make-series operator

Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer ✅ Azure Monitor ✅ Microsoft Sentinel

Create series of specified aggregated values along a specified axis.

Syntax

T | make-series [MakeSeriesParameters] [Column =] Aggregation [default = DefaultValue] [, ...] on AxisColumn [from start] [to end] step step [by [Column =] GroupExpression [, ...]]

Learn more about syntax conventions.

Parameters

Name	Type	Required	Description
Column	string		The name for the result column. Defaults to a name derived from the expression.
DefaultValue	scalar		A default value to use instead of absent values. If there's no row with specific values of AxisColumn and GroupExpression, then the corresponding element of the array will be assigned a DefaultValue. Default is 0.
Aggregation	string	✔️	A call to an aggregation function, such as count() or avg(), with column names as arguments. See the list of aggregation functions. Only aggregation functions that return numeric results can be used with the make-series operator.
AxisColumn	string	✔️	The column by which the series will be ordered. Usually the column values will be of type datetime or timespan but all numeric types are accepted.
start	scalar	✔️	The low bound value of the AxisColumn for each of the series to be built. If start is not specified, it will be the first bin, or step, that has data in each series.
end	scalar	✔️	The high bound non-inclusive value of the AxisColumn. The last index of the time series is smaller than this value and will be start plus integer multiple of step that is smaller than end. If end is not specified, it will be the upper bound of the last bin, or step, that has data per each series.
step	scalar	✔️	The difference, or bin size, between two consecutive elements of the AxisColumn array. For a list of possible time intervals, see timespan.
GroupExpression			An expression over the columns that provides a set of distinct values. Typically it's a column name that already provides a restricted set of values.
MakeSeriesParameters			Zero or more space-separated parameters in the form of Name = Value that control the behavior. See supported make series parameters.

Note

The start, end, and step parameters are used to build an array of AxisColumn values. The array consists of values between start and end, with the step value representing the difference between one array element to the next. All Aggregation values are ordered respectively to this array.

Supported make series parameters

Name	Description
kind	Produces default result when the input of make-series operator is empty. Value: nonempty
hint.shufflekey=<key>	The shufflekey query shares the query load on cluster nodes, using a key to partition data. See shuffle query

Note

The arrays generated by make-series are limited to 1048576 values (2^20). Trying to generate a larger array with make-series would result in either an error or a truncated array.

Alternate Syntax

T | make-series [Column =] Aggregation [default = DefaultValue] [, ...] on AxisColumn in range(start, stop, step) [by [Column =] GroupExpression [, ...]]

The generated series from the alternate syntax differs from the main syntax in two aspects:

• The stop value is inclusive.
• Binning the index axis is generated with bin() and not bin_at(), which means that start may not be included in the generated series.

It's recommended to use the main syntax of make-series and not the alternate syntax.

Returns

The input rows are arranged into groups having the same values of the by expressions and the bin_at(AxisColumn,step,start) expression. Then the specified aggregation functions are computed over each group, producing a row for each group. The result contains the by columns, AxisColumn column and also at least one column for each computed aggregate. (Aggregations over multiple columns or non-numeric results aren't supported.)

This intermediate result has as many rows as there are distinct combinations of by and bin_at(AxisColumn,step,start) values.

Finally the rows from the intermediate result arranged into groups having the same values of the by expressions and all aggregated values are arranged into arrays (values of dynamic type). For each aggregation, there's one column containing its array with the same name. The last column is an array containing the values of AxisColumn binned according to the specified step.

Note

Although you can provide arbitrary expressions for both the aggregation and grouping expressions, it's more efficient to use simple column names.

List of aggregation functions

Function	Description
avg()	Returns an average value across the group
avgif()	Returns an average with the predicate of the group
count()	Returns a count of the group
countif()	Returns a count with the predicate of the group
dcount()	Returns an approximate distinct count of the group elements
dcountif()	Returns an approximate distinct count with the predicate of the group
max()	Returns the maximum value across the group
maxif()	Returns the maximum value with the predicate of the group
min()	Returns the minimum value across the group
minif()	Returns the minimum value with the predicate of the group
percentile()	Returns the percentile value across the group
take_any()	Returns a random non-empty value for the group
stdev()	Returns the standard deviation across the group
sum()	Returns the sum of the elements within the group
sumif()	Returns the sum of the elements with the predicate of the group
variance()	Returns the variance across the group

List of series analysis functions

Function	Description
series_fir()	Applies Finite Impulse Response filter
series_iir()	Applies Infinite Impulse Response filter
series_fit_line()	Finds a straight line that is the best approximation of the input
series_fit_line_dynamic()	Finds a line that is the best approximation of the input, returning dynamic object
series_fit_2lines()	Finds two lines that are the best approximation of the input
series_fit_2lines_dynamic()	Finds two lines that are the best approximation of the input, returning dynamic object
series_outliers()	Scores anomaly points in a series
series_periods_detect()	Finds the most significant periods that exist in a time series
series_periods_validate()	Checks whether a time series contains periodic patterns of given lengths
series_stats_dynamic()	Return multiple columns with the common statistics (min/max/variance/stdev/average)
series_stats()	Generates a dynamic value with the common statistics (min/max/variance/stdev/average)

For a complete list of series analysis functions, see: Series processing functions

List of series interpolation functions

Function	Description
series_fill_backward()	Performs backward fill interpolation of missing values in a series
series_fill_const()	Replaces missing values in a series with a specified constant value
series_fill_forward()	Performs forward fill interpolation of missing values in a series
series_fill_linear()	Performs linear interpolation of missing values in a series

• Note: Interpolation functions by default assume null as a missing value. Therefore specify default=double(null) in make-series if you intend to use interpolation functions for the series.

