Great, I am glad that it worked. I do not use multi-index searches, so I
was not sure if it would. Good to know that shards from different indices
can be aggregated with DFS queries.

-- 
Ivan


On Tue, Feb 25, 2014 at 6:04 PM, Luiz Guilherme Pais dos Santos <
luizgpsan...@gmail.com> wrote:

> Hi Ivan,
>
> The DFS query then fetch worked very well!
>
> Thank you!
>
> Cheers,
> Luiz Guilherme
>
>
> On Tue, Feb 25, 2014 at 5:15 PM, Ivan Brusic <i...@brusic.com> wrote:
>
>> I have never tried or looked at the code, but off the top of my head
>> perhaps the DFS query type would work:
>> http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-request-search-type.html#dfs-query-then-fetch
>>
>> Since the DFS query type calculates the TF/IDF values based on the values
>> in each individual shard, perhaps it ignores which index the shard belongs
>> to. Easy to test.
>>
>> If not, the solution might be tricky. You can eliminate term length
>> normalization, but your issue is with the IDF. You can create your own
>> Similarity, but the best you can do is ignore the IDF, which probably would
>> not be ideal.
>>
>> Ultimately, you can try script based scoring. The TF/IDF values are
>> exposed to the scripts, so you can try to apply some type of normalization
>> yourself. Kludgy and it would impact performance.
>>
>>
>> http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/modules-advanced-scripting.html
>>
>> Hopefully DFS queries would work or someone else has a better idea!
>>
>> Cheers,
>>
>> Ivan
>>
>>
>>  On Tue, Feb 25, 2014 at 12:00 PM, Luiz Guilherme Pais dos Santos <
>> luizgpsan...@gmail.com> wrote:
>>
>>>  Hi,
>>>
>>> I'm trying to search across multiple indexes and I couldn't understand
>>> the result of the TF/TDF function. I didn't expect for the indexes where
>>> the term is more frequent to get penalized.
>>>
>>> Here follows an example:
>>> https://gist.github.com/luizgpsantos/9216108
>>>
>>> When searching for the term "alice" the document {"_index": "index2",
>>> "_type": "type", "_id": "1"} got a score 0.8784157 while {"_index":
>>> "index1", "_type": "type", "_id": "1"} got a score 0.4451987.
>>>
>>> In my use case I got one index about sports and another about
>>> celebrities and when I search for a celebrity documents across sports and
>>> celebrities indexes, results from sports index tend to appear in first
>>> place due to the explanation above (we have few celebrities documents in
>>> sports index). But the point is that when searching for a celebrity I would
>>> expect results from the celebrity index.
>>>
>>> Is there any way to calculate the score not penalizing indexes where the
>>> frequency of a term is higher?
>>>
>>> Cheers,
>>>
>>> --
>>> Luiz Guilherme P. Santos
>>>
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>
>
>
> --
> Luiz Guilherme P. Santos
>
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