually each list is unsorted linked list

Insertion in Hash Tables

Alg.:CHAINED-HASH-INSERT(T, x)

insert x at the head of list T[h(key[x])]

- Worst-case running time is O(1)
- May or may not allow duplication based on the application

Deletion in Hash Tables

Alg.:CHAINED-HASH-DELETE(T, x)

delete x from the list T[h(key[x])]

- Need to find the element to be deleted.
- Worst-case running time:
- Deletion depends on searching the corresponding list

Searching in Hash Tables

Alg.:CHAINED-HASH-SEARCH(T, k)

search for an element with key k in list T[h(k)]

- Running time is proportional to the length of the list of elements in slot h(k)

What is the worst case and average case??

0

chain

m - 1

Analysis of Hashing with Chaining:Worst Case- All keys will go to only one chain
- Chain size is O(n)
- Searching is O(n) + time to apply h(k)

0

chain

chain

chain

chain

m - 1

Analysis of Hashing with Chaining:Average Case- With good hash function and uniform distribution of keys
- Any given element is equally likely to hash into any of the m slots

- All chain will have similar sizes
- Assume n (total # of keys), m is the hash table size
- Average chain size O (n/m)

Average Search Time O(n/m): The common case

Analysis of Hashing with Chaining:Average Case

- If m (# of slots) is proportional to n (# of keys):
- m = O(n)
- n/m = O(1)
Searching takes constant time on average

Hash Functions

- A hash function transforms a key (k) into a table address (0…m-1)
- What makes a good hash function?
(1) Easy to compute

(2) Approximates a random function: for every input, every output is equally likely (simple uniform hashing)

(3) Reduces the number of collisions

Hash Functions Make table size (m) a prime number Common function

- Goal: Map a key k into one of the m slots in the hash table

- Avoids even and power-of-2 numbers

h(k) = F(k) mod m

Some function or operation on K (usually generates an integer)

The output of the “mod” is number [0…m-1]

Examples of Hash Functions

Collection of images

F(k): Sum of the pixels colors

h(k) = F(k) mod m

Collection of strings

F(k): Sum of the ascii values

h(k) = F(k) mod m

Collection of numbers

F(k): just return k

h(k) = F(k) mod m

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