InfluxQL functions

Aggregate, select, transform, and predict data with InfluxQL functions.

Content

Aggregations

COUNT()

Returns the number of non-null field values.

Syntax

  1. 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
  1. SELECT COUNT(DISTINCT( [ * | <field_key> | /<regular_expression>/ ] )) [...]

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

Count the field values associated with a field key
  1. > SELECT COUNT("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time count
  4. ---- -----
  5. 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.

Count the field values associated with each field key in a measurement
  1. > SELECT COUNT(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time count_level description count_water_level
  4. ---- ----------------------- -----------------
  5. 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.

Count the field values associated with each field key that matches a regular expression
  1. > SELECT COUNT(/water/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time count_water_level
  4. ---- -----------------
  5. 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.

Count the field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. tags: location=coyote_creek
  4. time count
  5. ---- -----
  6. 2015-08-17T23:48:00Z 200
  7. 2015-08-18T00:00:00Z 2
  8. 2015-08-18T00:12:00Z 2
  9. 2015-08-18T00:24:00Z 2
  10. 2015-08-18T00:36:00Z 2
  11. 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.

Count the distinct field values associated with a field key
  1. > SELECT COUNT(DISTINCT("level description")) FROM "h2o_feet"
  2. name: h2o_feet
  3. time count
  4. ---- -----
  5. 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()

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.

  1. > 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)
  2. name: h2o_feet
  3. time count
  4. ---- -----
  5. 2015-09-18T21:24:00Z 2
  6. 2015-09-18T21:36:00Z 2
  7. 2015-09-18T21:48:00Z 0
  8. > 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)
  9. name: h2o_feet
  10. time count
  11. ---- -----
  12. 2015-09-18T21:24:00Z 2
  13. 2015-09-18T21:36:00Z 2
  14. 2015-09-18T21:48:00Z 800000

DISTINCT()

Returns the list of unique field values.

Syntax

  1. 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
  1. SELECT COUNT(DISTINCT( [ <field_key> | /<regular_expression>/ ] )) [...]

DISTINCT(field_key)
Returns the unique field values associated with the field key.

DISTINCT() supports all field value data types. InfluxQL supports nesting DISTINCT() with COUNT().

Examples

List the distinct field values associated with a field key
  1. > SELECT DISTINCT("level description") FROM "h2o_feet"
  2. name: h2o_feet
  3. time distinct
  4. ---- --------
  5. 1970-01-01T00:00:00Z between 6 and 9 feet
  6. 1970-01-01T00:00:00Z below 3 feet
  7. 1970-01-01T00:00:00Z between 3 and 6 feet
  8. 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.

List the distinct field values associated with each field key in a measurement
  1. > SELECT DISTINCT(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time distinct_level description distinct_water_level
  4. ---- -------------------------- --------------------
  5. 1970-01-01T00:00:00Z between 6 and 9 feet 8.12
  6. 1970-01-01T00:00:00Z between 3 and 6 feet 8.005
  7. 1970-01-01T00:00:00Z at or greater than 9 feet 7.887
  8. 1970-01-01T00:00:00Z below 3 feet 7.762
  9. [...]

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.

List the distinct field values associated with a field key and include several clauses

  1. > 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
  2. name: h2o_feet
  3. tags: location=coyote_creek
  4. time distinct
  5. ---- --------
  6. 2015-08-18T00:00:00Z between 6 and 9 feet
  7. 2015-08-18T00:12:00Z between 6 and 9 feet
  8. 2015-08-18T00:24:00Z between 6 and 9 feet
  9. 2015-08-18T00:36:00Z between 6 and 9 feet
  10. 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.

Count the distinct field values associated with a field key
  1. > SELECT COUNT(DISTINCT("level description")) FROM "h2o_feet"
  2. name: h2o_feet
  3. time count
  4. ---- -----
  5. 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()

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 code block 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.

  1. > SELECT DISTINCT("level description") FROM "h2o_feet"
  2. name: h2o_feet
  3. time distinct
  4. ---- --------
  5. 1970-01-01T00:00:00Z below 3 feet
  6. 1970-01-01T00:00:00Z between 6 and 9 feet
  7. 1970-01-01T00:00:00Z between 3 and 6 feet
  8. 1970-01-01T00:00:00Z at or greater than 9 feet
  9. > SELECT DISTINCT("level description") INTO "distincts" FROM "h2o_feet"
  10. name: result
  11. time written
  12. ---- -------
  13. 1970-01-01T00:00:00Z 4
  14. > SELECT * FROM "distincts"
  15. name: distincts
  16. time distinct
  17. ---- --------
  18. 1970-01-01T00:00:00Z at or greater than 9 feet

INTEGRAL()

Returns the area under the curve for subsequent field values.

Syntax

  1. 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]

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 area under the curve for subsequent field values associated with each field key that matches the regular expression.

INTEGRAL(*)
Returns the average field value associated with each field key in the measurement.

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:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the integral for the field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time integral
  4. ---- --------
  5. 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.

Calculate the integral for the field values associated with a field key and specify the unit option
  1. > 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'
  2. name: h2o_feet
  3. time integral
  4. ---- --------
  5. 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.

Calculate the integral for the field values associated with each field key in a measurement and specify the unit option
  1. > SELECT INTEGRAL(*,1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
  2. name: h2o_feet
  3. time integral_water_level
  4. ---- --------------------
  5. 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.

Calculate the integral for the field values associated with each field key that matches a regular expression and specify the unit option

  1. > 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'
  2. name: h2o_feet
  3. time integral_water_level
  4. ---- --------------------
  5. 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.

Calculate the integral for the field values associated with a field key and include several clauses

  1. > 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
  2. name: h2o_feet
  3. time integral
  4. ---- --------
  5. 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

  1. 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]

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

Calculate the mean field value associated with a field key
  1. > SELECT MEAN("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 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.

Calculate the mean field value associated with each field key in a measurement
  1. > SELECT MEAN(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time mean_water_level
  4. ---- ----------------
  5. 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.

Calculate the mean field value associated with each field key that matches a regular expression
  1. > SELECT MEAN(/water/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time mean_water_level
  4. ---- ----------------
  5. 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.

Calculate the mean field value associated with a field key and include several clauses

  1. > 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
  2. name: h2o_feet
  3. tags: location=coyote_creek
  4. time mean
  5. ---- ----
  6. 2015-08-17T23:48:00Z 9.01
  7. 2015-08-18T00:00:00Z 8.0625
  8. 2015-08-18T00:12:00Z 7.8245
  9. 2015-08-18T00:24:00Z 7.5675
  10. 2015-08-18T00:36:00Z 7.303
  11. 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

  1. 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]

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 to PERCENTILE(field_key, 50), except MEDIAN() returns the average of the two middle field values if the field contains an even number of values.

Examples

Calculate the median field value associated with a field key
  1. > SELECT MEDIAN("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time median
  4. ---- ------
  5. 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.

Calculate the median field value associated with each field key in a measurement
  1. > SELECT MEDIAN(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time median_water_level
  4. ---- ------------------
  5. 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.

Calculate the median field value associated with each field key that matches a regular expression
  1. > SELECT MEDIAN(/water/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time median_water_level
  4. ---- ------------------
  5. 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.

Calculate the median field value associated with a field key and include several clauses

  1. > 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
  2. name: h2o_feet
  3. tags: location=santa_monica
  4. time median
  5. ---- ------
  6. 2015-08-17T23:48:00Z 700
  7. 2015-08-18T00:00:00Z 2.09
  8. 2015-08-18T00:12:00Z 2.077
  9. 2015-08-18T00:24:00Z 2.0460000000000003
  10. 2015-08-18T00:36:00Z 2.0620000000000003
  11. 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

  1. 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]

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

Calculate the mode field value associated with a field key
  1. > SELECT MODE("level description") FROM "h2o_feet"
  2. name: h2o_feet
  3. time mode
  4. ---- ----
  5. 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.

Calculate the mode field value associated with each field key in a measurement
  1. > SELECT MODE(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time mode_level description mode_water_level
  4. ---- ---------------------- ----------------
  5. 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.

Calculate the mode field value associated with each field key that matches a regular expression
  1. > SELECT MODE(/water/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time mode_water_level
  4. ---- ----------------
  5. 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.

Calculate the mode field value associated with a field key and include several clauses

  1. > 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
  2. name: h2o_feet
  3. tags: location=santa_monica
  4. time mode
  5. ---- ----
  6. 2015-08-17T23:48:00Z
  7. 2015-08-18T00:00:00Z below 3 feet
  8. 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

  1. 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]

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

Calculate the spread for the field values associated with a field key
  1. > SELECT SPREAD("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time spread
  4. ---- ------
  5. 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.

Calculate the spread for the field values associated with each field key in a measurement
  1. > SELECT SPREAD(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time spread_water_level
  4. ---- ------------------
  5. 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.

Calculate the spread for the field values associated with each field key that matches a regular expression
  1. > SELECT SPREAD(/water/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time spread_water_level
  4. ---- ------------------
  5. 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.

Calculate the spread for the field values associated with a field key and include several clauses

  1. > 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
  2. name: h2o_feet
  3. tags: location=santa_monica
  4. time spread
  5. ---- ------
  6. 2015-08-17T23:48:00Z 18
  7. 2015-08-18T00:00:00Z 0.052000000000000046
  8. 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 2015-08-18T00:54:00Zand 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

  1. 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]

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

Calculate the standard deviation for the field values associated with a field key
  1. > SELECT STDDEV("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time stddev
  4. ---- ------
  5. 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.

Calculate the standard deviation for the field values associated with each field key in a measurement
  1. > SELECT STDDEV(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time stddev_water_level
  4. ---- ------------------
  5. 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.

Calculate the standard deviation for the field values associated with each field key that matches a regular expression
  1. > SELECT STDDEV(/water/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time stddev_water_level
  4. ---- ------------------
  5. 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.

Calculate the standard deviation for the field values associated with a field key and include several clauses

  1. > 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
  2. name: h2o_feet
  3. tags: location=santa_monica
  4. time stddev
  5. ---- ------
  6. 2015-08-17T23:48:00Z 18000
  7. 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

  1. 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]

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

Calculate the sum of the field values associated with a field key

  1. > SELECT SUM("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time sum
  4. ---- ---
  5. 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.

Calculate the sum of the field values associated with each field key in a measurement

  1. > SELECT SUM(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time sum_water_level
  4. ---- ---------------
  5. 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.

Calculate the sum of the field values associated with each field key that matches a regular expression

  1. > SELECT SUM(/water/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time sum_water_level
  4. ---- ---------------
  5. 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.

Calculate the sum of the field values associated with a field key and include several clauses

  1. > 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
  2. name: h2o_feet
  3. tags: location=coyote_creek
  4. time sum
  5. ---- ---
  6. 2015-08-17T23:48:00Z 18000
  7. 2015-08-18T00:00:00Z 16.125
  8. 2015-08-18T00:12:00Z 15.649
  9. 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

  1. 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]

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 an INTO clause. See the Common Issues section for more information.

Examples

Select the bottom three field values associated with a field key
  1. > SELECT BOTTOM("water_level",3) FROM "h2o_feet"
  2. name: h2o_feet
  3. time bottom
  4. ---- ------
  5. 2015-08-29T14:30:00Z -0.61
  6. 2015-08-29T14:36:00Z -0.591
  7. 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.

Select the bottom field value associated with a field key for two tags
  1. > SELECT BOTTOM("water_level","location",2) FROM "h2o_feet"
  2. name: h2o_feet
  3. time bottom location
  4. ---- ------ --------
  5. 2015-08-29T10:36:00Z -0.243 santa_monica
  6. 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.

Select the bottom four field values associated with a field key and the relevant tags and fields
  1. > SELECT BOTTOM("water_level",4),"location","level description" FROM "h2o_feet"
  2. name: h2o_feet
  3. time bottom location level description
  4. ---- ------ -------- -----------------
  5. 2015-08-29T14:24:00Z -0.587 coyote_creek below 3 feet
  6. 2015-08-29T14:30:00Z -0.61 coyote_creek below 3 feet
  7. 2015-08-29T14:36:00Z -0.591 coyote_creek below 3 feet
  8. 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.

