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sliding_window_counts plugin

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Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

Calculates counts and distinct count of values in a sliding window over a lookback period, using the technique described in the Perform aggregations over a sliding window example. The plugin is invoked with the evaluate operator.

Syntax

T | evaluate sliding_window_counts(IdColumn, TimelineColumn, Start, End, LookbackWindow, Bin , [dim1, dim2, ...])

Learn more about syntax conventions.

Parameters

Name Type Required Description
T string ✔️ The input tabular expression.
IdColumn string ✔️ The name of the column with ID values that represent user activity.
TimelineColumn string ✔️ The name of the column representing the timeline.
Start int, long, real, datetime, or timespan ✔️ The analysis start period.
End int, long, real, datetime, or timespan ✔️ The analysis end period.
LookbackWindow int, long, real, datetime, or timespan ✔️ The lookback period. This value should be a multiple of the Bin value, otherwise the LookbackWindow will be rounded down to a multiple of the Bin value. For example, for dcount users in past 7d: LookbackWindow = 7d.
Bin int, long, real, datetime, timespan, or string ✔️ The analysis step period. The possible string values are week, month, and year for which all periods will be startofweek, startofmonth, startofyear respectively.
dim1, dim2, ... string A list of the dimensions columns that slice the activity metrics calculation.

Returns

Returns a table that has the count and distinct count values of Ids in the lookback period, for each timeline period (by bin) and for each existing dimensions combination.

Output table schema is:

TimelineColumn dim1 .. dim_n count dcount
type: as of TimelineColumn .. .. .. long long

Example

Calculate counts and dcounts for users in past week, for each day in the analysis period.

let start = datetime(2017 - 08 - 01);
let end = datetime(2017 - 08 - 07); 
let lookbackWindow = 3d;  
let bin = 1d;
let T = datatable(UserId: string, Timestamp: datetime)
    [
    'Bob', datetime(2017 - 08 - 01), 
    'David', datetime(2017 - 08 - 01), 
    'David', datetime(2017 - 08 - 01), 
    'John', datetime(2017 - 08 - 01), 
    'Bob', datetime(2017 - 08 - 01), 
    'Ananda', datetime(2017 - 08 - 02),  
    'Atul', datetime(2017 - 08 - 02), 
    'John', datetime(2017 - 08 - 02), 
    'Ananda', datetime(2017 - 08 - 03), 
    'Atul', datetime(2017 - 08 - 03), 
    'Atul', datetime(2017 - 08 - 03), 
    'John', datetime(2017 - 08 - 03), 
    'Bob', datetime(2017 - 08 - 03), 
    'Betsy', datetime(2017 - 08 - 04), 
    'Bob', datetime(2017 - 08 - 05), 
];
T
| evaluate sliding_window_counts(UserId, Timestamp, start, end, lookbackWindow, bin)

Output

Timestamp Count dcount
2017-08-01 00:00:00.0000000 5 3
2017-08-02 00:00:00.0000000 8 5
2017-08-03 00:00:00.0000000 13 5
2017-08-04 00:00:00.0000000 9 5
2017-08-05 00:00:00.0000000 7 5
2017-08-06 00:00:00.0000000 2 2
2017-08-07 00:00:00.0000000 1 1