chapter 9: maps, dictionaries, hashing

Chapter 9: Maps, Dictionaries, Hashing

Nancy AmatoParasol Lab, Dept. CSE, Texas A&M University

Acknowledgement: These slides are adapted from slides provided with Data Structures and Algorithms in C++, Goodrich, Tamassia and Mount (Wiley 2004)

http://parasol.tamu.edu

Dictionaries & Hashing 04/20/23 15:59Dictionaries 2

Outline and Reading

• Map ADT (§9.1)• Dictionary ADT (§9.5)• Ordered Maps (§9.3) • Hash Tables (§9.2)


Map ADT

• The map ADT models a searchable collection of key-element items

• The main operations of a map are searching, inserting, and deleting items

• Multiple items with the same key are not allowed

• Applications:• address book• credit card authorization• mapping host names (e.g.,

cs16.net) to internet addresses (e.g., 128.148.34.101)

• Map ADT methods:• find(k): if M has an entry with

key k, return an iterator p referring to this element, else, return special end iterator.

• put(k, v): if M has no entry with key k, then add entry (k, v) to M, otherwise replace the value of the entry with v; return iterator to the inserted/modified entry

• erase(k) or erase(p): remove from M entry with key k or iterator p; An error occurs if there is no such element.

• size(), empty()


Map - Direct Address Table

• A direct address table is a map in which• The keys are in the range {0,1,2,…,N}• Stored in an array of size N - T[0,N]• Item with key k stored in T[k]

• Performance:• insertItem, find, and removeElement all take O(1) time • Space - requires space O(N), independent of n, the number of items

stored in the map

• The direct address table is not space efficient unless the range of the keys is close to the number of elements to be stored in the map, I.e., unless n is close to N.


Dictionary ADT

• The dictionary ADT models a searchable collection of key-element items

• The main difference from a map is that multiple items with the same key are allowed

• Any data structure that supports a dictionary also supports a map

• Applications:• Dictionary which has multiple

definitions for the same word

• Dictionary ADT methods:• find(k): if the dictionary has an

entry with key k, returns an iterator p to an arbitrary elt.

• findAll(k): Return iterators (b,e) s.t. that all entries with key k are between them.

• insert(k, v): insert entry (k, v) into D, return iterator to it.

• erase(k), erase(p): remove arbitrary entry with key k or entry referenced by p. Error occurs if there is no such entry

• Begin(), end(): return iterator to first or just beyond last entry of D

• size(), isEmpty()


Dictionary - Log File (unordered sequence implementation)

• A log file is a dictionary implemented by means of an unsorted sequence• We store the items of the dictionary in a sequence (based on a doubly-linked

lists or a circular array), in arbitrary order

• Performance:• insert takes O(1) time since we can insert the new item at the beginning or at

the end of the sequence• find and erase take O(n) time since in the worst case (the item is not found)

we traverse the entire sequence to look for an item with the given key• Space - can be O(n), where n is the number of elements in the dictionary

• The log file is effective only for dictionaries of small size or for dictionaries on which insertions are the most common operations, while searches and removals are rarely performed (e.g., historical record of logins to a workstation)

Map/Dictionary implementations

• n - #elements in map/Dictionary

04/20/23 15:59Vectors 7

Insert Find Space

Log File O(1) O(n) O(n)

Direct Address Table (map only)

O(1) O(1) O(N)

04/20/23 15:598

Ordered Map/Dictionary ADT

• An Ordered Map/Dictionary supports the usual map/dictionary operations, but also maintains an order relation for the keys.

• Naturally supports • Look-Up Tables - store dictionary

in a vector by non-decreasing order of the keys

• Binary Search

• Ordered Map/Dictionary ADT:• In addition to the generic

map/dictionary ADT, supports the following functions:

• closestBefore(k): return the position of an item with the largest key less than or equal to k

• closestAfter(k): return the position of an item with the smallest key greater than or equal to k

04/20/23 15:59Dictionaries 9

Lookup Table

• A lookup table is a dictionary implemented by means of a sorted sequence

• We store the items of the dictionary in an array-based sequence, sorted by key

• We use an external comparator for the keys

• Performance:• find takes O(log n) time, using binary search• insertItem takes O(n) time since in the worst case we have to shift

n2 items to make room for the new item• removeElement take O(n) time since in the worst case we have to

shift n2 items to compact the items after the removal

• The lookup table is effective only for dictionaries of small size or for dictionaries on which searches are the most common operations, while insertions and removals are rarely performed (e.g., credit card authorizations)

