Best Practices

Performance Optimization 

 Limit the region: Restrict the lambda to the smallest area necessary to reduce compute costand time 

 Efficient algorithms: Use vectorized operations where possible for better performance 

 Memory management: Be mindful of memory usage when processing large datasets 

   

 Development Workflow 

 Test on a sample: Validate your code on a small subset before running it on the full dataset 

 Incremental development: Start with simple logic and gradually add complexity 

 Error handling: Include proper error handling for robust execution 

   

 Resource Management 

 Reuse templates: Start with VoxelSpace's built-in lambdas for common tasks; modify themrather than writing from scratch 

 Queue awareness: Plan your processing and lambda jobs to avoid long queues, especially onthe free tier where concurrency is limited 

 Monitor usage: Track your compute usage to stay within plan limits 

   

 Debugging and Validation 

 Use logs: Include log statements in your lambda to help diagnose issues and verifyintermediate results 

 Validate inputs: Check that your data has the expected properties before processing 

 Test edge cases: Consider what happens with empty regions, missing properties, or extremevalues