What happens to your scraper when the server returns a 429 or the job stops halfway? This guide walks through timeouts, idempotent storage, persistent job state, and tests for failure scenarios, with Python examples.

Description and use

The data scraper becomes resilient to 429 errors and mid‑job stops by using timeouts, idempotent storage, persistent job state, and failure‑scenario tests.

Problem

Scrapers often fail when the target server returns a 429 Too Many Requests or when the job stops midway, leading to incomplete data collection.

Outcome

The scraper becomes resilient to rate limiting and unexpected stops, ensuring complete and consistent data collection.

Workflow

• Set request timeouts and retry logic
• Store scraped data idempotently to avoid duplicates
• Persist job state (e.g., current URL or page number) to resume after interruption
• Write tests that simulate 429 responses and mid‑job failures
• Use assertions to verify data integrity after recovery

Evidence

This guide walks through timeouts, idempotent storage, persistent job state, and tests for failure scenarios, with Python examples.

Source