Hashes: Tables, Lists and Maps, Oh My?

I've been trying to find some concrete (laymen; non super-academic) definitions for the various types of hash data structures, specifically hash tables, hash lists and hash maps. Online searches provide many useful links to all of these, but never give clear definitions of when it is appropriate to use each over the others.

(1) From a practical standpoint, what's th开发者_如何学编程e difference between these 3?

(2) How do their operations' run times differ? Are there clear instances when one should be used or avoided over the other types of hashes?

(3) How do each of these relate back to the Map ADT? Are they all just different implementations of it, or different beasts altogether?

Thanks for any insight here!

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There's an abstract data structure that contains mapping between keys and values. It has several different names, including Map, Dictionary, Table, Association Table, and more.

The most basic operations that should be supported by this data-structure are adding, removing and retrieving a value, given its associated key. There are variations and additions around this basic concept - for instance, some structures support iterating over all the key-value pairs, some structures support multiple values per key, etc. There's also a difference in time and space complexity between the various implementations.

Of the multiple implementations available for this data structure, some of the most popular ones utilize hash functions for fast access times. Those implementations are sometimes called by the name Hash Table or Hash Map, you can read more about them in Wikipedia. The performance also varies between hash table implementations, with some reaching amortized O(1) insertion and access complexity (for the price of a lot of space used).

A hash list, on the other hand, is a different thing, and is more about the usage of a data structure, than its actual structures. A hash list is usually just a regular list of hash values, nothing special about it. It's used when verifying the integrity of a large piece of data - in that case it allows various data chunks to be verified independently, allowing for fixing or retrieving of just the bad chunks. This is as opposed to using a single hash value to hash the entire piece of data, in which case a failure means all the data has to be fixed or retrieved again.