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Example of Ordered Map: Binary Search

• Binary search performs operation find(k) on a dictionary implemented by means of an array-based sequence, sorted by key

• similar to the high-low game• at each step, the number of candidate items is halved• terminates after a logarithmic number of steps

• Example: find(7)

1 3 4 5 7 8 9 11 14 16 18 19

1 3 4 5 7 8 9 11 14 16 18 19

1 3 4 5 7 8 9 11 14 16 18 19

1 3 4 5 7 8 9 11 14 16 18 19

0

0

0

0

ml h

ml h

ml h

lm h

Map/Dictionary implementations

• n - #elements in map/Dictionary

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Insert Find Space



O(1) O(1) O(N)

Lookup Table (ordered map/dictionary)

O(n) O(logn) O(n)


Hash Tables

• Hashing• Hash table (an array) of size N, H[0,N]• Hash function h that maps keys to indices in H

• Issues• Hash functions - need method to transform key to an index in H that

will have nice properties.• Collisions - some keys will map to the same index of H (otherwise

we have a Direct Address Table). Several methods to resolve the collisions

• Chaining - put elts that hash to same location in a linked list• Open addressing - if a collision occurs, have a method to select another

location in the table.• Probe sequences


Hash Functions and Hash Tables

• A hash function h maps keys of a given type to integers in a fixed interval [0, N1]

• Example:h(x) x mod N

is a hash function for integer keys• The integer h(x) is called the hash value of key x

• A hash table for a given key type consists of• Hash function h• Array (called table) of size N

• When implementing a dictionary with a hash table, the goal is to store item (k, o) at index i h(k)


Example

• We design a hash table for a dictionary storing items (SSN, Name), where SSN (social security number) is a nine-digit positive integer

• Our hash table uses an array of size N10,000 and the hash functionh(x)last four digits of x

01234

999799989999

…

451-229-0004

981-101-0002

200-751-9998

025-612-0001


Collisions

• Collisions occur when different elements are mapped to the same cell

• collisions must be resolved

• Chaining (store in list outside the table)

• Open addressing (store in another cell in the table)

• Example with Division Method

• h(k) = k mod N

• If N=10, then • h(k)=0 for k=0,10,20, …• h(k)= 1 for k=1, 11, 21, etc• …


Collision Resolution with Chaining

• Collisions occur when different elements are mapped to the same cell

• Chaining: let each cell in the table point to a linked list of elements that map there

• Chaining is simple, but requires additional memory outside the table

01234 451-229-0004 981-101-0004

025-612-0001


Exercise: chaining

• Assume you have a hash table H with N=9 slots (H[0,8]) and let the hash function be h(k)=k mod N.

• Demonstrate (by picture) the insertion of the following keys into a hash table with collisions resolved by chaining.

• 5, 28, 19, 15, 20, 33, 12, 17, 10


Collision Resolution in Open Addressing - Linear Probing

• Open addressing: the colliding item is placed in a different cell of the table

• Linear probing handles collisions by placing the colliding item in the next (circularly) available table cell. So the i-th cell checked is:

• H(k,i) = (h(k)+i)mod N

• Each table cell inspected is referred to as a “probe”

• Colliding items lump together, causing future collisions to cause a longer sequence of probes

• Example:• h(x) x mod 13• Insert keys 18, 41, 22, 44,

59, 32, 31, 73, in this order

0 1 2 3 4 5 6 7 8 9 10 11 12

41 18 44 59 32 22 31 73 0 1 2 3 4 5 6 7 8 9 10 11 12


Search with Linear Probing

• Consider a hash table A that uses linear probing

• find(k)• We start at cell h(k) • We probe consecutive

locations until one of the following occurs

• An item with key k is found, or

• An empty cell is found, or• N cells have been

unsuccessfully probed

Algorithm find(k)i h(k)p 0repeat

c A[i]if c

return Position(null) else if c.key () k

return Position(c) else

i (i 1) mod Np p 1

until p Nreturn Position(null)


Updates with Linear Probing

• To handle insertions and deletions, we introduce a special object, called AVAILABLE, which replaces deleted elements

• removeElement(k)• We search for an item with

key k • If such an item (k, o) is found,

we replace it with the special item AVAILABLE and we return the position of this item

• Else, we return a null position

• insertItem(k, o)• We throw an exception if

the table is full• We start at cell h(k) • We probe consecutive

cells until one of the following occurs

• A cell i is found that is either empty or stores AVAILABLE, or

• N cells have been unsuccessfully probed

• We store item (k, o) in cell i


Exercise: Linear Probing

• Assume you have a hash table H with N=11 slots (H[0,10]) and let the hash function be h(k)=k mod N.

