Splunk Processing Language (SPL) is the core language used in Splunk to search, transform, and analyze data. It allows users to unlock insights from machine data quickly. SPL is composed of commands, functions, and clauses that work together to extract meaning from raw logs.
Among these commands, mvcombine plays a critical role in data transformation. It is particularly useful when consolidating multivalue fields, which often occur when data is grouped or aggregated. For example, it can combine multiple usernames associated with a single session ID into one field. This ability to merge field values simplifies analysis and makes results easier to read.
Understanding the mvcombine Command
The mvcombine command is used to merge field values across events that share a common value. The output is a single event per grouped field, with the target field showing a multivalue list.
Benefits of Using mvcombine
- Data Simplification: mvcombine merges values into one field, reducing visual clutter in results and improving clarity.
- Improved Correlation: When multiple actions share an identifier, mvcombine helps correlate related data in a single event.
- Enhanced Reporting: Reports become more user-friendly, especially when dealing with alerting or summary dashboards.
Basic Syntax
The syntax for using mvcombine is simple:
... | mvcombine [field]
... | sort session_id
| mvcombine user
| fields session_id, user
Example Use Cases
Example #1: Combine Users by Session ID
Use Case:You want to combine user values across multiple events that share the same session ID, IP address, and action.
index=authentication sourcetype=access_combined
| fields session_id ip action user
| sort session_id ip action
| mvcombine user
Explanation: This search begins by narrowing down the dataset to only the essential fields—session_id, ip, action, and user—to remove any extraneous data that could interfere with combining events. Sorting the results by these same fields ensures that events sharing identical values in session_id, ip, and action are placed adjacent to one another, which is important for the performance and effectiveness of the mvcombine command. Once sorted, mvcombine merges those events where everything matches except the user field, resulting in a single event where user is displayed as a multivalue list. This method preserves all other event details while consolidating repeated user entries, making it a better fit than using stats when full event context needs to be maintained.
Example #2: Identify Spam Senders Per Workstation
Use Case: You want to identify all unique sender email addresses that have delivered spam to a specific workstation.
index=email_logs sourcetype=mail_activity spam_flag=true
| fields workstation sender contents
| sort workstation contents
| mvcombine sender
Explanation: This search focuses on identifying patterns in spam activity by isolating the workstation, sender, and contents fields from email logs where spam was flagged. Sorting the results by workstation and contents ensures that events involving the same spam message and destination machine are grouped together. The mvcombine command is then applied to the sender field to merge events that share the same workstation and contents, but originate from different senders. The result is a single event per spam message per workstation, with a multivalue sender field showing all the email addresses responsible. This helps identify coordinated spam campaigns while preserving contextual details of each message.
Example #3: Correlating Destination IPs Using CIM without Aggregation
Use Case: You want to correlate multiple destination IPs tied to allowed traffic from the same source IP, preserving event-level detail.
| datamodel Network_Traffic All_Traffic search
| fields All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.action
| search All_Traffic.action="allowed"
| sort All_Traffic.src_ip All_Traffic.action
| mvcombine All_Traffic.dest_ip
Explanation: This search retrieves raw events from the Network Traffic data model, specifically focusing on those marked as “allowed”. The fields command limits the dataset to just the source IP, destination IP, and action, helping reduce noise and make the mvcombine operation more precise. Sorting ensures that events with the same source IP and action are adjacent. Then, mvcombine is applied to the destination IP field, merging all events that share the same src_ip and action but differ in dest_ip. The result is a single event per source IP and action combination, with dest_ip represented as a multivalue field. This method is useful for threat hunting or auditing outbound connections, especially when you want to maintain the context of the original events without aggregating them.
Conclusion
The mvcombine command is a powerful utility for organizing and summarizing Splunk results. It is especially useful when analyzing large volumes of grouped data.
- Simplifies results by consolidating values
- Enhances reports and dashboards with readable summaries
Mastering mvcombine allows you to build cleaner, more effective Splunk queries and dashboards.
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.
