Which data structure to add/look up/keep count of strings?

I'm trying to figure out what data structure to quickly support the following operations:

• Add a string (if it's not there, add it, if it is there, increment a counter for the word)
• Count a given string (look up by stri开发者_JS百科ng and then read the counter)

I'm debating between a hash table or a trie. From my understanding a hash table is fast to look up and add as long as you avoid collisions. If I don't know my inputs ahead of time would a trie be a better way to go?

It really depends on the types of strings you're going to be using as "keys". If you're using highly variable strings, plus you do not have a good hash algorithm for your strings, then a trie can outperform a hash.

However, given a good hash, the lookup will be faster than in a trie. (Given a very bad hash, the opposite is true, though.) If you don't know your inputs, but do have a decent hashing algorithm, I personally prefer using a hash.

Also, most modern languages/frameworks have very good hashing algorithms, so chances are, you'll be able to implement a good lookup using a hash with very little work, that will perform quite well.

A trie won't buy you much; they're only interesting when prefixes are important. Hash tables are simpler, and usually part of your language's standard library, if not directly part of the language itself (Ruby, Python, etc). Here's a dead-simple way to do this in Ruby:

strings = %w(some words that may be repeated repeated)
counts = Hash.new(0)
strings.each { |s| counts[s] += 1 }
#counts => {"words"=>1, "be"=>1, "repeated"=>2, "may"=>1, "that"=>1, "some"=>1}

Addenda: For C++, you can probably use Boost's hash implementation.

Either one is reasonably fast.

It isn't necessary to completely avoid collisions.

Looking at performance a little more closely, usually, hash tables are faster than trees, but I doubt if a real life program ever ran too slow simply because it used a tree instead of a HT, and some trees are faster than some hash tables.

What else can we say, well, hash tables are more common than trees.

One advantage of the complex trees is that they have predictable access times. With hash tables and simple binary trees, the performance you see depends on the data and with an HT performance depends strongly on the quality of the implementation and its configuration with respect to the data set size.