Select the bottom three field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time bottom location
  4. ---- ------ --------
  5. 2015-08-18T00:48:00Z 1.991 santa_monica
  6. 2015-08-18T00:54:00Z 2.054 santa_monica
  7. 2015-08-18T00:54:00Z 6.982 coyote_creek
  8. 2015-08-18T00:24:00Z 2.041 santa_monica
  9. 2015-08-18T00:30:00Z 2.051 santa_monica
  10. 2015-08-18T00:42:00Z 2.057 santa_monica
  11. 2015-08-18T00:00:00Z 2.064 santa_monica
  12. 2015-08-18T00:06:00Z 2.116 santa_monica
  13. 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()

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.

  1. > 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)
  2. name: h2o_feet
  3. time bottom
  4. ---- ------
  5. __
  6. 2015-08-18T00:00:00Z 2.064 |
  7. 2015-08-18T00:12:00Z 2.028 | <------- Smallest points for the first time interval
  8. --
  9. __
  10. 2015-08-18T00:24:00Z 2.041 |
  11. 2015-08-18T00:30:00Z 2.051 | <------- Smallest points for the second time interval --
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.

  1. > SELECT BOTTOM("water_level","location",3) FROM "h2o_feet"
  2. name: h2o_feet
  3. time bottom location
  4. ---- ------ --------
  5. 2015-08-29T10:36:00Z -0.243 santa_monica
  6. 2015-08-29T14:30:00Z -0.61 coyote_creek
BOTTOM(), tags, and the INTO clause

When combined with an INTO clause and no GROUP BY tag clause, most InfluxQL functions convert 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

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 that InfluxDB preserved the location tag as a tag in the bottom_water_levels measurement.

  1. > SELECT BOTTOM("water_level","location",2) INTO "bottom_water_levels" FROM "h2o_feet"
  2. name: result
  3. time written
  4. ---- -------
  5. 1970-01-01T00:00:00Z 2
  6. > SHOW TAG KEYS FROM "bottom_water_levels"
  7. name: bottom_water_levels
  8. tagKey
  9. ------
  10. location

FIRST()

Returns the field value with the oldest timestamp.

Syntax

  1. 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]

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.

FIRST(*)
Returns the oldest field value (determined by timestamp) associated with each field key in the 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 and/or field.

FIRST() supports all field value data types.

Examples

Select the first field value associated with a field key
  1. > SELECT FIRST("level description") FROM "h2o_feet"
  2. name: h2o_feet
  3. time first
  4. ---- -----
  5. 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.

Select the first field value associated with each field key in a measurement
  1. > SELECT FIRST(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time first_level description first_water_level
  4. ---- ----------------------- -----------------
  5. 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.

Select the first field value associated with each field key that matches a regular expression
  1. > SELECT FIRST(/level/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time first_level description first_water_level
  4. ---- ----------------------- -----------------
  5. 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.

Select the first value associated with a field key and the relevant tags and fields
  1. > SELECT FIRST("level description"),"location","water_level" FROM "h2o_feet"
  2. name: h2o_feet
  3. time first location water_level
  4. ---- ----- -------- -----------
  5. 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.

Select the first field value associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. tags: location=coyote_creek
  4. time first
  5. ---- -----
  6. 2015-08-17T23:48:00Z 9.01
  7. 2015-08-18T00:00:00Z 8.12
  8. 2015-08-18T00:12:00Z 7.887
  9. 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 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 it limits the number of points and series returned to four and one.

Notice that the GROUP BY time() clause 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 with the most recent timestamp.

Syntax

  1. 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]

LAST(field_key)
Returns the newest field value (determined by timestamp) associated with the field key.

LAST(/regular_expression/)
Returns the newest field value (determined by timestamp) associated with each field key that matches the regular expression.

LAST(*)
Returns the newest field value (determined by timestamp) associated with each field key in the 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 and/or field.

LAST() supports all field value data types.

Examples

Select the last field values associated with a field key
  1. > SELECT LAST("level description") FROM "h2o_feet"
  2. name: h2o_feet
  3. time last
  4. ---- ----
  5. 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.

Select the last field values associated with each field key in a measurement
  1. > SELECT LAST(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time last_level description last_water_level
  4. ---- ----------------------- -----------------
  5. 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.

Select the last field value associated with each field key that matches a regular expression
  1. > SELECT LAST(/level/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time last_level description last_water_level
  4. ---- ----------------------- -----------------
  5. 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.

Select the last field value associated with a field key and the relevant tags and fields
  1. > SELECT LAST("level description"),"location","water_level" FROM "h2o_feet"
  2. name: h2o_feet
  3. time last location water_level
  4. ---- ---- -------- -----------
  5. 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.

Select the last field value associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. tags: location=coyote_creek
  4. time last
  5. ---- ----
  6. 2015-08-17T23:48:00Z 9.01
  7. 2015-08-18T00:00:00Z 8.005
  8. 2015-08-18T00:12:00Z 7.762
  9. 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 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 it limits the number of points and series returned to four and one.

Notice that the GROUP BY time() clause 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.

Syntax

  1. 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]

MAX(field_key)
Returns the greatest field value associated with the field key.

MAX(/regular_expression/)
Returns the greatest field value associated with each field key that matches the regular expression.

MAX(*)
Returns the greatest field value associated with each field key in the 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 and/or field.

MAX() supports int64 and float64 field value data types.

Examples

Select the maximum field value associated with a field key
  1. > SELECT MAX("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time max
  4. ---- ---
  5. 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.

Select the maximum field value associated with each field key in a measurement
  1. > SELECT MAX(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time max_water_level
  4. ---- ---------------
  5. 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.

Select the maximum field value associated with each field key that matches a regular expression
  1. > SELECT MAX(/level/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time max_water_level
  4. ---- ---------------
  5. 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.

Select the maximum field value associated with a field key and the relevant tags and fields
  1. > SELECT MAX("water_level"),"location","level description" FROM "h2o_feet"
  2. name: h2o_feet
  3. time max location level description
  4. ---- --- -------- -----------------
  5. 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.

Select the maximum field value associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. tags: location=coyote_creek
  4. time max
  5. ---- ---
  6. 2015-08-17T23:48:00Z 9.01
  7. 2015-08-18T00:00:00Z 8.12
  8. 2015-08-18T00:12:00Z 7.887
  9. 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 between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results in to 12-minute time intervals and per tag. The query fills empty time intervals with 9.01, and it limits the number of points and series returned to four and one.

Notice that the GROUP BY time() clause 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.

Syntax

  1. 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]

MIN(field_key)
Returns the lowest field value associated with the field key.

MIN(/regular_expression/)
Returns the lowest field value associated with each field key that matches the regular expression.

MIN(*)
Returns the lowest field value associated with each field key in the 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 and/or field.

MIN() supports int64 and float64 field value data types.

Examples

Select the minimum field value associated with a field key
  1. > SELECT MIN("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time min
  4. ---- ---
  5. 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.

Select the minimum field value associated with each field key in a measurement
  1. > SELECT MIN(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time min_water_level
  4. ---- ---------------
  5. 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.

Select the minimum field value associated with each field key that matches a regular expression
  1. > SELECT MIN(/level/) FROM "h2o_feet"
  2. name: h2o_feet
  3. time min_water_level
  4. ---- ---------------
  5. 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.

Select the minimum field value associated with a field key and the relevant tags and fields
  1. > SELECT MIN("water_level"),"location","level description" FROM "h2o_feet"
  2. name: h2o_feet
  3. time min location level description
  4. ---- --- -------- -----------------
  5. 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.

Select the minimum field value associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. tags: location=coyote_creek
  4. time min
  5. ---- ---
  6. 2015-08-17T23:48:00Z 9.01
  7. 2015-08-18T00:00:00Z 8.005
  8. 2015-08-18T00:12:00Z 7.762
  9. 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 between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results in to 12-minute time intervals and per tag. The query fills empty time intervals with 9.01, and it limits the number of points and series returned to four and one.

Notice that the GROUP BY time() clause 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 Nth percentile field value.

Syntax

  1. SELECT PERCENTILE(<field_key>, <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]

PERCENTILE(field_key,N)
Returns the Nth percentile field value associated with the field key.

PERCENTILE(/regular_expression/,N)
Returns the Nth percentile field value associated with each field key that matches the regular expression.

PERCENTILE(*,N)
Returns the Nth percentile field value associated with each field key in the 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 and/or field.

N must be an integer or floating point number between 0 and 100, inclusive. PERCENTILE() supports int64 and float64 field value data types.

Examples

Select the fifth percentile field value associated with a field key
  1. > SELECT PERCENTILE("water_level",5) FROM "h2o_feet"
  2. name: h2o_feet
  3. time percentile
  4. ---- ----------
  5. 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.

Select the fifth percentile field value associated with each field key in a measurement
  1. > SELECT PERCENTILE(*,5) FROM "h2o_feet"
  2. name: h2o_feet
  3. time percentile_water_level
  4. ---- ----------------------
  5. 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.

Select fifth percentile field value associated with each field key that matches a regular expression
  1. > SELECT PERCENTILE(/level/,5) FROM "h2o_feet"
  2. name: h2o_feet
  3. time percentile_water_level
  4. ---- ----------------------
  5. 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.

Select the fifth percentile field values associated with a field key and the relevant tags and fields
  1. > SELECT PERCENTILE("water_level",5),"location","level description" FROM "h2o_feet"
  2. name: h2o_feet
  3. time percentile location level description
  4. ---- ---------- -------- -----------------
  5. 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.

Select the twentieth percentile field value associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time percentile
  4. ---- ----------
  5. 2015-08-17T23:36:00Z 15
  6. 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 between 2015-08-17T23:48:00Z and 2015-08-18T00:54:00Z and groups results into 24-minute intervals. It fills empty time intervals with 15 and it limits the number of points returned to two.

Notice that the GROUP BY time() clause 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()

PERCENTILE() compared to other InfluxQL functions
  • PERCENTILE(<field_key>,100) is equivalent to MAX(<field_key>).
  • PERCENTILE(<field_key>, 50) is nearly equivalent to MEDIAN(<field_key>), 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>). This is a known issue.

SAMPLE()

Returns a random sample of N field values. SAMPLE() uses reservoir sampling to generate the random points.

Syntax

  1. SELECT SAMPLE(<field_key>, <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]

SAMPLE(field_key,N)
Returns N randomly selected field values associated with the field key.

SAMPLE(/regular_expression/,N)
Returns N randomly selected field values associated with each field key that matches the regular expression.

SAMPLE(*,N)
Returns N randomly selected field values associated with each field key in the 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 and/or field.

N must be an integer. SAMPLE() supports all field value data types.

Examples

Select a sample of the field values associated with a field key
  1. > SELECT SAMPLE("water_level",2) FROM "h2o_feet"
  2. name: h2o_feet
  3. time sample
  4. ---- ------
  5. 2015-09-09T21:48:00Z 5.659
  6. 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.

Select a sample of the field values associated with each field key in a measurement

  1. > SELECT SAMPLE(*,2) FROM "h2o_feet"
  2. name: h2o_feet
  3. time sample_level description sample_water_level
  4. ---- ------------------------ ------------------
  5. 2015-08-25T17:06:00Z 3.284
  6. 2015-09-03T04:30:00Z below 3 feet
  7. 2015-09-03T20:06:00Z between 3 and 6 feet
  8. 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.

Select a sample of the field values associated with each field key that matches a regular expression
  1. > SELECT SAMPLE(/level/,2) FROM "h2o_feet"
  2. name: h2o_feet
  3. time sample_level description sample_water_level
  4. ---- ------------------------ ------------------
  5. 2015-08-30T05:54:00Z between 6 and 9 feet
  6. 2015-09-07T01:18:00Z 7.854
  7. 2015-09-09T20:30:00Z 7.32
  8. 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.

