Using dataweave in mule

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})`]