Aggregation Functions

Aggregate functions return a single result for a group of rows.

Aggregate functions return a single result for a group of rows. The following table shows supported aggregate functions in Pinot.

Function	Description	Example	Default Value When No Record Selected
ARG_MIN /ARG_MAX	Project a column where the maxima appears in a series of measuring columns.	ARG_MAX(measuring1, measuring2, measuring3, projection)	Will return no result
COUNT	Returns the count of the records as Long	COUNT(*)	0
COVAR_POP	Returns the population covariance between of 2 numerical columns as Double	COVAR_POP(col1, col2)	Double.NEGATIVE_INFINITY
COVAR_SAMP	Returns the sample covariance between of 2 numerical columns as Double	COVAR_SAMP(col1, col2)	Double.NEGATIVE_INFINITY
HISTOGRAM	Calculate the histogram of a numeric column as Double[]	HISTOGRAM(numberOfGames,0,200,10)	0, 0, ..., 0
MIN	Returns the minimum value of a numeric column as Double	MIN(playerScore)	Double.POSITIVE_INFINITY
MAX	Returns the maximum value of a numeric column as Double	MAX(playerScore)	Double.NEGATIVE_INFINITY
SUM	Returns the sum of the values for a numeric column as Double	SUM(playerScore)	0
SUMPRECISION	Returns the sum of the values for a numeric column with optional precision and scale as BigDecimal	SUMPRECISION(salary), SUMPRECISION(salary, precision, scale)	0.0
AVG	Returns the average of the values for a numeric column as Double	AVG(playerScore)	Double.NEGATIVE_INFINITY
MODE	Returns the most frequent value of a numeric column as Double. When multiple modes are present it gives the minimum of all the modes. This behavior can be overridden to get the maximum or the average mode.	MODE(playerScore) MODE(playerScore, 'MIN') MODE(playerScore, 'MAX') MODE(playerScore, 'AVG')	Double.NEGATIVE_INFINITY
MINMAXRANGE	Returns the max - min value for a numeric column as Double	MINMAXRANGE(playerScore)	Double.NEGATIVE_INFINITY
PERCENTILE(column, N)	Returns the Nth percentile of the values for a numeric column as Double. N is a decimal number between 0 and 100 inclusive.	PERCENTILE(playerScore, 50) PERCENTILE(playerScore, 99.9)	Double.NEGATIVE_INFINITY
PERCENTILEEST(column, N)	Returns the Nth percentile of the values for a numeric column using Quantile Digest as Long	PERCENTILEEST(playerScore, 50) PERCENTILEEST(playerScore, 99.9)	Long.MIN_VALUE
PERCENTILETDIGEST(column, N)	Returns the Nth percentile of the values for a numeric column using T-digest as Double	PERCENTILETDIGEST(playerScore, 50) PERCENTILETDIGEST(playerScore, 99.9)	Double.NaN
PERCENTILETDIGEST(column, N, CF)	Returns the Nth percentile (using compression factor of CF) of the values for a numeric column using T-digest as Double	PERCENTILETDIGEST(playerScore, 50, 1000) PERCENTILETDIGEST(playerScore, 99.9, 500)	Double.NaN
PERCENTILESMARTTDIGEST	Returns the Nth percentile of the values for a numeric column as Double. When there are too many values, automatically switch to approximate percentile using TDigest. The switch threshold (100_000 by default) and compression (100 by default) for the TDigest can be configured via the optional second argument.	PERCENTILESMARTTDIGEST(playerScore, 50) PERCENTILESMARTTDIGEST(playerScore, 99.9, 'threshold=100;compression=50)	Double.NEGATIVE_INFINITY
DISTINCTCOUNT	Returns the count of distinct values of a column as Integer	DISTINCTCOUNT(playerName)	0
DISTINCTCOUNTBITMAP	Returns the count of distinct values of a column as Integer. This function is accurate for INT column, but approximate for other cases where hash codes are used in distinct counting and there may be hash collisions.	DISTINCTCOUNTBITMAP(playerName)	0
DISTINCTCOUNTHLL	Returns an approximate distinct count using HyperLogLog as Long. It also takes an optional second argument to configure the log2m for the HyperLogLog.	DISTINCTCOUNTHLL(playerName, 12)	0
DISTINCTCOUNTRAWHLL	Returns HyperLogLog response serialized as String. The serialized HLL can be converted back into an HLL and then aggregated with other HLLs. A common use case may be to merge HLL responses from different Pinot tables, or to allow aggregation after client-side batching.	DISTINCTCOUNTRAWHLL(playerName)	0
DISTINCTCOUNTSMARTHLL	Returns the count of distinct values of a column as Integer. When there are too many distinct values, automatically switch to approximate distinct count using HyperLogLog. The switch threshold (100_000 by default) and log2m (12 by default) for the HyperLogLog can be configured via the optional second argument.	DISTINCTCOUNTSMARTHLL(playerName), DISTINCTCOUNTSMARTHLL(playerName, 'threshold=100;log2m=8')	0
DISTINCTCOUNTTHETASKETCH	See Cardinality Estimation		0
DISTINCTCOUNTRAWTHETASKETCH	See Cardinality Estimation		0
SEGMENTPARTITIONEDDISTINCTCOUNT	Returns the count of distinct values of a column as Long when the column is pre-partitioned for each segment, where there is no common value within different segments. This function calculates the exact count of distinct values within the segment, then simply sums up the results from different segments to get the final result.	SEGMENTPARTITIONEDDISTINCTCOUNT(playerName)	0
LASTWITHTIME(dataColumn, timeColumn, 'dataType')	Get the last value of dataColumn where the timeColumn is used to define the time of dataColumn and the dataType specifies the type of dataColumn, which can be BOOLEAN, INT, LONG, FLOAT, DOUBLE, STRING	LASTWITHTIME(playerScore, timestampColumn, 'BOOLEAN') LASTWITHTIME(playerScore, timestampColumn, 'INT') LASTWITHTIME(playerScore, timestampColumn, 'LONG') LASTWITHTIME(playerScore, timestampColumn, 'FLOAT') LASTWITHTIME(playerScore, timestampColumn, 'DOUBLE') LASTWITHTIME(playerScore, timestampColumn, 'STRING')	INT: Int.MIN_VALUE LONG: Long.MIN_VALUE FLOAT: Float.NaN DOUBLE: Double.NaN STRING: ""
FIRSTWITHTIME(dataColumn, timeColumn, 'dataType')	Get the first value of dataColumn where the timeColumn is used to define the time of dataColumn and the dataType specifies the type of dataColumn, which can be BOOLEAN, INT, LONG, FLOAT, DOUBLE, STRING	FIRSTWITHTIME(playerScore, timestampColumn, 'BOOLEAN') FIRSTWITHTIME(playerScore, timestampColumn, 'INT') FIRSTWITHTIME(playerScore, timestampColumn, 'LONG') FIRSTWITHTIME(playerScore, timestampColumn, 'FLOAT') FIRSTWITHTIME(playerScore, timestampColumn, 'DOUBLE') FIRSTWITHTIME(playerScore, timestampColumn, 'STRING')	INT: Int.MIN_VALUE LONG: Long.MIN_VALUE FLOAT: Float.NaN DOUBLE: Double.NaN STRING: ""

