InfluxQL functions
This page documents an earlier version of InfluxDB. InfluxDB v2.6 is the latest stable version.
Aggregate, select, transform, and predict data with InfluxQL functions.
Content
Aggregations
COUNT()
Returns the number of non-null field values.
Syntax
SELECT COUNT( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Nested Syntax
SELECT COUNT(DISTINCT( [ * | <field_key> | /<regular_expression>/ ] )) [...]
Description of Syntax
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.
COUNT(*)
Returns the number of field values associated with each field key in the measurement.
COUNT() supports all field value data types.
InfluxQL supports nesting DISTINCT() with COUNT().
Examples
Example: Count the field values associated with a field key
> SELECT COUNT("water_level") FROM "h2o_feet"
name: h2o_feet
time count
---- -----
1970-01-01T00:00:00Z 15258
The query returns the number of non-null field values in the water_level field key in the h2o_feet measurement.
Example: Count the field values associated with each field key in a measurement
> SELECT COUNT(*) FROM "h2o_feet"
name: h2o_feet
time count_level description count_water_level
---- ----------------------- -----------------
1970-01-01T00:00:00Z 15258 15258
The query returns 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.
Example: Count the field values associated with each field key that matches a regular expression
> SELECT COUNT(/water/) FROM "h2o_feet"
name: h2o_feet
time count_water_level
---- -----------------
1970-01-01T00:00:00Z 15258
The query returns the number of non-null field values for every field key that contains the word water in the h2o_feet measurement.
Example 4: Count the field values associated with a field key and include several clauses
> SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(200) LIMIT 7 SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time count
---- -----
2015-08-17T23:48:00Z 200
2015-08-18T00:00:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:24:00Z 2
2015-08-18T00:36:00Z 2
2015-08-18T00:48:00Z 2
The query returns the number of non-null field values in the water_level field key.
It covers the time range between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results into 12-minute time intervals and per tag.
The query fills empty time intervals with 200 and limits the number of points and series returned to seven and one.
Example 5: Count the distinct field values associated with a field key
> SELECT COUNT(DISTINCT("level description")) FROM "h2o_feet"
name: h2o_feet
time count
---- -----
1970-01-01T00:00:00Z 4
The query returns the number of unique field values for the level description field key and the h2o_feet measurement.
Common Issues with COUNT()
Issue 1: COUNT() and fill()
Most InfluxQL functions report null values for time intervals with no data, and
fill(<fill_option>)
replaces that null value with the fill_option.
COUNT() reports 0 for time intervals with no data, and fill(<fill_option>) replaces any 0 values with the fill_option.
Example
The first query in the codeblock below does not include `fill()`. The last time interval has no data so the reported value for that time interval is zero. The second query includes `fill(800000)`; it replaces the zero in the last interval with `800000`.
> SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-09-18T21:24:00Z' AND time <= '2015-09-18T21:54:00Z' GROUP BY time(12m)
name: h2o_feet
time count
---- -----
2015-09-18T21:24:00Z 2
2015-09-18T21:36:00Z 2
2015-09-18T21:48:00Z 0
> SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-09-18T21:24:00Z' AND time <= '2015-09-18T21:54:00Z' GROUP BY time(12m) fill(800000)
name: h2o_feet
time count
---- -----
2015-09-18T21:24:00Z 2
2015-09-18T21:36:00Z 2
2015-09-18T21:48:00Z 800000
DISTINCT()
Returns the list of unique field values.
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]
Nested Syntax
SELECT COUNT(DISTINCT( [ * | <field_key> | /<regular_expression>/ ] )) [...]
Description of Syntax
DISTINCT(field_key)
Returns the unique field values associated with the field key.
DISTINCT(/regular_expression/)
Returns the unique field values associated with each field key that matches the regular expression.
DISTINCT(*)
Returns the unique field values associated with each field key in the measurement.
DISTINCT() supports all field value data types.
InfluxQL supports nesting DISTINCT() with COUNT().
Examples
Example: List the distinct field values associated with a field key
> 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
The query returns a tabular list of the unique field values in the level description field key in the h2o_feet measurement.
Example: List the distinct field values associated with each field key in a measurement
> SELECT DISTINCT(*) FROM "h2o_feet"
name: h2o_feet
time distinct_level description distinct_water_level
---- -------------------------- --------------------
1970-01-01T00:00:00Z between 6 and 9 feet 8.12
1970-01-01T00:00:00Z between 3 and 6 feet 8.005
1970-01-01T00:00:00Z at or greater than 9 feet 7.887
1970-01-01T00:00:00Z below 3 feet 7.762
[...]
The query returns a tabular list of the unique field values for each field key in the h2o_feet measurement.
The h2o_feet measurement has two field keys: level description and water_level.
Example: List the distinct field values associated with each field key that matches a regular expression
> SELECT DISTINCT(/description/) FROM "h2o_feet"
name: h2o_feet
time distinct_level description
---- --------------------------
1970-01-01T00:00:00Z below 3 feet
1970-01-01T00:00:00Z between 6 and 9 feet
1970-01-01T00:00:00Z between 3 and 6 feet
1970-01-01T00:00:00Z at or greater than 9 feet
The query returns a tabular list of the unique field values for each field key in the h2o_feet measurement that contains the word description.
Example 4: List the distinct field values associated with a field key and include several clauses
> SELECT DISTINCT("level description") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time distinct
---- --------
2015-08-18T00:00:00Z between 6 and 9 feet
2015-08-18T00:12:00Z between 6 and 9 feet
2015-08-18T00:24:00Z between 6 and 9 feet
2015-08-18T00:36:00Z between 6 and 9 feet
2015-08-18T00:48:00Z between 6 and 9 feet
The query returns a tabular list of the unique field values in the level description field key.
It covers the time range between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results into 12-minute time intervals and per tag.
The query also limits the number of series returned to one.
Example 5: Count the distinct field values associated with a field key
> SELECT COUNT(DISTINCT("level description")) FROM "h2o_feet"
name: h2o_feet
time count
---- -----
1970-01-01T00:00:00Z 4
The query returns the number of unique field values in the level description field key and the h2o_feet measurement.
Common Issues with DISTINCT()
Issue 1: DISTINCT() and the INTO clause
Using DISTINCT() with the INTO clause can cause InfluxDB to overwrite points in the destination measurement.
DISTINCT() often returns several results with the same timestamp; InfluxDB assumes points with the same series and timestamp are duplicate points and simply overwrites any duplicate point with the most recent point in the destination measurement.
Example
The first query in the codeblock below uses the `DISTINCT()` function and returns four results. Notice that each result has the same timestamp. The second query adds an `INTO` clause to the initial query and writes the query results to the `distincts` measurement. The last query in the codeblock selects all the data in the `distincts` measurement.
The last query returns one point because the four initial results are duplicate points; they belong to the same series and have the same timestamp. When the system encounters duplicate points, it simply overwrites the previous point with the most recent point.
> SELECT DISTINCT("level description") FROM "h2o_feet"
name: h2o_feet
time distinct
---- --------
1970-01-01T00:00:00Z below 3 feet
1970-01-01T00:00:00Z between 6 and 9 feet
1970-01-01T00:00:00Z between 3 and 6 feet
1970-01-01T00:00:00Z at or greater than 9 feet
> SELECT DISTINCT("level description") INTO "distincts" FROM "h2o_feet"
name: result
time written
---- -------
1970-01-01T00:00:00Z 4
> SELECT * FROM "distincts"
name: distincts
time distinct
---- --------
1970-01-01T00:00:00Z at or greater than 9 feet
INTEGRAL()
Returns the area under the curve for subsequent field values.
Syntax
SELECT INTEGRAL( [ * | <field_key> | /<regular_expression>/ ] [ , <unit> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of Syntax
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 a duration literal and it is optional.
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 are 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.
INTEGRAL() does not support fill(). INTEGRAL() supports int64 and float64 field value data types.
Examples
Examples 1-5 use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the integral for the field values associated with a field key
> SELECT INTEGRAL("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time integral
---- --------
1970-01-01T00:00:00Z 3732.66
The query returns the area under the curve (in seconds) for the field values associated with the water_level field key and in the h2o_feet measurement.
Example: Calculate the integral for the field values associated with a field key and specify the unit option
> SELECT INTEGRAL("water_level",1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time integral
---- --------
1970-01-01T00:00:00Z 62.211
The query returns the area under the curve (in minutes) for the field values associated with the water_level field key and in the h2o_feet measurement.
Example: Calculate the integral for the field values associated with each field key in a measurement and specify the unit option
> SELECT INTEGRAL(*,1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time integral_water_level
---- --------------------
1970-01-01T00:00:00Z 62.211
The query returns the area under the curve (in minutes) for the field values associated with each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has on numerical field: water_level.
Example 4: Calculate the integral for the field values associated with each field key that matches a regular expression and specify the unit option
> SELECT INTEGRAL(/water/,1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time integral_water_level
---- --------------------
1970-01-01T00:00:00Z 62.211
The query returns the area under the curve (in minutes) for the field values associated with each field key that stores numerical values includes the word water in the h2o_feet measurement.
Example 5: Calculate the integral for the field values associated with a field key and include several clauses
> SELECT INTEGRAL("water_level",1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m) LIMIT 1
name: h2o_feet
time integral
---- --------
2015-08-18T00:00:00Z 24.972
The query returns the area under the curve (in minutes) for the field values associated with the water_level field key and in the h2o_feet measurement.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z, groups results into 12-minute intervals, and limits the number of results returned to one.
MEAN()
Returns the arithmetic mean (average) of field values.
Syntax
SELECT MEAN( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of Syntax
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.
MEAN() supports int64 and float64 field value data types.
Examples
Example: Calculate the mean field value associated with a field key
> SELECT MEAN("water_level") FROM "h2o_feet"
name: h2o_feet
time mean
---- ----
1970-01-01T00:00:00Z 4.442107025822522
The query returns the average field value in the water_level field key in the h2o_feet measurement.
Example: Calculate the mean field value associated with each field key in a measurement
> SELECT MEAN(*) FROM "h2o_feet"
name: h2o_feet
time mean_water_level
---- ----------------
1970-01-01T00:00:00Z 4.442107025822522
The query returns the average field value for every field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the mean field value associated with each field key that matches a regular expression
> SELECT MEAN(/water/) FROM "h2o_feet"
name: h2o_feet
time mean_water_level
---- ----------------
1970-01-01T00:00:00Z 4.442107025822523
The query returns the average field value for each field key that stores numerical values and includes the word water in the h2o_feet measurement.
Example 4: Calculate the mean field value associated with a field key and include several clauses
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(9.01) LIMIT 7 SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
2015-08-17T23:48:00Z 9.01
2015-08-18T00:00:00Z 8.0625
2015-08-18T00:12:00Z 7.8245
2015-08-18T00:24:00Z 7.5675
2015-08-18T00:36:00Z 7.303
2015-08-18T00:48:00Z 7.046
The query returns the average of the values in the water_level field key.
It covers the time range between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results into 12-minute time intervals and per tag.
The query fills empty time intervals with 9.01 and limits the number of points and series returned to seven and one.
MEDIAN()
Returns the middle value from a sorted list of field values.
Syntax
SELECT MEDIAN( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of Syntax
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.
MEDIAN() supports int64 and float64 field value data types.
Note:
MEDIAN()is nearly equivalent toPERCENTILE(field_key, 50), exceptMEDIAN()returns the average of the two middle field values if the field contains an even number of values.
Examples
Example: Calculate the median field value associated with a field key
> SELECT MEDIAN("water_level") FROM "h2o_feet"
name: h2o_feet
time median
---- ------
1970-01-01T00:00:00Z 4.124
The query returns the middle field value in the water_level field key and in the h2o_feet measurement.
Example: Calculate the median field value associated with each field key in a measurement
> SELECT MEDIAN(*) FROM "h2o_feet"
name: h2o_feet
time median_water_level
---- ------------------
1970-01-01T00:00:00Z 4.124
The query returns the middle field value for every field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the median field value associated with each field key that matches a regular expression
> SELECT MEDIAN(/water/) FROM "h2o_feet"
name: h2o_feet
time median_water_level
---- ------------------
1970-01-01T00:00:00Z 4.124
The query returns the middle field value for every field key that stores numerical values and includes the word water in the h2o_feet measurement.
Example 4: Calculate the median field value associated with a field key and include several clauses
> SELECT MEDIAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(700) LIMIT 7 SLIMIT 1 SOFFSET 1
name: h2o_feet
tags: location=santa_monica
time median
---- ------
2015-08-17T23:48:00Z 700
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
2015-08-18T00:36:00Z 2.0620000000000003
2015-08-18T00:48:00Z 700
The query returns the middle field value in the water_level field key.
It covers the time range between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results into 12-minute time intervals and per tag.
The query fills empty time intervals with 700 , limits the number of points and series returned to seven and one, and offsets the series returned by one.
MODE()
Returns the most frequent value in a list of field values.
Syntax
SELECT MODE( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of Syntax
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.
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.
Examples
Example: Calculate the mode field value associated with a field key
> SELECT MODE("level description") FROM "h2o_feet"
name: h2o_feet
time mode
---- ----
1970-01-01T00:00:00Z between 3 and 6 feet
The query returns the most frequent field value in the level description field key and in the h2o_feet measurement.
Example: Calculate the mode field value associated with each field key in a measurement
> 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 2.69
The query returns 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.
Example: Calculate the mode field value associated with each field key that matches a regular expression
> SELECT MODE(/water/) FROM "h2o_feet"
name: h2o_feet
time mode_water_level
---- ----------------
1970-01-01T00:00:00Z 2.69
The query returns the most frequent field value for every field key that includes the word /water/ in the h2o_feet measurement.
Example 4: Calculate the mode field value associated with a field key and include several clauses
> SELECT MODE("level description") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* LIMIT 3 SLIMIT 1 SOFFSET 1
name: h2o_feet
tags: location=santa_monica
time mode
---- ----
2015-08-17T23:48:00Z
2015-08-18T00:00:00Z below 3 feet
2015-08-18T00:12:00Z below 3 feet
The query returns the mode of the values associated with the water_level field key.
It covers the time range between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results into 12-minute time intervals and per tag.
The query limits the number of points and series returned to three and one, and it offsets the series returned by one.
SPREAD()
Returns the difference between the minimum and maximum field values.
Syntax
SELECT SPREAD( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of Syntax
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.
SPREAD() supports int64 and float64 field value data types.
Examples
Example: Calculate the spread for the field values associated with a field key
> SELECT SPREAD("water_level") FROM "h2o_feet"
name: h2o_feet
time spread
---- ------
1970-01-01T00:00:00Z 10.574
The query returns the difference between the minimum and maximum field values in the water_level field key and in the h2o_feet measurement.
Example: Calculate the spread for the field values associated with each field key in a measurement
> SELECT SPREAD(*) FROM "h2o_feet"
name: h2o_feet
time spread_water_level
---- ------------------
1970-01-01T00:00:00Z 10.574
The query returns the difference between the minimum and maximum field values for every field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the spread for the field values associated with each field key that matches a regular expression
> SELECT SPREAD(/water/) FROM "h2o_feet"
name: h2o_feet
time spread_water_level
---- ------------------
1970-01-01T00:00:00Z 10.574
The query returns the difference between the minimum and maximum field values for every field key that stores numerical values and includes the word water in the h2o_feet measurement.
Example 4: Calculate the spread for the field values associated with a field key and include several clauses
> SELECT SPREAD("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(18) LIMIT 3 SLIMIT 1 SOFFSET 1
name: h2o_feet
tags: location=santa_monica
time spread
---- ------
2015-08-17T23:48:00Z 18
2015-08-18T00:00:00Z 0.052000000000000046
2015-08-18T00:12:00Z 0.09799999999999986
The query returns the difference between the minimum and maximum field values in the water_level field key.
It covers the time range between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results into 12-minute time intervals and per tag.
The query fills empty time intervals with 18, limits the number of points and series returned to three and one, and offsets the series returned by one.
STDDEV()
Returns the standard deviation of field values.
Syntax
SELECT STDDEV( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of Syntax
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.
STDDEV() supports int64 and float64 field value data types.
Examples
Example: Calculate the standard deviation for the field values associated with a field key
> SELECT STDDEV("water_level") FROM "h2o_feet"
name: h2o_feet
time stddev
---- ------
1970-01-01T00:00:00Z 2.279144584196141
The query returns the standard deviation of the field values in the water_level field key and in the h2o_feet measurement.
Example: Calculate the standard deviation for the field values associated with each field key in a measurement
> SELECT STDDEV(*) FROM "h2o_feet"
name: h2o_feet
time stddev_water_level
---- ------------------
1970-01-01T00:00:00Z 2.279144584196141
The query returns the standard deviation of the field values for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the standard deviation for the field values associated with each field key that matches a regular expression
> SELECT STDDEV(/water/) FROM "h2o_feet"
name: h2o_feet
time stddev_water_level
---- ------------------
1970-01-01T00:00:00Z 2.279144584196141
The query returns the standard deviation of the field values for each field key that stores numerical values and includes the word water in the h2o_feet measurement.
Example 4: Calculate the standard deviation for the field values associated with a field key and include several clauses
> SELECT STDDEV("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(18000) LIMIT 2 SLIMIT 1 SOFFSET 1
name: h2o_feet
tags: location=santa_monica
time stddev
---- ------
2015-08-17T23:48:00Z 18000
2015-08-18T00:00:00Z 0.03676955262170051
The query returns the standard deviation of the field values in the water_level field key.
It covers the time range between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results into 12-minute time intervals and per tag.
The query fills empty time intervals with 18000, limits the number of points and series returned to two and one, and offsets the series returned by one.
SUM()
Returns the sum of field values.
Syntax
SELECT SUM( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of Syntax
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.
SUM() supports int64 and float64 field value data types.
Examples:
Example: Calculate the sum of the field values associated with a field key
> SELECT SUM("water_level") FROM "h2o_feet"
name: h2o_feet
time sum
---- ---
1970-01-01T00:00:00Z 67777.66900000004
The query returns the summed total of the field values in the water_level field key and in the h2o_feet measurement.
Example: Calculate the sum of the field values associated with each field key in a measurement
> SELECT SUM(*) FROM "h2o_feet"
name: h2o_feet
time sum_water_level
---- ---------------
1970-01-01T00:00:00Z 67777.66900000004
The query returns the summed total of the field values for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the sum of the field values associated with each field key that matches a regular expression
> SELECT SUM(/water/) FROM "h2o_feet"
name: h2o_feet
time sum_water_level
---- ---------------
1970-01-01T00:00:00Z 67777.66900000004
The query returns the summed total of the field values for each field key that stores numerical values and includes the word water in the h2o_feet measurement.
Example 4: Calculate the sum of the field values associated with a field key and include several clauses
> SELECT SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(18000) LIMIT 4 SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time sum
---- ---
2015-08-17T23:48:00Z 18000
2015-08-18T00:00:00Z 16.125
2015-08-18T00:12:00Z 15.649
2015-08-18T00:24:00Z 15.135
The query returns the summed total of the field values in the water_level field key.
It covers the time range between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results into 12-minute time intervals and per tag. The query fills empty time intervals with 18000, and it limits the number of points and series returned to four and one.
Selectors
BOTTOM()
Returns the smallest N field values.
Syntax
SELECT BOTTOM(<field_key>[,<tag_key(s)>],<N> )[,<tag_key(s)>|<field_key(s)>] [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of Syntax
BOTTOM(field_key,N)
Returns the smallest N field values associated with the field key.
BOTTOM(field_key,tag_key(s),N)
Returns the smallest field value for N tag values of the tag key.
BOTTOM(field_key,N),tag_key(s),field_key(s)
Returns the smallest N field values associated with the field key in the parentheses and the relevant tag and/or field.
BOTTOM() supports int64 and float64 field value data types.
Notes:
BOTTOM()returns the field value with the earliest timestamp if there’s a tie between two or more values for the smallest value.BOTTOM()differs from other InfluxQL functions when combined with anINTOclause. See the Common Issues section for more information.
Examples
Example: Select the bottom three field values associated with a field key
> SELECT BOTTOM("water_level",3) FROM "h2o_feet"
name: h2o_feet
time bottom
---- ------
2015-08-29T14:30:00Z -0.61
2015-08-29T14:36:00Z -0.591
2015-08-30T15:18:00Z -0.594
The query returns the smallest three field values in the water_level field key and in the h2o_feet measurement.
Example: Select the bottom field value associated with a field key for two tags
> SELECT BOTTOM("water_level","location",2) FROM "h2o_feet"
name: h2o_feet
time bottom location
---- ------ --------
2015-08-29T10:36:00Z -0.243 santa_monica
2015-08-29T14:30:00Z -0.61 coyote_creek
The query returns the smallest field values in the water_level field key for two tag values associated with the location tag key.
Example: Select the bottom four field values associated with a field key and the relevant tags and fields
> SELECT BOTTOM("water_level",4),"location","level description" FROM "h2o_feet"
name: h2o_feet
time bottom location level description
---- ------ -------- -----------------
2015-08-29T14:24:00Z -0.587 coyote_creek below 3 feet
2015-08-29T14:30:00Z -0.61 coyote_creek below 3 feet
2015-08-29T14:36:00Z -0.591 coyote_creek below 3 feet
2015-08-30T15:18:00Z -0.594 coyote_creek below 3 feet
The query returns the smallest four field values in the water_level field key and the relevant values of the location tag key and the level description field key.
Example 4: Select the bottom three field values associated with a field key and include several clauses
> SELECT BOTTOM("water_level",3),"location" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(24m) ORDER BY time DESC
name: h2o_feet
time bottom location
---- ------ --------
2015-08-18T00:48:00Z 1.991 santa_monica
2015-08-18T00:54:00Z 2.054 santa_monica
2015-08-18T00:54:00Z 6.982 coyote_creek
2015-08-18T00:24:00Z 2.041 santa_monica
2015-08-18T00:30:00Z 2.051 santa_monica
2015-08-18T00:42:00Z 2.057 santa_monica
2015-08-18T00:00:00Z 2.064 santa_monica
2015-08-18T00:06:00Z 2.116 santa_monica
2015-08-18T00:12:00Z 2.028 santa_monica
The query returns the smallest three values in the water_level field key for each 24-minute interval between 2015-08-18T00:00:00Z and 2015-08-18T00:54:00Z.
