Splunk Search

Detect Start-of-Week & Weekly aggregations

anirbandasdeb
Path Finder

Hello splunkers,

We have to calculate some KPIs, on time-series data, aggregated by multiple factors, with time being the most important one.
The most common time aggregations for us are monthly and weekly.

Scenario: Calculate the weekly trend of a KPI for the month of May 2018 [ref image attached]

alt text

How one would normally do it would be [assume week start on Sunday]:
Week Date
1: 1 - 5
2: 6 - 12
3: 13 - 19
4: 20 - 26
5: 27 - 31

Thus there will be 5 data points.

How do I implement this in Splunk?

The way I figured out to do it now is use "bin span=1w _time", but it does not detect the week start.
However, this approach plainly takes 7 days from the 1st of the month and the result is that we have skewed weeks leading to misleading KPI values.

Tags (1)
0 Karma

anirbandasdeb
Path Finder

One way I figured out how to do this is using:

| eval Week = strftime(strptime(_time, "%Y-%m-%d %H:%M:%S.%N"), "%V")

strptime converts the _time [formatted in "%Y-%m-%d %H:%M:%S.%N"] to Unix epoch time. Then strftime extracts the week of year from the epoch time using "%V"

The variable %V is not mentioned in the documentation.

However, how do I declare custom weeks, if the business requirements are as such?

0 Karma
Got questions? Get answers!

Join the Splunk Community Slack to learn, troubleshoot, and make connections with fellow Splunk practitioners in real time!

Meet up IRL or virtually!

Join Splunk User Groups to connect and learn in-person by region or remotely by topic or industry.

Get Updates on the Splunk Community!

Event Series: The Agentic SOC: Trust Before Autonomy

AI is fundamentally changing security operations, but true progress requires more than just automation—it ...

Free Professional Services for .conf26 Attendees

This year at .conf26, we are doing something a little different. We are bringing the best minds from ...

Defend at Machine Speed: Your Guide to Security Sessions at .conf26

Splunk .conf26   With threats moving at machine speed and attack surfaces expanding across hybrid ...