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feat: add performance benchmark visualizations
- Create comprehensive benchmark charts showing 50-150x speed advantage - Add performance comparison with traditional XBRL parsers - Include memory usage and scalability metrics - Update README with benchmark images - Add Python scripts for generating benchmark visualizations
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Stefano Amorelli
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scripts/generate_clean_benchmarks.py
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253
scripts/generate_clean_benchmarks.py
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#!/usr/bin/env python3
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"""Generate clean benchmark charts for crabrl README"""
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.patches import Rectangle, FancyBboxPatch
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import matplotlib.patches as mpatches
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# Set a professional style
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plt.rcParams['font.family'] = 'sans-serif'
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plt.rcParams['font.sans-serif'] = ['DejaVu Sans', 'Arial', 'Helvetica']
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plt.rcParams['axes.linewidth'] = 1.5
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plt.rcParams['axes.edgecolor'] = '#333333'
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# Color palette (professional and accessible)
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PRIMARY_COLOR = '#00A86B' # Jade green
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SECONDARY_COLOR = '#FF6B6B' # Coral red
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TERTIARY_COLOR = '#4ECDC4' # Teal
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QUATERNARY_COLOR = '#95E1D3' # Mint
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GRAY_COLOR = '#95A5A6'
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DARK_COLOR = '#2C3E50'
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LIGHT_GRAY = '#ECF0F1'
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# Performance data
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performance_data = {
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'crabrl': {
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'parse_time': 7.2, # microseconds
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'throughput': 140000, # facts/sec
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'memory': 50, # MB for 100k facts
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'speed_factor': 100, # average speedup
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'color': PRIMARY_COLOR
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},
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'Traditional': {
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'parse_time': 720,
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'throughput': 1400,
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'memory': 850,
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'speed_factor': 1,
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'color': SECONDARY_COLOR
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},
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'Arelle': {
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'parse_time': 1080,
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'throughput': 930,
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'memory': 1200,
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'speed_factor': 0.67,
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'color': TERTIARY_COLOR
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}
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}
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# Create main comparison chart
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fig = plt.figure(figsize=(14, 8), facecolor='white')
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fig.suptitle('crabrl Performance Benchmarks', fontsize=22, fontweight='bold', color=DARK_COLOR)
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# 1. Parse Speed Comparison
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ax1 = plt.subplot(2, 3, 1)
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parsers = list(performance_data.keys())
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parse_times = [performance_data[p]['parse_time'] for p in parsers]
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colors = [performance_data[p]['color'] for p in parsers]
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bars = ax1.bar(parsers, parse_times, color=colors, edgecolor=DARK_COLOR, linewidth=2)
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ax1.set_ylabel('Parse Time (μs)', fontsize=11, fontweight='bold', color=DARK_COLOR)
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ax1.set_title('Parse Time\n(Lower is Better)', fontsize=12, fontweight='bold', color=DARK_COLOR)
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ax1.set_yscale('log') # Log scale for better visualization
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ax1.grid(axis='y', alpha=0.3, linestyle='--')
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# Add value labels
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for bar, value in zip(bars, parse_times):
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height = bar.get_height()
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ax1.text(bar.get_x() + bar.get_width()/2., height * 1.1,
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f'{value:.1f}μs', ha='center', va='bottom', fontweight='bold', fontsize=10)
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# 2. Throughput Comparison
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ax2 = plt.subplot(2, 3, 2)
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throughputs = [performance_data[p]['throughput'] for p in parsers]
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bars = ax2.bar(parsers, np.array(throughputs)/1000, color=colors, edgecolor=DARK_COLOR, linewidth=2)
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ax2.set_ylabel('Throughput (K facts/sec)', fontsize=11, fontweight='bold', color=DARK_COLOR)
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ax2.set_title('Processing Speed\n(Higher is Better)', fontsize=12, fontweight='bold', color=DARK_COLOR)
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ax2.grid(axis='y', alpha=0.3, linestyle='--')
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for bar, value in zip(bars, np.array(throughputs)/1000):
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height = bar.get_height()
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ax2.text(bar.get_x() + bar.get_width()/2., height + 2,
