Cross-Language Performance Analysis

Comprehensive performance comparison between Rust Veloxx (native) and Python Pandas implementations across identical operations and datasets.

🎯 Executive Summary

Rust Veloxx excels in DataFrame creation and small data operations, showing up to 8.8x faster performance in DataFrame creation tasks.

Key Findings:

• Rust wins: 5 out of 15 operations
• Python wins: 10 out of 15 operations
• Average performance difference: 2.0x
• Maximum speedup: 8.8x (Rust DataFrame creation)

🔧 System Configuration

• Platform: Windows 11
• Python: 3.13.6 + Pandas 2.3.1
• Rust: Latest stable (release build with optimizations)
• Hardware: Multi-core Windows system
• Methodology: Criterion.rs (Rust) + time.perf_counter() (Python)

📊 Detailed Results

Small Dataset (1,000 rows)

Operation	Python Pandas	Rust Veloxx	Speedup	Winner
Creation	1.3ms	150.6μs	8.8x	🦀 Rust
Filter	242.4μs	65.7μs	3.7x	🦀 Rust
Groupby	477.7μs	335.5μs	1.4x	🦀 Rust
Sort	125.4μs	642.5μs	0.5x slower	🐍 Python
Arithmetic	60.3μs	309.3μs	0.5x slower	🐍 Python

Medium Dataset (10,000 rows)

Operation	Python Pandas	Rust Veloxx	Speedup	Winner
Creation	11.8ms	1.4ms	8.2x	🦀 Rust
Filter	426.3μs	633.2μs	0.7x slower	🐍 Python
Groupby	571.7μs	1.9ms	0.3x slower	🐍 Python
Sort	623.3μs	6.8ms	0.1x slower	🐍 Python
Arithmetic	61.4μs	3.0ms	0.02x slower	🐍 Python

Large Dataset (100,000 rows)

Operation	Python Pandas	Rust Veloxx	Speedup	Winner
Creation	124.5ms	19.2ms	6.5x	🦀 Rust
Filter	1.9ms	8.5ms	0.2x slower	🐍 Python
Groupby	3.9ms	20.2ms	0.2x slower	🐍 Python
Sort	7.6ms	110.7ms	0.07x slower	🐍 Python
Arithmetic	108.0μs	33.1ms	0.003x slower	🐍 Python

🚀 Performance Analysis

Rust Veloxx Strengths

🏗️ DataFrame Creation

• Consistent dominance: 6.5x - 8.8x faster across all data sizes
• Zero-cost abstractions: Compile-time optimizations eliminate runtime overhead
• Memory efficiency: Stack allocation and controlled heap usage

🔍 Small Data Operations

• Filter operations: 3.7x faster on small datasets (1K rows)
• Groupby aggregations: 1.4x faster on small datasets
• Low latency: Predictable performance for real-time applications

⚡ Technical Advantages

• No interpreter overhead: Direct machine code execution
• SIMD optimizations: Hardware-accelerated vectorized operations
• Memory safety: Zero-cost bounds checking and memory management

Python Pandas Strengths

📈 Large Data Scaling

• Sorting algorithms: Significantly better performance on large datasets
• Arithmetic operations: Highly optimized NumPy backend
• Memory layout: Contiguous array optimizations

🧮 Mathematical Operations

• NumPy integration: Decades of optimization in mathematical libraries
• Vectorization: Mature BLAS/LAPACK integration
• Algorithm maturity: Well-tested implementations for complex operations

🌐 Ecosystem Integration

• Rich ecosystem: Extensive library compatibility
• Development speed: Rapid prototyping and debugging
• Community support: Large user base and documentation

🎯 Use Case Recommendations

Choose Rust Veloxx When:

• High-frequency operations: Creating many small DataFrames
• Memory constraints: Limited RAM environments
• Predictable latency: Real-time or embedded systems
• System integration: Building high-performance libraries or services
• Small to medium data: < 50K rows with frequent operations

Choose Python Pandas When:

• Large datasets: > 100K rows with complex operations
• Rapid development: Prototyping and exploratory data analysis
• Rich ecosystem: Need integration with ML/AI libraries
• Complex algorithms: Leveraging mature statistical functions
• Team expertise: Python-focused development teams

🔬 Technical Deep Dive

Rust Implementation Details

// Zero-cost abstractions example
let df = DataFrame::new(columns)?;  // No runtime overhead
let filtered = df.filter(&condition)?;  // SIMD-optimized

Performance Characteristics:

• Compilation: Release mode with LTO and codegen optimizations
• Memory: Stack allocation where possible, arena allocation for large data
• Concurrency: Rayon for data-parallel operations
• SIMD: Automatic vectorization with explicit SIMD where beneficial

Python Implementation Details

# NumPy-optimized operations
df = pd.DataFrame(data)  # Interpreted construction
filtered = df[df['col'] > threshold]  # C-accelerated filtering

Performance Characteristics:

• Runtime: CPython interpreter with C extensions
• Memory: NumPy contiguous arrays with copy-on-write
• Concurrency: Limited by GIL for CPU-bound operations
• Optimization: Mature BLAS/LAPACK integration

📈 Benchmark Methodology

Data Generation

# Consistent test data across languages
np.random.seed(42)  # Reproducible results
data = {
    'integers': range(n_rows),
    'floats': np.random.random(n_rows),
    'categories': random_categories(n_rows)
}

Measurement Approach

• Rust: Criterion.rs with statistical analysis
• Python: time.perf_counter() with multiple iterations
• Consistency: Identical algorithms and data structures
• Environment: Same hardware, isolated processes

🚀 Future Optimizations

Planned Rust Enhancements

• Advanced SIMD operations (AVX-512)
• Lazy evaluation for complex query chains
• GPU acceleration for compute-heavy operations
• Columnar compression for memory efficiency

Performance Goals

• Target 10x+ improvement in arithmetic operations
• Sub-microsecond filtering for small datasets
• Memory usage reduction by 50% through compression

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Benchmarks performed using Criterion.rs for Rust and time.perf_counter() for Python on Windows 11. Results represent median times across multiple iterations.

Source Code:

• Rust Benchmark
• Python Benchmark