Contact Us 1-800-596-4880
  1. Homepage
  2. DataWeave (2.8)
  3. DataWeave Examples

  4. Map an Object

Map Objects

The following DataWeave examples use the mapObject function to iterate through the keys and values of objects. Before you begin, note that 2.x versions of DataWeave are used by Mule 4 apps. For DataWeave in Mule 3 apps, refer to DataWeave version 1.2 examples. For other DataWeave versions, you can use the version selector in the DataWeave table of contents.

First Example

This example uses both the map and mapObject functions to iterate through the input and set all of the keys to upper case.

The example uses these DataWeave functions:

  • map to go through the elements in the "books" array.

  • mapObject to go through the keys and values in each of the objects of the array.

  • upper to set each key to upper case.

DataWeave Script:
%dw 2.0
output application/json
---
items: payload.books map (item, index) -> {
      book: item mapObject (value, key) -> {
      (upper(key)): value
      }
}
Input JSON Payload:
{
    "books": [
      {
        "-category": "cooking",
        "title":"Everyday Italian",
        "author": "Giada De Laurentiis",
        "year": "2005",
        "price": "30.00"
      },
      {
        "-category": "children",
        "title": "Harry Potter",
        "author": "J K. Rowling",
        "year": "2005",
        "price": "29.99"
      },
      {
        "-category": "web",
        "title":  "XQuery Kick Start",
        "author": [
          "James McGovern",
          "Per Bothner",
          "Kurt Cagle",
          "James Linn",
          "Vaidyanathan Nagarajan"
        ],
        "year": "2003",
        "price": "49.99"
      },
      {
        "-category": "web",
        "-cover": "paperback",
        "title": "Learning XML",
        "author": "Erik T. Ray",
        "year": "2003",
        "price": "39.95"
      }
    ]
}
Output JSON:
{
  "items": [
    {
      "book": {
        "-CATEGORY": "cooking",
        "TITLE": "Everyday Italian",
        "AUTHOR": "Giada De Laurentiis",
        "YEAR": "2005",
        "PRICE": "30.00"
      }
    },
    {
      "book": {
        "-CATEGORY": "children",
        "TITLE": "Harry Potter",
        "AUTHOR": "J K. Rowling",
        "YEAR": "2005",
        "PRICE": "29.99"
      }
    },
    {
      "book": {
        "-CATEGORY": "web",
        "TITLE": "XQuery Kick Start",
        "AUTHOR": [
          "James McGovern",
          "Per Bothner",
          "Kurt Cagle",
          "James Linn",
          "Vaidyanathan Nagarajan"
        ],
        "YEAR": "2003",
        "PRICE": "49.99"
      }
    },
    {
      "book": {
        "-CATEGORY": "web",
        "-COVER": "paperback",
        "TITLE": "Learning XML",
        "AUTHOR": "Erik T. Ray",
        "YEAR": "2003",
        "PRICE": "39.95"
      }
    }
  ]
}

Second Example

This example uses the mapObject function to iterate through the keys and values of the object that results from using groupBy on the payload. If some objects of the input payload have the same values in the FirstName, LastName and Age keys, the DataWeave script transforms those objects into a single row in a CSV file. The remaining values in the Team Name and Role keys for those objects are concatenated with : in the single CSV row.

Input JSON Payload:
[
{
"Sr.No.": 1,
"FirstName": "Charles",
"LastName": "Lock",
"Age": 40,
"Team Name": "Scrum team 1",
"Role": "developer"
},
{
"Sr.No.": 2,
"FirstName": "Josh",
"LastName": "Rodriguez",
"Age": 45,
"Team Name": "architecture",
"Role": "SA"
},
{
"Sr.No.": 3,
"FirstName": "Josh",
"LastName": "Rodriguez",
"Age": 45,
"Team Name": "technology",
"Role": "advisor"
},
{
"Sr.No.": 4,
"FirstName": "Josh",
"LastName": "Rodriguez",
"Age": 35,
"Team Name": "development",
"Role": "developer"
},
{
"Sr.No.": 5,
"FirstName": "Jane",
"LastName": "Rodriguez",
"Age": 30,
"Team Name": "architecture",
"Role": "SA"
},
{
"Sr.No.": 6,
"FirstName": "Jane",
"LastName": "Rodriguez",
"Age": 30,
"Team Name": "Scrum team 1",
"Role": "developer"
},
{
"Sr.No.": 7,
"FirstName": "Josh",
"LastName": "Lee",
"Age": 42,
"Team Name": "Scrum team1",
"Role": "developer"
}
]

In the previous input example, Sr.No. 2 and Sr.No. 3, as well as Sr.No. 5 and Sr.No. 6, refer to the same person, as they have the same first name, last name, and age. However, Sr.No. 4 refers to a different person, as it has the same first and last name but a different age.

DataWeave Script:
output application/csv
---
valuesOf(
    payload
        groupBy ((item, index) -> (
            item.FirstName ++ item.LastName ++ item.Age))
        mapObject ((value, key, index) ->
            (index): {
                "Sr.No.": value."Sr.No." joinBy ":",
                "FirstName": value.FirstName[0],
                "LastName": value.LastName[0],
                "Age": value.Age[0],
                "Team Name": value."Team Name" joinBy ":",
                "Role": value.Role joinBy ":"
            })
)

The DataWeave script merges the repeated values of keys FirstName, LastName and Age of the objects with key-value Sr.No. 2 and Sr.No. 3, as well as Sr.No. 5 and Sr.No. 6. These values are separated with , in the single CSV row. The values of Team Name and Role for those objects are concatenated with : in the single CSV row.

Output CSV:
Sr.No.,FirstName,LastName,Age,Team Name,Role
1,Charles,Lock,40,Scrum team 1,developer
2:3,Josh,Rodriguez,45,architecture:technology,SA:advisor
4,Josh,Rodriguez,35,development,developer
5:6,Jane,Rodriguez,30,architecture:Scrum team 1,SA:developer
7,Josh,Lee,42,Scrum team1,developer

See Also