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Histogram Facets

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Histogram Facets

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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 histogram aggregation.

The histogram facet works with numeric data by building a histogram across intervals of the field values. Each value is "rounded" into an interval (or placed in a bucket), and statistics are provided per interval/bucket (count and total). Here is a simple example:

{
    "query" : {
        "match_all" : {}
    },
    "facets" : {
        "histo1" : {
            "histogram" : {
                "field" : "field_name",
                "interval" : 100
            }
        }
    }
}

The above example will run a histogram facet on the field_name field, with an interval of 100 (so, for example, a value of 1055 will be placed within the 1000 bucket).

The interval can also be provided as a time based interval (using the time format). This mainly make sense when working on date fields or field that represent absolute milliseconds, here is an example:

{
    "query" : {
        "match_all" : {}
    },
    "facets" : {
        "histo1" : {
            "histogram" : {
                "field" : "field_name",
                "time_interval" : "1.5h"
            }
        }
    }
}

Key and Value

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The histogram facet allows to use a different key and value. The key is used to place the hit/document within the appropriate bucket, and the value is used to compute statistical data (for example, total). Here is an example:

{
    "query" : {
        "match_all" : {}
    },
    "facets" : {
        "histo1" : {
            "histogram" : {
                "key_field" : "key_field_name",
                "value_field" : "value_field_name",
                "interval" : 100
            }
        }
    }
}

Script Key and Value

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Sometimes, some munging of both the key and the value are needed. In the key case, before it is rounded into a bucket, and for the value, when the statistical data is computed per bucket scripts can be used. Here is an example:

{
    "query" : {
        "match_all" : {}
    },
    "facets" : {
        "histo1" : {
            "histogram" : {
                "key_script" : "doc['date'].date.minuteOfHour",
                "value_script" : "doc['num1'].value"
            }
        }
    }
}

In the above sample, we can use a date type field called date to get the minute of hour from it, and the total will be computed based on another field num1. Note, in this case, no interval was provided, so the bucket will be based directly on the key_script (no rounding).

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" : {
        "histo1" : {
            "histogram" : {
                "key_script" : "doc['date'].date.minuteOfHour * factor1",
                "value_script" : "doc['num1'].value + factor2",
                "params" : {
                    "factor1" : 2,
                    "factor2" : 3
                }
            }
        }
    }
}

Memory Considerations

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In order to implement the histogram 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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