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Overview

JSON columns allow you to store complex, structured data that contains multiple related pieces of information. They display a preview of your data in the sheet view and offer a detailed side panel view when clicked, making it easy to work with complex information.

Unlike simple text or number columns, JSON columns keep all related information together in a structured format. This is especially useful for storing information from external systems, API responses, or any data that has multiple parts that should be kept together (like contact details with addresses, phone numbers, and social profiles all in one column).

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Use Cases

  • API response storage

    • Store complete REST API responses while preserving structure
    • Capture webhook payloads for processing and reference
    • Save third-party integration data in its native format
    • Maintain GraphQL query results with nested relationships
    • Keep a historical record of API interactions

  • Complex data structures

    • Store objects with multiple properties and values
    • Manage nested arrays of related items
    • Preserve hierarchical relationships between data elements
    • Handle mixed data types within a single column
    • Maintain dynamically changing data schemas

  • Nested relationship data

    • Track parent-child relationships within records
    • Store organization hierarchies and reporting structures
    • Maintain product catalogs with variant information
    • Capture geographic hierarchies (country > state > city)
    • Track project dependencies and relationships

  • Configuration settings

    • Store application configurations in structured format
    • Maintain user preference settings with nested options
    • Save feature flag settings and rules
    • Track environment-specific configurations
    • Store form field configurations and validation rules

  • Technology stack information

    • Track complex tech stack compositions with versions
    • Store infrastructure configurations with nested components
    • Maintain software dependency graphs
    • Document system architecture maps
    • Catalog development environments and tools

  • Feature matrices

    • Store product capability comparisons
    • Track feature availability across different plans
    • Maintain compliance requirement checklists
    • Document compatibility matrices
    • Store multi-dimensional comparison data

  • Metadata collections

    • Store file metadata with nested attributes
    • Track rich document properties and settings
    • Maintain extended attribution data
    • Store SEO metadata collections
    • Capture media asset properties (dimensions, format, encoding)

  • Hierarchical taxonomies

    • Store category trees with multiple levels
    • Maintain product classification hierarchies
    • Track knowledge base organization structures
    • Store menu structures and navigation trees
    • Document complex classification systems

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Features

  • Detailed View: See and explore complex data easily

    • Get a preview in the main table view
    • Click on any JSON cell to open a detailed side panel view
    • Browse through nested information in a user-friendly format
    • See the entire structure without truncation in the side panel
    • Copy specific values or the entire JSON structure as needed

  • Keep Related Information Together: Store multiple data points in one place

    • Keep all related information organized in a single column
    • Store objects with multiple properties (like name, address, phone)
    • Maintain lists of related items (like skills, interests, or preferences)
    • Save nested information (like addresses with street, city, state, zip)
    • Preserve the relationship between connected data points

  • Data Integrity: Ensure your structured data stays structured

    • JSON format validation ensures properly formatted data
    • Prevent accidental format breaking when editing
    • Maintain consistency of data structures
    • Ensure all required properties are included
    • Validate against expected schemas when needed

  • Advanced Operations: Work with your structured data effectively

    • Search within JSON structures for specific values
    • Transform data between different JSON structures
    • Filter table rows based on nested JSON properties
    • Sort by values within your JSON structure
    • Extract specific values for use in other columns

  • Visualization Options: See your structured data the way you want

    • Choose between tree view or raw JSON view
    • Collapse and expand nested structures as needed
    • Customize the preview to show the most important fields
    • Color-coding for different value types (strings, numbers, arrays)
    • Dark and light mode support for comfortable viewing

  • Integration Capabilities: Connect JSON data with other systems

    • Export JSON data for use in APIs and integrations
    • Import structured data from external sources
    • Transform between different data formats automatically
    • Keep data synchronized across multiple systems
    • Use as a bridge between different data structures

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JSON Column Examples

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API Response Data

{
  "status": "success",
  "code": 200,
  "data": {
    "company_info": {
      "name": "Acme Technologies",
      "founded": "2015-03-12",
      "employees": 342,
      "headquarters": {
        "city": "San Francisco",
        "state": "CA",
        "country": "USA"
      }
    },
    "metrics": {
      "annual_revenue": "$24.5M",
      "growth_rate": 18.7,
      "customer_count": 1250
    },
    "categories": ["SaaS", "Enterprise", "Analytics"]
  },
  "meta": {
    "retrieved_at": "2023-11-14T08:32:15Z",
    "source": "Company API v2.3"
  }
}

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Technology Stack Information

{
  "frontend": {
    "framework": "React",
    "version": "18.2.0",
    "state_management": "Redux",
    "ui_library": "Material UI",
    "responsive": true
  },
  "backend": {
    "language": "Node.js",
    "version": "18.12.1",
    "framework": "Express",
    "database": {
      "primary": "PostgreSQL",
      "version": "14.5",
      "caching": "Redis"
    },
    "authentication": "JWT"
  },
  "infrastructure": {
    "hosting": "AWS",
    "services": [
      "EC2",
      "RDS",
      "S3",
      "CloudFront"
    ],
    "deployment": "CI/CD with GitHub Actions",
    "monitoring": "Datadog"
  }
}

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Working with JSON Columns

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Creating JSON Columns

  1. Add a new column to your sheet
  2. Select “JSON” as the column type
  3. Configure the preview settings (which fields to show in the table view)
  4. Set any validation requirements for your JSON structure

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Editing JSON Data

  1. Click on a JSON cell to open the editor
  2. Use the tree view for easy navigation or the raw editor for direct JSON editing
  3. Add or modify properties as needed
  4. Validate your changes before saving

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Advanced Features

  • JSON Path Queries: Use JSONPath syntax to query specific values within your JSON structure
  • Schema Validation: Define a schema to ensure all JSON entries follow a specific structure
  • Template Creation: Save commonly used JSON structures as templates for quick entry
  • Transformation Rules: Set up rules to automatically transform data between different JSON formats

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Best Practices

  • Structure Consistency: Maintain the same JSON structure across all rows in a column for easier filtering and analysis
  • Key Naming: Use clear, consistent naming conventions for JSON keys (e.g., camelCase or snake_case, but not mixed)
  • Nesting Depth: Avoid extremely deep nesting (more than 4-5 levels) as it becomes harder to navigate
  • Preview Configuration: Configure the cell preview to show the most important fields for at-a-glance information
  • Documentation: Document your JSON structure if it’s complex or used by multiple team members
  • Validation: Use schema validation to ensure data quality when the structure is critical
  • Size Management: For very large JSON structures, consider breaking them into logical smaller structures