LEN Function
Returns the number of characters in a specified string. String value can be a column reference or string literal.
Wrangle vs. SQL: This function is part of Wrangle, a proprietary data transformation language. Wrangle is not SQL. For more information, see Wrangle Language.
Basic Usage
Column reference example:
len(MyName)
Output: Returns the number of characters in the value in column MyName.
String literal example:
len('Hello, World')
Output: Returns the value 12.
Syntax and Arguments
len(column_string)
Argument | Required? | Data Type | Description |
|---|---|---|---|
column_string | Y | string | Name of the column or string literal to be applied to the function |
For more information on syntax standards, see Language Documentation Syntax Notes.
column_string
Name of the column or string constant to be searched.
Missing string or column values generate missing string results.
String constants must be quoted (
'Hello, World').Multiple columns and wildcards are not supported.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | String literal or column reference | myColumn |
Examples
ヒント
For additional examples, see Common Tasks.
Example - Fixed Length Strings
Source:
Your product identifiers follow a specific structure that you'd like to validate in your recipe. In the following example data, the productId column should contain values of length 6.
You can see that there is already a column containing validation errors for the ProductName column. Values in the ProductId column that are not this length should be flagged in a new column. Then, you should merge the two columns together to create a ValidationError column.
ProductName | ProductId | ErrProductName |
|---|---|---|
Chocolate Bunnie | 123456 | Error-ProductName |
Chocolate Squirl | 88442286 | Error-ProductName |
Chocolate Gopher | 12345 |
Transformation:
To validate the length of the values in ProductId, enter the following transform. Note that the as parameter enables you to rename the column as part of the transform.
Transformation Name | |
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | if(len(ProductId) <> 6, 'Error-length-ProductId','') |
Parameter: New column name | 'ErrProductIdLength' |
The dataset now looks like the following:
ProductName | ProductId | ErrProductName | ErrProductIdLength |
|---|---|---|---|
Chocolate Bunnie | 123456 | Error-ProductName | |
Chocolate Squirrel | 88442286 | Error-ProductName | Error-length-ProductId |
Chocolate Gopher | 12345 | Error-length-ProductId |
You can blend the two error columns into a single DataValidationErrors error column using the following merge transform. Note again the use of the as parameter:
Transformation Name | |
|---|---|
Parameter: Columns | ErrProductName,ErrProductIdlength |
Parameter: Separator | '' |
Parameter: New column name | 'DataValidationErrors' |
To clean up the data, you might want to do the following, which trims out the whitespace in the DataValidationErrors column and removes the two individual error columns:
Transformation Name | |
|---|---|
Parameter: Columns | DataValidationErrors |
Parameter: Formula | trim(DataValidationErrors) |
Transformation Name | |
|---|---|
Parameter: Columns | ErrProductName,ErrProductIdLength |
Parameter: Action | Delete selected columns |
Results:
The final dataset should look like the following:
ProductName | ProductId | DataValidationErrors |
|---|---|---|
Chocolate Bunnie | 123456 | Error-ProductName |
Chocolate Squirrel | 88442286 | Error-ProductName Error-length-ProductId |
Chocolate Gopher | 12345 | Error-length-ProductId |