--- title: InfluxQL functions description: > Aggregate, select, transform, and predict data with InfluxQL functions. menu: influxdb_v1: name: Functions weight: 60 parent: InfluxQL --- Aggregate, select, transform, and predict data with InfluxQL functions. #### Content * [Aggregations](#aggregations) * [COUNT()](#count) * [DISTINCT()](#distinct) * [INTEGRAL()](#integral) * [MEAN()](#mean) * [MEDIAN()](#median) * [MODE()](#mode) * [SPREAD()](#spread) * [STDDEV()](#stddev) * [SUM()](#sum) * [Selectors](#selectors) * [BOTTOM()](#bottom) * [FIRST()](#first) * [LAST()](#last) * [MAX()](#max) * [MIN()](#min) * [PERCENTILE()](#percentile) * [SAMPLE()](#sample) * [TOP()](#top) * [Transformations](#transformations) * [ABS()](#abs) * [ACOS()](#acos) * [ASIN()](#asin) * [ATAN()](#atan) * [ATAN2()](#atan2) * [CEIL()](#ceil) * [COS()](#cos) * [CUMULATIVE_SUM()](#cumulative_sum) * [DERIVATIVE()](#derivative) * [DIFFERENCE()](#difference) * [ELAPSED()](#elapsed) * [EXP()](#exp) * [FLOOR()](#floor) * [HISTOGRAM()](#histogram) * [LN()](#ln) * [LOG()](#log) * [LOG2()](#log2) * [LOG10()](#log10) * [MOVING_AVERAGE()](#moving_average) * [NON_NEGATIVE_DERIVATIVE()](#non_negative_derivative) * [NON_NEGATIVE_DIFFERENCE()](#non_negative_difference) * [POW()](#pow) * [ROUND()](#round) * [SIN()](#sin) * [SQRT()](#sqrt) * [TAN()](#tan) * [Predictors](#predictors) * [HOLT_WINTERS()](#holt_winters) * [Technical Analysis](#technical-analysis) * [CHANDE_MOMENTUM_OSCILLATOR()](#chande_momentum_oscillator) * [EXPONENTIAL_MOVING_AVERAGE()](#exponential_moving_average) * [DOUBLE_EXPONENTIAL_MOVING_AVERAGE()](#double_exponential_moving_average) * [KAUFMANS_EFFICIENCY_RATIO()](#kaufmans_efficiency_ratio) * [KAUFMANS_ADAPTIVE_MOVING_AVERAGE()](#kaufmans_adaptive_moving_average) * [TRIPLE_EXPONENTIAL_MOVING_AVERAGE()](#triple_exponential_moving_average) * [TRIPLE_EXPONENTIAL_DERIVATIVE()](#triple_exponential_derivative) * [RELATIVE_STRENGTH_INDEX()](#relative_strength_index) * [Other](#other) * [Sample Data](#sample_data) * [General Syntax for Functions](#general_syntax_for_functions) * [Specify Multiple Functions in the SELECT clause](#specify_multiple_functions_in_the_select_clause) * [Rename the Output Field Key](#rename_the_output_field_key) * [Change the Values Reported for Intervals with no Data](#change_the_values_reported_for_intervals_with_no_data) * [Common Issues with Functions](#common_issues_with_functions) ## Aggregations ### COUNT() Returns the number of non-null [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT COUNT( [ * | | // ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause] ``` ##### Nested Syntax ``` SELECT COUNT(DISTINCT( [ * | | // ] )) [...] ``` `COUNT(field_key)` Returns the number of field values associated with the [field key](/influxdb/v1/concepts/glossary/#field-key). `COUNT(/regular_expression/)` Returns the number of field values associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `COUNT(*)` Returns the number of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `COUNT()` supports all field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). InfluxQL supports nesting [`DISTINCT()`](#distinct) with `COUNT()`. #### Examples ##### Count the field values associated with a field key ```sql > SELECT COUNT("water_level") FROM "h2o_feet" name: h2o_feet time count ---- ----- 1970-01-01T00:00:00Z 15258 ``` The query returns the number of non-null field values in the `water_level` field key in the `h2o_feet` measurement. ##### Count the field values associated with each field key in a measurement ```sql > SELECT COUNT(*) FROM "h2o_feet" name: h2o_feet time count_level description count_water_level ---- ----------------------- ----------------- 1970-01-01T00:00:00Z 15258 15258 ``` The query returns the number of non-null field values for each field key associated with the `h2o_feet` measurement. The `h2o_feet` measurement has two field keys: `level description` and `water_level`. ##### Count the field values associated with each field key that matches a regular expression ```sql > SELECT COUNT(/water/) FROM "h2o_feet" name: h2o_feet time count_water_level ---- ----------------- 1970-01-01T00:00:00Z 15258 ``` The query returns the number of non-null field values for every field key that contains the word `water` in the `h2o_feet` measurement. ##### Count the field values associated with a field key and include several clauses ```sql > SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(200) LIMIT 7 SLIMIT 1 name: h2o_feet tags: location=coyote_creek time count ---- ----- 2015-08-17T23:48:00Z 200 2015-08-18T00:00:00Z 2 2015-08-18T00:12:00Z 2 2015-08-18T00:24:00Z 2 2015-08-18T00:36:00Z 2 2015-08-18T00:48:00Z 2 ``` The query returns the number of non-null field values in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `200` and [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to seven and one. ##### Count the distinct field values associated with a field key ```sql > SELECT COUNT(DISTINCT("level description")) FROM "h2o_feet" name: h2o_feet time count ---- ----- 1970-01-01T00:00:00Z 4 ``` The query returns the number of unique field values for the `level description` field key and the `h2o_feet` measurement. ### Common Issues with COUNT() #### COUNT() and fill() Most InfluxQL functions report `null` values for time intervals with no data, and [`fill()`](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) replaces that `null` value with the `fill_option`. `COUNT()` reports `0` for time intervals with no data, and `fill()` 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`. ```sql > SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-09-18T21:24:00Z' AND time <= '2015-09-18T21:54:00Z' GROUP BY time(12m) name: h2o_feet time count ---- ----- 2015-09-18T21:24:00Z 2 2015-09-18T21:36:00Z 2 2015-09-18T21:48:00Z 0 > SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-09-18T21:24:00Z' AND time <= '2015-09-18T21:54:00Z' GROUP BY time(12m) fill(800000) name: h2o_feet time count ---- ----- 2015-09-18T21:24:00Z 2 2015-09-18T21:36:00Z 2 2015-09-18T21:48:00Z 800000 ``` ### DISTINCT() Returns the list of unique [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT DISTINCT( [ | // ] ) FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause] ``` ##### Nested Syntax ``` SELECT COUNT(DISTINCT( [ | // ] )) [...] ``` `DISTINCT(field_key)` Returns the unique field values associated with the [field key](/influxdb/v1/concepts/glossary/#field-key). `DISTINCT()` supports all field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). InfluxQL supports nesting `DISTINCT()` with [`COUNT()`](#count). #### Examples ##### List the distinct field values associated with a field key ```sql > SELECT DISTINCT("level description") FROM "h2o_feet" name: h2o_feet time distinct ---- -------- 1970-01-01T00:00:00Z between 6 and 9 feet 1970-01-01T00:00:00Z below 3 feet 1970-01-01T00:00:00Z between 3 and 6 feet 1970-01-01T00:00:00Z at or greater than 9 feet ``` The query returns a tabular list of the unique field values in the `level description` field key in the `h2o_feet` measurement. ##### List the distinct field values associated with each field key in a measurement ```sql > SELECT DISTINCT(*) FROM "h2o_feet" name: h2o_feet time distinct_level description distinct_water_level ---- -------------------------- -------------------- 1970-01-01T00:00:00Z between 6 and 9 feet 8.12 1970-01-01T00:00:00Z between 3 and 6 feet 8.005 1970-01-01T00:00:00Z at or greater than 9 feet 7.887 1970-01-01T00:00:00Z below 3 feet 7.762 [...] ``` The query returns a tabular list of the unique field values for each field key in the `h2o_feet` measurement. The `h2o_feet` measurement has two field keys: `level description` and `water_level`. #### List the distinct field values associated with a field key and include several clauses ```sql > SELECT DISTINCT("level description") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* SLIMIT 1 name: h2o_feet tags: location=coyote_creek time distinct ---- -------- 2015-08-18T00:00:00Z between 6 and 9 feet 2015-08-18T00:12:00Z between 6 and 9 feet 2015-08-18T00:24:00Z between 6 and 9 feet 2015-08-18T00:36:00Z between 6 and 9 feet 2015-08-18T00:48:00Z between 6 and 9 feet ``` The query returns a tabular list of the unique field values in the `level description` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of series returned to one. ##### Count the distinct field values associated with a field key ```sql > SELECT COUNT(DISTINCT("level description")) FROM "h2o_feet" name: h2o_feet time count ---- ----- 1970-01-01T00:00:00Z 4 ``` The query returns the number of unique field values in the `level description` field key and the `h2o_feet` measurement. ### Common Issues with DISTINCT() #### DISTINCT() and the INTO clause Using `DISTINCT()` with the [`INTO` clause](/influxdb/v1/query_language/explore-data/#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](/influxdb/v1/concepts/glossary/#point) with the same [series](/influxdb/v1/concepts/glossary/#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. ```sql > SELECT DISTINCT("level description") FROM "h2o_feet" name: h2o_feet time distinct ---- -------- 1970-01-01T00:00:00Z below 3 feet 1970-01-01T00:00:00Z between 6 and 9 feet 1970-01-01T00:00:00Z between 3 and 6 feet 1970-01-01T00:00:00Z at or greater than 9 feet > SELECT DISTINCT("level description") INTO "distincts" FROM "h2o_feet" name: result time written ---- ------- 1970-01-01T00:00:00Z 4 > SELECT * FROM "distincts" name: distincts time distinct ---- -------- 1970-01-01T00:00:00Z at or greater than 9 feet ``` ### INTEGRAL() Returns the area under the curve for subsequent [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT INTEGRAL( [ * | | // ] [ , ] ) [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](/influxdb/v1/query_language/spec/#literals) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/query_language/explore-data/#regular-expressions). `INTEGRAL(*)` Returns the average field value associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `INTEGRAL()` does not support [`fill()`](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill). `INTEGRAL()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples Examples 1-5 use the following subsample of the [`NOAA_water_database` data](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ##### Calculate the integral for the field values associated with a field key ```sql > SELECT INTEGRAL("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time integral ---- -------- 1970-01-01T00:00:00Z 3732.66 ``` The query returns the area under the curve (in seconds) for the field values associated with the `water_level` field key and in the `h2o_feet` measurement. ##### Calculate the integral for the field values associated with a field key and specify the unit option ```sql > SELECT INTEGRAL("water_level",1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time integral ---- -------- 1970-01-01T00:00:00Z 62.211 ``` The query returns the area under the curve (in minutes) for the field values associated with the `water_level` field key and in the `h2o_feet` measurement. ##### Calculate the integral for the field values associated with each field key in a measurement and specify the unit option ```sql > SELECT INTEGRAL(*,1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time integral_water_level ---- -------------------- 1970-01-01T00:00:00Z 62.211 ``` The query returns the area under the curve (in minutes) for the field values associated with each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has on numerical field: `water_level`. #### Calculate the integral for the field values associated with each field key that matches a regular expression and specify the unit option ```sql > SELECT INTEGRAL(/water/,1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time integral_water_level ---- -------------------- 1970-01-01T00:00:00Z 62.211 ``` The query returns the area under the curve (in minutes) for the field values associated with each field key that stores numerical values includes the word `water` in the `h2o_feet` measurement. #### Calculate the integral for the field values associated with a field key and include several clauses ```sql > SELECT INTEGRAL("water_level",1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m) LIMIT 1 name: h2o_feet time integral ---- -------- 2015-08-18T00:00:00Z 24.972 ``` The query returns the area under the curve (in minutes) for the field values associated with the `water_level` field key and in the `h2o_feet` measurement. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z`, [groups](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) results into 12-minute intervals, and [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of results returned to one. ### MEAN() Returns the arithmetic mean (average) of [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT MEAN( [ * | | // ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `MEAN(/regular_expression/)` Returns the average field value associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `MEAN(*)` Returns the average field value associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `MEAN()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Calculate the mean field value associated with a field key ```sql > SELECT MEAN("water_level") FROM "h2o_feet" name: h2o_feet time mean ---- ---- 1970-01-01T00:00:00Z 4.442107025822522 ``` The query returns the average field value in the `water_level` field key in the `h2o_feet` measurement. ##### Calculate the mean field value associated with each field key in a measurement ```sql > SELECT MEAN(*) FROM "h2o_feet" name: h2o_feet time mean_water_level ---- ---------------- 1970-01-01T00:00:00Z 4.442107025822522 ``` The query returns the average field value for every field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ##### Calculate the mean field value associated with each field key that matches a regular expression ```sql > SELECT MEAN(/water/) FROM "h2o_feet" name: h2o_feet time mean_water_level ---- ---------------- 1970-01-01T00:00:00Z 4.442107025822523 ``` The query returns the average field value for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. #### Calculate the mean field value associated with a field key and include several clauses ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(9.01) LIMIT 7 SLIMIT 1 name: h2o_feet tags: location=coyote_creek time mean ---- ---- 2015-08-17T23:48:00Z 9.01 2015-08-18T00:00:00Z 8.0625 2015-08-18T00:12:00Z 7.8245 2015-08-18T00:24:00Z 7.5675 2015-08-18T00:36:00Z 7.303 2015-08-18T00:48:00Z 7.046 ``` The query returns the average of the values in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `9.01` and [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to seven and one. ### MEDIAN() Returns the middle value from a sorted list of [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT MEDIAN( [ * | | // ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `MEDIAN(/regular_expression/)` Returns the middle field value associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `MEDIAN(*)` Returns the middle field value associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `MEDIAN()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). > **Note:** `MEDIAN()` is nearly equivalent to [`PERCENTILE(field_key, 50)`](#percentile), 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 ```sql > SELECT MEDIAN("water_level") FROM "h2o_feet" name: h2o_feet time median ---- ------ 1970-01-01T00:00:00Z 4.124 ``` The query returns the middle field value in the `water_level` field key and in the `h2o_feet` measurement. ##### Calculate the median field value associated with each field key in a measurement ```sql > SELECT MEDIAN(*) FROM "h2o_feet" name: h2o_feet time median_water_level ---- ------------------ 1970-01-01T00:00:00Z 4.124 ``` The query returns the middle field value for every field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ##### Calculate the median field value associated with each field key that matches a regular expression ```sql > SELECT MEDIAN(/water/) FROM "h2o_feet" name: h2o_feet time median_water_level ---- ------------------ 1970-01-01T00:00:00Z 4.124 ``` The query returns the middle field value for every field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. #### Calculate the median field value associated with a field key and include several clauses ```sql > SELECT MEDIAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(700) LIMIT 7 SLIMIT 1 SOFFSET 1 name: h2o_feet tags: location=santa_monica time median ---- ------ 2015-08-17T23:48:00Z 700 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 2015-08-18T00:36:00Z 2.0620000000000003 2015-08-18T00:48:00Z 700 ``` The query returns the middle field value in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `700 `, [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to seven and one, and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) the series returned by one. ### MODE() Returns the most frequent value in a list of [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT MODE( [ * | | // ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `MODE(/regular_expression/)` Returns the most frequent field value associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `MODE(*)` Returns the most frequent field value associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `MODE()` supports all field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). > **Note:** `MODE()` returns the field value with the earliest [timestamp](/influxdb/v1/concepts/glossary/#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 ```sql > SELECT MODE("level description") FROM "h2o_feet" name: h2o_feet time mode ---- ---- 1970-01-01T00:00:00Z between 3 and 6 feet ``` The query returns the most frequent field value in the `level description` field key and in the `h2o_feet` measurement. ##### Calculate the mode field value associated with each field key in a measurement ```sql > SELECT MODE(*) FROM "h2o_feet" name: h2o_feet time mode_level description mode_water_level ---- ---------------------- ---------------- 1970-01-01T00:00:00Z between 3 and 6 feet 2.69 ``` The query returns the most frequent field value for every field key in the `h2o_feet` measurement. The `h2o_feet` measurement has two field keys: `level description` and `water_level`. ##### Calculate the mode field value associated with each field key that matches a regular expression ```sql > SELECT MODE(/water/) FROM "h2o_feet" name: h2o_feet time mode_water_level ---- ---------------- 1970-01-01T00:00:00Z 2.69 ``` The query returns the most frequent field value for every field key that includes the word `/water/` in the `h2o_feet` measurement. #### Calculate the mode field value associated with a field key and include several clauses ```sql > SELECT MODE("level description") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* LIMIT 3 SLIMIT 1 SOFFSET 1 name: h2o_feet tags: location=santa_monica time mode ---- ---- 2015-08-17T23:48:00Z 2015-08-18T00:00:00Z below 3 feet 2015-08-18T00:12:00Z below 3 feet ``` The query returns the mode of the values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to three and one, and it [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) the series returned by one. ### SPREAD() Returns the difference between the minimum and maximum [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT SPREAD( [ * | | // ] ) [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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/query_language/explore-data/#regular-expressions). `SPREAD(*)` Returns the difference between the minimum and maximum field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `SPREAD()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Calculate the spread for the field values associated with a field key ```sql > SELECT SPREAD("water_level") FROM "h2o_feet" name: h2o_feet time spread ---- ------ 1970-01-01T00:00:00Z 10.574 ``` The query returns the difference between the minimum and maximum field values in the `water_level` field key and in the `h2o_feet` measurement. ##### Calculate the spread for the field values associated with each field key in a measurement ```sql > SELECT SPREAD(*) FROM "h2o_feet" name: h2o_feet time spread_water_level ---- ------------------ 1970-01-01T00:00:00Z 10.574 ``` The query returns the difference between the minimum and maximum field values for every field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ##### Calculate the spread for the field values associated with each field key that matches a regular expression ```sql > SELECT SPREAD(/water/) FROM "h2o_feet" name: h2o_feet time spread_water_level ---- ------------------ 1970-01-01T00:00:00Z 10.574 ``` The query returns the difference between the minimum and maximum field values for every field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. #### Calculate the spread for the field values associated with a field key and include several clauses ```sql > SELECT