Splunk Search Processing Language (SPL), also known as the Splunk query language, is a powerful tool for analyzing and visualizing data. At its core, SPL enables users to search, manipulate, and report on machine-generated data. Among its many commands, the collect command stands out as a crucial component for efficient data management and analysis.
The collect command plays a vital role in SPL by allowing users to gather and store search results for future use. This capability is particularly useful when dealing with large datasets or when you need to perform repetitive analyses.
Understanding the collect Command
The collect command in Splunk SPL is designed to save search results to a summary index. This process, known as data collection, enables users to store specific subsets of data for quick access and analysis later. By using collect, you can significantly reduce the time and resources required for complex searches on large datasets.
Moreover, the collect command allows you to create custom summary indexes tailored to your specific needs. This flexibility makes it an invaluable tool for Splunk administrators and power users alike.
Proper Syntax
To use the collect command effectively, it’s essential to understand its syntax. The basic structure of the command is as follows:
| collect [index=<string>] [sourcetype=<string>] [addinfo=<bool>]
The only required parameter is ‘index’, but these additional parameters are some of the most common ones you will run across:
- index: Specifies the summary index where the results will be stored.
- sourcetype / host / source: Defines the sourcetype, host, or source field for the collected events. Use this to correlate your collected data to previously defined sourcetypes from your props.conf definitions, or tag events with information on how this data was created, such as source=”MyCollectionSearch.”
- addinfo: Adds metadata to the collected events:
- info_min_time / info_max_time: The time rage of the data your collect command ran over (for example, you may be running a summary of data pertaining to the past work week. Info_min_time would indicate the start of the week, say Monday at 8 am, and info_max_time would indicate the end of the week, say Friday at 5 pm.)
- info_search_time: The time the collection search was ran – this is essentially index time for the newly indexed summary data.
Understanding these parameters allows you to customize how and where your data is stored, making retrieval and analysis more efficient.
Benefits of Using the collect Command
Incorporating the collect command into your Splunk workflow offers several advantages:
- Improved Performance: By storing frequently used data in summary indexes, you can significantly reduce search times and resource usage.
- Data Preservation: The collect command allows you to preserve specific subsets of data, ensuring that important information is always available, even if the original data is no longer accessible.
- Customized Data Views: You can create tailored summary indexes that focus on specific aspects of your data, making it easier to perform specialized analyses.
Example Use Cases
Let’s explore some practical examples of how to use the collect command effectively:
Example #1: Capturing Security Events
Suppose you want to create a summary of security events for quick analysis. You could use the following SPL:
index=security sourcetype=wineventlog EventCode=4625
| stats count by src_ip, user
| collect index=security_summary sourcetype=failed_logins
This search queries the Common Information Model (CIM) for failed login attempts, counts them by source IP and user, and stores the results in a summary index called “security_summary“.
Example #2: Summarizing Network Traffic
To capture daily network traffic summaries, you might use:
index=network sourcetype=firewall
| bucket _time span=1d
| stats sum(bytes) as total_bytes by _time, src_ip, dest_ip
| collect index=network_summary sourcetype=daily_traffic_summary
This example summarizes network traffic data and stores it in a summary index, allowing for quick access to historical traffic patterns.
Example #3: Capturing Performance Metrics
To track system performance over time, you could use:
index=os sourcetype=cpu_metrics
| timechart span=1h avg(cpu_usage) as avg_cpu by host
| collect index=performance_summary sourcetype=hourly_cpu_usage
This search captures hourly CPU usage averages and stores them in a summary index, facilitating long-term performance analysis.
Using Summary Indexes
Using summary indexes in these examples offers several advantages:
- Faster query times for frequently accessed data
- Reduced load on your Splunk infrastructure
- Customization of access and retention to important subsets of data
Benefits of Summary Indexes
Expanding on this, consider the outcome of Example 1. Users can now search and create dashboards using the “security_summary” index and will find that searches execute much quicker, as the volume of data is much smaller. After all, each summary event may have been created from aggregating hundreds or thousands of events. The less information Splunk needs to sort through, the faster it can provide your results.
As an administrator, you can set up specific permissions to allow all users to access the “security_summary” index, as the data may not contain the same sensitive information that the original “security” index has. This can help to control sensitive data, while still enabling your users to access the data critical for their jobs.
Additionally, if your company only cares about the information captured in the “security_summary” index, you can modify retention times such that the “security” index only retains a month worth of data, while your summary index retains years’ worth of data! This could end up saving your company big bucks on storage costs, as it can dump the heavy logs with tons of noise and focus on retention of lean, mission critical information.
Conclusion
The collect command is a powerful tool in Splunk SPL that can significantly enhance your data analysis capabilities. By understanding and utilizing this command, you can:
- Improve search performance by storing frequently used data in summary indexes
- Preserve important data subsets for long-term analysis
- Create customized views of your data to support specific analytical needs
Mastering the collect command will undoubtedly make you a more effective Splunk user, enabling you to handle large datasets with ease and extract valuable insights more efficiently.
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.
