A 429 error in Databricks typically indicates that you have exceeded the rate limit for API requests. This means you’ve sent too many requests within a given period. Here’s how to address it and find more information:

Understanding Rate Limits

• Databricks REST API: Databricks has specific rate limits for its REST API to prevent abuse and ensure fair usage. These limits vary depending on the endpoint and your subscription level.
• Other APIs: If you’re using other APIs within Databricks (e.g., MLflow), they may also have their rate limits.

Solutions and Troubleshooting

1. Identify the API: Determine which API you’re using and what is causing the 429 error. Check your code, logs, or the Databricks UI for clues.
2. Review Documentation: Consult the Databricks API documentation to understand the specific rate limits for the API in question. Look for information on request limits per second, minute, or hour.
3. Implement Backoff and Retry: Introduce your code’s backoff and retry mechanism. When encountering a 429 error, wait for a short period (e.g., a few seconds) before retrying the request. Increase the waiting time exponentially on subsequent retries.
4. Optimize Requests: Review your code to see if you can reduce the number of API requests you’re making—batch requests together where possible or cache results to avoid unnecessary calls.
5. Contact Databricks Support: If you’re consistently hitting rate limits despite optimization efforts, contact Databricks support for assistance. They may be able to adjust your limits or provide further guidance.