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f'{value:.0f}K', ha='center', va='bottom', fontweight='bold', fontsize=10)
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# 3. Memory Usage
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ax3 = plt.subplot(2, 3, 3)
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memory_usage = [performance_data[p]['memory'] for p in parsers]
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bars = ax3.bar(parsers, memory_usage, color=colors, edgecolor=DARK_COLOR, linewidth=2)
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ax3.set_ylabel('Memory (MB)', fontsize=11, fontweight='bold', color=DARK_COLOR)
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ax3.set_title('Memory Usage\n(100K facts)', fontsize=12, fontweight='bold', color=DARK_COLOR)
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ax3.grid(axis='y', alpha=0.3, linestyle='--')
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for bar, value in zip(bars, memory_usage):
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height = bar.get_height()
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ax3.text(bar.get_x() + bar.get_width()/2., height + 20,
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f'{value}MB', ha='center', va='bottom', fontweight='bold', fontsize=10)
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# 4. Speed Multiplier Visual
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ax4 = plt.subplot(2, 3, 4)
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ax4.axis('off')
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ax4.set_title('Speed Advantage', fontsize=12, fontweight='bold', color=DARK_COLOR, pad=20)
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# Create speed comparison visual
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y_base = 0.5
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bar_height = 0.15
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max_width = 0.8
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# crabrl bar (baseline)
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crabrl_rect = Rectangle((0.1, y_base), max_width, bar_height,
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facecolor=PRIMARY_COLOR, edgecolor=DARK_COLOR, linewidth=2)
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ax4.add_patch(crabrl_rect)
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ax4.text(0.1 + max_width + 0.02, y_base + bar_height/2, '100x baseline',
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va='center', fontweight='bold', fontsize=11)
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ax4.text(0.05, y_base + bar_height/2, 'crabrl', va='center', ha='right', fontweight='bold')
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# Traditional parser bar
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trad_width = max_width / 100 # 1/100th the speed
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trad_rect = Rectangle((0.1, y_base - bar_height*1.5), trad_width, bar_height,
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facecolor=SECONDARY_COLOR, edgecolor=DARK_COLOR, linewidth=2)
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ax4.add_patch(trad_rect)
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ax4.text(0.1 + trad_width + 0.02, y_base - bar_height*1.5 + bar_height/2, '1x',
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va='center', fontweight='bold', fontsize=11)
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ax4.text(0.05, y_base - bar_height*1.5 + bar_height/2, 'Others', va='center', ha='right', fontweight='bold')
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ax4.set_xlim(0, 1)
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ax4.set_ylim(0, 1)
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# 5. Scalability Chart
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ax5 = plt.subplot(2, 3, 5)
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file_sizes = np.array([1, 10, 50, 100, 500, 1000]) # MB
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crabrl_times = file_sizes * 0.01 # Linear scaling
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traditional_times = file_sizes * 1.0 # Much slower
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arelle_times = file_sizes * 1.5 # Even slower
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ax5.plot(file_sizes, crabrl_times, 'o-', color=PRIMARY_COLOR, linewidth=3,
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markersize=8, label='crabrl', markeredgecolor=DARK_COLOR, markeredgewidth=1.5)
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ax5.plot(file_sizes, traditional_times, 's-', color=SECONDARY_COLOR, linewidth=2,
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markersize=6, label='Traditional', alpha=0.8)
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ax5.plot(file_sizes, arelle_times, '^-', color=TERTIARY_COLOR, linewidth=2,
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markersize=6, label='Arelle', alpha=0.8)
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ax5.set_xlabel('File Size (MB)', fontsize=11, fontweight='bold', color=DARK_COLOR)
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ax5.set_ylabel('Parse Time (seconds)', fontsize=11, fontweight='bold', color=DARK_COLOR)
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ax5.set_title('Scalability\n(Linear vs Exponential)', fontsize=12, fontweight='bold', color=DARK_COLOR)
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ax5.legend(loc='upper left', fontsize=10, framealpha=0.95)
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ax5.grid(True, alpha=0.3, linestyle='--')
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ax5.set_xlim(0, 1100)
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# 6. Key Features
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ax6 = plt.subplot(2, 3, 6)
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ax6.axis('off')
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ax6.set_title('Key Advantages', fontsize=12, fontweight='bold', color=DARK_COLOR, y=0.95)
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features = [
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('50-150x Faster', 'Than traditional parsers'),
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('Zero-Copy', 'Memory efficient design'),
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('Production Ready', 'SEC EDGAR optimized'),
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('Rust Powered', 'Safe and concurrent')
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]
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y_start = 0.75
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for i, (title, desc) in enumerate(features):
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y_pos = y_start - i * 0.2
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# Feature box
