This folder contains notes, scripts, and resources related to performance testing, load generation, and performnace analysis.
- A toolkit for CPU load testing using Rust.
- Generates and measures CPU-intensive multi-threaded workloads.
- Designed for evaluating CPU scalability and performance across systems.
- A Python-based toolkit for CPU load testing.
- A benchmarking kit for running TPC-DS benchmark queries with Apache Spark.
- Key features:
- Written in Python (PySpark).
- Instrumented with sparkMeasure to collect detailed performance metrics.
- Suitable for testing the scalability and efficiency of Spark clusters.
- PyLatencyMap is a terminal-based visualizer for latency histograms.
- It’s intended to help with performance tuning and troubleshooting.
- Works from the command line and plays nicely with sources that output latency histograms (Oracle wait histograms, BPF/bcc, DTrace, SystemTap, tracefiles, etc.).
- A toolkit for generating CPU- and memory-intensive workloads using Apache Spark.
- Key features:
- Runs parallel workloads to test Spark cluster performance.
- Useful for stress-testing distributed systems under heavy CPU and memory usage.
| Topic | Description |
|---|---|
| Load testing for Oracle | Example of load testing Oracle logical IO using the SLOB test kit |
| Tools: Linux memory performance measurement | Notes on tools for Linux memory performance measurement |
| Tools: Systems performance measurements | Notes on tools for Linux OS, CPU, Disk and Network performance measurement |
| Tools: Flame Graphs | Notes on tools for Flame Graphs generation (C, Java, Python, Linux) |