It also returns results in descending timestamp order.
Notice that the GROUP BY time() clause does not override the points’ original timestamps. See Issue 1 in the section below for a more detailed explanation of that behavior.
Common Issues with BOTTOM()
Issue 1: BOTTOM() with a GROUP BY time() clause
Queries with BOTTOM() and a GROUP BY time() clause return the specified
number of points per GROUP BY time() interval.
For
most GROUP BY time() queries,
the returned timestamps mark the start of the GROUP BY time() interval.
GROUP BY time() queries with the BOTTOM() function behave differently;
they maintain the timestamp of the original data point.
Example
The query below returns two points per 18-minute `GROUP BY time()` interval. Notice that the returned timestamps are the points' original timestamps; they are not forced to match the start of the `GROUP BY time()` intervals.
> SELECT BOTTOM("water_level",2) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(18m)
name: h2o_feet
time bottom
---- ------
__
2015-08-18T00:00:00Z 2.064 |
2015-08-18T00:12:00Z 2.028 | <------- Smallest points for the first time interval
--
__
2015-08-18T00:24:00Z 2.041 |
2015-08-18T00:30:00Z 2.051 | <------- Smallest points for the second time interval
--
Issue 2: BOTTOM() and a tag key with fewer than N tag values
Queries with the syntax SELECT BOTTOM(<field_key>,<tag_key>,<N>) can return fewer points than expected.
If the tag key has X tag values, the query specifies N values, and X is smaller than N, then the query returns X points.
Example
The query below asks for the smallest field values of `water_level` for three tag values of the `location` tag key. Because the `location` tag key has two tag values (`santa_monica` and `coyote_creek`), the query returns two points instead of three. ``` > SELECT BOTTOM("water_level","location",3) FROM "h2o_feet"
name: h2o_feet time bottom location
2015-08-29T10:36:00Z -0.243 santa_monica 2015-08-29T14:30:00Z -0.61 coyote_creek
#### Issue 3: BOTTOM(), tags, and the INTO clause
When combined with an [`INTO` clause](/influxdb/v1.6/query_language/data_exploration/#the-into-clause) and no [`GROUP BY tag` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-tags), most InfluxQL functions [convert](/influxdb/v1.6/troubleshooting/frequently-asked-questions/#why-are-my-into-queries-missing-data) any tags in the initial data to fields in the newly written data.
This behavior also applies to the `BOTTOM()` function unless `BOTTOM()` includes a tag key as an argument: `BOTTOM(field_key,tag_key(s),N)`.
In those cases, the system preserves the specified tag as a tag in the newly written data.
##### Example
<br>
The first query in the codeblock below returns the smallest field values in the `water_level` field key for two tag values associated with the `location` tag key.
It also writes those results to the `bottom_water_levels` measurement.
The second query [shows](/influxdb/v1.6/query_language/schema_exploration/#show-tag-keys) that InfluxDB preserved the `location` tag as a tag in the `bottom_water_levels` measurement.
SELECT BOTTOM(“water_level”,“location”,2) INTO “bottom_water_levels” FROM “h2o_feet”
name: result time written
1970-01-01T00:00:00Z 2
SHOW TAG KEYS FROM “bottom_water_levels”
name: bottom_water_levels tagKey
location
## FIRST()
Returns the [field value ](/influxdb/v1.6/concepts/glossary/#field-value) with the oldest timestamp.
### Syntax
SELECT FIRST(<field_key>)[,<tag_key(s)>|<field_key(s)>] [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of Syntax
`FIRST(field_key)`
Returns the oldest field value (determined by timestamp) associated with the field key.
`FIRST(/regular_expression/)`
Returns the oldest field value (determined by timestamp) associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`FIRST(*)`
Returns the oldest field value (determined by timestamp) associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`FIRST(field_key),tag_key(s),field_key(s)`
Returns the oldest field value (determined by timestamp) associated with the field key in the parentheses and the relevant [tag](/influxdb/v1.6/concepts/glossary/#tag) and/or [field](/influxdb/v1.6/concepts/glossary/#field).
`FIRST()` supports all field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
### Examples
#### Example: Select the first field value associated with a field key
SELECT FIRST(“level description”) FROM “h2o_feet”
name: h2o_feet time first
2015-08-18T00:00:00Z between 6 and 9 feet
The query returns the oldest field value (determined by timestamp) associated with the `level description` field key and in the `h2o_feet` measurement.
#### Example: Select the first field value associated with each field key in a measurement
SELECT FIRST(*) FROM “h2o_feet”
name: h2o_feet time first_level description first_water_level
1970-01-01T00:00:00Z between 6 and 9 feet 8.12
The query returns the oldest field value (determined by timestamp) for each field key in the `h2o_feet` measurement.
The `h2o_feet` measurement has two field keys: `level description` and `water_level`.
#### Example: Select the first field value associated with each field key that matches a regular expression
SELECT FIRST(/level/) FROM “h2o_feet”
name: h2o_feet time first_level description first_water_level
1970-01-01T00:00:00Z between 6 and 9 feet 8.12
The query returns the oldest field value for each field key that includes the word `level` in the `h2o_feet` measurement.
#### Example 4: Select the first value associated with a field key and the relevant tags and fields
SELECT FIRST(“level description”),“location”,“water_level” FROM “h2o_feet”
name: h2o_feet time first location water_level
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
The query returns the oldest field value (determined by timestamp) in the `level description` field key and the relevant values of the `location` tag key and the `water_level` field key.
#### Example 5: Select the first field value associated with a field key and include several clauses
SELECT FIRST(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-17T23:48:00Z’ AND time <= ‘2015-08-18T00:54:00Z’ GROUP BY time(12m),* fill(9.01) LIMIT 4 SLIMIT 1
name: h2o_feet tags: location=coyote_creek time first
2015-08-17T23:48:00Z 9.01 2015-08-18T00:00:00Z 8.12 2015-08-18T00:12:00Z 7.887 2015-08-18T00:24:00Z 7.635
The query returns the oldest field value (determined by timestamp) in the `water_level` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1.6/query_language/data_exploration/#the-group-by-clause) results into 12-minute time intervals and per tag.
The query [fills](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals-and-fill) empty time intervals with `9.01`, and it [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points and series returned to four and one.
Notice that the [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) overrides the points' original timestamps.
The timestamps in the results indicate the the start of each 12-minute time interval;
the first point in the results covers the time interval between `2015-08-17T23:48:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:24:00Z` and just before `2015-08-18T00:36:00Z`.
## LAST()
Returns the [field value](/influxdb/v1.6/concepts/glossary/#field-value) with the most recent timestamp.
### Syntax
SELECT LAST(<field_key>)[,<tag_key(s)>|<field_keys(s)>] [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of Syntax
`LAST(field_key)`
Returns the newest field value (determined by timestamp) associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`LAST(/regular_expression/)`
Returns the newest field value (determined by timestamp) associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`LAST(*)`
Returns the newest field value (determined by timestamp) associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`LAST(field_key),tag_key(s),field_key(s)`
Returns the newest field value (determined by timestamp) associated with the field key in the parentheses and the relevant [tag](/influxdb/v1.6/concepts/glossary/#tag) and/or [field](/influxdb/v1.6/concepts/glossary/#field).
`LAST()` supports all field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
### Examples
#### Example: Select the last field values associated with a field key
SELECT LAST(“level description”) FROM “h2o_feet”
name: h2o_feet time last
2015-09-18T21:42:00Z between 3 and 6 feet
The query returns the newest field value (determined by timestamp) associated with the `level description` field key and in the `h2o_feet` measurement.
#### Example: Select the last field values associated with each field key in a measurement
SELECT LAST(*) FROM “h2o_feet”
name: h2o_feet time last_level description last_water_level
1970-01-01T00:00:00Z between 3 and 6 feet 4.938
The query returns the newest field value (determined by timestamp) for each field key in the `h2o_feet` measurement.
The `h2o_feet` measurement has two field keys: `level description` and `water_level`.
#### Example: Select the last field value associated with each field key that matches a regular expression
SELECT LAST(/level/) FROM “h2o_feet”
name: h2o_feet time last_level description last_water_level
1970-01-01T00:00:00Z between 3 and 6 feet 4.938
The query returns the newest field value for each field key that includes the word `level` in the `h2o_feet` measurement.
#### Example 4: Select the last field value associated with a field key and the relevant tags and fields
SELECT LAST(“level description”),“location”,“water_level” FROM “h2o_feet”
name: h2o_feet time last location water_level
2015-09-18T21:42:00Z between 3 and 6 feet santa_monica 4.938
The query returns the newest field value (determined by timestamp) in the `level description` field key and the relevant values of the `location` tag key and the `water_level` field key.
#### Example 5: Select the last field value associated with a field key and include several clauses
SELECT LAST(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-17T23:48:00Z’ AND time <= ‘2015-08-18T00:54:00Z’ GROUP BY time(12m),* fill(9.01) LIMIT 4 SLIMIT 1
name: h2o_feet tags: location=coyote_creek time last
2015-08-17T23:48:00Z 9.01 2015-08-18T00:00:00Z 8.005 2015-08-18T00:12:00Z 7.762 2015-08-18T00:24:00Z 7.5
The query returns the newest field value (determined by timestamp) in the `water_level` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1.6/query_language/data_exploration/#the-group-by-clause) results into 12-minute time intervals and per tag.
The query [fills](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals-and-fill) empty time intervals with `9.01`, and it [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points and series returned to four and one.
Notice that the [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) overrides the points' original timestamps.
The timestamps in the results indicate the the start of each 12-minute time interval;
the first point in the results covers the time interval between `2015-08-17T23:48:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:24:00Z` and just before `2015-08-18T00:36:00Z`.
## MAX()
Returns the greatest [field value](/influxdb/v1.6/concepts/glossary/#field-value).
### Syntax
SELECT MAX(<field_key>)[,<tag_key(s)>|<field__key(s)>] [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of Syntax
`MAX(field_key)`
Returns the greatest field value associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`MAX(/regular_expression/)`
Returns the greatest field value associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`MAX(*)`
Returns the greatest field value associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`MAX(field_key),tag_key(s),field_key(s)`
Returns the greatest field value associated with the field key in the parentheses and the relevant [tag](/influxdb/v1.6/concepts/glossary/#tag) and/or [field](/influxdb/v1.6/concepts/glossary/#field).
`MAX()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
### Examples
#### Example: Select the maximum field value associated with a field key
SELECT MAX(“water_level”) FROM “h2o_feet”
name: h2o_feet time max
2015-08-29T07:24:00Z 9.964
The query returns the greatest field value in the `water_level` field key and in the `h2o_feet` measurement.
#### Example: Select the maximum field value associated with each field key in a measurement
SELECT MAX(*) FROM “h2o_feet”
name: h2o_feet time max_water_level
2015-08-29T07:24:00Z 9.964
The query returns the greatest field value for each field key that stores numerical values in the `h2o_feet` measurement.
The `h2o_feet` measurement has one numerical field: `water_level`.
#### Example: Select the maximum field value associated with each field key that matches a regular expression
SELECT MAX(/level/) FROM “h2o_feet”
name: h2o_feet time max_water_level
2015-08-29T07:24:00Z 9.964
The query returns the greatest field value for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement.
#### Example 4: Select the maximum field value associated with a field key and the relevant tags and fields
SELECT MAX(“water_level”),“location”,“level description” FROM “h2o_feet”
name: h2o_feet time max location level description
2015-08-29T07:24:00Z 9.964 coyote_creek at or greater than 9 feet
The query returns the greatest field value in the `water_level` field key and the relevant values of the `location` tag key and the `level description` field key.
#### Example 5: Select the maximum field value associated with a field key and include several clauses
SELECT MAX(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-17T23:48:00Z’ AND time <= ‘2015-08-18T00:54:00Z’ GROUP BY time(12m),* fill(9.01) LIMIT 4 SLIMIT 1
name: h2o_feet tags: location=coyote_creek time max
2015-08-17T23:48:00Z 9.01 2015-08-18T00:00:00Z 8.12 2015-08-18T00:12:00Z 7.887 2015-08-18T00:24:00Z 7.635
The query returns the greatest field value in the `water_level` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1.6/query_language/data_exploration/#the-group-by-clause) results in to 12-minute time intervals and per tag.
The query [fills](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals-and-fill) empty time intervals with `9.01`, and it [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points and series returned to four and one.
Notice that the [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) overrides the points’ original timestamps.
The timestamps in the results indicate the the start of each 12-minute time interval;
the first point in the results covers the time interval between `2015-08-17T23:48:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:24:00Z` and just before `2015-08-18T00:36:00Z`.
## MIN()
Returns the lowest [field value](/influxdb/v1.6/concepts/glossary/#field-value).
### Syntax
SELECT MIN(<field_key>)[,<tag_key(s)>|<field_key(s)>] [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of Syntax
`MIN(field_key)`
Returns the lowest field value associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`MIN(/regular_expression/)`
Returns the lowest field value associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`MIN(*)`
Returns the lowest field value associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`MIN(field_key),tag_key(s),field_key(s)`
Returns the lowest field value associated with the field key in the parentheses and the relevant [tag](/influxdb/v1.6/concepts/glossary/#tag) and/or [field](/influxdb/v1.6/concepts/glossary/#field).
`MIN()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
### Examples
#### Example: Select the minimum field value associated with a field key
SELECT MIN(“water_level”) FROM “h2o_feet”
name: h2o_feet time min
2015-08-29T14:30:00Z -0.61
The query returns the lowest field value in the `water_level` field key and in the `h2o_feet` measurement.
#### Example: Select the minimum field value associated with each field key in a measurement
SELECT MIN(*) FROM “h2o_feet”
name: h2o_feet time min_water_level
2015-08-29T14:30:00Z -0.61
The query returns the lowest field value for each field key that stores numerical values in the `h2o_feet` measurement.
The `h2o_feet` measurement has one numerical field: `water_level`.
#### Example: Select the minimum field value associated with each field key that matches a regular expression
SELECT MIN(/level/) FROM “h2o_feet”
name: h2o_feet time min_water_level
2015-08-29T14:30:00Z -0.61
The query returns the lowest field value for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement.
#### Example 4: Select the minimum field value associated with a field key and the relevant tags and fields
SELECT MIN(“water_level”),“location”,“level description” FROM “h2o_feet”
name: h2o_feet time min location level description
2015-08-29T14:30:00Z -0.61 coyote_creek below 3 feet
The query returns the lowest field value in the `water_level` field key and the relevant values of the `location` tag key and the `level description` field key.
#### Example 5: Select the minimum field value associated with a field key and include several clauses
SELECT MIN(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-17T23:48:00Z’ AND time <= ‘2015-08-18T00:54:00Z’ GROUP BY time(12m),* fill(9.01) LIMIT 4 SLIMIT 1
name: h2o_feet tags: location=coyote_creek time min
2015-08-17T23:48:00Z 9.01 2015-08-18T00:00:00Z 8.005 2015-08-18T00:12:00Z 7.762 2015-08-18T00:24:00Z 7.5
The query returns the lowest field value in the `water_level` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1.6/query_language/data_exploration/#the-group-by-clause) results in to 12-minute time intervals and per tag.
The query [fills](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals-and-fill) empty time intervals with `9.01`, and it [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points and series returned to four and one.
Notice that the [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) overrides the points’ original timestamps.
The timestamps in the results indicate the the start of each 12-minute time interval;
the first point in the results covers the time interval between `2015-08-17T23:48:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:24:00Z` and just before `2015-08-18T00:36:00Z`.
## PERCENTILE()
Returns the `N`th percentile [field value](/influxdb/v1.6/concepts/glossary/#field-value).
### Syntax
SELECT PERCENTILE(<field_key>,
### Description of Syntax
`PERCENTILE(field_key,N)`
Returns the Nth percentile field value associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`PERCENTILE(/regular_expression/,N)`
Returns the Nth percentile field value associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`PERCENTILE(*,N)`
Returns the Nth percentile field value associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`PERCENTILE(field_key,N),tag_key(s),field_key(s)`
Returns the Nth percentile field value associated with the field key in the parentheses and the relevant [tag](/influxdb/v1.6/concepts/glossary/#tag) and/or [field](/influxdb/v1.6/concepts/glossary/#field).
`N` must be an integer or floating point number between `0` and `100`, inclusive.
`PERCENTILE()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
### Examples
#### Example: Select the fifth percentile field value associated with a field key
SELECT PERCENTILE(“water_level”,5) FROM “h2o_feet”
name: h2o_feet time percentile
2015-08-31T03:42:00Z 1.122
The query returns the field value that is larger than five percent of the field values in the `water_level` field key and in the `h2o_feet` measurement.
#### Example: Select the fifth percentile field value associated with each field key in a measurement
SELECT PERCENTILE(*,5) FROM “h2o_feet”
name: h2o_feet time percentile_water_level
2015-08-31T03:42:00Z 1.122
The query returns the field value that is larger than five percent of the field values in each field key that stores numerical values in the `h2o_feet` measurement.
The `h2o_feet` measurement has one numerical field: `water_level`.
#### Example: Select fifth percentile field value associated with each field key that matches a regular expression
SELECT PERCENTILE(/level/,5) FROM “h2o_feet”
name: h2o_feet time percentile_water_level
2015-08-31T03:42:00Z 1.122
The query returns the field value that is larger than five percent of the field values in each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement.
#### Example 4: Select the fifth percentile field values associated with a field key and the relevant tags and fields
SELECT PERCENTILE(“water_level”,5),“location”,“level description” FROM “h2o_feet”
name: h2o_feet time percentile location level description
2015-08-31T03:42:00Z 1.122 coyote_creek below 3 feet
The query returns the field value that is larger than five percent of the field values in the `water_level` field key and the relevant values of the `location` tag key and the `level description` field key.
#### Example 5: Select the twentieth percentile field value associated with a field key and include several clauses
SELECT PERCENTILE(“water_level”,20) FROM “h2o_feet” WHERE time >= ‘2015-08-17T23:48:00Z’ AND time <= ‘2015-08-18T00:54:00Z’ GROUP BY time(24m) fill(15) LIMIT 2
name: h2o_feet time percentile
2015-08-17T23:36:00Z 15 2015-08-18T00:00:00Z 2.064
The query returns the field value that is larger than 20 percent of the values in the `water_level` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) results into 24-minute intervals.
It [fills](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals-and-fill) empty time intervals with `15` and it [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to two.
Notice that the [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) overrides the points’ original timestamps.
The timestamps in the results indicate the the start of each 24-minute time interval; the first point in the results covers the time interval between `2015-08-17T23:36:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:00:00Z` and just before `2015-08-18T00:24:00Z`.
### Common Issues with PERCENTILE()
#### Issue 1: PERCENTILE() vs. other InfluxQL functions
* `PERCENTILE(<field_key>,100)` is equivalent to [`MAX(<field_key>)`](#max).
* `PERCENTILE(<field_key>, 50)` is nearly equivalent to [`MEDIAN(<field_key>)`](#median), except the `MEDIAN()` function returns the average of the two middle values if the field key contains an even number of field values.
* `PERCENTILE(<field_key>,0)` is not equivalent to [`MIN(<field_key>)`](#min). This is a known [issue](https://github.com/influxdata/influxdb/issues/4418).
## SAMPLE()
Returns a random sample of `N` [field values](/influxdb/v1.6/concepts/glossary/#field-value).
`SAMPLE()` uses [reservoir sampling](https://en.wikipedia.org/wiki/Reservoir_sampling) to generate the random points.
### Syntax
SELECT SAMPLE(<field_key>,
### Description of Syntax
`SAMPLE(field_key,N)`
Returns N randomly selected field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`SAMPLE(/regular_expression/,N)`
Returns N randomly selected field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`SAMPLE(*,N)`
Returns N randomly selected field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`SAMPLE(field_key,N),tag_key(s),field_key(s)`
Returns N randomly selected field values associated with the field key in the parentheses and the relevant [tag](/influxdb/v1.6/concepts/glossary/#tag) and/or [field](/influxdb/v1.6/concepts/glossary/#field).
`N` must be an integer.
`SAMPLE()` supports all field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
### Examples
#### Example: Select a sample of the field values associated with a field key
SELECT SAMPLE(“water_level”,2) FROM “h2o_feet”
name: h2o_feet time sample
2015-09-09T21:48:00Z 5.659 2015-09-18T10:00:00Z 6.939
The query returns two randomly selected points from the `water_level` field key and in the `h2o_feet` measurement.
### Example 2: Select a sample of the field values associated with each field key in a measurement
SELECT SAMPLE(*,2) FROM “h2o_feet”
name: h2o_feet time sample_level description sample_water_level
2015-08-25T17:06:00Z 3.284 2015-09-03T04:30:00Z below 3 feet 2015-09-03T20:06:00Z between 3 and 6 feet 2015-09-08T21:54:00Z 3.412
The query returns two randomly selected points for each field key in the `h2o_feet` measurement.
The `h2o_feet` measurement has two field keys: `level description` and `water_level`.
#### Example: Select a sample of the field values associated with each field key that matches a regular expression
SELECT SAMPLE(/level/,2) FROM “h2o_feet”
name: h2o_feet time sample_level description sample_water_level
2015-08-30T05:54:00Z between 6 and 9 feet 2015-09-07T01:18:00Z 7.854 2015-09-09T20:30:00Z 7.32 2015-09-13T19:18:00Z between 3 and 6 feet
The query returns two randomly selected points for each field key that includes the word `level` in the `h2o_feet` measurement.
#### Example 4: Select a sample of the field values associated with a field key and the relevant tags and fields
SELECT SAMPLE(“water_level”,2),“location”,“level description” FROM “h2o_feet”
name: h2o_feet time sample location level description
2015-08-29T10:54:00Z 5.689 coyote_creek between 3 and 6 feet 2015-09-08T15:48:00Z 6.391 coyote_creek between 6 and 9 feet
The query returns two randomly selected points from the `water_level` field key and the relevant values of the `location` tag and the `level description` field.
