One idea has to do with the fact that there are only 26 (assuming Latin alphabet) possible first letters, so I would try splitting up the list of 10,000 into 26 lists in a dictionary indexed by the first letter. Just doing that is a big reduction of your search space. That way you won't be doing the same search every time for a particular first letter. It might even be worthwhile to split each of those into 26 sublists based on the second letter. Now you've chopped up your 10,000 words into 676 lists, each of which might be small enough to send to the client without further searching. (Too bad you won't have an even distribution across all letters. Then each list would only have 15 words in it.)
You could also try using SQLite. I'm using right now in a Django application, and I'm very happy with the setup and performance, especially for read operations. With Django, I'm using their ORM, which is quite nice, so I'm not doing any SQL directly. I think there can be problems with SQLite when you attempt concurrent writes, but you wouldn't have that. It's hard to predict which would perform better, a tailor made domain specific solution written in Python, or a general purpose in-memory database written in C. I would start with which ever direction you are most comfortable, and if you can't get satisfactory performance, try the other route. Greg From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Dinesh B Vadhia Sent: Thursday, April 10, 2008 8:13 AM To: tutor@python.org Subject: Re: [Tutor] Searching through large number of string items The 10,000 string items are sorted. The way the autocomplete works is that when a user enters a char eg. 'f', the 'f' is sent to the server and returns strings with the char 'f'. You can limit the number of items sent back to the browser (say, limit to between 15 and 100). The string items containing 'f' are displayed. The user can then enter another char eg. 'a' to make 'fa'. The autocomplete plugin will search the cache to find all items containing 'fa' but may need to go back to the server to collect others. And, so on. Equally, the user could backspace the 'f' and enter 'k'. The 'k' will be sent to the server to find strings containing 'k', and so on. One way to solve this is with linear search which as you rightly pointed out has horrible performance (and it has!). I'll try the binary search and let you know. I'll also look at the trie structure. An alternative is to create an in-memory SQLite database of the string items. Any thoughts on that? Dinesh ----- Original Message ----- From: Kent Johnson <mailto:[EMAIL PROTECTED]> To: Dinesh B Vadhia <mailto:[EMAIL PROTECTED]> Cc: tutor@python.org Sent: Thursday, April 10, 2008 5:20 AM Subject: Re: [Tutor] List comprehensions Dinesh B Vadhia wrote: > Kent > > I'm using a Javascript autocomplete plugin for an online web > application/service. Each time a user inputs a character, the character > is sent to the backend Python program which searches for the character > in a list of >10,000 string items. Once it finds the character, the > backend will return that string and N other adjacent string items where > N can vary from 20 to 150. Each string item is sent back to the JS in > separate print statements. Hence, the for loop. Ok, this sounds a little closer to a real spec. What kind of search are you doing? Do you really just search for individual characters or are you looking for the entire string entered so far as a prefix? Is the list of 10,000 items sorted? Can it be? You need to look at your real problem and find an appropriate data structure, rather than showing us what you think is the solution and asking how to make it faster. For example, if what you have a sorted list of strings and you want to find the first string that starts with a given prefix and return the N adjacent strings, you could use the bisect module to do a binary search rather than a linear search. Binary search of 10,000 items will take 13-14 comparisons to find the correct location. Your linear search will take an average of 5,000 comparisons. You might also want to use a trie structure though I'm not sure if that will let you find adjacent items. http://www.cs.mcgill.ca/~cs251/OldCourses/1997/topic7/ http://jtauber.com/blog/2005/02/10/updated_python_trie_implementation/ > I haven't done any profiling yet as we are still building the system but > it seemed sensible that replacing the for loop with a built-in would > help. Maybe not? Not. An algorithm with poor "big O" performance should be *replaced*, not optimized. Kent
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