Aggregations

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Apache Druid supports two query languages: Druid SQL and native queries. This document describes the native language. For information about aggregators available in SQL, refer to the SQL documentation.

You can use aggregations:

• in the ingestion spec during ingestion to summarize data before it enters Apache Druid.
• at query time to summarize result data.

The following sections list the available aggregate functions. Unless otherwise noted, aggregations are available at both ingestion and query time.

Exact aggregations

Count aggregator

count computes the count of Druid rows that match the filters.

Example:

{ "type" : "count", "name" : "count" }

The count aggregator counts the number of Druid rows, which does not always reflect the number of raw events ingested. This is because Druid can be configured to roll up data at ingestion time. To count the number of ingested rows of data, include a count aggregator at ingestion time and a longSum aggregator at query time.

Sum aggregators

longSum aggregator

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

Example:

{ "type" : "longSum", "name" : "sumLong", "fieldName" : "aLong" }

doubleSum aggregator

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

Example:

{ "type" : "doubleSum", "name" : "sumDouble", "fieldName" : "aDouble" }

floatSum aggregator

Computes and stores the sum of values as a 32-bit floating point value. Similar to longSum and doubleSum.

Example:

{ "type" : "floatSum", "name" : "sumFloat", "fieldName" : "aFloat" }

Min and max aggregators

doubleMin aggregator

doubleMin computes the minimum of all input values and null if druid.generic.useDefaultValueForNull is false or Double.POSITIVE_INFINITY if true.

Example:

{ "type" : "doubleMin", "name" : "maxDouble", "fieldName" : "aDouble" }

doubleMax aggregator

doubleMax computes the maximum of all input values and null if druid.generic.useDefaultValueForNull is false or Double.NEGATIVE_INFINITY if true.

Example:

{ "type" : "doubleMax", "name" : "minDouble", "fieldName" : "aDouble" }

floatMin aggregator

floatMin computes the minimum of all input values and null if druid.generic.useDefaultValueForNull is false or Float.POSITIVE_INFINITY if true.

Example:

{ "type" : "floatMin", "name" : "minFloat", "fieldName" : "aFloat" }

floatMax aggregator

floatMax computes the maximum of all input values and null if druid.generic.useDefaultValueForNull is false or Float.NEGATIVE_INFINITY if true.

Example:

{ "type" : "floatMax", "name" : "maxFloat", "fieldName" : "aFloat" }

longMin aggregator

longMin computes the minimum of all input values and null if druid.generic.useDefaultValueForNull is false or Long.MAX_VALUE if true.

Example:

{ "type" : "longMin", "name" : "minLong", "fieldName" : "aLong" }

longMax aggregator

longMax computes the maximum of all metric values and null if druid.generic.useDefaultValueForNull is false or Long.MIN_VALUE if true.

Example:

{ "type" : "longMax", "name" : "maxLong", "fieldName" : "aLong" }

doubleMean aggregator

Computes and returns the arithmetic mean of a column’s values as a 64-bit floating point value.

Example:

{ "type" : "doubleMean", "name" : "aMean", "fieldName" : "aDouble" }

doubleMean is a query time aggregator only. It is not available for indexing. To accomplish mean aggregation on ingestion, refer to the Quantiles aggregator from the DataSketches extension.

First and last aggregators

The first and last aggregators determine the metric values that respectively correspond to the earliest and latest values of a time column.

Queries with first or last aggregators on a segment created with rollup return the rolled up value, not the first or last value from the raw ingested data. The timeColumn will get ignored in such cases, and the aggregation will use the original value of the time column stored at the time the segment was created.

Numeric first and last aggregators

doubleFirst aggregator

doubleFirst computes the input value with the minimum value for time column or 0 in default mode, or null in SQL-compatible mode if no row exists.

Example:

{
  "type" : "doubleFirst",
  "name" : "firstDouble",
  "fieldName" : "aDouble"
}

doubleLast aggregator

doubleLast computes the input value with the maximum value for time column or 0 in default mode, or null in SQL-compatible mode if no row exists.

Example:

{
  "type" : "doubleLast",
  "name" : "lastDouble",
  "fieldName" : "aDouble",
  "timeColumn" : "longTime"
}

floatFirst aggregator

floatFirst computes the input value with the minimum value for time column or 0 in default mode, or null in SQL-compatible mode if no row exists.

Example:

{
  "type" : "floatFirst",
  "name" : "firstFloat",
  "fieldName" : "aFloat"
}

floatLast aggregator

floatLast computes the metric value with the maximum value for time column or 0 in default mode, or null in SQL-compatible mode if no row exists.

Example:

{
  "type" : "floatLast",
  "name" : "lastFloat",
  "fieldName" : "aFloat"
}

longFirst aggregator

longFirst computes the metric value with the minimum value for time column or 0 in default mode, or null in SQL-compatible mode if no row exists.

