What is Dataweave

Categories: Dataweave

DataWeave is a programming language designed for transforming data. It is MuleSoft’s primary language for data transformation, as well as the expression language used to configure components and connectors.

However, DataWeave is also available in other contexts, like as a command line tool. These tutorials will largely treat DataWeave as a standalone language, with Mule-specific info designated with (M).

DataWeave allows users to easily perform a common use case for integration developers: read and parse data from one format, transform it, and write it out as a different format.

For example, a DataWeave script could take in a simple CSV file and transform it into an array of complex JSON objects. It could take in XML and write the data out to a flat file format.

DataWeave allows the developer to focus on the transformation logic instead of worrying about the specifics of reading, parsing, and writing specific data formats in a performant way.

When DataWeave receives data, it puts it through the reader. The reader’s job is to parse the input data into a canonical model. It then passes that model to the DataWeave script where it is used to generate the output, which is another canonical model.

That last canonical model is passed into a writer. The writer is responsible for serializing the canonical model into the desired output data format.

While DataWeave can handle itself when it comes to parsing and serializing data, it does need to be told what data to expect. This is done by specifying MIME types for the inputs and output.

MIME types specify the data format of a particular document, file, or piece of data. We use them to inform DataWeave what data format to read and write.

There are many MIME types, but DataWeave only uses a subset of them that make sense for its data transformation domain. Of that subset, there are only 3 we will to concern ourselves with for this tutorial:

1. application/xml – XML
2. application/json – JSON
3. application/csv – CSV

Here’s an example which takes in an array of JSON objects and transforms it into a CSV without a header.

Input:

[
  {
    "firstName": "John",
    "lastName": "Smith",
    "age": 45
  },
  {
    "firstName": "Jane",
    "lastName": "Doe",
    "age": 34
  }
]

DataWeave 2.0 Script:

%dw 2.0
input payload application / json
output application / csv header = false
—--
payload

Output:

John,Smith,45,Jane,Doe,34

This tutorial series will use an output MIME type of application/json for most cases. Other MIME types will be used to shed light on certain language features that may seem odd or not very useable otherwise. It will review more specific considerations for other MIME types later in the tutorial.

Let’s go over the anatomy of a DataWeave script using the code from the last example:

%dw 2.0
input payload application/json
output application/csv header=false
---
payload

The first three lines of the script contain directives. The first directive, which is in every DataWeave file, defines which version the script is using. You can think of this more as a necessary formality, as other factors will determine which DataWeave version is used to run your script (e.g., the Mule Runtime).

(M) If you’re in a Mule 3 project, you will always use %dw 1.0. If you’re in a Mule 4 project, you will always use %dw 2.0

The second and third lines contain the input and output directives. They each have their own form:

input <var_name> <mime_type> [<reader_properties>] output <mime_type> [<writer_properties>]

(M) If you’re in a Mule 4 project, you won’t be using the input directive at all. Instead, set the MIME type and any reader properties on your message source (e.g., HTTP Listener).

After the first three lines of the script there is a line only containing three dashes. This is to separate your declarations from your script output logic.

You’ll see in later tutorials that you can do more than just specify input and output directives in the declarations section, you can also declare functions and variables that you can reuse in your script.

The last line of the script is the output section. In Mule projects, payload refers to a predefined variable that corresponds to the payload of the MuleEvent as it hits a DataWeave script.

Whatever the output section evaluates to is what gets sent to the writer, and is ultimately serialized into the specified output format.