### DataWeave: CSV to XML with Order Aggregation

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Transforms CSV customer data to XML format while joining with inline order data. This example demonstrates reading embedded CSV, custom functions for filtering, XML attribute creation, and calculating totals with null handling.

```DataWeave
%dw 2.0
output application/xml
var orders = "OrderID,CustomerID,Amount
101,1,250
102,2,180
103,1,320
104,3,150
105,4,null"
fun filterOrder(customer)= read(orders, "application/csv") filter $.CustomerID==customer.CustomerID
var removedAttributes=["CustomerID", "Amount"]
---
Customers: {
    (payload map ((item, index) -> "Customer" @(CustomerID: item.CustomerID, CustomerName: item.CustomerName, totalAmount: sum(filterOrder(item).Amount)  default 0):"Orders":{
     ((filterOrder(item)) map {
        Order @(lineItem: $$ + 1): $ -- removedAttributes ++{
            Amount: ($.Amount default 0)
        }
    })
}))
}
```

--------------------------------

### Filtering Inventory Status with DataWeave Custom Function

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Filters inventory items based on their availability status using a reusable custom function. This example demonstrates function parameters with type annotations, object manipulation using `-` and `++` operators, and the `isEmpty` function for filtering out empty results. It processes an array of order items and returns filtered lists.

```DataWeave
%dw 2.0
output application/json
fun filterStatus(state: String)= payload map ($ - "items" ++{
        "items": ($.items filter ($.status == state)).name
    }) filter (!isEmpty($.items))
---
{
    "availableItem": filterStatus("available"),
    "unAvailableItem": filterStatus("out_of_stock")
}
```

--------------------------------

### Grouping Employees by Name Initial and Department from XML with DataWeave

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Groups XML employee data first by the initial letter of the employee's name and then by their department. This script demonstrates DataWeave's XML selector syntax, nested `groupBy` operations, and dynamic key creation using `mapObject`. It processes XML input and outputs a JSON object.

```DataWeave
%dw 2.0
output application/json
---
(payload.employees.*emp groupBy ($.name[0])) mapObject ((value, key, index) -> {
    (key): (value groupBy ($.dept)) mapObject ({
        ($$): $.name
    } )
})
```

--------------------------------

### Calculate Factorial with Tail Recursion

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Implements a tail-recursive function to calculate factorials efficiently. This approach prevents stack overflow errors by using an accumulator pattern for large input numbers.

```dataweave
%dw 2.0
output application/json
fun facRec(num, facValue)= if(num==1) facValue else facRec(num-1, num * facValue)
---
payload map ({
    (("factorial of ") ++ ($)): facRec($, 1)
})
```

--------------------------------

### XML Order Transformation to Flat CSV with DataWeave

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Transforms hierarchical XML orders into a flat CSV format. This script utilizes XML attribute access (`@id`), nested `map` operations with index correlation, and the `flatten` function to denormalize order items, including calculations for line totals. The output is a CSV string.

```DataWeave
%dw 2.0
output application/csv
var orders=payload.Orders.*Order
---
flatten(orders.Items map ((item1, index1) -> item1.*Item map ({
    "OrderId": orders.@id[index1],
    "CustomerName": orders.Customer.Name[index1],
    "Location": orders.Customer.Country[index1],
    "Product": $.Product,
    "Qty": $.Qty,
    "Price": $.Price,
    "LineTotal": (($.Qty default 0) * ($.Price default 0)),
    "OrderDate": $.OrderDate
})))
```

--------------------------------

### Aggregate Sales Data by Region

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Groups order records by region and calculates total sales per region. Demonstrates the use of groupBy, pluck, and sum functions to transform nested objects into summarized arrays.

```dataweave
%dw 2.0
output application/json
---
{
    "salesByRegion": (payload.orders groupBy $.region) pluck ({
    "region": $$,
    "total": sum($.amount),
    "id": $.id
})
}
```

--------------------------------

### Flattening Order Items with DataWeave flatMap

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Flattens nested order items into individual records using `flatMap` and `map`. It preserves parent order context (orderId, customerName, orderDate) while expanding each item with calculated total price. Expects a JSON structure with orders and their items.

```DataWeave
%dw 2.0
output application/json
---
payload.orders.items flatMap ((item1, index1) -> item1 map ({
    "orderId": payload.orders.orderId[index1],
    "customerName": payload.orders.customer.name[index1],
    "item": $.description,
    "totalPrice": $.qty * $.price,
    "orderDate": payload.orders.orderDate[index1]
}))
```

