[Yandex Cloud documentation](../../index.md) > [Monium](../index.md) > Concepts > Functions for searching metrics # Using functions when searching metrics This section describes functions you can use in a query searching for metrics. - [Aggregation](#aggregation-functions) - [avg](#avg) - [count](#count) - [integrate](#integrate) - [iqr](#iqr) - [last](#last) - [max](#max) - [median](#median) - [min](#min) - [percentile](#percentile) - [random](#random) - [std](#std) - [sum](#sum) - [Combining](#combine-functions) - [histogram_avg](#histogram_avg) - [histogram_cdfp](#histogram_cdfp) - [histogram_count](#histogram_count) - [histogram_percentile](#histogram_percentile) - [histogram_sum](#histogram_sum) - [series_avg](#series_avg) - [series_max](#series_max) - [series_min](#series_min) - [series_percentile](#series_percentile) - [series_sum](#series_sum) - [Ranking](#rank-functions) - [bottom_avg](#bottom_avg) - [bottom_count](#bottom_count) - [bottom_last](#bottom_last) - [bottom_max](#bottom_max) - [bottom_min](#bottom_min) - [bottom_sum](#bottom_sum) - [top_avg](#top_avg) - [top_count](#top_count) - [top_last](#top_last) - [top_max](#top_max) - [top_min](#top_min) - [top_sum](#top_sum) - [Transformation](#transform-functions) - [abs](#abs) - [asap](#asap) - [ceil](#ceil) - [derivative](#derivative) - [diff](#diff) - [drop_above](#drop_above) - [drop_below](#drop_below) - [drop_nan](#drop_nan) - [exp](#exp) - [floor](#floor) - [fract](#fract) - [heaviside](#heaviside) - [integral](#integral) - [log](#log) - [moving_avg](#moving_avg) - [moving_percentile](#moving_percentile) - [moving_sum](#moving_sum) - [non_negative_derivative](#non_negative_derivative) - [pow](#pow) - [ramp](#ramp) - [replace_nan](#replace_nan) - [round](#round) - [shift](#shift) - [sign](#sign) - [sqrt](#sqrt) - [trunc](#trunc) - [Other](#other-functions) - [alias](#alias) - [constant_line](#constant_line) - [drop_empty_series](#drop_empty_series) ## Aggregation {#aggregation-functions} Aggregation functions aggregate values of a timeseries in the current time range. {% note warning %} As an input argument, aggregation functions accept a metric vector (_timeseries_vector_). It must only include a single timeseries. Otherwise, the function returns a runtime error. When using aggregation functions, make sure that the selector returns a single timeseries. Use [combining functions](#combine-functions) if needed. {% endnote %} ### avg Returns an average value (for timeseries, a weighted average) for a set of elements or `NaN` for an empty timeseries. The **avg** function has the following function overloading options depending on the type of the input _arg0_ parameter (an array of numbers, a metric, or a vector of metrics): * **avg**(_arg0_: _scalar[]_): _scalar_ * **avg**(_arg0_: _timeseries_vector_): _scalar_ ### count Returns the count of metric points or number vectors. The **count** function has the following overloading options depending on the input _arg0_ parameter type (an array of numbers, a metric, or a vector of metrics): * **count**(_arg0_: _scalar[]_): _scalar_ * **count**(_arg0_: _timeseries_vector_): _scalar_ ### integrate Returns an integrated sum of values or 0 for an empty timeseries. The **integrate** function has the following function overloading options depending on the type of the input _arg0_ parameter (an array of numbers, a metric, or a vector of metrics): * **integrate**(_arg0: scalar[]_): _scalar_ * **integrate**(_arg0_: _timeseries_vector_): _scalar_ ### iqr Returns the [interquartile range](https://en.wikipedia.org/wiki/Interquartile_range) for a set of values. The **iqr** function has the following function overloading options depending on the type of the input _arg0_ parameter (an array of numbers, a metric, or a vector of metrics): * **iqr**(_arg0_: _scalar[]_): _scalar_ * **iqr**(_arg0_: _timeseries_vector_): _scalar_ ### last Returns either the last non-`NaN` value or `NaN` for an empty timeseries. The **last** function has the following function overloading options depending on the type of the input _arg0_ parameter (an array of numbers, a metric, or a vector of metrics): * **last**(_arg0_: _scalar[]_): _scalar_ * **last**(_arg0_: _timeseries_vector_): _scalar_ ### max Returns the maximum value or `NaN` for an empty timeseries. The **max** function has the following function overloading options depending on the type of the input _arg0_ parameter (an array of numbers, a metric, or a vector of metrics): * **max**(_arg0_: _scalar[]_): _scalar_ * **max**(_arg0_: _timeseries_vector_): _scalar_ ### median Returns the median of values or `NaN` for an empty timeseries. The **median** function has the following function overloading options depending on the type of the input _arg0_ parameter (an array of numbers, a metric, or a vector of metrics): * **median**(_arg0_: _scalar[]_): _scalar_ * **median**(_arg0_: _timeseries_vector_): _scalar_ ### min Returns the minimum value or `NaN` for an empty timeseries. The **min** function has the following function overloading options depending on the type of the input _arg0_ parameter (an array of numbers, a metric, or a vector of metrics): * **min**(_arg0_: _scalar[]_): _scalar_ * **min**(_arg0_: _timeseries_vector_): _scalar_ ### percentile Returns the percentile value for a set of values. The percentile level is set in the required _level_ parameter as a number between 0 and 100. The **percentile** function has the following function overloading options depending on the type of the input *values* parameter (an array of numbers, a metric, or a vector of metrics): * **percentile**(_level_: _scalar_, _values: scalar[]_): _scalar_ * **percentile**(_level_: _scalar_, _values: timeseries_vector_): _scalar_ ### random Returns a random item from a set of values. The **random** function has the following overloading options depending on the input _arg0_ parameter type (an array of numbers, a metric, or a vector of metrics): * **random**(_arg0_: _scalar[]_): _scalar_ * **random**(_arg0_: _timeseries_vector_): _scalar_ ### std Returns an unbiased estimation of standard deviation for a set of values (or `NaN` for an empty timeseries) calculated using this formula: $$\begin{array}{c} s=\sqrt{\frac{1}{n-1}\sum_{i=1}^n\left(x_i-\bar{x}\right)^2} \end{array}{} , $$ Where: * $x_i$: Value from the vector of values (or points in a timeseries). * $\bar{x}$: Average value. * $n$: Number of values. The **std** function has the following overloading options depending on the input _arg0_ parameter type (an array of numbers, a metric, or a vector of metrics): * **std**(_arg0_: _scalar[]_): _scalar_ * **std**(_arg0_: _timeseries_vector_): _scalar_ ### sum Returns a sum of all values of a set or 0 for an empty timeseries. The **sum** function has the following function overloading options depending on the type of the input _arg0_ parameter (an array of numbers, a metric, or a vector of metrics): * **sum**(_arg0_: _scalar[]_): _scalar_ * **sum**(_arg0_: _timeseries_vector_): _scalar_ ## Combining {#combine-functions} The combine functions aggregate a metric vector into a single metric or a metric vector. ### histogram_avg **histogram_avg**(*[bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* Calculates the average value of the distribution set by the histogram. The optional _bucketLabel_ parameter specifies which label contains the values of histogram intervals. ### histogram_cdfp The **histogram_cdfp** function has the following use cases (function overloading) depending on the type of _from_ and _to_ input parameters (a number or an array of numbers): - **histogram_cdfp**(*[from: number*, *to: number*, *bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* - **histogram_cdfp**(*[from: number*, *to: number[]*, *bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* - **histogram_cdfp**(*[from: number[]*, *to: number*, *bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* - **histogram_cdfp**(*[from: number[]*, *to: number[]*, *bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* Calculates the percentage of values in the histogram between the intervals specified in the _from_ and _to_ optional parameters. If no parameters are specified, the first and last intervals are used, respectively. The optional _bucketLabel_ parameter specifies which label contains the values of histogram intervals. ### histogram_count The **histogram_count** function has the following use cases (function overloading) depending on the type of _from_ and _to_ input parameters (a number or an array of numbers): - **histogram_count**(*[from: number*, *to: number*, *bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* - **histogram_count**(*[from: number*, *to: number[]*, *bucketLabel: string*], *source: timeseries_vector*): *timeseries_vector* - **histogram_count**(*[from: number[]*, *to: number*, *bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* - **histogram_count**(*[from: number[]*, *to: number[]*, *bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* Counts the number of values in the histogram between the intervals specified in the _from_ and _to_ optional parameters. If no parameters are specified, the first and last intervals will be used, respectively. The optional _bucketLabel_ parameter specifies which label contains the values of histogram intervals. ### histogram_percentile The **histogram_percentile** function has the following use cases (function overloading) depending on the type of _from_ and _to_ input parameters (a number or an array of numbers): - **histogram_percentile**(*percentileLevel: number*, *[bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* - **histogram_percentile**(*percentileLevel: number[]*, *[bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* Calculates the percentile values of the distribution set by the histogram. The percentile level is set in the required _percentileLevel_ parameter as a single number or an array of numbers from 0 to 100. The optional _bucketLabel_ parameter specifies which label contains the values of histogram intervals. ### histogram_sum **histogram_sum**(*[bucketLabel: string]*, *source: timeseries_vector*): *timeseries_vector* Calculates the sum of histogram values. The optional _bucketLabel_ parameter specifies which label contains the values of histogram intervals. ### series_avg The **series_avg** function has the following use cases (function overloading) depending on the type of _key_ input parameter (a string or an array of strings): - **series_avg**(*[key: string]*, *source: timeseries_vector*): *timeseries_vector* - **series_avg**(*[key: string[]]*, *source: timeseries_vector*): *timeseries_vector* Aggregates timeseries into one (or multiple ones) by applying the avg (average) aggregation function for each time point. The optional _key_ parameter contains a string or an array of strings with a list of labels to group by. For example, the `series_avg({...