Comparison of full text search engine - Lucene, Sphinx, Postgresql, MySQL?
I'm building a Django site and I am looking for a search engine.
A few candidates:
Lucene/Lucene with Compass/Solr
Sphinx
Postgresql built-in full text search
MySQl built-in full text search
Selection criteria:
- result relevance and ranking
- searching and indexing speed
- ease of use and ease of integration with Django
- resource requirements - site will be hosted on a VPS, so ideally the search engine wouldn't require a lot of RAM and CPU
- scalability
- extra features such as "did you mean?", related searches, etc
Anyone who has had experience with the search engines above, or other engines not in the list -- I would love to hear your opinions.
EDIT: As for indexing needs, as users keep entering data into the site, those data would need to be indexed continuously. It doesn't have to be real time, but ideally new data would show up in index with no more than 15 - 30 minutes delay
Answer
Good to see someone's chimed in about Lucene - because I've no idea about that.
Sphinx, on the other hand, I know quite well, so let's see if I can be of some help.
- Result relevance ranking is the default. You can set up your own sorting should you wish, and give specific fields higher weightings.
- Indexing speed is super-fast, because it talks directly to the database. Any slowness will come from complex SQL queries and un-indexed foreign keys and other such problems. I've never noticed any slowness in searching either.
- I'm a Rails guy, so I've no idea how easy it is to implement with Django. There is a Python API that comes with the Sphinx source though.
- The search service daemon (searchd) is pretty low on memory usage - and you can set limits on how much memory the indexer process uses too.
- Scalability is where my knowledge is more sketchy - but it's easy enough to copy index files to multiple machines and run several searchd daemons. The general impression I get from others though is that it's pretty damn good under high load, so scaling it out across multiple machines isn't something that needs to be dealt with.
- There's no support for 'did-you-mean', etc - although these can be done with other tools easily enough. Sphinx does stem words though using dictionaries, so 'driving' and 'drive' (for example) would be considered the same in searches.
- Sphinx doesn't allow partial index updates for field data though. The common approach to this is to maintain a delta index with all the recent changes, and re-index this after every change (and those new results appear within a second or two). Because of the small amount of data, this can take a matter of seconds. You will still need to re-index the main dataset regularly though (although how regularly depends on the volatility of your data - every day? every hour?). The fast indexing speeds keep this all pretty painless though.
I've no idea how applicable to your situation this is, but Evan Weaver compared a few of the common Rails search options (Sphinx, Ferret (a port of Lucene for Ruby) and Solr), running some benchmarks. Could be useful, I guess.
I've not plumbed the depths of MySQL's full-text search, but I know it doesn't compete speed-wise nor feature-wise with Sphinx, Lucene or Solr.