Hash

Discrete Mathematics andIts Applications

Baojian Huabjhua@ustc.edu.cn

Searching A dictionary-like data structure

contains a collection of tuple data: <k1, v1>, <k2, v2>, … keys are comparable and pair-wise distinct

supports these operations: new () insert (dict, k, v) lookup (dict, k) delete (dict, k)

Examples

Application Purpose Key Value

Phone Book phone name phone No.

Bank transaction

visa $$$

Dictionary lookup word meaning

compiler symbol variable type

www.google.com

search key words contents

… … … …

Summary So Far

rep’op’

array sorted array

linked list

sorted linked list

binarysearch tree

lookup()

O(n) O(lg n) O(n) O(n) O(n)

insert()

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

delete()

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

What’s the Problem?

For every mapping (k, v)s After we insert it into the dictionary dict,

we don’t know it’s position! Ex: insert (d, “li”, 97), (d, “wang”, 99),

(d, “zhang”, 100), … and then lookup (d, “zhang”);

(“li”, 97) …(“wang”,

99)(“zhang”,

100)

Basic Plan

Start from the array-based approach Use an array A to hold elements (k, v)s For every key k:

if we know its position (array index) i from k then lookup, insert and delete are simple:

A[i] done in constant time O(1)

…

(k, v)

i

Example

Ex: insert (d, “li”, 97), (d, “wang”, 99), (d, “zhang”, 100), …;and then lookup (d, “zhang”);

…

(“li”, 97)

?

Problem#1: How to calculate index from the given key?

Example

Ex: insert (d, “li”, 97), (d, “wang”, 99), (d, “zhang”, 100), …;and then lookup (d, “zhang”);

…

(“li”, 97)

?

Problem#2: How long should array be?

Basic Plan

Save (k, v)s in an array, index i calculated from key k

Hash function: a method for computing index from given keys

…

(“li”, 97)

hash (“li”)

Hash Function Given any key, compute an index

Efficiently computable Ideal goals: for any key, the index is uniform

different keys to different indexes However, thorough research problem, :-(

Next, we assume that the array is of infinite length, so the hash function has type: int hash (key k); To get some idea, next we perform a “case analy

sis” on how different key types affect “hash”

Hash Function On “int”// If the key of hash is of “int” type, the hash

// function is trivial:

int hash (int i)

{

return i;

}

Hash Function On “char”// If the key of hash is of “char” type, the hash

// function comes with type conversion:

int hash (char c)

{

return c;

}

Hash Function On “float”// Also type conversion:

int hash (float f)

{

return (int)f;

}

// how to deal with 0.aaa, say 0.5?

Hash Function On “string”// Example: “BillG”:// A trivial one, but not so good:int hash (char *s){ int i=0, sum=0; while (s[i]) { sum += s[i]; i++; } return sum;}

Hash Function On “Point”// Suppose we have a user-define type:struct Point2d{

int x;int y;

};

int hash (struct Point2d pt){ // ???}

From “int” Hash to Index Recall the type:

int hash (T data); Problems with “int” return type

At any time, the array is finite no negative index (say -10)

Our goal: int i ==> [0, N-1] Ok, that’s easy! It’s just:abs(i) % N

Bug! Note that “int”s range: -231~231-1

So abs(-231) = 231 Overflow!

The key step is to wipe the sign bit offint t = i & 0x7fffffff;int hc = t % N; In summary:hc = (i & 0x7fffffff) % N;

Collision

Given two keys k1 and k2, we compute two hash codes hc1, hc2[0, N-1]

If k1<>k2, but h1==h2, then a collision occurs

…

(k1, v1)

i

(k2, v2)

Collision Resolution

Open Addressing Re-hash Chaining (Multi-map)

Chaining

For collision index i, we keep a separate linear list (chain) at index i

…

(k1, v1)

i

(k2, v2)

k1

k2

General Scheme

k1

k2

k5k8

k43

Load Factor

loadFactor=numItems/numBuckets defaultLoadFactor: default value of the l

oad factor

k1

k2

k5k8

k43

“hash” ADT: interface#ifndef HASH_H#define HASH_H

typedef void *poly;typedef poly key;typedef poly value;

typedef struct hashStruct *hash;

hash newHash ();hash newHash2 (double lf);void insert (hash h, key k, value v);poly lookup (hash h, key k);void delete (hash h, key k);

#endif

Hash Implementation#include “hash.h”

#define EXT_FACTOR 2

#define INIT_BUCKETS 16

struct hashStruct

{

linkedList *buckets;

int numBuckets;

int numItems;

double loadFactor;

};

In Figure

k1

k2

k5k8

k43

buckets

loadFactor

numItems

numBuckets

h

“newHash ()”hash newHash (){ hash h = (hash)malloc (sizeof (*h)); h->buckets = malloc (INIT_BUCKETS * sizeof (linkedList));

for (…) // init the array

h->numBuckets = INIT_BUCKETS; h->numItems = 0; h->loadFactor = 0.25;

return h;}

“newHash2 ()”hash newHash2 (double lf){ hash h = (hash)malloc (sizeof (*h)); h->buckets=(linkedList *)malloc (INIT_BUCKETS * sizeof (linkedList));

for (…) // init the array

h->numBuckets = INIT_BUCKETS; h->numItems = 0; h->loadFactor = lf;

return h;}

“lookup (hash, key)”value lookup (hash h, key k, compTy cmp)

{

int i = k->hashCode (); // how to perform this?

int hc = (i & 0x7fffffff) % (h->numBuckets);

value t =linkedListSearch ((h->buckets)[hc], k);

return t;

}

Ex: lookup (ha, k43)

k1

k2

k5k8

k43

bucketsha

hc = (hash (k43) & 0x7fffffff) % 8;

// hc = 1

Ex: lookup (ha, k43)

k1

k2

k5k8

k43

bucketsha

hc = (hash (k43) & 0x7fffffff) % 8;

// hc = 1

compare k43 with k8,

Ex: lookup (ha, k43)

k1

k2

k5k8

k43

bucketsha

hc = (hash (k43) & 0x7fffffff) % 8;

// hc = 1

compare k43 with k43,

found!

“insert”void insert (hash h, poly k, poly v){ if (1.0*numItems/numBuckets >=defaultLoadFactor) // buckets extension & items re-hash; int i = k->hashCode (); // how to perform this? int hc = (i & 0x7fffffff) % (h->numBuckets); tuple t = newTuple (k, v);

linkedListInsertHead ((h->buckets)[hc], t); return;}

Ex: insert (ha, k13)

k1

k2

k5k8

k43

bucketsha

hc = (hash (k13) & 0x7fffffff) % 8;

// suppose hc==4

Ex: insert (ha, k13)

k13

k1

k5k8

k43

bucketsha

hc = (hash (k13) & 0x7fffffff) % 8;

// suppose hc==4

k2

Complexity

rep’op’

array sorted array

linked list

sorted linked list

hash

lookup()

O(n) O(lg n) O(n) O(n) O(1)

insert()

O(n) O(n) O(n) O(n) O(1)

delete()

O(n) O(n) O(n) O(n) O(1)