Comment on page

# Aggregation Functions

Pinot provides support for aggregations using GROUP BY. You can use the following functions to get the aggregated value.
Function
Description
Example
Default Value When No Record Selected
COUNT
Get the count of rows in a group
`COUNT(*)`
`0`
MIN
Get the minimum value in a group
`MIN(playerScore)`
`Double.POSITIVE_INFINITY`
MAX
Get the maximum value in a group
`MAX(playerScore)`
`Double.NEGATIVE_INFINITY`
SUM
Get the sum of values in a group
`SUM(playerScore)`
`0`
AVG
Get the average of the values in a group
`AVG(playerScore)`
`Double.NEGATIVE_INFINITY`
MODE
Get the most frequent value in a group. 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`
Returns the `max - min` value in a group
`MINMAXRANGE(playerScore)`
`Double.NEGATIVE_INFINITY`
Returns the Nth percentile of the group where N is a decimal number between 0 and 100 inclusive
`PERCENTILE(playerScore, 50), PERCENTILE(playerScore, 99.9)`
`Double.NEGATIVE_INFINITY`
Returns the Nth percentile of the group using Quantile Digest algorithm
`PERCENTILEEST(playerScore, 50), PERCENTILEEST(playerScore, 99.9)`
`Long.MIN_VALUE`
Returns the Nth percentile of the group using T-digest algorithm
`PERCENTILETDIGEST(playerScore, 50), PERCENTILETDIGEST(playerScore, 99.9)`
`Double.NaN`
Returns the count of distinct row values in a group
`DISTINCTCOUNT(playerName)`
`0`
Returns the count of distinct row values in a group. 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`
Returns an approximate distinct count using HyperLogLog. It also takes an optional second argument to configure the log2m for the HyperLogLog.
`DISTINCTCOUNTHLL(playerName, 12)`
`0`
Returns HLL 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`
`0`
`0`
Returns the count of distinct values of a column 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: ""`
Deprecated functions:
Function
Description
Example
FASTHLL
`FASTHLL(playerName)`

## Multi-value column functions

The following aggregation functions can be used for multi-value columns
Function
COUNTMV Get the count of rows in a group
MINMV Get the minimum value in a group
MAXMV Get the maximum value in a group
SUMMV Get the sum of values in a group
AVGMV Get the avg of values in a group
MINMAXRANGEMV Returns the `max - min` value in a group
PERCENTILEMV(column, N) Returns the Nth percentile of the group where N is a decimal number between 0 and 100 inclusive
PERCENTILEESTMV(column, N) Returns the Nth percentile of the group using Quantile Digest
PERCENTILETDIGESTMV(column, N) Returns the Nth percentile of the group using T-digest algorithm
DISTINCTCOUNTMV Returns the count of distinct row values in a group
DISTINCTCOUNTBITMAPMV Returns the count of distinct row values in a group. 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.
`FASTHLLMV(playerNames)`