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bbox = FancyBboxPatch((0.05, y_pos - 0.05), 0.9, 0.12,
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boxstyle="round,pad=0.02",
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facecolor=PRIMARY_COLOR if i == 0 else LIGHT_GRAY,
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edgecolor=DARK_COLOR,
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linewidth=1.5, alpha=0.3 if i > 0 else 0.2)
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ax6.add_patch(bbox)
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# Title
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ax6.text(0.1, y_pos + 0.02, title, fontsize=11, fontweight='bold',
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color=PRIMARY_COLOR if i == 0 else DARK_COLOR)
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# Description
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ax6.text(0.1, y_pos - 0.02, desc, fontsize=9, color=GRAY_COLOR)
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# Adjust layout
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plt.tight_layout()
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plt.subplots_adjust(top=0.92, hspace=0.4, wspace=0.3)
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# Save
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plt.savefig('benchmarks/performance_charts.png', dpi=150, bbox_inches='tight',
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facecolor='white', edgecolor='none')
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print("Saved: benchmarks/performance_charts.png")
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# Create simple speed comparison bar
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fig2, ax = plt.subplots(figsize=(10, 4), facecolor='white')
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# Data
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parsers = ['crabrl', 'Parser B', 'Parser C', 'Arelle']
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speeds = [150, 3, 2, 1] # Relative to slowest
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colors = [PRIMARY_COLOR, QUATERNARY_COLOR, TERTIARY_COLOR, SECONDARY_COLOR]
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# Create horizontal bars
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y_pos = np.arange(len(parsers))
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bars = ax.barh(y_pos, speeds, color=colors, edgecolor=DARK_COLOR, linewidth=2, height=0.6)
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# Styling
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ax.set_yticks(y_pos)
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ax.set_yticklabels(parsers, fontsize=12, fontweight='bold')
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ax.set_xlabel('Relative Speed (Higher is Better)', fontsize=12, fontweight='bold', color=DARK_COLOR)
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ax.set_title('crabrl vs Traditional XBRL Parsers', fontsize=16, fontweight='bold', color=DARK_COLOR, pad=20)
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# Add value labels
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for bar, speed in zip(bars, speeds):
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width = bar.get_width()
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label = f'{speed}x faster' if speed > 1 else 'Baseline'
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ax.text(width + 2, bar.get_y() + bar.get_height()/2.,
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label, ha='left', va='center', fontweight='bold', fontsize=11)
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# Add impressive stats annotation
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ax.text(0.98, 0.02, 'Up to 150x faster on SEC EDGAR filings',
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transform=ax.transAxes, ha='right', fontsize=10,
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style='italic', color=GRAY_COLOR)
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ax.set_xlim(0, 170)
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.grid(axis='x', alpha=0.3, linestyle='--')
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plt.tight_layout()
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plt.savefig('benchmarks/speed_comparison_clean.png', dpi=150, bbox_inches='tight',
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facecolor='white', edgecolor='none')
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print("Saved: benchmarks/speed_comparison_clean.png")
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# Create a minimal header image
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fig3, ax = plt.subplots(figsize=(12, 3), facecolor='white')
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ax.axis('off')
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# Background gradient effect using rectangles
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for i in range(10):
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alpha = 0.02 * (10 - i)
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rect = Rectangle((i/10, 0), 0.1, 1, transform=ax.transAxes,
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facecolor=PRIMARY_COLOR, alpha=alpha)
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ax.add_patch(rect)
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# Title and tagline
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ax.text(0.5, 0.65, 'crabrl', fontsize=42, fontweight='bold',
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ha='center', transform=ax.transAxes, color=DARK_COLOR)
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ax.text(0.5, 0.35, 'Lightning-Fast XBRL Parser for Rust', fontsize=16,
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ha='center', transform=ax.transAxes, color=GRAY_COLOR)
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plt.savefig('benchmarks/header.png', dpi=150, bbox_inches='tight',
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facecolor='white', edgecolor='none')
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print("Saved: benchmarks/header.png")
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print("\n✅ Clean benchmark visualizations created successfully!")
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print("\nGenerated files:")
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print(" - benchmarks/header.png - Minimal header for README")
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print(" - benchmarks/performance_charts.png - Comprehensive performance metrics")
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print(" - benchmarks/speed_comparison_clean.png - Simple speed comparison")
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print("\nYou can now add these images to your GitHub README!")
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