How to Optimize Search Performance in Splunk for Large Datasets?

[emmanuelkatto23]

Hi everyone,

My name is Emmanuel Katto. I’m currently working on a project where I need to analyze large datasets in Splunk, and I've noticed that the search performance tends to degrade as the dataset size increases. I'm looking for best practices or tips on how to optimize search performance in Splunk.

• What are the recommended indexing strategies for managing large volumes of data efficiently?

• Are there particular search query optimizations I should consider to speed up the execution time, especially with complex queries?

• How can I effectively utilize data models to improve performance in my searches?

I appreciate any insights or experiences you can share.

Thank you in advance for your help!

Best,

Emmanuel Katto

[inventsekar]

Hi @emmanuelkatto23 

>>> Are there particular search query optimizations I should consider to speed up the execution time, especially with complex queries?

On the DMC Distributed Management Console, you can find many dashboard(s)/Panels... using these  you can find which Searches took long time to run, which searches took more resources, etc.

There are many other considerations like avoiding the join's etc. 

1) Pls suggest us which Splunk Apps you use,
2) which Splunk things(user searches, reports or alerts or dashboards) are taking high Splunk performance..  then you can start fine-tuning one by one, thanks. 

[PickleRick]

Oh, mate...

You're trying to tackle several years of experience with a quick forum post.

Optimizing searches (just like any programming optimizations) is partly science, partly art.

You need to understand how Splunk works - how it breaks an event into single terms, how it stores those terms in indexes, how it searches for data, especially in distributed architecture, know the different command types and understand how they impact your search processing, undertand what the datamodels are and what they aren't (and what accelerated datamodel means; datamodel acceleration is not the same as datamodel itself), how accelerations work.

It's not straightforward but it's not impossible of course.

One thing - datamodel on its own doesn't accelerate searching - it can accelerate writing searches because the datamodel definition separates your high-level search from actual low-level details of your data. You don't have to - for example - care whether your firewall produces logs with a source IP field called src_ip, src, source_ip or whatever the developers wanted. If your logs were made CIM-compliant by relevant add-on, you can just search from Network_Traffic datamodel using the src_ip field. And that's it. Datamodel on its own doesn't give you more than that.

But if you enable datamodel acceleration, Splunk periodically searches through your data covered by particular datamodel and builds a pre-indexed summary which you can search faster than raw data from underlying datamodel.

[gcusello]

Hi @emmanuelkatto23 ,

when you have a large amount of datasets, my hint is to use an accelerated Data Model (https://docs.splunk.com/Documentation/SplunkCloud/latest/Knowledge/Acceleratedatamodels ) or a summary index (https://docs.splunk.com/Documentation/SplunkCloud/9.2.2406/Knowledge/Aboutsummaryindexing ) or a report acceleration (https://docs.splunk.com/Documentation/SplunkCloud/9.2.2406/Knowledge/Manageacceleratedsearchsummarie... ).

Obviously the first hint is to exactly define the time range to use in your searches avoiding large time ranges and delimitating them to what you need for your use case.

Then (always obvious), you need a very well performant storage: Splunk requires at least 800 IOPS, but if you can use SSD disks (with more tham 10,000 IOPS) at least for the Hot and Warm buckets you'll have more performant searches.

Ciao.

Giuseppe