# Performance

URL: /docs/about/performance/
Section: about
Tags: performance, benchmarks

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Performance Patitas is designed for consistent, predictable performance. Why State Machine? Regex-based parsers have unpredictable performance: Input Pattern Regex Parser State Machine Normal text Fast Fast Nested emphasis Slower Fast Pathological input Exponential Fast State machines guarantee O(n) regardless of input. Benchmarks Tested on MacBook Pro M3, Python 3.14t: Document Size Patitas mistune Speedup Small (1KB) 0.2ms 0.3ms 1.5x Medium (10KB) 1.8ms 2.8ms 1.6x Large (100KB) 15ms 25ms 1.7x Pathological 20ms >10s 500x+ Optimization Strategies 1. Zero-Copy Text The renderer extracts text directly from source using slices: # Instead of copying strings text = source[start:end] # Zero-copy slice 2. StringBuilder Output uses StringBuilder for O(n) concatenation: # O(n) total builder = StringBuilder() for part in parts: builder.append(part) result = str(builder) # Single allocation 3. Frozen Dataclasses with Slots @dataclass(frozen=True, slots=True) class Node: # 40% less memory than regular classes # Faster attribute access 4. Tuple Children Using tuples instead of lists: children: tuple[Inline, ...] # Immutable, hashable Memory Usage Component Per Node Notes Heading ~120 bytes Plus children Paragraph ~100 bytes Plus children Text ~80 bytes Plus string SourceLocation ~48 bytes Optional Free-Threading Performance With Python 3.14t (GIL disabled): Threads Documents Time Speedup 1 1000 1.5s 1x 4 1000 0.4s 3.75x 8 1000 0.25s 6x Near-linear scaling due to no shared mutable state.

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