Why My Single-Core Server Handles Metadata, PDFs And Covers With Just Pillow And Reportlab

Описание и применение

Скрипты автоматически генерируют правильные заголовки CSV, отслеживают загрузки, создают PDF и обложки с Pillow и Reportlab, позволяя обрабатывать 39 продуктов на однопроцессорном сервере с 2 ГБ RAM без отклонений и флагов.

Задача

Adobe and other stock platforms reject CSV uploads due to incorrect headers, and duplicate submissions flag accounts as spam.

Результат

The pipeline processed 39 products, producing 856 MB of output on a 1‑core, 2 GB server, avoiding CSV rejections and duplicate upload flags.

Рабочий процесс

• 1. Run make_metadata.py to generate platform‑specific CSV headers and rows from filenames or titles.json.
• 2. Use upload_tracker.py to record completed uploads and prevent re‑uploads.
• 3. Convert Markdown to PDF with md2pdf.py using Reportlab.
• 4. Render 1280x720 cover and 600x600 thumbnail with Pillow via pack_product.py and zip them.
• 5. Monitor memory with resource.getrusage and gc.collect() after each asset.
• 6. Organize files in assets/, output/, packs/, scripts/, research/ folders.

Доказательства

Adobe had rejected the entire CSV. One wrong column in the header.

Источник

Adobe Stock wants a 5-column CSV. Vecteezy wants 4 columns. Dreamstime wants 15.

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upload_tracker.py is a ledger, standard library only, no database.

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md2pdf.py uses reportlab alone: headings, tables, lists, links, clean styling, one command: python md2pdf.py content.md product.pdf "My Product Title"

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Pack_product.py renders both with Pillow, zips the files, and writes everything into a _system folder.

Источник

One process at a time. Never fan out image jobs in parallel on a 1-core box.

Источник

I validated the whole thing on that box: 39 finished products, 856 MB of output, running under a pipeline of roughly 19,000 lines with an agent orchestrator on top.

Источник