})` query will calculate the average value among all uploaded metrics at each point. The `series_avg("host", {...})` query will calculate the average value among all uploaded metrics for each value of the `host` label. The `series_avg(["host", "disk"], {...})` query will calculate the average value among all uploaded metrics for each combination of `host` and `disk` label values. ### series_max The **series_max** function has the following use cases (function overloading) depending on the type of _key_ input parameter (a string or an array of strings): - **series_max**(*[key: string]*, *source: timeseries_vector*): *timeseries_vector* - **series_max**(*[key: string[]]*, *source: timeseries_vector*): *timeseries_vector* Aggregates timeseries into one (or multiple ones) by applying the max aggregation function for each time point. The optional _key_ parameter contains a string or an array of strings with a list of labels to group by. See examples of queries using the _key_ parameter in [series_avg](#series_avg). ### series_min The **series_min** function has the following use cases (function overloading) depending on the type of _key_ input parameter (a string or an array of strings): - **series_min**(*[key: string]*, *source: timeseries_vector*): *timeseries_vector* - **series_min**(*[key: string[]]*, *source: timeseries_vector*): *timeseries_vector* Aggregates timeseries into one (or multiple ones) by applying the min aggregation function for each time point. The optional _key_ parameter contains a string or an array of strings with a list of labels to group by. See examples of queries using the _key_ parameter in [series_avg](#series_avg). ### series_percentile The **series_percentile** function has the following use cases (function overloading) depending on the type of _rank_ input parameter (a number or an array of numbers): - **series_percentile**(*rank: number*, *source: timeseries_vector*): *timeseries_vector* - **series_percentile**(*rank: number[]*, *source: timeseries_vector*): *timeseries_vector* Aggregates timeseries into one (or multiple ones) by applying the percentile aggregation function for each time point. ### series_sum The **series_sum** function has the following use cases (function overloading) depending on the type of _key_ input parameter (a string or an array of strings): - **series_sum**(*[key: string]*, *source: timeseries_vector*): *timeseries_vector* - **series_sum**(*[key: string[]]*, *source: timeseries_vector*): *timeseries_vector* Aggregates timeseries into one (or multiple ones) by applying the sum aggregation function for each time point. The optional _key_ parameter contains a string or an array of strings with a list of labels to group by. See examples of queries using the _key_ parameter in [series_avg](#series_avg). ## Ranking {#rank-functions} The ranking functions order a metric vector based on the aggregation function value in the current time window and return some of the first (upper) or last (lower) timeseries from it. The _limit_ parameter specifies how many metrics a function returns. ### bottom_avg **bottom_avg**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with a minimum average value. ### bottom_count **bottom_count**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with a minimum number of values. ### bottom_last **bottom_last**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with the minimum last value. ### bottom_max **bottom_max**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with the lowest max value. ### bottom_min **bottom_min**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with the lowest min value. ### bottom_sum **bottom_sum**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with the lowest sum value. ### top_avg **top_avg**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with the top average value. ### top_count **top_count**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with a maximum number of values. ### top_last **top_last**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with the top last value. ### top_max **top_max**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with the top max value. ### top_min **top_min**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with the top minimum value. ### top_sum **top_sum**(*limit: number*, *source: timeseries_vector*): *timeseries_vector* Returns the _limit_ of metrics with the top sum value. ## Transformation {#transform-functions} The transform metric functions calculate a new value in each point for each timeseries from a set of metrics. ### abs **abs**(*source: timeseries_vector*): *timeseries_vector* Calculates the absolute value. ### asap **asap**(*source: timeseries_vector*): *timeseries_vector* Smooths timeseries based on the [ASAP algorithm](http://futuredata.stanford.edu/asap/). Timeseries points are averaged using a moving average with a dynamic window. The window width is automatically selected so as to remove as much noise as possible while retaining important information. ### ceil **ceil**(*source: timeseries_vector*): *timeseries_vector* Rounds the point values up to the nearest integer. ### derivative **derivative**(*source: timeseries_vector*): *timeseries_vector* Calculates the derivative: the difference between the values of neighboring points divided by the interval between