#### Example 5: Select a sample of the field values associated with a field key and include several clauses
SELECT SAMPLE(“water_level”,1) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(18m)
name: h2o_feet time sample
2015-08-18T00:12:00Z 2.028 2015-08-18T00:30:00Z 2.051
The query returns one randomly selected point from the `water_level` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and [groups](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) results into 18-minute intervals.
Notice that the [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) does not override the points' original timestamps.
See [Issue 1](#issue-1-sample-with-a-group-by-time-clause) in the section below for a more detailed explanation of that behavior.
### Common Issues with `SAMPLE()`
#### Issue 1: `SAMPLE()` with a `GROUP BY time()` clause
Queries with `SAMPLE()` and a `GROUP BY time()` clause return the specified
number of points (`N`) per `GROUP BY time()` interval.
For
[most `GROUP BY time()` queries](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals),
the returned timestamps mark the start of the `GROUP BY time()` interval.
`GROUP BY time()` queries with the `SAMPLE()` function behave differently;
they maintain the timestamp of the original data point.
##### Example
<br>
The query below returns two randomly selected points per 18-minute
`GROUP BY time()` interval.
Notice that the returned timestamps are the points' original timestamps; they
are not forced to match the start of the `GROUP BY time()` intervals.
SELECT SAMPLE(“water_level”,2) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(18m)
name: h2o_feet time sample
__
2015-08-18T00:06:00Z 2.116 | 2015-08-18T00:12:00Z 2.028 | <——- Randomly-selected points for the first time interval – __ 2015-08-18T00:18:00Z 2.126 | 2015-08-18T00:30:00Z 2.051 | <——- Randomly-selected points for the second time interval –
## TOP()
Returns the greatest `N` [field values](/influxdb/v1.6/concepts/glossary/#field-value).
### Syntax
SELECT TOP( <field_key>[,<tag_key(s)>],
### Description of Syntax
`TOP(field_key,N)`
Returns the greatest N field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`TOP(field_key,tag_key(s),N)`
Returns the greatest field value for N tag values of the [tag key](/influxdb/v1.6/concepts/glossary/#tag-key).
`TOP(field_key,N),tag_key(s),field_key(s)`
Returns the greatest N field values associated with the field key in the parentheses and the relevant [tag](/influxdb/v1.6/concepts/glossary/#tag) and/or [field](/influxdb/v1.6/concepts/glossary/#field).
`TOP()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
> **Notes:**
>
* `TOP()` returns the field value with the earliest timestamp if there's a tie between two or more values for the greatest value.
* `TOP()` differs from other InfluxQL functions when combined with an [`INTO` clause](/influxdb/v1.6/query_language/data_exploration/#the-into-clause).
See the [Common Issues](#common-issues-with-top) section for more information.
### Examples
#### Example: Select the top three field values associated with a field key
SELECT TOP(“water_level”,3) FROM “h2o_feet”
name: h2o_feet time top
2015-08-29T07:18:00Z 9.957 2015-08-29T07:24:00Z 9.964 2015-08-29T07:30:00Z 9.954
The query returns the greatest three field values in the `water_level` field key and in the `h2o_feet` [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
#### Example: Select the top field value associated with a field key for two tags
SELECT TOP(“water_level”,“location”,2) FROM “h2o_feet”
name: h2o_feet time top location
2015-08-29T03:54:00Z 7.205 santa_monica 2015-08-29T07:24:00Z 9.964 coyote_creek
The query returns the greatest field values in the `water_level` field key for two tag values associated with the `location` tag key.
#### Example: Select the top four field values associated with a field key and the relevant tags and fields
SELECT TOP(“water_level”,4),“location”,“level description” FROM “h2o_feet”
name: h2o_feet time top location level description
2015-08-29T07:18:00Z 9.957 coyote_creek at or greater than 9 feet 2015-08-29T07:24:00Z 9.964 coyote_creek at or greater than 9 feet 2015-08-29T07:30:00Z 9.954 coyote_creek at or greater than 9 feet 2015-08-29T07:36:00Z 9.941 coyote_creek at or greater than 9 feet
The query returns the greatest four field values in the `water_level` field key and the relevant values of the `location` tag key and the `level description` field key.
#### Example 4: Select the top three field values associated with a field key and include several clauses
SELECT TOP(“water_level”,3),“location” FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:54:00Z’ GROUP BY time(24m) ORDER BY time DESC
name: h2o_feet time top location
2015-08-18T00:48:00Z 7.11 coyote_creek 2015-08-18T00:54:00Z 6.982 coyote_creek 2015-08-18T00:54:00Z 2.054 santa_monica 2015-08-18T00:24:00Z 7.635 coyote_creek 2015-08-18T00:30:00Z 7.5 coyote_creek 2015-08-18T00:36:00Z 7.372 coyote_creek 2015-08-18T00:00:00Z 8.12 coyote_creek 2015-08-18T00:06:00Z 8.005 coyote_creek 2015-08-18T00:12:00Z 7.887 coyote_creek
The query returns the greatest three values in the `water_level` field key for each 24-minute [interval](/influxdb/v1.6/query_language/data_exploration/#basic-group-by-time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:54:00Z`.
It also returns results in [descending timestamp](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc) order.
Notice that the [GROUP BY time() clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) does not override the points’ original timestamps.
See [Issue 1](#issue-1-top-with-a-group-by-time-clause) in the section below for a more detailed explanation of that behavior.
### Common Issues with `TOP()`
#### Issue 1: `TOP()` with a `GROUP BY time()` clause
Queries with `TOP()` and a `GROUP BY time()` clause return the specified
number of points per `GROUP BY time()` interval.
For
[most `GROUP BY time()` queries](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals),
the returned timestamps mark the start of the `GROUP BY time()` interval.
`GROUP BY time()` queries with the `TOP()` function behave differently;
they maintain the timestamp of the original data point.
##### Example
<br>
The query below returns two points per 18-minute
`GROUP BY time()` interval.
Notice that the returned timestamps are the points' original timestamps; they
are not forced to match the start of the `GROUP BY time()` intervals.
SELECT TOP(“water_level”,2) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(18m)
name: h2o_feet time top
__
2015-08-18T00:00:00Z 2.064 | 2015-08-18T00:06:00Z 2.116 | <——- Greatest points for the first time interval – __ 2015-08-18T00:18:00Z 2.126 | 2015-08-18T00:30:00Z 2.051 | <——- Greatest points for the second time interval –
#### Issue 2: TOP() and a tag key with fewer than N tag values
Queries with the syntax `SELECT TOP(<field_key>,<tag_key>,<N>)` can return fewer points than expected.
If the tag key has `X` tag values, the query specifies `N` values, and `X` is smaller than `N`, then the query returns `X` points.
##### Example
<br>
The query below asks for the greatest field values of `water_level` for three tag values of the `location` tag key.
Because the `location` tag key has two tag values (`santa_monica` and `coyote_creek`), the query returns two points instead of three.
SELECT TOP(“water_level”,“location”,3) FROM “h2o_feet”
name: h2o_feet time top location
2015-08-29T03:54:00Z 7.205 santa_monica 2015-08-29T07:24:00Z 9.964 coyote_creek
#### Issue 3: TOP(), tags, and the INTO clause
When combined with an [`INTO` clause](/influxdb/v1.6/query_language/data_exploration/#the-into-clause) and no [`GROUP BY tag` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-tags), most InfluxQL functions [convert](/influxdb/v1.6/troubleshooting/frequently-asked-questions/#why-are-my-into-queries-missing-data) any tags in the initial data to fields in the newly written data.
This behavior also applies to the `TOP()` function unless `TOP()` includes a tag key as an argument: `TOP(field_key,tag_key(s),N)`.
In those cases, the system preserves the specified tag as a tag in the newly written data.
##### Example
<br>
The first query in the codeblock below returns the greatest field values in the `water_level` field key for two tag values associated with the `location` tag key.
It also writes those results to the `top_water_levels` measurement.
The second query [shows](/influxdb/v1.6/query_language/schema_exploration/#show-tag-keys) that InfluxDB preserved the `location` tag as a tag in the `top_water_levels` measurement.
SELECT TOP(“water_level”,“location”,2) INTO “top_water_levels” FROM “h2o_feet”
name: result time written
1970-01-01T00:00:00Z 2
SHOW TAG KEYS FROM “top_water_levels”
name: top_water_levels tagKey
location
# Transformations
## ABS()
Returns the absolute value of the field value.
### Basic syntax
SELECT ABS( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`ABS(field_key)`
Returns the absolute values of field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
<!-- `ABS(/regular_expression/)`
Returns the absolute value of field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions). -->
`ABS(*)`
Returns the absolute values of field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`ABS()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-abs) section for how to use `ABS()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following subsample of this [sample data](https://gist.github.com/sanderson/8f8aec94a60b2c31a61f44a37737bfea):
SELECT * FROM “data” WHERE time >= ‘2018-06-24T12:00:00Z’ AND time <= ‘2018-06-24T12:05:00Z’
name: data time a b
1529841600000000000 1.33909108671076 -0.163643058925645 1529841660000000000 -0.774984088561186 0.137034364053949 1529841720000000000 -0.921037167720451 -0.482943221384294 1529841780000000000 -1.73880754843378 -0.0729732928756677 1529841840000000000 -0.905980032168252 1.77857552719844 1529841900000000000 -0.891164752631417 0.741147445214238
#### Example: Calculate the absolute values of field values associated with a field key
SELECT ABS(“a”) FROM “data” WHERE time >= ‘2018-06-24T12:00:00Z’ AND time <= ‘2018-06-24T12:05:00Z’
name: data time abs
1529841600000000000 1.33909108671076 1529841660000000000 0.774984088561186 1529841720000000000 0.921037167720451 1529841780000000000 1.73880754843378 1529841840000000000 0.905980032168252 1529841900000000000 0.891164752631417
The query returns the absolute values of field values in the `a` field key in the `data` measurement.
#### Example: Calculate the absolute Values of field values associated with each field key in a measurement
SELECT ABS(*) FROM “data” WHERE time >= ‘2018-06-24T12:00:00Z’ AND time <= ‘2018-06-24T12:05:00Z’
name: data time abs_a abs_b
1529841600000000000 1.33909108671076 0.163643058925645 1529841660000000000 0.774984088561186 0.137034364053949 1529841720000000000 0.921037167720451 0.482943221384294 1529841780000000000 1.73880754843378 0.0729732928756677 1529841840000000000 0.905980032168252 1.77857552719844 1529841900000000000 0.891164752631417 0.741147445214238
The query returns the absolute values of field values for each field key that stores
numerical values in the `data` measurement.
The `data` measurement has two numerical fields: `a` and `b`.
<!-- #### Example: Calculate the absolute values of field values associated with each field key that matches a regular expression
SELECT ABS(/a/) FROM “h2o_feet” WHERE time >= ‘2018-06-24T12:00:00Z’ AND time <= ‘2018-06-24T12:05:00Z’ AND “location” = ‘santa_monica’
name: data time abs
1529841600000000000 1.33909108671076 1529841660000000000 0.774984088561186 1529841720000000000 0.921037167720451 1529841780000000000 1.73880754843378 1529841840000000000 0.905980032168252 1529841900000000000 0.891164752631417
The query returns the absolute values of field values for each field key that stores numerical values and includes `a` in the `data` measurement. -->
#### Example: Calculate the absolute values of field values associated with a field key and include several clauses
SELECT ABS(“a”) FROM “data” WHERE time >= ‘2018-06-24T12:00:00Z’ AND time <= ‘2018-06-24T12:05:00Z’ ORDER BY time DESC LIMIT 4 OFFSET 2
name: data time abs
1529841780000000000 1.73880754843378 1529841720000000000 0.921037167720451 1529841660000000000 0.774984088561186 1529841600000000000 1.33909108671076
The query returns the absolute values of field values associated with the `a` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2018-06-24T12:00:00Z` and `2018-06-24T12:05:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
### Advanced syntax of ABS()
SELECT ABS(
### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ABS()` function to those results.
`ABS()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate the absolute values of mean values.
SELECT ABS(MEAN(“a”)) FROM “data” WHERE time >= ‘2018-06-24T12:00:00Z’ AND time <= ‘2018-06-24T13:00:00Z’ GROUP BY time(12m)
name: data time abs
1529841600000000000 0.3960977256302787 1529842320000000000 0.0010541018316373302 1529843040000000000 0.04494733240283668 1529843760000000000 0.2553594777104415 1529844480000000000 0.20382988543108413 1529845200000000000 0.790836070736962
The query returns the absolute values of [average](#mean) `a`s that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average `a`s at 12-minute intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ABS()`:
SELECT MEAN(“a”) FROM “data” WHERE time >= ‘2018-06-24T12:00:00Z’ AND time <= ‘2018-06-24T13:00:00Z’ GROUP BY time(12m)
name: data time mean
1529841600000000000 -0.3960977256302787 1529842320000000000 0.0010541018316373302 1529843040000000000 0.04494733240283668 1529843760000000000 0.2553594777104415 1529844480000000000 0.20382988543108413 1529845200000000000 -0.790836070736962
InfluxDB then calculates absolute values of those averages.
## ACOS()
Returns the arccosine (in radians) of the field value. Field values must be between -1 and 1.
### Basic syntax
SELECT ACOS( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`ACOS(field_key)`
Returns the arccosine of field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
<!-- `ACOS(/regular_expression/)`
Returns the arccosine of field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions). -->
`ACOS(*)`
Returns the arccosine of field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`ACOS()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types) with values between -1 and 1.
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-acos) section for how to use `ACOS()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following data sample of simulated park occupancy relative to total capacity. The important thing to note is that all field values fall within the calculable range (-1 to 1) of the `ACOS()` function:
SELECT “of_capacity” FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time capacity
2017-05-01T00:00:00Z 0.83 2017-05-02T00:00:00Z 0.3 2017-05-03T00:00:00Z 0.84 2017-05-04T00:00:00Z 0.22 2017-05-05T00:00:00Z 0.17 2017-05-06T00:00:00Z 0.77 2017-05-07T00:00:00Z 0.64 2017-05-08T00:00:00Z 0.72 2017-05-09T00:00:00Z 0.16
#### Example: Calculate the arccosine of field values associated with a field key
SELECT ACOS(“of_capacity”) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time acos
2017-05-01T00:00:00Z 0.591688642426544 2017-05-02T00:00:00Z 1.266103672779499 2017-05-03T00:00:00Z 0.5735131044230969 2017-05-04T00:00:00Z 1.3489818562981022 2017-05-05T00:00:00Z 1.399966657665792 2017-05-06T00:00:00Z 0.6919551751263169 2017-05-07T00:00:00Z 0.8762980611683406 2017-05-08T00:00:00Z 0.7669940078618667 2017-05-09T00:00:00Z 1.410105673842986
The query returns arccosine of field values in the `of_capacity` field key in the `park_occupancy` measurement.
#### Example: Calculate the arccosine of field values associated with each field key in a measurement
SELECT ACOS(*) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time acos_of_capacity
2017-05-01T00:00:00Z 0.591688642426544 2017-05-02T00:00:00Z 1.266103672779499 2017-05-03T00:00:00Z 0.5735131044230969 2017-05-04T00:00:00Z 1.3489818562981022 2017-05-05T00:00:00Z 1.399966657665792 2017-05-06T00:00:00Z 0.6919551751263169 2017-05-07T00:00:00Z 0.8762980611683406 2017-05-08T00:00:00Z 0.7669940078618667 2017-05-09T00:00:00Z 1.410105673842986
The query returns arccosine of field values for each field key that stores numerical values in the `park_occupancy` measurement.
The `park_occupancy` measurement has one numerical field: `of_capacity`.
<!-- #### Example: Calculate the arccosine of field values associated with each field key that matches a regular expression
SELECT ACOS(/capacity/) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time acos_of_capacity
2017-05-01T00:00:00Z 0.591688642426544 2017-05-02T00:00:00Z 1.266103672779499 2017-05-03T00:00:00Z 0.5735131044230969 2017-05-04T00:00:00Z 1.3489818562981022 2017-05-05T00:00:00Z 1.399966657665792 2017-05-06T00:00:00Z 0.6919551751263169 2017-05-07T00:00:00Z 0.8762980611683406 2017-05-08T00:00:00Z 0.7669940078618667 2017-05-09T00:00:00Z 1.410105673842986
The query returns arccosine of field values for each field key that stores numerical values and includes the word `capacity` in the `park_occupancy` measurement. -->
#### Example: Calculate the arccosine of field values associated with a field key and include several clauses
SELECT ACOS(“of_capacity”) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’ ORDER BY time DESC LIMIT 4 OFFSET 2
name: park_occupancy time acos
2017-05-07T00:00:00Z 0.8762980611683406 2017-05-06T00:00:00Z 0.6919551751263169 2017-05-05T00:00:00Z 1.399966657665792 2017-05-04T00:00:00Z 1.3489818562981022
The query returns arccosine of field values associated with the `of_capacity` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2017-05-01T00:00:00Z` and `2017-05-09T00:00:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
### Advanced syntax of ACOS()
SELECT ACOS(
### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ACOS()` function to those results.
`ACOS()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate the arccosine of mean values
SELECT ACOS(MEAN(“of_capacity”)) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’ GROUP BY time(3d)
name: park_occupancy time acos
2017-04-30T00:00:00Z 0.9703630732143733 2017-05-03T00:00:00Z 1.1483422646081407 2017-05-06T00:00:00Z 0.7812981174487247 2017-05-09T00:00:00Z 1.410105673842986
The query returns arccosine of [average](#mean) `of_capacity`s that are calculated at 3-day intervals.
To get those results, InfluxDB first calculates the average `of_capacity`s at 3-day intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ACOS()`:
SELECT MEAN(“of_capacity”) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’ GROUP BY time(3d)
name: park_occupancy time mean
2017-04-30T00:00:00Z 0.565 2017-05-03T00:00:00Z 0.41 2017-05-06T00:00:00Z 0.71 2017-05-09T00:00:00Z 0.16
InfluxDB then calculates arccosine of those averages.
## ASIN()
Returns the arcsine (in radians) of the field value. Field values must be between -1 and 1.
### Basic syntax
SELECT ASIN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`ASIN(field_key)`
Returns the arcsine of field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
<!-- `ASIN(/regular_expression/)`
Returns the arcsine of field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions). -->
`ASIN(*)`
Returns the arcsine of field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`ASIN()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types) with values between -1 and 1.
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-asin) section for how to use `ASIN()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following data sample of simulated park occupancy relative to total capacity. The important thing to note is that all field values fall within the calculable range (-1 to 1) of the `ASIN()` function:
SELECT “of_capacity” FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time capacity
2017-05-01T00:00:00Z 0.83 2017-05-02T00:00:00Z 0.3 2017-05-03T00:00:00Z 0.84 2017-05-04T00:00:00Z 0.22 2017-05-05T00:00:00Z 0.17 2017-05-06T00:00:00Z 0.77 2017-05-07T00:00:00Z 0.64 2017-05-08T00:00:00Z 0.72 2017-05-09T00:00:00Z 0.16
#### Example: Calculate the arcsine of field values associated with a field key
SELECT ASIN(“of_capacity”) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time asin
2017-05-01T00:00:00Z 0.9791076843683526 2017-05-02T00:00:00Z 0.3046926540153975 2017-05-03T00:00:00Z 0.9972832223717997 2017-05-04T00:00:00Z 0.22181447049679442 2017-05-05T00:00:00Z 0.1708296691291045 2017-05-06T00:00:00Z 0.8788411516685797 2017-05-07T00:00:00Z 0.6944982656265559 2017-05-08T00:00:00Z 0.8038023189330299 2017-05-09T00:00:00Z 0.1606906529519106
The query returns arcsine of field values in the `of_capacity` field key in the `park_capacity` measurement.
#### Example: Calculate the arcsine of field values associated with each field key in a measurement
SELECT ASIN(*) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time asin_of_capacity
2017-05-01T00:00:00Z 0.9791076843683526 2017-05-02T00:00:00Z 0.3046926540153975 2017-05-03T00:00:00Z 0.9972832223717997 2017-05-04T00:00:00Z 0.22181447049679442 2017-05-05T00:00:00Z 0.1708296691291045 2017-05-06T00:00:00Z 0.8788411516685797 2017-05-07T00:00:00Z 0.6944982656265559 2017-05-08T00:00:00Z 0.8038023189330299 2017-05-09T00:00:00Z 0.1606906529519106
The query returns arcsine of field values for each field key that stores numerical values in the `park_capacity` measurement.
The `h2o_feet` measurement has one numerical field: `of_capacity`.
<!-- #### Example: Calculate the arcsine of field values associated with each field key that matches a regular expression
SELECT ASIN(/capacity/) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time asin
2017-05-01T00:00:00Z 0.9791076843683526 2017-05-02T00:00:00Z 0.3046926540153975 2017-05-03T00:00:00Z 0.9972832223717997 2017-05-04T00:00:00Z 0.22181447049679442 2017-05-05T00:00:00Z 0.1708296691291045 2017-05-06T00:00:00Z 0.8788411516685797 2017-05-07T00:00:00Z 0.6944982656265559 2017-05-08T00:00:00Z 0.8038023189330299 2017-05-09T00:00:00Z 0.1606906529519106
The query returns arcsine of field values for each field key that stores numerical values and includes the word `of_capacity` in the `park_occupancy` measurement. -->
#### Example: Calculate the arcsine of field values associated with a field key and include several clauses
SELECT ASIN(“of_capacity”) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’ ORDER BY time DESC LIMIT 4 OFFSET 2
name: park_occupancy time asin
2017-05-07T00:00:00Z 0.6944982656265559 2017-05-06T00:00:00Z 0.8788411516685797 2017-05-05T00:00:00Z 0.1708296691291045 2017-05-04T00:00:00Z 0.22181447049679442
The query returns arcsine of field values associated with the `of_capacity` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2017-05-01T00:00:00Z` and `2017-05-09T00:00:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
### Advanced syntax of ASIN()
SELECT ASIN(
### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ASIN()` function to those results.
