📈 Performance & Scalability

Code-y is designed to handle projects of varying sizes efficiently, from small libraries to large-scale applications. Several strategies are employed to ensure good performance and scalability.

Optimized Processing Techniques

  • Incremental Analysis: This is a key performance feature. After the initial full analysis, Code-y intelligently reprocesses only the files that have changed since the last run. This dramatically speeds up subsequent documentation generations, especially in large projects where only a few files are modified between runs.
  • Parallel Processing (Planned/Experimental): For certain CPU-intensive tasks like parsing multiple files or generating embeddings for many code blocks, Code-y aims to leverage multi-threading or parallel processing capabilities to utilize modern multi-core processors effectively.
  • Smart Caching:
    • Analysis Results: Parsed ASTs, relationship data, and other analysis artifacts are cached.
    • AI Responses: AI-generated descriptions and explanations are cached based on the hash of the input code. If the code hasn't changed, the cached AI response is used, avoiding redundant API calls or local model processing.
    • Embeddings: Vector embeddings for code snippets are stored and only re-generated if the underlying code changes.
  • Efficient Memory Management: Code-y is mindful of memory usage, especially when dealing with very large projects or numerous files. Techniques are used to process files and data in a way that minimizes the memory footprint.

Scalable Architecture Considerations

  • Batch Processing: For operations like generating embeddings for an entire codebase, Code-y can process files in batches to manage resources and provide progress feedback.
  • Streaming Analysis (Where Applicable): For very large individual files, strategies involving streaming or chunked processing might be used to avoid loading the entire file into memory at once, though full AST parsing often requires the complete file content.
  • Background Processing for AI Tasks: AI operations, especially those involving calls to Ollama or OpenAI, can be time-consuming. Code-y often performs these in the background or asynchronously to keep the main analysis thread responsive and provide a smoother user experience.
  • Resource Management & Cleanup:
    • Python Virtual Environment: Code-y manages its Python dependencies (like those for FAISS or specific AI model libraries) in an isolated virtual environment. This environment can be kept (`--keep-environment`) for faster subsequent runs or cleaned up (`--cleanup-on-exit` or `code-y cleanup`) to save disk space.
    • Docker Integration (If Used for AI Services): If Code-y manages AI services via Docker containers (e.g., an Ollama container), it also handles starting and stopping these containers as needed.

Performance is an ongoing focus. As Code-y evolves, we continuously look for ways to optimize its speed and efficiency for even the most demanding codebases.