Hi All,
Thanks for the suggestions. But there is a slight difference in the
requirements.
1. We don't index/ search 10 million documents for a keyword; instead we do it
on only 500 documents because we are supposed to get the final result only from
the 500 set of documents.
2.We have already f
On 26/05/2014 05:40, Shruthi wrote:
Hi All,
Thanks for the suggestions. But there is a slight difference in the
requirements.
1. We don't index/ search 10 million documents for a keyword; instead we do it
on only 500 documents because we are supposed to get the final result only from
the 500
bq: We don’t want to search on the complete document store
Why not? Alexandre's comment is spot on. For 500 docs you could easily
form a filter query like
&fq=id1 OR id2 OR id3 (solr-style, but easily done in Lucene). You
get these IDs from the DB
search. This will still be MUCH faster than in
You don't need to worry about the 1024 maxBooleanClauses, just use a
TermsFilter.
https://lucene.apache.org/core/4_8_0/queries/org/apache/lucene/queries/TermsFilter.html
I use it for a similar scenario, where we have a data structure that
determines a subset of 1.5 million documents from outsi
1000+ is solr, lucenen more fast.
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very different one is search , another is ml.
But, I want use ml results to improve solr performance, for example, buy
more, view more.
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Hi Furkan
Actually i have set of CandidateResumes and comments which are related to
whether resumes have been selected or rejected now I have to make machine
learn itself that if next time such or similar resume comes based on the
pre history it should go in which bag selected or rejected .
Than
