Unfortunately, the FST based suggesters currently must be HEAP resident. In theory this is fixable, e.g. if we could map the FST and then access it via DirectByteBuffer ... maybe open a Jira issue to explore this possibility?
You could also try AnalyzingInfixSuggester; it uses a "normal" Lucene index (though, it does load things up into in-memory DocValues fields by default). And of course it differs from the other suggesters in that it's not "pure prefix" matching. You can see it running at http://jirasearch.mikemccandless.com ... try typing fst, for example. Mike McCandless http://blog.mikemccandless.com On Wed, Aug 7, 2013 at 9:32 AM, Anna Björk Nikulásdóttir <anna.b....@gmx.de> wrote: > Hi, > > I am using Lucene 4.3 on Android for terms auto suggestions (>500.000). I am > using both FuzzySuggester and AnalyzingSuggester, each for their specific > strengths. Everything works great but my app consumes 69MB of RAM with most > of that dedicated to the suggester classes. This is too much for many older > devices and Android imposes RAM limits for those. > As I understand, these suggester classes consume RAM because they use in > memory automatons. Is it possible - similar to Lucene indexes - to have these > automatons rather on "disk" than in memory or is there an alternative > approach with similarly good results that works with most data from > disk/flash ? > > regards, > > Anna. > --------------------------------------------------------------------- > To unsubscribe, e-mail: java-user-unsubscr...@lucene.apache.org > For additional commands, e-mail: java-user-h...@lucene.apache.org > --------------------------------------------------------------------- To unsubscribe, e-mail: java-user-unsubscr...@lucene.apache.org For additional commands, e-mail: java-user-h...@lucene.apache.org
