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## Statistical Facetedit

Statistical facet allows to compute statistical data on a numeric fields. The statistical data include count, total, sum of squares, mean (average), minimum, maximum, variance, and standard deviation. Here is an example:

{ "query" : { "match_all" : {} }, "facets" : { "stat1" : { "statistical" : { "field" : "num1" } } } }

### Script fieldedit

When using `field`

, the numeric value of the field is used to compute
the statistical information. Sometimes, several fields values represent
the statistics we want to compute, or some sort of mathematical
evaluation. The script field allows to define a
script to evaluate, with
its value used to compute the statistical information. For example:

{ "query" : { "match_all" : {} }, "facets" : { "stat1" : { "statistical" : { "script" : "doc['num1'].value + doc['num2'].value" } } } }

Parameters can also be provided to the different scripts (preferable if the script is the same, with different values for a specific parameter, like "factor"):

{ "query" : { "match_all" : {} }, "facets" : { "stat1" : { "statistical" : { "script" : "(doc['num1'].value + doc['num2'].value) * factor", "params" : { "factor" : 5 } } } } }

### Multi Fieldedit

The statistical facet can be executed against more than one field, returning the aggregation result across those fields. For example:

{ "query" : { "match_all" : {} }, "facets" : { "stat1" : { "statistical" : { "fields" : ["num1", "num2"] } } } }

### Memory Considerationsedit

In order to implement the statistical facet, the relevant field values
are loaded into memory from the index. This means that per shard, there
should be enough memory to contain them. Since by default, dynamic
introduced types are `long`

and `double`

, one option to reduce the
memory footprint is to explicitly set the types for the relevant fields
to either `short`

, `integer`

, or `float`

when possible.