InfluxQL aggregate functions
Use aggregate functions to assess, aggregate, and return values in your data. Aggregate functions return one row containing the aggregate values from each InfluxQL group.
Each aggregate function below covers syntax including parameters to pass to the function, and examples of how to use the function. Examples use NOAA water sample data.
COUNT()
Returns the number of non-null field values. Supports all field value data types.
Syntax
SELECT COUNT( [ * | <field_key> | /<regular_expression>/ ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
COUNT(*)
Returns the number of field values associated with each field key in the measurement.
COUNT(field_key)
Returns the number of field values associated with the field key.
COUNT(/regular_expression/)
Returns the number of field values associated with each field key that matches the regular expression.
Examples
Count values for a field
Return the number of non-null field values in the water_level
field key in the h2o_feet
measurement.
SELECT COUNT("water_level") FROM "h2o_feet"
name: h2o_feet
time | count |
---|---|
1970-01-01T00:00:00Z | 61026.0000000000 |
Count values for each field in a measurement
Return the number of non-null field values for each field key associated with the h2o_feet
measurement. The h2o_feet
measurement has two field keys: level description
and water_level
.
SELECT COUNT(*) FROM "h2o_feet"
name: h2o_feet
time | count_level description | count_water_level |
---|---|---|
1970-01-01T00:00:00Z | 61026.0000000000 | 61026.0000000000 |
Count the values that match a regular expression
Return the number of non-null field values for every field key that contains the word water
in the h2o_feet
measurement.
SELECT COUNT(/water/) FROM "h2o_feet"
name: h2o_feet
time | count_water_level |
---|---|
1970-01-01T00:00:00Z | 61026.0000000000 |
Count distinct values for a field
Return the number of unique field values for the level description
field key and the h2o_feet
measurement. InfluxQL supports nesting DISTINCT() in COUNT()
.
SELECT COUNT(DISTINCT("level description")) FROM "h2o_feet"
name: h2o_feet
time | count |
---|---|
1970-01-01T00:00:00Z | 4.0000000000 |
DISTINCT()
Returns the list of unique field values. Supports all field value data types.
InfluxQL supports nesting DISTINCT()
with COUNT().
Syntax
SELECT DISTINCT( [ <field_key> | /<regular_expression>/ ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
DISTINCT(field_key)
Returns the unique field values associated with the field key.
Examples
List the distinct field values associated with a field key
Return a tabular list of the unique field values in the level description
field key in the h2o_feet
measurement.
SELECT DISTINCT("level description") FROM "h2o_feet"
name: h2o_feet
time | distinct |
---|---|
1970-01-01T00:00:00Z | between 6 and 9 feet |
1970-01-01T00:00:00Z | below 3 feet |
1970-01-01T00:00:00Z | between 3 and 6 feet |
1970-01-01T00:00:00Z | at or greater than 9 feet |
INTEGRAL()
Returns the area under the curve for subsequent field values.
INTEGRAL()
does not support fill(). INTEGRAL()
supports int64 and float64 field value data types.
Syntax
SELECT INTEGRAL( [ * | <field_key> | /<regular_expression>/ ] [ , <unit> ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
InfluxDB calculates the area under the curve for subsequent field values and converts those results into the summed area per unit
. The unit
argument is an integer followed by an optional duration literal. If the query does not specify the unit
, the unit defaults to one second (1s
).
INTEGRAL(field_key)
Returns the area under the curve for subsequent field values associated with the field key.
INTEGRAL(/regular_expression/)
Returns the area under the curve for subsequent field values associated with each field key that matches the regular expression.
INTEGRAL(*)
Returns the average field value associated with each field key in the measurement.
Examples
The following examples use a subset of the NOAA water sample data data:
SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z'
name: h2o_feet
time | water_level |
---|---|
2019-08-18T00:00:00Z | 2.3520000000 |
2019-08-18T00:06:00Z | 2.3790000000 |
2019-08-18T00:12:00Z | 2.3430000000 |
2019-08-18T00:18:00Z | 2.3290000000 |
2019-08-18T00:24:00Z | 2.2640000000 |
2019-08-18T00:30:00Z | 2.2670000000 |
Calculate the integral for the field values associated with a field key
Return the area under the curve (in seconds) for the field values associated with the water_level
field key and in the h2o_feet
measurement.