Select a sample of the field values associated with a field key and the relevant tags and fields
  1. > SELECT SAMPLE("water_level",2),"location","level description" FROM "h2o_feet"
  2. name: h2o_feet
  3. time sample location level description
  4. ---- ------ -------- -----------------
  5. 2015-08-29T10:54:00Z 5.689 coyote_creek between 3 and 6 feet
  6. 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.

Select a sample of the field values associated with a field key and include several clauses
  1. > 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)
  2. name: h2o_feet
  3. time sample
  4. ---- ------
  5. 2015-08-18T00:12:00Z 2.028
  6. 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 between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and groups results into 18-minute intervals.

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 SAMPLE()

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, 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

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.

  1. > 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)
  2. name: h2o_feet
  3. time sample
  4. ---- ------
  5. __
  6. 2015-08-18T00:06:00Z 2.116 |
  7. 2015-08-18T00:12:00Z 2.028 | <------- Randomly-selected points for the first time interval
  8. --
  9. __
  10. 2015-08-18T00:18:00Z 2.126 |
  11. 2015-08-18T00:30:00Z 2.051 | <------- Randomly-selected points for the second time interval
  12. --

TOP()

Returns the greatest N field values.

Syntax

  1. SELECT TOP( <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]

TOP(field_key,N)
Returns the greatest N field values associated with the field key.

TOP(field_key,tag_key(s),N)
Returns the greatest field value for N tag values of the 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 and/or field.

TOP() supports int64 and float64 field value 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. See the Common Issues section for more information.

Examples

Select the top three field values associated with a field key
  1. > SELECT TOP("water_level",3) FROM "h2o_feet"
  2. name: h2o_feet
  3. time top
  4. ---- ---
  5. 2015-08-29T07:18:00Z 9.957
  6. 2015-08-29T07:24:00Z 9.964
  7. 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.

Select the top field value associated with a field key for two tags
  1. > SELECT TOP("water_level","location",2) FROM "h2o_feet"
  2. name: h2o_feet
  3. time top location
  4. ---- --- --------
  5. 2015-08-29T03:54:00Z 7.205 santa_monica
  6. 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.

Select the top four field values associated with a field key and the relevant tags and fields
  1. > SELECT TOP("water_level",4),"location","level description" FROM "h2o_feet"
  2. name: h2o_feet
  3. time top location level description
  4. ---- --- -------- -----------------
  5. 2015-08-29T07:18:00Z 9.957 coyote_creek at or greater than 9 feet
  6. 2015-08-29T07:24:00Z 9.964 coyote_creek at or greater than 9 feet
  7. 2015-08-29T07:30:00Z 9.954 coyote_creek at or greater than 9 feet
  8. 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.

Select the top three field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time top location
  4. ---- --- --------
  5. 2015-08-18T00:48:00Z 7.11 coyote_creek
  6. 2015-08-18T00:54:00Z 6.982 coyote_creek
  7. 2015-08-18T00:54:00Z 2.054 santa_monica
  8. 2015-08-18T00:24:00Z 7.635 coyote_creek
  9. 2015-08-18T00:30:00Z 7.5 coyote_creek
  10. 2015-08-18T00:36:00Z 7.372 coyote_creek
  11. 2015-08-18T00:00:00Z 8.12 coyote_creek
  12. 2015-08-18T00:06:00Z 8.005 coyote_creek
  13. 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 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 TOP()

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, 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

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.

  1. > 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)
  2. name: h2o_feet
  3. time top
  4. ---- ------
  5. __
  6. 2015-08-18T00:00:00Z 2.064 |
  7. 2015-08-18T00:06:00Z 2.116 | <------- Greatest points for the first time interval
  8. --
  9. __
  10. 2015-08-18T00:18:00Z 2.126 |
  11. 2015-08-18T00:30:00Z 2.051 | <------- Greatest points for the second time interval
  12. --
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

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.

  1. > SELECT TOP("water_level","location",3) FROM "h2o_feet"
  2. name: h2o_feet
  3. time top location
  4. ---- --- --------
  5. 2015-08-29T03:54:00Z 7.205 santa_monica
  6. 2015-08-29T07:24:00Z 9.964 coyote_creek
TOP(), tags, and the INTO clause

When combined with an INTO clause and no GROUP BY tag clause, most InfluxQL functions convert 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

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 that InfluxDB preserved the location tag as a tag in the top_water_levels measurement.

  1. > SELECT TOP("water_level","location",2) INTO "top_water_levels" FROM "h2o_feet"
  2. name: result
  3. time written
  4. ---- -------
  5. 1970-01-01T00:00:00Z 2
  6. > SHOW TAG KEYS FROM "top_water_levels"
  7. name: top_water_levels
  8. tagKey
  9. ------
  10. location

Transformations

ABS()

Returns the absolute value of the field value.

Basic syntax

  1. SELECT ABS( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

ABS(field_key)
Returns the absolute values of field values associated with the field key.

ABS(*)
Returns the absolute values of field values associated with each field key in the measurement.

ABS() 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 ABS() with a GROUP BY time() clause.

Examples

The examples below use the following subsample of this sample data:

  1. > SELECT * FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T12:05:00Z'
  2. name: data
  3. time a b
  4. ---- - -
  5. 1529841600000000000 1.33909108671076 -0.163643058925645
  6. 1529841660000000000 -0.774984088561186 0.137034364053949
  7. 1529841720000000000 -0.921037167720451 -0.482943221384294
  8. 1529841780000000000 -1.73880754843378 -0.0729732928756677
  9. 1529841840000000000 -0.905980032168252 1.77857552719844
  10. 1529841900000000000 -0.891164752631417 0.741147445214238
Calculate the absolute values of field values associated with a field key
  1. > SELECT ABS("a") FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T12:05:00Z'
  2. name: data
  3. time abs
  4. ---- ---
  5. 1529841600000000000 1.33909108671076
  6. 1529841660000000000 0.774984088561186
  7. 1529841720000000000 0.921037167720451
  8. 1529841780000000000 1.73880754843378
  9. 1529841840000000000 0.905980032168252
  10. 1529841900000000000 0.891164752631417

The query returns the absolute values of field values in the a field key in the data measurement.

Calculate the absolute Values of field values associated with each field key in a measurement
  1. > SELECT ABS(*) FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T12:05:00Z'
  2. name: data
  3. time abs_a abs_b
  4. ---- ----- -----
  5. 1529841600000000000 1.33909108671076 0.163643058925645
  6. 1529841660000000000 0.774984088561186 0.137034364053949
  7. 1529841720000000000 0.921037167720451 0.482943221384294
  8. 1529841780000000000 1.73880754843378 0.0729732928756677
  9. 1529841840000000000 0.905980032168252 1.77857552719844
  10. 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.

Calculate the absolute values of field values associated with a field key and include several clauses
  1. > 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
  2. name: data
  3. time abs
  4. ---- ---
  5. 1529841780000000000 1.73880754843378
  6. 1529841720000000000 0.921037167720451
  7. 1529841660000000000 0.774984088561186
  8. 1529841600000000000 1.33909108671076

The query returns the absolute values of field values associated with the a field key. It covers the time range between 2018-06-24T12:00:00Z and 2018-06-24T12:05:00Z 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

  1. SELECT ABS(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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 ABS() function to those results.

ABS() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples
Calculate the absolute values of mean values
  1. > SELECT ABS(MEAN("a")) FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T13:00:00Z' GROUP BY time(12m)
  2. name: data
  3. time abs
  4. ---- ---
  5. 1529841600000000000 0.3960977256302787
  6. 1529842320000000000 0.0010541018316373302
  7. 1529843040000000000 0.04494733240283668
  8. 1529843760000000000 0.2553594777104415
  9. 1529844480000000000 0.20382988543108413
  10. 1529845200000000000 0.790836070736962

The query returns the absolute values of average as that are calculated at 12-minute intervals.

To get those results, InfluxDB first calculates the average as at 12-minute intervals. This step is the same as using the MEAN() function with the GROUP BY time() clause and without ABS():

  1. > SELECT MEAN("a") FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T13:00:00Z' GROUP BY time(12m)
  2. name: data
  3. time mean
  4. ---- ----
  5. 1529841600000000000 -0.3960977256302787
  6. 1529842320000000000 0.0010541018316373302
  7. 1529843040000000000 0.04494733240283668
  8. 1529843760000000000 0.2553594777104415
  9. 1529844480000000000 0.20382988543108413
  10. 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

  1. SELECT ACOS( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

ACOS(field_key)
Returns the arccosine of field values associated with the field key.

ACOS(*)
Returns the arccosine of field values associated with each field key in the measurement.

ACOS() supports int64 and float64 field value data types with values between -1 and 1.

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 ACOS() with a GROUP BY time() clause.

Examples

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:

  1. > SELECT "of_capacity" FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z'
  2. name: park_occupancy
  3. time capacity
  4. ---- --------
  5. 2017-05-01T00:00:00Z 0.83
  6. 2017-05-02T00:00:00Z 0.3
  7. 2017-05-03T00:00:00Z 0.84
  8. 2017-05-04T00:00:00Z 0.22
  9. 2017-05-05T00:00:00Z 0.17
  10. 2017-05-06T00:00:00Z 0.77
  11. 2017-05-07T00:00:00Z 0.64
  12. 2017-05-08T00:00:00Z 0.72
  13. 2017-05-09T00:00:00Z 0.16
Calculate the arccosine of field values associated with a field key
  1. > SELECT ACOS("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z'
  2. name: park_occupancy
  3. time acos
  4. ---- ----
  5. 2017-05-01T00:00:00Z 0.591688642426544
  6. 2017-05-02T00:00:00Z 1.266103672779499
  7. 2017-05-03T00:00:00Z 0.5735131044230969
  8. 2017-05-04T00:00:00Z 1.3489818562981022
  9. 2017-05-05T00:00:00Z 1.399966657665792
  10. 2017-05-06T00:00:00Z 0.6919551751263169
  11. 2017-05-07T00:00:00Z 0.8762980611683406
  12. 2017-05-08T00:00:00Z 0.7669940078618667
  13. 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.

Calculate the arccosine of field values associated with each field key in a measurement
  1. > SELECT ACOS(*) FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z'
  2. name: park_occupancy
  3. time acos_of_capacity
  4. ---- -------------
  5. 2017-05-01T00:00:00Z 0.591688642426544
  6. 2017-05-02T00:00:00Z 1.266103672779499
  7. 2017-05-03T00:00:00Z 0.5735131044230969
  8. 2017-05-04T00:00:00Z 1.3489818562981022
  9. 2017-05-05T00:00:00Z 1.399966657665792
  10. 2017-05-06T00:00:00Z 0.6919551751263169
  11. 2017-05-07T00:00:00Z 0.8762980611683406
  12. 2017-05-08T00:00:00Z 0.7669940078618667
  13. 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.

Calculate the arccosine of field values associated with a field key and include several clauses
  1. > 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
  2. name: park_occupancy
  3. time acos
  4. ---- ----
  5. 2017-05-07T00:00:00Z 0.8762980611683406
  6. 2017-05-06T00:00:00Z 0.6919551751263169
  7. 2017-05-05T00:00:00Z 1.399966657665792
  8. 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 between 2017-05-01T00:00:00Z and 2017-05-09T00:00:00Z 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

  1. SELECT ACOS(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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 ACOS() function to those results.

ACOS() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples
Calculate the arccosine of mean values
  1. > 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)
  2. name: park_occupancy
  3. time acos
  4. ---- ----
  5. 2017-04-30T00:00:00Z 0.9703630732143733
  6. 2017-05-03T00:00:00Z 1.1483422646081407
  7. 2017-05-06T00:00:00Z 0.7812981174487247
  8. 2017-05-09T00:00:00Z 1.410105673842986

The query returns arccosine of average of_capacitys that are calculated at 3-day intervals.

