IO pattern visualization tool.

Purpose

Visualizing the IO-related data via the Heat Map. What makes it unique is the additional info about the process that performs the IO operations.

Heatmap example

Usage

$ pip install plotly 
$ python3 ./biosnoop-statistics-visualization.py <path_to_bcc_tools> <amount_of_metrics_to_collect>

• <path_to_bcc_tools> - path to the valid BCC tools file. The implementation has been tested using BIOSNOOP;
• <amount_of_metrics_to_collect> - amount of metrics to collection from the script launched from <path_to_bcc_tools>;

3.2 Available options

To get actual list of all options run: ./utils/pks_manager.py -h

Option	Short flag	Params	Description
--help	-h		Get available options.
--filepath	-f	[FILEPATH] [KIND]	Visualize data from given file. [FILEPATH] - Path to file, [KIND] - string description of the data.
--execute	-e	[PATH] [AMOUNT] [KIND}	Visualize data based on the captured output of iosnoop. [PATH] - path to IOSNOOP executable, [AMOUNT] - amount of logs to process, [KIND] - string description of the data.
--load	-l	[FOLDERPATH]	Visualize data from every file within given folder. [FOLDERPATH] - path to folder that contains files with logs.

Note: It is recommended use a test-id for the name of the new cluster, which can be generated with ./test.py -id command.

After that, the heatmap will be opened within your browser.