SELECT INTEGRAL("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z'
name: h2o_feet
time | integral |
---|---|
1970-01-01T00:00:00Z | 4184.8200000000 |
Calculate the integral for the field values associated with a field key and specify the unit option
Return the area under the curve (in minutes) for the field values associated with the water_level
field key and in the h2o_feet
measurement.
SELECT INTEGRAL("water_level",1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z'
name: h2o_feet
time | integral |
---|---|
1970-01-01T00:00:00Z | 69.7470000000 |
Calculate the integral for the field values associated with each field key in a measurement and specify the unit option
Return the area under the curve (in minutes) for the field values associated with each field key that stores numeric values in the h2o_feet
measurement. The h2o_feet
measurement has on numeric field: water_level
.
SELECT INTEGRAL(*,1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z'
name: h2o_feet
time | integral_water_level |
---|---|
1970-01-01T00:00:00Z | 69.7470000000 |
Calculate the integral for the field values associated with each field key that matches a regular expression and specify the unit option
Return the area under the curve (in minutes) for the field values associated with each field key that stores numeric values includes the word water
in the h2o_feet
measurement.
SELECT INTEGRAL(/water/,1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z'
name: h2o_feet
time | integral_water_level |
---|---|
1970-01-01T00:00:00Z | 69.7470000000 |
Calculate the integral for the field values associated with a field key and include several clauses
Return the area under the curve (in minutes) for the field values associated with the water_level
field key and in the h2o_feet
measurement in the time range between 2019-08-18T00:00:00Z
and 2019-08-18T00:30:00Z
, grouped results into 12-minute intervals, and limit the number of results returned to one.
SELECT INTEGRAL("water_level",1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z' GROUP BY time(12m) LIMIT 1
name: h2o_feet
time | integral |
---|---|
2019-08-18T00:00:00Z | 28.3590000000 |
MEAN()
Returns the arithmetic mean (average) of field values. MEAN()
supports int64 and float64 field value data types.
Syntax
SELECT MEAN( [ * | <field_key> | /<regular_expression>/ ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
MEAN(field_key)
Returns the average field value associated with the field key.
`MEAN(/regular_expression/)
Returns the average field value associated with each field key that matches the regular expression.
MEAN(*)
Returns the average field value associated with each field key in the measurement.
Examples
Calculate the mean field value associated with a field key
Return the average field value in the water_level
field key in the h2o_feet
measurement.
SELECT MEAN("water_level") FROM "h2o_feet"
name: h2o_feet
time | mean |
---|---|
1970-01-01T00:00:00Z | 4.4418674882 |
Calculate the mean field value associated with each field key in a measurement
Return the average field value for every field key that stores numeric values in the h2o_feet
measurement. The h2o_feet
measurement has one numeric field: water_level
.
SELECT MEAN(*) FROM "h2o_feet"
name: h2o_feet
time | mean_water_level |
---|---|
1970-01-01T00:00:00Z | 4.4418674882 |
Calculate the mean field value associated with each field key that matches a regular expression
Return the average field value for each field key that stores numeric values and includes the word water
in the h2o_feet
measurement.
SELECT MEAN(/water/) FROM "h2o_feet"
name: h2o_feet
time | mean_water_level |
---|---|
1970-01-01T00:00:00Z | 4.4418674882 |
Calculate the mean field value associated with a field key and include several clauses
Return the average of the values in the water_level
field key in the time range between 2019-08-18T00:00:00Z
and 2019-08-18T00:30:00Z
and group results into 12-minute time intervals and per tag. Then fill empty time intervals with 9.01
and limit the number of points and series returned to seven and one.
SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z' GROUP BY time(12m),* fill(9.01) LIMIT 7 SLIMIT 1
name: h2o_feet tags: location=coyote_creek
time | mean |
---|---|
2019-08-18T00:00:00Z | 8.4615000000 |
2019-08-18T00:12:00Z | 8.2725000000 |
2019-08-18T00:24:00Z | 8.0710000000 |
MEDIAN()
Returns the middle value from a sorted list of field values. MEDIAN()
supports int64 and float64 field value data types.
Note: MEDIAN()
is nearly equivalent to PERCENTILE(field_key, 50), except MEDIAN()
returns the average of the two middle field values if the field contains an even number of values.
Syntax
SELECT MEDIAN( [ * | <field_key> | /<regular_expression>/ ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
MEDIAN(field_key)
Returns the middle field value associated with the field key.
MEDIAN(/regular_expression/)
Returns the middle field value associated with each field key that matches the regular expression.
MEDIAN(*)
Returns the middle field value associated with each field key in the measurement.
Examples
Calculate the median field value associated with a field key
Return the middle field value in the water_level
field key and in the h2o_feet
measurement.
SELECT MEDIAN("water_level") FROM "h2o_feet"
name: h2o_feet
time | median |
---|---|
1970-01-01T00:00:00Z | 4.1240000000 |
Calculate the median field value associated with each field key in a measurement
Return the middle field value for every field key that stores numeric values in the h2o_feet
measurement. The h2o_feet
measurement has one numeric field: water_level
.
SELECT MEDIAN(*) FROM "h2o_feet"
name: h2o_feet
time | median_water_level |
---|---|
1970-01-01T00:00:00Z | 4.1240000000 |
Calculate the median field value associated with each field key that matches a regular expression
Return the middle field value for every field key that stores numeric values and includes the word water
in the h2o_feet
measurement.
SELECT MEDIAN(/water/) FROM "h2o_feet"
name: h2o_feet
time | median_water_level |
---|---|
1970-01-01T00:00:00Z | 4.1240000000 |
Calculate the median field value associated with a field key and include several clauses
Return the middle field value in the water_level
field key in the time range between 2019-08-18T00:00:00Z
and 2019-08-18T00:30:00Z
and group results into 12-minute time intervals and per tag. Then fill empty time intervals with 700
, limit the number of points and series returned to seven and one, and offset the series returned by one.
SELECT MEDIAN("water_level") FROM "h2o_feet" WHERE time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z' GROUP BY time(12m),* fill(700) LIMIT 7 SLIMIT 1 SOFFSET 1
name: h2o_feet tags: location=santa_monica
time | median |
---|---|
2019-08-18T00:00:00Z | 2.3655000000 |
2019-08-18T00:12:00Z | 2.3360000000 |
2019-08-18T00:24:00Z | 2.2655000000 |
MODE()
Returns the most frequent value in a list of field values. MODE()
supports all field value data types.
Note: MODE()
returns the field value with the earliest timestamp if there’s a tie between two or more values for the maximum number of occurrences.
Syntax
SELECT MODE( [ * | <field_key> | /<regular_expression>/ ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
MODE(field_key)
Returns the most frequent field value associated with the field key.
MODE(/regular_expression/)
Returns the most frequent field value associated with each field key that matches the regular expression.
MODE(*)
Returns the most frequent field value associated with each field key in the measurement.
Examples
Calculate the mode field value associated with a field key
Return the most frequent field value in the level description
field key and in the h2o_feet
measurement.
SELECT MODE("level description") FROM "h2o_feet"
name: h2o_feet
time | mode |
---|---|
1970-01-01T00:00:00Z | between 3 and 6 feet |
Calculate the mode field value associated with each field key in a measurement
Return the most frequent field value for every field key in the h2o_feet
measurement. The h2o_feet
measurement has two field keys: level description
and water_level
.
SELECT MODE(*) FROM "h2o_feet"
name: h2o_feet
time | mode_level description | mode_water_level |
---|---|---|
1970-01-01T00:00:00Z | between 3 and 6 feet | 2.6900000000 |
Calculate the mode field value associated with each field key that matches a regular expression
Return the most frequent field value for every field key that includes the word /water/
in the h2o_feet
measurement.
