Databricks Rate Limits
Overview
Handle Databricks API rate limits gracefully with exponential backoff.
Prerequisites
- Databricks SDK installed
- Understanding of async/await patterns
- Access to Databricks workspace
Instructions
Step 1: Understand Rate Limit Tiers
Step 2: Implement Exponential Backoff with Jitter
Step 3: Implement Request Queue for Bulk Operations
Step 4: Async Batch Processing
Step 5: Idempotency for Job Submissions
For full implementation details and code examples, load: references/implementation-guide.md
Output
- Reliable API calls with automatic retry
- Rate-limited request queue
- Async batch processing for bulk operations
- Idempotent job submissions
Error Handling
| Scenario | Behavior | Configuration |
|---|---|---|
| HTTP 429 | Exponential backoff | max_retries=5 |
| HTTP 503 | Retry with delay | base_delay=1.0 |
| Conflict (409) | Retry once | Check idempotency |
| Timeout | Retry with increased timeout | max_delay=60 |
Resources
Next Steps
For security configuration, see databricks-security-basics.
Examples
Basic usage: Apply databricks rate limits to a standard project setup with default configuration options.
Advanced scenario: Customize databricks rate limits for production environments with multiple constraints and team-specific requirements.