Schema Free Nosql Structure

{
  "title": "Schema-Free NoSQL Data Structure Patterns",
  "description": "Common structural patterns used in schema-free NoSQL databases for organizing flexible document data.",
  "patterns": {
    "embeddedDocument": {
      "label": "Embedded Document",
      "description": "Nested document embedded directly within a parent document. Used when related data is always accessed together and has a one-to-one or one-to-few relationship.",
      "example": {
        "orderId": "ORD-001",
        "customer": {
          "name": "Jane Smith",
          "email": "jane@example.com"
        },
        "items": [
          {"sku": "PROD-A", "qty": 2, "price": 49.99}
        ]
      },
      "useCases": ["Order with customer info", "Blog post with author details"]
    },
    "documentReference": {
      "label": "Document Reference",
      "description": "A document stores only the ID of a related document, requiring a separate lookup. Used for many-to-many relationships or when referenced data changes frequently.",
      "example": {
        "orderId": "ORD-001",
        "customerId": "CUST-12345",
        "productIds": ["PROD-A", "PROD-B"]
      },
      "useCases": ["Order referencing customer", "Post referencing tag IDs"]
    },
    "bucketPattern": {
      "label": "Bucket Pattern",
      "description": "Groups related time-series or high-volume documents into bucket documents to reduce document count and improve read efficiency. Common for IoT sensor data.",
      "example": {
        "sensorId": "SENSOR-001",
        "bucketStart": "2026-05-02T00:00:00Z",
        "bucketEnd": "2026-05-02T01:00:00Z",
        "count": 60,
        "readings": [
          {"timestamp": "2026-05-02T00:00:00Z", "value": 23.5},
          {"timestamp": "2026-05-02T00:01:00Z", "value": 23.7}
        ]
      }
    },
    "outlierPattern": {
      "label": "Outlier Pattern",
      "description": "Handles documents that have significantly more data than the average, preventing unbounded array growth by externalizing overflow data to separate documents.",
      "example": {
        "productId": "BEST-SELLER-001",
        "reviewCount": 50000,
        "hasOverflow": true,
        "recentReviews": ["...first 100 reviews..."],
        "overflowIds": ["OVERFLOW-001", "OVERFLOW-002"]
      }
    },
    "typeDiscriminator": {
      "label": "Type Discriminator",
      "description": "Using a 'type' field to distinguish between different document variants stored in the same collection. Enables polymorphic collections with application-level routing.",
      "example": {
        "type": "physical-product",
        "id": "PROD-001",
        "name": "Barcode Scanner",
        "weight": 0.5,
        "shippingDimensions": {"l": 8, "w": 4, "h": 3}
      }
    }
  },
  "keyDesignConsiderations": [
    "Design schemas around access patterns, not relational normalization",
    "Embed when data is always accessed together",
    "Reference when data is accessed independently or shared across documents",
    "Denormalize for read performance where appropriate",
    "Consider document size limits (MongoDB: 16MB, DynamoDB: 400KB)",
    "Use consistent naming conventions even in schemaless environments",
    "Always include version or timestamp metadata for auditing"
  ]
}