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session_count plugin

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Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

Calculates the session count based on the ID column over a timeline. The plugin is invoked with the evaluate operator.

Syntax

TabularExpression | evaluate session_count(IdColumn, TimelineColumn, Start, End, Bin, LookBackWindow [, dim1, dim2, ...])

Learn more about syntax conventions.

Parameters

Name Type Required Description
TabularExpression string ✔️ The tabular expression that serves as input.
IdColumn string ✔️ The name of the column with ID values that represents user activity.
TimelineColumn string ✔️ The name of the column that represents the timeline.
Start scalar ✔️ The start of the analysis period.
End scalar ✔️ The end of the analysis period.
Bin scalar ✔️ The session's analysis step period.
LookBackWindow scalar ✔️ The session lookback period. If the ID from IdColumn appears in a time window within LookBackWindow, the session is considered to be an existing one. If the ID doesn't appear, then the session is considered to be new.
dim1, dim2, ... string A list of the dimensions columns that slice the session count calculation.

Returns

Returns a table that has the session count values for each timeline period and for each existing dimensions combination.

Output table schema is:

TimelineColumn dim1 .. dim_n count_sessions
type: as of TimelineColumn .. .. .. long

Examples

For this example, the data is deterministic, and we use a table with two columns:

  • Timeline: a running number from 1 to 10,000
  • Id: ID of the user from 1 to 50

Id appears at the specific Timeline slot if it's a divider of Timeline (Timeline % Id == 0).

An event with Id==1 will appear at any Timeline slot, an event with Id==2 at every second Timeline slot, and so on.

Here are 20 lines of the data:

let _data = range Timeline from 1 to 10000 step 1
    | extend __key = 1
    | join kind=inner (range Id from 1 to 50 step 1 | extend __key=1) on __key
    | where Timeline % Id == 0
    | project Timeline, Id;
// Look on few lines of the data
_data
| order by Timeline asc, Id asc
| take 20

Output

Timeline Id
1 1
2 1
2 2
3 1
3 3
4 1
4 2
4 4
5 1
5 5
6 1
6 2
6 3
6 6
7 1
7 7
8 1
8 2
8 4
8 8

Let's define a session in next terms: session considered to be active as long as user (Id) appears at least once at a timeframe of 100 time slots, while session look-back window is 41 time slots.

The next query shows the count of active sessions according to the above definition.

let _data = range Timeline from 1 to 9999 step 1
    | extend __key = 1
    | join kind=inner (range Id from 1 to 50 step 1 | extend __key=1) on __key
    | where Timeline % Id == 0
    | project Timeline, Id;
// End of data definition
_data
| evaluate session_count(Id, Timeline, 1, 10000, 100, 41)
| render linechart

Example session count.