# 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: 1. **Explore built-in templates **available in the VoxelSpace platform 2. **Experiment with different processing techniques **and algorithms 3. **Study advanced examples **in the developer documentation 4. **Consider upgrading your plan **for increased compute resources and concurrent processing 5. **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.