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From Anna Björk Nikulásdóttir <>
Subject Re: Avoid automaton Memory Usage
Date Tue, 13 Aug 2013 13:44:38 GMT
I created these 3 issues for the discussed items:

On disk FST objects:

FuzzySuggester should boost terms with minimal Levenshtein Distance:

AnalyzingSuggester and FuzzySuggester should be able to share same FST:

Thanks a lot for your suggestions (pun intended) ;)



Am 7.8.2013 um 14:01 schrieb Michael McCandless <>:

> 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
> ... try typing fst, for example.
> Mike McCandless
> On Wed, Aug 7, 2013 at 9:32 AM, Anna Björk Nikulásdóttir
> <> 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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