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funnel_sequence_completion plugin

  • Članak

Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

Calculates a funnel of completed sequence steps while comparing different time periods. The plugin is invoked with the evaluate operator.

Syntax

T | evaluate funnel_sequence_completion(IdColumn, TimelineColumn, Start, End, BinSize, StateColumn, Sequence, MaxSequenceStepWindows)

Learn more about syntax conventions.

Parameters

Name Type Required Description
T string ✔️ The input tabular expression.
IdColum string ✔️ The column reference representing the ID. The column must be present in T.
TimelineColumn string ✔️ The column reference representing the timeline. The column must be present in T.
Start datetime, timespan, or long ✔️ The analysis start period.
End datetime, timespan, or long ✔️ The analysis end period.
BinSize datetime, timespan, or long ✔️ The analysis window size. Each window is analyzed separately.
StateColumn string ✔️ The column reference representing the state. The column must be present in T.
Sequence dynamic ✔️ An array with the sequence values that are looked up in StateColumn.
MaxSequenceStepPeriods dynamic ✔️ An array with the values of the max allowed timespan between the first and last sequential steps in the sequence. Each period in the array generates a funnel analysis result.

Returns

Returns a single table useful for constructing a funnel diagram for the analyzed sequence:

  • TimelineColumn: the analyzed time window (bin), each bin in the analysis timeframe (Start to End) generates a funnel analysis separately.
  • StateColumn: the state of the sequence.
  • Period: the maximal period allowed for completing steps in the funnel sequence measured from the first step in the sequence. Each value in MaxSequenceStepPeriods generates a funnel analysis with a separate period.
  • dcount: distinct count of IdColumn in time window that transitioned from first sequence state to the value of StateColumn.

Examples

Exploring Storm Events

The following query checks the completion funnel of the sequence: Hail -> Tornado -> Thunderstorm Wind in "overall" time of 1hour, 4hours, 1day.

let _start = datetime(2007-01-01);
let _end =  datetime(2008-01-01);
let _windowSize = 365d;
let _sequence = dynamic(['Hail', 'Tornado', 'Thunderstorm Wind']);
let _periods = dynamic([1h, 4h, 1d]);
StormEvents
| evaluate funnel_sequence_completion(EpisodeId, StartTime, _start, _end, _windowSize, EventType, _sequence, _periods)

Output

StartTime EventType Period dcount
2007-01-01 00:00:00.0000000 Hail 01:00:00 2877
2007-01-01 00:00:00.0000000 Tornado 01:00:00 208
2007-01-01 00:00:00.0000000 Thunderstorm Wind 01:00:00 87
2007-01-01 00:00:00.0000000 Hail 04:00:00 2877
2007-01-01 00:00:00.0000000 Tornado 04:00:00 231
2007-01-01 00:00:00.0000000 Thunderstorm Wind 04:00:00 141
2007-01-01 00:00:00.0000000 Hail 1.00:00:00 2877
2007-01-01 00:00:00.0000000 Tornado 1.00:00:00 244
2007-01-01 00:00:00.0000000 Thunderstorm Wind 1.00:00:00 155

Understanding the results:
The outcome is three funnels (for periods: One hour, 4 hours, and one day). For each funnel step, a number of distinct counts of are shown. You can see that the more time is given to complete the whole sequence of Hail -> Tornado -> Thunderstorm Wind, the higher dcount value is obtained. In other words, there were more occurrences of the sequence reaching the funnel step.

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