The log function in the dw::Core module allows to log a value or an expression. The function returns the input unchanged. This means we can wrap our code with the log function and the code is still executed as is, but also logged in a system log. As an extra argument we can specify a String value that will be a prefix to the expression value in the logging output. The fact that the input is also returned makes it very easy to add the log function to our DataWeave expressions.

In a previous post we learned we can turn a string into a string with kebab casing using dasherize from the dw::core::Strings module. If we want to turn a string into a string with camel casing we can use the underscore function. The underscore function will replace spaces, dashes and camel-casing with underscores, which makes the result snake-casing. Any uppercase characters are transformed to lowercase characters.

The dw::core::Strings module has useful functions for working with string values. One of the functions is dasherize. The function takes a string argument and replaces spaces, underscores and camel-casing into dashes. The resulting string value with hyphens is also called kebab-casing. The dasherize function also turns any uppercase character to lowercase.

DataWeave has some very nice features to transform data objects. One of those nice features is the update operator. With the update operator we can change values of keys in an object using a very concise syntax. We don’t have to go through all keys and create a new object, but we can pinpoint the exact key and change the value. To get the correct key we use selectors. Once we have the key we can set a new value. We can define a variable to contain the current value if we want to use it to define a new value. Also is it possible to add a condition that needs to be true to change the value. Finally the update operator supports upserting a value if the key might not exist yet.

DataWeave has a nice language feature called literal types. Literal types are types with a single predefined values and can be defined using a String, Number or Boolean value. So the value of a literal type is a fixed value. We can combine multiple literal types into a new type using a union type to define an enumaration in DataWeave. The enumaration can only be one of the literal types used to define it.
Together with overloaded functions literal types are very useful. We can define a function where one of the input arguments is a literal type to define specific behaviour based on the literal type. Then we can overload the function for other literal types with different behaviour. DataWeave will make sure the correct function is called based on the value of the input argument and how it matches to the literal type value.

The dw::core::Strings has a lot of functions to deal with strings. One of the functions is ordinalize that takes a number as argument and returns the ordinal value as string.

To check if a value is of a certain type in DataWeave we must use the is operator. We must specify the type after the is operator and the value before the is operator. For example to check if the value 42 is a Number we write 42 is Number. The result is a Boolean that is either true or false. In the previous example the result is true.

To calculate the modulo of two numbers in DataWeave we can use the mod function from the dw::Core module. We provide two arguments where the first argument is the number that needs to be divided by the number provided as second argument. The number that remains after a division of the input arguments is returned as a result.

In a previous blog post we learned about the zip function. DataWeave also gives us the unzip function that will do the opposite for an array with arrays. The input argument of the unzip function is an array where the elements are also arrays. This could be created by the zip function or just defined as data structure directly. The unzip function will take from each array the same index element and return it as an array with the index elements. For example with the input array [[1, "A"], [2, "B"]] will be unzipped to [[1, 2], ["A", "B"]]. When the number of elements in the arrays that need to unzipped are not equal, the unzip function will only return the elements from the index with the most elements.

DataWeave has a zip function in the dw::Core module. The function will merge two arrays into a new array. Each element in the new array is also an array and will have a value from the two original arrays from the same index grouped together. So for example we have an input array ["A", "B"] and another input array [1, 2]. The result of the zip function will be [["A", 1], ["B", 2]]. The size of the resulting array is the same as the minimal size of both input arrays. Any value from an array that cannot be merged is simply ignored and left out of the resulting array.

To measure the time it takes to execute a function in DataWeave we can use the time and duration functions from the module dw::util::Timer. Both functions take a zero argument function that needs to be executed as argument (() → T). But the output of the functions is different. The time function returns a TimeMeasurement object that has a result key containing the result of the function we passed as argument. We also get a start key that has the date and time value when the function gets executed. And finally we have the end key that stores the date and time value when the function is finished. To calculate the total duration time of the function we could use the start and end keys, but when we want the duration time we can better use the duration function. The duration function returns a DurationMeasurement object with also a key result that has the output of the function that is executed. The other key is time and contains the time it took for the function to be executed in milliseconds.

In DataWeave we can partition the items in an array using a predicate function by using the partition function from the dw::core::Arrays module. The function takes an array as first argument and a predicate function as second argument. The predicate function should return true or false for each item of the array. The result is an object with the key success containing all items from the array that returned true for the predicate function and a key failure for the items that returned false.