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copied!<p>Good to see someone's chimed in about Lucene - because I've no idea about that.</p> <p>Sphinx, on the other hand, I know quite well, so let's see if I can be of some help.</p> <ul> <li>Result relevance ranking is the default. You can set up your own sorting should you wish, and give specific fields higher weightings.</li> <li>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.</li> <li>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.</li> <li>The search service daemon (searchd) is pretty low on memory usage - and you can set limits on <a href="http://www.sphinxsearch.com/docs/manual-0.9.8.html#conf-mem-limit" rel="noreferrer">how much memory</a> the indexer process uses too.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ul> <p>I've no idea how applicable to your situation this is, but <a href="http://blog.evanweaver.com/articles/2008/03/17/rails-search-benchmarks/" rel="noreferrer">Evan Weaver compared a few of the common Rails search options</a> (Sphinx, Ferret (a port of Lucene for Ruby) and Solr), running some benchmarks. Could be useful, I guess.</p> <p>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.</p>

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