Why does my pipeline break when an API rate limits?

Because most pipelines assume each step can be retried independently and complete in a predictable amount of time. Rate limiting breaks that assumption.

Common reasons:

1. The API returns errors or 429s

   • If your code doesn’t handle HTTP 429 (“Too Many Requests”) gracefully, the step fails immediately.

2. No retry/backoff logic

   • A temporary limit should usually trigger retries with exponential backoff.
   • Without this, the pipeline treats a transient condition as a hard failure.

3. Timeouts cascade

   • Waiting for the API longer than expected can cause downstream tasks to time out, fail, or be skipped.

4. Batch jobs exceed quotas

   • Pipelines often process many records in parallel, which can exceed per-minute/per-second limits even if each individual request is valid.

5. The pipeline is not checkpointed

   • If the job fails halfway through, it may restart from scratch and re-hit the limit.

6. Concurrency is too high

   • Multiple workers or threads can hammer the API at once, causing repeated throttling.

7. The pipeline treats partial failure as total failure

   • One rate-limited request can cause the whole batch or DAG to fail instead of isolating the bad item.

How to fix it:

• Add retry with exponential backoff + jitter
• Respect Retry-After headers if provided
• Reduce parallelism / request rate
• Add queueing or throttling
• Use checkpointing so retries resume from the last successful step
• Make the pipeline idempotent so retries don’t duplicate work
• Separate transient failures (429/503) from permanent ones

If you want, I can also help you design a rate-limit-safe pipeline pattern for your specific stack (Airflow, Dagster, Prefect, Lambda, GitHub Actions, etc.).