GCGC uses the Jupyter Notebook interface to profile GC (Garbage Collection) log file.

There are 17 graphs that can be generated to analyze latency, concurrent and STW events, heap information, allocation rates, event frequency, and event summaries, comparing any number of log files and external data sources. The tool uses Jupyter notebook data visualization, and the provided charts can be easily customized.

Analysis is built into the provided Jupyter notebook and generates graphs and tables from the collected GC information. The data collected for each log is parsed as a python pandas “event log”.

Then, using the event log as a persistent database, event information can be sorted, filtered, and grouped in preset and customizable ways to reveal relevant trends and outliers.

Collectors in JDK11 and JDK 16 are currently supported.

• Python3
• The following Python3 packages
  • numpy
  • pandas
  • matplotlib
  • Jupyter notebook

Installation instructions are here:docs/setup.md

according to docs/how-to-run.md Instructions in the operation.