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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

`doubleMin` aggregator

doubleMin computes the minimum of all metric values and Double.POSITIVE_INFINITY

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

`doubleMax` aggregator

doubleMax computes the maximum of all metric values and Double.NEGATIVE_INFINITY

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

`longMin` aggregator

longMin computes the minimum of all metric values and Long.MAX_VALUE

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

`longMax` aggregator

longMax computes the maximum of all metric values and Long.MIN_VALUE

{ "type" : "longMax", "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(DISTINCT(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> }

Miscellaneous Aggregations

Filtered Aggregator

A filtered aggregator wraps any given aggregator, but only aggregates the values for which the given dimension filter matches.

This makes it possible to compute the results of a filtered and an unfiltered aggregation simultaneously, without having to issue multiple queries, and use both results as part of post-aggregations.

Limitations: The filtered aggregator currently only supports 'or', 'and', 'selector' and 'not' filters, i.e. matching one or multiple dimensions against a single value.

Note: If only the filtered results are required, consider putting the filter on the query itself, which will be much faster since it does not require scanning all the data.

{
  "type" : "filtered",
  "filter" : {
    "type" : "selector",
    "dimension" : <dimension>,
    "value" : <dimension value>
  }
  "aggregator" : <aggregation>
}

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