Function main

benchmark/memory_benchmark.py:112–158

Run memory benchmarks.

()

def main():
    """Run memory benchmarks."""
    from sample_datasets import DATASETS

    print("="*60)
    print("TOON Memory Usage Benchmark")
    print("="*60)
    print("\nThis benchmark shows actual memory consumption when")
    print("working with JSON vs TOON serialized data.")

    results = []

    # Test a subset of datasets for memory benchmarks
    test_datasets = {
        "E-commerce Products": DATASETS["E-commerce Products"],
        "Database Results": DATASETS["Database Results"],
        "Large Inventory (100 items)": DATASETS["Large Inventory (100 items)"],
    }

    for dataset_name, dataset in test_datasets.items():
        result = benchmark_memory(dataset_name, dataset)
        results.append(result)

    # Summary
    print(f"\n{'='*60}")
    print("MEMORY SAVINGS SUMMARY")
    print(f"{'='*60}")
    print(f"\n{'Dataset':<35} {'Bytes Saved':<15} {'% Saved':<10}")
    print("-" * 60)

    for result in results:
        bytes_saved = result['json_bytes_memory'] - result['toon_bytes_memory']
        print(f"{result['name']:<35} {format_size(bytes_saved):<15} {result['bytes_savings']:>6.1f}%")

    total_json = sum(r['json_bytes_memory'] for r in results)
    total_toon = sum(r['toon_bytes_memory'] for r in results)
    total_saved = total_json - total_toon
    avg_savings = ((total_json - total_toon) / total_json) * 100

    print("-" * 60)
    print(f"{'TOTAL':<35} {format_size(total_saved):<15} {avg_savings:>6.1f}%")

    print(f"\n🎉 Key Findings:")
    print(f"  • TOON reduces serialized data size by {avg_savings:.1f}% on average")
    print(f"  • This means less memory usage, faster network transfers")
    print(f"  • And most importantly: lower LLM API costs!")
    print(f"\n✅ Memory benchmark completed successfully!")


if __name__ == "__main__":