Prefect Best Practices
This guide covers battle-tested patterns and practices for building production-ready Prefect workflows. These recommendations come from real-world implementations across diverse data engineering teams.
Project Structure & Organization
Recommended Directory Layout
Separation of Concerns
❌ Don't: Mix everything in one file
✅ Do: Organize by responsibility
Flow Design Patterns
Keep Flows Focused
Principle: One flow should accomplish one logical workflow.
❌ Don't: Mega-flow that does everything
✅ Do: Separate flows by domain
Flow Composition
Use subflows for reusable workflows:
Task Design Best Practices
Task Granularity
Rule of Thumb: Tasks should be independently retryable units of work.
❌ Too Fine-Grained:
❌ Too Coarse-Grained:
✅ Right Balance:
Task Configuration
Always configure retries for external operations:
Task Dependencies
Use explicit returns and parameters for clarity:
❌ Don't: Use global state
✅ Do: Pass data explicitly
Error Handling Strategies
Graceful Degradation
Failure Notifications
Partial Failure Handling
Configuration Management
Use Blocks for Credentials
✅ Best Practice: Store credentials in Blocks
Use in flows:
Environment-Based Configuration
Deployment Best Practices
Use prefect.yaml for Declarative Deployments
Deployment Lifecycle
Logging and Observability
Structured Logging
Metrics and Monitoring
Testing Strategies
Unit Testing Tasks
Integration Testing Flows
Testing with pytest-prefect
Performance Optimization
Parallel Task Execution
Dask Integration for Large-Scale Processing
Caching Strategies
Production Deployment Checklist
Pre-Deployment
- All tests passing
- Credentials stored in Blocks (not hardcoded)
- Error handling implemented
- Retries configured appropriately
- Logging comprehensive and structured
- Notifications configured for failures
- Resource limits set (timeouts, memory)
- Documentation updated
- Code reviewed
Deployment Configuration
- Work pool created and configured
- Workers running with adequate resources
- Schedules defined correctly (timezone aware)
- Parameters validated
- Tags applied for organization
- Version control integrated
Post-Deployment
- Monitor first few runs
- Verify logs are being captured
- Test alerting works
- Document any issues
- Update runbooks
Common Anti-Patterns to Avoid
❌ Anti-Pattern 1: Not Using Tasks
❌ Anti-Pattern 2: Hardcoded Configuration
❌ Anti-Pattern 3: Ignoring Idempotency
Quick Reference
Task Retry Matrix
| Operation Type | Retries | Delay | Example |
|---|---|---|---|
| API Call | 3-5 | 60s | External REST APIs |
| Database Write | 2-3 | 10s | INSERT/UPDATE |
| File I/O | 2 | 5s | S3, GCS uploads |
| Webhook | 5-10 | 30s | Notification services |
| Pure Computation | 0 | 0s | Transformations |
When to Use Subflows
- ✅ Reusable workflow components
- ✅ Logical grouping of tasks
- ✅ Independent retry boundaries
- ✅ Separate monitoring/observability
- ❌ Simple task sequences
- ❌ Overhead concerns
Resources
Need expert guidance implementing these practices? Contact me for consulting on Prefect architecture, migrations, and best practices.
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