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From "Cassandra Targett (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SOLR-12235) Incomplete debugQuery info when using edismax and boost param
Date Wed, 09 May 2018 21:57:00 GMT

    [ https://issues.apache.org/jira/browse/SOLR-12235?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16469565#comment-16469565
] 

Cassandra Targett commented on SOLR-12235:
------------------------------------------

bq. then to 'zoom in' towards the unknown commit point(s) in between at which the info went
missing

If you can write such a test, {{git bisect}} could help you a lot with that to find the commit
where your test breaks (just in case you didn't know about it already).

> Incomplete debugQuery info when using edismax and boost param
> -------------------------------------------------------------
>
>                 Key: SOLR-12235
>                 URL: https://issues.apache.org/jira/browse/SOLR-12235
>             Project: Solr
>          Issue Type: Bug
>      Security Level: Public(Default Security Level. Issues are Public) 
>          Components: query parsers, search
>    Affects Versions: 7.3
>         Environment: Solr 7.3.0, Java 1.8.0_162
>            Reporter: Bogdan Stoica
>            Priority: Minor
>
> There is an issue with the way SOLR 7.3 outputs explain information when using edismax
and 
> boost param.
>  
> Example query: /select?boost=results&debugQuery=on&defType=edismax&q=word&qf=text
>  
> Solr 7.3 outputs:
>  
> {code:java}
> <str name="word"> 
> 31349.63 = product of: 1.0 = boost 31349.63 = boost(double(results)) 
> </str>{code}
>  
>  
> In comparrison, Solr 7.2.1 returns the following:
>  
> {code:java}
> <str name="word"> 
> 31349.63 = boost(text:word,double(results)), product of: 14.400382 = weight(text:word
in 18142) [SchemaSimilarity], result of: 14.400382 = score(doc=18142,freq=1.0 = termFreq=1.0
), product of: 10.677335 = idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq
+ 0.5)) from: 6.0 = docFreq 281851.0 = docCount 1.3486869 = tfNorm, computed as (freq * (k1
+ 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from: 1.0 = termFreq=1.0
1.2 = parameter k1 0.75 = parameter b 2.7172585 = avgFieldLength 1.0 = fieldLength 2177.0
= double(results)=2177.0 
> </str>{code}
>  



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