Insights on troubleshooting scripted inputs causing high CPU

Applicability

Description

Splunk Instance consistently running at close to 100% CPU utilization due to scipted inputs

Investigation

Scripted inputs are causing high CPU usage on your Splunk instance, it’s important to carefully analyze and optimize these scripts to mitigate the impact on system performance. Here are some steps you can take to troubleshoot and resolve the issue:

Steps to Diagnose and Optimize

1. Identify Resource-Intensive Scripts:

Check Process List: Use commands like `top` or `htop` to identify which specific scripts or processes are consuming high CPU.

Splunk Internal Logs: Review $SPLUNK_HOME/var/log/splunk/splunkd.log for any errors or warnings related to scripted inputs.

2. Script Efficiency:

Optimize Code: Review the scripts for any inefficiencies or bottlenecks. Efficient coding practices include minimizing loops, using effective data structures, and avoiding unnecessary operations.

Reduce Frequency: If possible, reduce how frequently the scripted inputs run. This can often be modified in the script’s configuration file by setting a higher interval.

Use Pagination/Batch Processing: For tasks involving large data, consider processing in smaller batches or implementing pagination to reduce CPU load.

3. Resource Limits:

Set Resource Limits: Restrict the CPU and memory usage of individual scripts using tools like `cgroups` or `ulimit`. This prevents any single script from using excessive resources.

4. Splunk Configuration:

Balances Input Intervals: Adjust the `interval` parameter in the inputs.conf file to increase the interval between script runs.

Throttling: Implement throttling controls if the script can handle data in smaller, controlled bursts.

5. Monitoring and Alerts:

Implement Monitoring: Set up monitoring to track CPU usage over time and trigger alerts when CPU usage exceeds a certain threshold. Splunk itself can be used to monitor these metrics.

Automated Alerts: Generate alerts based on resource usage patterns to proactively manage and troubleshoot high resource consumption before it impacts overall performance.

Example: Modifying inputs.conf

Edit the inputs.conf file to adjust the interval and other configurations for the scripted input:

[script:///path/to/your/script.sh]interval = 300 # Run the script every 300 seconds (or 5 minutes)disabled = 0 # Ensure the script is enabled

Example: Limiting CPU Usage with `ulimit`

You can add a `ulimit` command at the beginning of your script to limit the CPU usage:

#!/bin/shulimit -t 600 # Set CPU time limit to 600 seconds (10 minutes)# The rest of your script here

Monitoring Script Performance with `time`

Use the `time` command to monitor how long the script takes to execute:

time /path/to/your/script.sh

This helps identify any portions of the script that may be causing delays or high resource usage.

Splunk-Specific Optimization

If the issue persists, consider leveraging Splunk-specific optimizations:

Distributed Environment: Offload heavy processing to more powerful indexers or use a distributed search environment.

Splunk Add-Ons: Utilize Splunk-supported add-ons for data collection instead of custom scripts when possible, as they are often optimized for performance.

By systematically addressing each of these areas, you can identify the root cause of the high CPU usage and implement effective solutions to optimize the performance of your scripted inputs.

Resolution

For scripted inputs, both the run interval and the script’s activity are important factors. If a script runs every hour and only takes a few seconds to complete, it typically does not cause resource issues. However, if a script runs every minute and consumes significant CPU resources, this can create performance problems (using the top command on Linux can help identify high-usage processes). In such cases, consider increasing the interval between runs and optimizing the script or code to reduce CPU usage.

If a script’s runtime approaches or exceeds its scheduled interval set in inputs.conf, it is recommend running the script manually (Adhoc run) to evaluate its efficiency before deploying it on a schedule.