Languages

Learn about Languages in Laminar

Overview

Laminar supports both JavaScript and JQ for writing flow transformations. Each language has its own strengths and use cases.

JavaScript

JavaScript transformations use a simple function pattern:

(payload) => {
  // Access data from previous steps or input
  const { input, step_1 } = payload;
  
  // Perform transformations
  const transformed = {
    // ... transformation logic
  };
  
  // Return result object
  return {
    "data": transformed,
    // Optional Laminar keywords
    "lam.httpRequest": {/*...*/}
  };
}

Advantages

• Familiar syntax for most developers

• Rich ecosystem of npm packages

• Native JSON handling

• Powerful array/object manipulation

Example Transformation

(payload) => {
  const { step_1 } = payload;
  
  // Filter users who spent over $20
  const highValueUsers = step_1.data.users
    .filter(user => user.totalSpent > 20)
    .map(user => ({
      "lam.workflowId": 49,
      "lam.payload": {
        "lam.queryParams": {
          "email": user.email
        },
        "lam.body": user
      }
    }));

  return {
    "lam.result": highValueUsers
  };
}

JQ

JQ is a lightweight, powerful language specifically designed for JSON processing:

# Access input data using dot notation
.input | 
# Transform using JQ operators
map(select(.totalSpent > 20)) |
# Output transformed data
{
  "lam.result": .
}

Advantages

• Concise syntax for JSON manipulation

• Powerful built-in functions

• Excellent performance for JSON processing

• Native streaming support

Example Transformation

.step_1.data.users |
map(
  select(.totalSpent > 20) |
  {
    "lam.workflowId": 49,
    "lam.payload": {
      "lam.queryParams": {
        "email": .email
      },
      "lam.body": .
    }
  }
)

Choosing a Language

• Choose JavaScript when:

  • You need complex business logic

  • Your team is more familiar with JavaScript

  • You want to use npm packages

  • You need advanced string/array operations

• Choose JQ when:

  • You're primarily doing JSON transformation

  • You want concise, readable transformations

  • Performance is critical

  • You're working with streaming data