them. ### diff **diff**(*source: timeseries_vector*): *timeseries_vector* Calculates the difference between the values of each pair of neighboring points. ### drop_above **drop_above**(*source: timeseries_vector*, *threshold: number*): *timeseries_vector* Drops points with a value above the _threshold_ (not including the value itself). In dropped points, the metric value will be equal to `NaN`. ### drop_below **drop_below**(*source: timeseries_vector*, *threshold: number*): *timeseries_vector* Drops points with a value above the _threshold_ (not including the value itself). In dropped points, the metric value will be equal to `NaN`. ### drop_nan **drop_nan**(*source: timeseries_vector*): *timeseries_vector* Drops points with the `NaN` value. ### exp Calculates an exponential function: raises _e_ to a power equal to the point value, where _e = 2,718281..._ is the base of the natural logarithm. ### floor **floor**(*source: timeseries_vector*): *timeseries_vector* Rounds point values down to the nearest integer. ### fract **fract**(*source: timeseries_vector*): *timeseries_vector* Selects the real part of point values. ### heaviside **heaviside**(*source: timeseries_vector*): *timeseries_vector* Calculates the [Heaviside step function](https://en.wikipedia.org/wiki/Heaviside_step_function) value. The function is 1 if the point values are positive, and 0 if the point values are negative. ### integral **integral**(*source: timeseries_vector*): *timeseries_vector* Calculates an indefinite integral using the [trapezoidal rule](https://en.wikipedia.org/wiki/Trapezoidal_rule). ### log **log**(*source: timeseries_vector*): *timeseries_vector* Calculates the natural logarithm. ### moving_avg **moving_avg**(*source: timeseries_vector*, *window: duration*): *timeseries_vector* Calculates the moving average across a window _window_ width. For example, the `moving_avg({...}, 1d)` query will return the moving average with a 1 day window. ### moving_percentile **moving_percentile**(*source: timeseries_vector*, *window: duration*, *rank: number*): *timeseries_vector* Calculates the moving percentile: the percentile of the _rank_ level (from 0 to 100) among the points in a window with a _window_ width. For example, the `moving_percentile({...}, 1h, 99.9)` query will return the moving 99.9 percentile with a 1 hour window. ### moving_sum **moving_sum**(*source: timeseries_vector*, *window: duration*): *timeseries_vector* Calculates the moving sum across a _window_ window width. For example, the `moving_sum({...}, 1d)` query will return a moving sum with a 1 day window. ### non_negative_derivative **non_negative_derivative**(*source: timeseries_vector*): *timeseries_vector* Calculates the derivative: the difference between the values of neighboring points divided by the interval between them. If the derivative value is negative, it is substituted with the `NaN` value. ### pow **pow**(*source: timeseries_vector*, *power: number*): *timeseries_vector* Calculates the power function: raises the point value to the *power* power. ### ramp **ramp**(*source: timeseries_vector*): *timeseries_vector* Resets points with a negative value to 0. ### replace_nan **replace_nan**(*source: timeseries_vector*, *replace: number*): *timeseries_vector* Replaces points with the `NaN` value with the `replace` value. ### round **round**(*source: timeseries_vector*): *timeseries_vector* Rounds values to the nearest integer. ### shift **shift**(*source: timeseries_vector*, *window: duration*): *timeseries_vector* Adds the `window` value to point timestamps. This function lets you compare current metric values with the values for a different time interval. For example, `shift({...}, 1w)` will return metrics shifted a week ahead, i.e., the chosen time window will contain values that are week old. ### sign **sign**(*source: timeseries_vector*): *timeseries_vector* Calculates the *sgn(x)* function. The function is 1 for positive point values, 0 for zero values, and -1 for negative values. ### sqrt **sqrt**(*source: timeseries_vector*): *timeseries_vector* Calculates the square root of point values. ### trunc **trunc**(*source: timeseries_vector*): *timeseries_vector* Truncates the real part of point values. ## Other {#other-functions} ### alias **alias**(*source: timeseries_vector*, *arg1: string*): *timeseries_vector* Renames metrics. As an argument, you can use [mustache templates](https://mustache.github.io/) in `not_var{{label}}` format to substitute a label value in the new metric name. ### constant_line Returns a constant line consisting of two points in the beginning and end of the interval equal to *value*. **constant_line**(*value: scalar*): *timeseries_vector* When you specify an optional *grid* parameter, the function populates the current time interval with points with the value of *value* and the step of *grid* between the points. **constant_line**(*value: scalar*, *grid: duration*): *timeseries_vector* {% note warning %} Use the **constant_line** function only to plot lines on charts. Using this function in calculations produces an incorrect result, because the function returns a timeseries of only two points: at the beginning and end of the definition interval. {% endnote %} ### drop_empty_series **drop_empty_series**(*source: timeseries_vector*): *timeseries_vector* Drops timeseries with no points in the specified time range or with the `NaN` value for all points.