Getting Data In

Does anyone have experience with how indexing latency affects accelerated data models and how to handle this?

romedome
Path Finder

Has anyone had any experience on how indexing lag affects accelerated data models and ways to mitigate the issue?

Thanks!

0 Karma
1 Solution

woodcock
Esteemed Legend

It is all handled automatically in a lossless way (unlike Summary Indices). So your only concern, is to not search for (rely on) data that has happened more recently than the max duration of your input + indexer pipeline latency.

View solution in original post

woodcock
Esteemed Legend

It is all handled automatically in a lossless way (unlike Summary Indices). So your only concern, is to not search for (rely on) data that has happened more recently than the max duration of your input + indexer pipeline latency.

romedome
Path Finder

That's reassuring. Is there any documentation that shows how this "losslessness" works?

Thanks!

0 Karma

woodcock
Esteemed Legend

It is because all of the magic happens WHENEVER the event is indexed as PART OF the indexing process. Therefore, it is impossible for the acceleration to be missed, even if event/pipeline latency is very large.

https://helgeklein.com/blog/2015/10/splunk-accelerated-data-models-part-1/
http://docs.splunk.com/Documentation/Splunk/6.4.1/Knowledge/Acceleratedatamodels

romedome
Path Finder

Cool blog post! Thanks!

0 Karma
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