DataWeave can be used in Mule to transform message payloads. The Transform Message element allows writing DataWeave code to transform incoming message elements into outgoing message elements. The editor provides input/output previews and autocomplete. DataWeave expressions can also be used directly in other Mule components using the dw() function.

1. DATAWEAVE IN MULE

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 that involve aggregation, filtering,
calculations, defining custom functions, etc and there you
must write this out in DataWeave code.

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 button if you’ve made any
changes to elements that come after the Transform
Message element on the flow that expose metadata and
don’t mind loosing what you’ve already written into the
transform’s body.

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 corresponding to the currently selected transform.

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})`]