How do I process uploaded PDFs in a serverless app?

A good serverless PDF-processing flow usually looks like this:

1. Upload PDF to object storage
2. Trigger a serverless job/event
3. Extract text / metadata / images / OCR as needed
4. Store results in a database/search index
5. Return status to the client

Common architecture

1) Direct upload from client

Have the browser/mobile app upload the PDF directly to storage like:

• AWS S3
• Google Cloud Storage
• Azure Blob Storage

Use a pre-signed URL so your serverless backend doesn’t handle the file bytes directly.

2) Trigger processing

Once the upload completes, trigger a function using:

• S3 event → Lambda
• GCS finalize event → Cloud Function
• Blob trigger → Azure Function
• Or an event queue like SQS/PubSub/Service Bus

3) Process asynchronously

In the serverless function, do PDF work such as:

• Extract text
• Read metadata
• Generate thumbnails
• Run OCR for scanned documents
• Split into pages/sections
• Classify or index content

For larger files or longer jobs, fan out to:

• Step Functions / Workflows
• Durable Functions
• Cloud Tasks / queues

4) Save outputs

Store extracted data in:

• Database: DynamoDB, Firestore, Postgres
• Search: OpenSearch, Algolia, Elasticsearch
• Vector store if doing RAG/semantic search
• Another bucket for derived artifacts

5) Track job status

Write a job record with states like:

• uploaded
• processing
• completed
• failed

Then the client can poll or subscribe for updates.

---

Important implementation tips

Keep the function small

Serverless runtimes have limits on:

• execution time
• memory
• disk space
• file size
• cold start latency

For large PDFs, don’t try to do everything in one function.

Stream when possible

Avoid loading the whole PDF into memory if you can. Prefer:

• streaming downloads from storage
• page-by-page parsing
• chunked processing

Use OCR for scanned PDFs

If the PDF is image-based, text extraction libraries won’t help much. Use:

• AWS Textract
• Google Document AI / Vision OCR
• Azure Document Intelligence
• Tesseract in a container, if acceptable

Use a container-based serverless option for heavy libraries

If you need native dependencies or large PDF libraries, consider:

• AWS Lambda container images
• Cloud Run
• Azure Container Apps
• Google Cloud Run

This is often easier than squeezing everything into a zip-based function.

---

Example flow on AWS

1. Client requests a pre-signed S3 upload URL
2. Client uploads PDF to S3
3. S3 event triggers Lambda
4. Lambda:
   • downloads PDF
   • extracts text with a library or Textract
   • stores extracted content in DynamoDB/OpenSearch
   • updates job status
5. Client checks job status via API

---

Example tools/libraries

Depending on language:

• Python
  • pypdf
  • pdfplumber
  • PyMuPDF (fitz)
  • OCR via AWS Textract or Tesseract
• Node.js
  • pdf-lib
  • pdf-parse
• Java
  • Apache PDFBox
• Go
  • unidoc (commercial for some use cases)

For OCR or enterprise workflows, managed document AI services are often the simplest option.

---

Recommended pattern for most apps

If you want a robust serverless setup:

• Upload PDF directly to storage
• Put a job message on a queue
• Process with a worker function
• Store results in DB/search
• Use OCR service when needed
• Split large jobs into page batches

This gives you:

• better reliability
• retries
• lower latency for uploads
• fewer timeout issues

If you want, I can give you:

1. a reference architecture diagram,
2. an AWS/GCP/Azure example, or
3. sample code for processing PDFs in Lambda/Cloud Functions.