Thursday 21 March 2024

Reduce in data weave ?180

 Reduce in data weave ?

The reduce function in DataWeave is a powerful tool for iterating over an array and transforming it into a single scalar value, a map, or another array. Here's a detailed explanation:


array reduce ( (item: T, accumulator: T) -> T): T | Null


  • array: This represents the array you want to iterate through. It can contain elements of any data type.

  • (item: T, accumulator: T) -> T: This is the lambda expression that defines the logic for each iteration.

  • item: This refers to the current element within the array during each iteration.

  • accumulator: This acts as a variable that stores the intermediate result after processing each element. It's initialized with the first element of the array by default (unless an initial value is explicitly provided). You can modify the accumulator within the lambda expression to build the final result.

  • The return type of the lambda expression (T) represents the data type of both the item and the modified accumulator at each step.

Optional Initial Value:

array reduce ( (item: T, accumulator: T = initialValue) -> T): T | Null

  • You can optionally provide an initial value for the accumulator using accumulator: T = initialValue. This value will be used instead of the first element of the array for the first iteration.

Return Type:

  • The reduce function returns a single value of type T (the same type as the elements in the lambda expression), or Null if the array is empty. The final return value depends on how you manipulate the accumulator within the lambda expression.

Common Use Cases:

  1. Sum of Elements:

%dw 2.0
output application/json

var numbers = [1, 2, 3, 4];

var sum = numbers reduce ($ + $$); // $$ refers to the current accumulator value


  "sum": 10  // Output: Sum of all elements

  1. Maximum Value:

%dw 2.0
output application/json

var numbers = [1, 5, 2, 8];

var max = numbers reduce (max, $ -> max > $ ? max : $);


  "max": 8  // Output: Maximum element in the array

  1. Filtering and Transforming:

%dw 2.0
output application/json

var data = [
  { id: 1, name: "Alice" },
  { id: 2, name: "Bob" },
  { id: 3, name: "Charlie" }

var filteredNames = data reduce ([], (item, acc) -> acc ++ { id:, name:toupper( });


  "filteredNames": [
    { "id": 1, "name": "ALICE" },
    { "id": 2, "name": "BOB" },
    { "id": 3, "name": "CHARLIE" }

Key Points:

  • The reduce function allows for concise and efficient array processing within DataWeave.

  • By modifying the lambda expression, you can achieve various tasks like summation, filtering, transformation, and more.

  • The initial value for the accumulator provides flexibility for handling empty arrays or specific starting points.

By understanding the reduce function, you can effectively manipulate arrays and create powerful data processing flows in MuleSoft 4 applications.

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