The JSON Management system will be implemented as a collection of custom nodes for ComfyUI, built in Python. Each node will inherit from a common base class that provides shared functionality for JSON handling and error management.
Common base class providing:
- JSON validation utilities
- Error handling mechanisms
- Type checking functions
- Performance optimization helpers
Purpose: Extract random values from JSON structures Processing Logic:
- For arrays: Use random.choice() for direct selection
- For objects: First select random key, then return corresponding value
- For nested structures: Support depth parameter for multi-level random selection
Edge Cases:
- Empty JSON structures
- Mixed data types
- Nested structures exceeding maximum depth
Purpose: Iterate through JSON object key-value pairs Processing Logic:
- Implement iterator protocol
- Maintain iteration state
- Support pause/resume operations
State Management:
- Current iteration index
- Iteration history
- Reset capability
Purpose: Sequential array element access Processing Logic:
- Linear array traversal
- Index tracking
- Element type handling
Features:
- Bidirectional iteration
- Index access
- Batch processing capability
Purpose: Combine multiple JSON structures Processing Logic:
- Deep merge for objects
- Concatenation for arrays
- Conflict resolution strategies
Merge Strategies:
- Override existing keys
- Preserve existing keys
- Custom conflict resolution
Purpose: Update JSON object values Processing Logic:
- Path-based value location
- Type-safe value replacement
- Structure preservation
Validation:
- Key existence checking
- Type compatibility verification
- Structure integrity maintenance
Purpose: Convert string to JSON structure Processing Logic:
- JSON syntax validation
- Schema validation (optional)
- Error recovery options
Error Handling:
- Syntax error detection
- Schema violation reporting
- Malformed input recovery
Purpose: Create new JSON objects Processing Logic:
- Dynamic object construction
- Type inference
- Schema compliance
Validation:
- Key uniqueness
- Value type validation
- Structure consistency
Purpose: Calculate JSON structure size Processing Logic:
- Object key counting
- Array length calculation
- Nested structure handling
Features:
- Depth-aware counting
- Type-specific calculations
- Performance optimization for large structures
Purpose: Verify key existence Processing Logic:
- Key path resolution
- Nested key checking
- Case sensitivity options
Features:
- Deep key checking
- Multiple key verification
- Partial path matching
Purpose: Convert JSON to string Processing Logic:
- Pretty printing
- Compact formatting
- Custom formatting options
Features:
- Indentation control
- Encoding options
- Format customization
- Syntax Errors
- Type Errors
- Value Errors
- Structure Errors
- Detailed error messages
- Error recovery options
- User-friendly notifications
- Lazy evaluation for large structures
- Caching for frequently accessed values
- Stream processing for large arrays
- Efficient data structure usage
- Memory-conscious operations
- Resource cleanup
- Node registration
- UI component integration
- Event handling
- Input validation
- Output formatting
- Inter-node communication
- Unit tests for individual nodes
- Integration tests for node combinations
- Performance tests for large datasets
- Edge cases
- Error conditions
- Performance benchmarks
- Function documentation
- Class documentation
- Usage examples
- Node usage guides
- Example workflows
- Troubleshooting guides