--------------------------------

### DataWeave: Group Customer Orders by Category

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Groups customer orders by customer ID and calculates total amounts per category using groupBy and mapObject. It computes overall amounts and category-specific totals with nested sum operations, filter, and distinctBy.

```DataWeave
%dw 2.0
output application/json
---
payload groupBy ($.customerId ) mapObject ((value, key) -> {
    (key): {
         "overallAmount": sum(value.amount),
        "categories":
            {
                (value map ((customers, index2) ->{
            (customers.category): (sum((value filter ($.category == customers.category )).amount) )
        } ) distinctBy ($))
            }
    }
})
```

--------------------------------

### Recursive JSON Flattening with DataWeave

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Flattens deeply nested JSON objects into dot-notation keys using tail recursion. It traverses nested objects, builds prefixed key paths, and iterates over key-value pairs. Handles JSON input and produces a flattened JSON output.

```DataWeave
%dw 2.0
output application/json
fun recursionOrder(orderObj, prefix="") =
orderObj pluck ((value, key, index) -> if(value is Object)
flatten(recursionOrder(value, (prefix ++ key ++ ".")) ) else (prefix ++ key): value)
---
recursionOrder(payload) map {
    (flatten($))
}
```

--------------------------------

### Convert Roman Numerals to Decimal

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Parses Roman numeral strings into decimal integers. Uses a lookup map and array reduction to handle standard values and subtractive notation logic.

```dataweave
%dw 2.0
output application/json
var romanValues= {
    "I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000
}
fun div(x)= (x splitBy "") map (romanValues[$])
var arr= div(payload.romanNumber)
---
{
    "wholeNumber": (arr map (if(($$ < sizeOf(arr)-1) and ($ < arr[$$ + 1])) -$ else $) reduce ($+$$))
}
```

--------------------------------

### Prepare Null Values for Salesforce Upsert

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Filters out null or empty fields from an object and collects them into a separate 'fieldsToNull' array. Utilizes the dw::core::Objects module for key extraction.

```dataweave
%dw 2.0
output application/json
import * from dw::core::Objects
---
payload mapObject ((($$): $) if( $ !=null)) ++ {
    "filedsToNull": keySet(payload mapObject ((($$): $) if($ == null or $ =="")))
}
```

--------------------------------

### Summarize Salesforce Upsert Results

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Categorizes batch upsert results into insert, update, or fail counts. Uses conditional logic and groupBy to aggregate status flags from the Salesforce response.

```dataweave
%dw 2.0
output application/json
var recordList= payload.items.payload
fun getStatistics(created, success)= if((created and success)==true) "insert" else if ((created or success)==true) "update" else "fail"
---
((recordList map ({
    final: getStatistics($.created, $.success)
} )) groupBy ($.final) ) mapObject (
    {
    ($$): sizeOf($)
})
```

--------------------------------

### Flatten Nested Structures with Reduce

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Flattens deeply nested JSON structures using nested reduce operations. Includes conditional logic to reorder object keys based on specific field values.

```dataweave
%dw 2.0
output application/json
---
payload reduce ((item, acc= []) -> acc ++ (item.order.place.name.asisiselm reduce ((subItem, subAcc=[]) ->subAcc ++ [if(item.order.place.name.firstName == "sachin")
    {
        "asisslem": subItem.asile,
        "Lastname": item.order.place.name.lastname
    }
    else
    {
        "Lastname": item.order.place.name.lastname,
        "asisslem": subItem.asile
    }
] )))
```

--------------------------------

### DataWeave: Mask Sensitive Fields with dw::util::Values

Source: https://context7.com/subhash-builds/mulesoft-dataweave-examples/llms.txt

Masks sensitive fields like passwords using the dw::util::Values module's mask function. It replaces all occurrences of a specified field name with a masked value throughout the payload.

```DataWeave
%dw 2.0
output application/json
import * from dw::util::Values
---
payload mask field("password") with "XXXXXX"
```

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