Examples

A table that shows arrays of the numbers and average prices of each fruit from each supplier ordered by the timestamp with specified range. There's a row in the output for each distinct combination of fruit and supplier. The output columns show the fruit, supplier, and arrays of: count, average, and the whole timeline (from 2016-01-01 until 2016-01-10). All arrays are sorted by the respective timestamp and all gaps are filled with default values (0 in this example). All other input columns are ignored.

T | make-series PriceAvg=avg(Price) default=0
on Purchase from datetime(2016-09-10) to datetime(2016-09-13) step 1d by Supplier, Fruit

Run the query

let data=datatable(timestamp:datetime, metric: real)
[
  datetime(2016-12-31T06:00), 50,
  datetime(2017-01-01), 4,
  datetime(2017-01-02), 3,
  datetime(2017-01-03), 4,
  datetime(2017-01-03T03:00), 6,
  datetime(2017-01-05), 8,
  datetime(2017-01-05T13:40), 13,
  datetime(2017-01-06), 4,
  datetime(2017-01-07), 3,
  datetime(2017-01-08), 8,
  datetime(2017-01-08T21:00), 8,
  datetime(2017-01-09), 2,
  datetime(2017-01-09T12:00), 11,
  datetime(2017-01-10T05:00), 5,
];
let interval = 1d;
let stime = datetime(2017-01-01);
let etime = datetime(2017-01-10);
data
| make-series avg(metric) on timestamp from stime to etime step interval 

avg_metric	timestamp
[ 4.0, 3.0, 5.0, 0.0, 10.5, 4.0, 3.0, 8.0, 6.5 ]	[ "2017-01-01T00:00:00.0000000Z", "2017-01-02T00:00:00.0000000Z", "2017-01-03T00:00:00.0000000Z", "2017-01-04T00:00:00.0000000Z", "2017-01-05T00:00:00.0000000Z", "2017-01-06T00:00:00.0000000Z", "2017-01-07T00:00:00.0000000Z", "2017-01-08T00:00:00.0000000Z", "2017-01-09T00:00:00.0000000Z" ]

When the input to make-series is empty, the default behavior of make-series produces an empty result.

Run the query

let data=datatable(timestamp:datetime, metric: real)
[
  datetime(2016-12-31T06:00), 50,
  datetime(2017-01-01), 4,
  datetime(2017-01-02), 3,
  datetime(2017-01-03), 4,
  datetime(2017-01-03T03:00), 6,
  datetime(2017-01-05), 8,
  datetime(2017-01-05T13:40), 13,
  datetime(2017-01-06), 4,
  datetime(2017-01-07), 3,
  datetime(2017-01-08), 8,
  datetime(2017-01-08T21:00), 8,
  datetime(2017-01-09), 2,
  datetime(2017-01-09T12:00), 11,
  datetime(2017-01-10T05:00), 5,
];
let interval = 1d;
let stime = datetime(2017-01-01);
let etime = datetime(2017-01-10);
data
| take 0
| make-series avg(metric) default=1.0 on timestamp from stime to etime step interval 
| count 

Output

Count
0

Using kind=nonempty in make-series will produce a non-empty result of the default values:

Run the query

let data=datatable(timestamp:datetime, metric: real)
[
  datetime(2016-12-31T06:00), 50,
  datetime(2017-01-01), 4,
  datetime(2017-01-02), 3,
  datetime(2017-01-03), 4,
  datetime(2017-01-03T03:00), 6,
  datetime(2017-01-05), 8,
  datetime(2017-01-05T13:40), 13,
  datetime(2017-01-06), 4,
  datetime(2017-01-07), 3,
  datetime(2017-01-08), 8,
  datetime(2017-01-08T21:00), 8,
  datetime(2017-01-09), 2,
  datetime(2017-01-09T12:00), 11,
  datetime(2017-01-10T05:00), 5,
];
let interval = 1d;
let stime = datetime(2017-01-01);
let etime = datetime(2017-01-10);
data
| take 0
| make-series kind=nonempty avg(metric) default=1.0 on timestamp from stime to etime step interval 

Output

avg_metric	timestamp
[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ]	[ "2017-01-01T00:00:00.0000000Z", "2017-01-02T00:00:00.0000000Z", "2017-01-03T00:00:00.0000000Z", "2017-01-04T00:00:00.0000000Z", "2017-01-05T00:00:00.0000000Z", "2017-01-06T00:00:00.0000000Z", "2017-01-07T00:00:00.0000000Z", "2017-01-08T00:00:00.0000000Z", "2017-01-09T00:00:00.0000000Z" ]