SPREAD("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(18) LIMIT 3 SLIMIT 1 SOFFSET 1 name: h2o_feet tags: location=santa_monica time spread ---- ------ 2015-08-17T23:48:00Z 18 2015-08-18T00:00:00Z 0.052000000000000046 2015-08-18T00:12:00Z 0.09799999999999986 ``` The query returns the difference between the minimum and maximum field values in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z `and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `18`, [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to three and one, and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) the series returned by one. ### STDDEV() Returns the standard deviation of [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT STDDEV( [ * | | // ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `STDDEV(/regular_expression/)` Returns the standard deviation of field values associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `STDDEV(*)` Returns the standard deviation of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `STDDEV()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Calculate the standard deviation for the field values associated with a field key ```sql > SELECT STDDEV("water_level") FROM "h2o_feet" name: h2o_feet time stddev ---- ------ 1970-01-01T00:00:00Z 2.279144584196141 ``` The query returns the standard deviation of the field values in the `water_level` field key and in the `h2o_feet` measurement. ##### Calculate the standard deviation for the field values associated with each field key in a measurement ```sql > SELECT STDDEV(*) FROM "h2o_feet" name: h2o_feet time stddev_water_level ---- ------------------ 1970-01-01T00:00:00Z 2.279144584196141 ``` The query returns the standard deviation of the field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ##### Calculate the standard deviation for the field values associated with each field key that matches a regular expression ```sql > SELECT STDDEV(/water/) FROM "h2o_feet" name: h2o_feet time stddev_water_level ---- ------------------ 1970-01-01T00:00:00Z 2.279144584196141 ``` The query returns the standard deviation of the field values for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. #### Calculate the standard deviation for the field values associated with a field key and include several clauses ```sql > SELECT STDDEV("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(18000) LIMIT 2 SLIMIT 1 SOFFSET 1 name: h2o_feet tags: location=santa_monica time stddev ---- ------ 2015-08-17T23:48:00Z 18000 2015-08-18T00:00:00Z 0.03676955262170051 ``` The query returns the standard deviation of the field values in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `18000`, [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to two and one, and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) the series returned by one. ### SUM() Returns the sum of [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT SUM( [ * | | // ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `SUM(/regular_expression/)` Returns the sum of field values associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `SUM(*)` Returns the sums of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `SUM()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples #### Calculate the sum of the field values associated with a field key ```sql > SELECT SUM("water_level") FROM "h2o_feet" name: h2o_feet time sum ---- --- 1970-01-01T00:00:00Z 67777.66900000004 ``` The query returns the summed total of the field values in the `water_level` field key and in the `h2o_feet` measurement. #### Calculate the sum of the field values associated with each field key in a measurement ```sql > SELECT SUM(*) FROM "h2o_feet" name: h2o_feet time sum_water_level ---- --------------- 1970-01-01T00:00:00Z 67777.66900000004 ``` The query returns the summed total of the field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. #### Calculate the sum of the field values associated with each field key that matches a regular expression ```sql > SELECT SUM(/water/) FROM "h2o_feet" name: h2o_feet time sum_water_level ---- --------------- 1970-01-01T00:00:00Z 67777.66900000004 ``` The query returns the summed total of the field values for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. #### Calculate the sum of the field values associated with a field key and include several clauses ```sql > SELECT SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(18000) LIMIT 4 SLIMIT 1 name: h2o_feet tags: location=coyote_creek time sum ---- --- 2015-08-17T23:48:00Z 18000 2015-08-18T00:00:00Z 16.125 2015-08-18T00:12:00Z 15.649 2015-08-18T00:24:00Z 15.135 ``` The query returns the summed total of the field values in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with 18000, and it [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to four and one. ## Selectors ### BOTTOM() Returns the smallest `N` [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT BOTTOM([,], )[,|] [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](/influxdb/v1/concepts/glossary/#field-key). `BOTTOM(field_key,tag_key(s),N)` Returns the smallest field value for N tag values of the [tag key](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#tag) and/or [field](/influxdb/v1/concepts/glossary/#field). `BOTTOM()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#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](/influxdb/v1/query_language/explore-data/#the-into-clause). See the [Common Issues](#common-issues-with-bottom) section for more information. #### Examples ##### Select the bottom three field values associated with a field key ```sql > SELECT BOTTOM("water_level",3) FROM "h2o_feet" name: h2o_feet time bottom ---- ------ 2015-08-29T14:30:00Z -0.61 2015-08-29T14:36:00Z -0.591 2015-08-30T15:18:00Z -0.594 ``` The query returns the smallest three field values in the `water_level` field key and in the `h2o_feet` [measurement](/influxdb/v1/concepts/glossary/#measurement). ##### Select the bottom field value associated with a field key for two tags ```sql > SELECT BOTTOM("water_level","location",2) FROM "h2o_feet" name: h2o_feet time bottom location ---- ------ -------- 2015-08-29T10:36:00Z -0.243 santa_monica 2015-08-29T14:30:00Z -0.61 coyote_creek ``` The query returns the smallest field values in the `water_level` field key for two tag values associated with the `location` tag key. ##### Select the bottom four field values associated with a field key and the relevant tags and fields ```sql > SELECT BOTTOM("water_level",4),"location","level description" FROM "h2o_feet" name: h2o_feet time bottom location level description ---- ------ -------- ----------------- 2015-08-29T14:24:00Z -0.587 coyote_creek below 3 feet 2015-08-29T14:30:00Z -0.61 coyote_creek below 3 feet 2015-08-29T14:36:00Z -0.591 coyote_creek below 3 feet 2015-08-30T15:18:00Z -0.594 coyote_creek below 3 feet ``` The query returns the smallest four field values in the `water_level` field key and the relevant values of the `location` tag key and the `level description` field key. ##### Select the bottom three field values associated with a field key and include several clauses ```sql > SELECT BOTTOM("water_level",3),"location" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(24m) ORDER BY time DESC name: h2o_feet time bottom location ---- ------ -------- 2015-08-18T00:48:00Z 1.991 santa_monica 2015-08-18T00:54:00Z 2.054 santa_monica 2015-08-18T00:54:00Z 6.982 coyote_creek 2015-08-18T00:24:00Z 2.041 santa_monica 2015-08-18T00:30:00Z 2.051 santa_monica 2015-08-18T00:42:00Z 2.057 santa_monica 2015-08-18T00:00:00Z 2.064 santa_monica 2015-08-18T00:06:00Z 2.116 santa_monica 2015-08-18T00:12:00Z 2.028 santa_monica ``` The query returns the smallest three values in the `water_level` field key for each 24-minute [interval](/influxdb/v1/query_language/explore-data/#basic-group-by-time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:54:00Z`. It also returns results in [descending timestamp](/influxdb/v1/query_language/explore-data/#order-by-time-desc) order. Notice that the [GROUP BY time() clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) does not override the points’ original timestamps. See [Issue 1](#bottom-with-a-group-by-time-clause) 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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals), 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. ```sql > SELECT BOTTOM("water_level",2) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(18m) name: h2o_feet time bottom ---- ------ __ 2015-08-18T00:00:00Z 2.064 | 2015-08-18T00:12:00Z 2.028 | <------- Smallest points for the first time interval -- __ 2015-08-18T00:24:00Z 2.041 | 2015-08-18T00:30:00Z 2.051 | <------- Smallest points for the second time interval -- ``` ##### BOTTOM() and a tag key with fewer than N tag values Queries with the syntax `SELECT BOTTOM(,,)` 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. ```sql > SELECT BOTTOM("water_level","location",3) FROM "h2o_feet" name: h2o_feet time bottom location ---- ------ -------- 2015-08-29T10:36:00Z -0.243 santa_monica 2015-08-29T14:30:00Z -0.61 coyote_creek ``` ##### BOTTOM(), tags, and the INTO clause When combined with an [`INTO` clause](/influxdb/v1/query_language/explore-data/#the-into-clause) and no [`GROUP BY tag` clause](/influxdb/v1/query_language/explore-data/#group-by-tags), most InfluxQL functions [convert](/influxdb/v1/troubleshooting/frequently-asked-questions/#why-are-my-into-queries-missing-data) any tags in the initial data to fields in the newly written data. This behavior also applies to the `BOTTOM()` function unless `BOTTOM()` includes a tag key as an argument: `BOTTOM(field_key,tag_key(s),N)`. In those cases, the system preserves the specified tag as a tag in the newly written data. ###### Example The first query in the codeblock below returns the smallest field values in the `water_level` field key for two tag values associated with the `location` tag key. It also writes those results to the `bottom_water_levels` measurement. The second query [shows](/influxdb/v1/query_language/explore-schema/#show-tag-keys) that InfluxDB preserved the `location` tag as a tag in the `bottom_water_levels` measurement. ```sql > SELECT BOTTOM("water_level","location",2) INTO "bottom_water_levels" FROM "h2o_feet" name: result time written ---- ------- 1970-01-01T00:00:00Z 2 > SHOW TAG KEYS FROM "bottom_water_levels" name: bottom_water_levels tagKey ------ location ``` ### FIRST() Returns the [field value ](/influxdb/v1/concepts/glossary/#field-value) with the oldest timestamp. #### Syntax ``` SELECT FIRST()[,|] [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](/influxdb/v1/query_language/explore-data/#regular-expressions). `FIRST(*)` Returns the oldest field value (determined by timestamp) associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `FIRST(field_key),tag_key(s),field_key(s)` Returns the oldest field value (determined by timestamp) associated with the field key in the parentheses and the relevant [tag](/influxdb/v1/concepts/glossary/#tag) and/or [field](/influxdb/v1/concepts/glossary/#field). `FIRST()` supports all field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Select the first field value associated with a field key ```sql > SELECT FIRST("level description") FROM "h2o_feet" name: h2o_feet time first ---- ----- 2015-08-18T00:00:00Z between 6 and 9 feet ``` The query returns the oldest field value (determined by timestamp) associated with the `level description` field key and in the `h2o_feet` measurement. ##### Select the first field value associated with each field key in a measurement ```sql > SELECT FIRST(*) FROM "h2o_feet" name: h2o_feet time first_level description first_water_level ---- ----------------------- ----------------- 1970-01-01T00:00:00Z between 6 and 9 feet 8.12 ``` The query returns the oldest field value (determined by timestamp) for each field key in the `h2o_feet` measurement. The `h2o_feet` measurement has two field keys: `level description` and `water_level`. ##### Select the first field value associated with each field key that matches a regular expression ```sql > SELECT FIRST(/level/) FROM "h2o_feet" name: h2o_feet time first_level description first_water_level ---- ----------------------- ----------------- 1970-01-01T00:00:00Z between 6 and 9 feet 8.12 ``` The query returns the oldest field value for each field key that includes the word `level` in the `h2o_feet` measurement. ##### Select the first value associated with a field key and the relevant tags and fields ```sql > SELECT FIRST("level description"),"location","water_level" FROM "h2o_feet" name: h2o_feet time first location water_level ---- ----- -------- ----------- 2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12 ``` The query returns the oldest field value (determined by timestamp) in the `level description` field key and the relevant values of the `location` tag key and the `water_level` field key. ##### Select the first field value associated with a field key and include several clauses ```sql > SELECT FIRST("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(9.01) LIMIT 4 SLIMIT 1 name: h2o_feet tags: location=coyote_creek time first ---- ----- 2015-08-17T23:48:00Z 9.01 2015-08-18T00:00:00Z 8.12 2015-08-18T00:12:00Z 7.887 2015-08-18T00:24:00Z 7.635 ``` The query returns the oldest field value (determined by timestamp) in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `9.01`, and it [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to four and one. Notice that the [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) overrides the points' original timestamps. The timestamps in the results indicate the the start of each 12-minute time interval; the first point in the results covers the time interval between `2015-08-17T23:48:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:24:00Z` and just before `2015-08-18T00:36:00Z`. ### LAST() Returns the [field value](/influxdb/v1/concepts/glossary/#field-value) with the most recent timestamp. #### Syntax ```sql SELECT LAST()[,|] [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](/influxdb/v1/concepts/glossary/#field-key). `LAST(/regular_expression/)` Returns the newest field value (determined by timestamp) associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `LAST(*)` Returns the newest field value (determined by timestamp) associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `LAST(field_key),tag_key(s),field_key(s)` Returns the newest field value (determined by timestamp) associated with the field key in the parentheses and the relevant [tag](/influxdb/v1/concepts/glossary/#tag) and/or [field](/influxdb/v1/concepts/glossary/#field). `LAST()` supports all field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Select the last field values associated with a field key ```sql > SELECT LAST("level description") FROM "h2o_feet" name: h2o_feet time last ---- ---- 2015-09-18T21:42:00Z between 3 and 6 feet ``` The query returns the newest field value (determined by timestamp) associated with the `level description` field key and in the `h2o_feet` measurement. ##### Select the last field values associated with each field key in a measurement ```sql > SELECT LAST(*) FROM "h2o_feet" name: h2o_feet time last_level description last_water_level ---- ----------------------- ----------------- 1970-01-01T00:00:00Z between 3 and 6 feet 4.938 ``` The query returns the newest field value (determined by timestamp) for each field key in the `h2o_feet` measurement. The `h2o_feet` measurement has two field keys: `level description` and `water_level`. ##### Select the last field value associated with each field key that matches a regular expression ```sql > SELECT LAST(/level/) FROM "h2o_feet" name: h2o_feet time last_level description last_water_level ---- ----------------------- ----------------- 1970-01-01T00:00:00Z between 3 and 6 feet 4.938 ``` The query returns the newest field value for each field key that includes the word `level` in the `h2o_feet` measurement. ##### Select the last field value associated with a field key and the relevant tags and fields ```sql > SELECT LAST("level description"),"location","water_level" FROM "h2o_feet" name: h2o_feet time last location water_level ---- ---- -------- ----------- 2015-09-18T21:42:00Z between 3 and 6 feet santa_monica 4.938 ``` The query returns the newest field value (determined by timestamp) in the `level description` field key and the relevant values of the `location` tag key and the `water_level` field key. ##### Select the last field value associated with a field key and include several clauses ```sql > SELECT LAST("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(9.01) LIMIT 4 SLIMIT 1 name: h2o_feet tags: location=coyote_creek time last ---- ---- 2015-08-17T23:48:00Z 9.01 2015-08-18T00:00:00Z 8.005 2015-08-18T00:12:00Z 7.762 2015-08-18T00:24:00Z 7.5 ``` The query returns the newest field value (determined by timestamp) in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results into 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `9.01`, and it [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to four and one. Notice that the [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) overrides the points' original timestamps. The timestamps in the results indicate the the start of each 12-minute time interval; the first point in the results covers the time interval between `2015-08-17T23:48:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:24:00Z` and just before `2015-08-18T00:36:00Z`. ### MAX() Returns the greatest [field value](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT MAX()[,|] [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](/influxdb/v1/concepts/glossary/#field-key). `MAX(/regular_expression/)` Returns the greatest field value associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `MAX(*)` Returns the greatest field value associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `MAX(field_key),tag_key(s),field_key(s)` Returns the greatest field value associated with the field key in the parentheses and the relevant [tag](/influxdb/v1/concepts/glossary/#tag) and/or [field](/influxdb/v1/concepts/glossary/#field). `MAX()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Select the maximum field value associated with a field key ```sql > SELECT MAX("water_level") FROM "h2o_feet" name: h2o_feet time max ---- --- 2015-08-29T07:24:00Z 9.964 ``` The query returns the greatest field value in the `water_level` field key and in the `h2o_feet` measurement. ##### Select the maximum field value associated with each field key in a measurement ```sql > SELECT MAX(*) FROM "h2o_feet" name: h2o_feet time max_water_level ---- --------------- 2015-08-29T07:24:00Z 9.964 ``` The query returns the greatest field value for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ##### Select the maximum field value associated with each field key that matches a regular expression ```sql > SELECT MAX(/level/) FROM "h2o_feet" name: h2o_feet time max_water_level ---- --------------- 2015-08-29T07:24:00Z 9.964 ``` The query returns the greatest field value for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. ##### Select the maximum field value associated with a field key and the relevant tags and fields ```sql > SELECT MAX("water_level"),"location","level description" FROM "h2o_feet" name: h2o_feet time max location level description ---- --- -------- ----------------- 2015-08-29T07:24:00Z 9.964 coyote_creek at or greater than 9 feet ``` The query returns the greatest field value in the `water_level` field key and the relevant values of the `location` tag key and the `level description` field key. ##### Select the maximum field value associated with a field key and include several clauses ```sql > SELECT MAX("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(9.01) LIMIT 4 SLIMIT 1 name: h2o_feet tags: location=coyote_creek time max ---- --- 2015-08-17T23:48:00Z 9.01 2015-08-18T00:00:00Z 8.12 2015-08-18T00:12:00Z 7.887 2015-08-18T00:24:00Z 7.635 ``` The query returns the greatest field value in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results in to 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `9.01`, and it [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to four and one. Notice that the [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) overrides the points’ original timestamps. The timestamps in the results indicate the the start of each 12-minute time interval; the first point in the results covers the time interval between `2015-08-17T23:48:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:24:00Z` and just before `2015-08-18T00:36:00Z`. ### MIN() Returns the lowest [field value](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT MIN()[,|] [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](/influxdb/v1/concepts/glossary/#field-key). `MIN(/regular_expression/)` Returns the lowest field value associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `MIN(*)` Returns the lowest field value associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `MIN(field_key),tag_key(s),field_key(s)` Returns the lowest field value associated with the field key in the parentheses and the relevant [tag](/influxdb/v1/concepts/glossary/#tag) and/or [field](/influxdb/v1/concepts/glossary/#field). `MIN()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Select the minimum field value associated with a field key ```sql > SELECT MIN("water_level") FROM "h2o_feet" name: h2o_feet time min ---- --- 2015-08-29T14:30:00Z -0.61 ``` The query returns the lowest field value in the `water_level` field key and in the `h2o_feet` measurement. ##### Select the minimum field value associated with each field key in a measurement ```sql > SELECT MIN(*) FROM "h2o_feet" name: h2o_feet time min_water_level ---- --------------- 2015-08-29T14:30:00Z -0.61 ``` The query returns the lowest field value for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ##### Select the minimum field value associated with each field key that matches a regular expression ```sql > SELECT MIN(/level/) FROM "h2o_feet" name: h2o_feet time min_water_level ---- --------------- 2015-08-29T14:30:00Z -0.61 ``` The query returns the lowest field value for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. ##### Select the minimum field value associated with a field key and the relevant tags and fields ```sql > SELECT MIN("water_level"),"location","level description" FROM "h2o_feet" name: h2o_feet time min location level description ---- --- -------- ----------------- 2015-08-29T14:30:00Z -0.61 coyote_creek below 3 feet ``` The query returns the lowest field value in the `water_level` field key and the relevant values of the `location` tag key and the `level description` field key. ##### Select the minimum field value associated with a field key and include several clauses ```sql > SELECT MIN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(9.01) LIMIT 4 SLIMIT 1 name: h2o_feet tags: location=coyote_creek time min ---- --- 2015-08-17T23:48:00Z 9.01 2015-08-18T00:00:00Z 8.005 2015-08-18T00:12:00Z 7.762 2015-08-18T00:24:00Z 7.5 ``` The query returns the lowest field value in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#the-group-by-clause) results in to 12-minute time intervals and per tag. The query [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `9.01`, and it [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points and series returned to four and one. Notice that the [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) overrides the points’ original timestamps. The timestamps in the results indicate the the start of each 12-minute time interval; the first point in the results covers the time interval between `2015-08-17T23:48:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:24:00Z` and just before `2015-08-18T00:36:00Z`. ### PERCENTILE() Returns the `N`th percentile [field value](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT PERCENTILE(, )[,|] [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](/influxdb/v1/concepts/glossary/#field-key). `PERCENTILE(/regular_expression/,N)` Returns the Nth percentile field value associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `PERCENTILE(*,N)` Returns the Nth percentile field value associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `PERCENTILE(field_key,N),tag_key(s),field_key(s)` Returns the Nth percentile field value associated with the field key in the parentheses and the relevant [tag](/influxdb/v1/concepts/glossary/#tag) and/or [field](/influxdb/v1/concepts/glossary/#field). `N` must be an integer or floating point number between `0` and `100`, inclusive. `PERCENTILE()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Select the fifth percentile field value associated with a field key ```sql > SELECT PERCENTILE("water_level",5) FROM "h2o_feet" name: h2o_feet time percentile ---- ---------- 2015-08-31T03:42:00Z 1.122 ``` The query returns the field value that is larger than five percent of the field values in the `water_level` field key and in the `h2o_feet` measurement. ##### Select the fifth percentile field value associated with each field key in a measurement ```sql > SELECT PERCENTILE(*,5) FROM "h2o_feet" name: h2o_feet time percentile_water_level ---- ---------------------- 2015-08-31T03:42:00Z 1.122 ``` The query returns the field value that is larger than five percent of the field values in each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ##### Select fifth percentile field value associated with each field key that matches a regular expression ```sql > SELECT PERCENTILE(/level/,5) FROM "h2o_feet" name: h2o_feet time percentile_water_level ---- ---------------------- 2015-08-31T03:42:00Z 1.122 ``` The query returns the field value that is larger than five percent of the field values in each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. ##### Select the fifth percentile field values associated with a field key and the relevant tags and fields ```sql > SELECT PERCENTILE("water_level",5),"location","level description" FROM "h2o_feet" name: h2o_feet time percentile location level description ---- ---------- -------- ----------------- 2015-08-31T03:42:00Z 1.122 coyote_creek below 3 feet ``` The query returns the field value that is larger than five percent of the field values in the `water_level` field key and the relevant values of the `location` tag key and the `level description` field key. ##### Select the twentieth percentile field value associated with a field key and include several clauses ```sql > SELECT PERCENTILE("water_level",20) FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(24m) fill(15) LIMIT 2 name: h2o_feet time percentile ---- ---------- 2015-08-17T23:36:00Z 15 2015-08-18T00:00:00Z 2.064 ``` The query returns the field value that is larger than 20 percent of the values in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-17T23:48:00Z` and `2015-08-18T00:54:00Z` and [groups](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) results into 24-minute intervals. It [fills](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) empty time intervals with `15` and it [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to two. Notice that the [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) overrides the points’ original timestamps. The timestamps in the results indicate the the start of each 24-minute time interval; the first point in the results covers the time interval between `2015-08-17T23:36:00Z` and just before `2015-08-18T00:00:00Z` and the last point in the results covers the time interval between `2015-08-18T00:00:00Z` and just before `2015-08-18T00:24:00Z`. #### Common Issues with PERCENTILE() ##### PERCENTILE() compared to other InfluxQL functions * `PERCENTILE(,100)` is equivalent to [`MAX()`](#max). * `PERCENTILE(, 50)` is nearly equivalent to [`MEDIAN()`](#median), except the `MEDIAN()` function returns the average of the two middle values if the field key contains an even number of field values. * `PERCENTILE(,0)` is not equivalent to [`MIN()`](#min). This is a known [issue](https://github.com/influxdata/influxdb/issues/4418). ### SAMPLE() Returns a random sample of `N` [field values](/influxdb/v1/concepts/glossary/#field-value). `SAMPLE()` uses [reservoir sampling](https://en.wikipedia.org/wiki/Reservoir_sampling) to generate the random points. #### Syntax ``` SELECT SAMPLE(, )[,|] [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](/influxdb/v1/concepts/glossary/#field-key). `SAMPLE(/regular_expression/,N)` Returns N randomly selected field values associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `SAMPLE(*,N)` Returns N randomly selected field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `SAMPLE(field_key,N),tag_key(s),field_key(s)` Returns N randomly selected field values associated with the field key in the parentheses and the relevant [tag](/influxdb/v1/concepts/glossary/#tag) and/or [field](/influxdb/v1/concepts/glossary/#field). `N` must be an integer. `SAMPLE()` supports all field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Select a sample of the field values associated with a field key ```sql > SELECT SAMPLE("water_level",2) FROM "h2o_feet" name: h2o_feet time sample ---- ------ 2015-09-09T21:48:00Z 5.659 2015-09-18T10:00:00Z 6.939 ``` The query returns two randomly selected points from the `water_level` field key and in the `h2o_feet` measurement. #### Select a sample of the field values associated with each field key in a measurement ```sql > SELECT SAMPLE(*,2) FROM "h2o_feet" name: h2o_feet time sample_level description sample_water_level ---- ------------------------ ------------------ 2015-08-25T17:06:00Z 3.284 2015-09-03T04:30:00Z below 3 feet 2015-09-03T20:06:00Z between 3 and 6 feet 2015-09-08T21:54:00Z 3.412 ``` The query returns two randomly selected points for each field key in the `h2o_feet` measurement. The `h2o_feet` measurement has two field keys: `level description` and `water_level`. ##### Select a sample of the field values associated with each field key that matches a regular expression ```sql > SELECT SAMPLE(/level/,2) FROM "h2o_feet" name: h2o_feet time sample_level description sample_water_level ---- ------------------------ ------------------ 2015-08-30T05:54:00Z between 6 and 9 feet 2015-09-07T01:18:00Z 7.854 2015-09-09T20:30:00Z 7.32 2015-09-13T19:18:00Z between 3 and 6 feet ``` The query returns two randomly selected points for each field key that includes the word `level` in the `h2o_feet` measurement. ##### Select a sample of the field values associated with a field key and the relevant tags and fields ```sql > SELECT SAMPLE("water_level",2),"location","level description" FROM "h2o_feet" name: h2o_feet time sample location level description ---- ------ -------- ----------------- 2015-08-29T10:54:00Z 5.689 coyote_creek between 3 and 6 feet 2015-09-08T15:48:00Z 6.391 coyote_creek between 6 and 9 feet ``` The query returns two randomly selected points from the `water_level` field key and the relevant values of the `location` tag and the `level description` field. ##### Select a sample of the field values associated with a field key and include several clauses ```sql > SELECT SAMPLE("water_level",1) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(18m) name: h2o_feet time sample ---- ------ 2015-08-18T00:12:00Z 2.028 2015-08-18T00:30:00Z 2.051 ``` The query returns one randomly selected point from the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and [groups](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) results into 18-minute intervals. Notice that the [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) does not override the points' original timestamps. See [Issue 1](#sample-with-a-group-by-time-clause) in the section below for a more detailed explanation of that behavior. #### Common Issues with `SAMPLE()` ##### `SAMPLE()` with a `GROUP BY time()` clause Queries with `SAMPLE()` and a `GROUP BY time()` clause return the specified number of points (`N`) per `GROUP BY time()` interval. For [most `GROUP BY time()` queries](/influxdb/v1/query_language/explore-data/#group-by-time-intervals), the returned timestamps mark the start of the `GROUP BY time()` interval. `GROUP BY time()` queries with the `SAMPLE()` function behave differently; they maintain the timestamp of the original data point. ###### Example 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. ```sql > SELECT SAMPLE("water_level",2) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(18m) name: h2o_feet time sample ---- ------ __ 2015-08-18T00:06:00Z 2.116 | 2015-08-18T00:12:00Z 2.028 | <------- Randomly-selected points for the first time interval -- __ 2015-08-18T00:18:00Z 2.126 | 2015-08-18T00:30:00Z 2.051 | <------- Randomly-selected points for the second time interval -- ``` ### TOP() Returns the greatest `N` [field values](/influxdb/v1/concepts/glossary/#field-value). #### Syntax ``` SELECT TOP( [,], )[,|] [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](/influxdb/v1/concepts/glossary/#field-key). `TOP(field_key,tag_key(s),N)` Returns the greatest field value for N tag values of the [tag key](/influxdb/v1/concepts/glossary/#tag-key). `TOP(field_key,N),tag_key(s),field_key(s)` Returns the greatest N field values associated with the field key in the parentheses and the relevant [tag](/influxdb/v1/concepts/glossary/#tag) and/or [field](/influxdb/v1/concepts/glossary/#field). `TOP()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). > **Notes:** > * `TOP()` returns the field value with the earliest timestamp if there's a tie between two or more values for the greatest value. * `TOP()` differs from other InfluxQL functions when combined with an [`INTO` clause](/influxdb/v1/query_language/explore-data/#the-into-clause). See the [Common Issues](#common-issues-with-top) section for more information. #### Examples ##### Select the top three field values associated with a field key ```sql > SELECT TOP("water_level",3) FROM "h2o_feet" name: h2o_feet time top ---- --- 2015-08-29T07:18:00Z 9.957 2015-08-29T07:24:00Z 9.964 2015-08-29T07:30:00Z 9.954 ``` The query returns the greatest three field values in the `water_level` field key and in the `h2o_feet` [measurement](/influxdb/v1/concepts/glossary/#measurement). ##### Select the top field value associated with a field key for two tags ```sql > SELECT TOP("water_level","location",2) FROM "h2o_feet" name: h2o_feet time top location ---- --- -------- 2015-08-29T03:54:00Z 7.205 santa_monica 2015-08-29T07:24:00Z 9.964 coyote_creek ``` The query returns the greatest field values in the `water_level` field key for two tag values associated with the `location` tag key. ##### Select the top four field values associated with a field key and the relevant tags and fields ```sql > SELECT TOP("water_level",4),"location","level description" FROM "h2o_feet" name: h2o_feet time top location level description ---- --- -------- ----------------- 2015-08-29T07:18:00Z 9.957 coyote_creek at or greater than 9 feet 2015-08-29T07:24:00Z 9.964 coyote_creek at or greater than 9 feet 2015-08-29T07:30:00Z 9.954 coyote_creek at or greater than 9 feet 2015-08-29T07:36:00Z 9.941 coyote_creek at or greater than 9 feet ``` The query returns the greatest four field values in the `water_level` field key and the relevant values of the `location` tag key and the `level description` field key. ##### Select the top three field values associated with a field key and include several clauses ```sql > SELECT TOP("water_level",3),"location" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(24m) ORDER BY time DESC name: h2o_feet time top location ---- --- -------- 2015-08-18T00:48:00Z 7.11 coyote_creek 2015-08-18T00:54:00Z 6.982 coyote_creek 2015-08-18T00:54:00Z 2.054 santa_monica 2015-08-18T00:24:00Z 7.635 coyote_creek 2015-08-18T00:30:00Z 7.5 coyote_creek 2015-08-18T00:36:00Z 7.372 coyote_creek 2015-08-18T00:00:00Z 8.12 coyote_creek 2015-08-18T00:06:00Z 8.005 coyote_creek 2015-08-18T00:12:00Z 7.887 coyote_creek ``` The query returns the greatest three values in the `water_level` field key for each 24-minute [interval](/influxdb/v1/query_language/explore-data/#basic-group-by-time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:54:00Z`. It also returns results in [descending timestamp](/influxdb/v1/query_language/explore-data/#order-by-time-desc) order. Notice that the [GROUP BY time() clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) does not override the points’ original timestamps. See [Issue 1](#top-with-a-group-by-time-clause) in the section below for a more detailed explanation of that behavior. #### Common Issues with `TOP()` ##### `TOP()` with a `GROUP BY time()` clause Queries with `TOP()` and a `GROUP BY time()` clause return the specified number of points per `GROUP BY time()` interval. For [most `GROUP BY time()` queries](/influxdb/v1/query_language/explore-data/#group-by-time-intervals), the returned timestamps mark the start of the `GROUP BY time()` interval. `GROUP BY time()` queries with the `TOP()` function behave differently; they maintain the timestamp of the original data point. ###### Example 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. ```sql > SELECT TOP("water_level",2) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(18m) name: h2o_feet time top ---- ------ __ 2015-08-18T00:00:00Z 2.064 | 2015-08-18T00:06:00Z 2.116 | <------- Greatest points for the first time interval -- __ 2015-08-18T00:18:00Z 2.126 | 2015-08-18T00:30:00Z 2.051 | <------- Greatest points for the second time interval -- ``` ##### TOP() and a tag key with fewer than N tag values Queries with the syntax `SELECT TOP(,,)` 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. ```sql > SELECT TOP("water_level","location",3) FROM "h2o_feet" name: h2o_feet time top location ---- --- -------- 2015-08-29T03:54:00Z 7.205 santa_monica 2015-08-29T07:24:00Z 9.964 coyote_creek ``` ##### TOP(), tags, and the INTO clause When combined with an [`INTO` clause](/influxdb/v1/query_language/explore-data/#the-into-clause) and no [`GROUP BY tag` clause](/influxdb/v1/query_language/explore-data/#group-by-tags), most InfluxQL functions [convert](/influxdb/v1/troubleshooting/frequently-asked-questions/#why-are-my-into-queries-missing-data) any tags in the initial data to fields in the newly written data. This behavior also applies to the `TOP()` function unless `TOP()` includes a tag key as an argument: `TOP(field_key,tag_key(s),N)`. In those cases, the system preserves the specified tag as a tag in the newly written data. ###### Example The first query in the codeblock below returns the greatest field values in the `water_level` field key for two tag values associated with the `location` tag key. It also writes those results to the `top_water_levels` measurement. The second query [shows](/influxdb/v1/query_language/explore-schema/#show-tag-keys) that InfluxDB preserved the `location` tag as a tag in the `top_water_levels` measurement. ```sql > SELECT TOP("water_level","location",2) INTO "top_water_levels" FROM "h2o_feet" name: result time written ---- ------- 1970-01-01T00:00:00Z 2 > SHOW TAG KEYS FROM "top_water_levels" name: top_water_levels tagKey ------ location ``` ## Transformations ### ABS() Returns the absolute value of the field value. #### Basic syntax ``` SELECT ABS( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `ABS(*)` Returns the absolute values of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `ABS()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](https://gist.github.com/sanderson/8f8aec94a60b2c31a61f44a37737bfea): ```sql > SELECT * FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T12:05:00Z' name: data time a b ---- - - 1529841600000000000 1.33909108671076 -0.163643058925645 1529841660000000000 -0.774984088561186 0.137034364053949 1529841720000000000 -0.921037167720451 -0.482943221384294 1529841780000000000 -1.73880754843378 -0.0729732928756677 1529841840000000000 -0.905980032168252 1.77857552719844 1529841900000000000 -0.891164752631417 0.741147445214238 ``` ###### Calculate the absolute values of field values associated with a field key ```sql > SELECT ABS("a") FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T12:05:00Z' name: data time abs ---- --- 1529841600000000000 1.33909108671076 1529841660000000000 0.774984088561186 1529841720000000000 0.921037167720451 1529841780000000000 1.73880754843378 1529841840000000000 0.905980032168252 1529841900000000000 0.891164752631417 ``` The query returns the absolute values of field values in the `a` field key in the `data` measurement. ###### Calculate the absolute Values of field values associated with each field key in a measurement ```sql > SELECT ABS(*) FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T12:05:00Z' name: data time abs_a abs_b ---- ----- ----- 1529841600000000000 1.33909108671076 0.163643058925645 1529841660000000000 0.774984088561186 0.137034364053949 1529841720000000000 0.921037167720451 0.482943221384294 1529841780000000000 1.73880754843378 0.0729732928756677 1529841840000000000 0.905980032168252 1.77857552719844 1529841900000000000 0.891164752631417 0.741147445214238 ``` The query returns the absolute values of field values for each field key that stores numerical values in the `data` measurement. The `data` measurement has two numerical fields: `a` and `b`. ###### Calculate the absolute values of field values associated with a field key and include several clauses ```sql > SELECT ABS("a") FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T12:05:00Z' ORDER BY time DESC LIMIT 4 OFFSET 2 name: data time abs ---- --- 1529841780000000000 1.73880754843378 1529841720000000000 0.921037167720451 1529841660000000000 0.774984088561186 1529841600000000000 1.33909108671076 ``` The query returns the absolute values of field values associated with the `a` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2018-06-24T12:00:00Z` and `2018-06-24T12:05:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT ABS(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ABS()` function to those