SELECT MODE(/water/) FROM "h2o_feet"
name: h2o_feet
time | mode_water_level |
---|---|
1970-01-01T00:00:00Z | 2.6900000000 |
Calculate the mode field value associated with a field key and include several clauses
Return the mode of the values associated with the water_level
field key in the time range between 2019-08-18T00:00:00Z
and 2019-08-18T00:30:00Z
and group results into 12-minute time intervals and per tag. Then limits the number of points and series returned to three and one, and it [offsets](/influxdb/v2/query-data/influxql/explore-data #the-offset-and-soffset-clauses) the series returned by one.
SELECT MODE("level description") FROM "h2o_feet" WHERE time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z' GROUP BY time(12m),* LIMIT 3 SLIMIT 1 SOFFSET 1
name: h2o_feet tags: location=santa_monica
time | mode |
---|---|
2019-08-18T00:00:00Z | below 3 feet |
2019-08-18T00:12:00Z | below 3 feet |
2019-08-18T00:24:00Z | below 3 feet |
SPREAD()
Returns the difference between the minimum and maximum field values. SPREAD()
supports int64 and float64 field value data types.
Syntax
SELECT SPREAD( [ * | <field_key> | /<regular_expression>/ ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
SPREAD(field_key)
Returns the difference between the minimum and maximum field values associated with the field key.
SPREAD(/regular_expression/)
Returns the difference between the minimum and maximum field values associated with each field key that matches the regular expression.
SPREAD(*)
Returns the difference between the minimum and maximum field values associated with each field key in the measurement.
Examples
Calculate the spread for the field values associated with a field key
Return the difference between the minimum and maximum field values in the water_level
field key and in the h2o_feet
measurement.
SELECT SPREAD("water_level") FROM "h2o_feet"
name: h2o_feet
time | spread |
---|---|
1970-01-01T00:00:00Z | 10.5740000000 |
Calculate the spread for the field values associated with each field key in a measurement
Return the difference between the minimum and maximum field values for every field key that stores numeric values in the h2o_feet
measurement. The h2o_feet
measurement has one numeric field: water_level
.
SELECT SPREAD(*) FROM "h2o_feet"
name: h2o_feet
time | spread_water_level |
---|---|
1970-01-01T00:00:00Z | 10.5740000000 |
Calculate the spread for the field values associated with each field key that matches a regular expression
Return the difference between the minimum and maximum field values for every field key that stores numeric values and includes the word water
in the h2o_feet
measurement.
SELECT SPREAD(/water/) FROM "h2o_feet"
name: h2o_feet
time | spread_water_level |
---|---|
1970-01-01T00:00:00Z | 10.5740000000 |
Calculate the spread for the field values associated with a field key and include several clauses
Return the difference between the minimum and maximum field values in the water_level
field key in the time range between 2019-08-18T00:00:00Z
and 2019-08-18T00:30:00Z
and group results into 12-minute time intervals and per tag. Then fill empty time intervals with 18
, lim ts the number of points and series returned to three and one, and offsets the series returned by one.
SELECT SPREAD("water_level") FROM "h2o_feet" WHERE time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z' GROUP BY time(12m),* fill(18) LIMIT 3 SLIMIT 1 SOFFSET 1
name: h2o_feet tags: location=santa_monica
time | spread |
---|---|
2019-08-18T00:00:00Z | 0.0270000000 |
2019-08-18T00:12:00Z | 0.0140000000 |
2019-08-18T00:24:00Z | 0.0030000000 |
STDDEV()
Returns the standard deviation of field values. STDDEV()
supports int64 and float64 field value data types.
Syntax
SELECT STDDEV( [ * | <field_key> | /<regular_expression>/ ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
STDDEV(field_key)
Returns the standard deviation of field values associated with the field key.
STDDEV(/regular_expression/)
Returns the standard deviation of field values associated with each field key that matches the regular expression.
STDDEV(*)
Returns the standard deviation of field values associated with each field key in the measurement.
