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