Time Columns
How to add a time column to your importer through code.
You can add a column to your importer directly through the UI of your Fuse account, or you can do so in code using the documentation below.
The Time column type validates that values are a specific time format.
addColumn Function
The addColumn
function accepts an object as an argument.
internal_key
(String) - required: The internal key used to access a column’s value.label
(String) - required: The user-facing label shown in the interface.column_type
(String) - required: Should be “time”.required
(String) - required: Whether or not the column is required.pattern
(String) - required: must be one of these patterns.position
(Optional): The position or order of the column.validations
(Optional): An array of built-in data validations. See Built-in Time Validations.transformations
(Optional): An array of built-in data transformations. See Built-in Time Transformations.
Example Code
Patterns
HH:mm
Example: 12:30; 23:45
HH:mm:ss
Example: 12:30:45; 23:45:30
hh:mm a
Example: 01:30 PM; 11:45 AM
hh:mm:ss a
Example: 01:30:45 PM; 11:45:30 AM
Built-in Time Validations
The options property of addColumn
accepts an array of validations. For the Time validation, you can specify two properties:
validation_type:
the name of the validationmessage:
the message showed to the end user if the validation fails
The validations property accepts an array of objects that contains these possible values: validation_type
, message
. As the example below:
unique_case_sensitive
Specifies that the value must be unique using case-sensitive comparison.
unique_case_insensitive
Specifies that the value must be unique using case-insensitive comparison.
Built-in Time Transformations
The options property of addColumn
accepts an array of transformations. For the time transformations, you can specify one property:
transformation_type:
the name of the transformation
The transformations property accepts an array of objects that contains these possible values: transformation_type
. As the example below:
autoformat
The autoformat transformation automatically transforms the date in the column to a standardized format specified by the pattern
on the column.