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FAQ
LoSkroefie edited this page Jan 19, 2025
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FLEXON is a binary serialization format designed as a modern alternative to JSON, offering better performance, smaller file sizes, and richer type support.
- 60-80% smaller file sizes
- 2-3x faster processing
- Native support for binary data, DateTime, and UUID
- Built-in schema validation
- Better type safety
Yes, FLEXON can seamlessly convert to and from JSON. All valid JSON can be represented in FLEXON.
FLEXON supports all JSON types plus:
- DateTime
- UUID
- Binary data
- Rich numeric types
- Custom type extensions
FLEXON uses a combination of:
- Efficient binary encoding
- Built-in GZIP compression
- Type-specific optimizations
Yes, FLEXON operations are thread-safe when using separate instances.
Typically:
- 60-80% smaller than JSON
- 40-60% smaller than BSON
- 30-50% smaller than MessagePack
- Serialization: 2-3x faster than JSON
- Deserialization: 2-3x faster than JSON
- Validation: 2x faster than JSON Schema
FLEXON includes streaming support for efficient processing of large files with minimal memory usage.
FLEXON provides bindings for:
- .NET
- Python
- JavaScript/Node.js
- Java
- Go
- Rust
FLEXON supports:
- Optional fields
- Schema versioning
- Backward compatibility
- Migration tools
FLEXON can handle:
- Individual files up to 2GB
- Streaming for larger datasets
- Efficient memory usage
Yes, FLEXON works well for:
- REST APIs
- WebSocket data
- File uploads/downloads
- Configuration files
Yes, FLEXON integrates with:
- Document databases
- Key-value stores
- Object storage
- File systems
Yes, FLEXON is production-ready with:
- Comprehensive testing
- Error handling
- Performance optimization
- Security considerations
- Cause: Corrupted FLEXON file
- Solution: Verify file integrity
- Cause: Data doesn't match schema
- Solution: Check validation errors
- Cause: Memory or I/O issues
- Solution: Check system resources
- Use streaming for large files
- Enable compression for network transfer
- Cache compiled schemas
- Process data in chunks
- Use appropriate buffer sizes
- Clean up resources properly
- Implement proper validation
- Handle edge cases
- Log errors appropriately
- GitHub Issues
- Documentation
- Community Forums
- Email Support
Create a GitHub issue with:
- Version information
- Error message
- Reproduction steps
- Sample data (if possible)
Submit a feature request with:
- Use case description
- Expected behavior
- Technical requirements
- Impact assessment