Transforming JSON Arrays in DataWeave: A Guide to Handling Flat Structures with Repeating Keys

Discover how to transform JSON arrays in DataWeave 2.0, focusing on iterating through flat structures with repeating keys like `Part` and `Labor`. Learn the technique to create a new structured format for improved data handling.
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Transforming JSON Arrays in DataWeave: A Guide to Handling Flat Structures with Repeating Keys

When working with JSON data structures, especially in applications like Mulesoft, you may encounter scenarios where you need to transform a JSON array into a new structure with repeated keys. A common problem arises when trying to manage flat structures consisting of keys like Part and Labor. This guide aims to address that challenge and provide you with a step-by-step guide on how to achieve the desired transformation using DataWeave 2.0.

The Problem

Imagine you have a JSON array that consists of multiple objects containing details about Parts and Labor. Each object has a LineDescription that can be associated with multiple labor types or parts:

[[See Video to Reveal this Text or Code Snippet]]

Your goal is to transform this JSON array into a new structured format where each LineDescription is associated with its corresponding Labor or Part, like this:

[[See Video to Reveal this Text or Code Snippet]]

The Solution

To achieve this transformation, we can leverage the power of DataWeave 2.0’s expressive features. The approach involves using the pluck function to extract keys, followed by filtering and mapping to create the correct output structure.

Step-by-step Breakdown

Define valid keys: First, we define which keys we consider valid for our transformation, in this case, Part and Labor.

[[See Video to Reveal this Text or Code Snippet]]

Apply flatMap: The transformation uses flatMap to iterate through each item in the JSON payload.

Use pluck: The pluck function extracts the key-value pairs, which we filter based on our valid keys.

Mapping the output structure: Finally, we map the extracted information into the desired output format.

Here’s how the complete DataWeave code looks:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code

%dw 2.0 indicates the version of DataWeave you are using.

output application/json specifies the output format.

flatMap allows us to create a new array based on the original array while flattening any nested structures.

pluck extracts the key-value pairs from each item, and filter ensures we only keep the relevant keys.

The final map creates a new object containing the LineDescription, LaborOrPart, and the corresponding parts or labor details.

Conclusion

Transforming JSON arrays with repeating keys in DataWeave can be complex, but by following the structured approach outlined above, it becomes manageable. This method helps create a clear and organized output structure that can be invaluable for further data analysis or processing.

If you have any questions or need further clarification about DataWeave transformations, feel free to ask in the comments below!