druid/docs/querying/sql-aggregations.md

id	title	sidebar_label
sql-aggregations	SQL aggregation functions	Aggregation functions

:::info Apache Druid supports two query languages: Druid SQL and native queries. This document describes the SQL language. :::

You can use aggregation functions in the SELECT clause of any Druid SQL query.

Filter any aggregator using the FILTER clause, for example:

SELECT 
  SUM(added) FILTER(WHERE channel = '#en.wikipedia')
FROM wikipedia

The FILTER clause limits an aggregation query to only the rows that match the filter. Druid translates the FILTER clause to a native filtered aggregator. Two aggregators in the same SQL query may have different filters.

When no rows are selected, aggregation functions return their initial value. This can occur from the following:

• When no rows match the filter while aggregating values across an entire table without a grouping, or
• When using filtered aggregations within a grouping.

The initial value varies by aggregator. COUNT and the approximate count distinct sketch functions always return 0 as the initial value.

In the aggregation functions supported by Druid, only COUNT, ARRAY_AGG, and STRING_AGG accept the DISTINCT keyword.

:::info The order of aggregation operations across segments is not deterministic. This means that non-commutative aggregation functions can produce inconsistent results across the same query.

Functions that operate on an input type of "float" or "double" may also see these differences in aggregation results across multiple query runs because of this. If precisely the same value is desired across multiple query runs, consider using the ROUND function to smooth out the inconsistencies between queries. :::

