Troubleshooting Common Issues
Performance Problems
- Large memory usage: Process voxels in batches rather than loading all into memory
- Slow execution: Optimize algorithms and reduce unnecessary computations
- Timeout errors: Break large jobs into smaller regions
Data Issues
- Missing properties: Always use the .get() method with default values
- Unexpected data types: Validate and convert data types as needed
- Empty regions: Check for zero voxel counts before performing calculations
Runtime Errors
- Import errors: Ensure all required libraries are available in the runtime environment
- Syntax errors: Test your code locally before uploading
- Logic errors: Use logging extensively to trace execution flow
Conclusion
Spatial lambdas bring high-performance analytics directly to your data, enabling custom computations at massivescale. By understanding how to author, configure, and run these serverless functions, you can:
- Extract deeper insights from voxel models
- Automate complex spatial analyses
- Process trillions of voxels efficiently
- Create custom workflows tailored to your specific needs
The power of spatial lambdas lies in their ability to execute near your data, eliminating the need to transfermassive datasets and enabling real-time analysis at unprecedented scales.
Next Steps
To advance your spatial lambda expertise:
- Explore built-in templates available in the VoxelSpace platform
- Experiment with different processing techniques and algorithms
- Study advanced examples in the developer documentation
- Consider upgrading your plan for increased compute resources and concurrent processing
- Join the VoxelSpace community to share techniques and learn from other users
For comprehensive developer documentation and advanced tutorials, consult the full manuals within VoxelSpace once you have platform access.