To get those results, InfluxDB first calculates the average of_capacitys at 3-day intervals. This step is the same as using the MEAN() function with the GROUP BY time() clause and without ACOS():

  1. > 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)
  2. name: park_occupancy
  3. time mean
  4. ---- ----
  5. 2017-04-30T00:00:00Z 0.565
  6. 2017-05-03T00:00:00Z 0.41
  7. 2017-05-06T00:00:00Z 0.71
  8. 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

  1. SELECT ASIN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

ASIN(field_key)
Returns the arcsine of field values associated with the field key.

ASIN(*)
Returns the arcsine of field values associated with each field key in the measurement.

ASIN() supports int64 and float64 field value data types with values between -1 and 1.

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 ASIN() with a GROUP BY time() clause.

Examples

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:

  1. > SELECT "of_capacity" FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z'
  2. name: park_occupancy
  3. time capacity
  4. ---- --------
  5. 2017-05-01T00:00:00Z 0.83
  6. 2017-05-02T00:00:00Z 0.3
  7. 2017-05-03T00:00:00Z 0.84
  8. 2017-05-04T00:00:00Z 0.22
  9. 2017-05-05T00:00:00Z 0.17
  10. 2017-05-06T00:00:00Z 0.77
  11. 2017-05-07T00:00:00Z 0.64
  12. 2017-05-08T00:00:00Z 0.72
  13. 2017-05-09T00:00:00Z 0.16
Calculate the arcsine of field values associated with a field key
  1. > SELECT ASIN("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z'
  2. name: park_occupancy
  3. time asin
  4. ---- ----
  5. 2017-05-01T00:00:00Z 0.9791076843683526
  6. 2017-05-02T00:00:00Z 0.3046926540153975
  7. 2017-05-03T00:00:00Z 0.9972832223717997
  8. 2017-05-04T00:00:00Z 0.22181447049679442
  9. 2017-05-05T00:00:00Z 0.1708296691291045
  10. 2017-05-06T00:00:00Z 0.8788411516685797
  11. 2017-05-07T00:00:00Z 0.6944982656265559
  12. 2017-05-08T00:00:00Z 0.8038023189330299
  13. 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.

Calculate the arcsine of field values associated with each field key in a measurement
  1. > SELECT ASIN(*) FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z'
  2. name: park_occupancy
  3. time asin_of_capacity
  4. ---- -------------
  5. 2017-05-01T00:00:00Z 0.9791076843683526
  6. 2017-05-02T00:00:00Z 0.3046926540153975
  7. 2017-05-03T00:00:00Z 0.9972832223717997
  8. 2017-05-04T00:00:00Z 0.22181447049679442
  9. 2017-05-05T00:00:00Z 0.1708296691291045
  10. 2017-05-06T00:00:00Z 0.8788411516685797
  11. 2017-05-07T00:00:00Z 0.6944982656265559
  12. 2017-05-08T00:00:00Z 0.8038023189330299
  13. 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.

Calculate the arcsine of field values associated with a field key and include several clauses
  1. > 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
  2. name: park_occupancy
  3. time asin
  4. ---- ----
  5. 2017-05-07T00:00:00Z 0.6944982656265559
  6. 2017-05-06T00:00:00Z 0.8788411516685797
  7. 2017-05-05T00:00:00Z 0.1708296691291045
  8. 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 between 2017-05-01T00:00:00Z and 2017-05-09T00:00:00Z 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

  1. SELECT ASIN(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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 ASIN() function to those results.

ASIN() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples
Calculate the arcsine of mean values.
  1. > 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)
  2. name: park_occupancy
  3. time asin
  4. ---- ----
  5. 2017-04-30T00:00:00Z 0.6004332535805232
  6. 2017-05-03T00:00:00Z 0.42245406218675574
  7. 2017-05-06T00:00:00Z 0.7894982093461719
  8. 2017-05-09T00:00:00Z 0.1606906529519106

The query returns arcsine of average of_capacitys that are calculated at 3-day intervals.

To get those results, InfluxDB first calculates the average of_capacitys at 3-day intervals. This step is the same as using the MEAN() function with the GROUP BY time() clause and without ASIN():

  1. > 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)
  2. name: park_occupancy
  3. time mean
  4. ---- ----
  5. 2017-04-30T00:00:00Z 0.565
  6. 2017-05-03T00:00:00Z 0.41
  7. 2017-05-06T00:00:00Z 0.71
  8. 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

  1. SELECT ATAN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

ATAN(field_key)
Returns the arctangent of field values associated with the field key.

ATAN(*)
Returns the arctangent of field values associated with each field key in the measurement.

ATAN() supports int64 and float64 field value data types with values between -1 and 1.

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 ATAN() with a GROUP BY time() clause.

Examples

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:

  1. > SELECT "of_capacity" FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z'
  2. name: park_occupancy
  3. time capacity
  4. ---- --------
  5. 2017-05-01T00:00:00Z 0.83
  6. 2017-05-02T00:00:00Z 0.3
  7. 2017-05-03T00:00:00Z 0.84
  8. 2017-05-04T00:00:00Z 0.22
  9. 2017-05-05T00:00:00Z 0.17
  10. 2017-05-06T00:00:00Z 0.77
  11. 2017-05-07T00:00:00Z 0.64
  12. 2017-05-08T00:00:00Z 0.72
  13. 2017-05-09T00:00:00Z 0.16
Calculate the arctangent of field values associated with a field key
  1. > SELECT ATAN("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z'
  2. name: park_occupancy
  3. time atan
  4. ---- ----
  5. 2017-05-01T00:00:00Z 0.6927678353971222
  6. 2017-05-02T00:00:00Z 0.2914567944778671
  7. 2017-05-03T00:00:00Z 0.6986598247214632
  8. 2017-05-04T00:00:00Z 0.2165503049760893
  9. 2017-05-05T00:00:00Z 0.16839015714752992
  10. 2017-05-06T00:00:00Z 0.6561787179913948
  11. 2017-05-07T00:00:00Z 0.5693131911006619
  12. 2017-05-08T00:00:00Z 0.6240230529767568
  13. 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.

Calculate the arctangent of field values associated with each field key in a measurement
  1. > SELECT ATAN(*) FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z'
  2. name: park_occupancy
  3. time atan_of_capacity
  4. ---- -------------
  5. 2017-05-01T00:00:00Z 0.6927678353971222
  6. 2017-05-02T00:00:00Z 0.2914567944778671
  7. 2017-05-03T00:00:00Z 0.6986598247214632
  8. 2017-05-04T00:00:00Z 0.2165503049760893
  9. 2017-05-05T00:00:00Z 0.16839015714752992
  10. 2017-05-06T00:00:00Z 0.6561787179913948
  11. 2017-05-07T00:00:00Z 0.5693131911006619
  12. 2017-05-08T00:00:00Z 0.6240230529767568
  13. 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.

Calculate the arctangent of field values associated with a field key and include several clauses
  1. > 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
  2. name: park_occupancy
  3. time atan
  4. ---- ----
  5. 2017-05-07T00:00:00Z 0.5693131911006619
  6. 2017-05-06T00:00:00Z 0.6561787179913948
  7. 2017-05-05T00:00:00Z 0.16839015714752992
  8. 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 between 2017-05-01T00:00:00Z and 2017-05-09T00:00:00Z 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

  1. SELECT ATAN(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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 ATAN() function to those results.

ATAN() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples of advanced syntax
Calculate the arctangent of mean values.
  1. > 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)
  2. name: park_occupancy
  3. time atan
  4. ---- ----
  5. 2017-04-30T00:00:00Z 0.5142865412694495
  6. 2017-05-03T00:00:00Z 0.3890972310552784
  7. 2017-05-06T00:00:00Z 0.6174058917515726
  8. 2017-05-09T00:00:00Z 0.1586552621864014

The query returns arctangent of average of_capacitys that are calculated at 3-day intervals.

To get those results, InfluxDB first calculates the average of_capacitys at 3-day intervals. This step is the same as using the MEAN() function with the GROUP BY time() clause and without ATAN():

  1. > 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)
  2. name: park_occupancy
  3. time mean
  4. ---- ----
  5. 2017-04-30T00:00:00Z 0.565
  6. 2017-05-03T00:00:00Z 0.41
  7. 2017-05-06T00:00:00Z 0.71
  8. 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

  1. 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]

ATAN2(field_key_y, field_key_x)
Returns the arctangent of field values associated with the field key, field_key_y, 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 divided by field values associated with field_key_x.

ATAN2() 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 ATAN2() with a GROUP BY time() clause.

Examples

The examples below use the following sample of simulated flight data:

  1. > SELECT "altitude_ft", "distance_ft" FROM "flight_data" WHERE time >= '2018-05-16T12:01:00Z' AND time <= '2018-05-16T12:10:00Z'
  2. name: flight_data
  3. time altitude_ft distance_ft
  4. ---- ----------- -----------
  5. 2018-05-16T12:01:00Z 1026 50094
  6. 2018-05-16T12:02:00Z 2549 53576
  7. 2018-05-16T12:03:00Z 4033 55208
  8. 2018-05-16T12:04:00Z 5579 58579
  9. 2018-05-16T12:05:00Z 7065 61213
  10. 2018-05-16T12:06:00Z 8589 64807
  11. 2018-05-16T12:07:00Z 10180 67707
  12. 2018-05-16T12:08:00Z 11777 69819
  13. 2018-05-16T12:09:00Z 13321 72452
  14. 2018-05-16T12:10:00Z 14885 75881
Calculate the arctangent of field_key_y over field_key_x
  1. > SELECT ATAN2("altitude_ft", "distance_ft") FROM "flight_data" WHERE time >= '2018-05-16T12:01:00Z' AND time <= '2018-05-16T12:10:00Z'
  2. name: flight_data
  3. time atan2
  4. ---- -----
  5. 2018-05-16T12:01:00Z 0.020478631571881498
  6. 2018-05-16T12:02:00Z 0.04754142349303296
  7. 2018-05-16T12:03:00Z 0.07292147724575364
  8. 2018-05-16T12:04:00Z 0.09495251193874832
  9. 2018-05-16T12:05:00Z 0.11490822875441563
  10. 2018-05-16T12:06:00Z 0.13176409347584003
  11. 2018-05-16T12:07:00Z 0.14923587589682233
  12. 2018-05-16T12:08:00Z 0.1671059946640312
  13. 2018-05-16T12:09:00Z 0.18182893717409565
  14. 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.

Calculate the arctangent of values associated with each field key in a measurement divided by field_key_x
  1. > SELECT ATAN2(*, "distance_ft") FROM "flight_data" WHERE time >= '2018-05-16T12:01:00Z' AND time <= '2018-05-16T12:10:00Z'
  2. name: flight_data
  3. time atan2_altitude_ft atan2_distance_ft
  4. ---- ----------------- -----------------
  5. 2018-05-16T12:01:00Z 0.020478631571881498 0.7853981633974483
  6. 2018-05-16T12:02:00Z 0.04754142349303296 0.7853981633974483
  7. 2018-05-16T12:03:00Z 0.07292147724575364 0.7853981633974483
  8. 2018-05-16T12:04:00Z 0.09495251193874832 0.7853981633974483
  9. 2018-05-16T12:05:00Z 0.11490822875441563 0.7853981633974483
  10. 2018-05-16T12:06:00Z 0.13176409347584003 0.7853981633974483
  11. 2018-05-16T12:07:00Z 0.14923587589682233 0.7853981633974483
  12. 2018-05-16T12:08:00Z 0.1671059946640312 0.7853981633974483
  13. 2018-05-16T12:09:00Z 0.18182893717409565 0.7853981633974483
  14. 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.

Calculate the arctangents of field values and include several clauses
  1. > 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
  2. name: flight_data
  3. time atan2
  4. ---- -----
  5. 2018-05-16T12:08:00Z 0.1671059946640312
  6. 2018-05-16T12:07:00Z 0.14923587589682233
  7. 2018-05-16T12:06:00Z 0.13176409347584003
  8. 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 between 2018-05-16T12:10:00Z and 2018-05-16T12:10:00Z 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

  1. SELECT ATAN2(<function()>, <function()>) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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 ATAN2() function to those results.