Deprecated functions:

Function	Description	Example
FASTHLL	FASTHLL stores serialized HyperLogLog in String format, which performs worse than DISTINCTCOUNTHLL, which supports serialized HyperLogLog in BYTES (byte array) format	FASTHLL(playerName)

Multi-value column functions

The following aggregation functions can be used for multi-value columns

Function
COUNTMV Returns the count of a multi-value column as Long
MINMV Returns the minimum value of a numeric multi-value column as Double
MAXMV Returns the maximum value of a numeric multi-value column as Double
SUMMV Returns the sum of the values for a numeric multi-value column as Double
AVGMV Returns the average of the values for a numeric multi-value column as Double
MINMAXRANGEMV Returns the max - min value for a numeric multi-value column as Double
PERCENTILEMV(column, N) Returns the Nth percentile of the values for a numeric multi-value column as Double
PERCENTILEESTMV(column, N) Returns the Nth percentile using Quantile Digest as Long
PERCENTILETDIGESTMV(column, N) Returns the Nth percentile using T-digest as Double
PERCENTILETDIGESTMV(column, N, CF) Returns the Nth percentile (using compression factor CF) using T-digest as Double
DISTINCTCOUNTMV Returns the count of distinct values for a multi-value column as Integer
DISTINCTCOUNTBITMAPMV Returns the count of distinct values for a multi-value column as Integer. This function is accurate for INT or dictionary encoded column, but approximate for other cases where hash codes are used in distinct counting and there may be hash collision.
DISTINCTCOUNTHLLMV Returns an approximate distinct count using HyperLogLog as Long
DISTINCTCOUNTRAWHLLMV Returns HyperLogLog response serialized as string. The serialized HLL can be converted back into an HLL and then aggregated with other HLLs. A common use case may be to merge HLL responses from different Pinot tables, or to allow aggregation after client-side batching.

FILTER Clause in aggregation

Pinot supports FILTER clause in aggregation queries as follows:

SELECT SUM(COL1) FILTER (WHERE COL2 > 300),
       AVG(COL2) FILTER (WHERE COL2 < 50) 
FROM MyTable WHERE COL3 > 50

In the query above, COL1 is aggregated only for rows where COL2 > 300 and COL3 > 50 . Similarly, COL2 is aggregated where COL2 < 50 and COL3 > 50.

With NULL Value Support enabled, this allows to filter out the null values while performing aggregation as follows:

SELECT SUM(COL1) FILTER (WHERE COL1 IS NOT NULL)
FROM MyTable WHERE COL3 > 50

In the above query, COL1 is aggregated only for the non-null values. Without NULL value support, we would have to filter using the default null value.

NOTE: TheFILTER clause is currently supported for aggregation-only queries, i.e., GROUP BY

is not supported.

Deprecated functions:

Function	Description	Example
FASTHLLMV (Deprecated)	stores serialized HyperLogLog in String format, which performs worse than DISTINCTCOUNTHLL, which supports serialized HyperLogLog in BYTES (byte array) format	FASTHLLMV(playerNames)