This project was designed as a hands-on learning exercise to explore the Rust programming language through a performance focused lens. It serves as my first structured Rust project.
The objective is to compare function-level and end-to-end runtime performance between Rust and Python. The pipeline involves generating large synthetic datasets, processing them with Polars, timing the operations, and saving the results to disk.
- Benchmark results available in:
results/*.txt
- Rust
1.85.1(rustc) - Cargo (build and dependency manager)
- Python
3.12.7 - Polars
- Ownership & Borrowing
- Scope & Data Flow
- Traits, Generics, & Type Inference
- Threading & Parallelism
- Thread Tuning & Join Handles
- Error Propagation & Handling
- Formatting & File I/O
- Benchmarking & Runtime Analysis
Each language runs:
- 10 iterations (runs)
- Each iteration generates 1 million rows
- Timings include:
- Data creation
- Data processing
- Total program runtime
Rust uses a thread-per-run strategy with Polars parallelism tuned via POLARS_MAX_THREADS.
For questions, feedback, or collaboration, feel free to reach out.
Email: [email protected]