Example:

{
  "type" : "longFirst",
  "name" : "firstLong",
  "fieldName" : "aLong"
}

longLast aggregator

longLast computes the metric value with the maximum value for time column or 0 in default mode, or null in SQL-compatible mode if no row exists.

Example:

{
  "type" : "longLast",
  "name" : "lastLong",
  "fieldName" : "aLong",
  "timeColumn" : "longTime"
}

String first and last aggregators

stringFirst aggregator

stringFirst computes the metric value with the minimum value for time column or null if no row exists.

Example:

{
  "type" : "stringFirst",
  "name" : "firstString",
  "fieldName" : "aString",
  "maxStringBytes" : 2048,
  "timeColumn" : "longTime"
}

stringLast aggregator

stringLast computes the metric value with the maximum value for time column or null if no row exists.

Example:

{
  "type" : "stringLast",
  "name" : "lastString",
  "fieldName" : "aString"
}

ANY aggregators

(Double/Float/Long/String) ANY aggregator cannot be used in ingestion spec, and should only be specified as part of queries.

Returns any value including null. This aggregator can simplify and optimize the performance by returning the first encountered value (including null)

Numeric any aggregators

doubleAny aggregator

doubleAny returns any double metric value.

Example:

{
  "type" : "doubleAny",
  "name" : "anyDouble",
  "fieldName" : "aDouble"
}

floatAny aggregator

floatAny returns any float metric value.

Example:

{
  "type" : "floatAny",
  "name" : "anyFloat",
  "fieldName" : "aFloat"
}

longAny aggregator

longAny returns any long metric value.

Example:

{
  "type" : "longAny",
  "name" : "anyLong",
  "fieldName" : "aLong"
}

stringAny aggregator

stringAny returns any string value present in the input.

Example:

{
  "type" : "stringAny",
  "name" : "anyString",
  "fieldName" : "aString",
  "maxStringBytes" : 2048
}

Approximate aggregations

Count distinct

Apache DataSketches Theta Sketch

The DataSketches Theta Sketch extension-provided aggregator gives distinct count estimates with support for set union, intersection, and difference post-aggregators, using Theta sketches from the Apache DataSketches library.

Apache DataSketches HLL Sketch

The DataSketches HLL Sketch extension-provided aggregator gives distinct count estimates using the HyperLogLog algorithm.

Compared to the Theta sketch, the HLL sketch does not support set operations and has slightly slower update and merge speed, but requires significantly less space.

Cardinality, hyperUnique

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For new use cases, we recommend evaluating DataSketches Theta Sketch or DataSketches HLL Sketch instead. The DataSketches aggregators are generally able to offer more flexibility and better accuracy than the classic Druid cardinality and hyperUnique aggregators.

The Cardinality and HyperUnique aggregators are older aggregator implementations available by default in Druid that also provide distinct count estimates using the HyperLogLog algorithm. The newer DataSketches Theta and HLL extension-provided aggregators described above have superior accuracy and performance and are recommended instead.

The DataSketches team has published a comparison study between Druid’s original HLL algorithm and the DataSketches HLL algorithm. Based on the demonstrated advantages of the DataSketches implementation, we are recommending using them in preference to Druid’s original HLL-based aggregators. However, to ensure backwards compatibility, we will continue to support the classic aggregators.

Please note that hyperUnique aggregators are not mutually compatible with Datasketches HLL or Theta sketches.

Multi-column handling

Note the DataSketches Theta and HLL aggregators currently only support single-column inputs. If you were previously using the Cardinality aggregator with multiple-column inputs, equivalent operations using Theta or HLL sketches are described below:

• Multi-column byValue Cardinality can be replaced with a union of Theta sketches on the individual input columns
• Multi-column byRow Cardinality can be replaced with a Theta or HLL sketch on a single virtual column that combines the individual input columns.

Histograms and quantiles

DataSketches Quantiles Sketch

The DataSketches Quantiles Sketch extension-provided aggregator provides quantile estimates and histogram approximations using the numeric quantiles DoublesSketch from the datasketches library.

We recommend this aggregator in general for quantiles/histogram use cases, as it provides formal error bounds and has distribution-independent accuracy.

Moments Sketch (Experimental)

The Moments Sketch extension-provided aggregator is an experimental aggregator that provides quantile estimates using the Moments Sketch.

The Moments Sketch aggregator is provided as an experimental option. It is optimized for merging speed and it can have higher aggregation performance compared to the DataSketches quantiles aggregator. However, the accuracy of the Moments Sketch is distribution-dependent, so users will need to empirically verify that the aggregator is suitable for their input data.

As a general guideline for experimentation, the Moments Sketch paper points out that this algorithm works better on inputs with high entropy. In particular, the algorithm is not a good fit when the input data consists of a small number of clustered discrete values.

Fixed Buckets Histogram

Druid also provides a simple histogram implementation that uses a fixed range and fixed number of buckets with support for quantile estimation, backed by an array of bucket count values.

