Developer Rebuilds Law Firm Document Pipeline in 200 Lines of Python, Cuts RAM Use by 97%

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A developer automating his law firm's document workflows replaced a Node.js and Puppeteer-based pipeline with a Python rewrite after the original system crashed at 3 AM while processing 5,000 PDFs, consuming 6.2GB of RAM. The new system uses only Python's standard library and a ThreadPoolExecutor, reducing peak memory usage from 6.2GB to approximately 180MB. Key fixes included replacing an unbounded Redis queue with a bounded asyncio queue to enforce backpressure and prevent out-of-memory crashes. The developer also addressed a court API rate-limiting issue by implementing exponential backoff with jitter using pure asyncio, eliminating the need for external dependencies. The refactored stack processed 48,000 documents compared to 4,200 under the previous Node.js setup, while running within an 8GB instance constraint.

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