Dataweave is a powerful data transformation language used in MuleSoft to transform data from one format to another. When working with data, one of the most common types of data that needs to be transformed is dates and times. Dates and times can be represented in various formats and can be tricky to manipulate, so it’s important to have a good understanding of how to handle them in Dataweave.
In this article, we’ll cover some of the key functions and syntax used in Dataweave for working with dates and times. We’ll provide examples of how to format dates and times, perform calculations on them, and convert them to different time zones.
Table of Contents
Formatting Dates and Times
Formatting dates and times is a common task when working with data. Dataweave provides several functions to help format dates and times in a specific way. The most commonly used function for formatting dates and times is the “format” function.
The “format” function takes two parameters: the date or time to be formatted and the format string. The format string defines how the date or time should be formatted. Here’s an example:
%dw 2.0
output application/json
var myDate = now()
---
{
formattedDate: myDate as String {format: "yyyy-MM-dd"},
formattedTime: myDate as String {format: "KK:mm:ss a"},
formattedDateTime: myDate as String {format: "KK:mm:ss a, MMMM dd, uuuu"}
}
In this example, we’re using the “format” function to format the current date and time as a string in the format “yyyy-MM-dd”, “KK:mm:ss a” and “KK:mm:ss a MMMM dd, uuuu”. The result of this transformation will be a JSON object with a property called “formattedDate”, “formattedTime” and “formattedDateTime” that contains the formatted date, time and date time.
Performing Calculations on Dates and Times
Another common task when working with dates and times is performing calculations on them. Dataweave provides several functions to help perform calculations on dates and times. The most commonly used functions for performing calculations on dates and times are the “daysBetween”, “hoursBetween”, and “minutesBetween” functions.
Here’s an example of using the “daysBetween” function to calculate the number of days between two dates:
%dw 2.0
output application/json
var startDate = "2022-01-01" as Date
var endDate = "2022-01-10" as Date
---
{
"daysBetween": daysBetween(startDate, endDate)
}
In this example, we’re using the “daysBetween” function to calculate the number of days between January 1st, 2022 and January 10th, 2022. The result of this transformation will be a JSON object with a property called “daysBetween” that contains the number of days between the two dates.
Converting Time Zones
Converting dates and times to different time zones is another common task when working with data. Dataweave provides a function called “asTimezone” to help convert dates and times to different time zones.
The “asTimezone” function takes two parameters: the date or time to be converted and the target time zone. Here’s an example:
%dw 2.0
output application/json
var myDate = now()
---
{
"originalDate": myDate as String { format: "yyyy-MM-dd'T'HH:mm:ss'Z'" },
"convertedDate": myDate as DateTime { timezone: "America/New_York" } as String { format: "yyyy-MM-dd'T'HH:mm:ss'Z'" }
}
In this example, we’re using the “asTimezone” function to convert the current date and time to the “America/New_York” time zone. The result of this transformation will be a JSON object with two properties: “originalDate” contains the original date and time in the UTC time zone, and “convertedDate” contains the date and time converted to the “America/New_York” time zone.
Extracting Date and Time Components
Sometimes, you may need to extract specific components of a date or time, such as the year, month, day, hour, minute, or second. Dataweave provides several functions to help extract these components. The most commonly used functions for extracting date and time components are “yearOf”, “monthOf”, “dayOf”, “hourOf”, “minuteOf”, and “secondOf”.
Here’s an example of using the “yearOf” function to extract the year from a date:
%dw 2.0
output application/json
var myDate = "2022-04-25" as LocalDate
---
{
"year": yearOf(myDate)
}
In this example, we’re using the “yearOf” function to extract the year from April 25th, 2022. The result of this transformation will be a JSON object with a property called “year” that contains the extracted year.
Parsing Dates and Times
When working with data, you may encounter dates and times in various formats. Dataweave provides a function called “parse” to help parse dates and times from strings in different formats.
The “parse” function takes two parameters: the string to be parsed and the format string. The format string defines how the date or time is formatted in the string. Here’s an example:
%dw 2.0
output application/json
var dateString = "2022-04-25T13:30:00.000Z"
---
{
"parsedDate": parse(dateString, "yyyy-MM-dd'T'HH:mm:ss.SSS'Z'") as DateTime
}
In this example, we’re using the “parse” function to parse a string in the format “yyyy-MM-dd’T’HH:mm:ss.SSS’Z'” into a DateTime object. The result of this transformation will be a JSON object with a property called “parsedDate” that contains the parsed DateTime object.
Handling Time Zones with Offset
In addition to converting dates and times to different time zones, you may also need to handle time zones with an offset. Dataweave provides a function called “fromOffsetDateTime” to help handle time zones with an offset.
The “fromOffsetDateTime” function takes a string representing a date and time with an offset and returns a DateTime object. Here’s an example:
%dw 2.0
output application/json
var dateString = "2022-04-25T13:30:00-04:00"
---
{
"parsedDate": fromOffsetDateTime(dateString) as DateTime
}
In this example, we’re using the “fromOffsetDateTime” function to parse a string in the format “yyyy-MM-dd’T’HH:mm:ssXXX” into a DateTime object. The result of this transformation will be a JSON object with a property called “parsedDate” that contains the parsed DateTime object.
Working with Time Intervals
Time intervals are periods of time that represent a duration between two points in time. Dataweave provides several functions to work with time intervals, including “intervalBetween”, “durationOf”, and “periodOf”.
Here’s an example of using the “intervalBetween” function to calculate the duration between two dates:
%dw 2.0
output application/json
var startDate = "2022-04-25" as LocalDate
var endDate = "2022-04-30" as LocalDate
---
{
"durationInDays": intervalBetween(startDate, endDate).days
}
In this example, we’re using the “intervalBetween” function to calculate the duration between two dates, April 25th, 2022 and April 30th, 2022. The result of this transformation will be a JSON object with a property called “durationInDays” that contains the duration in days between the two dates.
Formatting Dates and Times with Locale
When formatting dates and times, you may want to take into account the user’s locale to display the date and time in a format that is familiar to them. Dataweave provides a function called “format” to help format dates and times with a locale.
The “format” function takes two parameters: the date or time to be formatted and a format string that defines the format to be used. Here’s an example:
%dw 2.0
output application/json
var myDate = "2022-04-25" as LocalDate
---
{
"formattedDate": format(myDate, "dd MMMM yyyy", "en_US")
}
In this example, we’re using the “format” function to format a date in the format “dd MMMM yyyy” with the “en_US” locale. The result of this transformation will be a JSON object with a property called “formattedDate” that contains the formatted date in the specified locale.
Handling Time Zones with Names
In addition to handling time zones with offsets, you may also need to handle time zones with names, such as “America/New_York” or “Europe/London”. Dataweave provides a function called “fromTimeZone” to help handle time zones with names.
The “fromTimeZone” function takes a string representing a time zone name and returns a TimeZone object. Here’s an example:
%dw 2.0
output application/json
var timeZoneName = "America/New_York"
---
{
"timeZone": fromTimeZone(timeZoneName)
}
In this example, we’re using the “fromTimeZone” function to parse a time zone name and return a TimeZone object. The result of this transformation will be a JSON object with a property called “timeZone” that contains the TimeZone object.
Conclusion
Handling dates and times in Dataweave can be a challenging task, but with the right tools and knowledge, it can be made much easier. In this blog post, we covered some of the key functions and syntax used in Dataweave for working with dates and times. We explored how to format dates and times, perform calculations on them, and convert them to different time zones.
By understanding these functions and how to use them effectively, you can improve the accuracy and reliability of your data transformations in MuleSoft’s Anypoint Platform.
