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Aggregations

Aggregations are specifications of processing over metrics available in Druid. Available aggregations are:

Count aggregator

count computes the row count that match the filters

{ "type" : "count", "name" : <output_name> }

Sum aggregators

longSum aggregator

computes the sum of values as a 64-bit, signed integer

{ "type" : "longSum", "name" : <output_name>, "fieldName" : <metric_name> }

name – output name for the summed value fieldName – name of the metric column to sum over

doubleSum aggregator

Computes the sum of values as 64-bit floating point value. Similar to longSum

{ "type" : "doubleSum", "name" : <output_name>, "fieldName" : <metric_name> }

Min / Max aggregators

min aggregator

min computes the minimum metric value

{ "type" : "min", "name" : <output_name>, "fieldName" : <metric_name> }

max aggregator

max computes the maximum metric value

{ "type" : "max", "name" : <output_name>, "fieldName" : <metric_name> }

JavaScript aggregator

Computes an arbitrary JavaScript function over a set of columns (both metrics and dimensions).

All JavaScript functions must return numerical values.

{ "type": "javascript",
  "name": "<output_name>",
  "fieldNames"  : [ <column1>, <column2>, ... ],
  "fnAggregate" : "function(current, column1, column2, ...) {
                     <updates partial aggregate (current) based on the current row values>
                     return <updated partial aggregate>
                   }",
  "fnCombine"   : "function(partialA, partialB) { return <combined partial results>; }",
  "fnReset"     : "function()                   { return <initial value>; }"
}

Example

{
  "type": "javascript",
  "name": "sum(log(x)/y) + 10",
  "fieldNames": ["x", "y"],
  "fnAggregate" : "function(current, a, b)      { return current + (Math.log(a) * b); }",
  "fnCombine"   : "function(partialA, partialB) { return partialA + partialB; }",
  "fnReset"     : "function()                   { return 10; }"
}

Cardinality aggregator

Computes the cardinality of a set of Druid dimensions, using HyperLogLog to estimate the cardinality.

{
  "type": "cardinality",
  "name": "<output_name>",
  "fieldNames": [ <dimension1>, <dimension2>, ... ],
  "byRow": <false | true> # (optional, defaults to false)
}

Cardinality by value

When setting byRow to false (the default) it computes the cardinality of the set composed of the union of all dimension values for all the given dimensions.

• For a single dimension, this is equivalent to

SELECT COUNT(DISCTINCT(dimension)) FROM <datasource>

• For multiple dimensions, this is equivalent to something akin to

SELECT COUNT(DISTINCT(value)) FROM (
  SELECT dim_1 as value FROM <datasource>
  UNION
  SELECT dim_2 as value FROM <datasource>
  UNION
  SELECT dim_3 as value FROM <datasource>
)

Cardinality by row

When setting byRow to true it computes the cardinality by row, i.e. the cardinality of distinct dimension combinations This is equivalent to something akin to

SELECT COUNT(*) FROM ( SELECT DIM1, DIM2, DIM3 FROM <datasource> GROUP BY DIM1, DIM2, DIM3

Example

Determine the number of distinct categories items are assigned to.

{
  "type": "cardinality",
  "name": "distinct_values",
  "fieldNames": [ "main_category", "secondary_category" ]
}

Determine the number of distinct are assigned to.

{
  "type": "cardinality",
  "name": "distinct_values",
  "fieldNames": [ "", "secondary_category" ],
  "byRow" : true
}

Complex Aggregations

HyperUnique aggregator

Uses HyperLogLog to compute the estimated cardinality of a dimension that has been aggregated as a “hyperUnique” metric at indexing time.

{ "type" : "hyperUnique", "name" : <output_name>, "fieldName" : <metric_name> }