Version: 0.7.0

windowFunnel

Similar to windowFunnel in ClickHouse (they were created by the same author), it searches for event chains in a sliding time window and calculates the maximum number of events that occurred from the chain.

The function works according to the algorithm:

The function searches for data that triggers the first condition in the chain and sets the event counter to 1. This is the moment when the sliding window starts.
If events from the chain occur sequentially within the window, the counter is incremented. If the sequence of events is disrupted, the counter isn’t incremented.
If the data has multiple event chains at varying points of completion, the function will only output the size of the longest chain.

windowFunnel(window)(timestamp, cond1, cond2, ..., condN)

Arguments

timestamp — Name of the column containing the timestamp. Data types supported: unsigned integer types.
cond — Conditions or data describing the chain of events. Must be Boolean datatype.

Parameters

window — Length of the sliding window, it is the time interval between the first and the last condition. The unit of window depends on the timestamp itself and varies. Determined using the expression timestamp of cond1 <= timestamp of cond2 <= ... <= timestamp of condN <= timestamp of cond1 + window.

Returned value

The maximum number of consecutive triggered conditions from the chain within the sliding time window. All the chains in the selection are analyzed.

Type: UInt8.

Example

Determine if a set period of time is enough for the user to select a phone and purchase it twice in the online store.

Set the following chain of events:

The user logged in to their account on the store (eventID = 1003).
The user searches for a phone (eventID = 1007, product = 'phone').
The user placed an order (eventID = 1009).
The user made the order again (eventID = 1010).

Input table:

┌─user_id─┬─timestamp─┬─eventID─┬─product─┐
│       1 │        20 │    1003 │ phone   │
│       1 │       200 │    1007 │ phone   │
│       1 │        50 │    1009 │ phone   │
│       1 │       400 │    1010 │ phone   │
└─────────┴───────────┴─────────┴─────────┘

Find out how far the user user_id could get through the chain.

Query:

SELECT
    level,
    count() AS c
FROM
(
    SELECT
        user_id,
        windowFunnel(500)(timestamp, eventID = 1003, eventID = 1009, eventID = 1007, eventID = 1010) AS level
    FROM test
    GROUP BY user_id
)
GROUP BY level ORDER BY level ASC;

Result:

┌─level─┬─c─┐
│     4 │ 1 │
└───────┴───┘

windowFunnel​

windowFunnel