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This project is experimental and in its very beginning stages. APIs may change significantly, and it is not yet ready for production use.
4dba8915 Fe5c 4857 Bbe7 D75cb12f9a40(2) Pn

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Key Features

🦆 Format Agnostic: JSON, XML, CSV, YAML, and more. ⚡ High Performance: Stream-based processing to handle large datasets efficiently. 🔄 Two-Way Mapping: Configure bidirectional mappings easily. 🔍 Schema Inference: Automatically detects schemas from sample data. ✨ Data Transformation: Apply powerful transformations on-the-fly. 🔄 Conditional Mapping: Map fields based on conditional logic. 🔗 Extensible Adapters: Plugin support for custom formats and transformations. 🐛 Rich Validation & Error Handling: Detailed validation before processing.

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Getting Started

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Installation

<!-- Maven -->

// Gradle

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Basic Usage

// Create a simple mapping from customer to profile
        val customerMapping = Platymap.flow("customer")
            .withFormat(Format.JSON)
            .to("profile")
            .withFormat(Format.JSON)
            .map("customer.firstName").to("profile.name.first").end()
            .map("customer.lastName").to("profile.name.last").end()
            .map("customer.email").to("profile.contact.email").end()
            .build()

        val jsonInput =
            "{ \"customer\": { \"firstName\": \"John\", \"lastName\": \"Doe\", \"email\": \"[email protected]\" } }"
        val jsonOutput = customerMapping.executeToJson(jsonInput)
        val xmlOutput = customerMapping.executeToXml(jsonInput)
Output: 
{
  "profile": {
    "name": {
      "first": "John",
      "last": "Doe"
    },
    "contact": {
      "email": "[email protected]"
    }
  }
}

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Examples

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Wildcard Mapping

Map all fields at once from one structure to another: 
    val customerMapping = Platymap.flow("customer")
        .withFormat(Format.JSON)
        .to("profile")
        .withFormat(Format.JSON)
        .mapAll("customer.*").to("profile").end()
        .build()

    val jsonInput = """
        {
            "customer": {
                "firstName": "John",
                "lastName": "Doe",
                "email": "[email protected]",
                "age": 30,
                "address": {
                    "street": "123 Main St",
                    "city": "Anytown",
                    "zipCode": "12345"
                }
            }
        }
    """.trimIndent()
    
    val result = customerMapping.executeToJson(jsonInput)

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Flatten Nested Structures

Transform nested objects into flat fields: 
        val addressMapping = Platymap.flow("customer")
            .withFormat(Format.JSON)
            .to("profile")
            .withFormat(Format.JSON)
            .flatten("customer.address").flattenWithPrefix("addr_").to("profile").end()
            .map("customer.firstName").to("profile.name").end()
            .map("customer.lastName").to("profile.surname").end()
            .build()

        // Create test input JSON with nested address structure
        val jsonInput = """
            {
                "customer": {
                    "firstName": "John",
                    "lastName": "Doe",
                    "address": {
                        "street": "123 Main St",
                        "city": "Anytown",
                        "state": "CA",
                        "zipCode": "12345",
                        "country": "USA"
                    }
                }
            }
            """.trimIndent()

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Nest Flat Fields

Group related flat fields into a structured object: 
        val productMapping = Platymap.flow("order")
            .withFormat(Format.JSON)
            .to("invoice")
            .withFormat(Format.JSON)
            .nest("order.item_*").asCollection("lineItems").to("invoice")
            .map("order.orderNumber").to("invoice.reference").end()
            .map("order.orderDate").to("invoice.date").end()
            .build()
        val jsonInput = """
        {
            "order": {
                "orderNumber": "ORD-5678",
                "orderDate": "2023-05-15",
                "item_1_name": "Laptop",
                "item_1_price": 999.99,
                "item_1_quantity": 1,
                "item_2_name": "Mouse",
                "item_2_price": 25.50,
                "item_2_quantity": 2,
                "item_3_name": "Keyboard",
                "item_3_price": 75.00,
                "item_3_quantity": 1,
                "customerName": "Jane Smith"
            }
        }
        """.trimIndent()

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Collection Mapping

Process arrays and collections: 
        val orderMapping = Platymap.flow("order")
            .to("receipt")
            .map("orderNumber").to("receipt.reference").end()
            .map("customerName").to("receipt.buyer").end()
            .forEach("items").`as`("item")
            .create("receipt.lines")
            .map("\$item.productName").to("description").end()
            .map("\$item.quantity").to("amount").end()
            .map("\$item.price").to("unitPrice").end()
            .map("\$item.price * \$item.quantity").to("total").end()
            .end()
            .end()
            .build()

        val jsonInput = """
        {
            "orderNumber": "ORD-12345",
            "customerName": "Jane Smith",
            "items": [
                {
                    "productName": "Laptop",
                    "quantity": 1,
                    "price": 999.99
                },
                {
                    "productName": "Mouse",
                    "quantity": 2,
                    "price": 25.50
                },
                {
                    "productName": "Keyboard",
                    "quantity": 1,
                    "price": 75.00
                }
            ]
        }
    """.trimIndent()

        val result = orderMapping.executeToJson(jsonInput)

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Conditional Mapping

Apply different mapping logic based on conditions: 
Mapping userMapping = Platymap.flow("user")
        .to("account")
        .branch()
        .when(user -> {
            // Check if user is admin
            DataNode dataNode = (DataNode) user;
            return "admin".equals(dataNode.getAsObject().get("type").getAsString());
        })
        .then()
        .map("name").to("account.adminName").end()
        .map("'Full Access'").to("account.permissions").end()
        .endBranch()
        .otherwise()
        .map("name").to("account.userName").end()
        .map("'Limited Access'").to("account.permissions").end()
        .endBranch()
        .end()
        .build();

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Field Exclusion and Transformation

Exclude sensitive fields and transform data during mapping: 
Mapping userMapping = Platymap.flow("user")
        .withFormat(Format.JSON)
        .to("profile")
        .withFormat(Format.JSON)
        .mapAll("user.*")
        .excluding("user.password", "user.ssn", "user.creditCard")
        .transformEach((key, value) -> {
            // Uppercase all string values
            if (value instanceof String) {
                return ((String) value).toUpperCase();
            }
            return value;
        })
        .to("profile.userInfo")
        .end()
        .build();

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Architecture

Architecture of Platymap library Platymap follows a modular architecture with:
  • Core Engine: Handles the mapping execution and transformation logic
  • Format Adapters: Convert between different data formats
  • Function Registry: Manages custom transformation functions
  • Validation Layer: Ensures data integrity during mapping

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Roadmap

  • Enhanced schema validation
  • Performance optimizations for large datasets
  • Additional format adapters
  • Visual mapping designer
  • Cloud integration for mapping as a service

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Contributing

We welcome contributions! Please feel free to submit a Pull Request.

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License

Platymap is released under the MIT License.