Getting Data In

Transaction Command: Determine Outliers/Mismatches Only

bcarr12
Path Finder

I am using the transaction command in Splunk to group the events of an identical log file across two hosts. Essentially, the field=value pairs across both hosts should be identical at all times. From time to time, issues can issue that cause the two hosts to become out of sync. I'd like to have a search that only identifies transactions where the field=value pairs do not match exactly. What would be the best way to accomplish this?

For instance, using the search below groups the log files from multiple hosts into a single transaction by second.
"searchterm" source="mylog.log" | transaction field maxspan=1s

I want to only return events with the below pattern (mismatches)
2020-01-10 17:30:00,348 INFO field=true
2020-01-10 17:30:00,351 INFO field=false

But ignore events with this pattern (identical)
2020-01-10 17:30:00,348 INFO field=true
2020-01-10 17:30:00,351 INFO field=true

Or this pattern (identical)
2020-01-10 17:30:00,348 INFO field=false
2020-01-10 17:30:00,351 INFO field=false

0 Karma

to4kawa
Ultra Champion
"searchterm" source="mylog.log" 
| streamstats time_window=1s dc(field) as flag
| where flag >1

how about this?

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, ...