`ASIN()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate the arcsine of mean values.
SELECT ASIN(MEAN(“of_capacity”)) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’ GROUP BY time(3d)
name: park_occupancy time asin
2017-04-30T00:00:00Z 0.6004332535805232 2017-05-03T00:00:00Z 0.42245406218675574 2017-05-06T00:00:00Z 0.7894982093461719 2017-05-09T00:00:00Z 0.1606906529519106
The query returns arcsine of [average](#mean) `of_capacity`s that are calculated at 3-day intervals.
To get those results, InfluxDB first calculates the average `of_capacity`s at 3-day intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ASIN()`:
SELECT MEAN(“of_capacity”) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’ GROUP BY time(3d)
name: park_occupancy time mean
2017-04-30T00:00:00Z 0.565 2017-05-03T00:00:00Z 0.41 2017-05-06T00:00:00Z 0.71 2017-05-09T00:00:00Z 0.16
InfluxDB then calculates arcsine of those averages.
## ATAN()
Returns the arctangent (in radians) of the field value. Field values must be between -1 and 1.
### Basic syntax
SELECT ATAN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`ATAN(field_key)`
Returns the arctangent of field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
<!-- `ATAN(/regular_expression/)`
Returns the arctangent of field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions). -->
`ATAN(*)`
Returns the arctangent of field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`ATAN()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types) with values between -1 and 1.
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-atan) section for how to use `ATAN()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following data sample of simulated park occupancy relative to total capacity. The important thing to note is that all field values fall within the calculable range (-1 to 1) of the `ATAN()` function:
SELECT “of_capacity” FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time capacity
2017-05-01T00:00:00Z 0.83 2017-05-02T00:00:00Z 0.3 2017-05-03T00:00:00Z 0.84 2017-05-04T00:00:00Z 0.22 2017-05-05T00:00:00Z 0.17 2017-05-06T00:00:00Z 0.77 2017-05-07T00:00:00Z 0.64 2017-05-08T00:00:00Z 0.72 2017-05-09T00:00:00Z 0.16
#### Example: Calculate the arctangent of field values associated with a field key
SELECT ATAN(“of_capacity”) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time atan
2017-05-01T00:00:00Z 0.6927678353971222 2017-05-02T00:00:00Z 0.2914567944778671 2017-05-03T00:00:00Z 0.6986598247214632 2017-05-04T00:00:00Z 0.2165503049760893 2017-05-05T00:00:00Z 0.16839015714752992 2017-05-06T00:00:00Z 0.6561787179913948 2017-05-07T00:00:00Z 0.5693131911006619 2017-05-08T00:00:00Z 0.6240230529767568 2017-05-09T00:00:00Z 0.1586552621864014
The query returns arctangent of field values in the `of_capacity` field key in the `park_occupancy` measurement.
#### Example: Calculate the arctangent of field values associated with each field key in a measurement
SELECT ATAN(*) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time atan_of_capacity
2017-05-01T00:00:00Z 0.6927678353971222 2017-05-02T00:00:00Z 0.2914567944778671 2017-05-03T00:00:00Z 0.6986598247214632 2017-05-04T00:00:00Z 0.2165503049760893 2017-05-05T00:00:00Z 0.16839015714752992 2017-05-06T00:00:00Z 0.6561787179913948 2017-05-07T00:00:00Z 0.5693131911006619 2017-05-08T00:00:00Z 0.6240230529767568 2017-05-09T00:00:00Z 0.1586552621864014
The query returns arctangent of field values for each field key that stores numerical values in the `park_occupancy` measurement.
The `park_occupancy` measurement has one numerical field: `of_capacity`.
<!-- #### Example: Calculate the arctangent of field values associated with each field key that matches a regular expression
SELECT ATAN(/capacity/) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’
name: park_occupancy time atan_of_capacity
2017-05-01T00:00:00Z 0.6927678353971222 2017-05-02T00:00:00Z 0.2914567944778671 2017-05-03T00:00:00Z 0.6986598247214632 2017-05-04T00:00:00Z 0.2165503049760893 2017-05-05T00:00:00Z 0.16839015714752992 2017-05-06T00:00:00Z 0.6561787179913948 2017-05-07T00:00:00Z 0.5693131911006619 2017-05-08T00:00:00Z 0.6240230529767568 2017-05-09T00:00:00Z 0.1586552621864014
The query returns arctangent of field values for each field key that stores numerical values and includes the word `capacity` in the `park_occupancy` measurement. -->
#### Example: Calculate the arctangent of field values associated with a field key and include several clauses
SELECT ATAN(“of_capacity”) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’ ORDER BY time DESC LIMIT 4 OFFSET 2
name: park_occupancy time atan
2017-05-07T00:00:00Z 0.5693131911006619 2017-05-06T00:00:00Z 0.6561787179913948 2017-05-05T00:00:00Z 0.16839015714752992 2017-05-04T00:00:00Z 0.2165503049760893
The query returns arctangent of field values associated with the `of_capacity` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2017-05-01T00:00:00Z` and `2017-05-09T00:00:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
### Advanced syntax of ATAN()
SELECT ATAN(
### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ATAN()` function to those results.
`ATAN()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate the arctangent of mean values.
SELECT ATAN(MEAN(“of_capacity”)) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’ GROUP BY time(3d)
name: park_occupancy time atan
2017-04-30T00:00:00Z 0.5142865412694495 2017-05-03T00:00:00Z 0.3890972310552784 2017-05-06T00:00:00Z 0.6174058917515726 2017-05-09T00:00:00Z 0.1586552621864014
The query returns arctangent of [average](#mean) `of_capacity`s that are calculated at 3-day intervals.
To get those results, InfluxDB first calculates the average `of_capacity`s at 3-day intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ATAN()`:
SELECT MEAN(“of_capacity”) FROM “park_occupancy” WHERE time >= ‘2017-05-01T00:00:00Z’ AND time <= ‘2017-05-09T00:00:00Z’ GROUP BY time(3d)
name: park_occupancy time mean
2017-04-30T00:00:00Z 0.565 2017-05-03T00:00:00Z 0.41 2017-05-06T00:00:00Z 0.71 2017-05-09T00:00:00Z 0.16
InfluxDB then calculates arctangent of those averages.
## ATAN2()
Returns the the arctangent of `y/x` in radians.
### Basic syntax
SELECT ATAN2( [ * | <field_key> | num ], [ <field_key> | num ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`ATAN2(field_key_y, field_key_x)`
Returns the arctangent of field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key), `field_key_y`, divided by field values associated with `field_key_x`.
<!-- `ATAN2(/regular_expression/, field_key_x)`
Returns the arctangent of field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions)
divided by field values associated with `field_key_x`. -->
`ATAN2(*, field_key_x)`
Returns the field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement)
divided by field values associated with `field_key_x`.
`ATAN2()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-atan2) section for how to use `ATAN2()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following sample of simulated flight data:
SELECT “altitude_ft”, “distance_ft” FROM “flight_data” WHERE time >= ‘2018-05-16T12:01:00Z’ AND time <= ‘2018-05-16T12:10:00Z’
name: flight_data time altitude_ft distance_ft
2018-05-16T12:01:00Z 1026 50094 2018-05-16T12:02:00Z 2549 53576 2018-05-16T12:03:00Z 4033 55208 2018-05-16T12:04:00Z 5579 58579 2018-05-16T12:05:00Z 7065 61213 2018-05-16T12:06:00Z 8589 64807 2018-05-16T12:07:00Z 10180 67707 2018-05-16T12:08:00Z 11777 69819 2018-05-16T12:09:00Z 13321 72452 2018-05-16T12:10:00Z 14885 75881
#### Example: Calculate the arctangent of field_key_y over field_key_x
SELECT ATAN2(“altitude_ft”, “distance_ft”) FROM “flight_data” WHERE time >= ‘2018-05-16T12:01:00Z’ AND time <= ‘2018-05-16T12:10:00Z’
name: flight_data time atan2
2018-05-16T12:01:00Z 0.020478631571881498 2018-05-16T12:02:00Z 0.04754142349303296 2018-05-16T12:03:00Z 0.07292147724575364 2018-05-16T12:04:00Z 0.09495251193874832 2018-05-16T12:05:00Z 0.11490822875441563 2018-05-16T12:06:00Z 0.13176409347584003 2018-05-16T12:07:00Z 0.14923587589682233 2018-05-16T12:08:00Z 0.1671059946640312 2018-05-16T12:09:00Z 0.18182893717409565 2018-05-16T12:10:00Z 0.1937028631495223
The query returns the arctangents of field values in the `altitude_ft` field key divided by values in the `distance_ft` field key. Both are part of the `flight_data` measurement.
#### Example: Calculate the arctangent of values associated with each field key in a measurement divided by field_key_x
SELECT ATAN2(*, “distance_ft”) FROM “flight_data” WHERE time >= ‘2018-05-16T12:01:00Z’ AND time <= ‘2018-05-16T12:10:00Z’
name: flight_data time atan2_altitude_ft atan2_distance_ft
2018-05-16T12:01:00Z 0.020478631571881498 0.7853981633974483 2018-05-16T12:02:00Z 0.04754142349303296 0.7853981633974483 2018-05-16T12:03:00Z 0.07292147724575364 0.7853981633974483 2018-05-16T12:04:00Z 0.09495251193874832 0.7853981633974483 2018-05-16T12:05:00Z 0.11490822875441563 0.7853981633974483 2018-05-16T12:06:00Z 0.13176409347584003 0.7853981633974483 2018-05-16T12:07:00Z 0.14923587589682233 0.7853981633974483 2018-05-16T12:08:00Z 0.1671059946640312 0.7853981633974483 2018-05-16T12:09:00Z 0.18182893717409565 0.7853981633974483 2018-05-16T12:10:00Z 0.19370286314952234 0.7853981633974483
The query returns the arctangents of all numeric field values in the `flight_data` measurement divided by values in the `distance_ft` field key.
The `flight_data` measurement has two numeric fields: `altitude_ft` and `distance_ft`.
<!-- #### Example: Calculate the arctangent of values associated with each field key matching a regular expression divided by field_key_x
SELECT ATAN2(/ft/, “distance_ft”) FROM “flight_data” WHERE time >= ‘2018-05-16T12:01:00Z’ AND time <= ‘2018-05-16T12:10:00Z’
name: flight_data time atan2_altitude_ft atan2_distance_ft
2018-05-16T12:01:00Z 0.020478631571881498 0.7853981633974483 2018-05-16T12:02:00Z 0.04754142349303296 0.7853981633974483 2018-05-16T12:03:00Z 0.07292147724575364 0.7853981633974483 2018-05-16T12:04:00Z 0.09495251193874832 0.7853981633974483 2018-05-16T12:05:00Z 0.11490822875441563 0.7853981633974483 2018-05-16T12:06:00Z 0.13176409347584003 0.7853981633974483 2018-05-16T12:07:00Z 0.14923587589682233 0.7853981633974483 2018-05-16T12:08:00Z 0.1671059946640312 0.7853981633974483 2018-05-16T12:09:00Z 0.18182893717409565 0.7853981633974483 2018-05-16T12:10:00Z 0.19370286314952234 0.7853981633974483
The query returns the arctangents of all numeric field values in the `flight_data` measurement that match the `/ft/` regular expression divided by values in the `distance_ft` field key.
The `flight_data` measurement has two matching numeric fields: `altitude_ft` and `distance_ft`.
-->
#### Example: Calculate the arctangents of field values and include several clauses
SELECT ATAN2(“altitude_ft”, “distance_ft”) FROM “flight_data” WHERE time >= ‘2018-05-16T12:01:00Z’ AND time <= ‘2018-05-16T12:10:00Z’ ORDER BY time DESC LIMIT 4 OFFSET 2
name: flight_data time atan2
2018-05-16T12:08:00Z 0.1671059946640312 2018-05-16T12:07:00Z 0.14923587589682233 2018-05-16T12:06:00Z 0.13176409347584003 2018-05-16T12:05:00Z 0.11490822875441563
The query returns the arctangent of field values associated with the `altitude_ft` field key divided by the `distance_ft` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2018-05-16T12:10:00Z` and `2018-05-16T12:10:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
### Advanced syntax of ATAN2
SELECT ATAN2(<function()>, <function()>) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ATAN2()` function to those results.
`ATAN2()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate arctangents of mean values
SELECT ATAN2(MEAN(“altitude_ft”), MEAN(“distance_ft”)) FROM “flight_data” WHERE time >= ‘2018-05-16T12:01:00Z’ AND time <= ‘2018-05-16T13:01:00Z’ GROUP BY time(12m)
name: flight_data time atan2
2018-05-16T12:00:00Z 0.133815587896842 2018-05-16T12:12:00Z 0.2662716308351908 2018-05-16T12:24:00Z 0.2958845306108965 2018-05-16T12:36:00Z 0.23783439588429497 2018-05-16T12:48:00Z 0.1906803720242831 2018-05-16T13:00:00Z 0.17291511946158172
The query returns the argtangents of [average](#mean) `altitude_ft`s divided by average `distance_ft`s. Averages are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average `altitude_ft`s and `distance_ft` at 12-minute intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ATAN2()`:
SELECT MEAN(“altitude_ft”), MEAN(“distance_ft”) FROM “flight_data” WHERE time >= ‘2018-05-16T12:01:00Z’ AND time <= ‘2018-05-16T13:01:00Z’ GROUP BY time(12m)
name: flight_data time mean mean_1
2018-05-16T12:00:00Z 8674 64433.181818181816 2018-05-16T12:12:00Z 26419.833333333332 96865.25 2018-05-16T12:24:00Z 40337.416666666664 132326.41666666666 2018-05-16T12:36:00Z 41149.583333333336 169743.16666666666 2018-05-16T12:48:00Z 41230.416666666664 213600.91666666666 2018-05-16T13:00:00Z 41184.5 235799
InfluxDB then calculates the arctangents of those averages.
## CEIL()
Returns the subsequent value rounded up to the nearest integer.
### Basic syntax
SELECT CEIL( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`CEIL(field_key)`
Returns the field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key) rounded up to the nearest integer.
<!-- `CEIL(/regular_expression/)`
Returns the field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions) rounded up to the nearest integer. -->
`CEIL(*)`
Returns the field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement) rounded up to the nearest integer.
`CEIL()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-ceil) section for how to use `CEIL()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following subsample of the [`NOAA_water_database` data](/influxdb/v1.6/query_language/data_download/):
SELECT “water_level” FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time water_level
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051
#### Example: Calculate the ceiling of field values associated with a field key
SELECT CEIL(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time ceil
2015-08-18T00:00:00Z 3 2015-08-18T00:06:00Z 3 2015-08-18T00:12:00Z 3 2015-08-18T00:18:00Z 3 2015-08-18T00:24:00Z 3 2015-08-18T00:30:00Z 3
The query returns field values in the `water_level` field key in the `h2o_feet` measurement rounded up to the nearest integer.
#### Example: Calculate the ceiling of field values associated with each field key in a measurement
SELECT CEIL(*) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time ceil_water_level
2015-08-18T00:00:00Z 3 2015-08-18T00:06:00Z 3 2015-08-18T00:12:00Z 3 2015-08-18T00:18:00Z 3 2015-08-18T00:24:00Z 3 2015-08-18T00:30:00Z 3
The query returns field values for each field key that stores numerical values in the `h2o_feet` measurement rounded up to the nearest integer.
The `h2o_feet` measurement has one numerical field: `water_level`.
<!-- #### Example: Calculate the ceiling of the field values associated with each field key that matches a regular expression
SELECT CEIL(/water/) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time ceil_water_level
2015-08-18T00:00:00Z 3 2015-08-18T00:06:00Z 3 2015-08-18T00:12:00Z 3 2015-08-18T00:18:00Z 3 2015-08-18T00:24:00Z 3 2015-08-18T00:30:00Z 3
The query returns field values for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement rounded up to the nearest integer. -->
#### Example: Calculate the ceiling of field values associated with a field key and include several clauses
SELECT CEIL(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet time ceil
2015-08-18T00:18:00Z 3 2015-08-18T00:12:00Z 3 2015-08-18T00:06:00Z 3 2015-08-18T00:00:00Z 3
The query returns field values associated with the `water_level` field key rounded up to the nearest integer.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
### Advanced syntax of CEIL()
SELECT CEIL(
### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `CEIL()` function to those results.
`CEIL()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate mean values rounded up to the nearest integer.
SELECT CEIL(MEAN(“water_level”)) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(12m)
name: h2o_feet time ceil
2015-08-18T00:00:00Z 3 2015-08-18T00:12:00Z 3 2015-08-18T00:24:00Z 3
The query returns the [average](#mean) `water_level`s that are calculated at 12-minute intervals and rounds them up to the nearest integer.
To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `CEIL()`:
SELECT MEAN(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(12m)
name: h2o_feet time mean
2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then rounds those averages up to the nearest integer.
## COS()
Returns the cosine of the field value.
### Basic syntax
SELECT COS( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`COS(field_key)`
Returns the cosine of field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
<!-- `COS(/regular_expression/)`
Returns the cosine of field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions). -->
`COS(*)`
Returns the cosine of field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`COS()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-cos) section for how to use `COS()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following subsample of the [`NOAA_water_database` data](/influxdb/v1.6/query_language/data_download/):
SELECT “water_level” FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time water_level
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051
#### Example: Calculate the cosine of field values associated with a field key
SELECT COS(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time cos
2015-08-18T00:00:00Z -0.47345017433543124 2015-08-18T00:06:00Z -0.5185922462666872 2015-08-18T00:12:00Z -0.4414407189100776 2015-08-18T00:18:00Z -0.5271163912192579 2015-08-18T00:24:00Z -0.45306786455514825 2015-08-18T00:30:00Z -0.4619598230611262
The query returns cosine of field values in the `water_level` field key in the `h2o_feet` measurement.
#### Example: Calculate the cosine of field values associated with each field key in a measurement
SELECT COS(*) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time cos_water_level
2015-08-18T00:00:00Z -0.47345017433543124 2015-08-18T00:06:00Z -0.5185922462666872 2015-08-18T00:12:00Z -0.4414407189100776 2015-08-18T00:18:00Z -0.5271163912192579 2015-08-18T00:24:00Z -0.45306786455514825 2015-08-18T00:30:00Z -0.4619598230611262
The query returns cosine of field values for each field key that stores numerical values in the `h2o_feet` measurement.
The `h2o_feet` measurement has one numerical field: `water_level`.
<!-- #### Example: Calculate the cosine of field values associated with each field key that matches a regular expression
SELECT COS(/water/) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time cos
2015-08-18T00:00:00Z -0.47345017433543124 2015-08-18T00:06:00Z -0.5185922462666872 2015-08-18T00:12:00Z -0.4414407189100776 2015-08-18T00:18:00Z -0.5271163912192579 2015-08-18T00:24:00Z -0.45306786455514825 2015-08-18T00:30:00Z -0.4619598230611262
The query returns cosine of field values for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. -->
#### Example: Calculate the cosine of field values associated with a field key and include several clauses
SELECT COS(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet time cos
2015-08-18T00:18:00Z -0.5271163912192579 2015-08-18T00:12:00Z -0.4414407189100776 2015-08-18T00:06:00Z -0.5185922462666872 2015-08-18T00:00:00Z -0.47345017433543124
The query returns cosine of field values associated with the `water_level` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
### Advanced syntax of COS()
SELECT COS(
### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `COS()` function to those results.
`COS()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate the cosine of mean values.
SELECT COS(MEAN(“water_level”)) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(12m)
name: h2o_feet time cos
2015-08-18T00:00:00Z -0.49618891270599885 2015-08-18T00:12:00Z -0.4848605136571181 2015-08-18T00:24:00Z -0.4575195627907578
The query returns cosine of [average](#mean) `water_level`s that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `COS()`:
SELECT MEAN(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(12m)
name: h2o_feet time mean
2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates cosine of those averages.
## CUMULATIVE_SUM()
Returns the running total of subsequent [field values](/influxdb/v1.6/concepts/glossary/#field-value).
### Basic syntax
SELECT CUMULATIVE_SUM( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`CUMULATIVE_SUM(field_key)`
Returns the running total of subsequent field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`CUMULATIVE_SUM(/regular_expression/)`
Returns the running total of subsequent field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`CUMULATIVE_SUM(*)`
Returns the running total of subsequent field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`CUMULATIVE_SUM()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-cumulative-sum) section for how to use `CUMULATIVE_SUM()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following subsample of the [`NOAA_water_database` data](/influxdb/v1.6/query_language/data_download/):
SELECT “water_level” FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time water_level
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051
#### Example: Calculate the cumulative sum of the field values associated with a field key
SELECT CUMULATIVE_SUM(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time cumulative_sum
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 4.18 2015-08-18T00:12:00Z 6.208 2015-08-18T00:18:00Z 8.334 2015-08-18T00:24:00Z 10.375 2015-08-18T00:30:00Z 12.426
The query returns the running total of the field values in the `water_level` field key and in the `h2o_feet` measurement.
#### Example: Calculate the cumulative sum of the field values associated with each field key in a measurement
SELECT CUMULATIVE_SUM(*) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time cumulative_sum_water_level
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 4.18 2015-08-18T00:12:00Z 6.208 2015-08-18T00:18:00Z 8.334 2015-08-18T00:24:00Z 10.375 2015-08-18T00:30:00Z 12.426
The query returns the running total of the field values for each field key that stores numerical values in the `h2o_feet` measurement.
The `h2o_feet` measurement has one numerical field: `water_level`.
#### Example: Calculate the cumulative sum of the field values associated with each field key that matches a regular expression
SELECT CUMULATIVE_SUM(/water/) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time cumulative_sum_water_level
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 4.18 2015-08-18T00:12:00Z 6.208 2015-08-18T00:18:00Z 8.334 2015-08-18T00:24:00Z 10.375 2015-08-18T00:30:00Z 12.426
The query returns the running total of the field values for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement.
