How hashmap retrieves value with hash key?

Question

I'm more confused with Hashmap or Hashtable concept, when people say Hashmap is faster over List. I'm clear with hashing concept, in which the value is stored in hash code for the given key.

But when I want to retrieve the data how it works, For example, I'm storing n number of strings with n different keys in a HashMap. If I want to retrieve a specific value associated with specific key, how it will return it in O(1) of time ? Because the hashed key will be compared with all other keys right ?

Answer 1

Lets go on a word journey, say you have a bunch weird m&m's with all the letters.

Now it's your job is to vend people m&m's in the letter color combo of their choosing.

You have some choices about how to organize your shop. ( This act of organization will be metaphorically our hash function. )

You can sort your M&M's into buckets by color or by letter or by both. The question follows, what provides you the fastest retrieval time of a specific request?

The answer is rather intuitive, being that the sorting providing the fewest different M&Ms in each bucket facilitates the most efficient queering.

Lets say someone asked if you had any green Q ; if all your M&M's are in a single bin or list or bucket or otherwise unstructured container the answer will be far from accessible in O(1) as compared to keeping an organized shop.

This analogy relies on the concept of Separate chaining where each hash-Key corresponds to a container of multiple elements.

Without this concept the idea of hashing is more generally to use keys from uniformly throughout an array such that the amortized performance is constant. Collisions can be resolved through a variety of method variations and the Wikipedia article will tell you all about it. http://en.wikipedia.org/wiki/Hash_table

"If the set of key-value pairs is fixed and known ahead of time (so insertions and deletions are not allowed), one may reduce the average lookup cost by a careful choice of the hash function, bucket table size, and internal data structures. In particular, one may be able to devise a hash function that is collision-free, or even perfect "