The fixed buckets histogram can perform well when the distribution of the input data allows a small number of buckets to be used.

We do not recommend the fixed buckets histogram for general use, as its usefulness is extremely data dependent. However, it is made available for users that have already identified use cases where a fixed buckets histogram is suitable.

Approximate Histogram (deprecated)

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The Approximate Histogram aggregator is deprecated. There are a number of other quantile estimation algorithms that offer better performance, accuracy, and memory footprint. We recommend using DataSketches Quantiles instead.

The Approximate Histogram extension-provided aggregator also provides quantile estimates and histogram approximations, based on http://jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf.

The algorithm used by this deprecated aggregator is highly distribution-dependent and its output is subject to serious distortions when the input does not fit within the algorithm’s limitations.

A study published by the DataSketches team demonstrates some of the known failure modes of this algorithm:

• The algorithm’s quantile calculations can fail to provide results for a large range of rank values (all ranks less than 0.89 in the example used in the study), returning all zeroes instead.
• The algorithm can completely fail to record spikes in the tail ends of the distribution
• In general, the histogram produced by the algorithm can deviate significantly from the true histogram, with no bounds on the errors.

It is not possible to determine a priori how well this aggregator will behave for a given input stream, nor does the aggregator provide any indication that serious distortions are present in the output.

For these reasons, we have deprecated this aggregator and recommend using the DataSketches Quantiles aggregator instead for new and existing use cases, although we will continue to support Approximate Histogram for backwards compatibility.

Expression aggregations

Expression aggregator

Aggregator applicable only at query time. Aggregates results using Druid expressions functions to facilitate building custom functions.

Example: a “count” aggregator

The initial value is 0. fold adds 1 for each row processed.

{
  "type": "expression",
  "name": "expression_count",
  "fields": [],
  "initialValue": "0",
  "fold": "__acc + 1",
  "combine": "__acc + expression_count"
}

Example: a “sum” aggregator

The initial value is 0. fold adds the numeric value column_a for each row processed.

{
  "type": "expression",
  "name": "expression_sum",
  "fields": ["column_a"],
  "initialValue": "0",
  "fold": "__acc + column_a"
}

Example: a “distinct array element” aggregator, sorted by array_length

The initial value is an empty array. fold adds the elements of column_a to the accumulator using set semantics, combine merges the sets, and compare orders the values by array_length.

{
  "type": "expression",
  "name": "expression_array_agg_distinct",
  "fields": ["column_a"],
  "initialValue": "[]",
  "fold": "array_set_add(__acc, column_a)",
  "combine": "array_set_add_all(__acc, expression_array_agg_distinct)",
  "compare": "if(array_length(o1) > array_length(o2), 1, if (array_length(o1) == array_length(o2), 0, -1))"
}

Example: an “approximate count” aggregator using the built-in hyper-unique

Similar to the cardinality aggregator, the default value is an empty hyper-unique sketch, fold adds the value of column_a to the sketch, combine merges the sketches, and finalize gets the estimated count from the accumulated sketch.

{
  "type": "expression",
  "name": "expression_cardinality",
  "fields": ["column_a"],
  "initialValue": "hyper_unique()",
  "fold": "hyper_unique_add(column_a, __acc)",
  "combine": "hyper_unique_add(expression_cardinality, __acc)",
  "finalize": "hyper_unique_estimate(o)"
}

JavaScript aggregator

Computes an arbitrary JavaScript function over a set of columns (both metrics and dimensions are allowed). Your JavaScript functions are expected to return floating-point values.

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; }"
}

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JavaScript-based functionality is disabled by default. Refer to the Druid JavaScript programming guide for guidelines about using Druid’s JavaScript functionality, including instructions on how to enable it.

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.

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

Example:

{
  "type": "filtered",
  "name": "filteredSumLong",
  "filter": {
    "type" : "selector",
    "dimension" : "someColumn",
    "value" : "abcdef"
  },
  "aggregator": {
    "type": "longSum",
    "name": "sumLong",
    "fieldName": "aLong"
  }
}

Grouping aggregator

A grouping aggregator can only be used as part of GroupBy queries which have a subtotal spec. It returns a number for each output row that lets you infer whether a particular dimension is included in the sub-grouping used for that row. You can pass a non-empty list of dimensions to this aggregator which must be a subset of dimensions that you are grouping on.

For example, the following aggregator has ["dim1", "dim2"] as input dimensions:

{ "type" : "grouping", "name" : "someGrouping", "groupings" : ["dim1", "dim2"] }

and used in a grouping query with [["dim1", "dim2"], ["dim1"], ["dim2"], []] as subtotals, the possible output of the aggregator is:

As the example illustrates, you can think of the output number as an unsigned n bit number where n is the number of dimensions passed to the aggregator. Druid sets the bit at position X for the number to 0 if the sub-grouping includes a dimension at position X in the aggregator input. Otherwise, Druid sets this bit to 1.