#### Example 4: Calculate the cumulative sum of the field values associated with a field key and include several clauses
SELECT CUMULATIVE_SUM(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet time cumulative_sum
2015-08-18T00:18:00Z 6.218 2015-08-18T00:12:00Z 8.246 2015-08-18T00:06:00Z 10.362 2015-08-18T00:00:00Z 12.426
The query returns the running total of the field values associated with the `water_level` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
### Advanced syntax of CUMULATIVE_SUM()
SELECT CUMULATIVE_SUM(
### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `CUMULATIVE_SUM()` function to those results.
`CUMULATIVE_SUM()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate the cumulative sum of mean values
SELECT CUMULATIVE_SUM(MEAN(“water_level”)) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(12m)
name: h2o_feet time cumulative_sum
2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 4.167 2015-08-18T00:24:00Z 6.213
The query returns the running total of [average](#mean) `water_level`s that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `CUMULATIVE_SUM()`:
SELECT MEAN(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(12m)
name: h2o_feet time mean
2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003
Next, InfluxDB calculates the running total of those averages.
The second point in the final results (`4.167`) is the sum of `2.09` and `2.077`
and the third point (`6.213`) is the sum of `2.09`, `2.077`, and `2.0460000000000003`.
## DERIVATIVE()
Returns the rate of change between subsequent [field values](/influxdb/v1.6/concepts/glossary/#field-value).
### Basic syntax
SELECT DERIVATIVE( [ * | <field_key> | /<regular_expression>/ ] [ ,
### Description of basic syntax
InfluxDB calculates the difference between subsequent field values and converts those results into the rate of change per `unit`.
The `unit` argument is an integer followed by a [duration literal](/influxdb/v1.6/query_language/spec/#literals) and it is optional.
If the query does not specify the `unit` the unit defaults to one second (`1s`).
`DERIVATIVE(field_key)`
Returns the rate of change between subsequent field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`DERIVATIVE(/regular_expression/)`
Returns the rate of change between subsequent field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`DERIVATIVE(*)`
Returns the rate of change between subsequent field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`DERIVATIVE()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-derivative) section for how to use `DERIVATIVE()` with a `GROUP BY time()` clause.
### Examples of basic syntax
Examples 1-5 use the following subsample of the [`NOAA_water_database` data](/influxdb/v1.6/query_language/data_download/):
SELECT “water_level” FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’
name: h2o_feet time water_level
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051
#### Example: Calculate the derivative between the field values associated with a field key
SELECT DERIVATIVE(“water_level”) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’
name: h2o_feet time derivative
2015-08-18T00:06:00Z 0.00014444444444444457 2015-08-18T00:12:00Z -0.00024444444444444465 2015-08-18T00:18:00Z 0.0002722222222222218 2015-08-18T00:24:00Z -0.000236111111111111 2015-08-18T00:30:00Z 2.777777777777842e-05
The query returns the one-second rate of change between the field values associated with the `water_level` field key and in the `h2o_feet` measurement.
The first result (`0.00014444444444444457`) is the one-second rate of change between the first two subsequent field values in the raw data.
InfluxDB calculates the difference between the field values and normalizes that value to the one-second rate of change:
(2.116 - 2.064) / (360s / 1s)
| |
| the difference between the field values' timestamps / the default unit
second field value - first field value
#### Example: Calculate the derivative between the field values associated with a field key and specify the unit option
SELECT DERIVATIVE(“water_level”,6m) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’
name: h2o_feet time derivative
2015-08-18T00:06:00Z 0.052000000000000046 2015-08-18T00:12:00Z -0.08800000000000008 2015-08-18T00:18:00Z 0.09799999999999986 2015-08-18T00:24:00Z -0.08499999999999996 2015-08-18T00:30:00Z 0.010000000000000231
The query returns the six-minute rate of change between the field values associated with the `water_level` field key and in the `h2o_feet` measurement.
The first result (`0.052000000000000046`) is the six-minute rate of change between the first two subsequent field values in the raw data.
InfluxDB calculates the difference between the field values and normalizes that value to the six-minute rate of change:
(2.116 - 2.064) / (6m / 6m)
| |
| the difference between the field values' timestamps / the specified unit
second field value - first field value
#### Example: Calculate the derivative between the field values associated with each field key in a measurement and specify the unit option
SELECT DERIVATIVE(*,3m) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’
name: h2o_feet time derivative_water_level
2015-08-18T00:06:00Z 0.026000000000000023 2015-08-18T00:12:00Z -0.04400000000000004 2015-08-18T00:18:00Z 0.04899999999999993 2015-08-18T00:24:00Z -0.04249999999999998 2015-08-18T00:30:00Z 0.0050000000000001155
The query returns the three-minute rate of change between the field values associated with each field key that stores numerical values in the `h2o_feet` measurement.
The `h2o_feet` measurement has one numerical field: `water_level`.
The first result (`0.026000000000000023`) is the three-minute rate of change between the first two subsequent field values in the raw data.
InfluxDB calculates the difference between the field values and normalizes that value to the three-minute rate of change:
(2.116 - 2.064) / (6m / 3m)
| |
| the difference between the field values' timestamps / the specified unit
second field value - first field value
#### Example 4: Calculate the derivative between the field values associated with each field key that matches a regular expression and specify the unit option
SELECT DERIVATIVE(/water/,2m) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’
name: h2o_feet time derivative_water_level
2015-08-18T00:06:00Z 0.01733333333333335 2015-08-18T00:12:00Z -0.02933333333333336 2015-08-18T00:18:00Z 0.03266666666666662 2015-08-18T00:24:00Z -0.02833333333333332 2015-08-18T00:30:00Z 0.0033333333333334103
The query returns the two-minute rate of change between the field values associated with each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement.
The first result (`0.01733333333333335`) is the two-minute rate of change between the first two subsequent field values in the raw data.
InfluxDB calculates the difference between the field values and normalizes that value to the two-minute rate of change:
(2.116 - 2.064) / (6m / 2m)
| |
| the difference between the field values' timestamps / the specified unit
second field value - first field value
#### Example 5: Calculate the derivative between the field values associated with a field key and include several clauses
SELECT DERIVATIVE(“water_level”) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ ORDER BY time DESC LIMIT 1 OFFSET 2
name: h2o_feet time derivative
2015-08-18T00:12:00Z -0.0002722222222222218
The query returns the one-second rate of change between the field values associated with the `water_level` field key and in the `h2o_feet` measurement.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to one and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
The only result (`-0.0002722222222222218`) is the one-second rate of change between the relevant subsequent field values in the raw data.
InfluxDB calculates the difference between the field values and normalizes that value to the one-second rate of change:
(2.126 - 2.028) / (360s / 1s)
| |
| the difference between the field values' timestamps / the default unit
second field value - first field value
### Advanced syntax of DERIVATIVE()
SELECT DERIVATIVE(
### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `DERIVATIVE()` function to those results.
The `unit` argument is an integer followed by a [duration literal](/influxdb/v1.6/query_language/spec/#literals) and it is optional.
If the query does not specify the `unit` the `unit` defaults to the `GROUP BY time()` interval.
Note that this behavior is different from the [basic syntax's](#basic-syntax-1) default behavior.
`DERIVATIVE()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate the derivative of mean values
SELECT DERIVATIVE(MEAN(“water_level”)) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ GROUP BY time(12m)
name: h2o_feet time derivative
2015-08-18T00:12:00Z -0.0129999999999999 2015-08-18T00:24:00Z -0.030999999999999694
The query returns the 12-minute rate of change between [average](#mean) `water_level`s that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `DERIVATIVE()`:
SELECT MEAN(“water_level”) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ GROUP BY time(12m)
name: h2o_feet time mean
2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003
Next, InfluxDB calculates the 12-minute rate of change between those averages.
The first result (`-0.0129999999999999`) is the 12-minute rate of change between the first two averages.
InfluxDB calculates the difference between the field values and normalizes that value to the 12-minute rate of change.
(2.077 - 2.09) / (12m / 12m)
| |
| the difference between the field values' timestamps / the default unit
second field value - first field value
#### Example: Calculate the derivative of mean values and specify the unit option
SELECT DERIVATIVE(MEAN(“water_level”),6m) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ GROUP BY time(12m)
name: h2o_feet time derivative
2015-08-18T00:12:00Z -0.00649999999999995 2015-08-18T00:24:00Z -0.015499999999999847
The query returns the six-minute rate of change between average `water_level`s that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals.
This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `DERIVATIVE()`:
SELECT MEAN(“water_level”) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ GROUP BY time(12m)
name: h2o_feet time mean
2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003
Next, InfluxDB calculates the six-minute rate of change between those averages.
The first result (`-0.00649999999999995`) is the six-minute rate of change between the first two averages.
InfluxDB calculates the difference between the field values and normalizes that value to the six-minute rate of change.
(2.077 - 2.09) / (12m / 6m)
| |
| the difference between the field values' timestamps / the specified unit
second field value - first field value
## DIFFERENCE()
Returns the result of subtraction between subsequent [field values](/influxdb/v1.6/concepts/glossary/#field-value).
### Basic syntax
SELECT DIFFERENCE( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`DIFFERENCE(field_key)`
Returns the difference between subsequent field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`DIFFERENCE(/regular_expression/)`
Returns the difference between subsequent field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`DIFFERENCE(*)`
Returns the difference between subsequent field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`DIFFERENCE()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-difference) section for how to use `DIFFERENCE()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following subsample of the [`NOAA_water_database` data](/influxdb/v1.6/query_language/data_download/):
SELECT “water_level” FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time water_level
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051
#### Example: Calculate the difference between the field values associated with a field key
SELECT DIFFERENCE(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time difference
2015-08-18T00:06:00Z 0.052000000000000046 2015-08-18T00:12:00Z -0.08800000000000008 2015-08-18T00:18:00Z 0.09799999999999986 2015-08-18T00:24:00Z -0.08499999999999996 2015-08-18T00:30:00Z 0.010000000000000231
The query returns the difference between the subsequent field values in the `water_level` field key and in the `h2o_feet` measurement.
#### Example: Calculate the difference between the field values associated with each field key in a measurement
SELECT DIFFERENCE(*) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time difference_water_level
2015-08-18T00:06:00Z 0.052000000000000046 2015-08-18T00:12:00Z -0.08800000000000008 2015-08-18T00:18:00Z 0.09799999999999986 2015-08-18T00:24:00Z -0.08499999999999996 2015-08-18T00:30:00Z 0.010000000000000231
The query returns the difference between the subsequent field values for each field key that stores numerical values in the `h2o_feet` measurement.
The `h2o_feet` measurement has one numerical field: `water_level`.
#### Example: Calculate the difference between the field values associated with each field key that matches a regular expression
SELECT DIFFERENCE(/water/) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time difference_water_level
2015-08-18T00:06:00Z 0.052000000000000046 2015-08-18T00:12:00Z -0.08800000000000008 2015-08-18T00:18:00Z 0.09799999999999986 2015-08-18T00:24:00Z -0.08499999999999996 2015-08-18T00:30:00Z 0.010000000000000231
The query returns the difference between the subsequent field values for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement.
#### Example 4: Calculate the difference between the field values associated with a field key and include several clauses
SELECT DIFFERENCE(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ ORDER BY time DESC LIMIT 2 OFFSET 2
name: h2o_feet time difference
2015-08-18T00:12:00Z -0.09799999999999986 2015-08-18T00:06:00Z 0.08800000000000008
The query returns the difference between the subsequent field values in the `water_level` field key.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
They query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to two and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by two points.
### Advanced syntax of DIFFERENCE()
SELECT DIFFERENCE(
#### Description of advanced syntax
The advanced syntax requires a [`GROUP BY time() ` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `DIFFERENCE()` function to those results.
`DIFFERENCE()` supports the following nested functions:
[`COUNT()`](#count),
[`MEAN()`](#mean),
[`MEDIAN()`](#median),
[`MODE()`](#mode),
[`SUM()`](#sum),
[`FIRST()`](#first),
[`LAST()`](#last),
[`MIN()`](#min),
[`MAX()`](#max), and
[`PERCENTILE()`](#percentile).
### Examples of advanced syntax
#### Example: Calculate the difference between maximum values
SELECT DIFFERENCE(MAX(“water_level”)) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(12m)
name: h2o_feet time difference
2015-08-18T00:12:00Z 0.009999999999999787 2015-08-18T00:24:00Z -0.07499999999999973
The query returns the difference between [maximum](#max) `water_level`s that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the maximum `water_level`s at 12-minute intervals.
This step is the same as using the `MAX()` function with the `GROUP BY time()` clause and without `DIFFERENCE()`:
SELECT MAX(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’ GROUP BY time(12m)
name: h2o_feet time max
2015-08-18T00:00:00Z 2.116 2015-08-18T00:12:00Z 2.126 2015-08-18T00:24:00Z 2.051
Next, InfluxDB calculates the difference between those maximum values.
The first point in the final results (`0.009999999999999787`) is the difference between `2.126` and `2.116`, and the second point in the final results (`-0.07499999999999973`) is the difference between `2.051` and `2.126`.
## ELAPSED()
Returns the difference between subsequent [field value's](/influxdb/v1.6/concepts/glossary/#field-value) timestamps.
### Syntax
SELECT ELAPSED( [ * | <field_key> | /<regular_expression>/ ] [ ,
### Description of Syntax
InfluxDB calculates the difference between subsequent timestamps.
The `unit` option is an integer followed by a [duration literal](/influxdb/v1.6/query_language/spec/#literals) and it determines the unit of the returned difference.
If the query does not specify the `unit` option the query returns the difference between timestamps in nanoseconds.
`ELAPSED(field_key)`
Returns the difference between subsequent timestamps associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
`ELAPSED(/regular_expression/)`
Returns the difference between subsequent timestamps associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions).
`ELAPSED(*)`
Returns the difference between subsequent timestamps associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`ELAPSED()` supports all field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
### Examples
Examples 1-5 use the following subsample of the [`NOAA_water_database` data](/influxdb/v1.6/query_language/data_download/):
SELECT “water_level” FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:12:00Z’
name: h2o_feet time water_level
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028
#### Example: Calculate the elapsed time between field values associated with a field key
SELECT ELAPSED(“water_level”) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:12:00Z’
name: h2o_feet time elapsed
2015-08-18T00:06:00Z 360000000000 2015-08-18T00:12:00Z 360000000000
The query returns the difference (in nanoseconds) between subsequent timestamps in the `water_level` field key and in the `h2o_feet` measurement.
#### Example: Calculate the elapsed time between field values associated with a field key and specify the unit option
SELECT ELAPSED(“water_level”,1m) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:12:00Z’
name: h2o_feet time elapsed
2015-08-18T00:06:00Z 6 2015-08-18T00:12:00Z 6
The query returns the difference (in minutes) between subsequent timestamps in the `water_level` field key and in the `h2o_feet` measurement.
#### Example: Calculate the elapsed time between field values associated with each field key in a measurement and specify the unit option
SELECT ELAPSED(*,1m) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:12:00Z’
name: h2o_feet time elapsed_level description elapsed_water_level
2015-08-18T00:06:00Z 6 6 2015-08-18T00:12:00Z 6 6
The query returns the difference (in minutes) between subsequent timestamps associated with each field key in the `h2o_feet`
measurement.
The `h2o_feet` measurement has two field keys: `level description` and `water_level`.
#### Example 4: Calculate the elapsed time between field values associated with each field key that matches a regular expression and specify the unit option
SELECT ELAPSED(/level/,1s) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:12:00Z’
name: h2o_feet time elapsed_level description elapsed_water_level
2015-08-18T00:06:00Z 360 360 2015-08-18T00:12:00Z 360 360
The query returns the difference (in seconds) between subsequent timestamps associated with each field key that includes the word `level` in the `h2o_feet` measurement.
#### Example 5: Calculate the elapsed time between field values associated with a field key and include several clauses
SELECT ELAPSED(“water_level”,1ms) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:12:00Z’ ORDER BY time DESC LIMIT 1 OFFSET 1
name: h2o_feet time elapsed
2015-08-18T00:00:00Z -360000
The query returns the difference (in milliseconds) between subsequent timestamps in the `water_level` field key and in the `h2o_feet` measurement.
It covers the [time range](/influxdb/v1.6/query_language/data_exploration/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:12:00Z` and sorts timestamps in [descending order](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc).
The query also [limits](/influxdb/v1.6/query_language/data_exploration/#the-limit-and-slimit-clauses) the number of points returned to one and [offsets](/influxdb/v1.6/query_language/data_exploration/#the-offset-and-soffset-clauses) results by one point.
Notice that the result is negative; the [`ORDER BY time DESC` clause](/influxdb/v1.6/query_language/data_exploration/#order-by-time-desc) sorts timestamps in descending order so `ELAPSED()` calculates the difference between timestamps in reverse order.
### Common Issues with ELAPSED()
#### Issue 1: ELAPSED() and units greater than the elapsed time
InfluxDB returns `0` if the `unit` option is greater than the difference between the timestamps.
##### Example
<br>
The timestamps in the `h2o_feet` measurement occur at six-minute intervals.
If the query sets the `unit` option to one hour, InfluxDB returns `0`:
SELECT ELAPSED(“water_level”,1h) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:12:00Z’
name: h2o_feet time elapsed
2015-08-18T00:06:00Z 0 2015-08-18T00:12:00Z 0
#### Issue 2: ELAPSED() with GROUP BY time() clauses
The `ELAPSED()` function supports the [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) but the query results aren't particularly useful.
Currently, an `ELAPSED()` query with a nested function and a `GROUP BY time()` clause simply returns the interval specified in the `GROUP BY time()` clause.
The `GROUP BY time()` clause determines the timestamps in the results; each timestamp marks the start of a time interval.
That behavior also applies to nested selector functions (like [`FIRST()`](#first) or [`MAX()`](#max)) which would, in all other cases, return a specific timestamp from the raw data.
Because the `GROUP BY time()` clause overrides the original timestamps, the `ELAPSED()` calculation always returns the same value as the `GROUP BY time()` interval.
##### Example
<br>
In the codeblock below, the first query attempts to use the `ELAPSED()` function with a `GROUP BY time()` clause to find the time elapsed (in minutes) between [minimum](#min) `water_level`s.
The query returns 12 minutes for both time intervals.
To get those results, InfluxDB first calculates the minimum `water_level`s at 12-minute intervals.
The second query in the codeblock shows the results of that step.
The step is the same as using the `MIN()` function with the `GROUP BY time()` clause and without the `ELAPSED()` function.
Notice that the timestamps returned by the second query are 12 minutes apart.
In the raw data, the first result (`2.057`) occurs at `2015-08-18T00:42:00Z` but the `GROUP BY time()` clause overrides that original timestamp.
Because the timestamps are determined by the `GROUP BY time()` interval and not by the original data, the `ELAPSED()` calculation always returns the same value as the `GROUP BY time()` interval.
SELECT ELAPSED(MIN(“water_level”),1m) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:36:00Z’ AND time <= ‘2015-08-18T00:54:00Z’ GROUP BY time(12m)
name: h2o_feet time elapsed
2015-08-18T00:36:00Z 12 2015-08-18T00:48:00Z 12
SELECT MIN(“water_level”) FROM “h2o_feet” WHERE “location” = ‘santa_monica’ AND time >= ‘2015-08-18T00:36:00Z’ AND time <= ‘2015-08-18T00:54:00Z’ GROUP BY time(12m)
name: h2o_feet time min
2015-08-18T00:36:00Z 2.057 <— Actually occurs at 2015-08-18T00:42:00Z 2015-08-18T00:48:00Z 1.991
## EXP()
Returns the exponential of the field value.
### Basic syntax
SELECT EXP( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
### Description of basic syntax
`EXP(field_key)`
Returns the exponential of field values associated with the [field key](/influxdb/v1.6/concepts/glossary/#field-key).
<!-- `EXP(/regular_expression/)`
Returns the exponential of field values associated with each field key that matches the [regular expression](/influxdb/v1.6/query_language/data_exploration/#regular-expressions). -->
`EXP(*)`
Returns the exponential of field values associated with each field key in the [measurement](/influxdb/v1.6/concepts/glossary/#measurement).
`EXP()` supports int64 and float64 field value [data types](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types).
The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1.6/query_language/data_exploration/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals).
See the [Advanced Syntax](#advanced-syntax-of-exp) section for how to use `EXP()` with a `GROUP BY time()` clause.
### Examples of basic syntax
The examples below use the following subsample of the [`NOAA_water_database` data](/influxdb/v1.6/query_language/data_download/):
SELECT “water_level” FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time water_level
2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051
#### Example: Calculate the exponential of field values associated with a field key
SELECT EXP(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time exp
2015-08-18T00:00:00Z 7.877416541092307 2015-08-18T00:06:00Z 8.297879498060171 2015-08-18T00:12:00Z 7.598873404088091 2015-08-18T00:18:00Z 8.381274573459967 2015-08-18T00:24:00Z 7.6983036546645645 2015-08-18T00:30:00Z 7.775672892658607
The query returns the exponential of field values in the `water_level` field key in the `h2o_feet` measurement.
#### Example: Calculate the exponential of field values associated with each field key in a measurement
SELECT EXP(*) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time exp_water_level
2015-08-18T00:00:00Z 7.877416541092307 2015-08-18T00:06:00Z 8.297879498060171 2015-08-18T00:12:00Z 7.598873404088091 2015-08-18T00:18:00Z 8.381274573459967 2015-08-18T00:24:00Z 7.6983036546645645 2015-08-18T00:30:00Z 7.775672892658607
The query returns the exponential of field values for each field key that stores numerical values in the `h2o_feet` measurement.
The `h2o_feet` measurement has one numerical field: `water_level`.