ATAN2() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples
Calculate arctangents of mean values
  1. > 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)
  2. name: flight_data
  3. time atan2
  4. ---- -----
  5. 2018-05-16T12:00:00Z 0.133815587896842
  6. 2018-05-16T12:12:00Z 0.2662716308351908
  7. 2018-05-16T12:24:00Z 0.2958845306108965
  8. 2018-05-16T12:36:00Z 0.23783439588429497
  9. 2018-05-16T12:48:00Z 0.1906803720242831
  10. 2018-05-16T13:00:00Z 0.17291511946158172

The query returns the argtangents of average altitude_fts divided by average distance_fts. Averages are calculated at 12-minute intervals.

To get those results, InfluxDB first calculates the average altitude_fts 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(): ^

  1. > 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)
  2. name: flight_data
  3. time mean mean_1
  4. ---- ---- ------
  5. 2018-05-16T12:00:00Z 8674 64433.181818181816
  6. 2018-05-16T12:12:00Z 26419.833333333332 96865.25
  7. 2018-05-16T12:24:00Z 40337.416666666664 132326.41666666666
  8. 2018-05-16T12:36:00Z 41149.583333333336 169743.16666666666
  9. 2018-05-16T12:48:00Z 41230.416666666664 213600.91666666666
  10. 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

  1. SELECT CEIL( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

CEIL(field_key)
Returns the field values associated with the field key rounded up to the nearest integer.

CEIL(*)
Returns the field values associated with each field key in the measurement rounded up to the nearest integer.

CEIL() 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 CEIL() with a GROUP BY time() clause.

Examples

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the ceiling of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time ceil
  4. ---- ----
  5. 2015-08-18T00:00:00Z 3
  6. 2015-08-18T00:06:00Z 3
  7. 2015-08-18T00:12:00Z 3
  8. 2015-08-18T00:18:00Z 3
  9. 2015-08-18T00:24:00Z 3
  10. 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.

Calculate the ceiling of field values associated with each field key in a measurement
  1. > SELECT CEIL(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time ceil_water_level
  4. ---- ----------------
  5. 2015-08-18T00:00:00Z 3
  6. 2015-08-18T00:06:00Z 3
  7. 2015-08-18T00:12:00Z 3
  8. 2015-08-18T00:18:00Z 3
  9. 2015-08-18T00:24:00Z 3
  10. 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.

Calculate the ceiling of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time ceil
  4. ---- ----
  5. 2015-08-18T00:18:00Z 3
  6. 2015-08-18T00:12:00Z 3
  7. 2015-08-18T00:06:00Z 3
  8. 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 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

  1. SELECT CEIL(<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]

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 CEIL() function to those results.

CEIL() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples
Calculate mean values rounded up to the nearest integer
  1. > 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)
  2. name: h2o_feet
  3. time ceil
  4. ---- ----
  5. 2015-08-18T00:00:00Z 3
  6. 2015-08-18T00:12:00Z 3
  7. 2015-08-18T00:24:00Z 3

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 CEIL():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. SELECT COS( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

COS(field_key)
Returns the cosine of field values associated with the field key.

COS(*)
Returns the cosine of field values associated with each field key in the measurement.

COS() 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 COS() with a GROUP BY time() clause.

Examples

The examples below use the following subsample of the NOAA_water_database data: ^

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the cosine of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time cos
  4. ---- ---
  5. 2015-08-18T00:00:00Z -0.47345017433543124
  6. 2015-08-18T00:06:00Z -0.5185922462666872
  7. 2015-08-18T00:12:00Z -0.4414407189100776
  8. 2015-08-18T00:18:00Z -0.5271163912192579
  9. 2015-08-18T00:24:00Z -0.45306786455514825
  10. 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.

Calculate the cosine of field values associated with each field key in a measurement
  1. > SELECT COS(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time cos_water_level
  4. ---- ---------------
  5. 2015-08-18T00:00:00Z -0.47345017433543124
  6. 2015-08-18T00:06:00Z -0.5185922462666872
  7. 2015-08-18T00:12:00Z -0.4414407189100776
  8. 2015-08-18T00:18:00Z -0.5271163912192579
  9. 2015-08-18T00:24:00Z -0.45306786455514825
  10. 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.

Calculate the cosine of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time cos
  4. ---- ---
  5. 2015-08-18T00:18:00Z -0.5271163912192579
  6. 2015-08-18T00:12:00Z -0.4414407189100776
  7. 2015-08-18T00:06:00Z -0.5185922462666872
  8. 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 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

  1. SELECT COS(<function>( [ * | <field_key> ] )) [INTO_clause] FROM_clause [WHERE_clause] GROUP_BY_clause [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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 COS() function to those results.

COS() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples

Calculate the cosine of mean values
  1. > 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)
  2. name: h2o_feet
  3. time cos
  4. ---- ---
  5. 2015-08-18T00:00:00Z -0.49618891270599885
  6. 2015-08-18T00:12:00Z -0.4848605136571181
  7. 2015-08-18T00:24:00Z -0.4575195627907578

The query returns cosine 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 COS():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 2015-08-18T00:24:00Z 2.0460000000000003

InfluxDB then calculates cosine of those averages.

CUMULATIVE_SUM()

Returns the running total of subsequent field values.

Basic syntax

  1. 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]

CUMULATIVE_SUM(field_key)
Returns the running total of subsequent field values associated with the field key.

CUMULATIVE_SUM(/regular_expression/)
Returns the running total of subsequent field values associated with each field key that matches the regular expression.

CUMULATIVE_SUM(*)
Returns the running total of subsequent field values associated with each field key in the measurement.

CUMULATIVE_SUM() 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 CUMULATIVE_SUM() with a GROUP BY time() clause.

Examples

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the cumulative sum of the field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time cumulative_sum
  4. ---- --------------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 4.18
  7. 2015-08-18T00:12:00Z 6.208
  8. 2015-08-18T00:18:00Z 8.334
  9. 2015-08-18T00:24:00Z 10.375
  10. 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.

Calculate the cumulative sum of the field values associated with each field key in a measurement
  1. > SELECT CUMULATIVE_SUM(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time cumulative_sum_water_level
  4. ---- --------------------------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 4.18
  7. 2015-08-18T00:12:00Z 6.208
  8. 2015-08-18T00:18:00Z 8.334
  9. 2015-08-18T00:24:00Z 10.375
  10. 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.

Calculate the cumulative sum of the field values associated with each field key that matches a regular expression
  1. > 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'
  2. name: h2o_feet
  3. time cumulative_sum_water_level
  4. ---- --------------------------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 4.18
  7. 2015-08-18T00:12:00Z 6.208
  8. 2015-08-18T00:18:00Z 8.334
  9. 2015-08-18T00:24:00Z 10.375
  10. 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.

Calculate the cumulative sum of the field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time cumulative_sum
  4. ---- --------------
  5. 2015-08-18T00:18:00Z 6.218
  6. 2015-08-18T00:12:00Z 8.246
  7. 2015-08-18T00:06:00Z 10.362
  8. 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 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

  1. SELECT CUMULATIVE_SUM(<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]

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 CUMULATIVE_SUM() function to those results.

CUMULATIVE_SUM() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples
Calculate the cumulative sum of mean values
  1. > 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)
  2. name: h2o_feet
  3. time cumulative_sum
  4. ---- --------------
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 4.167
  7. 2015-08-18T00:24:00Z 6.213

The query returns the running total 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 CUMULATIVE_SUM():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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.

Basic syntax

  1. SELECT 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]

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).

DERIVATIVE(field_key)
Returns the rate of change between subsequent field values associated with the field key.

DERIVATIVE(/regular_expression/)
Returns the rate of change between subsequent field values associated with each field key that matches the regular expression.

DERIVATIVE(*)
Returns the rate of change between subsequent field values associated with each field key in the measurement.

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 DERIVATIVE() with a GROUP BY time() clause.

Examples

Examples 1-5 use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the derivative between the field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time derivative
  4. ---- ----------
  5. 2015-08-18T00:06:00Z 0.00014444444444444457
  6. 2015-08-18T00:12:00Z -0.00024444444444444465
  7. 2015-08-18T00:18:00Z 0.0002722222222222218
  8. 2015-08-18T00:24:00Z -0.000236111111111111
  9. 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:

  1. (2.116 - 2.064) / (360s / 1s)
  2. -------------- ----------
  3. | |
  4. | the difference between the field values' timestamps / the default unit
  5. second field value - first field value
Calculate the derivative between the field values associated with a field key and specify the unit option
  1. > 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'
  2. name: h2o_feet
  3. time derivative
  4. ---- ----------
  5. 2015-08-18T00:06:00Z 0.052000000000000046
  6. 2015-08-18T00:12:00Z -0.08800000000000008
  7. 2015-08-18T00:18:00Z 0.09799999999999986
  8. 2015-08-18T00:24:00Z -0.08499999999999996
  9. 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:

  1. (2.116 - 2.064) / (6m / 6m)
  2. -------------- ----------
  3. | |
  4. | the difference between the field values' timestamps / the specified unit
  5. second field value - first field value
Calculate the derivative between the field values associated with each field key in a measurement and specify the unit option
  1. > SELECT DERIVATIVE(*,3m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
  2. name: h2o_feet
  3. time derivative_water_level
  4. ---- ----------------------
  5. 2015-08-18T00:06:00Z 0.026000000000000023
  6. 2015-08-18T00:12:00Z -0.04400000000000004
  7. 2015-08-18T00:18:00Z 0.04899999999999993
  8. 2015-08-18T00:24:00Z -0.04249999999999998
  9. 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:

  1. (2.116 - 2.064) / (6m / 3m)
  2. -------------- ----------
  3. | |
  4. | the difference between the field values' timestamps / the specified unit
  5. second field value - first field value
Calculate the derivative between the field values associated with each field key that matches a regular expression and specify the unit option
  1. > 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'
  2. name: h2o_feet
  3. time derivative_water_level
  4. ---- ----------------------
  5. 2015-08-18T00:06:00Z 0.01733333333333335
  6. 2015-08-18T00:12:00Z -0.02933333333333336
  7. 2015-08-18T00:18:00Z 0.03266666666666662
  8. 2015-08-18T00:24:00Z -0.02833333333333332
  9. 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:

  1. (2.116 - 2.064) / (6m / 2m)
  2. -------------- ----------
  3. | |
  4. | the difference between the field values' timestamps / the specified unit
  5. second field value - first field value
Calculate the derivative between the field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time derivative
  4. ---- ----------
  5. 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 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 one and offsets 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:

  1. (2.126 - 2.028) / (360s / 1s)
  2. -------------- ----------
  3. | |
  4. | the difference between the field values' timestamps / the default unit
  5. second field value - first field value

Advanced syntax

  1. SELECT 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]

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 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.

DERIVATIVE() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples
Calculate the derivative of mean values
  1. > 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)
  2. name: h2o_feet
  3. time derivative
  4. ---- ----------
  5. 2015-08-18T00:12:00Z -0.0129999999999999
  6. 2015-08-18T00:24:00Z -0.030999999999999694

The query returns the 12-minute rate of change between 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 DERIVATIVE():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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.

  1. (2.077 - 2.09) / (12m / 12m)
  2. ------------- ----------
  3. | |
  4. | the difference between the field values' timestamps / the default unit
  5. second field value - first field value
Calculate the derivative of mean values and specify the unit option
  1. > 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)
  2. name: h2o_feet
  3. time derivative
  4. ---- ----------
  5. 2015-08-18T00:12:00Z -0.00649999999999995
  6. 2015-08-18T00:24:00Z -0.015499999999999847

The query returns the six-minute rate of change between 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 DERIVATIVE():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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.

  1. (2.077 - 2.09) / (12m / 6m)
  2. ------------- ----------
  3. | |
  4. | the difference between the field values' timestamps / the specified unit
  5. second field value - first field value

DIFFERENCE()

Returns the result of subtraction between subsequent field values.

Basic syntax

  1. 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]

DIFFERENCE(field_key)
Returns the difference between subsequent field values associated with the field key.

DIFFERENCE(/regular_expression/)
Returns the difference between subsequent field values associated with each field key that matches the regular expression.

