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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:


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


ArgumentRequired?Data TypeDescription
column_stringYstringName of the column or string literal to be applied to the function

For more information on syntax standards, see Language Documentation Syntax Notes.


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 TypeExample Value
YesString literal or column referencemyColumn


Tip: For additional examples, see Common Tasks.

Example - Fixed Length Strings


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.

Chocolate Bunnie123456Error-ProductName
Chocolate Squirl88442286Error-ProductName
Chocolate Gopher12345 


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 New formula
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:

Chocolate Bunnie123456Error-ProductName 
Chocolate Squirrel88442286Error-ProductNameError-length-ProductId
Chocolate Gopher12345 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 Merge columns
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 Edit column with formula
Parameter: Columns DataValidationErrors
Parameter: Formula trim(DataValidationErrors)

Transformation Name Delete columns
Parameter: Columns ErrProductName,ErrProductIdLength
Parameter: Action Delete selected columns


The final dataset should look like the following:

Chocolate Bunnie123456Error-ProductName
Chocolate Squirrel88442286Error-ProductName Error-length-ProductId
Chocolate Gopher12345Error-length-ProductId

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