<!-- #### Example: Calculate the exponential of field values associated with each field key that matches a regular expression
SELECT EXP(/water/) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:30:00Z’ AND “location” = ‘santa_monica’
name: h2o_feet time exp_water_level
2015-08-18T00:00:00Z 7.877416541092307 2015-08-18T00:06:00Z 8.297879498060171 2015-08-18T00:12:00Z 7.598873404088091 2015-08-18T00:18:00Z 8.381274573459967 2015-08-18T00:24:00Z 7.6983036546645645 2015-08-18T00:30:00Z 7.775672892658607
The query returns the exponential of field values for each field key that stores numerical values and includes the word water in the h2o_feet measurement. –>
Example: Calculate the exponential of field values associated with a field key and include several clauses
> SELECT EXP("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time exp
---- ---
2015-08-18T00:18:00Z 8.381274573459967
2015-08-18T00:12:00Z 7.598873404088091
2015-08-18T00:06:00Z 8.297879498060171
2015-08-18T00:00:00Z 7.877416541092307
The query returns the exponentials of field values associated with the water_level field key.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of EXP()
SELECT EXP(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the EXP() function to those results.
EXP() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate the exponential of mean values.
> SELECT EXP(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time exp
---- ---
2015-08-18T00:00:00Z 8.084915164305059
2015-08-18T00:12:00Z 7.980491491670466
2015-08-18T00:24:00Z 7.736891562315577
The query returns the exponential of average water_levels that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without EXP():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates the exponentials of those averages.
FLOOR()
Returns the subsequent value rounded down to the nearest integer.
Basic syntax
SELECT FLOOR( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
FLOOR(field_key)
Returns the field values associated with the field key rounded down to the nearest integer.
FLOOR(*)
Returns the field values associated with each field key in the measurement rounded down to the nearest integer.
FLOOR() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use FLOOR() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the floor of field values associated with a field key
> SELECT FLOOR("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time floor
---- -----
2015-08-18T00:00:00Z 2
2015-08-18T00:06:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:18:00Z 2
2015-08-18T00:24:00Z 2
2015-08-18T00:30:00Z 2
The query returns field values in the water_level field key in the h2o_feet measurement rounded down to the nearest integer.
Example: Calculate the floor of field values associated with each field key in a measurement
> SELECT FLOOR(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time floor_water_level
---- -----------------
2015-08-18T00:00:00Z 2
2015-08-18T00:06:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:18:00Z 2
2015-08-18T00:24:00Z 2
2015-08-18T00:30:00Z 2
The query returns field values for each field key that stores numerical values in the h2o_feet measurement rounded down to the nearest integer.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the floor of field values associated with a field key and include several clauses
> SELECT FLOOR("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time floor
---- -----
2015-08-18T00:18:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:06:00Z 2
2015-08-18T00:00:00Z 2
The query returns field values associated with the water_level field key rounded down to the nearest integer.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of FLOOR()
SELECT FLOOR(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the FLOOR() function to those results.
FLOOR() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate mean values rounded down to the nearest integer.
> SELECT FLOOR(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time floor
---- -----
2015-08-18T00:00:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:24:00Z 2
The query returns the average water_levels that are calculated at 12-minute intervals and rounds them up to the nearest integer.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without FLOOR():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then rounds those averages down to the nearest integer.
HISTOGRAM()
HISTOGRAM() is not yet functional.
See GitHub Issue #5930 for more information.
LN()
Returns the natural logarithm of the field value.
Basic syntax
SELECT LN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
LN(field_key)
Returns the natural logarithm of field values associated with the field key.
LN(*)
Returns the natural logarithm of field values associated with each field key in the measurement.
LN() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use LN() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the natural logarithm of field values associated with a field key
> SELECT LN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time ln
---- --
2015-08-18T00:00:00Z 0.7246458476193163
2015-08-18T00:06:00Z 0.749527513996053
2015-08-18T00:12:00Z 0.7070500857289368
2015-08-18T00:18:00Z 0.7542422799197561
2015-08-18T00:24:00Z 0.7134398838277077
2015-08-18T00:30:00Z 0.7183274790902436
The query returns the natural logarithm of field values in the water_level field key in the h2o_feet measurement.
Example: Calculate the natural logarithm of field values associated with each field key in a measurement
> SELECT LN(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time ln_water_level
---- --------------
2015-08-18T00:00:00Z 0.7246458476193163
2015-08-18T00:06:00Z 0.749527513996053
2015-08-18T00:12:00Z 0.7070500857289368
2015-08-18T00:18:00Z 0.7542422799197561
2015-08-18T00:24:00Z 0.7134398838277077
2015-08-18T00:30:00Z 0.7183274790902436
The query returns the natural logarithm of field values for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the natural logarithm of field values associated with a field key and include several clauses
> SELECT LN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time ln
---- --
2015-08-18T00:18:00Z 0.7542422799197561
2015-08-18T00:12:00Z 0.7070500857289368
2015-08-18T00:06:00Z 0.749527513996053
2015-08-18T00:00:00Z 0.7246458476193163
The query returns the natural logarithms of field values associated with the water_level field key.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of LN()
SELECT LN(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the LN() function to those results.
LN() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate the natural logarithm of mean values.
> SELECT LN(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time ln
---- --
2015-08-18T00:00:00Z 0.7371640659767196
2015-08-18T00:12:00Z 0.7309245448939752
2015-08-18T00:24:00Z 0.7158866675294349
The query returns the natural logarithm of average water_levels that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without LN():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates the natural logarithms of those averages.
LOG()
Returns the logarithm of the field value with base b.
Basic syntax
SELECT LOG( [ * | <field_key> ], <b> ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
LOG(field_key, b)
Returns the logarithm of field values associated with the field key with base b.
LOG(*, b)
Returns the logarithm of field values associated with each field key in the measurement with base b.
LOG() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use LOG() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the logarithm base 4 of field values associated with a field key
> SELECT LOG("water_level", 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time log
---- ---
2015-08-18T00:00:00Z 0.5227214853805835
2015-08-18T00:06:00Z 0.5406698137259695
2015-08-18T00:12:00Z 0.5100288261706268
2015-08-18T00:18:00Z 0.5440707984345088
2015-08-18T00:24:00Z 0.5146380911853161
2015-08-18T00:30:00Z 0.5181637459088826
The query returns the logarithm base 4 of field values in the water_level field key in the h2o_feet measurement.
Example: Calculate the logarithm base 4 of field values associated with each field key in a measurement
> SELECT LOG(*, 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time log_water_level
---- ---------------
2015-08-18T00:00:00Z 0.5227214853805835
2015-08-18T00:06:00Z 0.5406698137259695
2015-08-18T00:12:00Z 0.5100288261706268
2015-08-18T00:18:00Z 0.5440707984345088
2015-08-18T00:24:00Z 0.5146380911853161
2015-08-18T00:30:00Z 0.5181637459088826
The query returns the logarithm base 4 of field values for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the logarithm base 4 of field values associated with a field key and include several clauses
> SELECT LOG("water_level", 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time log
---- ---
2015-08-18T00:18:00Z 0.5440707984345088
2015-08-18T00:12:00Z 0.5100288261706268
2015-08-18T00:06:00Z 0.5406698137259695
2015-08-18T00:00:00Z 0.5227214853805835
The query returns the logarithm base 4 of field values associated with the water_level field key.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of LOG()
SELECT LOG(<function>( [ * | <field_key> ] ), <b>) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the LOG() function to those results.
LOG() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate the logarithm base 4 of mean values.
> SELECT LOG(MEAN("water_level"), 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time log
---- ---
2015-08-18T00:00:00Z 0.531751471153079
2015-08-18T00:12:00Z 0.5272506080912802
2015-08-18T00:24:00Z 0.5164030725416209
The query returns the logarithm base 4 of average water_levels that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without LOG():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates the logarithm base 4 of those averages.
LOG2()
Returns the logarithm of the field value to the base 2.
Basic syntax
SELECT LOG2( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
LOG2(field_key)
Returns the logarithm of field values associated with the field key to the base 2.
LOG2(*)
Returns the logarithm of field values associated with each field key in the measurement to the base 2.
LOG2() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use LOG2() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the logarithm base 2 of field values associated with a field key
> SELECT LOG2("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time log2
---- ----
2015-08-18T00:00:00Z 1.045442970761167
2015-08-18T00:06:00Z 1.081339627451939
2015-08-18T00:12:00Z 1.0200576523412537
2015-08-18T00:18:00Z 1.0881415968690176
2015-08-18T00:24:00Z 1.0292761823706322
2015-08-18T00:30:00Z 1.0363274918177652
The query returns the logarithm base 2 of field values in the water_level field key in the h2o_feet measurement.
Example: Calculate the logarithm base 2 of field values associated with each field key in a measurement
> SELECT LOG2(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time log2_water_level
---- ----------------
2015-08-18T00:00:00Z 1.045442970761167
2015-08-18T00:06:00Z 1.081339627451939
2015-08-18T00:12:00Z 1.0200576523412537
2015-08-18T00:18:00Z 1.0881415968690176
2015-08-18T00:24:00Z 1.0292761823706322
2015-08-18T00:30:00Z 1.0363274918177652
The query returns the logarithm base 2 of field values for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the logarithm base 2 of field values associated with a field key and include several clauses
> SELECT LOG2("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time log2
---- ----
2015-08-18T00:18:00Z 1.0881415968690176
2015-08-18T00:12:00Z 1.0200576523412537
2015-08-18T00:06:00Z 1.081339627451939
2015-08-18T00:00:00Z 1.045442970761167
The query returns the logarithm base 2 of field values associated with the water_level field key.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of LOG2()
SELECT LOG2(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the LOG2() function to those results.
LOG2() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate the logarithm base 2 of mean values.
> SELECT LOG2(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time log2
---- ----
2015-08-18T00:00:00Z 1.063502942306158
2015-08-18T00:12:00Z 1.0545012161825604
2015-08-18T00:24:00Z 1.0328061450832418
The query returns the logarithm base 2 of average water_levels that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without LOG2():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates the logarithm base 2 of those averages.
LOG10()
Returns the logarithm of the field value to the base 10.
Basic syntax
SELECT LOG10( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
LOG10(field_key)
Returns the logarithm of field values associated with the field key to the base 10.
LOG10(*)
Returns the logarithm of field values associated with each field key in the measurement to the base 10.
LOG10() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use LOG10() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the logarithm base 10 of field values associated with a field key
> SELECT LOG10("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time log10
---- -----
2015-08-18T00:00:00Z 0.3147096929551737
2015-08-18T00:06:00Z 0.32551566336314813
2015-08-18T00:12:00Z 0.3070679506612984
2015-08-18T00:18:00Z 0.32756326018727794
2015-08-18T00:24:00Z 0.3098430047160705
2015-08-18T00:30:00Z 0.3119656603683663
The query returns the logarithm base 10 of field values in the water_level field key in the h2o_feet measurement.
Example: Calculate the logarithm base 10 of field values associated with each field key in a measurement
> SELECT LOG10(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time log10_water_level
---- -----------------
2015-08-18T00:00:00Z 0.3147096929551737
2015-08-18T00:06:00Z 0.32551566336314813
2015-08-18T00:12:00Z 0.3070679506612984
2015-08-18T00:18:00Z 0.32756326018727794
2015-08-18T00:24:00Z 0.3098430047160705
2015-08-18T00:30:00Z 0.3119656603683663
The query returns the logarithm base 10 of field values for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the logarithm base 10 of field values associated with a field key and include several clauses
> SELECT LOG10("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time log10
---- -----
2015-08-18T00:18:00Z 0.32756326018727794
2015-08-18T00:12:00Z 0.3070679506612984
2015-08-18T00:06:00Z 0.32551566336314813
2015-08-18T00:00:00Z 0.3147096929551737
The query returns the logarithm base 10 of field values associated with the water_level field key.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of LOG10()
SELECT LOG10(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the LOG10() function to those results.
LOG10() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate the logarithm base 10 of mean values.
> SELECT LOG10(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time log10
---- -----
2015-08-18T00:00:00Z 0.32014628611105395
2015-08-18T00:12:00Z 0.3174364965350991
2015-08-18T00:24:00Z 0.3109056293761414
The query returns the logarithm base 10 of average water_levels that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without LOG10():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates the logarithm base 10 of those averages.
MOVING_AVERAGE()
Returns the rolling average across a window of subsequent field values.
Basic syntax
SELECT MOVING_AVERAGE( [ * | <field_key> | /<regular_expression>/ ] , <N> ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
MOVING_AVERAGE() calculates the rolling average across a window of N subsequent field values.
The N argument is an integer and it is required.
MOVING_AVERAGE(field_key,N)
Returns the rolling average across N field values associated with the field key.
MOVING_AVERAGE(/regular_expression/,N)
Returns the rolling average across N field values associated with each field key that matches the regular expression.
MOVING_AVERAGE(*,N)
Returns the rolling average across N field values associated with each field key in the measurement.
MOVING_AVERAGE() int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use MOVING_AVERAGE() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the moving average of the field values associated with a field key
> SELECT MOVING_AVERAGE("water_level",2) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time moving_average
---- --------------
2015-08-18T00:06:00Z 2.09
2015-08-18T00:12:00Z 2.072
2015-08-18T00:18:00Z 2.077
2015-08-18T00:24:00Z 2.0835
2015-08-18T00:30:00Z 2.0460000000000003
The query returns the rolling average across a two-field-value window for the water_level field key and the h2o_feet measurement.
The first result (2.09) is the average of the first two points in the raw data: (2.064 + 2.116) / 2).
The second result (2.072) is the average of the second two points in the raw data: (2.116 + 2.028) / 2).
Example: Calculate the moving average of the field values associated with each field key in a measurement
> SELECT MOVING_AVERAGE(*,3) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time moving_average_water_level
---- --------------------------
2015-08-18T00:12:00Z 2.0693333333333332
2015-08-18T00:18:00Z 2.09
2015-08-18T00:24:00Z 2.065
2015-08-18T00:30:00Z 2.0726666666666667
The query returns the rolling average across a three-field-value window for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the moving average of the field values associated with each field key that matches a regular expression
> SELECT MOVING_AVERAGE(/level/,4) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time moving_average_water_level
---- --------------------------
2015-08-18T00:18:00Z 2.0835
2015-08-18T00:24:00Z 2.07775
2015-08-18T00:30:00Z 2.0615
The query returns the rolling average across a four-field-value window for each field key that stores numerical values and includes the word level in the h2o_feet measurement.
Example 4: Calculate the moving average of the field values associated with a field key and include several clauses
> SELECT MOVING_AVERAGE("water_level",2) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' ORDER BY time DESC LIMIT 2 OFFSET 3
name: h2o_feet
time moving_average
---- --------------
2015-08-18T00:06:00Z 2.072
2015-08-18T00:00:00Z 2.09
The query returns the rolling average across a two-field-value window for the water_level field key in the h2o_feet measurement.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to two and offsets results by three points.
Advanced syntax of MOVING_AVERAGE()
SELECT MOVING_AVERAGE(<function> ([ * | <field_key> | /<regular_expression>/ ]) , N ) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the MOVING_AVERAGE() function to those results.
MOVING_AVERAGE() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate the moving average of maximum values
> SELECT MOVING_AVERAGE(MAX("water_level"),2) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m)
name: h2o_feet
time moving_average
---- --------------
2015-08-18T00:12:00Z 2.121
2015-08-18T00:24:00Z 2.0885
The query returns the rolling average across a two-value window of maximum water_levels that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the maximum water_levels at 12-minute intervals.
This step is the same as using the MAX() function with the GROUP BY time() clause and without MOVING_AVERAGE():
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m)
name: h2o_feet
time max
---- ---
2015-08-18T00:00:00Z 2.116
2015-08-18T00:12:00Z 2.126
2015-08-18T00:24:00Z 2.051
Next, InfluxDB calculates the rolling average across a two-value window using those maximum values.
The first final result (2.121) is the average of the first two maximum values ((2.116 + 2.126) / 2).
NON_NEGATIVE_DERIVATIVE()
Returns the non-negative rate of change between subsequent field values. Non-negative rates of change include positive rates of change and rates of change that equal zero.
Basic syntax
SELECT NON_NEGATIVE_DERIVATIVE( [ * | <field_key> | /<regular_expression>/ ] [ , <unit> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
InfluxDB calculates the difference between subsequent field values and converts those results into the rate of change per unit.
The unit argument is an integer followed by a duration literal and it is optional.
If the query does not specify the unit, the unit defaults to one second (1s).
NON_NEGATIVE_DERIVATIVE() returns only positive rates of change or rates of change that equal zero.
NON_NEGATIVE_DERIVATIVE(field_key)
Returns the non-negative rate of change between subsequent field values associated with the field key.
NON_NEGATIVE_DERIVATIVE(/regular_expression/)
Returns the non-negative rate of change between subsequent field values associated with each field key that matches the regular expression.
NON_NEGATIVE_DERIVATIVE(*)
Returns the non-negative rate of change between subsequent field values associated with each field key in the measurement.
NON_NEGATIVE_DERIVATIVE() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use NON_NEGATIVE_DERIVATIVE() with a GROUP BY time() clause.
Examples of basic syntax
See the examples in the DERIVATIVE() documentation.
NON_NEGATIVE_DERIVATIVE() behaves the same as the DERIVATIVE() function but NON_NEGATIVE_DERIVATIVE() returns only positive rates of change or rates of change that equal zero.
Advanced syntax of NON_NEGATIVE_DERIVATIVE()
SELECT NON_NEGATIVE_DERIVATIVE(<function> ([ * | <field_key> | /<regular_expression>/ ]) [ , <unit> ] ) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the NON_NEGATIVE_DERIVATIVE() function to those results.
The unit argument is an integer followed by a duration literal and it is optional.
If the query does not specify the unit, the unit defaults to the GROUP BY time() interval.
Note that this behavior is different from the basic syntax’s default behavior.
NON_NEGATIVE_DERIVATIVE() returns only positive rates of change or rates of change that equal zero.
NON_NEGATIVE_DERIVATIVE() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
See the examples in the DERIVATIVE() documentation.
NON_NEGATIVE_DERIVATIVE() behaves the same as the DERIVATIVE() function but NON_NEGATIVE_DERIVATIVE() returns only positive rates of change or rates of change that equal zero.
NON_NEGATIVE_DIFFERENCE()
Returns the non-negative result of subtraction between subsequent field values. Non-negative results of subtraction include positive differences and differences that equal zero.
Basic syntax
SELECT NON_NEGATIVE_DIFFERENCE( [ * | <field_key> | /<regular_expression>/ ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
NON_NEGATIVE_DIFFERENCE(field_key)
Returns the non-negative difference between subsequent field values associated with the field key.
NON_NEGATIVE_DIFFERENCE(/regular_expression/)
Returns the non-negative difference between subsequent field values associated with each field key that matches the regular expression.
NON_NEGATIVE_DIFFERENCE(*)
Returns the non-negative difference between subsequent field values associated with each field key in the measurement.
NON_NEGATIVE_DIFFERENCE() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use NON_NEGATIVE_DIFFERENCE() with a GROUP BY time() clause.
Examples of basic syntax
See the examples in the DIFFERENCE() documentation.
NON_NEGATIVE_DIFFERENCE() behaves the same as the DIFFERENCE() function but NON_NEGATIVE_DIFFERENCE() returns only positive differences or differences that equal zero.
Advanced syntax of NON_NEGATIVE_DIFFERENCE()
SELECT NON_NEGATIVE_DIFFERENCE(<function>( [ * | <field_key> | /<regular_expression>/ ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the NON_NEGATIVE_DIFFERENCE() function to those results.
NON_NEGATIVE_DIFFERENCE() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
See the examples in the DIFFERENCE() documentation.
NON_NEGATIVE_DIFFERENCE() behaves the same as the DIFFERENCE() function but NON_NEGATIVE_DIFFERENCE() returns only positive differences or differences that equal zero.
POW()
Returns the field value to the power of x.
Basic syntax
SELECT POW( [ * | <field_key> ], <x> ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
POW(field_key, x)
Returns the field values associated with the field key to the power of x.
POW(*, x)
Returns the field values associated with each field key in the measurement to the power of x.
POW() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use POW() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate field values associated with a field key to the power of 4
> SELECT POW("water_level", 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time pow
---- ---
2015-08-18T00:00:00Z 18.148417929216
2015-08-18T00:06:00Z 20.047612231936
2015-08-18T00:12:00Z 16.914992230656004
2015-08-18T00:18:00Z 20.429279055375993
2015-08-18T00:24:00Z 17.352898193760993
2015-08-18T00:30:00Z 17.69549197320101
The query returns field values in the water_level field key in the h2o_feet measurement multiplied to a power of 4.
Example: Calculate field values associated with each field key in a measurement to the power of 4
> SELECT POW(*, 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time pow_water_level
---- ---------------
2015-08-18T00:00:00Z 18.148417929216
2015-08-18T00:06:00Z 20.047612231936
2015-08-18T00:12:00Z 16.914992230656004
2015-08-18T00:18:00Z 20.429279055375993
2015-08-18T00:24:00Z 17.352898193760993
2015-08-18T00:30:00Z 17.69549197320101
The query returns field values for each field key that stores numerical values in the h2o_feet measurement multiplied to the power of 4.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate field values associated with a field key to the power of 4 and include several clauses
> SELECT POW("water_level", 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time pow
---- ---
2015-08-18T00:18:00Z 20.429279055375993
2015-08-18T00:12:00Z 16.914992230656004
2015-08-18T00:06:00Z 20.047612231936
2015-08-18T00:00:00Z 18.148417929216
The query returns field values associated with the water_level field key multiplied to the power of 4.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of POW()
SELECT POW(<function>( [ * | <field_key> ] ), <x>) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the POW() function to those results.
POW() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate mean values to the power of 4
> SELECT POW(MEAN("water_level"), 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time pow
---- ---
2015-08-18T00:00:00Z 19.08029760999999
2015-08-18T00:12:00Z 18.609983417041
2015-08-18T00:24:00Z 17.523567165456008
The query returns average water_levels that are calculated at 12-minute intervals multiplied to the power of 4.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without POW():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates those averages multiplied to the power of 4.
ROUND()
Returns the subsequent value rounded to the nearest integer.
Basic syntax
SELECT ROUND( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
ROUND(field_key)
Returns the field values associated with the field key rounded to the nearest integer.
ROUND(*)
Returns the field values associated with each field key in the measurement rounded to the nearest integer.
