Introduction: SPL & the Value of multisearch
Splunk’s Search Processing Language (SPL) enables detailed investigations across large datasets. It allows users to extract insights, detect anomalies, and support operational decisions. The multisearch command is part of this language and offers a powerful way to merge several searches into one result set.
This command becomes important when analysts need to compare activity patterns, correlate different CIM-aligned datasets, or test multiple hypotheses quickly. It combines independent searches without forcing users to stitch results manually. Therefore, the command simplifies investigations and improves search efficiency during daily Splunk operations.
Understanding the multisearch Command
The multisearch command allows multiple search pipelines to run in parallel and return a unified table of events. Each pipeline operates independently, which means field extraction and logic remain separate until the results merge. As a result, complex analysis becomes easier because the command handles the consolidation step.
Although multisearch behaves similarly to a UNION operation in traditional SQL, it also works smoothly with SPL’s event-driven nature. Additionally, the command offers predictable behavior because the structure of each subsearch is preserved in the final output. This allows analysts to rely on consistent fields when analyzing merged data.
Benefits of the multisearch Command
The multisearch command provides meaningful advantages in daily Splunk usage:
- Efficient comparison of datasets: It merges outcomes from independent searches, which helps users review patterns side by side.
- Flexible correlation opportunities: It lets analysts compare data sources without creating temporary lookups or summary indexes.
- Cleaner search pipelines: It reduces repeated logic, so teams maintain fewer queries and simplify long-term search management.
Basic Syntax
The basic syntax for the command is simple and readable. Each search is placed within its own brackets, and all searches are joined by the multisearch keyword:
| multisearch
[ search <query A> ]
[ search <query B> ]
[ search <query C> ]
Each block must contain a complete search. The command then returns the combined results as one dataset.
Usage Examples & Practical Applications
Example #1: Comparing Authentication Failures & Successful Logins (CIM: Authentication Data Model)
This use case helps analysts understand whether spikes in failed authentication attempts align with successful logins. The multisearch command provides a unified view that reveals potential attack patterns or brute-force attempts.
| multisearch
[ tstats summariesonly=true count from datamodel=Authentication.Authentication
where Authentication.action="failure"
by Authentication.user ]
[ tstats summariesonly=true count from datamodel=Authentication.Authentication
where Authentication.action="success"
by Authentication.user ]
| rename Authentication.user AS user
| table user count
Example #2: Reviewing Web Errors vs Web Traffic Volume (CIM: Web Data Model)
This example helps teams evaluate whether increased traffic correlates with user-facing errors. Because the data originates from two pipelines, multisearch provides an easy way to analyze them together.
| multisearch
[ tstats count from datamodel=Web.Web
where Web.status>=500
by Web.src ]
[ tstats count from datamodel=Web.Web
where Web.status<400
by Web.src ]
| rename Web.src AS src
| table src count
Example #3: Tracking Endpoint Malware Detections Alongside Process Activity (CIM: Endpoint Data Model)
Sometimes analysts need to know whether malware detections align with unusual process execution. Multisearch allows both perspectives in a single dataset.
| multisearch
[ tstats count from datamodel=Endpoint.Filesystem
where Filesystem.signature!=""
by Filesystem.dest ]
[ tstats count from datamodel=Endpoint.Processes
where Processes.process_name="powershell.exe"
by Processes.dest ]
| rename Filesystem.dest AS host Processes.dest AS host
| table host count
Conclusion
The multisearch command plays a valuable role in analytical workflows because it consolidates independent searches into one dataset. Its flexible structure supports CIM-aligned investigations and keeps search logic clean. Splunk users benefit from faster exploration and clearer correlations during everyday operations.
Key Takeaways
- It merges results from multiple searches into one dataset.
- It helps analysts compare independent CIM datasets efficiently.
- It simplifies investigations by reducing repeated search logic.
To access more Splunk searches, check out Atlas Search Library, which is part of the Atlas Platform. Specifically, Atlas Search Library offers a curated list of optimized searches. These searches empower Splunk users without requiring SPL knowledge. Furthermore, you can create, customize, and maintain your own search library. By doing so, you ensure your users get the most from using Splunk.
