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Statistical Facet

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Statistical Facet

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Facets are deprecated and will be removed in a future release. You are encouraged to migrate to aggregations instead.

The equivalent aggregation would be the stats aggregation.

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 field

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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 Field

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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 Considerations

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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.

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