ROUND() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use ROUND() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Round field values associated with a field key
> SELECT ROUND("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time round
---- -----
2015-08-18T00:00:00Z 2
2015-08-18T00:06:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:18:00Z 2
2015-08-18T00:24:00Z 2
2015-08-18T00:30:00Z 2
The query returns field values in the water_level field key in the h2o_feet measurement rounded to the nearest integer.
Example: Round field values associated with each field key in a measurement
> SELECT ROUND(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time round_water_level
---- -----------------
2015-08-18T00:00:00Z 2
2015-08-18T00:06:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:18:00Z 2
2015-08-18T00:24:00Z 2
2015-08-18T00:30:00Z 2
The query returns field values for each field key that stores numerical values in the h2o_feet measurement rounded to the nearest integer.
The h2o_feet measurement has one numerical field: water_level.
Example: Round field values associated with a field key and include several clauses
> SELECT ROUND("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time round
---- -----
2015-08-18T00:18:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:06:00Z 2
2015-08-18T00:00:00Z 2
The query returns field values associated with the water_level field key rounded to the nearest integer.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of ROUND()
SELECT ROUND(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the ROUND() function to those results.
ROUND() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate mean values rounded to the nearest integer.
> SELECT ROUND(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time round
---- -----
2015-08-18T00:00:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:24:00Z 2
The query returns the average water_levels that are calculated at 12-minute intervals and rounds to the nearest integer.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without ROUND():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then rounds those averages to the nearest integer.
SIN()
Returns the sine of the field value.
Basic syntax
SELECT SIN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
SIN(field_key)
Returns the sine of field values associated with the field key.
SIN(*)
Returns the sine of field values associated with each field key in the measurement.
SIN() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use SIN() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the sine of field values associated with a field key
> SELECT SIN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time sin
---- ---
2015-08-18T00:00:00Z 0.8808206017241819
2015-08-18T00:06:00Z 0.8550216851706579
2015-08-18T00:12:00Z 0.8972904165810275
2015-08-18T00:18:00Z 0.8497930984115993
2015-08-18T00:24:00Z 0.8914760289023131
2015-08-18T00:30:00Z 0.8869008523376968
The query returns sine of field values in the water_level field key in the h2o_feet measurement.
Example: Calculate the sine of field values associated with each field key in a measurement
> SELECT SIN(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time sin_water_level
---- ---------------
2015-08-18T00:00:00Z 0.8808206017241819
2015-08-18T00:06:00Z 0.8550216851706579
2015-08-18T00:12:00Z 0.8972904165810275
2015-08-18T00:18:00Z 0.8497930984115993
2015-08-18T00:24:00Z 0.8914760289023131
2015-08-18T00:30:00Z 0.8869008523376968
The query returns sine of field values for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the sine of field values associated with a field key and include several clauses
> SELECT SIN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time sin
---- ---
2015-08-18T00:18:00Z 0.8497930984115993
2015-08-18T00:12:00Z 0.8972904165810275
2015-08-18T00:06:00Z 0.8550216851706579
2015-08-18T00:00:00Z 0.8808206017241819
The query returns sine of field values associated with the water_level field key.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of SIN()
SELECT SIN(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the SIN() function to those results.
SIN() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate the sine of mean values.
> SELECT SIN(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time sin
---- ---
2015-08-18T00:00:00Z 0.8682145834456126
2015-08-18T00:12:00Z 0.8745914945253902
2015-08-18T00:24:00Z 0.8891995555912935
The query returns sine of average water_levels that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without SIN():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates sine of those averages.
SQRT()
Returns the square root of field value.
Basic syntax
SELECT SQRT( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
SQRT(field_key)
Returns the square root of field values associated with the field key.
SQRT(*)
Returns the square root field values associated with each field key in the measurement.
SQRT() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use SQRT() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the square root of field values associated with a field key
> SELECT SQRT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time sqrt
---- ----
2015-08-18T00:00:00Z 1.4366627996854378
2015-08-18T00:06:00Z 1.4546477236774544
2015-08-18T00:12:00Z 1.4240786495134319
2015-08-18T00:18:00Z 1.4580809305384939
2015-08-18T00:24:00Z 1.4286357128393508
2015-08-18T00:30:00Z 1.4321312788986909
The query returns the square roots of field values in the water_level field key in the h2o_feet measurement.
Example: Calculate the square root of field values associated with each field key in a measurement
> SELECT SQRT(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time sqrt_water_level
---- ----------------
2015-08-18T00:00:00Z 1.4366627996854378
2015-08-18T00:06:00Z 1.4546477236774544
2015-08-18T00:12:00Z 1.4240786495134319
2015-08-18T00:18:00Z 1.4580809305384939
2015-08-18T00:24:00Z 1.4286357128393508
2015-08-18T00:30:00Z 1.4321312788986909
The query returns the square roots of field values for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the square root of field values associated with a field key and include several clauses
> SELECT SQRT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time sqrt
---- ----
2015-08-18T00:18:00Z 1.4580809305384939
2015-08-18T00:12:00Z 1.4240786495134319
2015-08-18T00:06:00Z 1.4546477236774544
2015-08-18T00:00:00Z 1.4366627996854378
The query returns the square roots of field values associated with the water_level field key.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of SQRT()
SELECT SQRT(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the SQRT() function to those results.
SQRT() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate the square root of mean values.
> SELECT SQRT(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time sqrt
---- ----
2015-08-18T00:00:00Z 1.445683229480096
2015-08-18T00:12:00Z 1.4411800720243115
2015-08-18T00:24:00Z 1.430384563675098
The query returns the square roots of average water_levels that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without SQRT():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates the square roots of those averages.
TAN()
Returns the tangent of the field value.
Basic syntax
SELECT TAN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of basic syntax
TAN(field_key)
Returns the tangent of field values associated with the field key.
TAN(*)
Returns the tangent of field values associated with each field key in the measurement.
TAN() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
See the Advanced Syntax section for how to use TAN() with a GROUP BY time() clause.
Examples of basic syntax
The examples below use the following subsample of the NOAA_water_database data:
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
Example: Calculate the tangent of field values associated with a field key
> SELECT TAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time tan
---- ---
2015-08-18T00:00:00Z -1.8604293534384375
2015-08-18T00:06:00Z -1.6487359603347427
2015-08-18T00:12:00Z -2.0326408012302273
2015-08-18T00:18:00Z -1.6121545688343464
2015-08-18T00:24:00Z -1.9676434782626282
2015-08-18T00:30:00Z -1.9198657720074992
The query returns tangent of field values in the water_level field key in the h2o_feet measurement.
Example: Calculate the tangent of field values associated with each field key in a measurement
> SELECT TAN(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time tan_water_level
---- ---------------
2015-08-18T00:00:00Z -1.8604293534384375
2015-08-18T00:06:00Z -1.6487359603347427
2015-08-18T00:12:00Z -2.0326408012302273
2015-08-18T00:18:00Z -1.6121545688343464
2015-08-18T00:24:00Z -1.9676434782626282
2015-08-18T00:30:00Z -1.9198657720074992
The query returns tangent of field values for each field key that stores numerical values in the h2o_feet measurement.
The h2o_feet measurement has one numerical field: water_level.
Example: Calculate the tangent of field values associated with a field key and include several clauses
> SELECT TAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2
name: h2o_feet
time tan
---- ---
2015-08-18T00:18:00Z -1.6121545688343464
2015-08-18T00:12:00Z -2.0326408012302273
2015-08-18T00:06:00Z -1.6487359603347427
2015-08-18T00:00:00Z -1.8604293534384375
The query returns tangent of field values associated with the water_level field key.
It covers the time range between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order.
The query also limits the number of points returned to four and offsets results by two points.
Advanced syntax of TAN()
SELECT TAN(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of advanced syntax
The advanced syntax requires a GROUP BY time() clause and a nested InfluxQL function.
The query first calculates the results for the nested function at the specified GROUP BY time() interval and then applies the TAN() function to those results.
TAN() supports the following nested functions:
COUNT(),
MEAN(),
MEDIAN(),
MODE(),
SUM(),
FIRST(),
LAST(),
MIN(),
MAX(), and
PERCENTILE().
Examples of advanced syntax
Example: Calculate the tangent of mean values.
> SELECT TAN(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time tan
---- ---
2015-08-18T00:00:00Z -1.7497661902817365
2015-08-18T00:12:00Z -1.8038002062256624
2015-08-18T00:24:00Z -1.9435224805850773
The query returns tangent of average water_levels that are calculated at 12-minute intervals.
To get those results, InfluxDB first calculates the average water_levels at 12-minute intervals.
This step is the same as using the MEAN() function with the GROUP BY time() clause and without TAN():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
InfluxDB then calculates tangent of those averages.
Predictors
HOLT_WINTERS()
Returns N number of predicted field values using the Holt-Winters seasonal method.
Use HOLT_WINTERS() to:
- Predict when data values will cross a given threshold
- Compare predicted values with actual values to detect anomalies in your data
Syntax
SELECT HOLT_WINTERS[_WITH-FIT](<function>(<field_key>),<N>,<S>) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
Description of Syntax
HOLT_WINTERS(function(field_key),N,S) returns N seasonally adjusted
predicted field values for the specified field key.
The N predicted values occur at the same interval as the GROUP BY time() interval.
If your GROUP BY time() interval is 6m and N is 3 you’ll
receive three predicted values that are each six minutes apart.
S is the seasonal pattern parameter and delimits the length of a seasonal
pattern according to the GROUP BY time() interval.
If your GROUP BY time() interval is 2m and S is 3, then the
seasonal pattern occurs every six minutes, that is, every three data points.
If you do not want to seasonally adjust your predicted values, set S to 0
or 1.
HOLT_WINTERS_WITH_FIT(function(field_key),N,S) returns the fitted values in
addition to N seasonally adjusted predicted field values for the specified field key.
HOLT_WINTERS() and HOLT_WINTERS_WITH_FIT() work with data that occur at
consistent time intervals; the nested InfluxQL function and the
GROUP BY time() clause ensure that the Holt-Winters functions operate on regular data.
HOLT_WINTERS() and HOLT_WINTERS_WITH_FIT() support int64 and float64 field value data types.
Examples
Example: Predict field values associated with a field key
Raw Data
Example 1 uses [Chronograf](https://github.com/influxdata/chronograf) to visualize the data. The example focuses the following subsample of the [`NOAA_water_database` data](/influxdb/v1.6/query_language/data_download/):
SELECT "water_level" FROM "NOAA_water_database"."autogen"."h2o_feet" WHERE "location"='santa_monica' AND time >= '2015-08-22 22:12:00' AND time <= '2015-08-28 03:00:00'
Step 1: Match the Trends of the Raw Data
Write a `GROUP BY time()` query that matches the general trends of the raw `water_level` data. Here, we use the [`FIRST()`](#first) function:
SELECT FIRST("water_level") FROM "NOAA_water_database"."autogen"."h2o_feet" WHERE "location"='santa_monica' and time >= '2015-08-22 22:12:00' and time <= '2015-08-28 03:00:00' GROUP BY time(379m,348m)
In the GROUP BY time() clause, the first argument (379m) matches
the length of time that occurs between each peak and trough in the water_level data.
The second argument (348m) is the
offset interval.
The offset interval alters InfluxDB’s default GROUP BY time() boundaries to
match the time range of the raw data.
The blue line shows the results of the query:
Step 2: Determine the Seasonal Pattern
Identify the seasonal pattern in the data using the information from the query in step 1.
Focusing on the blue line in the graph below, the pattern in the water_level data repeats about every 25 hours and 15 minutes.
There are four data points per season, so 4 is the seasonal pattern argument.
Step 3: Apply the HOLT_WINTERS() function
Add the Holt-Winters function to the query. Here, we use `HOLT_WINTERS_WITH_FIT()` to view both the fitted values and the predicted values:
SELECT HOLT_WINTERS_WITH_FIT(FIRST("water_level"),10,4) FROM "NOAA_water_database"."autogen"."h2o_feet" WHERE "location"='santa_monica' AND time >= '2015-08-22 22:12:00' AND time <= '2015-08-28 03:00:00' GROUP BY time(379m,348m)
In the HOLT_WINTERS_WITH_FIT() function, the first argument (10) requests 10 predicted field values.
Each predicted point is 379m apart, the same interval as the first argument in the GROUP BY time() clause.
The second argument in the HOLT_WINTERS_WITH_FIT() function (4) is the seasonal pattern that we determined in the previous step.
The blue line shows the results of the query:
Common Issues with HOLT_WINTERS()
Issue 1: HOLT_WINTERS() and receiving fewer than N points
In some cases, users may receive fewer predicted points than
requested by the N parameter.
That behavior occurs when the math becomes unstable and cannot forecast more
points.
It implies that either HOLT_WINTERS() is not suited for the dataset or that
the seasonal adjustment parameter is invalid and is confusing the algorithm.
Technical Analysis
The following technical analysis functions apply widely used algorithms to your data. While they are primarily used in the world of finance and investing, they have application in other industries and use cases as well.
CHANDE_MOMENTUM_OSCILLATOR()
EXPONENTIAL_MOVING_AVERAGE()
DOUBLE_EXPONENTIAL_MOVING_AVERAGE()
KAUFMANS_EFFICIENCY_RATIO()
KAUFMANS_ADAPTIVE_MOVING_AVERAGE()
TRIPLE_EXPONENTIAL_MOVING_AVERAGE()
TRIPLE_EXPONENTIAL_DERIVATIVE()
RELATIVE_STRENGTH_INDEX()
Arguments
Along with a field key, technical analysis function accept the following arguments:
PERIOD
Required, integer, min=1
The sample size of the algorithm.
This is essentially the number of historical samples which have any significant
effect on the output of the algorithm.
E.G. 2 means the current point and the point before it.
The algorithm uses an exponential decay rate to determine the weight of a historical point,
generally known as the alpha (α). The PERIOD controls the decay rate.
NOTE: Older points can still have an impact.
HOLD_PERIOD
integer, min=-1
How many samples the algorithm needs before it will start emitting results.
The default of -1 means the value is based on the algorithm, the PERIOD,
and the WARMUP_TYPE, but is a value in which the algorithm can emit meaningful results.
Default Hold Periods:
For most of the available technical analysis, the default HOLD_PERIOD is
determined by which technical analysis algorithm you’re using and the WARMUP_TYPE
| Algorithm \ Warmup Type | simple | exponential | none |
|---|---|---|---|
| EXPONENTIAL_MOVING_AVERAGE | PERIOD - 1 | PERIOD - 1 | n/a |
| DOUBLE_EXPONENTIAL_MOVING_AVERAGE | ( PERIOD - 1 ) * 2 | PERIOD - 1 | n/a |
| TRIPLE_EXPONENTIAL_MOVING_AVERAGE | ( PERIOD - 1 ) * 3 | PERIOD - 1 | n/a |
| TRIPLE_EXPONENTIAL_DERIVATIVE | ( PERIOD - 1 ) * 3 + 1 | PERIOD | n/a |
| RELATIVE_STRENGTH_INDEX | PERIOD | PERIOD | n/a |
| CHANDE_MOMENTUM_OSCILLATOR | PERIOD | PERIOD | PERIOD - 1 |
Kaufman Algorithm Default Hold Periods:
| Algorithm | Default Hold Period |
|---|---|
| KAUFMANS_EFFICIENCY_RATIO() | PERIOD |
| KAUFMANS_ADAPTIVE_MOVING_AVERAGE() | PERIOD |
WARMUP_TYPE
default=‘exponential’
This controls how the algorithm initializes itself for the first PERIOD samples.
It is essentially the duration for which it has an incomplete sample set.
simple
Simple moving average (SMA) of the first PERIOD samples.
This is the method used by ta-lib.
exponential
Exponential moving average (EMA) with scaling alpha (α).
This basically uses an EMA with PERIOD=1 for the first point, PERIOD=2
for the second point, etc., until algorithm has consumed PERIOD number of points.
As the algorithm immediately starts using an EMA, when this method is used and
HOLD_PERIOD is unspecified or -1, the algorithm may start emitting points
after a much smaller sample size than with simple.
none
The algorithm does not perform any smoothing at all.
This is the method used by ta-lib.
When this method is used and HOLD_PERIOD is unspecified, HOLD_PERIOD
defaults to PERIOD - 1.
The
nonewarmup type is only available with theCHANDE_MOMENTUM_OSCILLATOR()function.
CHANDE_MOMENTUM_OSCILLATOR()
The Chande Momentum Oscillator (CMO) is a technical momentum indicator developed by Tushar Chande. The CMO indicator is created by calculating the difference between the sum of all recent higher data points and the sum of all recent lower data points, then dividing the result by the sum of all data movement over a given time period. The result is multiplied by 100 to give the -100 to +100 range. Source
Basic syntax
CHANDE_MOMENTUM_OSCILLATOR([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period>, [warmup_type]])
Available Arguments:
period
hold_period (Optional)
warmup_type (Optional)
Description of basic syntax
CHANDE_MOMENTUM_OSCILLATOR(field_key, 2)
Returns the field values associated with the field key
processed using the Chande Momentum Oscillator algorithm with a 2-value period
and the default hold period and warmup type.
CHANDE_MOMENTUM_OSCILLATOR(field_key, 10, 9, 'none')
Returns the field values associated with the field key
processed using the Chande Momentum Oscillator algorithm with a 10-value period
a 9-value hold period, and the none warmup type.
CHANDE_MOMENTUM_OSCILLATOR(MEAN(<field_key>), 2) ... GROUP BY time(1d)
Returns the mean of field values associated with the field key
processed using the Chande Momentum Oscillator algorithm with a 2-value period
and the default hold period and warmup type.
Note: When aggregating data with a
GROUP BYclause, you must include an aggregate function in your call to theCHANDE_MOMENTUM_OSCILLATOR()function.
CHANDE_MOMENTUM_OSCILLATOR(/regular_expression/, 2)
Returns the field values associated with each field key that matches the regular expression
processed using the Chande Momentum Oscillator algorithm with a 2-value period
and the default hold period and warmup type.
CHANDE_MOMENTUM_OSCILLATOR(*, 2)
Returns the field values associated with each field key in the measurement
processed using the Chande Momentum Oscillator algorithm with a 2-value period
and the default hold period and warmup type.
CHANDE_MOMENTUM_OSCILLATOR() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
EXPONENTIAL_MOVING_AVERAGE()
An exponential moving average (EMA) is a type of moving average that is similar to a simple moving average, except that more weight is given to the latest data. It’s also known as the “exponentially weighted moving average.” This type of moving average reacts faster to recent data changes than a simple moving average. Source
Basic syntax
EXPONENTIAL_MOVING_AVERAGE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])
Available Arguments:
period
hold_period (Optional)
warmup_type (Optional)
Description of basic syntax
EXPONENTIAL_MOVING_AVERAGE(field_key, 2)
Returns the field values associated with the field key
processed using the Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
EXPONENTIAL_MOVING_AVERAGE(field_key, 10, 9, 'exponential')
Returns the field values associated with the field key
processed using the Exponential Moving Average algorithm with a 10-value period
a 9-value hold period, and the exponential warmup type.
EXPONENTIAL_MOVING_AVERAGE(MEAN(<field_key>), 2) ... GROUP BY time(1d)
Returns the mean of field values associated with the field key
processed using the Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
Note: When aggregating data with a
GROUP BYclause, you must include an aggregate function in your call to theEXPONENTIAL_MOVING_AVERAGE()function.
EXPONENTIAL_MOVING_AVERAGE(/regular_expression/, 2)
Returns the field values associated with each field key that matches the regular expression
processed using the Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
EXPONENTIAL_MOVING_AVERAGE(*, 2)
Returns the field values associated with each field key in the measurement
processed using the Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
EXPONENTIAL_MOVING_AVERAGE() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
DOUBLE_EXPONENTIAL_MOVING_AVERAGE()
The Double Exponential Moving Average (DEMA) attempts to remove the inherent lag associated to Moving Averages by placing more weight on recent values. The name suggests this is achieved by applying a double exponential smoothing which is not the case. The name double comes from the fact that the value of an EMA is doubled. To keep it in line with the actual data and to remove the lag, the value “EMA of EMA” is subtracted from the previously doubled EMA. Source
Basic syntax
DOUBLE_EXPONENTIAL_MOVING_AVERAGE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])
Available Arguments:
period
hold_period (Optional)
warmup_type (Optional)
Description of basic syntax
DOUBLE_EXPONENTIAL_MOVING_AVERAGE(field_key, 2)
Returns the field values associated with the field key
processed using the Double Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
DOUBLE_EXPONENTIAL_MOVING_AVERAGE(field_key, 10, 9, 'exponential')
Returns the field values associated with the field key
processed using the Double Exponential Moving Average algorithm with a 10-value period
a 9-value hold period, and the exponential warmup type.
DOUBLE_EXPONENTIAL_MOVING_AVERAGE(MEAN(<field_key>), 2) ... GROUP BY time(1d)
Returns the mean of field values associated with the field key
processed using the Double Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
Note: When aggregating data with a
GROUP BYclause, you must include an aggregate function in your call to theDOUBLE_EXPONENTIAL_MOVING_AVERAGE()function.
DOUBLE_EXPONENTIAL_MOVING_AVERAGE(/regular_expression/, 2)
Returns the field values associated with each field key that matches the regular expression
processed using the Double Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
DOUBLE_EXPONENTIAL_MOVING_AVERAGE(*, 2)
Returns the field values associated with each field key in the measurement
processed using the Double Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
DOUBLE_EXPONENTIAL_MOVING_AVERAGE() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
KAUFMANS_EFFICIENCY_RATIO()
Kaufman’s Efficiency Ration, or simply “Efficiency Ratio” (ER), is calculated by dividing the data change over a period by the absolute sum of the data movements that occurred to achieve that change. The resulting ratio ranges between 0 and 1 with higher values representing a more efficient or trending market.
