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Mulesoft DataWeave is a simple powerful tool to transform data inside a flow. Numerous core operators and functions are already present to perform various operations such as capitalize, camelize, upper, and lower.
For string operations, there are no core functions present to resolve a wildcard. I hope the DataWeave(DWL 1.0) function below helps you to perform a requirement where you want to resolve a wildcard using a set of parameters.
In this blog, I'll demonstrate how to use DataWeave filter functions in various ways.
All the examples below use the same input.
Using the DataWeave exercise below, I'll demonstrate how to use the reduce, map, valuesOf, splitBy functions, format, and ++ operator.
[
{
"empId": "ex12345",
"fullName": "Peter parker",
"street": "Second Ave 46",
"city": "San Francisco",
"state": "veniam",
"postal": "sit",
"country": "Mexico",
"dept": "hr",
"joinedDate": "2021-12-22",
"miles": 68
},
{
"empId": "ex54321",
"fullName": "Joseph charles",
"street": "First Ave 47",
"city": "Bangalore",
"state": "Karnataka",
"postal": "560100",
"country": "India",
"dept": "finance",
"joinedDate": "2021-11-15",
"miles": 49
}
][
{
"firstName": "Peter",
"lastName": "parker",
"AddrWithValuesOf": "San Francisco,veniam,sit,Mexico",
"AddrWithPluck": "San Francisco,veniam,sit,Mexico",
"miles": 68.00,
"DateofJoin": "22-Dec-2021"
},
{
"firstName": "Joseph",
"lastName": "charles",
"AddrWithValuesOf": "Bangalore,Karnataka,560100,India",
"AddrWithPluck": "Bangalore,Karnataka,560100,India",
"miles": 49.00,
"DateofJoin": "15-Nov-2021"
}
]We can accomplish the desired output by writing the following DataWeave.
DataWeave is a programming language designed for transforming data. It is the primary language of MuleSoft for data transformation and an expression language for components and connectors configuration.
Mulesoft released Dataweave 2.4.0 for Mule Version 4.4. The 2.4.0 version of DataWeave introduced many new features. In this post, we will see one of the newly introduced DataWeave modules.
Both of these functions can be used for selecting elements from the array for a given condition, The difference is "filter" returns all the element which matches condition but 'takeWhile' returns all the elements till the first failure.
input :
[
{
"id": "100",
"name": "Dataweave by"
},
{
"id": "100",
"name": "Arpan"
},
{
"id": "200",
"name": "Kumar"
},
{
"id": "100",
"name": "Sharma"
}
]
=========dwl=============
%dw 2.0
import * from dw::core::Arrays
output application/json
---
{
// use 'takeWhile' when you wants to select the element till first failure of given condition.
"takeWhile" : payload takeWhile ((item) -> item.id == "100" ) ,
//use 'filter' when you wants to select all the element for given condition.
"filter": payload filter ((item, index) -> item.id == "100")
}
=========output=============
{
"takeWhile": [
{
"id": "100",
"name": "Dataweave by"
},
{
"id": "100",
"name": "Arpan"
}
],
"filter": [
{
"id": "100",
"name": "Dataweave by"
},
{
"id": "100",
"name": "Arpan"
},
{
"id": "100",
"name": "Sharma"
}
]
}This short article contains the Dataweave 2.0 code for reading a dynamic value from a property file based on the input passed.
Created a property file that contains the Abbreviation and Description for some defined values.
Hi Muleys,
This article is about writing complex DataWeave codes using (some, every, countBy, sumBy) operators after importing DW core libraries.
These days, I see quite a few posts that require using Counter in DataWeave. The problem faced is that users require a Sequence Number that is consistent throughout the different sections of data. Consider the below JSON:
xxxxxxxxxx
[{
"Item" : "ItemXYZ",
"SequenceNo" : 1,
"Parts": [
{ "Part" : "A1", "SequenceNo" : 2 }, { "Part" : "B1", "SequenceNo" : 3 },
{ "Part" : "A2", "SequenceNo" : 4 }, { "Part" : "B2", "SequenceNo" : 5 }
]
},
{
"Item" : "ItemRTY",
"SequenceNo" : 6,
"Parts": [
{ "Part" : "A1", "SequenceNo" : 7 }, { "Part" : "B1", "SequenceNo" : 8 },
{ "Part" : "A2", "SequenceNo" : 9 }, { "Part" : "B2", "SequenceNo" : 10 }
]
}
]