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This example shows how you can split data from a single column into multiple columns using delimiters.
  • single-pattern delimiter: One pattern is applied one or more times to the source column to define the delimiters for the output columns
  • multi-pattern delimiter: Multiple patterns, in the form of explicit strings, character index positions, or fixed-width fields, are used to split the column.


In this example, your CSV dataset contains status messages from a set of servers. In this case, the data about the server and the timestamp is contained in a single value within the CSV.

Server|Date Time,Status
admin.examplecom|2016-03-05 07:04:00,down
webapp.examplecom|2016-03-05 07:04:00,ok
admin.examplecom|2016-03-05 07:04:30,rebooting
webapp.examplecom|2016-03-05 07:04:00,ok
admin.examplecom|2016-03-05 07:05:00,ok
webapp.examplecom|2016-03-05 07:05:00,ok


When the data is first loaded into the Transformer page, the CSV data is split using the following two transformations:

Transformation Name Split into rows
Parameter: Column column1
Parameter: Split on \n

Transformation Name Split column
Parameter: Column column1
Parameter: Option On pattern
Parameter: Match pattern ','
Parameter: Ignore matches between \"

You might need to add a header as the first step:

Transformation Name Rename column with row(s)
Parameter: Option Use row(s) as column names
Parameter: Type Use a single row to name columns
Parameter: Row number 1

At this point, your data should look like the following:

Server_Date_TimeStatus|2016-03-05 07:04:00down|2016-03-05 07:04:00ok|2016-03-05 07:04:30rebooting|2016-03-05 07:04:30ok|2016-03-05 07:05:00ok|2016-03-05 07:05:00ok

The first column contains three distinct sets of data: the server name, the date, and the time. Note that the delimiters between these fields are different, so you should use a multi-pattern delimiter to break them apart:

Transformation Name Split column
Parameter: Column Server|Date Time
Parameter: Option Sequence of patterns
Parameter: Pattern1 ','
Parameter: Pattern2 ' '

When the above is added, you should see three separate columns with the individual fields of information. Note that the source column has been automatically dropped.

Now, you decide that it would be useful to break apart the date information column into separate columns for year, month, and day. Since the column delimiter of this field is consistently a dash (-), you can use a single-pattern delimiter with the following transformation:

Transformation Name Split by delimiter
Parameter: Column Server|Date Time2
Parameter: Option By delimiter
Parameter: Delimiter '-'
Parameter: Number of columns to create 2


After you rename the generated columns, your dataset should look like the following. Note that the source timestamp column has been automatically dropped.



See Also for EXAMPLE - Splitting with Different Delimiter Types:

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