Splunk’s Search Processing Language (SPL) offers powerful tools for data analysis, and the contingency command stands out as a specialized statistical function. This command creates contingency tables used to reveal relationships between two categorical fields in your data. This visualization helps analysts quickly identify correlations, dependencies, and unexpected patterns that might otherwise remain hidden in raw data. It can transform how to see the connection between values in our data.
Understanding the multisearch Command
The basic structure follows this pattern (brackets depict optional arguments):
| contingency [maxrows=<int>
| maxcols=<int>] [mincolcover=<num> | minrowcover=<num>] [usetotal=<bool>] [totalstr=<field>] <column-field> <row-field>
- maxrows=<int> | maxcols=<int> – You can set a limit on the number of rows or columns returned in the search. The default limit is 1000.
- mincolcover=<num> | minrowcover=<num> – Like maxrows, you can set a limit to a number of rows or columns, but you use it based on a percentage ratio of the total number of columns. By default, this is set to 1.0 which is equivalent to 100%.
- usetotal=<bool> – By default set to true, you can opt out of including a total field by setting to false.
- totalstr=<field> – You can change the Total field name to a custom one.
- <column-field> – This will be your field values in a column along the left side of the table.
- <row-field> – This will be your field values in the header of the table.
Splunk then calculates the frequency of each combination, presenting results in an easy-to-read matrix format.
Benefits of the contingency Command
Consider three common scenarios where contingency analysis proves invaluable:
Security Analysis: Correlate user authentication attempts with success rates across different geographic locations. This helps identify suspicious login patterns or compromised accounts by revealing unusual combinations of user identities and access points.
Application Performance: Examine relationships between HTTP response codes and specific application endpoints. By mapping status codes against URLs, you can quickly pinpoint which services experience the most errors and what types of failures occur most frequently.
Infrastructure Monitoring: Analyze the connection between server hostnames and error message types. This contingency view reveals whether specific infrastructure components consistently generate errors, guiding targeted remediation efforts.
Usage Examples & Practical Applications
Example #1: Web Traffic Analysis
index=web_logs sourcetype=access_combined
| contingency method status
This search examines the relationship between HTTP methods (GET, POST, PUT, DELETE) and response status codes. The resulting table helps identify whether specific HTTP methods experience higher failure rates, which can indicate API vulnerabilities or client implementation issues.
Example #2: Security Event Correlation
index=security sourcetype=authentication
| contingency action user_type
| where count > 10
By correlating authentication actions (success, failure, locked) with user types (admin, standard, service), security analysts can spot privilege escalation attempts or compromised service accounts. The where command filters out noise by showing only significant patterns.
Example #3: System Performance Diagnostics
index=infrastructure sourcetype=syslog severity>=3
| contingency host error_category
This search maps critical system events across your infrastructure, showing which hosts generate specific error categories most frequently. The severity filter focuses on important events, while sorting highlights the most problematic combinations first.
Best Practice with Contingency
Start by identifying categorical fields with low cardinality. This is typically fields with fewer than 20 unique values per field. Too many unique values create unwieldy tables that obscure patterns rather than revealing them. When working with high-cardinality fields like IP addresses, you should filter down to those you only want to see. If you want to preserve the scope of the search, use the eval command to bucket them in groups.
Combine contingency analysis with time-based searches to track how relationships evolve. Running the same contingency command across different time windows reveals whether correlations strengthen, weaken, or shift entirely. These insights are crucial for understanding system behavior changes.
Conclusion
The contingency command reveals hidden relationships between categorical variables. This empowers analysts to make data-driven decisions quickly. Whether you’re investigating security incidents, troubleshooting application issues, or optimizing infrastructure performance, mastering this command adds a powerful tool to your Splunk toolkit. With practice, contingency analysis becomes an instinctive step in your investigation workflow, consistently uncovering insights that drive meaningful improvements across your environment.
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.
