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Hash Tables

Dive into the world of hash tables, learn about magic address calculators, hash functions, collision resolution, open addressing, and restructuring techniques. Discover the secrets behind efficient search algorithms and how to optimize performance with the right hash function.

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Hash Tables

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  1. Ellen Walker CPSC 201 Data Structures Hiram College Hash Tables

  2. Breaking the Rules • The fastest possible search algorithm, if you only compare two items at once, is O(log n) where n is the number of items in the table. • But, if we can figure out a way to compare multiple items at once, we can beat that!

  3. Magic Address Calculator • Represent your table as an array • Add a new function, the “magic address calculator” • The input to this function is the key • The output of this function is the address to look in • No comparisons, so we’re not limited to log n. • In fact, if the calculator takes the same time for every input, it’s constant time search!

  4. Hash Function • The “magic calculator” function is called a hash function • It treats the key as a sequence of bits or an integer, regardless of its original type • Example hash functions (not very good ones) • Last two digits of the integer (table size 100) • Divide the bit string into sequences of 8 bits and XOR all sequences together (table size 256)

  5. Hash Table • Table is an array of TableSize, hash(key) is a function that returns a value from 0 to TableSize. • To insert: • Table[hash(key)] = key • To retrieve: • Result = Table[hash(key)] • To delete: • Table[hash(key)] = empty marker • Can it really be that simple?

  6. Hash Table Collisions • If the size of the the table is smaller than the number of possible keys, then there must be at least two keys with the same hash value. • E.g. 202 and 102 if key is last 2 digits • If we want to insert both values, we will get a collision • The item we retrieve might not really have a matching key • The location to insert into might already be full

  7. Avoiding Collisions • Make the table “big” (if you can afford it) • Pick the right hash function • If you know all possible keys, create a perfect hash function (unique value for each possible key) • Try to distribute all possible keys evenly among the addresses • Try to distribute the most likely keys evenly among the addresses

  8. Choosing a Hash Function • Should return integers in a fixed range • Should be quick to compute • Should avoid obvious patterns of results • Should involve the entire search key

  9. Typical Hash Functions • Taking an integer modulo a prime number • Prime number has only 1 and itself as factors • This avoids patterns of addresses • Easiest to analyze and most common • Folding (integer or bits) • Divide value into subgroups (k bits or digits) • Add or XOR together subgroups

  10. Resolving Collisions byOpen Addressing • Find another place within the table for the item • Linear probing: new item goes in first empty space after the result of the hash function (Offsets are sequence of numbers) • Quadratic probing: first look in next space, then skip to 4th space, then 9th, then 16th, etc. (Offsets are sequence of squares) • Double hashing: use a second hash function on the key to find the offset. (Offsets are multiples of the second hash value)

  11. Insertion with Open Addressing void insert(E item){ int address = hash(item); while(Table[address]!=null){ compute next offset address = address + offset } Table[address] = item; }

  12. Retrieval with Open Addressing E retrieve(E item){ int address = hash(item); while((!table[address].equals(item))&& (table[address] != null)){ compute next offset address = address + offset } return( table[address]); //returns null if not found }

  13. Issues with Open Addressing • Retrieval must follow same sequence of probes as insertion • If a collision fills a cell, then it forces a collision with the value that hashes directly to the cell. • Consider: • Hash(key) = key%11 • Sequence of items: 1,14,12,2,3,41,27,15 • Try linear, quadratic, double hash = key%7

  14. Comparing Open Addressing Schemes • Linear probing is most prone to clustering • Large clumps of cells fill, causing long sequences of probes for each insertion • Quadratic probing is less prone to clustering • Each probe is even further from the “cluster” • No guarantee every slot will be searched, though! • Double hashing depends on the other hash function • Its base should be relatively prime to the original base so there is no pattern • In this case, it is as good or better than quadratic

  15. Restructuring the Hash Table • Each “address” can contain multiple items • Bucket (set max # items per hash key) • Separate chaining (array of linked lists) • Our example again: • Hash(key) = key%11 • Sequence of items: 1,14,12,2,3,41,27,15

  16. Bucket: Multiple Cells per Hash Value

  17. Separate chaining • Hash table as array of linked lists

  18. Growing a Hash Table • Open addressing: • When the hash table is full, allocate a bigger one. • “Rehashing” = add each element from the original table to the full one using the new hash code. • Chaining: • When the lists are getting too long, allocate a bigger table • Rehash as above.

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