results. `ABS()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the absolute values of mean values ```sql > SELECT ABS(MEAN("a")) FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T13:00:00Z' GROUP BY time(12m) name: data time abs ---- --- 1529841600000000000 0.3960977256302787 1529842320000000000 0.0010541018316373302 1529843040000000000 0.04494733240283668 1529843760000000000 0.2553594777104415 1529844480000000000 0.20382988543108413 1529845200000000000 0.790836070736962 ``` The query returns the absolute values of [average](#mean) `a`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `a`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ABS()`: ```sql > SELECT MEAN("a") FROM "data" WHERE time >= '2018-06-24T12:00:00Z' AND time <= '2018-06-24T13:00:00Z' GROUP BY time(12m) name: data time mean ---- ---- 1529841600000000000 -0.3960977256302787 1529842320000000000 0.0010541018316373302 1529843040000000000 0.04494733240283668 1529843760000000000 0.2553594777104415 1529844480000000000 0.20382988543108413 1529845200000000000 -0.790836070736962 ``` InfluxDB then calculates absolute values of those averages. ### ACOS() Returns the arccosine (in radians) of the field value. Field values must be between -1 and 1. #### Basic syntax ``` SELECT ACOS( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `ACOS(*)` Returns the arccosine of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `ACOS()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types) with values between -1 and 1. The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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: ```sql > SELECT "of_capacity" FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' name: park_occupancy time capacity ---- -------- 2017-05-01T00:00:00Z 0.83 2017-05-02T00:00:00Z 0.3 2017-05-03T00:00:00Z 0.84 2017-05-04T00:00:00Z 0.22 2017-05-05T00:00:00Z 0.17 2017-05-06T00:00:00Z 0.77 2017-05-07T00:00:00Z 0.64 2017-05-08T00:00:00Z 0.72 2017-05-09T00:00:00Z 0.16 ``` ###### Calculate the arccosine of field values associated with a field key ```sql > SELECT ACOS("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' name: park_occupancy time acos ---- ---- 2017-05-01T00:00:00Z 0.591688642426544 2017-05-02T00:00:00Z 1.266103672779499 2017-05-03T00:00:00Z 0.5735131044230969 2017-05-04T00:00:00Z 1.3489818562981022 2017-05-05T00:00:00Z 1.399966657665792 2017-05-06T00:00:00Z 0.6919551751263169 2017-05-07T00:00:00Z 0.8762980611683406 2017-05-08T00:00:00Z 0.7669940078618667 2017-05-09T00:00:00Z 1.410105673842986 ``` The query returns arccosine of field values in the `of_capacity` field key in the `park_occupancy` measurement. ###### Calculate the arccosine of field values associated with each field key in a measurement ```sql > SELECT ACOS(*) FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' name: park_occupancy time acos_of_capacity ---- ------------- 2017-05-01T00:00:00Z 0.591688642426544 2017-05-02T00:00:00Z 1.266103672779499 2017-05-03T00:00:00Z 0.5735131044230969 2017-05-04T00:00:00Z 1.3489818562981022 2017-05-05T00:00:00Z 1.399966657665792 2017-05-06T00:00:00Z 0.6919551751263169 2017-05-07T00:00:00Z 0.8762980611683406 2017-05-08T00:00:00Z 0.7669940078618667 2017-05-09T00:00:00Z 1.410105673842986 ``` The query returns arccosine of field values for each field key that stores numerical values in the `park_occupancy` measurement. The `park_occupancy` measurement has one numerical field: `of_capacity`. ###### Calculate the arccosine of field values associated with a field key and include several clauses ```sql > SELECT ACOS("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' ORDER BY time DESC LIMIT 4 OFFSET 2 name: park_occupancy time acos ---- ---- 2017-05-07T00:00:00Z 0.8762980611683406 2017-05-06T00:00:00Z 0.6919551751263169 2017-05-05T00:00:00Z 1.399966657665792 2017-05-04T00:00:00Z 1.3489818562981022 ``` The query returns arccosine of field values associated with the `of_capacity` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2017-05-01T00:00:00Z` and `2017-05-09T00:00:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT ACOS(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ACOS()` function to those results. `ACOS()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the arccosine of mean values ```sql > SELECT ACOS(MEAN("of_capacity")) FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' GROUP BY time(3d) name: park_occupancy time acos ---- ---- 2017-04-30T00:00:00Z 0.9703630732143733 2017-05-03T00:00:00Z 1.1483422646081407 2017-05-06T00:00:00Z 0.7812981174487247 2017-05-09T00:00:00Z 1.410105673842986 ``` The query returns arccosine of [average](#mean) `of_capacity`s that are calculated at 3-day intervals. To get those results, InfluxDB first calculates the average `of_capacity`s at 3-day intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ACOS()`: ```sql > SELECT MEAN("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' GROUP BY time(3d) name: park_occupancy time mean ---- ---- 2017-04-30T00:00:00Z 0.565 2017-05-03T00:00:00Z 0.41 2017-05-06T00:00:00Z 0.71 2017-05-09T00:00:00Z 0.16 ``` InfluxDB then calculates arccosine of those averages. ### ASIN() Returns the arcsine (in radians) of the field value. Field values must be between -1 and 1. #### Basic syntax ``` SELECT ASIN( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `ASIN(*)` Returns the arcsine of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `ASIN()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types) with values between -1 and 1. The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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: ```sql > SELECT "of_capacity" FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' name: park_occupancy time capacity ---- -------- 2017-05-01T00:00:00Z 0.83 2017-05-02T00:00:00Z 0.3 2017-05-03T00:00:00Z 0.84 2017-05-04T00:00:00Z 0.22 2017-05-05T00:00:00Z 0.17 2017-05-06T00:00:00Z 0.77 2017-05-07T00:00:00Z 0.64 2017-05-08T00:00:00Z 0.72 2017-05-09T00:00:00Z 0.16 ``` ###### Calculate the arcsine of field values associated with a field key ```sql > SELECT ASIN("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' name: park_occupancy time asin ---- ---- 2017-05-01T00:00:00Z 0.9791076843683526 2017-05-02T00:00:00Z 0.3046926540153975 2017-05-03T00:00:00Z 0.9972832223717997 2017-05-04T00:00:00Z 0.22181447049679442 2017-05-05T00:00:00Z 0.1708296691291045 2017-05-06T00:00:00Z 0.8788411516685797 2017-05-07T00:00:00Z 0.6944982656265559 2017-05-08T00:00:00Z 0.8038023189330299 2017-05-09T00:00:00Z 0.1606906529519106 ``` The query returns arcsine of field values in the `of_capacity` field key in the `park_capacity` measurement. ###### Calculate the arcsine of field values associated with each field key in a measurement ```sql > SELECT ASIN(*) FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' name: park_occupancy time asin_of_capacity ---- ------------- 2017-05-01T00:00:00Z 0.9791076843683526 2017-05-02T00:00:00Z 0.3046926540153975 2017-05-03T00:00:00Z 0.9972832223717997 2017-05-04T00:00:00Z 0.22181447049679442 2017-05-05T00:00:00Z 0.1708296691291045 2017-05-06T00:00:00Z 0.8788411516685797 2017-05-07T00:00:00Z 0.6944982656265559 2017-05-08T00:00:00Z 0.8038023189330299 2017-05-09T00:00:00Z 0.1606906529519106 ``` The query returns arcsine of field values for each field key that stores numerical values in the `park_capacity` measurement. The `h2o_feet` measurement has one numerical field: `of_capacity`. ###### Calculate the arcsine of field values associated with a field key and include several clauses ```sql > SELECT ASIN("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' ORDER BY time DESC LIMIT 4 OFFSET 2 name: park_occupancy time asin ---- ---- 2017-05-07T00:00:00Z 0.6944982656265559 2017-05-06T00:00:00Z 0.8788411516685797 2017-05-05T00:00:00Z 0.1708296691291045 2017-05-04T00:00:00Z 0.22181447049679442 ``` The query returns arcsine of field values associated with the `of_capacity` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2017-05-01T00:00:00Z` and `2017-05-09T00:00:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT ASIN(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ASIN()` function to those results. `ASIN()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the arcsine of mean values. ```sql > SELECT ASIN(MEAN("of_capacity")) FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' GROUP BY time(3d) name: park_occupancy time asin ---- ---- 2017-04-30T00:00:00Z 0.6004332535805232 2017-05-03T00:00:00Z 0.42245406218675574 2017-05-06T00:00:00Z 0.7894982093461719 2017-05-09T00:00:00Z 0.1606906529519106 ``` The query returns arcsine of [average](#mean) `of_capacity`s that are calculated at 3-day intervals. To get those results, InfluxDB first calculates the average `of_capacity`s at 3-day intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ASIN()`: ```sql > SELECT MEAN("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' GROUP BY time(3d) name: park_occupancy time mean ---- ---- 2017-04-30T00:00:00Z 0.565 2017-05-03T00:00:00Z 0.41 2017-05-06T00:00:00Z 0.71 2017-05-09T00:00:00Z 0.16 ``` InfluxDB then calculates arcsine of those averages. ### ATAN() Returns the arctangent (in radians) of the field value. Field values must be between -1 and 1. #### Basic syntax ```sql SELECT ATAN( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `ATAN(*)` Returns the arctangent of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `ATAN()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types) with values between -1 and 1. The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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: ```sql > SELECT "of_capacity" FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' name: park_occupancy time capacity ---- -------- 2017-05-01T00:00:00Z 0.83 2017-05-02T00:00:00Z 0.3 2017-05-03T00:00:00Z 0.84 2017-05-04T00:00:00Z 0.22 2017-05-05T00:00:00Z 0.17 2017-05-06T00:00:00Z 0.77 2017-05-07T00:00:00Z 0.64 2017-05-08T00:00:00Z 0.72 2017-05-09T00:00:00Z 0.16 ``` ###### Calculate the arctangent of field values associated with a field key ```sql > SELECT ATAN("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' name: park_occupancy time atan ---- ---- 2017-05-01T00:00:00Z 0.6927678353971222 2017-05-02T00:00:00Z 0.2914567944778671 2017-05-03T00:00:00Z 0.6986598247214632 2017-05-04T00:00:00Z 0.2165503049760893 2017-05-05T00:00:00Z 0.16839015714752992 2017-05-06T00:00:00Z 0.6561787179913948 2017-05-07T00:00:00Z 0.5693131911006619 2017-05-08T00:00:00Z 0.6240230529767568 2017-05-09T00:00:00Z 0.1586552621864014 ``` The query returns arctangent of field values in the `of_capacity` field key in the `park_occupancy` measurement. ###### Calculate the arctangent of field values associated with each field key in a measurement ```sql > SELECT ATAN(*) FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' name: park_occupancy time atan_of_capacity ---- ------------- 2017-05-01T00:00:00Z 0.6927678353971222 2017-05-02T00:00:00Z 0.2914567944778671 2017-05-03T00:00:00Z 0.6986598247214632 2017-05-04T00:00:00Z 0.2165503049760893 2017-05-05T00:00:00Z 0.16839015714752992 2017-05-06T00:00:00Z 0.6561787179913948 2017-05-07T00:00:00Z 0.5693131911006619 2017-05-08T00:00:00Z 0.6240230529767568 2017-05-09T00:00:00Z 0.1586552621864014 ``` The query returns arctangent of field values for each field key that stores numerical values in the `park_occupancy` measurement. The `park_occupancy` measurement has one numerical field: `of_capacity`. ###### Calculate the arctangent of field values associated with a field key and include several clauses ```sql > SELECT ATAN("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' ORDER BY time DESC LIMIT 4 OFFSET 2 name: park_occupancy time atan ---- ---- 2017-05-07T00:00:00Z 0.5693131911006619 2017-05-06T00:00:00Z 0.6561787179913948 2017-05-05T00:00:00Z 0.16839015714752992 2017-05-04T00:00:00Z 0.2165503049760893 ``` The query returns arctangent of field values associated with the `of_capacity` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2017-05-01T00:00:00Z` and `2017-05-09T00:00:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT ATAN(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ATAN()` function to those results. `ATAN()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples of advanced syntax ###### Calculate the arctangent of mean values. ```sql > SELECT ATAN(MEAN("of_capacity")) FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' GROUP BY time(3d) name: park_occupancy time atan ---- ---- 2017-04-30T00:00:00Z 0.5142865412694495 2017-05-03T00:00:00Z 0.3890972310552784 2017-05-06T00:00:00Z 0.6174058917515726 2017-05-09T00:00:00Z 0.1586552621864014 ``` The query returns arctangent of [average](#mean) `of_capacity`s that are calculated at 3-day intervals. To get those results, InfluxDB first calculates the average `of_capacity`s at 3-day intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ATAN()`: ```sql > SELECT MEAN("of_capacity") FROM "park_occupancy" WHERE time >= '2017-05-01T00:00:00Z' AND time <= '2017-05-09T00:00:00Z' GROUP BY time(3d) name: park_occupancy time mean ---- ---- 2017-04-30T00:00:00Z 0.565 2017-05-03T00:00:00Z 0.41 2017-05-06T00:00:00Z 0.71 2017-05-09T00:00:00Z 0.16 ``` InfluxDB then calculates arctangent of those averages. ### ATAN2() Returns the the arctangent of `y/x` in radians. #### Basic syntax ``` SELECT ATAN2( [ * | | num ], [ | 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#measurement) divided by field values associated with `field_key_x`. `ATAN2()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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: ```sql > SELECT "altitude_ft", "distance_ft" FROM "flight_data" WHERE time >= '2018-05-16T12:01:00Z' AND time <= '2018-05-16T12:10:00Z' name: flight_data time altitude_ft distance_ft ---- ----------- ----------- 2018-05-16T12:01:00Z 1026 50094 2018-05-16T12:02:00Z 2549 53576 2018-05-16T12:03:00Z 4033 55208 2018-05-16T12:04:00Z 5579 58579 2018-05-16T12:05:00Z 7065 61213 2018-05-16T12:06:00Z 8589 64807 2018-05-16T12:07:00Z 10180 67707 2018-05-16T12:08:00Z 11777 69819 2018-05-16T12:09:00Z 13321 72452 2018-05-16T12:10:00Z 14885 75881 ``` ###### Calculate the arctangent of field_key_y over field_key_x ```sql > SELECT ATAN2("altitude_ft", "distance_ft") FROM "flight_data" WHERE time >= '2018-05-16T12:01:00Z' AND time <= '2018-05-16T12:10:00Z' name: flight_data time atan2 ---- ----- 2018-05-16T12:01:00Z 0.020478631571881498 2018-05-16T12:02:00Z 0.04754142349303296 2018-05-16T12:03:00Z 0.07292147724575364 2018-05-16T12:04:00Z 0.09495251193874832 2018-05-16T12:05:00Z 0.11490822875441563 2018-05-16T12:06:00Z 0.13176409347584003 2018-05-16T12:07:00Z 0.14923587589682233 2018-05-16T12:08:00Z 0.1671059946640312 2018-05-16T12:09:00Z 0.18182893717409565 2018-05-16T12:10:00Z 0.1937028631495223 ``` The query returns the arctangents of field values in the `altitude_ft` field key divided by values in the `distance_ft` field key. Both are part of the `flight_data` measurement. ###### Calculate the arctangent of values associated with each field key in a measurement divided by field_key_x ```sql > SELECT ATAN2(*, "distance_ft") FROM "flight_data" WHERE time >= '2018-05-16T12:01:00Z' AND time <= '2018-05-16T12:10:00Z' name: flight_data time atan2_altitude_ft atan2_distance_ft ---- ----------------- ----------------- 2018-05-16T12:01:00Z 0.020478631571881498 0.7853981633974483 2018-05-16T12:02:00Z 0.04754142349303296 0.7853981633974483 2018-05-16T12:03:00Z 0.07292147724575364 0.7853981633974483 2018-05-16T12:04:00Z 0.09495251193874832 0.7853981633974483 2018-05-16T12:05:00Z 0.11490822875441563 0.7853981633974483 2018-05-16T12:06:00Z 0.13176409347584003 0.7853981633974483 2018-05-16T12:07:00Z 0.14923587589682233 0.7853981633974483 2018-05-16T12:08:00Z 0.1671059946640312 0.7853981633974483 2018-05-16T12:09:00Z 0.18182893717409565 0.7853981633974483 2018-05-16T12:10:00Z 0.19370286314952234 0.7853981633974483 ``` The query returns the arctangents of all numeric field values in the `flight_data` measurement divided by values in the `distance_ft` field key. The `flight_data` measurement has two numeric fields: `altitude_ft` and `distance_ft`. ###### Calculate the arctangents of field values and include several clauses ```sql > SELECT ATAN2("altitude_ft", "distance_ft") FROM "flight_data" WHERE time >= '2018-05-16T12:01:00Z' AND time <= '2018-05-16T12:10:00Z' ORDER BY time DESC LIMIT 4 OFFSET 2 name: flight_data time atan2 ---- ----- 2018-05-16T12:08:00Z 0.1671059946640312 2018-05-16T12:07:00Z 0.14923587589682233 2018-05-16T12:06:00Z 0.13176409347584003 2018-05-16T12:05:00Z 0.11490822875441563 ``` The query returns the arctangent of field values associated with the `altitude_ft` field key divided by the `distance_ft` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2018-05-16T12:10:00Z` and `2018-05-16T12:10:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT ATAN2(, ) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ATAN2()` function to those results. `ATAN2()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate arctangents of mean values ```sql > SELECT ATAN2(MEAN("altitude_ft"), MEAN("distance_ft")) FROM "flight_data" WHERE time >= '2018-05-16T12:01:00Z' AND time <= '2018-05-16T13:01:00Z' GROUP BY time(12m) name: flight_data time atan2 ---- ----- 2018-05-16T12:00:00Z 0.133815587896842 2018-05-16T12:12:00Z 0.2662716308351908 2018-05-16T12:24:00Z 0.2958845306108965 2018-05-16T12:36:00Z 0.23783439588429497 2018-05-16T12:48:00Z 0.1906803720242831 2018-05-16T13:00:00Z 0.17291511946158172 ``` The query returns the argtangents of [average](#mean) `altitude_ft`s divided by average `distance_ft`s. Averages are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `altitude_ft`s and `distance_ft` at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ATAN2()`: ^ ```sql > SELECT MEAN("altitude_ft"), MEAN("distance_ft") FROM "flight_data" WHERE time >= '2018-05-16T12:01:00Z' AND time <= '2018-05-16T13:01:00Z' GROUP BY time(12m) name: flight_data time mean mean_1 ---- ---- ------ 2018-05-16T12:00:00Z 8674 64433.181818181816 2018-05-16T12:12:00Z 26419.833333333332 96865.25 2018-05-16T12:24:00Z 40337.416666666664 132326.41666666666 2018-05-16T12:36:00Z 41149.583333333336 169743.16666666666 2018-05-16T12:48:00Z 41230.416666666664 213600.91666666666 2018-05-16T13:00:00Z 41184.5 235799 ``` InfluxDB then calculates the arctangents of those averages. ### CEIL() Returns the subsequent value rounded up to the nearest integer. #### Basic syntax ``` SELECT CEIL( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key) rounded up to the nearest integer. `CEIL(*)` Returns the field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement) rounded up to the nearest integer. `CEIL()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the ceiling of field values associated with a field key ```sql > SELECT CEIL("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time ceil ---- ---- 2015-08-18T00:00:00Z 3 2015-08-18T00:06:00Z 3 2015-08-18T00:12:00Z 3 2015-08-18T00:18:00Z 3 2015-08-18T00:24:00Z 3 2015-08-18T00:30:00Z 3 ``` The query returns field values in the `water_level` field key in the `h2o_feet` measurement rounded up to the nearest integer. ###### Calculate the ceiling of field values associated with each field key in a measurement ```sql > SELECT CEIL(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time ceil_water_level ---- ---------------- 2015-08-18T00:00:00Z 3 2015-08-18T00:06:00Z 3 2015-08-18T00:12:00Z 3 2015-08-18T00:18:00Z 3 2015-08-18T00:24:00Z 3 2015-08-18T00:30:00Z 3 ``` The query returns field values for each field key that stores numerical values in the `h2o_feet` measurement rounded up to the nearest integer. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the ceiling of field values associated with a field key and include several clauses ```sql > SELECT CEIL("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time ceil ---- ---- 2015-08-18T00:18:00Z 3 2015-08-18T00:12:00Z 3 2015-08-18T00:06:00Z 3 2015-08-18T00:00:00Z 3 ``` The query returns field values associated with the `water_level` field key rounded up to the nearest integer. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT CEIL(( [ * | | // ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `CEIL()` function to those results. `CEIL()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate mean values rounded up to the nearest integer ```sql > SELECT CEIL(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time ceil ---- ---- 2015-08-18T00:00:00Z 3 2015-08-18T00:12:00Z 3 2015-08-18T00:24:00Z 3 ``` The query returns the [average](#mean) `water_level`s that are calculated at 12-minute intervals and rounds them up to the nearest integer. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `CEIL()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then rounds those averages up to the nearest integer. ### COS() Returns the cosine of the field value. #### Basic syntax ``` SELECT COS( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `COS(*)` Returns the cosine of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `COS()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ^ ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the cosine of field values associated with a field key ```sql > SELECT COS("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time cos ---- --- 2015-08-18T00:00:00Z -0.47345017433543124 2015-08-18T00:06:00Z -0.5185922462666872 2015-08-18T00:12:00Z -0.4414407189100776 2015-08-18T00:18:00Z -0.5271163912192579 2015-08-18T00:24:00Z -0.45306786455514825 2015-08-18T00:30:00Z -0.4619598230611262 ``` The query returns cosine of field values in the `water_level` field key in the `h2o_feet` measurement. ###### Calculate the cosine of field values associated with each field key in a measurement ```sql > SELECT COS(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time cos_water_level ---- --------------- 2015-08-18T00:00:00Z -0.47345017433543124 2015-08-18T00:06:00Z -0.5185922462666872 2015-08-18T00:12:00Z -0.4414407189100776 2015-08-18T00:18:00Z -0.5271163912192579 2015-08-18T00:24:00Z -0.45306786455514825 2015-08-18T00:30:00Z -0.4619598230611262 ``` The query returns cosine of field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the cosine of field values associated with a field key and include several clauses ```sql > SELECT COS("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time cos ---- --- 2015-08-18T00:18:00Z -0.5271163912192579 2015-08-18T00:12:00Z -0.4414407189100776 2015-08-18T00:06:00Z -0.5185922462666872 2015-08-18T00:00:00Z -0.47345017433543124 ``` The query returns cosine of field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT COS(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `COS()` function to those results. `COS()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). #### Examples ###### Calculate the cosine of mean values ```sql > SELECT COS(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time cos ---- --- 2015-08-18T00:00:00Z -0.49618891270599885 2015-08-18T00:12:00Z -0.4848605136571181 2015-08-18T00:24:00Z -0.4575195627907578 ``` The query returns cosine of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `COS()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates cosine of those averages. ### CUMULATIVE_SUM() Returns the running total of subsequent [field values](/influxdb/v1/concepts/glossary/#field-value). #### Basic syntax ``` SELECT CUMULATIVE_SUM( [ * | | // ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `CUMULATIVE_SUM(/regular_expression/)` Returns the running total of subsequent field values associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `CUMULATIVE_SUM(*)` Returns the running total of subsequent field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `CUMULATIVE_SUM()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the cumulative sum of the field values associated with a field key ```sql > SELECT CUMULATIVE_SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time cumulative_sum ---- -------------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 4.18 2015-08-18T00:12:00Z 6.208 2015-08-18T00:18:00Z 8.334 2015-08-18T00:24:00Z 10.375 2015-08-18T00:30:00Z 12.426 ``` The query returns the running total of the field values in the `water_level` field key and in the `h2o_feet` measurement. ###### Calculate the cumulative sum of the field values associated with each field key in a measurement ```sql > SELECT CUMULATIVE_SUM(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time cumulative_sum_water_level ---- -------------------------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 4.18 2015-08-18T00:12:00Z 6.208 2015-08-18T00:18:00Z 8.334 2015-08-18T00:24:00Z 10.375 2015-08-18T00:30:00Z 12.426 ``` The query returns the running total of the field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the cumulative sum of the field values associated with each field key that matches a regular expression ```sql > SELECT CUMULATIVE_SUM(/water/) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time cumulative_sum_water_level ---- -------------------------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 4.18 2015-08-18T00:12:00Z 6.208 2015-08-18T00:18:00Z 8.334 2015-08-18T00:24:00Z 10.375 2015-08-18T00:30:00Z 12.426 ``` The query returns the running total of the field values for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. ###### Calculate the cumulative sum of the field values associated with a field key and include several clauses ```sql > SELECT CUMULATIVE_SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time cumulative_sum ---- -------------- 2015-08-18T00:18:00Z 6.218 2015-08-18T00:12:00Z 8.246 2015-08-18T00:06:00Z 10.362 2015-08-18T00:00:00Z 12.426 ``` The query returns the running total of the field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT CUMULATIVE_SUM(( [ * | | // ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `CUMULATIVE_SUM()` function to those results. `CUMULATIVE_SUM()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the cumulative sum of mean values ```sql > SELECT CUMULATIVE_SUM(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time cumulative_sum ---- -------------- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 4.167 2015-08-18T00:24:00Z 6.213 ``` The query returns the running total of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `CUMULATIVE_SUM()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` Next, InfluxDB calculates the running total of those averages. The second point in the final results (`4.167`) is the sum of `2.09` and `2.077` and the third point (`6.213`) is the sum of `2.09`, `2.077`, and `2.0460000000000003`. ### DERIVATIVE() Returns the rate of change between subsequent [field values](/influxdb/v1/concepts/glossary/#field-value). #### Basic syntax ``` SELECT DERIVATIVE( [ * | | // ] [ , ] ) [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](/influxdb/v1/query_language/spec/#literals) and it is optional. If the query does not specify the `unit` the unit defaults to one second (`1s`). `DERIVATIVE(field_key)` Returns the rate of change between subsequent field values associated with the [field key](/influxdb/v1/concepts/glossary/#field-key). `DERIVATIVE(/regular_expression/)` Returns the rate of change between subsequent field values associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `DERIVATIVE(*)` Returns the rate of change between subsequent field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `DERIVATIVE()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#advanced-syntax-1) 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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the derivative between the field values associated with a field key ```sql > SELECT DERIVATIVE("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time derivative ---- ---------- 2015-08-18T00:06:00Z 0.00014444444444444457 2015-08-18T00:12:00Z -0.00024444444444444465 2015-08-18T00:18:00Z 0.0002722222222222218 2015-08-18T00:24:00Z -0.000236111111111111 2015-08-18T00:30:00Z 2.777777777777842e-05 ``` The query returns the one-second rate of change between the field values associated with the `water_level` field key and in the `h2o_feet` measurement. The first result (`0.00014444444444444457`) is the one-second rate of change between the first two subsequent field values in the raw data. InfluxDB calculates the difference between the field values and normalizes that value to the one-second rate of change: ``` (2.116 - 2.064) / (360s / 1s) -------------- ---------- | | | the difference between the field values' timestamps / the default unit second field value - first field value ``` ###### Calculate the derivative between the field values associated with a field key and specify the unit option ```sql > SELECT DERIVATIVE("water_level",6m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time derivative ---- ---------- 2015-08-18T00:06:00Z 0.052000000000000046 2015-08-18T00:12:00Z -0.08800000000000008 2015-08-18T00:18:00Z 0.09799999999999986 2015-08-18T00:24:00Z -0.08499999999999996 2015-08-18T00:30:00Z 0.010000000000000231 ``` The query returns the six-minute rate of change between the field values associated with the `water_level` field key and in the `h2o_feet` measurement. The first result (`0.052000000000000046`) is the six-minute rate of change between the first two subsequent field values in the raw data. InfluxDB calculates the difference between the field values and normalizes that value to the six-minute rate of change: ``` (2.116 - 2.064) / (6m / 6m) -------------- ---------- | | | the difference between the field values' timestamps / the specified unit second field value - first field value ``` ###### Calculate the derivative between the field values associated with each field key in a measurement and specify the unit option ```sql > SELECT DERIVATIVE(*,3m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time derivative_water_level ---- ---------------------- 2015-08-18T00:06:00Z 0.026000000000000023 2015-08-18T00:12:00Z -0.04400000000000004 2015-08-18T00:18:00Z 0.04899999999999993 2015-08-18T00:24:00Z -0.04249999999999998 2015-08-18T00:30:00Z 0.0050000000000001155 ``` The query returns the three-minute rate of change between the field values associated with each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. The first result (`0.026000000000000023`) is the three-minute rate of change between the first two subsequent field values in the raw data. InfluxDB calculates the difference between the field values and normalizes that value to the three-minute rate of change: ``` (2.116 - 2.064) / (6m / 3m) -------------- ---------- | | | the difference between the field values' timestamps / the specified unit second field value - first field value ``` ###### Calculate the derivative between the field values associated with each field key that matches a regular expression and specify the unit option ```sql > SELECT DERIVATIVE(/water/,2m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time derivative_water_level ---- ---------------------- 2015-08-18T00:06:00Z 0.01733333333333335 2015-08-18T00:12:00Z -0.02933333333333336 2015-08-18T00:18:00Z 0.03266666666666662 2015-08-18T00:24:00Z -0.02833333333333332 2015-08-18T00:30:00Z 0.0033333333333334103 ``` The query returns the two-minute rate of change between the field values associated with each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. The first result (`0.01733333333333335`) is the two-minute rate of change between the first two subsequent field values in the raw data. InfluxDB calculates the difference between the field values and normalizes that value to the two-minute rate of change: ``` (2.116 - 2.064) / (6m / 2m) -------------- ---------- | | | the difference between the field values' timestamps / the specified unit second field value - first field value ``` ###### Calculate the derivative between the field values associated with a field key and include several clauses ```sql > SELECT DERIVATIVE("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' ORDER BY time DESC LIMIT 1 OFFSET 2 name: h2o_feet time derivative ---- ---------- 2015-08-18T00:12:00Z -0.0002722222222222218 ``` The query returns the one-second rate of change between the field values associated with the `water_level` field key and in the `h2o_feet` measurement. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to one and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. The only result (`-0.0002722222222222218`) is the one-second rate of change between the relevant subsequent field values in the raw data. InfluxDB calculates the difference between the field values and normalizes that value to the one-second rate of change: ``` (2.126 - 2.028) / (360s / 1s) -------------- ---------- | | | the difference between the field values' timestamps / the default unit second field value - first field value ``` #### Advanced syntax ``` SELECT DERIVATIVE( ([ * | | // ]) [ , ] ) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `DERIVATIVE()` function to those results. The `unit` argument is an integer followed by a [duration literal](/influxdb/v1/query_language/spec/#literals) and it is optional. If the query does not specify the `unit` the `unit` defaults to the `GROUP BY time()` interval. Note that this behavior is different from the [basic syntax's](#basic-syntax-1) default behavior. `DERIVATIVE()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the derivative of mean values ```sql > SELECT DERIVATIVE(MEAN("water_level")) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m) name: h2o_feet time derivative ---- ---------- 2015-08-18T00:12:00Z -0.0129999999999999 2015-08-18T00:24:00Z -0.030999999999999694 ``` The query returns the 12-minute rate of change between [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `DERIVATIVE()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` Next, InfluxDB calculates the 12-minute rate of change between those averages. The first result (`-0.0129999999999999`) is the 12-minute rate of change between the first two averages. InfluxDB calculates the difference between the field values and normalizes that value to the 12-minute rate of change. ``` (2.077 - 2.09) / (12m / 12m) ------------- ---------- | | | the difference between the field values' timestamps / the default unit second field value - first field value ``` ###### Calculate the derivative of mean values and specify the unit option ```sql > SELECT DERIVATIVE(MEAN("water_level"),6m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m) name: h2o_feet time derivative ---- ---------- 2015-08-18T00:12:00Z -0.00649999999999995 2015-08-18T00:24:00Z -0.015499999999999847 ``` The query returns the six-minute rate of change between average `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `DERIVATIVE()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` Next, InfluxDB calculates the six-minute rate of change between those averages. The first result (`-0.00649999999999995`) is the six-minute rate of change between the first two averages. InfluxDB calculates the difference between the field values and normalizes that value to the six-minute rate of change. ``` (2.077 - 2.09) / (12m / 6m) ------------- ---------- | | | the difference between the field values' timestamps / the specified unit second field value - first field value ``` ### DIFFERENCE() Returns the result of subtraction between subsequent [field values](/influxdb/v1/concepts/glossary/#field-value). #### Basic syntax ``` SELECT DIFFERENCE( [ * | | // ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `DIFFERENCE(/regular_expression/)` Returns the difference between subsequent field values associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `DIFFERENCE(*)` Returns the difference between subsequent field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `DIFFERENCE()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#advanced-syntax-2) 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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the difference between the field values associated with a field key ```sql > SELECT DIFFERENCE("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time difference ---- ---------- 2015-08-18T00:06:00Z 0.052000000000000046 2015-08-18T00:12:00Z -0.08800000000000008 2015-08-18T00:18:00Z 0.09799999999999986 2015-08-18T00:24:00Z -0.08499999999999996 2015-08-18T00:30:00Z 0.010000000000000231 ``` The query returns the difference between the subsequent field values in the `water_level` field key and in the `h2o_feet` measurement. ###### Calculate the difference between the field values associated with each field key in a measurement ```sql > SELECT DIFFERENCE(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time difference_water_level ---- ---------------------- 2015-08-18T00:06:00Z 0.052000000000000046 2015-08-18T00:12:00Z -0.08800000000000008 2015-08-18T00:18:00Z 0.09799999999999986 2015-08-18T00:24:00Z -0.08499999999999996 2015-08-18T00:30:00Z 0.010000000000000231 ``` The query returns the difference between the subsequent field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the difference between the field values associated with each field key that matches a regular expression ```sql > SELECT DIFFERENCE(/water/) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time difference_water_level ---- ---------------------- 2015-08-18T00:06:00Z 0.052000000000000046 2015-08-18T00:12:00Z -0.08800000000000008 2015-08-18T00:18:00Z 0.09799999999999986 2015-08-18T00:24:00Z -0.08499999999999996 2015-08-18T00:30:00Z 0.010000000000000231 ``` The query returns the difference between the subsequent field values for each field key that stores numerical values and includes the word `water` in the `h2o_feet` measurement. ###### Calculate the difference between the field values associated with a field key and include several clauses ```sql > SELECT DIFFERENCE("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 2 OFFSET 2 name: h2o_feet time difference ---- ---------- 2015-08-18T00:12:00Z -0.09799999999999986 2015-08-18T00:06:00Z 0.08800000000000008 ``` The query returns the difference between the subsequent field values in the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). They query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to two and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT DIFFERENCE(( [ * | | // ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `DIFFERENCE()` function to those results. `DIFFERENCE()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the difference between maximum values ```sql > SELECT DIFFERENCE(MAX("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time difference ---- ---------- 2015-08-18T00:12:00Z 0.009999999999999787 2015-08-18T00:24:00Z -0.07499999999999973 ``` The query returns the difference between [maximum](#max) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the maximum `water_level`s at 12-minute intervals. This step is the same as using the `MAX()` function with the `GROUP BY time()` clause and without `DIFFERENCE()`: ```sql > SELECT MAX("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time max ---- --- 2015-08-18T00:00:00Z 2.116 2015-08-18T00:12:00Z 2.126 2015-08-18T00:24:00Z 2.051 ``` Next, InfluxDB calculates the difference between those maximum values. The first point in the final results (`0.009999999999999787`) is the difference between `2.126` and `2.116`, and the second point in the final results (`-0.07499999999999973`) is the difference between `2.051` and `2.126`. ### ELAPSED() Returns the difference between subsequent [field value's](/influxdb/v1/concepts/glossary/#field-value) timestamps. #### Syntax ``` SELECT ELAPSED( [ * | | // ] [ , ] ) [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](/influxdb/v1/query_language/spec/#literals) and it determines the unit of the returned difference. If the query does not specify the `unit` option the query returns the difference between timestamps in nanoseconds. `ELAPSED(field_key)` Returns the difference between subsequent timestamps associated with the [field key](/influxdb/v1/concepts/glossary/#field-key). `ELAPSED(/regular_expression/)` Returns the difference between subsequent timestamps associated with each field key that matches the [regular expression](/influxdb/v1/query_language/explore-data/#regular-expressions). `ELAPSED(*)` Returns the difference between subsequent timestamps associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `ELAPSED()` supports all field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples The examples use the following subsample of the [`NOAA_water_database` data](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:12:00Z' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 ``` ##### Calculate the elapsed time between field values associated with a field key ```sql > SELECT ELAPSED("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:12:00Z' name: h2o_feet time elapsed ---- ------- 2015-08-18T00:06:00Z 360000000000 2015-08-18T00:12:00Z 360000000000 ``` The query returns the difference (in nanoseconds) between subsequent timestamps in the `water_level` field key and in the `h2o_feet` measurement. ##### Calculate the elapsed time between field values associated with a field key and specify the unit option ```sql > SELECT ELAPSED("water_level",1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:12:00Z' name: h2o_feet time elapsed ---- ------- 2015-08-18T00:06:00Z 6 2015-08-18T00:12:00Z 6 ``` The query returns the difference (in minutes) between subsequent timestamps in the `water_level` field key and in the `h2o_feet` measurement. ##### Calculate the elapsed time between field values associated with each field key in a measurement and specify the unit option ```sql > SELECT ELAPSED(*,1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:12:00Z' name: h2o_feet time elapsed_level description elapsed_water_level ---- ------------------------- ------------------- 2015-08-18T00:06:00Z 6 6 2015-08-18T00:12:00Z 6 6 ``` The query returns the difference (in minutes) between subsequent timestamps associated with each field key in the `h2o_feet` measurement. The `h2o_feet` measurement has two field keys: `level description` and `water_level`. ##### Calculate the elapsed time between field values associated with each field key that matches a regular expression and specify the unit option ```sql > SELECT ELAPSED(/level/,1s) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:12:00Z' name: h2o_feet time elapsed_level description elapsed_water_level ---- ------------------------- ------------------- 2015-08-18T00:06:00Z 360 360 2015-08-18T00:12:00Z 360 360 ``` The query returns the difference (in seconds) between subsequent timestamps associated with each field key that includes the word `level` in the `h2o_feet` measurement. ##### Calculate the elapsed time between field values associated with a field key and include several clauses ```sql > SELECT ELAPSED("water_level",1ms) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:12:00Z' ORDER BY time DESC LIMIT 1 OFFSET 1 name: h2o_feet time elapsed ---- ------- 2015-08-18T00:00:00Z -360000 ``` The query returns the difference (in milliseconds) between subsequent timestamps in the `water_level` field key and in the `h2o_feet` measurement. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:12:00Z` and sorts timestamps in [descending order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to one and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by one point. Notice that the result is negative; the [`ORDER BY time DESC` clause](/influxdb/v1/query_language/explore-data/#order-by-time-desc) 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`: ```sql > SELECT ELAPSED("water_level",1h) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:12:00Z' name: h2o_feet time elapsed ---- ------- 2015-08-18T00:06:00Z 0 2015-08-18T00:12:00Z 0 ``` #### ELAPSED() with GROUP BY time() clauses The `ELAPSED()` function supports the [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) but the query results aren't particularly useful. Currently, an `ELAPSED()` query with a nested function and a `GROUP BY time()` clause simply returns the interval specified in the `GROUP BY time()` clause. The `GROUP BY time()` clause determines the timestamps in the results; each timestamp marks the start of a time interval. That behavior also applies to nested selector functions (like [`FIRST()`](#first) or [`MAX()`](#max)) which would, in all other cases, return a specific timestamp from the raw data. Because the `GROUP BY time()` clause overrides the original timestamps, the `ELAPSED()` calculation always returns the same value as the `GROUP BY time()` interval. ##### Example In the codeblock below, the first query attempts to use the `ELAPSED()` function with a `GROUP BY time()` clause to find the time elapsed (in minutes) between [minimum](#min) `water_level`s. The query returns 12 minutes for both time intervals. To get those results, InfluxDB first calculates the minimum `water_level`s at 12-minute intervals. The second query in the codeblock shows the results of that step. The step is the same as using the `MIN()` function with the `GROUP BY time()` clause and without the `ELAPSED()` function. Notice that the timestamps returned by the second query are 12 minutes apart. In the raw data, the first result (`2.057`) occurs at `2015-08-18T00:42:00Z` but the `GROUP BY time()` clause overrides that original timestamp. Because the timestamps are determined by the `GROUP BY time()` interval and not by the original data, the `ELAPSED()` calculation always returns the same value as the `GROUP BY time()` interval. ```sql > SELECT ELAPSED(MIN("water_level"),1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:36:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m) name: h2o_feet time elapsed ---- ------- 2015-08-18T00:36:00Z 12 2015-08-18T00:48:00Z 12 > SELECT MIN("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:36:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m) name: h2o_feet time min ---- --- 2015-08-18T00:36:00Z 2.057 <--- Actually occurs at 2015-08-18T00:42:00Z 2015-08-18T00:48:00Z 1.991 ``` ### EXP() Returns the exponential of the field value. #### Basic syntax ``` SELECT EXP( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `EXP(*)` Returns the exponential of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `EXP()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the exponential of field values associated with a field key ```sql > SELECT EXP("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time exp ---- --- 2015-08-18T00:00:00Z 7.877416541092307 2015-08-18T00:06:00Z 8.297879498060171 2015-08-18T00:12:00Z 7.598873404088091 2015-08-18T00:18:00Z 8.381274573459967 2015-08-18T00:24:00Z 7.6983036546645645 2015-08-18T00:30:00Z 7.775672892658607 ``` The query returns the exponential of field values in the `water_level` field key in the `h2o_feet` measurement. ###### Calculate the exponential of field values associated with each field key in a measurement ```sql > SELECT EXP(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time exp_water_level ---- --------------- 2015-08-18T00:00:00Z 7.877416541092307 2015-08-18T00:06:00Z 8.297879498060171 2015-08-18T00:12:00Z 7.598873404088091 2015-08-18T00:18:00Z 8.381274573459967 2015-08-18T00:24:00Z 7.6983036546645645 2015-08-18T00:30:00Z 7.775672892658607 ``` The query returns the exponential of field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the exponential of field values associated with a field key and include several clauses ```sql > SELECT EXP("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time exp ---- --- 2015-08-18T00:18:00Z 8.381274573459967 2015-08-18T00:12:00Z 7.598873404088091 2015-08-18T00:06:00Z 8.297879498060171 2015-08-18T00:00:00Z 7.877416541092307 ``` The query returns the exponentials of field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT EXP(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `EXP()` function to those results. `EXP()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the exponential of mean values. ```sql > SELECT EXP(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time exp ---- --- 2015-08-18T00:00:00Z 8.084915164305059 2015-08-18T00:12:00Z 7.980491491670466 2015-08-18T00:24:00Z 7.736891562315577 ``` The query returns the exponential of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `EXP()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates the exponentials of those averages. ### FLOOR() Returns the subsequent value rounded down to the nearest integer. #### Basic syntax ``` SELECT FLOOR( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key) rounded down to the nearest integer. `FLOOR(*)` Returns the field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement) rounded down to the nearest integer. `FLOOR()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the floor of field values associated with a field key ```sql > SELECT FLOOR("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time floor ---- ----- 2015-08-18T00:00:00Z 2 2015-08-18T00:06:00Z 2 2015-08-18T00:12:00Z 2 2015-08-18T00:18:00Z 2 2015-08-18T00:24:00Z 2 2015-08-18T00:30:00Z 2 ``` The query returns field values in the `water_level` field key in the `h2o_feet` measurement rounded down to the nearest integer. ###### Calculate the floor of field values associated with each field key in a measurement ```sql > SELECT FLOOR(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time floor_water_level ---- ----------------- 2015-08-18T00:00:00Z 2 2015-08-18T00:06:00Z 2 2015-08-18T00:12:00Z 2 2015-08-18T00:18:00Z 2 2015-08-18T00:24:00Z 2 2015-08-18T00:30:00Z 2 ``` The query returns field values for each field key that stores numerical values in the `h2o_feet` measurement rounded down to the nearest integer. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the floor of field values associated with a field key and include several clauses ```sql > SELECT FLOOR("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time floor ---- ----- 2015-08-18T00:18:00Z 2 2015-08-18T00:12:00Z 2 2015-08-18T00:06:00Z 2 2015-08-18T00:00:00Z 2 ``` The query returns field values associated with the `water_level` field key rounded down to the nearest integer. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT FLOOR(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `FLOOR()` function to those results. `FLOOR()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate mean values rounded down to the nearest integer. ```sql > SELECT FLOOR(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time floor ---- ----- 2015-08-18T00:00:00Z 2 2015-08-18T00:12:00Z 2 2015-08-18T00:24:00Z 2 ``` The query returns the [average](#mean) `water_level`s that are calculated at 12-minute intervals and rounds them up to the nearest integer. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `FLOOR()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then rounds those averages down to the nearest integer. ### HISTOGRAM() _InfluxQL does not currently support histogram generation. For information about creating histograms with data stored in InfluxDB, see [Flux's `histogram()` function](/flux/v0/stdlib/universe/histogram)._ ### LN() Returns the natural logarithm of the field value. #### Basic syntax ``` SELECT LN( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `LN(*)` Returns the natural logarithm of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `LN()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the natural logarithm of field values associated with a field key ```sql > SELECT LN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time ln ---- -- 2015-08-18T00:00:00Z 0.7246458476193163 2015-08-18T00:06:00Z 0.749527513996053 2015-08-18T00:12:00Z 0.7070500857289368 2015-08-18T00:18:00Z 0.7542422799197561 2015-08-18T00:24:00Z 0.7134398838277077 2015-08-18T00:30:00Z 0.7183274790902436 ``` The query returns the natural logarithm of field values in the `water_level` field key in the `h2o_feet` measurement. ###### Calculate the natural logarithm of field values associated with each field key in a measurement ```sql > SELECT LN(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time ln_water_level ---- -------------- 2015-08-18T00:00:00Z 0.7246458476193163 2015-08-18T00:06:00Z 0.749527513996053 2015-08-18T00:12:00Z 0.7070500857289368 2015-08-18T00:18:00Z 0.7542422799197561 2015-08-18T00:24:00Z 0.7134398838277077 2015-08-18T00:30:00Z 0.7183274790902436 ``` The query returns the natural logarithm of field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the natural logarithm of field values associated with a field key and include several clauses ```sql > SELECT LN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time ln ---- -- 2015-08-18T00:18:00Z 0.7542422799197561 2015-08-18T00:12:00Z 0.7070500857289368 2015-08-18T00:06:00Z 0.749527513996053 2015-08-18T00:00:00Z 0.7246458476193163 ``` The query returns the natural logarithms of field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT LN(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `LN()` function to those results. `LN()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the natural logarithm of mean values. ```sql > SELECT LN(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time ln ---- -- 2015-08-18T00:00:00Z 0.7371640659767196 2015-08-18T00:12:00Z 0.7309245448939752 2015-08-18T00:24:00Z 0.7158866675294349 ``` The query returns the natural logarithm of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `LN()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates the natural logarithms of those averages. ### LOG() Returns the logarithm of the field value with base `b`. #### Basic syntax ``` SELECT LOG( [ * | ], ) [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](/influxdb/v1/concepts/glossary/#field-key) with base `b`. `LOG(*, b)` Returns the logarithm of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement) with base `b`. `LOG()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the logarithm base 4 of field values associated with a field key ```sql > SELECT LOG("water_level", 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time log ---- --- 2015-08-18T00:00:00Z 0.5227214853805835 2015-08-18T00:06:00Z 0.5406698137259695 2015-08-18T00:12:00Z 0.5100288261706268 2015-08-18T00:18:00Z 0.5440707984345088 2015-08-18T00:24:00Z 0.5146380911853161 2015-08-18T00:30:00Z 0.5181637459088826 ``` The query returns the logarithm base 4 of field values in the `water_level` field key in the `h2o_feet` measurement. ###### Calculate the logarithm base 4 of field values associated with each field key in a measurement ```sql > SELECT LOG(*, 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time log_water_level ---- --------------- 2015-08-18T00:00:00Z 0.5227214853805835 2015-08-18T00:06:00Z 0.5406698137259695 2015-08-18T00:12:00Z 0.5100288261706268 2015-08-18T00:18:00Z 0.5440707984345088 2015-08-18T00:24:00Z 0.5146380911853161 2015-08-18T00:30:00Z 0.5181637459088826 ``` The query returns the logarithm base 4 of field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the logarithm base 4 of field values associated with a field key and include several clauses ```sql > SELECT LOG("water_level", 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time log ---- --- 2015-08-18T00:18:00Z 0.5440707984345088 2015-08-18T00:12:00Z 0.5100288261706268 2015-08-18T00:06:00Z 0.5406698137259695 2015-08-18T00:00:00Z 0.5227214853805835 ``` The query returns the logarithm base 4 of field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT LOG(( [ * | ] ), ) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `LOG()` function to those results. `LOG()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the logarithm base 4 of mean values ```sql > SELECT LOG(MEAN("water_level"), 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time log ---- --- 2015-08-18T00:00:00Z 0.531751471153079 2015-08-18T00:12:00Z 0.5272506080912802 2015-08-18T00:24:00Z 0.5164030725416209 ``` The query returns the logarithm base 4 of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `LOG()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates the logarithm base 4 of those averages. ### LOG2() Returns the logarithm of the field value to the base 2. #### Basic syntax ``` SELECT LOG2( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key) to the base 2. `LOG2(*)` Returns the logarithm of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement) to the base 2. `LOG2()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the logarithm base 2 of field values associated with a field key ```sql > SELECT LOG2("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time log2 ---- ---- 2015-08-18T00:00:00Z 1.045442970761167 2015-08-18T00:06:00Z 1.081339627451939 2015-08-18T00:12:00Z 1.0200576523412537 2015-08-18T00:18:00Z 1.0881415968690176 2015-08-18T00:24:00Z 1.0292761823706322 2015-08-18T00:30:00Z 1.0363274918177652 ``` The query returns the logarithm base 2 of field values in the `water_level` field key in the `h2o_feet` measurement. ###### Calculate the logarithm base 2 of field values associated with each field key in a measurement ```sql > SELECT LOG2(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time log2_water_level ---- ---------------- 2015-08-18T00:00:00Z 1.045442970761167 2015-08-18T00:06:00Z 1.081339627451939 2015-08-18T00:12:00Z 1.0200576523412537 2015-08-18T00:18:00Z 1.0881415968690176 2015-08-18T00:24:00Z 1.0292761823706322 2015-08-18T00:30:00Z 1.0363274918177652 ``` The query returns the logarithm base 2 of field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the logarithm base 2 of field values associated with a field key and include several clauses ```sql > SELECT LOG2("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time log2 ---- ---- 2015-08-18T00:18:00Z 1.0881415968690176 2015-08-18T00:12:00Z 1.0200576523412537 2015-08-18T00:06:00Z 1.081339627451939 2015-08-18T00:00:00Z 1.045442970761167 ``` The query returns the logarithm base 2 of field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ```sql SELECT LOG2(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `LOG2()` function to those results. `LOG2()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the logarithm base 2 of mean values ```sql > SELECT LOG2(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time log2 ---- ---- 2015-08-18T00:00:00Z 1.063502942306158 2015-08-18T00:12:00Z 1.0545012161825604 2015-08-18T00:24:00Z 1.0328061450832418 ``` The query returns the logarithm base 2 of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `LOG2()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates the logarithm base 2 of those averages. ### LOG10() Returns the logarithm of the field value to the base 10. #### Basic syntax ``` SELECT LOG10( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key) to the base 10. `LOG10(*)` Returns the logarithm of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement) to the base 10. `LOG10()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the logarithm base 10 of field values associated with a field key ```sql > SELECT LOG10("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time log10 ---- ----- 2015-08-18T00:00:00Z 0.3147096929551737 2015-08-18T00:06:00Z 0.32551566336314813 2015-08-18T00:12:00Z 0.3070679506612984 2015-08-18T00:18:00Z 0.32756326018727794 2015-08-18T00:24:00Z 0.3098430047160705 2015-08-18T00:30:00Z 0.3119656603683663 ``` The query returns the logarithm base 10 of field values in the `water_level` field key in the `h2o_feet` measurement. ###### Calculate the logarithm base 10 of field values associated with each field key in a measurement ```sql > SELECT LOG10(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time log10_water_level ---- ----------------- 2015-08-18T00:00:00Z 0.3147096929551737 2015-08-18T00:06:00Z 0.32551566336314813 2015-08-18T00:12:00Z 0.3070679506612984 2015-08-18T00:18:00Z 0.32756326018727794 2015-08-18T00:24:00Z 0.3098430047160705 2015-08-18T00:30:00Z 0.3119656603683663 ``` The query returns the logarithm base 10 of field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the logarithm base 10 of field values associated with a field key and include several clauses ```sql > SELECT LOG10("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time log10 ---- ----- 2015-08-18T00:18:00Z 0.32756326018727794 2015-08-18T00:12:00Z 0.3070679506612984 2015-08-18T00:06:00Z 0.32551566336314813 2015-08-18T00:00:00Z 0.3147096929551737 ``` The query returns the logarithm base 10 of field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT LOG10(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `LOG10()` function to those results. `LOG10()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the logarithm base 10 of mean values ```sql > SELECT LOG10(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time log10 ---- ----- 2015-08-18T00:00:00Z 0.32014628611105395 2015-08-18T00:12:00Z 0.3174364965350991 2015-08-18T00:24:00Z 0.3109056293761414 ``` The query returns the logarithm base 10 of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `LOG10()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates the logarithm base 10 of those averages. ### MOVING_AVERAGE() Returns the rolling average across a window of subsequent [field values](/influxdb/v1/concepts/glossary/#field-value). #### Basic syntax ``` SELECT MOVING_AVERAGE( [ * | | // ] , ) [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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/query_language/explore-data/#regular-expressions). `MOVING_AVERAGE(*,N)` Returns the rolling average across `N` field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `MOVING_AVERAGE()` int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#advanced-syntax-3) 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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the moving average of the field values associated with a field key ```sql > SELECT MOVING_AVERAGE("water_level",2) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time moving_average ---- -------------- 2015-08-18T00:06:00Z 2.09 2015-08-18T00:12:00Z 2.072 2015-08-18T00:18:00Z 2.077 2015-08-18T00:24:00Z 2.0835 2015-08-18T00:30:00Z 2.0460000000000003 ``` The query returns the rolling average across a two-field-value window for the `water_level` field key and the `h2o_feet` measurement. The first result (`2.09`) is the average of the first two points in the raw data: (`2.064 + 2.116) / 2`). The second result (`2.072`) is the average of the second two points in the raw data: (`2.116 + 2.028) / 2`). ###### Calculate the moving average of the field values associated with each field key in a measurement ```sql > SELECT MOVING_AVERAGE(*,3) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time moving_average_water_level ---- -------------------------- 2015-08-18T00:12:00Z 2.0693333333333332 2015-08-18T00:18:00Z 2.09 2015-08-18T00:24:00Z 2.065 2015-08-18T00:30:00Z 2.0726666666666667 ``` The query returns the rolling average across a three-field-value window for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the moving average of the field values associated with each field key that matches a regular expression ```sql > SELECT MOVING_AVERAGE(/level/,4) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' name: h2o_feet time moving_average_water_level ---- -------------------------- 2015-08-18T00:18:00Z 2.0835 2015-08-18T00:24:00Z 2.07775 2015-08-18T00:30:00Z 2.0615 ``` The query returns the rolling average across a four-field-value window for each field key that stores numerical values and includes the word `level` in the `h2o_feet` measurement. ###### Calculate the moving average of the field values associated with a field key and include several clauses ```sql > SELECT MOVING_AVERAGE("water_level",2) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' ORDER BY time DESC LIMIT 2 OFFSET 3 name: h2o_feet time moving_average ---- -------------- 2015-08-18T00:06:00Z 2.072 2015-08-18T00:00:00Z 2.09 ``` The query returns the rolling average across a two-field-value window for the `water_level` field key in the `h2o_feet` measurement. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to two and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by three points. #### Advanced syntax ``` SELECT MOVING_AVERAGE( ([ * | | // ]) , 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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `MOVING_AVERAGE()` function to those results. `MOVING_AVERAGE()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the moving average of maximum values ```sql > SELECT MOVING_AVERAGE(MAX("water_level"),2) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m) name: h2o_feet time moving_average ---- -------------- 2015-08-18T00:12:00Z 2.121 2015-08-18T00:24:00Z 2.0885 ``` The query returns the rolling average across a two-value window of [maximum](#max) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the maximum `water_level`s at 12-minute intervals. This step is the same as using the `MAX()` function with the `GROUP BY time()` clause and without `MOVING_AVERAGE()`: ```sql > SELECT MAX("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m) name: h2o_feet time max ---- --- 2015-08-18T00:00:00Z 2.116 2015-08-18T00:12:00Z 2.126 2015-08-18T00:24:00Z 2.051 ``` Next, InfluxDB calculates the rolling average across a two-value window using those maximum values. The first final result (`2.121`) is the average of the first two maximum values (`(2.116 + 2.126) / 2`). ### NON_NEGATIVE_DERIVATIVE() Returns the non-negative rate of change between subsequent [field values](/influxdb/v1/concepts/glossary/#field-value). Non-negative rates of change include positive rates of change and rates of change that equal zero. #### Basic syntax ``` SELECT NON_NEGATIVE_DERIVATIVE( [ * | | // ] [ , ] ) [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](/influxdb/v1/query_language/spec/#literals) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/query_language/explore-data/#regular-expressions). `NON_NEGATIVE_DERIVATIVE(*)` Returns the non-negative rate of change between subsequent field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `NON_NEGATIVE_DERIVATIVE()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#advanced-syntax-4) section for how to use `NON_NEGATIVE_DERIVATIVE()` with a `GROUP BY time()` clause. ##### Examples See the examples in the [`DERIVATIVE()` documentation](#basic-syntax-8). `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 ``` SELECT NON_NEGATIVE_DERIVATIVE( ([ * | | // ]) [ , ] ) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `NON_NEGATIVE_DERIVATIVE()` function to those results. The `unit` argument is an integer followed by a [duration literal](/influxdb/v1/query_language/spec/#literals) and it is optional. If the query does not specify the `unit`, the `unit` defaults to the `GROUP BY time()` interval. Note that this behavior is different from the [basic syntax's](#basic-syntax-4) 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()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples See the examples in the [`DERIVATIVE()` documentation](#advanced-syntax-8). `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](/influxdb/v1/concepts/glossary/#field-value). Non-negative results of subtraction include positive differences and differences that equal zero. #### Basic syntax ``` SELECT NON_NEGATIVE_DIFFERENCE( [ * | | // ] ) [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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/query_language/explore-data/#regular-expressions). `NON_NEGATIVE_DIFFERENCE(*)` Returns the non-negative difference between subsequent field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `NON_NEGATIVE_DIFFERENCE()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#advanced-syntax-5) section for how to use `NON_NEGATIVE_DIFFERENCE()` with a `GROUP BY time()` clause. ##### Examples See the examples in the [`DIFFERENCE()` documentation](#basic-syntax-9). `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 ``` SELECT NON_NEGATIVE_DIFFERENCE(( [ * | | // ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `NON_NEGATIVE_DIFFERENCE()` function to those results. `NON_NEGATIVE_DIFFERENCE()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples See the examples in the [`DIFFERENCE()` documentation](#advanced-syntax-9). `NON_NEGATIVE_DIFFERENCE()` behaves the same as the `DIFFERENCE()` function but `NON_NEGATIVE_DIFFERENCE()` returns only positive differences or differences that equal zero. ### POW() Returns the field value to the power of `x`. #### Basic syntax ``` SELECT POW( [ * | ], ) [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](/influxdb/v1/concepts/glossary/#field-key) to the power of `x`. `POW(*, x)` Returns the field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement) to the power of `x`. `POW()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate field values associated with a field key to the power of 4 ```sql > SELECT POW("water_level", 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time pow ---- --- 2015-08-18T00:00:00Z 18.148417929216 2015-08-18T00:06:00Z 20.047612231936 2015-08-18T00:12:00Z 16.914992230656004 2015-08-18T00:18:00Z 20.429279055375993 2015-08-18T00:24:00Z 17.352898193760993 2015-08-18T00:30:00Z 17.69549197320101 ``` The query returns field values in the `water_level` field key in the `h2o_feet` measurement multiplied to a power of 4. ###### Calculate field values associated with each field key in a measurement to the power of 4 ```sql > SELECT POW(*, 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time pow_water_level ---- --------------- 2015-08-18T00:00:00Z 18.148417929216 2015-08-18T00:06:00Z 20.047612231936 2015-08-18T00:12:00Z 16.914992230656004 2015-08-18T00:18:00Z 20.429279055375993 2015-08-18T00:24:00Z 17.352898193760993 2015-08-18T00:30:00Z 17.69549197320101 ``` The query returns field values for each field key that stores numerical values in the `h2o_feet` measurement multiplied to the power of 4. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate field values associated with a field key to the power of 4 and include several clauses ```sql > SELECT POW("water_level", 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time pow ---- --- 2015-08-18T00:18:00Z 20.429279055375993 2015-08-18T00:12:00Z 16.914992230656004 2015-08-18T00:06:00Z 20.047612231936 2015-08-18T00:00:00Z 18.148417929216 ``` The query returns field values associated with the `water_level` field key multiplied to the power of 4. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT POW(( [ * | ] ), ) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `POW()` function to those results. `POW()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate mean values to the power of 4 ```sql > SELECT POW(MEAN("water_level"), 4) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time pow ---- --- 2015-08-18T00:00:00Z 19.08029760999999 2015-08-18T00:12:00Z 18.609983417041 2015-08-18T00:24:00Z 17.523567165456008 ``` The query returns [average](#mean) `water_level`s that are calculated at 12-minute intervals multiplied to the power of 4. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `POW()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates those averages multiplied to the power of 4. ### ROUND() Returns the subsequent value rounded to the nearest integer. #### Basic syntax ``` SELECT ROUND( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key) rounded to the nearest integer. `ROUND(*)` Returns the field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement) rounded to the nearest integer. `ROUND()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Round field values associated with a field key ```sql > SELECT ROUND("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time round ---- ----- 2015-08-18T00:00:00Z 2 2015-08-18T00:06:00Z 2 2015-08-18T00:12:00Z 2 2015-08-18T00:18:00Z 2 2015-08-18T00:24:00Z 2 2015-08-18T00:30:00Z 2 ``` The query returns field values in the `water_level` field key in the `h2o_feet` measurement rounded to the nearest integer. ###### Round field values associated with each field key in a measurement ```sql > SELECT ROUND(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time round_water_level ---- ----------------- 2015-08-18T00:00:00Z 2 2015-08-18T00:06:00Z 2 2015-08-18T00:12:00Z 2 2015-08-18T00:18:00Z 2 2015-08-18T00:24:00Z 2 2015-08-18T00:30:00Z 2 ``` The query returns field values for each field key that stores numerical values in the `h2o_feet` measurement rounded to the nearest integer. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Round field values associated with a field key and include several clauses ```sql > SELECT ROUND("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time round ---- ----- 2015-08-18T00:18:00Z 2 2015-08-18T00:12:00Z 2 2015-08-18T00:06:00Z 2 2015-08-18T00:00:00Z 2 ``` The query returns field values associated with the `water_level` field key rounded to the nearest integer. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT ROUND(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `ROUND()` function to those results. `ROUND()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate mean values rounded to the nearest integer ```sql > SELECT ROUND(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time round ---- ----- 2015-08-18T00:00:00Z 2 2015-08-18T00:12:00Z 2 2015-08-18T00:24:00Z 2 ``` The query returns the [average](#mean) `water_level`s that are calculated at 12-minute intervals and rounds to the nearest integer. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `ROUND()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then rounds those averages to the nearest integer. ### SIN() Returns the sine of the field value. #### Basic syntax ``` SELECT SIN( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `SIN(*)` Returns the sine of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `SIN()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the sine of field values associated with a field key ```sql > SELECT SIN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time sin ---- --- 2015-08-18T00:00:00Z 0.8808206017241819 2015-08-18T00:06:00Z 0.8550216851706579 2015-08-18T00:12:00Z 0.8972904165810275 2015-08-18T00:18:00Z 0.8497930984115993 2015-08-18T00:24:00Z 0.8914760289023131 2015-08-18T00:30:00Z 0.8869008523376968 ``` The query returns sine of field values in the `water_level` field key in the `h2o_feet` measurement. ###### Calculate the sine of field values associated with each field key in a measurement ```sql > SELECT SIN(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time sin_water_level ---- --------------- 2015-08-18T00:00:00Z 0.8808206017241819 2015-08-18T00:06:00Z 0.8550216851706579 2015-08-18T00:12:00Z 0.8972904165810275 2015-08-18T00:18:00Z 0.8497930984115993 2015-08-18T00:24:00Z 0.8914760289023131 2015-08-18T00:30:00Z 0.8869008523376968 ``` The query returns sine of field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the sine of field values associated with a field key and include several clauses ```sql > SELECT SIN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time sin ---- --- 2015-08-18T00:18:00Z 0.8497930984115993 2015-08-18T00:12:00Z 0.8972904165810275 2015-08-18T00:06:00Z 0.8550216851706579 2015-08-18T00:00:00Z 0.8808206017241819 ``` The query returns sine of field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT SIN(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `SIN()` function to those results. `SIN()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the sine of mean values ```sql > SELECT SIN(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time sin ---- --- 2015-08-18T00:00:00Z 0.8682145834456126 2015-08-18T00:12:00Z 0.8745914945253902 2015-08-18T00:24:00Z 0.8891995555912935 ``` The query returns the sine of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `SIN()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates sine of those averages. ### SQRT() Returns the square root of field value. #### Basic syntax ``` SELECT SQRT( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `SQRT(*)` Returns the square root field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `SQRT()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the square root of field values associated with a field key ```sql > SELECT SQRT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time sqrt ---- ---- 2015-08-18T00:00:00Z 1.4366627996854378 2015-08-18T00:06:00Z 1.4546477236774544 2015-08-18T00:12:00Z 1.4240786495134319 2015-08-18T00:18:00Z 1.4580809305384939 2015-08-18T00:24:00Z 1.4286357128393508 2015-08-18T00:30:00Z 1.4321312788986909 ``` The query returns the square roots of field values in the `water_level` field key in the `h2o_feet` measurement. ###### Calculate the square root of field values associated with each field key in a measurement ```sql > SELECT SQRT(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time sqrt_water_level ---- ---------------- 2015-08-18T00:00:00Z 1.4366627996854378 2015-08-18T00:06:00Z 1.4546477236774544 2015-08-18T00:12:00Z 1.4240786495134319 2015-08-18T00:18:00Z 1.4580809305384939 2015-08-18T00:24:00Z 1.4286357128393508 2015-08-18T00:30:00Z 1.4321312788986909 ``` The query returns the square roots of field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the square root of field values associated with a field key and include several clauses ```sql > SELECT SQRT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time sqrt ---- ---- 2015-08-18T00:18:00Z 1.4580809305384939 2015-08-18T00:12:00Z 1.4240786495134319 2015-08-18T00:06:00Z 1.4546477236774544 2015-08-18T00:00:00Z 1.4366627996854378 ``` The query returns the square roots of field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT SQRT(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `SQRT()` function to those results. `SQRT()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the square root of mean values ```sql > SELECT SQRT(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time sqrt ---- ---- 2015-08-18T00:00:00Z 1.445683229480096 2015-08-18T00:12:00Z 1.4411800720243115 2015-08-18T00:24:00Z 1.430384563675098 ``` The query returns the square roots of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `SQRT()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates the square roots of those averages. ### TAN() Returns the tangent of the field value. #### Basic syntax ``` SELECT TAN( [ * | ] ) [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](/influxdb/v1/concepts/glossary/#field-key). `TAN(*)` Returns the tangent of field values associated with each field key in the [measurement](/influxdb/v1/concepts/glossary/#measurement). `TAN()` supports int64 and float64 field value [data types](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/): ```sql > SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time water_level ---- ----------- 2015-08-18T00:00:00Z 2.064 2015-08-18T00:06:00Z 2.116 2015-08-18T00:12:00Z 2.028 2015-08-18T00:18:00Z 2.126 2015-08-18T00:24:00Z 2.041 2015-08-18T00:30:00Z 2.051 ``` ###### Calculate the tangent of field values associated with a field key ```sql > SELECT TAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time tan ---- --- 2015-08-18T00:00:00Z -1.8604293534384375 2015-08-18T00:06:00Z -1.6487359603347427 2015-08-18T00:12:00Z -2.0326408012302273 2015-08-18T00:18:00Z -1.6121545688343464 2015-08-18T00:24:00Z -1.9676434782626282 2015-08-18T00:30:00Z -1.9198657720074992 ``` The query returns tangent of field values in the `water_level` field key in the `h2o_feet` measurement. ###### Calculate the tangent of field values associated with each field key in a measurement ```sql > SELECT TAN(*) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' name: h2o_feet time tan_water_level ---- --------------- 2015-08-18T00:00:00Z -1.8604293534384375 2015-08-18T00:06:00Z -1.6487359603347427 2015-08-18T00:12:00Z -2.0326408012302273 2015-08-18T00:18:00Z -1.6121545688343464 2015-08-18T00:24:00Z -1.9676434782626282 2015-08-18T00:30:00Z -1.9198657720074992 ``` The query returns tangent of field values for each field key that stores numerical values in the `h2o_feet` measurement. The `h2o_feet` measurement has one numerical field: `water_level`. ###### Calculate the tangent of field values associated with a field key and include several clauses ```sql > SELECT TAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' ORDER BY time DESC LIMIT 4 OFFSET 2 name: h2o_feet time tan ---- --- 2015-08-18T00:18:00Z -1.6121545688343464 2015-08-18T00:12:00Z -2.0326408012302273 2015-08-18T00:06:00Z -1.6487359603347427 2015-08-18T00:00:00Z -1.8604293534384375 ``` The query returns tangent of field values associated with the `water_level` field key. It covers the [time range](/influxdb/v1/query_language/explore-data/#time-syntax) between `2015-08-18T00:00:00Z` and `2015-08-18T00:30:00Z` and returns results in [descending timestamp order](/influxdb/v1/query_language/explore-data/#order-by-time-desc). The query also [limits](/influxdb/v1/query_language/explore-data/#the-limit-and-slimit-clauses) the number of points returned to four and [offsets](/influxdb/v1/query_language/explore-data/#the-offset-and-soffset-clauses) results by two points. #### Advanced syntax ``` SELECT TAN(( [ * | ] )) [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](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) and a nested InfluxQL function. The query first calculates the results for the nested function at the specified `GROUP BY time()` interval and then applies the `TAN()` function to those results. `TAN()` supports the following nested functions: [`COUNT()`](#count), [`MEAN()`](#mean), [`MEDIAN()`](#median), [`MODE()`](#mode), [`SUM()`](#sum), [`FIRST()`](#first), [`LAST()`](#last), [`MIN()`](#min), [`MAX()`](#max), and [`PERCENTILE()`](#percentile). ##### Examples ###### Calculate the tangent of mean values ```sql > SELECT TAN(MEAN("water_level")) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time tan ---- --- 2015-08-18T00:00:00Z -1.7497661902817365 2015-08-18T00:12:00Z -1.8038002062256624 2015-08-18T00:24:00Z -1.9435224805850773 ``` The query returns tangent of [average](#mean) `water_level`s that are calculated at 12-minute intervals. To get those results, InfluxDB first calculates the average `water_level`s at 12-minute intervals. This step is the same as using the `MEAN()` function with the `GROUP BY time()` clause and without `TAN()`: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica' GROUP BY time(12m) name: h2o_feet time mean ---- ---- 2015-08-18T00:00:00Z 2.09 2015-08-18T00:12:00Z 2.077 2015-08-18T00:24:00Z 2.0460000000000003 ``` InfluxDB then calculates tangent of those averages. ## Predictors ### HOLT_WINTERS() Returns N number of predicted [field values](/influxdb/v1/concepts/glossary/#field-value) using the [Holt-Winters](https://www.otexts.org/fpp/7/5) seasonal method. Use `HOLT_WINTERS()` to: * Predict when data values will cross a given threshold * Compare predicted values with actual values to detect anomalies in your data #### Syntax ``` SELECT HOLT_WINTERS[_WITH-FIT]((),,) [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](/influxdb/v1/concepts/glossary/#field-key). The `N` predicted values occur at the same interval as the [`GROUP BY time()` interval](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). 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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). #### Examples ##### Predict field values associated with a field key ###### Raw Data The example uses [Chronograf](https://github.com/influxdata/chronograf) to visualize the data. The example focuses on the following subsample of the [`NOAA_water_database` data](/influxdb/v1/query_language/data_download/): ```sql 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](/img/influxdb/1-3-hw-raw-data-1-2.png) ###### Step 1: Match the Trends of the Raw Data Write a `GROUP BY time()` query that matches the general trends of the raw `water_level` data. Here, we use the [`FIRST()`](#first) function: ```sql 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](/influxdb/v1/query_language/explore-data/#advanced-group-by-time-syntax). 