Getting Data In

Splunk CSV comma handling != Excel comma handling

virtualpony
Path Finder

I have a CSV file that lines up fields perfectly in Excel, but when Splunk parses that same CSV data, it gets tripped up when it gets an event with an extra comma in it, even if each field is encapsulated with "quotes" like this:

"5/1/2012 12:15:43 AM","GeneralHostWarningEvent","warning","","USLAB1","Compute HCS","Issue detected on uslab1esxi04, reboot host."

See in the last field, there is a comma before reboot host. This is where Splunk ends that field and then events after this get misaligned.

Is this a bug that should be fixed, or am I going to need to backfill all my data with a force dimlimiter of some other character other than a comma?

Thanks

Tags (1)

Stefan_van_de_R
Explorer

Your CSV file should work if you put the next line in your stanza in transforms.conf

[Your_stanza]
FIELDS="Time","EventType","Priority","somthing","location","client","description"
DELIMS=","

[Edit]
I did some more research and found out that multiple characters to split the CSV fields is not possible. You can use an regular expression instead to split your fields.
[/Edit]

0 Karma

virtualpony
Path Finder

Thats what my stanza currently looks like.

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!

Get Agentic with Splunk Lantern: Connect to Cisco Cloud Control, Transform ...

Splunk Lantern is Splunk’s customer success center that provides practical guidance from Splunk experts on key ...

July Community Events: Master ITSI 5.0 & Automate Splunk

Struggling with alert fatigue or feeling like you're spending more time on infrastructure maintenance than ...

New Release of Federated Search: Bringing Splunk Analytics to More of Your Data

Organizations today are generating more data than ever and storing it across cloud object stores, data lakes, ...