• Demonstrate (by picture) the insertion of the following keys into a hash table with collisions resolved by linear probing.

• 10, 22, 31, 4, 15, 28, 17, 88, 59


Open Addressing: Double Hashing

• Double hashing uses a secondary hash function h2(k) and handles collisions by placing an item in the first available cell of the seriesh(k,i) =(h1(k) ih2(k)) mod N for i 0, 1, … , N 1

• The secondary hash function h2(k) cannot have zero values

• The table size N must be a prime to allow probing of all the cells

• Common choice of compression map for the secondary hash function:

h2(k) q k mod q

where• q N• q is a prime

• The possible values for h2(k) are

1, 2, … , q


• Consider a hash table storing integer keys that handles collision with double hashing

• N13

• h1(k) k mod 13

• h2(k) 7 k mod 7

• Insert keys 18, 41, 22, 44, 59, 32, 31, 73, in this order

Example of Double Hashing

0 1 2 3 4 5 6 7 8 9 10 11 12

31 41 18 32 59 73 22 44 0 1 2 3 4 5 6 7 8 9 10 11 12


Exercise: Double Hashing

• Assume you have a hash table H with N=11 slots (H[0,10]) and let the hash functions for double hashing be

• h(k,i)=(h1(k) + ih2(k))mod N• h1(k)=k mod N • h2(k)=1 + (k mod (N-1))

• Demonstrate (by picture) the insertion of the following keys into H

• 10, 22, 31, 4, 15, 28, 17, 88, 59


Performance of Hashing

• In the worst case, searches, insertions and removals on a hash table take O(n) time

• The worst case occurs when all the keys inserted into the dictionary collide

• The load factor nN affects the performance of a hash table

• Assuming that the hash values are like random numbers, it can be shown that the expected number of probes for an insertion with open addressing is

1 (1 )

• The expected running time of all the dictionary ADT operations in a hash table is O(1)

• In practice, hashing is very fast provided the load factor is not close to 100%

• Applications of hash tables:• small databases• compilers• browser caches

Dictionaries & Hashing

Uniform Hashing Assumption • The probe sequence of a key k is the sequence of slots

that will be probed when looking for k• In open addressing, the probe sequence is h(k,0), h(k,1), h(k,2),

h(k,3), …

• Uniform Hashing Assumption: Each key is equally likely to have any one of the N! permutations of {0,1, 2, …, N-1} as is probe sequence

• Note: Linear probing and double hashing are far from achieving Uniform Hashing

• Linear probing: N distinct probe sequences• Double Hashing: N2 distinct probe sequences

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Dictionaries & Hashing

Performance of Uniform Hashing

• Theorem: Assuming uniform hashing and an open-address hash table with load factor a = n/N < 1, the expected number of probes in an unsuccessful search is at most 1/(1-a).

• Exercise: compute the expected number of probes in an unsuccessful search in an open address hash table with a = ½ , a=3/4, and a = 99/100.

31 04/20/23 15:59Dictionaries


Universal Hashing

• A family of hash functions is universal if, for any 0<i,j<M-1,

• Pr(h(j)=h(i)) < 1/N.

• Choose p as a prime between M and 2M.

• Randomly select 0<a<p and 0<b<p, and define h(k)=(ak+b mod p) mod N

• Theorem: The set of all functions, h, as defined here, is universal.

Maps/Dictionaries

• n = #elements in map/dictionary, • N=#possible keys (it could be N>>n) or size of hash table

04/20/23 15:59Vectors 35

Insert Find Space



O(1) O(1) O(N)

Lookup Table (ordered map/dictionary)

O(n) O(logn) O(n)

Hashing (chaining)

O(1) O(n/N) O(n+N)

Hashing (open addressing)

O(1/(1-n/N)) O(1/(1-n/N)) O(N)

chapter 9: maps, dictionaries, hashing

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arbitrary entry

return iterator

entry of dsize

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iterator p

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