DIFFERENCE(*)
Returns the difference between subsequent field values associated with each field key in the measurement.

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 DIFFERENCE() with a GROUP BY time() clause.

Examples

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the difference between the field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time difference
  4. ---- ----------
  5. 2015-08-18T00:06:00Z 0.052000000000000046
  6. 2015-08-18T00:12:00Z -0.08800000000000008
  7. 2015-08-18T00:18:00Z 0.09799999999999986
  8. 2015-08-18T00:24:00Z -0.08499999999999996
  9. 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.

Calculate the difference between the field values associated with each field key in a measurement
  1. > SELECT DIFFERENCE(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time difference_water_level
  4. ---- ----------------------
  5. 2015-08-18T00:06:00Z 0.052000000000000046
  6. 2015-08-18T00:12:00Z -0.08800000000000008
  7. 2015-08-18T00:18:00Z 0.09799999999999986
  8. 2015-08-18T00:24:00Z -0.08499999999999996
  9. 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.

Calculate the difference between the field values associated with each field key that matches a regular expression
  1. > SELECT DIFFERENCE(/water/) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time difference_water_level
  4. ---- ----------------------
  5. 2015-08-18T00:06:00Z 0.052000000000000046
  6. 2015-08-18T00:12:00Z -0.08800000000000008
  7. 2015-08-18T00:18:00Z 0.09799999999999986
  8. 2015-08-18T00:24:00Z -0.08499999999999996
  9. 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.

Calculate the difference between the field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time difference
  4. ---- ----------
  5. 2015-08-18T00:12:00Z -0.09799999999999986
  6. 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 between 2015-08-18T00:00:00Z and 2015-08-18T00:30:00Z and returns results in descending timestamp order. They query also limits the number of points returned to two and offsets results by two points.

Advanced syntax

  1. SELECT 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]

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 DIFFERENCE() function to those results.

DIFFERENCE() supports the following nested functions: COUNT(), MEAN(), MEDIAN(), MODE(), SUM(), FIRST(), LAST(), MIN(), MAX(), and PERCENTILE().

Examples
Calculate the difference between maximum values
  1. > 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)
  2. name: h2o_feet
  3. time difference
  4. ---- ----------
  5. 2015-08-18T00:12:00Z 0.009999999999999787
  6. 2015-08-18T00:24:00Z -0.07499999999999973

The query returns the difference between 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 DIFFERENCE():

  1. > 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)
  2. name: h2o_feet
  3. time max
  4. ---- ---
  5. 2015-08-18T00:00:00Z 2.116
  6. 2015-08-18T00:12:00Z 2.126
  7. 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 timestamps.

Syntax

  1. SELECT ELAPSED( [ * | <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]

InfluxDB calculates the difference between subsequent timestamps. The unit option is an integer followed by a duration literal 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.

ELAPSED(/regular_expression/)
Returns the difference between subsequent timestamps associated with each field key that matches the regular expression.

ELAPSED(*)
Returns the difference between subsequent timestamps associated with each field key in the measurement.

ELAPSED() supports all field value data types.

Examples

The examples use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:12:00Z'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
Calculate the elapsed time between field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time elapsed
  4. ---- -------
  5. 2015-08-18T00:06:00Z 360000000000
  6. 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.

Calculate the elapsed time between field values associated with a field key and specify the unit option
  1. > 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'
  2. name: h2o_feet
  3. time elapsed
  4. ---- -------
  5. 2015-08-18T00:06:00Z 6
  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.

Calculate the elapsed time between field values associated with each field key in a measurement and specify the unit option
  1. > SELECT ELAPSED(*,1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:12:00Z'
  2. name: h2o_feet
  3. time elapsed_level description elapsed_water_level
  4. ---- ------------------------- -------------------
  5. 2015-08-18T00:06:00Z 6 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.

Calculate the elapsed time between field values associated with each field key that matches a regular expression and specify the unit option
  1. > 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'
  2. name: h2o_feet
  3. time elapsed_level description elapsed_water_level
  4. ---- ------------------------- -------------------
  5. 2015-08-18T00:06:00Z 360 360
  6. 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.

Calculate the elapsed time between field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time elapsed
  4. ---- -------
  5. 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 between 2015-08-18T00:00:00Z and 2015-08-18T00:12:00Z and sorts timestamps in descending order. The query also limits the number of points returned to one and offsets results by one point.

Notice that the result is negative; the ORDER BY time DESC clause sorts timestamps in descending order so ELAPSED() calculates the difference between timestamps in reverse order.

Common Issues with ELAPSED()

ELAPSED() and units greater than the elapsed time

InfluxDB returns 0 if the unit option is greater than the difference between the timestamps.

Example

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:

  1. > 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'
  2. name: h2o_feet
  3. time elapsed
  4. ---- -------
  5. 2015-08-18T00:06:00Z 0
  6. 2015-08-18T00:12:00Z 0

ELAPSED() with GROUP BY time() clauses

The ELAPSED() function supports the GROUP BY time() clause 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() or 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

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 water_levels. The query returns 12 minutes for both time intervals.

To get those results, InfluxDB first calculates the minimum water_levels 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.

  1. > 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)
  2. name: h2o_feet
  3. time elapsed
  4. ---- -------
  5. 2015-08-18T00:36:00Z 12
  6. 2015-08-18T00:48:00Z 12
  7. > 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)
  8. name: h2o_feet
  9. time min
  10. ---- ---
  11. 2015-08-18T00:36:00Z 2.057 <--- Actually occurs at 2015-08-18T00:42:00Z
  12. 2015-08-18T00:48:00Z 1.991

EXP()

Returns the exponential of the field value.

Basic syntax

  1. SELECT EXP( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

EXP(field_key)
Returns the exponential of field values associated with the field key.

EXP(*)
Returns the exponential of field values associated with each field key in the measurement.

EXP() 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 EXP() with a GROUP BY time() clause.

Examples

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the exponential of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time exp
  4. ---- ---
  5. 2015-08-18T00:00:00Z 7.877416541092307
  6. 2015-08-18T00:06:00Z 8.297879498060171
  7. 2015-08-18T00:12:00Z 7.598873404088091
  8. 2015-08-18T00:18:00Z 8.381274573459967
  9. 2015-08-18T00:24:00Z 7.6983036546645645
  10. 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.

Calculate the exponential of field values associated with each field key in a measurement
  1. > SELECT EXP(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time exp_water_level
  4. ---- ---------------
  5. 2015-08-18T00:00:00Z 7.877416541092307
  6. 2015-08-18T00:06:00Z 8.297879498060171
  7. 2015-08-18T00:12:00Z 7.598873404088091
  8. 2015-08-18T00:18:00Z 8.381274573459967
  9. 2015-08-18T00:24:00Z 7.6983036546645645
  10. 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.

Calculate the exponential of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time exp
  4. ---- ---
  5. 2015-08-18T00:18:00Z 8.381274573459967
  6. 2015-08-18T00:12:00Z 7.598873404088091
  7. 2015-08-18T00:06:00Z 8.297879498060171
  8. 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

  1. 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]

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
Calculate the exponential of mean values.
  1. > 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)
  2. name: h2o_feet
  3. time exp
  4. ---- ---
  5. 2015-08-18T00:00:00Z 8.084915164305059
  6. 2015-08-18T00:12:00Z 7.980491491670466
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. SELECT FLOOR( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the floor of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time floor
  4. ---- -----
  5. 2015-08-18T00:00:00Z 2
  6. 2015-08-18T00:06:00Z 2
  7. 2015-08-18T00:12:00Z 2
  8. 2015-08-18T00:18:00Z 2
  9. 2015-08-18T00:24:00Z 2
  10. 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.

Calculate the floor of field values associated with each field key in a measurement
  1. > SELECT FLOOR(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time floor_water_level
  4. ---- -----------------
  5. 2015-08-18T00:00:00Z 2
  6. 2015-08-18T00:06:00Z 2
  7. 2015-08-18T00:12:00Z 2
  8. 2015-08-18T00:18:00Z 2
  9. 2015-08-18T00:24:00Z 2
  10. 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.

Calculate the floor of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time floor
  4. ---- -----
  5. 2015-08-18T00:18:00Z 2
  6. 2015-08-18T00:12:00Z 2
  7. 2015-08-18T00:06:00Z 2
  8. 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

  1. 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]

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
Calculate mean values rounded down to the nearest integer.
  1. > 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)
  2. name: h2o_feet
  3. time floor
  4. ---- -----
  5. 2015-08-18T00:00:00Z 2
  6. 2015-08-18T00:12:00Z 2
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 2015-08-18T00:24:00Z 2.0460000000000003

InfluxDB then rounds those averages down to the nearest integer.

HISTOGRAM()

InfluxQL does not currently support histogram generation. For information about creating histograms with data stored in InfluxDB, see Flux’s histogram() function.

LN()

Returns the natural logarithm of the field value.

Basic syntax

  1. SELECT LN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the natural logarithm of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time ln
  4. ---- --
  5. 2015-08-18T00:00:00Z 0.7246458476193163
  6. 2015-08-18T00:06:00Z 0.749527513996053
  7. 2015-08-18T00:12:00Z 0.7070500857289368
  8. 2015-08-18T00:18:00Z 0.7542422799197561
  9. 2015-08-18T00:24:00Z 0.7134398838277077
  10. 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.

Calculate the natural logarithm of field values associated with each field key in a measurement
  1. > SELECT LN(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time ln_water_level
  4. ---- --------------
  5. 2015-08-18T00:00:00Z 0.7246458476193163
  6. 2015-08-18T00:06:00Z 0.749527513996053
  7. 2015-08-18T00:12:00Z 0.7070500857289368
  8. 2015-08-18T00:18:00Z 0.7542422799197561
  9. 2015-08-18T00:24:00Z 0.7134398838277077
  10. 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.

Calculate the natural logarithm of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time ln
  4. ---- --
  5. 2015-08-18T00:18:00Z 0.7542422799197561
  6. 2015-08-18T00:12:00Z 0.7070500857289368
  7. 2015-08-18T00:06:00Z 0.749527513996053
  8. 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

  1. 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]

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
Calculate the natural logarithm of mean values.
  1. > 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)
  2. name: h2o_feet
  3. time ln
  4. ---- --
  5. 2015-08-18T00:00:00Z 0.7371640659767196
  6. 2015-08-18T00:12:00Z 0.7309245448939752
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. 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]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the logarithm base 4 of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time log
  4. ---- ---
  5. 2015-08-18T00:00:00Z 0.5227214853805835
  6. 2015-08-18T00:06:00Z 0.5406698137259695
  7. 2015-08-18T00:12:00Z 0.5100288261706268
  8. 2015-08-18T00:18:00Z 0.5440707984345088
  9. 2015-08-18T00:24:00Z 0.5146380911853161
  10. 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.

Calculate the logarithm base 4 of field values associated with each field key in a measurement
  1. > SELECT LOG(*, 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time log_water_level
  4. ---- ---------------
  5. 2015-08-18T00:00:00Z 0.5227214853805835
  6. 2015-08-18T00:06:00Z 0.5406698137259695
  7. 2015-08-18T00:12:00Z 0.5100288261706268
  8. 2015-08-18T00:18:00Z 0.5440707984345088
  9. 2015-08-18T00:24:00Z 0.5146380911853161
  10. 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.

Calculate the logarithm base 4 of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time log
  4. ---- ---
  5. 2015-08-18T00:18:00Z 0.5440707984345088
  6. 2015-08-18T00:12:00Z 0.5100288261706268
  7. 2015-08-18T00:06:00Z 0.5406698137259695
  8. 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

  1. 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]

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
Calculate the logarithm base 4 of mean values
  1. > 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)
  2. name: h2o_feet
  3. time log
  4. ---- ---
  5. 2015-08-18T00:00:00Z 0.531751471153079
  6. 2015-08-18T00:12:00Z 0.5272506080912802
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. SELECT LOG2( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the logarithm base 2 of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time log2
  4. ---- ----
  5. 2015-08-18T00:00:00Z 1.045442970761167
  6. 2015-08-18T00:06:00Z 1.081339627451939
  7. 2015-08-18T00:12:00Z 1.0200576523412537
  8. 2015-08-18T00:18:00Z 1.0881415968690176
  9. 2015-08-18T00:24:00Z 1.0292761823706322
  10. 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.