Examples
Calculate the standard deviation for the field values associated with a field key
Return the standard deviation of the field values in the water_level
field key and in the h2o_feet
measurement.
SELECT STDDEV("water_level") FROM "h2o_feet"
name: h2o_feet
time | stddev |
---|---|
1970-01-01T00:00:00Z | 2.2789744110 |
Calculate the standard deviation for the field values associated with each field key in a measurement
Return the standard deviation of numeric fields in the h2o_feet
measurement. The h2o_feet
measurement has one numeric field: water_level
.
SELECT STDDEV(*) FROM "h2o_feet"
name: h2o_feet
time | stddev_water_level |
---|---|
1970-01-01T00:00:00Z | 2.2789744110 |
Calculate the standard deviation for the field values associated with each field key that matches a regular expression
Return the standard deviation of numeric fields with water
in the field key in the h2o_feet
measurement.
SELECT STDDEV(/water/) FROM "h2o_feet"
name: h2o_feet
time | stddev_water_level |
---|---|
1970-01-01T00:00:00Z | 2.2789744110 |
Calculate the standard deviation for the field values associated with a field key and include several clauses
Return the standard deviation of the field values in the water_level
field key in the time range between 2019-08-18T00:00:00Z
and 2019-08-18T00:30:00Z
and group results into 12-minute time intervals and per tag. Then fill empty time intervals with 18000
, limit the number of points and series returned to two and one, and offsets the series returned by one.
SELECT STDDEV("water_level") FROM "h2o_feet" WHERE time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z' GROUP BY time(12m),* fill(18000) LIMIT 2 SLIMIT 1 SOFFSET 1
name: h2o_feet tags: location=santa_monica
time | stddev |
---|---|
2019-08-18T00:00:00Z | 0.0190918831 |
2019-08-18T00:12:00Z | 0.0098994949 |
SUM()
Returns the sum of field values. SUM()
supports int64 and float64 field value data types.
Syntax
SELECT SUM( [ * | <field_key> | /<regular_expression>/ ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
SUM(field_key)
Returns the sum of field values associated with the field key.
SUM(/regular_expression/)
Returns the sum of field values associated with each field key that matches the regular expression.
SUM(*)
Returns the sums of field values associated with each field key in the measurement.
Examples
Calculate the sum of the field values associated with a field key
Return the summed total of the field values in the water_level
field key and in the h2o_feet
measurement.
SELECT SUM("water_level") FROM "h2o_feet"
name: h2o_feet
time | sum |
---|---|
1970-01-01T00:00:00Z | 271069.4053333958 |
Calculate the sum of the field values associated with each field key in a measurement
Return the summed total of numeric fields in the h2o_feet
measurement. The h2o_feet
measurement has one numeric field: water_level
.
SELECT SUM(*) FROM "h2o_feet"
name: h2o_feet
time | sum_water_level |
---|---|
1970-01-01T00:00:00Z | 271069.4053333958 |
Calculate the sum of the field values associated with each field key that matches a regular expression
Return the summed total of numeric fields with water
in the field key in the h2o_feet
measurement.
SELECT SUM(/water/) FROM "h2o_feet"
name: h2o_feet
time | sum_water_level |
---|---|
1970-01-01T00:00:00Z | 271069.4053333958 |
Calculate the sum of the field values associated with a field key and include several clauses
Return the summed total of the field values in the water_level
field key in the time range between 2019-08-18T00:00:00Z
and 2019-08-18T00:30:00Z
and group results into 12-minute time intervals and per tag. Then fill empty time intervals with 18000, and limit the number of points and series returned to four and one.
SELECT SUM("water_level") FROM "h2o_feet" WHERE time >= '2019-08-18T00:00:00Z' AND time <= '2019-08-18T00:30:00Z' GROUP BY time(12m),* fill(18000) LIMIT 4 SLIMIT 1
name: h2o_feet tags: location=coyote_creek
time | sum |
---|---|
2019-08-18T00:00:00Z | 16.9230000000 |
2019-08-18T00:12:00Z | 16.5450000000 |
2019-08-18T00:24:00Z | 16.1420000000 |