Hash Tables1 Part E Hash Tables  

Hash Tables2 Motivations of Hash Tables We have n items, each contains a key and value (k, value). The key uniquely determines the item. Each key could be anything, e.g., a number in [0, 2 32 ], a string of length 32, etc. How to store the n items such that given the key k, we can find the position of the item with key= k in O(1) time. Another constraint: space required is O(n). Linked list? Space O(n) and Time O(n). Array? Time O(1) and space: too big, e.g.,  If the key is an integer in [0, 2 32 ], then the space required is  if the key is a string of length 30, the space required is Hash Table: space O(n) and time O(1).

Hash Tables3 Basic ideas of Hash Tables A hash function h maps keys of a given type with a wide range to integers in a fixed interval [0, N  1], where N is the size of the hash table such that if k≠k then h(k)≠h(k’) ….. (1). Problem: It is hard to design a function h such that (1) holds. What we can do: We can design a function h so that with high chance, (1) holds. i.e., (1) may not always holds, but (1) holds for most of the n keys.

Hash Tables4 Hash Functions A hash function h maps keys of a given type to integers in a fixed interval [0, N  1] 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 the goal is to store item (k, o) at index i  h(k)

Hash Tables5 Example We design a hash table storing entries as (HKID, Name), where HKID is a nine-digit positive integer Our hash table uses an array of size N  10,000 and the hash function h(x)  last four digits of x Need a method to handle collision.     …

Hash Tables6 Example We design a hash table storing entries as (studentID, Name), where studentID is a eight-digit positive integer Our hash table uses an array of size N  100 for our class of n= 49 students and the hash function h(x)  last two digits of x Need a method to handle collision.     … 86xxxx04 50xxxx02 51xxxx98 50xxxx01 51xxxx02 Task: to store the n items such that given the key k, we can find the position of the item with key= k in O(1) time. As long as the chance for collision is low, we can achieve this goal. Setting N=1000 and looking at the last three digits will reduce the chance of collision.

Hash Tables7 How to design a Hash Function A hash function is usually specified as the composition of two functions: Hash code: h 1 : keys  integers key could be anything, e.g., your name, an object, etc. Compression function: h 2 : integers  [0, N  1] The size of the array N cannot be too large in order to save space. Trade-off between space and time. The hash code is applied first, and the compression function is applied next on the result, i.e., h(x) = h 2 (h 1 (x)) The goal of the hash function is to “disperse” the keys in an apparently random way so that in most cases (1) holds.

Hash Tables8 Integer cast: We reinterpret the bits of the key as an integer Example: characters is mapped to its ASCII code. A=65= , B=66= , a=97, b=98,,=44,.=46. Suitable for keys of length less than or equal to the number of bits of the integer type (e.g., byte, short, int and float in Java)

Hash Tables9 Component sum: We partition the bits of the key into components of fixed length (e.g., 16 or 32 bits) a 0 a 1 … a n  1 and we sum the components (ignoring overflows) a 0  a 1  a 2  … +a n  1 Example 1: AB= h(AB)= Example 2: h( )= (ignore overflows) Suitable for numeric keys of fixed length greater than or equal to the number of bits of the integer type (e.g., long and double in Java)

Hash Tables10 Compression Functions Division: h 2 (y)  y mod N The size N of the hash table is usually chosen to be a prime The reason has to do with number theory and is beyond the scope of this course Example: keys: {200, 205, 210, 215, 220, 600}. If N=100, 200 and 600 have the same code, i.e., 0. It is better to choose N=101.

Hash Tables11 Compression Functions Multiply, Add and Divide (MAD): h 2 (y)  (ay  b) mod N a >0 and b>0 are nonnegative integers such that a mod N  0 and N is a prime number. Example: Keys={200, 205, 210, 215, 220, 600}. N=101. a=3 and b=7. h(200)=(600+7) mod 101 = 607 mod 101=1. H(205)=(615+7) mod 101 = 622 mod 101=16. h(210)=(630+7) mod 101 = 637 mod 101=31. h(215)=(645+7) mod 101 = 652 mod 101=46. h(220)=(660+7) mod 101 = 667 mod 101=61. H(600)=( ) mod 101=3607 mod 101=72.

Hash Tables12 Collision Handling Collisions occur when different elements are mapped to the same cell Separate Chaining: let each cell in the table point to a linked list of entries that map there Separate chaining is simple, but requires additional memory outside the table   

Hash Tables13 Open Addressing the colliding item is placed in a different cell of the table Load factor: n/N, where n is the number of items to store and N the size of the hash table. n/N≤1. To get a reasonable performance, n/N<0.5.

Hash Tables14 Linear Probing Linear probing handles collisions by placing the colliding item in the next (circularly) available table cell 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

Hash Tables15 Search with Linear Probing Consider a hash table A that uses linear probing get (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 To ensure the efficiency, if k is not in the table, we want to find an empty cell as soon as possible. The load factor can NOT be close to 1. Algorithm get(k) i  h(k) p  0 repeat c  A[i] if c   return null else if c.key ()  k return c.element() else i  (i  1) mod N p  p  1 until p  N return null

Hash Tables16 Linear Probing Search for key=20. h(20)=20 mod 13 =7. Go through rank 8, 9, …, 12, 0. Search for key=15 h(15)=15 mod 13=2. Go through rank 2, 3 and return null. Example: h(x)  x mod 13 Insert keys 18, 41, 22, 44, 59, 32, 31, 73, 12, 20 in this order

Hash Tables17 Updates with Linear Probing To handle insertions and deletions, we introduce a special object, called AVAILABLE, which replaces deleted elements remove (k) We search for an entry with key k If such an entry (k, o) is found, we replace it with the special item AVAILABLE and we return element o Else, we return null Have to modify other methods to skip available cells. put (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 entry (k, o) in cell i

Hash Tables18 Updates with Linear Probing Algorithm put(k,o) i  h(k) p  0 repeat c  A[i] if c   return A[i].key()=k and A[i].element=0 else if c.key ()  k A[i].element=o else i  (i  1) mod N p  p  1 until p  N if p=N+1 the array is full. Example: h(x) = x mod 13 Insert keys 18, 41, 22, 44, 59, 32, 31, 73, 20, 12 in this order Ti insert 12, we look at rank 12 and then rank

Hash Tables19 A complete example Example: h(x) = x mod 13 Insert keys 18, 41, 22, 44, 59, 32, 31, 73, 20, 12 in this order Remove(): 20, 12 Get(11): check the cell after AVAILABLE cells. Insert keys 10, is at rank 12 and 11 is at rank 0. The Available cells are hard to deal with. Separate Chaining approach is simpler A A

Hash Tables20 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 map collide The load factor   n  N 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 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