yep, as Isabel mentioned, you should use *dfs_query_then_fetch* search type 
(it is slower doo)


On Monday, February 17, 2014 3:23:27 PM UTC, Vallabh Bothre wrote:
>
> Thanks Karol for replying,
>
> As per your suggestion i used search type which execute the query on all 
> relevant shards and return the results.
> "search_type" => "query_then_fetch"
>
> But still i am getting different score for same keyword.
>
> On Monday, February 17, 2014 4:50:51 PM UTC+5:30, Karol Gwaj wrote:
>>
>> your query runs in parallel on multiple shards and score you seeing is 
>> computed independently on every shard
>> the default similarity is tf/idf based, which means it is using terms 
>> frequency across all documents (so your score on the shard will depend on 
>> data on this shard)
>> for score to be this same, every shard will have to have more or less 
>> this same documents
>>
>> you can change search_type to compute score when combining results from 
>> shards (slower):
>>
>> http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-request-search-type.html
>>
>> also check this out (to learn more about similarity algorithms used by 
>> elasticsearch):
>>
>> http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/index-modules-similarity.html
>>
>> because you are searching for user names, it is possible that you could 
>> wrap your query in constant (or function) score query and settle for less 
>> granular scoring:
>>
>> http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html
>>
>>
>> Cheers,
>> Karol Gwaj
>>
>>
>> On Monday, February 17, 2014 11:00:47 AM UTC, Vallabh Bothre wrote:
>>>
>>> Dear Friends,
>>>
>>> I am using phonetic analysis in elasticsearch to search best results.
>>>
>>> When i search keyword lets say "McDonald" elastic search returns many 
>>> listings with "McDonald's" but some of these have differrent scores.
>>>
>>> I am manipulating results based on score and due to this difference its 
>>> affecting my functionality.
>>>
>>> All returned listings with "McDonald" have same case and are exact.
>>>
>>> For Ex: 
>>> *Name                 score*
>>> McDonald's         *5.8059134*
>>> McDonald's         5.8059134
>>> McDonald's         5.8059134
>>> McDonald's         *5.7834973*
>>> McDonald's         5.7834973
>>> McDonald's         *5.4078074*
>>>
>>>
>>> As shown in example above there are 3 different scores which are 
>>> highlighted.
>>>
>>> Any help is very much appreciated.
>>>
>>> Regards,
>>> Vallabh
>>>
>>

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