The ER is very similar to the Chande Momentum Oscillator (CMO). The difference is that the CMO takes market direction into account, but if you take the absolute CMO and divide by 100, you you get the Efficiency Ratio. Source
Basic syntax
KAUFMANS_EFFICIENCY_RATIO([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period>])
Available Arguments:
period
hold_period (Optional)
Description of basic syntax
KAUFMANS_EFFICIENCY_RATIO(field_key, 2)
Returns the field values associated with the field key
processed using the Efficiency Index algorithm with a 2-value period
and the default hold period and warmup type.
KAUFMANS_EFFICIENCY_RATIO(field_key, 10, 10)
Returns the field values associated with the field key
processed using the Efficiency Index algorithm with a 10-value period and
a 10-value hold period.
KAUFMANS_EFFICIENCY_RATIO(MEAN(<field_key>), 2) ... GROUP BY time(1d)
Returns the mean of field values associated with the field key
processed using the Efficiency Index algorithm with a 2-value period
and the default hold period.
Note: When aggregating data with a
GROUP BYclause, you must include an aggregate function in your call to theKAUFMANS_EFFICIENCY_RATIO()function.
KAUFMANS_EFFICIENCY_RATIO(/regular_expression/, 2)
Returns the field values associated with each field key that matches the regular expression
processed using the Efficiency Index algorithm with a 2-value period
and the default hold period and warmup type.
KAUFMANS_EFFICIENCY_RATIO(*, 2)
Returns the field values associated with each field key in the measurement
processed using the Efficiency Index algorithm with a 2-value period
and the default hold period and warmup type.
KAUFMANS_EFFICIENCY_RATIO() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
KAUFMANS_ADAPTIVE_MOVING_AVERAGE()
Kaufman’s Adaptive Moving Average (KAMA) is a moving average designed to account for sample noise or volatility. KAMA will closely follow data points when the data swings are relatively small and noise is low. KAMA will adjust when the data swings widen and follow data from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter data movements. Source
Basic syntax
KAUFMANS_ADAPTIVE_MOVING_AVERAGE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period>])
Available Arguments:
period
hold_period (Optional)
Description of basic syntax
KAUFMANS_ADAPTIVE_MOVING_AVERAGE(field_key, 2)
Returns the field values associated with the field key
processed using the Kaufman Adaptive Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
KAUFMANS_ADAPTIVE_MOVING_AVERAGE(field_key, 10, 10)
Returns the field values associated with the field key
processed using the Kaufman Adaptive Moving Average algorithm with a 10-value period
and a 10-value hold period.
KAUFMANS_ADAPTIVE_MOVING_AVERAGE(MEAN(<field_key>), 2) ... GROUP BY time(1d)
Returns the mean of field values associated with the field key
processed using the Kaufman Adaptive Moving Average algorithm with a 2-value period
and the default hold period.
Note: When aggregating data with a
GROUP BYclause, you must include an aggregate function in your call to theKAUFMANS_ADAPTIVE_MOVING_AVERAGE()function.
KAUFMANS_ADAPTIVE_MOVING_AVERAGE(/regular_expression/, 2)
Returns the field values associated with each field key that matches the regular expression
processed using the Kaufman Adaptive Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
KAUFMANS_ADAPTIVE_MOVING_AVERAGE(*, 2)
Returns the field values associated with each field key in the measurement
processed using the Kaufman Adaptive Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
KAUFMANS_ADAPTIVE_MOVING_AVERAGE() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
TRIPLE_EXPONENTIAL_MOVING_AVERAGE()
The triple exponential moving average (TEMA) was developed to filter out volatility from conventional moving averages. While the name implies that it’s a triple exponential smoothing, it’s actually a composite of a single exponential moving average, a double exponential moving average, and a triple exponential moving average. Source
Basic syntax
TRIPLE_EXPONENTIAL_MOVING_AVERAGE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])
Available Arguments:
period
hold_period (Optional)
warmup_type (Optional)
Description of basic syntax
TRIPLE_EXPONENTIAL_MOVING_AVERAGE(field_key, 2)
Returns the field values associated with the field key
processed using the Triple Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
TRIPLE_EXPONENTIAL_MOVING_AVERAGE(field_key, 10, 9, 'exponential')
Returns the field values associated with the field key
processed using the Triple Exponential Moving Average algorithm with a 10-value period
a 9-value hold period, and the exponential warmup type.
TRIPLE_EXPONENTIAL_MOVING_AVERAGE(MEAN(<field_key>), 2) ... GROUP BY time(1d)
Returns the mean of field values associated with the field key
processed using the Triple Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
Note: When aggregating data with a
GROUP BYclause, you must include an aggregate function in your call to theTRIPLE_EXPONENTIAL_MOVING_AVERAGE()function.
TRIPLE_EXPONENTIAL_MOVING_AVERAGE(/regular_expression/, 2)
Returns the field values associated with each field key that matches the regular expression
processed using the Triple Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
TRIPLE_EXPONENTIAL_MOVING_AVERAGE(*, 2)
Returns the field values associated with each field key in the measurement
processed using the Triple Exponential Moving Average algorithm with a 2-value period
and the default hold period and warmup type.
TRIPLE_EXPONENTIAL_MOVING_AVERAGE() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
TRIPLE_EXPONENTIAL_DERIVATIVE()
The triple exponential derivative indicator, commonly referred to as “TRIX,” is an oscillator used to identify oversold and overbought markets, and can also be used as a momentum indicator. TRIX calculates a triple exponential moving average of the log of the data input over the period of time. The previous value is subtracted from the previous value. This prevents cycles that are shorter than the defined period from being considered by the indicator.
Like many oscillators, TRIX oscillates around a zero line. When used as an oscillator, a positive value indicates an overbought market while a negative value indicates an oversold market. When used as a momentum indicator, a positive value suggests momentum is increasing while a negative value suggests momentum is decreasing. Many analysts believe that when the TRIX crosses above the zero line it gives a buy signal, and when it closes below the zero line, it gives a sell signal. Source
Basic syntax
TRIPLE_EXPONENTIAL_DERIVATIVE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])
Available Arguments:
period
hold_period (Optional)
warmup_type (Optional)
Description of basic syntax
TRIPLE_EXPONENTIAL_DERIVATIVE(field_key, 2)
Returns the field values associated with the field key
processed using the Triple Exponential Derivative algorithm with a 2-value period
and the default hold period and warmup type.
TRIPLE_EXPONENTIAL_DERIVATIVE(field_key, 10, 10, 'exponential')
Returns the field values associated with the field key
processed using the Triple Exponential Derivative algorithm with a 10-value period,
a 10-value hold period, and the exponential warmup type.
TRIPLE_EXPONENTIAL_DERIVATIVE(MEAN(<field_key>), 2) ... GROUP BY time(1d)
Returns the mean of field values associated with the field key
processed using the Triple Exponential Derivative algorithm with a 2-value period
and the default hold period and warmup type.
Note: When aggregating data with a
GROUP BYclause, you must include an aggregate function in your call to theTRIPLE_EXPONENTIAL_DERIVATIVE()function.
TRIPLE_EXPONENTIAL_DERIVATIVE(/regular_expression/, 2)
Returns the field values associated with each field key that matches the regular expression
processed using the Triple Exponential Derivative algorithm with a 2-value period
and the default hold period and warmup type.
TRIPLE_EXPONENTIAL_DERIVATIVE(*, 2)
Returns the field values associated with each field key in the measurement
processed using the Triple Exponential Derivative algorithm with a 2-value period
and the default hold period and warmup type.
TRIPLE_EXPONENTIAL_DERIVATIVE() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
RELATIVE_STRENGTH_INDEX()
The relative strength index (RSI) is a momentum indicator that compares the magnitude of recent increases and decreases over a specified time period to measure speed and change of data movements. Source
Basic syntax
RELATIVE_STRENGTH_INDEX([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])
Available Arguments:
period
hold_period (Optional)
warmup_type (Optional)
Description of basic syntax
RELATIVE_STRENGTH_INDEX(field_key, 2)
Returns the field values associated with the field key
processed using the Relative Strength Index algorithm with a 2-value period
and the default hold period and warmup type.
RELATIVE_STRENGTH_INDEX(field_key, 10, 10, 'exponential')
Returns the field values associated with the field key
processed using the Relative Strength Index algorithm with a 10-value period,
a 10-value hold period, and the exponential warmup type.
RELATIVE_STRENGTH_INDEX(MEAN(<field_key>), 2) ... GROUP BY time(1d)
Returns the mean of field values associated with the field key
processed using the Relative Strength Index algorithm with a 2-value period
and the default hold period and warmup type.
Note: When aggregating data with a
GROUP BYclause, you must include an aggregate function in your call to theRELATIVE_STRENGTH_INDEX()function.
RELATIVE_STRENGTH_INDEX(/regular_expression/, 2)
Returns the field values associated with each field key that matches the regular expression
processed using the Relative Strength Index algorithm with a 2-value period
and the default hold period and warmup type.
RELATIVE_STRENGTH_INDEX(*, 2)
Returns the field values associated with each field key in the measurement
processed using the Relative Strength Index algorithm with a 2-value period
and the default hold period and warmup type.
RELATIVE_STRENGTH_INDEX() supports int64 and float64 field value data types.
The basic syntax supports GROUP BY clauses that group by tags but not GROUP BY clauses that group by time.
Other
Sample Data
The data used in this document are available for download on the Sample Data page.
General Syntax for Functions
Specify Multiple Functions in the SELECT Clause
Syntax
SELECT <function>(),<function>() FROM_clause [...]
Description of Syntax
Separate multiple functions in one SELECT statement with a comma (,).
The syntax applies to all InfluxQL functions except TOP() and BOTTOM().
The SELECT clause does not support specifying TOP() or BOTTOM() with another function.
Examples
Example: Calculate the mean and median field values in one query
``` > SELECT MEAN("water_level"),MEDIAN("water_level") FROM "h2o_feet"
name: h2o_feet time mean median
1970-01-01T00:00:00Z 4.442107025822522 4.124
The query returns the [average](#mean) and [median](#median) field values in the `water_level` field key.
##### Example: Calculate the mode of two fields in one query
<br>
SELECT MODE(“water_level”),MODE(“level description”) FROM “h2o_feet”
name: h2o_feet time mode mode_1
1970-01-01T00:00:00Z 2.69 between 3 and 6 feet
The query returns the [mode](#mode) field values for the `water_level` field key and for the `level description` field key.
The `water_level` mode is in the `mode` column and the `level description` mode is in the `mode_1` column.
The system can't return more than one column with the same name so it renames the second `mode` column to `mode_1`.
See [Rename the Output Field Key](#rename-the-output-field-key) for how to configure the output column headers.
##### Example: Calculate the minimum and maximum field values in one query
<br>
SELECT MIN(“water_level”), MAX(“water_level”) […]
name: h2o_feet time min max
1970-01-01T00:00:00Z -0.61 9.964
The query returns the [minimum](#min) and [maximum](#max) field values in the `water_level` field key.
Notice that the query returns `1970-01-01T00:00:00Z`, InfluxDB's null-timestamp equivalent, as the timestamp.
`MIN()` and `MAX()` are [selector](#selectors) functions; when a selector function is the only function in the `SELECT` clause, it returns a specific timestamp.
Because `MIN()` and `MAX()` return two different timestamps (see below), the system overrides those timestamps with the null timestamp equivalent.
SELECT MIN(“water_level”) FROM “h2o_feet”
name: h2o_feet time min
2015-08-29T14:30:00Z -0.61 <— Timestamp 1
SELECT MAX(“water_level”) FROM “h2o_feet”
name: h2o_feet time max
2015-08-29T07:24:00Z 9.964 <— Timestamp 2
### Rename the Output Field Key
#### Syntax
SELECT
#### Description of Syntax
By default, functions return results under a field key that matches the function name.
Include an `AS` clause to specify the name of the output field key.
#### Examples
##### Example: Specify the output field key
<br>
SELECT MEAN(“water_level”) AS “dream_name” FROM “h2o_feet”
name: h2o_feet time dream_name
1970-01-01T00:00:00Z 4.442107025822522
The query returns the [average](#mean) field value of the `water_level` field key and renames the output field key to `dream_name`.
Without the `AS` clause, the query returns `mean` as the output field key:
SELECT MEAN(“water_level”) FROM “h2o_feet”
name: h2o_feet time mean
1970-01-01T00:00:00Z 4.442107025822522
##### Example: Specify the output field key for multiple functions
<br>
SELECT MEDIAN(“water_level”) AS “med_wat”,MODE(“water_level”) AS “mode_wat” FROM “h2o_feet”
name: h2o_feet time med_wat mode_wat
1970-01-01T00:00:00Z 4.124 2.69
The query returns the [median](#median) and [mode](#mode) field values for the `water_level` field key and renames the output field keys to `med_wat` and `mode_wat`.
Without the `AS` clauses, the query returns `median` and `mode` as the output field keys:
SELECT MEDIAN(“water_level”),MODE(“water_level”) FROM “h2o_feet”
name: h2o_feet time median mode
1970-01-01T00:00:00Z 4.124 2.69
### Change the Values Reported for Intervals with no Data
By default, queries with an InfluxQL function and a [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) report null values for intervals with no data.
Include `fill()` at the end of the `GROUP BY` clause to change that value.
See [Data Exploration](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals-and-fill) for a complete discussion of `fill()`.
## Common Issues with Functions
The following sections describe frequent sources of confusion with all functions, aggregation functions, and selector functions.
See the function-specific documentation for common issues with individual functions:
* [DISTINCT()](#common-issues-with-distinct)
* [BOTTOM()](#common-issues-with-bottom)
* [PERCENTILE()](#common-issues-with-percentile)
* [SAMPLE()](#common-issues-with-sample)
* [TOP()](#common-issues-with-top)
* [ELAPSED()](#common-issues-with-elapsed)
* [HOLT_WINTERS()](#common-issues-with-holt-winters)
### All Functions
#### Issue 1: Nesting functions
Some InfluxQL functions support nesting in the [`SELECT` clause](/influxdb/v1.6/query_language/data_exploration/#select-clause):
* [`COUNT()`](#count) with [`DISTINCT()`](#distinct)
* [`CUMULATIVE_SUM()`](#cumulative-sum)
* [`DERIVATIVE()`](#derivative)
* [`DIFFERENCE()`](#difference)
* [`ELAPSED()`](#elapsed)
* [`MOVING_AVERAGE()`](#moving-average)
* [`NON_NEGATIVE_DERIVATIVE()`](#non-negative-derivative)
* [`HOLT_WINTERS()`](#holt-winters) and [`HOLT_WINTERS_WITH_FIT()`](#holt-winters)
For other functions, use InfluxQL's [subqueries](/influxdb/v1.6/query_language/data_exploration/#subqueries) to nest functions in the [`FROM` clause](/influxdb/v1.6/query_language/data_exploration/#from-clause).
See the [Data Exploration](/influxdb/v1.6/query_language/data_exploration/#subqueries) page more on using subqueries.
#### Issue 2: Querying time ranges after now()
Most `SELECT` statements have a default time range between [`1677-09-21 00:12:43.145224194` and `2262-04-11T23:47:16.854775806Z` UTC](/influxdb/v1.6/troubleshooting/frequently-asked-questions/#what-are-the-minimum-and-maximum-timestamps-that-influxdb-can-store).
For `SELECT` statements with an InfluxQL function and a [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals), the default time
range is between `1677-09-21 00:12:43.145224194` UTC and [`now()`](/influxdb/v1.6/concepts/glossary/#now).
To query data with timestamps that occur after `now()`, `SELECT` statements with
an InfluxQL function and a `GROUP BY time()` clause must provide an alternative upper bound in the
[`WHERE` clause](/influxdb/v1.6/query_language/data_exploration/#the-where-clause).
See the [Frequently Asked Questions](/influxdb/v1.6/troubleshooting/frequently-asked-questions/#why-don-t-my-group-by-time-queries-return-timestamps-that-occur-after-now) page for an example.
### Aggregation Functions
#### Issue 1: Understanding the returned timestamp
A query with an [aggregation function](#aggregations) and no time range in the [`WHERE` clause](/influxdb/v1.6/query_language/data_exploration/#the-where-clause) returns epoch 0 (`1970-01-01T00:00:00Z`) as the timestamp.
InfluxDB uses epoch 0 as the null timestamp equivalent.
A query with an aggregate function that includes a time range in the `WHERE` clause returns the lower time bound as the timestamp.
##### Examples
<br>
##### Example: Use an aggregate function without a specified time range
<br>
SELECT SUM(“water_level”) FROM “h2o_feet”
name: h2o_feet time sum
1970-01-01T00:00:00Z 67777.66900000004
The query returns InfluxDB's null timestamp equivalent (epoch 0: `1970-01-01T00:00:00Z`) as the timestamp.
[`SUM()`](#sum) aggregates points across several timestamps and has no single timestamp to return.
##### Example: Use an aggregate function with a specified time range
<br>
SELECT SUM(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’
name: h2o_feet time sum
2015-08-18T00:00:00Z 67777.66900000004
The query returns the lower time bound (`WHERE time >= '2015-08-18T00:00:00Z'`) as the timestamp.
##### Example: Use an aggregate function with a specified time range and a GROUP BY time() clause
<br>
SELECT SUM(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:18:00Z’ GROUP BY time(12m)
name: h2o_feet time sum
2015-08-18T00:00:00Z 20.305 2015-08-18T00:12:00Z 19.802999999999997
The query returns the lower time bound for each [`GROUP BY time()`](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) interval as the timestamps.
#### Issue 2: Mixing aggregation functions with non-aggregates
Aggregation functions do not support specifying standalone [field keys](/influxdb/v1.6/concepts/glossary/#field-key) or [tag keys](/influxdb/v1.6/concepts/glossary/#tag-key) in the [`SELECT` clause](/influxdb/v1.6/query_language/data_exploration/#select-clause).
Aggregation functions return a single calculated value and there is no obvious single value to return for any unaggregated fields or tags.
Including a standalone field key or tag key with an aggregation function in the `SELECT` clause returns an error:
SELECT SUM(“water_level”),“location” FROM “h2o_feet”
ERR: error parsing query: mixing aggregate and non-aggregate queries is not supported
#### Issue 3: Getting slightly different results
For some aggregation functions, executing the same function on the same set of [float64](/influxdb/v1.6/write_protocols/line_protocol_reference/#data-types) points may yield slightly different results.
InfluxDB does not sort points before it applies the aggregation function; that behavior can cause small discrepancies in the query results.
### Selector Functions
#### Issue 1: Understanding the returned timestamp
The timestamps returned by [selector functions](#selectors) depend on the number of functions in the query and on the other clauses in the query:
A query with a single selector function, a single [field key](/influxdb/v1.6/concepts/glossary/#field-key) argument, and no [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) returns the timestamp for the point that appears in the raw data.
A query with a single selector function, multiple field key arguments, and no [`GROUP BY time()` clause](/influxdb/v1.6/query_language/data_exploration/#group-by-time-intervals) returns the timestamp for the point that appears in the raw data or InfluxDB's null timestamp equivalent (epoch 0: `1970-01-01T00:00:00Z`).
A query with more than one function and no time range in the [`WHERE` clause](/influxdb/v1.6/query_language/data_exploration/#the-where-clause) returns InfluxDB's null timestamp equivalent (epoch 0: `1970-01-01T00:00:00Z`).
A query with more than one function and a time range in the `WHERE` clause returns the lower time bound as the timestamp.
A query with a selector function and a `GROUP BY time()` clause returns the lower time bound for each `GROUP BY time()` interval.
Note that the `SAMPLE()` function behaves differently from other selector functions when paired with the `GROUP BY time()` clause.
See [Common Issues with `SAMPLE()`](#common-issues-with-sample) for more information.
##### Examples
<br>
##### Example: Use a single selector function with a single field key and without a specified time range
<br>
SELECT MAX(“water_level”) FROM “h2o_feet”
name: h2o_feet time max
2015-08-29T07:24:00Z 9.964
SELECT MAX(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’
name: h2o_feet time max
2015-08-29T07:24:00Z 9.964
The queries return the timestamp for the [maximum](#max) point that appears in the raw data.
##### Example: Use a single selector function with multiple field keys and without a specified time range
<br>
SELECT FIRST(*) FROM “h2o_feet”
name: h2o_feet time first_level description first_water_level
1970-01-01T00:00:00Z between 6 and 9 feet 8.12
SELECT MAX(*) FROM “h2o_feet”
name: h2o_feet time max_water_level
2015-08-29T07:24:00Z 9.964
The first query returns InfluxDB's null timestamp equivalent (epoch 0: `1970-01-01T00:00:00Z`) as the timestamp.
`FIRST(*)` returns two timestamps (one for each field key in the `h2o_feet` [measurement](/influxdb/v1.6/concepts/glossary/#measurement)) so the system overrides those timestamps with the null timestamp equivalent.
The second query returns the timestamp for the maximum point that appears in the raw data.
`MAX(*)` returns one timestamp (the `h2o-feet` measurement has only one numerical field) so the system does not overwrite the original timestamp.
##### Example: Use a selector function with another function and without a specified time range
<br>
SELECT MAX(“water_level”),MIN(“water_level”) FROM “h2o_feet”
name: h2o_feet time max min
1970-01-01T00:00:00Z 9.964 -0.61
The query returns InfluxDB's null timestamp equivalent (epoch 0: `1970-01-01T00:00:00Z`) as the timestamp.
The `MAX()` and [`MIN()`](#min) functions return different timestamps so the system has no single timestamp to return.
##### Example 4: Use a selector function with another function and with a specified time range
<br>
SELECT MAX(“water_level”),MIN(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’
name: h2o_feet time max min
2015-08-18T00:00:00Z 9.964 -0.61
The query returns the lower time bound (`WHERE time >= '2015-08-18T00:00:00Z'`) as the timestamp.
##### Example 5: Use a selector function with a GROUP BY time() clause
<br>
SELECT MAX(“water_level”) FROM “h2o_feet” WHERE time >= ‘2015-08-18T00:00:00Z’ AND time <= ‘2015-08-18T00:18:00Z’ GROUP BY time(12m)
name: h2o_feet time max
2015-08-18T00:00:00Z 8.12 2015-08-18T00:12:00Z 7.887
The query returns the lower time bound for each `GROUP BY time()` interval as the timestamp.
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