2. 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,
and by building an initial scaffolding for your code, based
on the available metadata in your flow. This generates
a .dwl transformation file (or several of them) that stores
your code and is packaged with your Mule application.
Using the DataWeave Transformer
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. 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.
Setting Reader Parameters
8. 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.
Transform Section
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. 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.
Re-scaffolding
11. 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.
Referencing Existing Transforms
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. 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.
Output Section
16. 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.
Using DataWeave Language
Elsewhere
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})`]
