Hashing: Collision Function

A collision functionA collision function exists within the context of hashing and hash tables. Collision functions determine how to handle situations when the array underlying the hash table already has another datapoint in the slot chosen by the hash function. Here are some of the most popular collision functions: Linear Probing - Linear probing simply increments the hash index each time it... exists within the context of hashingA hash table (sometimes also called a hash map) is a data structure that is useful in situations when: you need very quick insertion and retrieval, you very rarely need to iterate over all your items, you don't care what order the items are stored (they don't need to be sorted, etc.), and the data you want to store has... and hash tables. Collision functions determine how to handle situations when the array underlying the hash tableA hash table (sometimes also called a hash map) is a data structure that is useful in situations when: you need very quick insertion and retrieval, you very rarely need to iterate over all your items, you don't care what order the items are stored (they don't need to be sorted, etc.), and the data you want to store has... already has another datapoint in the slot chosen by the hash functionA hash function exists within the context of hashing and hash tables. Data to be stored in hash tables includes a unique attribute (id number, username, etc.). Hash functions take the unique attribute and translate it into a hash index. Most algorithms take a numerical value as input, so if your unique attribute is text-based, translate your text into a....

Here are some of the most popular collision functions:

  • Linear ProbingA collision function exists within the context of hashing and hash tables. Collision functions determine how to handle situations when the array underlying the hash table already has another datapoint in the slot chosen by the hash function. Here are some of the most popular collision functions: Linear Probing - Linear probing simply increments the hash index each time it... – Linear probing simply increments the hash indexA hash index is an index into the underlying array that supports a hash table. A hash index is determined by a hash function.... each time it encounters a collision. So if your hash function returned 7 as its hash index, but there’s already an item in slot 7, you would try slot 8. If slot 8 is also occupied, you’d move on to slot 9. When you find an open slot, you store your item in it. This is a simple and elegant method, but when many collisions occur, it can cause clustering around popular hash indexes. Clustering can undermine your desire for the insert and retrieval functions to be very fast.
  • Quadratic ProbingA collision function exists within the context of hashing and hash tables. Collision functions determine how to handle situations when the array underlying the hash table already has another datapoint in the slot chosen by the hash function. Here are some of the most popular collision functions: Linear Probing - Linear probing simply increments the hash index each time it... – quadratic probing is the same concept as linear probing, but attempts to fight the ill effects of clustering. Instead of incrementing the hash index by 1 each time, quadratic probing increases the hash index by the number_of_collisions_encountered squared. So if your hash index is occupied, you have encountered 1 collision, so you move 1 squared slotsĀ (1 slot). If that slot is full, you’ve encountered two collisions, so you’ll move 2 squared slots (4 slots). If that slot is occupied, you’ve encountered 3 collisions, so you’ll move 3 squared slots (9 slots). You will continue this until you find an empty slot and store your data.
  • Robin Hood HashingA collision function exists within the context of hashing and hash tables. Collision functions determine how to handle situations when the array underlying the hash table already has another datapoint in the slot chosen by the hash function. Here are some of the most popular collision functions: Linear Probing - Linear probing simply increments the hash index each time it... – Robin Hood hashing works on the principle “Steal from the rich and give to the poor”. The “rich” are datapoints stored after very few collisions index and the “poor” are datapoints stored after very many collisions. For each item inserted into the hash table, store the number of collisions encountered before storing the item. If you try to insert an item into a slot, and the slot is occupied, before moving on, compare the number of collisions for each item. If the item you are trying to store has encountered more collisions than the item already stored, swap the two items. Store the item you’re trying to store in that slot and find a new home for the previous occupant. This process serves to minimize the number of collisions encountered by any single element. Fewer collisions means quicker retrieval. You spend a little extra time in insertion to ensure the retrieval is as quick as possible.
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