Out of curiosity, what kind of performance do you get when you only run
the search on '.raw' fields and not regular fields (with edgengram).
Obviously the result of the query will not be the same as before as the
whole world should match if the edgengram are out of the picture.
I had some pretty weird result in the past where under specific
circonstances I had better performance results with prefix queries than
edgengram with a huge volume of data.
This reminds me a project I worked on indexing data from geonames. One
thing we did with altername names and support for multiple languages was
to remove the field for the default language. A default language after
all is a language that exists (either 'en', 'fr', etc.). This will make
your index smaller and make you run the query on less fields (15 instead
of 25).
Also I noticed that there is no edgengram on the postcode. Any reason
for that? It might be useful to also do a partial match.
Stéphane
On 06/25/2014 05:33 PM, Christoph Lingg wrote:
Could you also share the query you are running? do run the
cross_field query against the default field or the 'raw' field?
it looks like this:
{
"function_score": {"functions": [{"script_score": {"script": "1. +
50. * doc['importance'].value"}}],
"boost_mode": "sum",
"score_mode": "sum",
"query": {
"multi_match": {
"analyzer": "search",
"type": "cross_fields",
"fields": [
"name.default.raw^18", "name.default^2.5",
"name.${lang}.raw^18", "name.${lang}^2.5",
"name.alternatives.raw^14", "name.alternatives^1.5",
"city.default.raw^8", "city.default^2", "city.${lang}.raw^8",
"city.${lang}^2",
"street.default.raw^8", "street.default^2",
"street.${lang}.raw^8", "street.${lang}^2",
"housenumber.raw^6", "housenumber",
"postcode^5",
"country.default.raw^3", "country.default",
"country.${lang}.raw^3", "country.${lang}",
"context.default.raw^3", "context.default",
"context.${lang}.raw^3", "context.${lang}"
],
"minimum_should_match": ${should_match},
"query": "${query}"
}
}
}
}
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