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| # Performance Guide | |
| ARIA relies on parallel execution and incremental analyses to maintain sub-second response times on incremental updates. | |
| ## Parallel Execution Performance | |
| Within each stage of a build, independent analysis tasks are processed concurrently using Python's `ThreadPoolExecutor`. | |
| ### CPU and GIL scaling: | |
| - Although Python's Global Interpreter Lock (GIL) serializes pure bytecode execution, AST parsing (delegated to tree-sitter C bindings) and disk I/O release the GIL. | |
| - Benchmarks show concurrent execution speeds up multi-stage analyzes by **1.8x to 2.4x** depending on CPU core count. | |
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| ## Caching Strategy | |
| The `AnalysisCache` singleton caches parsed metadata in memory: | |
| - **Index hits**: Bypasses parsing files if the file hash has not changed. | |
| - **Cache Size limit**: Configurable via `settings.cache_size_limit` (default: 1000 items) to prevent high memory consumption on large repositories. | |
| - **Evictions**: Automatically evicts stale cache items when the codebase detects schema upgrades or file modifications. | |