I'm building a batch document pipeline for thousands of PDFs per day

Got it — for thousands of PDFs/day, the main things to design for are throughput, reliability, idempotency, and observability.

A solid batch PDF pipeline usually looks like this:

1. Ingest

   • Upload PDFs to object storage
   • Write a job record to a queue or database
   • Assign each document a unique id for deduplication/idempotency

2. Preprocess

   • Validate file type, size, corruption
   • Extract metadata: page count, dimensions, encryption/password status
   • Split large PDFs if needed

3. Extract

   • Text extraction for digital PDFs
   • OCR for scanned/image PDFs
   • Layout extraction if you need tables, headers, paragraphs, etc.

4. Postprocess

   • Normalize text
   • Merge OCR + extracted text
   • Chunk content for downstream search/LLM/indexing
   • Store structured outputs

5. Store and index

   • Raw PDF in object storage
   • Extracted text/JSON in a database or document store
   • Index into search/vector systems if needed

6. Orchestrate

   • Use a queue + worker model
   • Make workers stateless and horizontally scalable
   • Add retries with backoff
   • Dead-letter queue for failures

7. Monitor

   • Track per-stage latency, failure rate, OCR rate, page throughput
   • Log document/job ids everywhere
   • Alert on backlog growth and error spikes

Key design choices

• Digital vs scanned detection: avoid OCR when text extraction is enough.
• Idempotency: same PDF can be safely reprocessed.
• Backpressure: prevent overload when OCR spikes.
• Chunking strategy: page-based, section-based, or semantic.
• Cost control: OCR and LLM steps can get expensive fast.

Common stack pattern

• Ingest: S3/GCS/Azure Blob
• Queue: SQS/RabbitMQ/Kafka
• Workers: Python/Celery/RQ, or containerized jobs on Kubernetes
• Metadata DB: Postgres
• Search: Elasticsearch/OpenSearch
• OCR: Tesseract, AWS Textract, Google Document AI, Azure Form Recognizer

Questions to narrow the architecture

1. Are these PDFs mostly digital text or scanned images?
2. Do you need just text extraction, or also tables/forms/layout?
3. What’s your latency target: minutes, hours, or overnight?
4. Do you need human review for low-confidence documents?
5. What cloud/on-prem environment are you using?

If you want, I can sketch a concrete architecture for your throughput target, or help you choose between self-hosted OCR vs managed document AI.