The Splunk Search Processing Language (SPL) grants us the ability to make calculated conclusions about our data in seconds that would otherwise take hours. One of the commands which helps with these rapid conclusions is fieldsummary. It generates comprehensive statistical information about the fields within your search. In this blog post, we explore the fieldsummary command, its capabilities, and the practical applications that will enhance your data analysis in Splunk.
Understanding the fieldsummary Command
The fieldsummary command analyzes your search results and provides detailed statistics about each field. This includes the number of events containing the field, distinct values, and frequency distributions. You can use the output for initial data analysis, troubleshooting data quality issues, or planning transformations. This command provides excellent insight through providing a wide range of statistical values.
Syntax of the fieldsummary Command
The basic syntax is straightforward:
| fieldsummary [maxvals=<unsigned_int] <field-list>
- Fieldsummary – This will return a table with the field name, a set of statistical calculations like count, minimum, and maximum as well as a list of the different values listed in the order from what values appear most at the beginning and the rarest at the end.
- Maxvals – This will limit the number of values returned, limiting it to the most occurring within that field. By default, this value is set to 100.
- Fieldlist – Name the field you want to generate a table for. You can also use wildcards to return various fields at one time. For example, if you want to return all fields that start with “ip” you would enter “ip*”.
Example Use Cases
Example #1: Basic Field Analysis
In this case, we want to generate a table of all the fields within the web_access index:
index=web_access
| fieldsummary
Since we do not specify anything with the fieldsummary command, it will return every field in the table. This is great to get an overview of statistics across the entire index. This table would help us get a start in our trek into data analysis for this index.
Example #2: Focus Analysis with Parameters
What if we wanted to study the most occurring events within our network_traffic index? We could use the following:
index=network_traffic
| fieldsummary maxvals=5 src_ip dest_ip status message
This query focuses on specific statistics for network traffic data, limiting to the top 5 distinct values per field. Despite limiting the number of values returned, our calculations will still apply to all values in the given fields. So even if there are more than 5 values, they will still contribute to the value of calculations like count and distinct_count.
Example #3: Combining with Other Commands
We have a backup server which is written to with changes every day. We must make sure this server does not run out of storage space. Every week we can schedule a search to run; writing a report on how much data is being written versus phased out.
index=data_storage sourcetype=backup_storage
| fieldsummary bytes_in bytes_out
| table fields max min mean
With a search like this, we can see the maximum, minimum, and mean of data being written and phased out over the same period. This table would be an excellent example of a report we can keep; tracking the rate at which our backup storage is being used.
Common Applications
- Data Quality Assessment – We can assess the quality of the data being ingested by the number of events coming in, the unique number of events, and the standard deviation of those events. This would let us identify unexpected patterns, validate consistency, and identify missing or incomplete fields.
- Data Exploration – We are able to understand how our data is distributed across different fields and values. You can discover fields relationships and identify potential correlations.
- Performance Optimization – We should be able to understand how fields and values affect the performance of our environment but also our ability to draw conclusions from our data. With this command we can identify field values unnecessary for Splunk and reduce our ingestion. This information also gives us insight into fields we could use in summary indexing. Both of these would help us optimize our search strategies and performance.
Best Practices
- Performance Considerations:
- Run searches restricted by time as much as possible
- Use maxvals to limit output size and table malformation
- Consider a sample data search for initial analysis
- Analysis Tips:
- Start with a broad search, then narrow focus
- Compare summaries across different time ranges
- Use in conjunction with other statistical commands
- Reporting Guidelines:
- Export summaries for documentation
- Track field changes over time
- Document unexpected patterns
Conclusion
By mastering fieldsummary, you’ll be better equipped to understand your data’s structure, identify patterns and anomalies, and make informed decisions about data handling and analysis strategies. Its ability to provide detailed statistical information makes it invaluable for:
- Initial data exploration and quality assessment
- Ongoing monitoring and validation
- Performance optimization and capacity planning
Start incorporating fieldsummary into your Splunk workflows today to gain deeper insights into your data and enhance your analysis capabilities!
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.