Calculate the logarithm base 2 of field values associated with each field key in a measurement
  1. > SELECT LOG2(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time log2_water_level
  4. ---- ----------------
  5. 2015-08-18T00:00:00Z 1.045442970761167
  6. 2015-08-18T00:06:00Z 1.081339627451939
  7. 2015-08-18T00:12:00Z 1.0200576523412537
  8. 2015-08-18T00:18:00Z 1.0881415968690176
  9. 2015-08-18T00:24:00Z 1.0292761823706322
  10. 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.

Calculate the logarithm base 2 of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time log2
  4. ---- ----
  5. 2015-08-18T00:18:00Z 1.0881415968690176
  6. 2015-08-18T00:12:00Z 1.0200576523412537
  7. 2015-08-18T00:06:00Z 1.081339627451939
  8. 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

  1. 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]

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
Calculate the logarithm base 2 of mean values
  1. > 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)
  2. name: h2o_feet
  3. time log2
  4. ---- ----
  5. 2015-08-18T00:00:00Z 1.063502942306158
  6. 2015-08-18T00:12:00Z 1.0545012161825604
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. SELECT LOG10( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the logarithm base 10 of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time log10
  4. ---- -----
  5. 2015-08-18T00:00:00Z 0.3147096929551737
  6. 2015-08-18T00:06:00Z 0.32551566336314813
  7. 2015-08-18T00:12:00Z 0.3070679506612984
  8. 2015-08-18T00:18:00Z 0.32756326018727794
  9. 2015-08-18T00:24:00Z 0.3098430047160705
  10. 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.

Calculate the logarithm base 10 of field values associated with each field key in a measurement
  1. > SELECT LOG10(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time log10_water_level
  4. ---- -----------------
  5. 2015-08-18T00:00:00Z 0.3147096929551737
  6. 2015-08-18T00:06:00Z 0.32551566336314813
  7. 2015-08-18T00:12:00Z 0.3070679506612984
  8. 2015-08-18T00:18:00Z 0.32756326018727794
  9. 2015-08-18T00:24:00Z 0.3098430047160705
  10. 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.

Calculate the logarithm base 10 of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time log10
  4. ---- -----
  5. 2015-08-18T00:18:00Z 0.32756326018727794
  6. 2015-08-18T00:12:00Z 0.3070679506612984
  7. 2015-08-18T00:06:00Z 0.32551566336314813
  8. 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

  1. 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]

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
Calculate the logarithm base 10 of mean values
  1. > 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)
  2. name: h2o_feet
  3. time log10
  4. ---- -----
  5. 2015-08-18T00:00:00Z 0.32014628611105395
  6. 2015-08-18T00:12:00Z 0.3174364965350991
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. 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]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the moving average of the field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time moving_average
  4. ---- --------------
  5. 2015-08-18T00:06:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.072
  7. 2015-08-18T00:18:00Z 2.077
  8. 2015-08-18T00:24:00Z 2.0835
  9. 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).

Calculate the moving average of the field values associated with each field key in a measurement
  1. > 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'
  2. name: h2o_feet
  3. time moving_average_water_level
  4. ---- --------------------------
  5. 2015-08-18T00:12:00Z 2.0693333333333332
  6. 2015-08-18T00:18:00Z 2.09
  7. 2015-08-18T00:24:00Z 2.065
  8. 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.

Calculate the moving average of the field values associated with each field key that matches a regular expression
  1. > 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'
  2. name: h2o_feet
  3. time moving_average_water_level
  4. ---- --------------------------
  5. 2015-08-18T00:18:00Z 2.0835
  6. 2015-08-18T00:24:00Z 2.07775
  7. 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.

Calculate the moving average of the field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time moving_average
  4. ---- --------------
  5. 2015-08-18T00:06:00Z 2.072
  6. 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

  1. 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]

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
Calculate the moving average of maximum values
  1. > 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)
  2. name: h2o_feet
  3. time moving_average
  4. ---- --------------
  5. 2015-08-18T00:12:00Z 2.121
  6. 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():

  1. > 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)
  2. name: h2o_feet
  3. time max
  4. ---- ---
  5. 2015-08-18T00:00:00Z 2.116
  6. 2015-08-18T00:12:00Z 2.126
  7. 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

  1. 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]

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

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

  1. 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]

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

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

  1. 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]

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

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

  1. 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]

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

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

  1. 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]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate field values associated with a field key to the power of 4
  1. > 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'
  2. name: h2o_feet
  3. time pow
  4. ---- ---
  5. 2015-08-18T00:00:00Z 18.148417929216
  6. 2015-08-18T00:06:00Z 20.047612231936
  7. 2015-08-18T00:12:00Z 16.914992230656004
  8. 2015-08-18T00:18:00Z 20.429279055375993
  9. 2015-08-18T00:24:00Z 17.352898193760993
  10. 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.

Calculate field values associated with each field key in a measurement to the power of 4
  1. > SELECT POW(*, 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time pow_water_level
  4. ---- ---------------
  5. 2015-08-18T00:00:00Z 18.148417929216
  6. 2015-08-18T00:06:00Z 20.047612231936
  7. 2015-08-18T00:12:00Z 16.914992230656004
  8. 2015-08-18T00:18:00Z 20.429279055375993
  9. 2015-08-18T00:24:00Z 17.352898193760993
  10. 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.

Calculate field values associated with a field key to the power of 4 and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time pow
  4. ---- ---
  5. 2015-08-18T00:18:00Z 20.429279055375993
  6. 2015-08-18T00:12:00Z 16.914992230656004
  7. 2015-08-18T00:06:00Z 20.047612231936
  8. 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

  1. 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]

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
Calculate mean values to the power of 4
  1. > 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)
  2. name: h2o_feet
  3. time pow
  4. ---- ---
  5. 2015-08-18T00:00:00Z 19.08029760999999
  6. 2015-08-18T00:12:00Z 18.609983417041
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. SELECT ROUND( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Round field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time round
  4. ---- -----
  5. 2015-08-18T00:00:00Z 2
  6. 2015-08-18T00:06:00Z 2
  7. 2015-08-18T00:12:00Z 2
  8. 2015-08-18T00:18:00Z 2
  9. 2015-08-18T00:24:00Z 2
  10. 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.

Round field values associated with each field key in a measurement
  1. > SELECT ROUND(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time round_water_level
  4. ---- -----------------
  5. 2015-08-18T00:00:00Z 2
  6. 2015-08-18T00:06:00Z 2
  7. 2015-08-18T00:12:00Z 2
  8. 2015-08-18T00:18:00Z 2
  9. 2015-08-18T00:24:00Z 2
  10. 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.

Round field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time round
  4. ---- -----
  5. 2015-08-18T00:18:00Z 2
  6. 2015-08-18T00:12:00Z 2
  7. 2015-08-18T00:06:00Z 2
  8. 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

  1. 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]

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
Calculate mean values rounded to the nearest integer
  1. > 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)
  2. name: h2o_feet
  3. time round
  4. ---- -----
  5. 2015-08-18T00:00:00Z 2
  6. 2015-08-18T00:12:00Z 2
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. SELECT SIN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the sine of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time sin
  4. ---- ---
  5. 2015-08-18T00:00:00Z 0.8808206017241819
  6. 2015-08-18T00:06:00Z 0.8550216851706579
  7. 2015-08-18T00:12:00Z 0.8972904165810275
  8. 2015-08-18T00:18:00Z 0.8497930984115993
  9. 2015-08-18T00:24:00Z 0.8914760289023131
  10. 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.

Calculate the sine of field values associated with each field key in a measurement
  1. > SELECT SIN(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time sin_water_level
  4. ---- ---------------
  5. 2015-08-18T00:00:00Z 0.8808206017241819
  6. 2015-08-18T00:06:00Z 0.8550216851706579
  7. 2015-08-18T00:12:00Z 0.8972904165810275
  8. 2015-08-18T00:18:00Z 0.8497930984115993
  9. 2015-08-18T00:24:00Z 0.8914760289023131
  10. 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.

Calculate the sine of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time sin
  4. ---- ---
  5. 2015-08-18T00:18:00Z 0.8497930984115993
  6. 2015-08-18T00:12:00Z 0.8972904165810275
  7. 2015-08-18T00:06:00Z 0.8550216851706579
  8. 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

  1. 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]

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
Calculate the sine of mean values
  1. > 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)
  2. name: h2o_feet
  3. time sin
  4. ---- ---
  5. 2015-08-18T00:00:00Z 0.8682145834456126
  6. 2015-08-18T00:12:00Z 0.8745914945253902
  7. 2015-08-18T00:24:00Z 0.8891995555912935

The query returns the 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 2015-08-18T00:24:00Z 2.0460000000000003

InfluxDB then calculates sine of those averages.

SQRT()

Returns the square root of field value.

Basic syntax

  1. SELECT SQRT( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the square root of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time sqrt
  4. ---- ----
  5. 2015-08-18T00:00:00Z 1.4366627996854378
  6. 2015-08-18T00:06:00Z 1.4546477236774544
  7. 2015-08-18T00:12:00Z 1.4240786495134319
  8. 2015-08-18T00:18:00Z 1.4580809305384939
  9. 2015-08-18T00:24:00Z 1.4286357128393508
  10. 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.

Calculate the square root of field values associated with each field key in a measurement
  1. > SELECT SQRT(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time sqrt_water_level
  4. ---- ----------------
  5. 2015-08-18T00:00:00Z 1.4366627996854378
  6. 2015-08-18T00:06:00Z 1.4546477236774544
  7. 2015-08-18T00:12:00Z 1.4240786495134319
  8. 2015-08-18T00:18:00Z 1.4580809305384939
  9. 2015-08-18T00:24:00Z 1.4286357128393508
  10. 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.

Calculate the square root of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time sqrt
  4. ---- ----
  5. 2015-08-18T00:18:00Z 1.4580809305384939
  6. 2015-08-18T00:12:00Z 1.4240786495134319
  7. 2015-08-18T00:06:00Z 1.4546477236774544
  8. 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

  1. 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]

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
Calculate the square root of mean values
  1. > 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)
  2. name: h2o_feet
  3. time sqrt
  4. ---- ----
  5. 2015-08-18T00:00:00Z 1.445683229480096
  6. 2015-08-18T00:12:00Z 1.4411800720243115
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. SELECT TAN( [ * | <field_key> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]

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

The examples below use the following subsample of the NOAA_water_database data:

  1. > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time water_level
  4. ---- -----------
  5. 2015-08-18T00:00:00Z 2.064
  6. 2015-08-18T00:06:00Z 2.116
  7. 2015-08-18T00:12:00Z 2.028
  8. 2015-08-18T00:18:00Z 2.126
  9. 2015-08-18T00:24:00Z 2.041
  10. 2015-08-18T00:30:00Z 2.051
Calculate the tangent of field values associated with a field key
  1. > 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'
  2. name: h2o_feet
  3. time tan
  4. ---- ---
  5. 2015-08-18T00:00:00Z -1.8604293534384375
  6. 2015-08-18T00:06:00Z -1.6487359603347427
  7. 2015-08-18T00:12:00Z -2.0326408012302273
  8. 2015-08-18T00:18:00Z -1.6121545688343464
  9. 2015-08-18T00:24:00Z -1.9676434782626282
  10. 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.

Calculate the tangent of field values associated with each field key in a measurement
  1. > SELECT TAN(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
  2. name: h2o_feet
  3. time tan_water_level
  4. ---- ---------------
  5. 2015-08-18T00:00:00Z -1.8604293534384375
  6. 2015-08-18T00:06:00Z -1.6487359603347427
  7. 2015-08-18T00:12:00Z -2.0326408012302273
  8. 2015-08-18T00:18:00Z -1.6121545688343464
  9. 2015-08-18T00:24:00Z -1.9676434782626282
  10. 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.