Function	Notes	Default
COUNT(*)	Counts the number of rows.	0
COUNT(DISTINCT expr)	Counts distinct values of expr. When useApproximateCountDistinct is set to "true" (the default), this is an alias for APPROX_COUNT_DISTINCT. The specific algorithm depends on the value of druid.sql.approxCountDistinct.function. In this mode, you can use strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm. When useApproximateCountDistinct is set to "false", the computation will be exact. In this case, expr must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless useGroupingSetForExactDistinct is enabled. Counts each distinct value in a multi-value-row separately.	0
SUM(expr)	Sums numbers.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
MIN(expr)	Takes the minimum of numbers.	null or 9223372036854775807 (maximum LONG value) if druid.generic.useDefaultValueForNull=true (legacy mode)
MAX(expr)	Takes the maximum of numbers.	null or -9223372036854775808 (minimum LONG value) if druid.generic.useDefaultValueForNull=true (legacy mode)
AVG(expr)	Averages numbers.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
APPROX_COUNT_DISTINCT(expr)	Counts distinct values of expr using an approximate algorithm. The expr can be a regular column or a prebuilt sketch column. The specific algorithm depends on the value of druid.sql.approxCountDistinct.function. By default, this is APPROX_COUNT_DISTINCT_BUILTIN. If the DataSketches extension is loaded, you can set it to APPROX_COUNT_DISTINCT_DS_HLL or APPROX_COUNT_DISTINCT_DS_THETA. When run on prebuilt sketch columns, the sketch column type must match the implementation of this function. For example: when druid.sql.approxCountDistinct.function is set to APPROX_COUNT_DISTINCT_BUILTIN, this function runs on prebuilt hyperUnique columns, but not on prebuilt HLLSketchBuild columns.
APPROX_COUNT_DISTINCT_BUILTIN(expr)	Usage note: consider using APPROX_COUNT_DISTINCT_DS_HLL instead, which offers better accuracy in many cases. Counts distinct values of expr using Druid's built-in "cardinality" or "hyperUnique" aggregators, which implement a variant of HyperLogLog. The expr can be a string, a number, or a prebuilt hyperUnique column. Results are always approximate, regardless of the value of useApproximateCountDistinct.
APPROX_QUANTILE(expr, probability, [resolution])	Deprecated. Use APPROX_QUANTILE_DS instead, which provides a superior distribution-independent algorithm with formal error guarantees. Computes approximate quantiles on numeric or approxHistogram expressions. probability should be between 0 and 1, exclusive. resolution is the number of centroids to use for the computation. Higher resolutions will give more precise results but also have higher overhead. If not provided, the default resolution is 50. Load the approximate histogram extension to use this function.	NaN
APPROX_QUANTILE_FIXED_BUCKETS(expr, probability, numBuckets, lowerLimit, upperLimit, [outlierHandlingMode])	Computes approximate quantiles on numeric or fixed buckets histogram expressions. probability should be between 0 and 1, exclusive. The numBuckets, lowerLimit, upperLimit, and outlierHandlingMode parameters are described in the fixed buckets histogram documentation. Load the approximate histogram extension to use this function.	0.0
BLOOM_FILTER(expr, numEntries)	Computes a bloom filter from values produced by expr, with numEntries maximum number of distinct values before false positive rate increases. See bloom filter extension documentation for additional details.	Empty base64 encoded bloom filter STRING
VAR_POP(expr)	Computes variance population of expr. See stats extension documentation for additional details.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
VAR_SAMP(expr)	Computes variance sample of expr. See stats extension documentation for additional details.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
VARIANCE(expr)	Computes variance sample of expr. See stats extension documentation for additional details.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
STDDEV_POP(expr)	Computes standard deviation population of expr. See stats extension documentation for additional details.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
STDDEV_SAMP(expr)	Computes standard deviation sample of expr. See stats extension documentation for additional details.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
STDDEV(expr)	Computes standard deviation sample of expr. See stats extension documentation for additional details.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
EARLIEST(expr, [maxBytesPerValue])	Returns the earliest value of expr. If expr comes from a relation with a timestamp column (like __time in a Druid datasource), the "earliest" is taken from the row with the overall earliest non-null value of the timestamp column. If the earliest non-null value of the timestamp column appears in multiple rows, the expr may be taken from any of those rows. If expr does not come from a relation with a timestamp, then it is simply the first value encountered. If expr is a string or complex type maxBytesPerValue amount of space is allocated for the aggregation. Strings longer than this limit are truncated. The maxBytesPerValue parameter should be set as low as possible, since high values will lead to wasted memory. If maxBytesPerValueis omitted; it defaults to 1024.	null or 0/'' if druid.generic.useDefaultValueForNull=true (legacy mode)
EARLIEST_BY(expr, timestampExpr, [maxBytesPerValue])	Returns the earliest value of expr. The earliest value of expr is taken from the row with the overall earliest non-null value of timestampExpr. If the earliest non-null value of timestampExpr appears in multiple rows, the expr may be taken from any of those rows. If expr is a string or complex type maxBytesPerValue amount of space is allocated for the aggregation. Strings longer than this limit are truncated. The maxBytesPerValue parameter should be set as low as possible, since high values will lead to wasted memory. If maxBytesPerValueis omitted; it defaults to 1024.	null or 0/'' if druid.generic.useDefaultValueForNull=true (legacy mode)
LATEST(expr, [maxBytesPerValue])	Returns the latest value of expr The expr must come from a relation with a timestamp column (like __time in a Druid datasource) and the "latest" is taken from the row with the overall latest non-null value of the timestamp column. If the latest non-null value of the timestamp column appears in multiple rows, the expr may be taken from any of those rows. If expr is a string or complex type maxBytesPerValue amount of space is allocated for the aggregation. Strings longer than this limit are truncated. The maxBytesPerValue parameter should be set as low as possible, since high values will lead to wasted memory. If maxBytesPerValueis omitted; it defaults to 1024.	null or 0/'' if druid.generic.useDefaultValueForNull=true (legacy mode)
LATEST_BY(expr, timestampExpr, [maxBytesPerValue])	Returns the latest value of expr. The latest value of expr is taken from the row with the overall latest non-null value of timestampExpr. If the overall latest non-null value of timestampExpr appears in multiple rows, the expr may be taken from any of those rows. If expr is a string or complex type maxBytesPerValue amount of space is allocated for the aggregation. Strings longer than this limit are truncated. The maxBytesPerValue parameter should be set as low as possible, since high values will lead to wasted memory. If maxBytesPerValueis omitted; it defaults to 1024.	null or 0/'' if druid.generic.useDefaultValueForNull=true (legacy mode)
ANY_VALUE(expr, [maxBytesPerValue])	Returns any value of expr including null. This aggregator can simplify and optimize the performance by returning the first encountered value (including null). If expr is a string or complex type maxBytesPerValue amount of space is allocated for the aggregation. Strings longer than this limit are truncated. The maxBytesPerValue parameter should be set as low as possible, since high values will lead to wasted memory. If maxBytesPerValueis omitted; it defaults to 1024.	null or 0/'' if druid.generic.useDefaultValueForNull=true (legacy mode)
GROUPING(expr, expr...)	Returns a number to indicate which groupBy dimension is included in a row, when using GROUPING SETS. Refer to additional documentation on how to infer this number.	N/A
ARRAY_AGG(expr, [size])	Collects all values of expr into an ARRAY, including null values, with size in bytes limit on aggregation size (default of 1024 bytes). If the aggregated array grows larger than the maximum size in bytes, the query will fail. Use of ORDER BY within the ARRAY_AGG expression is not currently supported, and the ordering of results within the output array may vary depending on processing order.	null
ARRAY_AGG(DISTINCT expr, [size])	Collects all distinct values of expr into an ARRAY, including null values, with size in bytes limit on aggregation size (default of 1024 bytes) per aggregate. If the aggregated array grows larger than the maximum size in bytes, the query will fail. Use of ORDER BY within the ARRAY_AGG expression is not currently supported, and the ordering of results will be based on the default for the element type.	null
ARRAY_CONCAT_AGG(expr, [size])	Concatenates all array expr into a single ARRAY, with size in bytes limit on aggregation size (default of 1024 bytes). Input expr must be an array. Null expr will be ignored, but any null values within an expr will be included in the resulting array. If the aggregated array grows larger than the maximum size in bytes, the query will fail. Use of ORDER BY within the ARRAY_CONCAT_AGG expression is not currently supported, and the ordering of results within the output array may vary depending on processing order.	null
ARRAY_CONCAT_AGG(DISTINCT expr, [size])	Concatenates all distinct values of all array expr into a single ARRAY, with size in bytes limit on aggregation size (default of 1024 bytes) per aggregate. Input expr must be an array. Null expr will be ignored, but any null values within an expr will be included in the resulting array. If the aggregated array grows larger than the maximum size in bytes, the query will fail. Use of ORDER BY within the ARRAY_CONCAT_AGG expression is not currently supported, and the ordering of results will be based on the default for the element type.	null
STRING_AGG([DISTINCT] expr, [separator, [size]])	Collects all values (or all distinct values) of expr into a single STRING, ignoring null values. Each value is joined by an optional separator, which must be a literal STRING. If the separator is not provided, strings are concatenated without a separator. An optional size in bytes can be supplied to limit aggregation size (default of 1024 bytes). If the aggregated string grows larger than the maximum size in bytes, the query will fail. Use of ORDER BY within the STRING_AGG expression is not currently supported, and the ordering of results within the output string may vary depending on processing order.	null or '' if druid.generic.useDefaultValueForNull=true (legacy mode)
LISTAGG([DISTINCT] expr, [separator, [size]])	Synonym for STRING_AGG.	null or '' if druid.generic.useDefaultValueForNull=true (legacy mode)
BIT_AND(expr)	Performs a bitwise AND operation on all input values.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
BIT_OR(expr)	Performs a bitwise OR operation on all input values.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)
BIT_XOR(expr)	Performs a bitwise XOR operation on all input values.	null or 0 if druid.generic.useDefaultValueForNull=true (legacy mode)

