Input & Output

The Node Data Envelope

Every node's input and output — not just DataMapping's — is a JSON object shaped like this. The top-level fields are run metadata; the real payload lives under items, one entry per record, each holding the record under "json":

{
  "timestamp": "2026-07-24T01:02:27.8652562Z",
  "triggerTime": "2026-07-24T01:02:27.8652562Z",
  "resource": "trigger",
  "operation": "manual-trigger",
  "status": "success",
  "portName": "main",
  "items": [
    {
      "json": {
        "timestamp": "2026-07-24T01:02:27.8652562Z",
        "triggerTime": "2026-07-24T01:02:27.8652562Z",
        "resource": "trigger",
        "operation": "manual-trigger",
        "status": "success",
        "portName": "main"
      }
    }
  ],
  "success": true
}

DataMapping's real input is InputData["items"] — it unwraps each entry's "json" value and treats that as one source record. It never reads the outer envelope fields (timestamp, resource, operation, status, portName, success) as mapping input; those describe the upstream node's run, not your data.

Output Ports

Output Data Schema

Example: Upstream Input Through to Mapped Output

Upstream input — an HTTP Request node's output, feeding DataMapping's default InputData["items"] read:

{
  "timestamp": "2026-07-24T08:15:02.1200000Z",
  "triggerTime": "2026-07-24T08:15:00.0000000Z",
  "resource": "http-request",
  "operation": "GET",
  "status": "success",
  "portName": "main",
  "items": [
    {
      "json": {
        "contact": {
          "fullName": "  Jane Doe  ",
          "emailAddress": "[email protected]",
          "phone": "(512) 555-1234",
          "createdAt": "2023-11-15T00:00:00Z",
          "accountBalance": 1234.5,
          "firstName": "Jane",
          "lastName": "Doe"
        }
      }
    }
  ],
  "success": true
}

DataMapping configuration:

{
  "dataMappings": [
    { "sourceField": "contact.fullName",     "targetField": "displayName", "transform": "trim" },
    { "sourceField": "contact.emailAddress", "targetField": "email",       "transform": "lowercase" },
    { "sourceField": "contact.phone",        "targetField": "phoneE164",   "transform": "regexreplace:[^0-9]:" },
    { "sourceField": "contact.createdAt",    "targetField": "memberSince", "transform": "todate" },
    { "sourceField": "contact.accountBalance","targetField": "balance",    "transform": "todecimal" },
    { "sourceField": "contact.firstName",    "targetField": "fullName",    "transform": "expression", "transformExpression": "item.contact.firstName + ' ' + item.contact.lastName" }
  ]
}

Output after Data Mapping runs with the configuration above (note: this node's own output is always wrapped in the same envelope shape shown at the top of this page — status/recordCount/mappingFieldCount alongside items):

{
  "status": "success",
  "mappingFieldCount": 6,
  "recordCount": 1,
  "items": [
    {
      "json": {
        "displayName": "Jane Doe",
        "email": "[email protected]",
        "phoneE164": "5125551234",
        "memberSince": "2023-11-15T00:00:00Z",
        "balance": 1234.5,
        "fullName": "Jane Doe"
      }
    }
  ]
}

Multiple Upstream Records In → Multiple Output Items

When the upstream items array holds many records (e.g. a database-query node's result set), DataMapping maps each independently and produces one output item per input record — never one item wrapping the whole array:

// Upstream input.items (2 records, e.g. from a SQL query node):
[
  { "json": { "contact": { "fullName": "Alice Wong",  "emailAddress": "[email protected]" } } },
  { "json": { "contact": { "fullName": "Marcus Reed", "emailAddress": "[email protected]" } } }
]

// DataMapping output.items (2 mapped records, recordCount: 2):
[
  { "json": { "displayName": "Alice Wong",  "email": "[email protected]" } },
  { "json": { "displayName": "Marcus Reed", "email": "[email protected]" } }
]

Passthrough Mode Output

With dataMappingMode: "passthrough" and the same upstream input, each item's json is the resolved source record unchanged:

{
  "status": "success",
  "mappingFieldCount": 0,
  "recordCount": 1,
  "items": [
    {
      "json": {
        "contact": {
          "fullName": "  Jane Doe  ",
          "emailAddress": "[email protected]",
          "phone": "(512) 555-1234",
          "createdAt": "2023-11-15T00:00:00Z",
          "accountBalance": 1234.5,
          "firstName": "Jane",
          "lastName": "Doe"
        }
      }
    }
  ]
}

Using inlineDataSource Instead of Upstream Input

Type plain JSON — no envelope needed — directly into inlineDataSource:

// inlineDataSource (exactly what you type in the designer):
{ "orderId": "ORD-7741", "total": 129.50, "customerEmail": "[email protected]" }

// DataMapping configuration:
{
  "inlineDataSource": "{ \"orderId\": \"ORD-7741\", \"total\": 129.50, \"customerEmail\": \"[email protected]\" }",
  "dataMappings": [
    { "sourceField": "orderId",       "targetField": "id" },
    { "sourceField": "total",         "targetField": "amount", "transform": "todecimal" },
    { "sourceField": "customerEmail", "targetField": "email",  "transform": "lowercase" }
  ]
}

// Output — normalized into the standard envelope automatically:
{
  "status": "success",
  "mappingFieldCount": 3,
  "recordCount": 1,
  "items": [
    { "json": { "id": "ORD-7741", "amount": 129.5, "email": "[email protected]" } }
  ]
}

Accessing Output Downstream

Error Output

When no mapping rules are configured (outside passthrough mode), the source cannot be resolved at all, or an unhandled exception occurs, the node logs a "data mapping failed" activity entry and routes to the configured error port instead of populating items.