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EXAMPLE - Splitting with Different Delimiter Types

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.

Source:

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

Transformation:

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_Time

Status

admin.example.com|2016-03-05 07:04:00

down

webapp.example.com|2016-03-05 07:04:00

ok

admin.example.com|2016-03-05 07:04:30

rebooting

webapp.example.com|2016-03-05 07:04:30

ok

admin.example.com|2016-03-05 07:05:00

ok

webapp.example.com|2016-03-05 07:05:00

ok

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

Results:

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

server

year

month

day

time

Status

admin.example.com

2016

03

05

07:04:00

down

webapp.example.com

2016

03

05

07:04:00

ok

admin.example.com

2016

03

05

07:04:30

rebooting

webapp.example.com

2016

03

05

07:04:30

ok

admin.example.com

2016

03

05

07:05:00

ok

webapp.example.com

2016

03

05

07:05:00

ok