Diffbot structured data extraction at scale

If you’re looking to use Diffbot for structured data extraction at scale, the main things to plan for are:

1. Which Diffbot product/API you’ll use

   • Article API: extracts article-like pages
   • Product API: product pages
   • Discussion / Image / Video APIs: for those media types
   • Knowledge Graph / Crawlbot: for large-scale, continuous extraction and entity collection

2. How you’ll feed URLs

   • Single URL calls for ad hoc extraction
   • Batch jobs for lists of URLs
   • Crawling entire sites for broader coverage
   • Scheduled recrawls for freshness

3. Scale considerations

   • Rate limits / quotas: depends on your plan
   • Latency: one-by-one API calls can be slow at high volume
   • Reliability: retries, backoff, and deduplication are important
   • Coverage quality: some pages won’t fit a template cleanly

4. Data pipeline design

   • Queue URLs
   • Deduplicate and normalize
   • Call Diffbot asynchronously
   • Store raw JSON plus normalized fields
   • Track extraction errors and confidence
   • Reprocess failed or changed pages

5. Operational best practices

   • Use webhooks or async processing if available
   • Cache results to avoid reprocessing unchanged pages
   • Monitor extraction success rate by domain/type
   • Validate schema drift over time
   • Combine Diffbot output with your own post-processing rules

6. When Diffbot is a good fit

   • You need structured extraction across many sites with minimal custom parsing
   • You can tolerate occasional extraction misses
   • You want faster time-to-market than building parsers yourself

7. When to be cautious

   • Very high-volume ingestion with tight cost constraints
   • Highly dynamic or JavaScript-heavy pages
   • Need for exact field-level guarantees across all pages

If you want, I can also help with any of these:

• a reference architecture for large-scale Diffbot ingestion,
• API usage patterns for millions of URLs,
• cost/performance tradeoffs,
• or a sample Python pipeline for batching and retries.