Scott, Yes, take your big index and split it into multiple smaller shards. Put those shards in different servers and then query them remotely (using the provided RMI thing in Lucene or using something custom), take top N results from each searcher, merge those, and take top N from the merged result set.
You could also experiment with a memory mapped Directory implementation. Otis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simpy -- http://www.simpy.com/ - Tag - Search - Share ----- Original Message ---- From: Scott Sellman <[EMAIL PROTECTED]> To: java-user@lucene.apache.org Sent: Thursday, May 24, 2007 1:31:49 PM Subject: Improving Search Performance on Large Indexes Hello, Currently we are attempting to optimize the search time against an index that is 26 GB in size (~35 million docs) and I was wondering what experiences others have had in similar attempts. Simple searches against the index are still fast even at 26GB, but the problem is our application allows the user a lot of options in searching, which can generate complicated queries. Based on previous posts we decided to try splitting our index into multiple indexes and use ParallelMultiSearcher. When we split our single index into 6 separate ones we recorded a 25% decrease in response time on minimal load. We haven't done any stress testing on it yet, has anyone noticed problems with increased load when using ParallelMultiSearcher? What about using machines with more processors in combination with the ParallelMultiSearcher, does this result in much response time improvement? Or is the slow down primarily with disk access? Any recommendations are welcome. Thanks in advance, Scott --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