Sketch functions

These functions create sketch objects that you can use to perform fast, approximate analyses. For advice on choosing approximate aggregation functions, check out our approximate aggregations documentation. To operate on sketch objects, also see the DataSketches post aggregator functions.

HLL sketch functions

Load the DataSketches extension to use the following functions.

Function	Notes	Default
APPROX_COUNT_DISTINCT_DS_HLL(expr, [lgK, tgtHllType])	Counts distinct values of expr, which can be a regular column or an HLL sketch column. Results are always approximate, regardless of the value of useApproximateCountDistinct. The lgK and tgtHllType parameters here are, like the equivalents in the aggregator, described in the HLL sketch documentation. See also COUNT(DISTINCT expr).	0
DS_HLL(expr, [lgK, tgtHllType])	Creates an HLL sketch on the values of expr, which can be a regular column or a column containing HLL sketches. The lgK and tgtHllType parameters are described in the HLL sketch documentation.	'0' (STRING)

Theta sketch functions

Load the DataSketches extension to use the following functions.

Function	Notes	Default
APPROX_COUNT_DISTINCT_DS_THETA(expr, [size])	Counts distinct values of expr, which can be a regular column or a Theta sketch column. Results are always approximate, regardless of the value of useApproximateCountDistinct. The size parameter is described in the Theta sketch documentation. See also COUNT(DISTINCT expr).	0
DS_THETA(expr, [size])	Creates a Theta sketch on the values of expr, which can be a regular column or a column containing Theta sketches. The size parameter is described in the Theta sketch documentation.	'0.0' (STRING)

Quantiles sketch functions

Load the DataSketches extension to use the following functions.

Function	Notes	Default
APPROX_QUANTILE_DS(expr, probability, [k])	Computes approximate quantiles on numeric or Quantiles sketch expressions. The probability value should be between 0 and 1, exclusive. The k parameter is described in the Quantiles sketch documentation. See the known issue with this function.	NaN
DS_QUANTILES_SKETCH(expr, [k])	Creates a Quantiles sketch on the values of expr, which can be a regular column or a column containing quantiles sketches. The k parameter is described in the Quantiles sketch documentation. See the known issue with this function.	'0' (STRING)

Tuple sketch functions

Load the DataSketches extension to use the following functions.

Function	Notes	Default
DS_TUPLE_DOUBLES(expr, [nominalEntries])	Creates a Tuple sketch on the values of expr which is a column containing Tuple sketches which contain an array of double values as their Summary Objects. The nominalEntries override parameter is optional and described in the Tuple sketch documentation.
DS_TUPLE_DOUBLES(dimensionColumnExpr, metricColumnExpr, ..., [nominalEntries])	Creates a Tuple sketch which contains an array of double values as its Summary Object based on the dimension value of dimensionColumnExpr and the numeric metric values contained in one or more metricColumnExpr columns. If the last value of the array is a numeric literal, Druid assumes that the value is an override parameter for nominal entries.

T-Digest sketch functions

Load the T-Digest extension to use the following functions. See the T-Digest extension for additional details and for more information on these functions.

Function	Notes	Default
TDIGEST_QUANTILE(expr, quantileFraction, [compression])	Builds a T-Digest sketch on values produced by expr and returns the value for the quantile. Compression parameter (default value 100) determines the accuracy and size of the sketch. Higher compression means higher accuracy but more space to store sketches.	Double.NaN
TDIGEST_GENERATE_SKETCH(expr, [compression])	Builds a T-Digest sketch on values produced by expr. Compression parameter (default value 100) determines the accuracy and size of the sketch Higher compression means higher accuracy but more space to store sketches.	Empty base64 encoded T-Digest sketch STRING