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](/img/influxdb/1-3-hw-first-step-1-2.png) ###### 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](/img/influxdb/1-3-hw-second-step-1-2.png) ###### 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: ```sql 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](/img/influxdb/1-3-hw-third-step-1-2.png) #### 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()](#chande_momentum_oscillator) [EXPONENTIAL_MOVING_AVERAGE()](#exponential_moving_average) [DOUBLE_EXPONENTIAL_MOVING_AVERAGE()](#double_exponential_moving_average) [KAUFMANS_EFFICIENCY_RATIO()](#kaufmans_efficiency_ratio) [KAUFMANS_ADAPTIVE_MOVING_AVERAGE()](#kaufmans_adaptive_moving_average) [TRIPLE_EXPONENTIAL_MOVING_AVERAGE()](#triple_exponential_moving_average) [TRIPLE_EXPONENTIAL_DERIVATIVE()](#triple_exponential_derivative) [RELATIVE_STRENGTH_INDEX()](#relative_strength_index) ### Arguments Along with a [field key](/influxdb/v1/concepts/glossary/#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`](#warmup_type) | Algorithm \ Warmup Type | simple | exponential | none | | --------------------------------- | ---------------------- | ----------- |:----------: | | [EXPONENTIAL_MOVING_AVERAGE](#exponential_moving_average) | PERIOD - 1 | PERIOD - 1 | n/a | | [DOUBLE_EXPONENTIAL_MOVING_AVERAGE](#double_exponential_moving_average) | ( PERIOD - 1 ) * 2 | PERIOD - 1 | n/a | | [TRIPLE_EXPONENTIAL_MOVING_AVERAGE](#triple_exponential_moving_average) | ( PERIOD - 1 ) * 3 | PERIOD - 1 | n/a | | [TRIPLE_EXPONENTIAL_DERIVATIVE](#triple_exponential_derivative) | ( PERIOD - 1 ) * 3 + 1 | PERIOD | n/a | | [RELATIVE_STRENGTH_INDEX](#relative_strength_index) | PERIOD | PERIOD | n/a | | [CHANDE_MOMENTUM_OSCILLATOR](#chande_momentum_oscillator) | PERIOD | PERIOD | PERIOD - 1 | _**Kaufman Algorithm Default Hold Periods:**_ | Algorithm | Default Hold Period | | --------- | ------------------- | | [KAUFMANS_EFFICIENCY_RATIO()](#kaufmans_efficiency_ratio) | PERIOD | | [KAUFMANS_ADAPTIVE_MOVING_AVERAGE()](#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](https://www.ta-lib.org/). `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](https://www.ta-lib.org/). 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()`](#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 ``` CHANDE_MOMENTUM_OSCILLATOR([ * | | /regular_expression/ ], [, , [warmup_type]]) ``` **Available Arguments:** [period](#period) [hold_period](#hold_period) (Optional) [warmup_type](#warmup_type) (Optional) `CHANDE_MOMENTUM_OSCILLATOR(field_key, 2)` Returns the field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#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(), 2) ... GROUP BY time(1d)` Returns the mean of field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](#aggregations) 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](/influxdb/v1/query_language/explore-data/#regular-expressions) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](#moving_average), except that more weight is given to the latest data. It's also known as the "exponentially weighted moving average." This type of moving average reacts faster to recent data changes than a simple moving average. Source #### Basic syntax ``` EXPONENTIAL_MOVING_AVERAGE([ * | | /regular_expression/ ], [, (Optional) [warmup_type](#warmup_type) (Optional) `EXPONENTIAL_MOVING_AVERAGE(field_key, 2)` Returns the field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#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(), 2) ... GROUP BY time(1d)` Returns the mean of field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](#aggregations) 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](/influxdb/v1/query_language/explore-data/#regular-expressions) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](#exponential_moving_average) is doubled. To keep it in line with the actual data and to remove the lag, the value "EMA of EMA" is subtracted from the previously doubled EMA. Source #### Basic syntax ``` DOUBLE_EXPONENTIAL_MOVING_AVERAGE([ * | | /regular_expression/ ], [, (Optional) [warmup_type](#warmup_type) (Optional) `DOUBLE_EXPONENTIAL_MOVING_AVERAGE(field_key, 2)` Returns the field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#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(), 2) ... GROUP BY time(1d)` Returns the mean of field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](#aggregations) 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](/influxdb/v1/query_language/explore-data/#regular-expressions) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](#chande-momentum-oscillator) (CMO). The difference is that the CMO takes market direction into account, but if you take the absolute CMO and divide by 100, you you get the Efficiency Ratio. Source #### Basic syntax ``` KAUFMANS_EFFICIENCY_RATIO([ * | | /regular_expression/ ], [, ]) ``` **Available Arguments:** [period](#period) [hold_period](#hold_period) (Optional) `KAUFMANS_EFFICIENCY_RATIO(field_key, 2)` Returns the field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#field-key) processed using the Efficiency Index algorithm with a 10-value period and a 10-value hold period. `KAUFMANS_EFFICIENCY_RATIO(MEAN(), 2) ... GROUP BY time(1d)` Returns the mean of field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](#aggregations) 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](/influxdb/v1/query_language/explore-data/#regular-expressions) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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 ``` KAUFMANS_ADAPTIVE_MOVING_AVERAGE([ * | | /regular_expression/ ], [, ]) ``` **Available Arguments:** [period](#period) [hold_period](#hold_period) (Optional) `KAUFMANS_ADAPTIVE_MOVING_AVERAGE(field_key, 2)` Returns the field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#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(), 2) ... GROUP BY time(1d)` Returns the mean of field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](#aggregations) 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](/influxdb/v1/query_language/explore-data/#regular-expressions) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](#exponential_moving_average), a [double exponential moving average](#double_exponential_moving_average), and a triple exponential moving average. Source #### Basic syntax ``` TRIPLE_EXPONENTIAL_MOVING_AVERAGE([ * | | /regular_expression/ ], [, (Optional) [warmup_type](#warmup_type) (Optional) `TRIPLE_EXPONENTIAL_MOVING_AVERAGE(field_key, 2)` Returns the field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#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(), 2) ... GROUP BY time(1d)` Returns the mean of field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](#aggregations) 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](/influxdb/v1/query_language/explore-data/#regular-expressions) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](#triple-exponential-moving-average) of the [log](#log) of the data input over the period of time. The previous value is subtracted from the previous value. This prevents cycles that are shorter than the defined period from being considered by the indicator. Like many oscillators, TRIX oscillates around a zero line. When used as an oscillator, a positive value indicates an overbought market while a negative value indicates an oversold market. When used as a momentum indicator, a positive value suggests momentum is increasing while a negative value suggests momentum is decreasing. Many analysts believe that when the TRIX crosses above the zero line it gives a buy signal, and when it closes below the zero line, it gives a sell signal. Source #### Basic syntax ``` TRIPLE_EXPONENTIAL_DERIVATIVE([ * | | /regular_expression/ ], [, (Optional) [warmup_type](#warmup_type) (Optional) `TRIPLE_EXPONENTIAL_DERIVATIVE(field_key, 2)` Returns the field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#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(), 2) ... GROUP BY time(1d)` Returns the mean of field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](#aggregations) 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](/influxdb/v1/query_language/explore-data/#regular-expressions) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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 ``` RELATIVE_STRENGTH_INDEX([ * | | /regular_expression/ ], [, (Optional) [warmup_type](#warmup_type) (Optional) `RELATIVE_STRENGTH_INDEX(field_key, 2)` Returns the field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/concepts/glossary/#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(), 2) ... GROUP BY time(1d)` Returns the mean of field values associated with the [field key](/influxdb/v1/concepts/glossary/#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](#aggregations) 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](/influxdb/v1/query_language/explore-data/#regular-expressions) 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](/influxdb/v1/concepts/glossary/#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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types). The basic syntax supports `GROUP BY` clauses that [group by tags](/influxdb/v1/query_language/explore-data/#group-by-tags) but not `GROUP BY` clauses that [group by time](/influxdb/v1/query_language/explore-data/#group-by-time-intervals). See the [Advanced Syntax](#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](/influxdb/v1/query_language/data_download/) page. ### General Syntax for Functions #### Specify Multiple Functions in the SELECT Clause ##### Syntax ``` SELECT (),() FROM_clause [...] ``` Separate multiple functions in one `SELECT` statement with a comma (`,`). The syntax applies to all InfluxQL functions except [`TOP()`](#top) and [`BOTTOM()`](#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 ```sql > SELECT MEAN("water_level"),MEDIAN("water_level") FROM "h2o_feet" name: h2o_feet time mean median ---- ---- ------ 1970-01-01T00:00:00Z 4.442107025822522 4.124 ``` The query returns the [average](#mean) and [median](#median) field values in the `water_level` field key. ###### Calculate the mode of two fields in one query ```sql > SELECT MODE("water_level"),MODE("level description") FROM "h2o_feet" name: h2o_feet time mode mode_1 ---- ---- ------ 1970-01-01T00:00:00Z 2.69 between 3 and 6 feet ``` The query returns the [mode](#mode) field values for the `water_level` field key and for the `level description` field key. The `water_level` mode is in the `mode` column and the `level description` mode is in the `mode_1` column. The system can't return more than one column with the same name so it renames the second `mode` column to `mode_1`. See [Rename the Output Field Key](#rename_the_output_field_key) for how to configure the output column headers. ###### Calculate the minimum and maximum field values in one query ```sql > SELECT MIN("water_level"), MAX("water_level") [...] name: h2o_feet time min max ---- --- --- 1970-01-01T00:00:00Z -0.61 9.964 ``` The query returns the [minimum](#min) and [maximum](#max) field values in the `water_level` field key. Notice that the query returns `1970-01-01T00:00:00Z`, the InfluxDB equivalent to a null timestamp, as the timestamp value. `MIN()` and `MAX()` are [selector](#selectors) functions; when a selector function is the only function in the `SELECT` clause, it returns a specific timestamp. Because `MIN()` and `MAX()` return two different timestamps (see below), the system overrides those timestamps with the null timestamp equivalent. ```sql > SELECT MIN("water_level") FROM "h2o_feet" name: h2o_feet time min ---- --- 2015-08-29T14:30:00Z -0.61 <--- Timestamp 1 > SELECT MAX("water_level") FROM "h2o_feet" name: h2o_feet time max ---- --- 2015-08-29T07:24:00Z 9.964 <--- Timestamp 2 ``` #### Rename the Output Field Key ##### Syntax ``` SELECT () AS [...] ``` 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 ```sql > SELECT MEAN("water_level") AS "dream_name" FROM "h2o_feet" name: h2o_feet time dream_name ---- ---------- 1970-01-01T00:00:00Z 4.442107025822522 ``` The query returns the [average](#mean) field value of the `water_level` field key and renames the output field key to `dream_name`. Without the `AS` clause, the query returns `mean` as the output field key: ```sql > SELECT MEAN("water_level") FROM "h2o_feet" name: h2o_feet time mean ---- ---- 1970-01-01T00:00:00Z 4.442107025822522 ``` ###### Specify the output field key for multiple functions ```sql > SELECT MEDIAN("water_level") AS "med_wat",MODE("water_level") AS "mode_wat" FROM "h2o_feet" name: h2o_feet time med_wat mode_wat ---- ------- -------- 1970-01-01T00:00:00Z 4.124 2.69 ``` The query returns the [median](#median) and [mode](#mode) field values for the `water_level` field key and renames the output field keys to `med_wat` and `mode_wat`. Without the `AS` clauses, the query returns `median` and `mode` as the output field keys: ```sql > SELECT MEDIAN("water_level"),MODE("water_level") FROM "h2o_feet" name: h2o_feet time median mode ---- ------ ---- 1970-01-01T00:00:00Z 4.124 2.69 ``` #### Change the Values Reported for Intervals with no Data By default, queries with an InfluxQL function and a [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) report null values for intervals with no data. Include `fill()` at the end of the `GROUP BY` clause to change that value. See [Data Exploration](/influxdb/v1/query_language/explore-data/#group-by-time-intervals-and-fill) for a complete discussion of `fill()`. ### Common Issues with Functions The following sections describe frequent sources of confusion with all functions, aggregation functions, and selector functions. See the function-specific documentation for common issues with individual functions: * [DISTINCT()](#common-issues-with-distinct) * [BOTTOM()](#common-issues-with-bottom) * [PERCENTILE()](#common-issues-with-percentile) * [SAMPLE()](#common-issues-with-sample) * [TOP()](#common-issues-with-top) * [ELAPSED()](#common-issues-with-elapsed) * [HOLT_WINTERS()](#common-issues-with-holt-winters) #### All Functions ##### Nesting functions Some InfluxQL functions support nesting in the [`SELECT` clause](/influxdb/v1/query_language/explore-data/#select-clause): * [`COUNT()`](#count) with [`DISTINCT()`](#distinct) * [`CUMULATIVE_SUM()`](#cumulative_sum) * [`DERIVATIVE()`](#derivative) * [`DIFFERENCE()`](#difference) * [`ELAPSED()`](#elapsed) * [`MOVING_AVERAGE()`](#moving_average) * [`NON_NEGATIVE_DERIVATIVE()`](#non_negative_derivative) * [`HOLT_WINTERS()`](#holt_winters) and [`HOLT_WINTERS_WITH_FIT()`](#holt_winters) For other functions, use InfluxQL's [subqueries](/influxdb/v1/query_language/explore-data/#subqueries) to nest functions in the [`FROM` clause](/influxdb/v1/query_language/explore-data/#from-clause). See the [Data Exploration](/influxdb/v1/query_language/explore-data/#subqueries) 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](/influxdb/v1/troubleshooting/frequently-asked-questions/#what-are-the-minimum-and-maximum-timestamps-that-influxdb-can-store). For `SELECT` statements with an InfluxQL function and a [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals), the default time range is between `1677-09-21 00:12:43.145224194` UTC and [`now()`](/influxdb/v1/concepts/glossary/#now). To query data with timestamps that occur after `now()`, `SELECT` statements with an InfluxQL function and a `GROUP BY time()` clause must provide an alternative upper bound in the [`WHERE` clause](/influxdb/v1/query_language/explore-data/#the-where-clause). See the [Frequently Asked Questions](/influxdb/v1/troubleshooting/frequently-asked-questions/#why-don-t-my-group-by-time-queries-return-timestamps-that-occur-after-now) page for an example. #### Aggregation Functions ##### Understanding the returned timestamp A query with an [aggregation function](#aggregations) and no time range in the [`WHERE` clause](/influxdb/v1/query_language/explore-data/#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 ```sql > SELECT SUM("water_level") FROM "h2o_feet" name: h2o_feet time sum ---- --- 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()`](#sum) aggregates points across several timestamps and has no single timestamp to return. ###### Use an aggregate function with a specified time range ```sql > SELECT SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' name: h2o_feet time sum ---- --- 2015-08-18T00:00:00Z 67777.66900000004 ``` The query returns the lower time bound (`WHERE time >= '2015-08-18T00:00:00Z'`) as the timestamp. ###### Use an aggregate function with a specified time range and a GROUP BY time() clause ```sql > SELECT SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:18:00Z' GROUP BY time(12m) name: h2o_feet time sum ---- --- 2015-08-18T00:00:00Z 20.305 2015-08-18T00:12:00Z 19.802999999999997 ``` The query returns the lower time bound for each [`GROUP BY time()`](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) interval as the timestamps. ###### Mixing aggregation functions with non-aggregates Aggregation functions do not support specifying standalone [field keys](/influxdb/v1/concepts/glossary/#field-key) or [tag keys](/influxdb/v1/concepts/glossary/#tag-key) in the [`SELECT` clause](/influxdb/v1/query_language/explore-data/#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: ```sql > SELECT SUM("water_level"),"location" FROM "h2o_feet" 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](/influxdb/v1/write_protocols/line_protocol_reference/#data-types) points may yield slightly different results. InfluxDB does not sort points before it applies the aggregation function; that behavior can cause small discrepancies in the query results. #### Selector Functions ##### Understanding the returned timestamp The timestamps returned by [selector functions](#selectors) depend on the number of functions in the query and on the other clauses in the query: A query with a single selector function, a single [field key](/influxdb/v1/concepts/glossary/#field-key) argument, and no [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) returns the timestamp for the point that appears in the raw data. A query with a single selector function, multiple field key arguments, and no [`GROUP BY time()` clause](/influxdb/v1/query_language/explore-data/#group-by-time-intervals) 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](/influxdb/v1/query_language/explore-data/#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()`](#common-issues-with-sample) for more information. ##### Examples ###### Use a single selector function with a single field key and without a specified time range ```sql > SELECT MAX("water_level") FROM "h2o_feet" name: h2o_feet time max ---- --- 2015-08-29T07:24:00Z 9.964 > SELECT MAX("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' name: h2o_feet time max ---- --- 2015-08-29T07:24:00Z 9.964 ``` The queries return the timestamp for the [maximum](#max) point that appears in the raw data. ###### Use a single selector function with multiple field keys and without a specified time range ```sql > SELECT FIRST(*) FROM "h2o_feet" name: h2o_feet time first_level description first_water_level ---- ----------------------- ----------------- 1970-01-01T00:00:00Z between 6 and 9 feet 8.12 > SELECT MAX(*) FROM "h2o_feet" name: h2o_feet time max_water_level ---- --------------- 2015-08-29T07:24:00Z 9.964 ``` The first query returns 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](/influxdb/v1/concepts/glossary/#measurement)) so the system overrides those timestamps with the null timestamp equivalent. The second query returns the timestamp for the maximum point that appears in the raw data. `MAX(*)` returns one timestamp (the `h2o-feet` measurement has only one numerical field) so the system does not overwrite the original timestamp. ###### Use a selector function with another function and without a specified time range ```sql > SELECT MAX("water_level"),MIN("water_level") FROM "h2o_feet" name: h2o_feet time max min ---- --- --- 1970-01-01T00:00:00Z 9.964 -0.61 ``` The query returns the InfluxDB equivalent of a null timestamp (epoch 0: `1970-01-01T00:00:00Z`) as the timestamp. The `MAX()` and [`MIN()`](#min) functions return different timestamps so the system has no single timestamp to return. ###### Use a selector function with another function and with a specified time range ```sql > SELECT MAX("water_level"),MIN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' name: h2o_feet time max min ---- --- --- 2015-08-18T00:00:00Z 9.964 -0.61 ``` The query returns the lower time bound (`WHERE time >= '2015-08-18T00:00:00Z'`) as the timestamp. ###### Use a selector function with a GROUP BY time() clause ```sql > SELECT MAX("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:18:00Z' GROUP BY time(12m) name: h2o_feet time max ---- --- 2015-08-18T00:00:00Z 8.12 2015-08-18T00:12:00Z 7.887 ``` The query returns the lower time bound for each `GROUP BY time()` interval as the timestamp.