Here's an example of a facet query, complete with the results of
post-processing it as a hierarchical combination facet (not yet available
in ES 0.90.X). The script is used to provide the response in such a way
that I can easily break it down into a hierarchical combination of
arbitrary depth. In this example, it shows the top ten words used in cities
in the USA and Puerto Rico by state.
First, the full query, including the facet. It was issued against
approximately 26K documents within a type that contains all cities in the
US and Puerto Rico (originally from the US Census). That's why the
match_all with no additional filtering is used. The query was built using
the Java API, and the JSON below was emitted by the Java API:
{
"from" : 0,
"size" : 50000,
"timeout" : 60000,
"query" : {
"match_all" : { }
},
"version" : true,
"explain" : false,
"fields" : [ "_ttl", "_source" ],
"facets" : {
"state_city_combinations" : {
"terms" : {
"size" : 10,
"script" : "doc['state'].value + \"~~~\" + doc['city'].value"
}
}
}
}
The response. The "terms" JSON object is what ElasticSearch returned, while
the "combinations" is what I calculated by processing what ElasticSearch
returned. Just as a point of interest:
{
"facets" : {
"state_city_combinations" : {
"_type" : "terms",
"total" : 25376,
"other" : 25123,
"missing" : 0,
"combinations" : {
"fl" : {
"beach" : 61,
"lake" : 18,
"citi" : 13
},
"mo" : {
"citi" : 36
},
"tx" : {
"citi" : 33
},
"il" : {
"citi" : 22
},
"ny" : {
"east" : 21
},
"mn" : {
"lake" : 19
},
"wi" : {
"lake" : 16
},
"ia" : {
"citi" : 14
}
},
"terms" : [ {
"term" : "fl~~~beach",
"count" : 61
}, {
"term" : "mo~~~citi",
"count" : 36
}, {
"term" : "tx~~~citi",
"count" : 33
}, {
"term" : "il~~~citi",
"count" : 22
}, {
"term" : "ny~~~east",
"count" : 21
}, {
"term" : "mn~~~lake",
"count" : 19
}, {
"term" : "fl~~~lake",
"count" : 18
}, {
"term" : "wi~~~lake",
"count" : 16
}, {
"term" : "ia~~~citi",
"count" : 14
}, {
"term" : "fl~~~citi",
"count" : 13
} ]
}
}
}
Brian
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