DataWeave can be used in Mule to transform message payloads. The DataWeave transformer allows writing DataWeave code to read input data, perform transformations, and output the results. It provides an editor with input/output previews, autocomplete, and scaffolding assistance. DataWeave expressions can also be used elsewhere in Mule via the dw() function.

1. DataWeave in MULE
By Nishant

2. Using the DataWeave Transformer
• In Anypoint Studio, you can place a Transform
Message element in a flow to create
transformations using the DataWeave language.
• The editor helps you do this by offering intelligent
autocomplete suggestions, an output preview
that is updated in real time as you make changes.
• This generates a .dwl transformation file (or
several of them) that stores your code and is
packaged with your Mule application.

3. • When adding a Transform Message element to
a Mule Flow, it takes the elements from the
incoming Mule Message as its inputs. It then
performs the necessary actions to produce a
Mule message as output for the next element
in the flow

4. • The DataWeave Text Editor GUI
• The Transform Message element allows you to
freely write DataWeave code.
• If you click on an instance of the Transform
Message element in your flow, its properties
editor will be displayed. This editor is divided
into three sections:
• Input
• Transform
• Output

5. Input Section
In the main tab, a tree view shows the known
metadata contents of the incoming Mule Message,
allowing you to explore it and know what data is
available for using as an input, and how to
reference each part of it.
If the Mule flow doesn’t expose Metadata about
the elements you need from the incoming message,
you can manually specify it. To do so, select the
element of the input you desire to define, note that
the pencil icon at the top right is no longer grayed
out. Click this icon to open a new tab in your input
section where you can define a sample with the
structure of this data.

6. When the input is of JSON or XML types, the sample input contains plain XML or JSON code.
When the input is of type POJO or DataWeave, the sample input is written in DataWeave for
more simplicity. In these cases the sample DataWeave code is merely a way to display the
sample data, not a transformation in itself.

7. Setting Reader Parameters
• Some input formats, like CSV, allow you to define a
reader with specific properties that make DataWeave
parse inputs differently. Select the input element you
wish to configure on the tree view of the input
section, then click the gear icon.

8. Transform Section
• you write the actual DataWeave code that
carries out the transform. Notice that
changing the type of your output directive
changes the output section of the editor.
Although DataWeave as a language supports
adding input directives and naming these by
any name you like, in Studio the elements of
the input message are implicitly considered
input directives and so they don’t need to be
defined in the header.

9. If Studio has any metadata about the
components that are upstream or downstream
from your Transform Message element at the
time when you add the component to your flow,
a scaffolding for your DataWeave code is
written out automatically, with as much depth
as Studio can intelligently deduce. In some
cases, this code may be enough to carry out the
transformation you need, and no additional
coding is needed. Sometimes, all you need to do
is fill in the blank spaces in the scaffolding with
references to the input fields, other times you
may want to carry out more complex operations

10. Re-scaffolding
• Once you’ve added the Transform
Message element to your flow, any further
changes you make to the surrounding
message processors and their metadata won’t
affect your `.dw`l file. You may still click the
Scaffolding button on the top left of
the DataWeave properties editor any time you
want and have a new scaffolding built, note
that this will erase anything you’ve written in
the DataWeave body. Doing this won’t affect
any directives you defined in your header
(except for the output directive). Use this

11. Referencing Existing Transforms
Instead of defining a new .dwl file, you can
reference an existing one by clicking theData
Source button, selecting On File as the source
and referencing the correct file.

12. Handling Multiple Outputs
A single Transform Message element can give
shape to several different components of the
output Mule message. Each of these output
components must be defined in a
separate .dwl file, written out in a separate tab
of the Transform section. For example in one tab
you may be defining the payload contents, in
another those of an outbound property, and
these will both be parts of the same output
Mule message

13. To add a new output, click the Plus sign at the bottom
right of the section:new+output.png[image]
A new tab will then appear, there you can specify
where in the output Mule message to place the output
of this DataWeave transform. In case you’re creating a
new variable or property, you must also set a name for
it.

14. In the XML editor you can do the same by adding
multiple child elements inside thedw:transform-
message component.
<dw:transform-message>
<dw:set-payload resource="classpath:path/transform.dwl"/> <dw:set-
variable variableName="myVariable"
resource="classpath:path/transform.dwl"/> <dw:set-session-variable
variableName="mySessionVariable" resource="classpath:path/transform.dwl"/>
</dw:transform-message>

15. Output Section
• This section has two tabs, one of them shows you
a neat expandable tree view of the output data
structure, the other shows you a preview of what
the actual output looks like, built from any
sample data you provide in the input section. As
you make changes in the transport section, notice
how the data structure changes. The output of
the transformer is made into the selected
component of the output mule message. If your
transformer has multiple outputs,
the Preview tab will display the one

16. Using DataWeave Language Elsewhere
• All components in Mule that support Mule
Expression Language also support expressions
written in DataWeave Language. To invoke an
expression written in DataWeave language,
simply invoke the dw() function, the expression
will return whatever the transform outputs.
• DataWeave expressions defined within this
function work just as those defined within a
Transform Message element, the only difference
is that the output is returned into the
expression’s result, wherever it may be.

17. • For example, you can define a custom object
and populate it with elements from the
payload:
• That same expression could be added inside a
Logger,
• within a MEL expression, to print out its
result:
dw(myobject:{id:payload.accountid, user:payload.user})
#[`dw(myobject:{id:payload.accountid, user:payload.user})`]