Run in parallel batches using multiprocessing.Pool for large archives.
Finally, the "modern strategy" is incomplete without a plan for production. Deploy robust monitoring and implement layered error handling. Use structured logging (e.g., structlog ) to track the source of parsing failures. Implement retries with exponential backoff for network-dependent services like OCR APIs. For large-scale batch processing, track progress and automatically resume from the last successful item, a pattern known as "checkpointing." These operational strategies are what differentiate a script from a production system. Run in parallel batches using multiprocessing
The book spotlights several "power tools" of the Python language that drastically change how software is built: Use structured logging (e
Use rlextra (commercial) or open-source xhtml2pdf with reportlab backend. The book spotlights several "power tools" of the
def merge_pdfs_smart(pdf_list: list, output_path: str): merger = PdfMerger() for pdf in pdf_list: merger.append(pdf, import_outline=False) # outlines can be heavy merger.write(output_path) merger.close()