JSONDetective 🔍
A powerful tool for analyzing and understanding JSON schemas. Built to handle large, complex JSON files by automatically detecting and abstracting patterns in your data.
Key features:
- Automatically recognizes and normalizes date formats in both keys and values
- Detects optional fields by analyzing multiple instances
- Abstracts repeated patterns into clean, readable schemas
Quick Start
# Install pip install jsondetective # Use jsondetective data.json
Pattern Recognition Example
Given a JSON with repeated date patterns like:
{
"2021-08-24": {"views": 100, "likes": 20},
"2021-08-25": {"views": 150, "likes": 30},
"2021-08-26": {"views": 200, "likes": 40}
}
JSONDetective recognizes the pattern and abstracts it as:
{
"yyyy-mm-dd_1": {
"type": "object",
"properties": {
"views": {"type": "integer"},
"likes": {"type": "integer"}
}
}
}
Note: The _1 suffix indicates the nesting level in the JSON structure.
Complex Structure Example
It also handles nested structures with various data types and patterns:
{
"users": [
{
"id": "123",
"joined_date": "2024-01-15",
"last_active": "2024-03-20T15:30:00Z",
"activity": {
"2024-03-19": {"posts": 5},
"2024-03-20": {"posts": 3}
},
"preferences": {
"theme": "dark",
"notifications": true
}
}
],
// many more users...
}
Produces this clean schema:
{
"users": {
"type": "array",
"items": {
"id": {
"type": "string",
"examples": ["123"]
},
"joined_date": {
"type": "string",
"format": "yyyy-mm-dd"
},
"last_active": {
"type": "string",
"format": "datetime"
},
"activity": {
"type": "object",
"properties": {
"yyyy-mm-dd_2": {
"type": "object",
"properties": {
"posts": {"type": "integer"}
}
}
}
},
"preferences": {
"type": "object",
"properties": {
"theme": {
"type": "string",
"optional": true
},
"notifications": {
"type": "boolean"
}
}
}
}
}
}
Features
-
Intelligent Pattern Detection:
- Recognizes date formats in both keys and values
- Abstracts repeated structures
- Identifies optional fields
-
Schema Intelligence:
- Detects data types
- Identifies nested structures
- Provides example values
- Experimental: Python dataclass generation (beta feature)
Advanced Usage
Experimental Python Dataclass Generation
# Print dataclass to console jsondetective data.json -d # Save to file jsondetective data.json -d -o my_dataclasses.py # Custom class name jsondetective data.json -d -c MyDataClass
CLI Options
jsondetective [JSON_FILE] [OPTIONS] Options: -d, --create-dataclass Generate Python dataclass code -o, --output-path PATH Save dataclass to file -c, --class-name TEXT Name for the root dataclass (default: Root) --help Show this message and exit
Why Use JSONDetective?
- Pattern Recognition: Automatically detects and abstracts repeated patterns
- Date Handling: Intelligent date format recognition and normalization
- Large Files: Efficiently processes and summarizes large JSON structures
- Clear Output: Clean, readable schema representation
- Time Saving: No manual inspection of large JSON files needed