benchmark-resource-usage

You can benchmark CPU and memory usage by running Python scripts that utilize `/usr/bin/time -l` to measure detailed resource metrics during process discovery and file monitoring loops at configurable polling frequencies.

Evaluating targeted vs full scan resource consumption involves benchmarking both optimization strategies at different polling frequencies to identify bottlenecks and determine which approach minimizes CPU, memory, and disk I/O costs.

Polling frequency directly impacts agent resource consumption by scaling the rate of process discovery and log parsing operations, which you can measure precisely using configurable benchmarking scripts to find the optimal balance between performance and overhead.

Yes, you can use the resource benchmarking data for capacity planning, as it provides data-driven insights into how much CPU, memory, and disk I/O your monitoring loops consume to guide infrastructure scaling decisions.

No external dependencies are required to measure disk I/O, as the benchmarking execution relies on built-in Python scripts and the standard `/usr/bin/time -l` utility to capture detailed resource metrics without additional packages.