Big Data Management User Guide

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GPS Pings Example

Your organization receives GPS pings from vehicles that include trip and event IDs and a time stamp. You want to calculate the time difference between each ping and flag the row as skipped if the time difference with the previous row is less than 60 seconds.

You order the events chronologically and partition the events by trip. You define a window function that accesses the event time from the previous row, and you use an ADD_TO_DATE function to calculate the time difference between the two events.

Windowing Properties

You define the following windowing properties on the Windowing tab:

Property	Description
Order key	_event_id Ascending . Arranges the data chronologically by ascending event ID.
Partition key	trip_id . Groups the rows according to trip ID so calculations are based on events from the same trip.
Frame	Not specified. Window functions access rows based on the offset argument and ignore the frame specification.

On the Windowing tab, _event_id is selected as the order key and trip_id is selected as the partition key. The frame specification is not defined.

Window Function

You define the following LAG function to get the event time from the previous row:

LAG ( _event_time, 1, NULL )

Where:

_event_time
is the column name whose value from the previous row is to be returned.

1
is the offset. This value represents the row immediately before the current row.

NULL
is the default value. The function returns NULL if the return value is outside the bounds of the partition.

You define the following DATE_DIFF function to calculate the length of time between the two dates:

DATE_DIFF ( _event_time, LAG ( _event_time, 1, NULL ), 'ss' )

You flag the row as skipped if the DATE_DIFF is less than 60 seconds, or if the _event_time is NULL:

IIF ( DATE_DIFF < 60 or ISNULL ( _event_time ), 'Skip', 'Valid' )

The LAG function, the DATE_DIFF function, and the IIF flag are defined on the Ports tab in the Expression transformation.

Output

The transformation produces the following outputs:

Trip ID	Event ID	Event Time	Time Difference	Flag
101	1	2017-05-03 12:00:00	NULL*	Skip
101	2	2017-05-03 12:00:34	34	Skip
101	3	2017-05-03 12:02:00	86	Valid
101	4	2017-05-03 12:02:23	23	Skip
102	1	2017-05-03 12:00:00	NULL*	Skip
102	2	2017-05-03 12:01:56	116	Valid
102	3	2017-05-03 12:02:00	4	Skip
102	4	2017-05-03 13:00:00	3480	Valid
103	1	2017-05-03 12:00:00	NULL*	Skip
103	2	2017-05-03 12:00:12	12	Skip
103	3	2017-05-03 12:01:12	60	Valid

*The rows preceding these rows are outside the bounds of the partition, so the transformation produced NULL values.

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Table of Contents

Big Data Management User Guide

Big Data Management User Guide

GPS Pings Example

GPS Pings Example

Windowing Properties

Window Function

Output