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Transform Node

The Transform node maps and transforms event data by creating new payloads from existing data.

The Transform Map node's mapping configuration. Each mapping value is an expression evaluated against the incoming payload.

Overview#

Transform nodes take input events and create new output events with transformed data structures.

Use Cases
  • Reshape data structures
  • Combine data from multiple sources
  • Calculate derived values
  • Normalize data formats

Configuration#

Field Type Required Description
mapping object Yes Object mapping new field names to expressions

Mapping Format#

The mapping field is a JSON object where:

json

{
  "new_field": "{{ expression }}",
  "another_field": "{{ another_expression }}"
}
Transform replaces the entire payload

The output payload contains only the keys you map; every field of the input payload that you don't reference is dropped. (Event meta is carried through unchanged.) To keep the original payload alongside your new fields, map it explicitly:

json

{
  "new_field": "{{ payload.something }}",
  "_original": "{{ payload | tojson }}"
}

Basic Examples#

Simple Field Mapping#

Input:

json

{
  "first_name": "John",
  "last_name": "Doe"
}

Mapping:

json

{
  "full_name": "{{ payload.first_name }} {{ payload.last_name }}",
  "email": "{{ payload.email }}"
}

Output:

json

{
  "full_name": "John Doe",
  "email": "john@example.com"
}

Using Direct Connection#

When directly connected to the previous node, use payload:

Mapping:

json

{
  "user_id": "{{ payload.id }}",
  "email": "{{ payload.email }}",
  "name": "{{ payload.first_name }} {{ payload.last_name }}"
}

Using Non-Direct Steps#

When accessing data from a non-directly connected node:

Mapping:

json

{
  "user_id": "{{ steps.user_lookup.output_payload.id }}",
  "email": "{{ steps.user_lookup.output_payload.email }}",
  "name": "{{ steps.user_lookup.output_payload.first_name }} {{ steps.user_lookup.output_payload.last_name }}"
}

Calculations#

Mapping:

json

{
  "total": "{{ payload.price * payload.quantity }}",
  "tax": "{{ payload.price * payload.quantity * 0.1 }}",
  "grand_total": "{{ payload.price * payload.quantity * 1.1 }}"
}

Conditional Values#

Mapping:

json

{
  "status": "{{ 'active' if payload.enabled == true else 'inactive' }}",
  "priority": "{{ 'high' if payload.amount > 1000 else 'normal' }}"
}

Advanced Examples#

Combining Multiple Sources#

Mapping:

json

{
  "user": {
    "id": "{{ steps.user_lookup.output_payload.id }}",
    "email": "{{ steps.user_lookup.output_payload.email }}"
  },
  "preferences": {{ steps.preferences_lookup.output_payload | tojson }},
  "metadata": {
    "source": "{{ initial.meta.trigger_source }}",
    "workflow": "{{ initial.meta.workflow_name }}"
  }
}

Array Transformations#

Mapping:

json

{
  "item_count": "{{ payload.items | length }}",
  "total_value": "{{ payload.items | sum(attribute='price') }}",
  "item_ids": "{{ payload.items | map(attribute='id') | list }}"
}

JSONPath Integration#

Mapping:

json

{
  "active_user_emails": "{{ payload | jsonpath('$.users[?(@.active == true)].email') }}",
  "user_count": "{{ payload | jsonpath('$.users.`len`') }}"
}

Best Practices#

Tips
  1. Use descriptive field names
  2. Keep mappings simple and readable
  3. Test with sample data
  4. Use default filter for missing data:
json

   {
     "email": "{{ payload.email | default('unknown@example.com') }}"
   }
  1. Document complex transformations with comments in expressions

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