Why MorphQL?

The Challenge

In modern software development, data transformation is ubiquitous. Whether you are shaping API responses, processing ETL pipelines, or integrating third-party services, you often face:

• Complex mapping logic scattered across your codebase.
• Performance bottlenecks when processing large datasets.
• Format juggling between JSON, XML, and native Objects.
• Inconsistent transformations across different microservices.
• Debug difficulty with deeply nested, imperative mapping code.

The MorphQL Solution

MorphQL provides a unified, declarative approach to data transformation.

Feature	Benefit
Declarative DSL	Write what you want, not how to loop and assign. Queries are self-documenting.
Native Performance	Queries are compiled to optimized JavaScript functions. Compile once, execute millions of times.
Format Agnostic	Built-in format conversion (JSON ↔ XML ↔ Object) in a single query.
Centralized Logic	Keep your transformation logic separate from your business logic.
Inspectable Code	The generated code is readable JavaScript, making it easy to debug if needed.

🤖 AI & LLM Efficiency

MorphQL is uniquely suited for the era of AI-driven development.

Token Efficiency

When using Large Language Models (LLMs) to orchestrate data pipelines, context window space is precious. Generative standard JavaScript transformations consumes significantly more tokens than the equivalent MorphQL query.

JavaScript (High Token Cost):

// Requires imports, function signatures, null checks, error handling...
if (data && data.user) {
  return {
    fullName: `${data.user.firstName} ${data.user.lastName}`,
    age: Number(data.user.age),
  };
}

MorphQL (Low Token Cost):

transform
  set fullName = user.firstName + " " + user.lastName
  set age = number(user.age)

Reduced Hallucinations

By constraining the model to a strict, declarative DSL, you reduce the surface area for logical errors (hallucinations). An LLM either generates valid MorphQL syntax or invalid syntax—there are fewer "valid but wrong" states compared to complex imperative code loops.

Use Cases

🔄 API Response Transformation

Shape backend responses into frontend-friendly formats without cluttering your application code.

const shapeOrder = await compile(morphQL`
  from json to json
  transform
    set id = orderId
    set customer = billing.customerName
    section multiple items(
      set name = productName
      set price = number(unitPrice)
    ) from lineItems
`);

📦 ETL Pipelines

Process large datasets with compiled transformations for optimal throughput.

const transform = await compile(morphQL`
  from xml to json
  transform
    set productId = root.product.id
    set price = extractnumber(root.product.price)
    set available = root.product.stock > 0
`);

// Process millions of records efficiently
data.map(transform);

🔧 Format Conversion

Convert between formats with zero transformation logic—just specify source and target.

const xmlToJson = await compile(morphQL`from xml to json`);
const jsonToXml = await compile(morphQL`from json to xml`);

🧩 Nested Data Processing

Handle complex nested structures with subqueries that can parse embedded data (e.g., XML inside a JSON field).

const engine = await compile(morphQL`
  from json to object
  transform
    set orderId = id
    section metadata(
      from xml to object
      transform
        set supplier = root.vendor.name
    ) from embeddedXmlField
`);