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From Michael McCandless <luc...@mikemccandless.com>
Subject Re: Avoid automaton Memory Usage
Date Wed, 07 Aug 2013 14:01:24 GMT
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.nik@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.
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