trie

By trie map I mean an associative array, where the payloads are stored in a trie instead of a hash table. When I'm using a hash map/table, the keys I use are typically strings. What are the advantages of a hash map over some trie based map? I have read that a hash map is faster - but it seems to me that a consistent hash functions would have to check each element of the (char) array for the final hash - iterating over the array once. In a trie you would similarly have to iterate over the array just once. It does seem to me that this would use a lot more memory when encoding small objects (even

What is the complexity of creating a trie of a list of words and what is complexity of searching other set of word in that trie? Should I use trie for string searching, when i have hashtable? The complexity of creating a trie is O(W*L) , where W is the number of words, and L is an average length of the word: you need to perform L lookups on the average for each of the W words in the set. Same goes for looking up words later: you perform L steps for each of the W words. Hash insertions and lookups have the same complexity: for each word you need to check equality, which takes O(L) , for the

I'm attempting to implement a Patricia Trie with the methods addWord() , isWord() , and isPrefix() as a means to store a large dictionary of words for quick retrieval (including prefix search). I've read up on the concepts but they just aren't clarifying into an implementation. I want to know (in Java or Python code) how to implement the Trie, particularly the nodes (or should I implement it recursively). I saw one person who implemented it with an array of 26 child nodes set to null/None. Is there a better strategy (such as treating the letters as bits) and how would you implement it? Someone

I need information about any standard python package which can be used for "longest prefix match" on URLs. I have gone through the two standard packages http://packages.python.org/PyTrie/#pytrie.StringTrie & 'http://pypi.python.org/pypi/trie/0.1.1' but they don't seem to be useful for longest prefix match task on URLs. Examlple, if my set has these URLs 1->http://www.google.com/mail , 2->http://www.google.com/document, 3->http://www.facebook.com, etc.. Now if I search for 'http://www.google.com/doc' then it should return 2 and search for 'http://www.face' should return 3. I wanted to confirm

I don't know if this is the place to ask about algorithms. But let's see if I get any answers ... :) If anything is unclear I'm very happy to clarify things. I just implemented a Trie in python. However, one bit seemed to be more complicated than it ought to (as someone who loves simplicity). Perhaps someone has had a similar problem? My aim was to minimize the number of nodes by storing the largest common prefix of a sub-trie in its root. For example, if we had the words stackoverflow , stackbase and stackbased , then the tree would look something like this: [s]tack [o]verflow ______/ \______

I am reading about Tries commonly known as Prefix trees and Suffix Trees . Although I have found code for a Trie I can not find an example for a Suffix Tree . Also I get the feeling that the code that builds a Trie is the same as the one for a Suffix Tree with the only difference that in the former case we store prefixes but in the latter suffixes. Is this true? Can anyone help me clear this out in my head? An example code would be great help! Ze Blob A suffix tree can be viewed as a data structure built on top of a trie where, instead of just adding the string itself into the trie, you would

I am working on an autocompletion script and was thinking about using a trie. My problem is I want everything that matches to be returned. So for example I type in the letter r I want all entries starting with r to be returned. Then all entries starting with re etc. Is this feasible with a trie and how would it work. Also, if there is a better way I am open to suggestions. The reason I ask is it seems like it would be complicated and a whole lot of processing to return all of the nodes off of say the r branch. And yes I may be reinventing the wheel, but I would like to learn how it works. You