Calculate the tangent of field values associated with a field key and include several clauses
  1. > 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
  2. name: h2o_feet
  3. time tan
  4. ---- ---
  5. 2015-08-18T00:18:00Z -1.6121545688343464
  6. 2015-08-18T00:12:00Z -2.0326408012302273
  7. 2015-08-18T00:06:00Z -1.6487359603347427
  8. 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

  1. 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]

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
Calculate the tangent of mean values
  1. > 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)
  2. name: h2o_feet
  3. time tan
  4. ---- ---
  5. 2015-08-18T00:00:00Z -1.7497661902817365
  6. 2015-08-18T00:12:00Z -1.8038002062256624
  7. 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():

  1. > 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)
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 2015-08-18T00:00:00Z 2.09
  6. 2015-08-18T00:12:00Z 2.077
  7. 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

  1. 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]

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

Predict field values associated with a field key
Raw Data

The example uses Chronograf to visualize the data. The example focuses on the following subsample of the NOAA_water_database data:

  1. 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'

Raw Data

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() function:

  1. 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 the default GROUP BY time() boundaries to match the time range of the raw data.

The blue line shows the results of the query:

First step

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.

Second step

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:

  1. 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:

Third step

Common Issues with HOLT_WINTERS()

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 Typesimpleexponentialnone
EXPONENTIAL_MOVING_AVERAGEPERIOD - 1PERIOD - 1n/a
DOUBLE_EXPONENTIAL_MOVING_AVERAGE( PERIOD - 1 ) 2PERIOD - 1n/a
TRIPLE_EXPONENTIAL_MOVING_AVERAGE( PERIOD - 1 ) 3PERIOD - 1n/a
TRIPLE_EXPONENTIAL_DERIVATIVE( PERIOD - 1 ) * 3 + 1PERIODn/a
RELATIVE_STRENGTH_INDEXPERIODPERIODn/a
CHANDE_MOMENTUM_OSCILLATORPERIODPERIODPERIOD - 1

Kaufman Algorithm Default Hold Periods:

AlgorithmDefault 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 none warmup type is only available with the CHANDE_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

  1. CHANDE_MOMENTUM_OSCILLATOR([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period>, [warmup_type]])

Available Arguments:

period
hold_period (Optional)
warmup_type (Optional)

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 BY clause, you must include an aggregate function in your call to the CHANDE_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. See the Advanced Syntax section for how to use CHANDE_MOMENTUM_OSCILLATOR() with a GROUP BY time() clause.

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

  1. EXPONENTIAL_MOVING_AVERAGE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])

Available Arguments:

period
hold_period (Optional)
warmup_type (Optional)

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 BY clause, you must include an aggregate function in your call to the EXPONENTIAL_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. See the Advanced Syntax section for how to use EXPONENTIAL_MOVING_AVERAGE() with a GROUP BY time() clause.

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

  1. DOUBLE_EXPONENTIAL_MOVING_AVERAGE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])

Available Arguments:

period
hold_period (Optional)
warmup_type (Optional)

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 BY clause, you must include an aggregate function in your call to the DOUBLE_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. See the Advanced Syntax section for how to use DOUBLE_EXPONENTIAL_MOVING_AVERAGE() with a GROUP BY time() clause.

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

  1. KAUFMANS_EFFICIENCY_RATIO([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period>])

Available Arguments:

period
hold_period (Optional)

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 BY clause, you must include an aggregate function in your call to the KAUFMANS_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. See the Advanced Syntax section for how to use KAUFMANS_EFFICIENCY_RATIO() with a GROUP BY time() clause.

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

  1. KAUFMANS_ADAPTIVE_MOVING_AVERAGE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period>])

Available Arguments:
period
hold_period (Optional)

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 BY clause, you must include an aggregate function in your call to the KAUFMANS_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. See the Advanced Syntax section for how to use KAUFMANS_ADAPTIVE_MOVING_AVERAGE() with a GROUP BY time() clause.

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

  1. TRIPLE_EXPONENTIAL_MOVING_AVERAGE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])

Available Arguments:

period
hold_period (Optional)
warmup_type (Optional)

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 BY clause, you must include an aggregate function in your call to the TRIPLE_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. See the Advanced Syntax section for how to use TRIPLE_EXPONENTIAL_MOVING_AVERAGE() with a GROUP BY time() clause.

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

  1. TRIPLE_EXPONENTIAL_DERIVATIVE([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])

Available Arguments:

period
hold_period (Optional)
warmup_type (Optional)

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 BY clause, you must include an aggregate function in your call to the TRIPLE_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. See the Advanced Syntax section for how to use TRIPLE_EXPONENTIAL_DERIVATIVE() with a GROUP BY time() clause.

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

  1. RELATIVE_STRENGTH_INDEX([ * | <field_key> | /regular_expression/ ], <period>[, <hold_period)[, <warmup_type]])

Available Arguments:

period
hold_period (Optional)
warmup_type (Optional)

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 BY clause, you must include an aggregate function in your call to the RELATIVE_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. See the Advanced Syntax section for how to use RELATIVE_STRENGTH_INDEX() with a GROUP BY time() clause.

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
  1. SELECT <function>(),<function>() FROM_clause [...]

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
Calculate the mean and median field values in one query
  1. > SELECT MEAN("water_level"),MEDIAN("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time mean median
  4. ---- ---- ------
  5. 1970-01-01T00:00:00Z 4.442107025822522 4.124

The query returns the average and median field values in the water_level field key.

Calculate the mode of two fields in one query
  1. > SELECT MODE("water_level"),MODE("level description") FROM "h2o_feet"
  2. name: h2o_feet
  3. time mode mode_1
  4. ---- ---- ------
  5. 1970-01-01T00:00:00Z 2.69 between 3 and 6 feet

The query returns the 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 for how to configure the output column headers.

Calculate the minimum and maximum field values in one query
  1. > SELECT MIN("water_level"), MAX("water_level") [...]
  2. name: h2o_feet
  3. time min max
  4. ---- --- ---
  5. 1970-01-01T00:00:00Z -0.61 9.964

The query returns the minimum and maximum field values in the water_level field key.

Notice that the query returns 1970-01-01T00:00:00Z, the InfluxDB equivalent to a null timestamp, as the timestamp value. MIN() and MAX() are selector 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.

  1. > SELECT MIN("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time min
  4. ---- ---
  5. 2015-08-29T14:30:00Z -0.61 <--- Timestamp 1
  6. > SELECT MAX("water_level") FROM "h2o_feet"
  7. name: h2o_feet
  8. time max
  9. ---- ---
  10. 2015-08-29T07:24:00Z 9.964 <--- Timestamp 2

Rename the Output Field Key

Syntax
  1. SELECT <function>() AS <field_key> [...]

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
Specify the output field key
  1. > SELECT MEAN("water_level") AS "dream_name" FROM "h2o_feet"
  2. name: h2o_feet
  3. time dream_name
  4. ---- ----------
  5. 1970-01-01T00:00:00Z 4.442107025822522

The query returns the average 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:

  1. > SELECT MEAN("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time mean
  4. ---- ----
  5. 1970-01-01T00:00:00Z 4.442107025822522
Specify the output field key for multiple functions
  1. > SELECT MEDIAN("water_level") AS "med_wat",MODE("water_level") AS "mode_wat" FROM "h2o_feet"
  2. name: h2o_feet
  3. time med_wat mode_wat
  4. ---- ------- --------
  5. 1970-01-01T00:00:00Z 4.124 2.69

The query returns the median and 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:

  1. > SELECT MEDIAN("water_level"),MODE("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time median mode
  4. ---- ------ ----
  5. 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 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 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:

All Functions

Nesting functions

Some InfluxQL functions support nesting in the SELECT clause:

For other functions, use InfluxQL’s subqueries to nest functions in the FROM clause. See the Data Exploration page more on using subqueries.

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. For SELECT statements with an InfluxQL function and a GROUP BY time() clause, the default time range is between 1677-09-21 00:12:43.145224194 UTC and 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. See the Frequently Asked Questions page for an example.

Aggregation Functions

Understanding the returned timestamp

A query with an aggregation function and no time range in 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
Use an aggregate function without a specified time range
  1. > SELECT SUM("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time sum
  4. ---- ---
  5. 1970-01-01T00:00:00Z 67777.66900000004

The query returns the InfluxDB equivalent of a null timestamp (epoch 0: 1970-01-01T00:00:00Z) as the timestamp. SUM() aggregates points across several timestamps and has no single timestamp to return.

Use an aggregate function with a specified time range
  1. > SELECT SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z'
  2. name: h2o_feet
  3. time sum
  4. ---- ---
  5. 2015-08-18T00:00:00Z 67777.66900000004

The query returns the lower time bound (WHERE time >= '2015-08-18T00:00:00Z') as the timestamp.

Use an aggregate function with a specified time range and a GROUP BY time() clause
  1. > 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)
  2. name: h2o_feet
  3. time sum
  4. ---- ---
  5. 2015-08-18T00:00:00Z 20.305
  6. 2015-08-18T00:12:00Z 19.802999999999997

The query returns the lower time bound for each GROUP BY time() interval as the timestamps.

Mixing aggregation functions with non-aggregates

Aggregation functions do not support specifying standalone field keys or tag keys in the 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:

  1. > SELECT SUM("water_level"),"location" FROM "h2o_feet"
  2. ERR: error parsing query: mixing aggregate and non-aggregate queries is not supported
Getting slightly different results

For some aggregation functions, executing the same function on the same set of float64 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

Understanding the returned timestamp

The timestamps returned by selector functions 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 argument, and no GROUP BY time() clause 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 returns the timestamp for the point that appears in the raw data or the InfluxDB equivalent of a null timestamp (epoch 0: 1970-01-01T00:00:00Z).

A query with more than one function and no time range in the WHERE clause returns the InfluxDB equivalent of a null timestamp (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() for more information.

Examples
Use a single selector function with a single field key and without a specified time range
  1. > SELECT MAX("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time max
  4. ---- ---
  5. 2015-08-29T07:24:00Z 9.964
  6. > SELECT MAX("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z'
  7. name: h2o_feet
  8. time max
  9. ---- ---
  10. 2015-08-29T07:24:00Z 9.964

The queries return the timestamp for the maximum point that appears in the raw data.

Use a single selector function with multiple field keys and without a specified time range
  1. > SELECT FIRST(*) FROM "h2o_feet"
  2. name: h2o_feet
  3. time first_level description first_water_level
  4. ---- ----------------------- -----------------
  5. 1970-01-01T00:00:00Z between 6 and 9 feet 8.12
  6. > SELECT MAX(*) FROM "h2o_feet"
  7. name: h2o_feet
  8. time max_water_level
  9. ---- ---------------
  10. 2015-08-29T07:24:00Z 9.964

The first query returns the InfluxDB equivalent of a null timestamp (epoch 0: 1970-01-01T00:00:00Z) as the timestamp. FIRST(*) returns two timestamps (one for each field key in the h2o_feet 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.

Use a selector function with another function and without a specified time range
  1. > SELECT MAX("water_level"),MIN("water_level") FROM "h2o_feet"
  2. name: h2o_feet
  3. time max min
  4. ---- --- ---
  5. 1970-01-01T00:00:00Z 9.964 -0.61

The query returns the InfluxDB equivalent of a null timestamp (epoch 0: 1970-01-01T00:00:00Z) as the timestamp. The MAX() and MIN() functions return different timestamps so the system has no single timestamp to return.

Use a selector function with another function and with a specified time range
  1. > SELECT MAX("water_level"),MIN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z'
  2. name: h2o_feet
  3. time max min
  4. ---- --- ---
  5. 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.

Use a selector function with a GROUP BY time() clause
  1. > 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)
  2. name: h2o_feet
  3. time max
  4. ---- ---
  5. 2015-08-18T00:00:00Z 8.12
  6. 2015-08-18T00:12:00Z 7.887

The query returns the lower time bound for each GROUP BY time() interval as the timestamp.