On Tue, Aug 13, 2013 at 9:44 AM, Anna Björk Nikulásdóttir
wrote:
> I created these 3 issues for the discussed items:
Thanks! If you (or anyone!) want to work up a patch that would be great ...
> Thanks a lot for your suggestions (pun intended) ;)
;)
Mike McCandless
http://blog.mikemccandless
I created these 3 issues for the discussed items:
On disk FST objects:
https://issues.apache.org/jira/browse/LUCENE-5174
FuzzySuggester should boost terms with minimal Levenshtein Distance:
https://issues.apache.org/jira/browse/LUCENE-5172
AnalyzingSuggester and FuzzySuggester should be able to
On Thu, Aug 8, 2013 at 12:54 PM, Anna Björk Nikulásdóttir
wrote:
>
> Am 8.8.2013 um 12:37 schrieb Michael McCandless :
>
>>
>>> What would help in my case as I use the same FST for both analyzers, if the
>>> same FST object could be shared among both analyzers. So what I am doing is
>>> to use
Am 8.8.2013 um 12:37 schrieb Michael McCandless :
>
>> What would help in my case as I use the same FST for both analyzers, if the
>> same FST object could be shared among both analyzers. So what I am doing is
>> to use AnalyzingSuggester.store() and use the stored file for
>> AnalyzingSugges
On Wed, Aug 7, 2013 at 1:18 PM, Anna Björk Nikulásdóttir
wrote:
> Ah I see. I will look into the AnalyzingInfixSuggester. I suppose it could be
> useful as an alternative rather to AnalyzingSuggester instead of
> FuzzySuggestor ?
Yes, but it's very different (it does no fuzzing, and it matches
Ah I see. I will look into the AnalyzingInfixSuggester. I suppose it could be
useful as an alternative rather to AnalyzingSuggester instead of FuzzySuggestor
?
What would help in my case as I use the same FST for both analyzers, if the
same FST object could be shared among both analyzers. So wh
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
i
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
