NOTE: This function has been superseded by the $sourcerownumber reference. While this function is still usable in the product, it is likely to be deprecated in a future release. Please use $sourcerownumber instead. For more information, see Source Metadata References.

Returns the row number of the current row as it appeared in the original source dataset before any steps had been applied.

The following transforms might make original row information invalid or otherwise unavailable. In these cases, the function returns null values:

  • pivot
  • flatten
  • join
  • lookup
  • union
  • unnest
  • unpivot

NOTE: If the dataset is sourced from multiple files, a predictable original source row number cannot be guaranteed, and null values are returned.

Tip: If the source row information is still available, you can hover over the left side of a row in the data grid to see the source row number in the original source data.

NOTE: When working with datasets sourced from Avro files, lineage information and the SOURCEROWNUMBER function are not supported.

Basic Usage

Derive Example:

derive type:single value:SOURCEROWNUMBER() as:'OriginalRowNums'

Output: Generates a new OriginalRowNums column containing the row numbers for each row as it appeared in the original data.Delete Example:

delete row:SOURCEROWNUMBER() > 101

Output: Deletes the rows in the dataset that were after row #101 in the original source data.


There are no arguments for this function.


Example - Header from row that is not the first one


You have imported the following racer data on heat times from a CSV file. When loaded in the Transformer page, it looks like the following:

1RacerHeat 1Heat 2Heat 3
2Racer X37.2238.2237.61
3Racer Y41.33DQ38.04
4Racer Z39.2739.0438.85

In the above, the (rowId) column references the row numbers displayed in the data grid; it is not part of the dataset. This information is available when you hover over the black dot on the left side of the screen.


You have examined the best performance in each heat according to the sample. You then notice that the data contains headers, but you forget how it was originally sorted. The data now looks like the following:

1Racer Y41.33DQ38.04
2RacerHeat 1Heat 2Heat 3
3Racer X37.2238.2237.61
4Racer Z39.2739.0438.85

You can use the following transformation to use the third row as your header for each column:

NOTE: The following does not use the header transform.

rename type: header method: index sourcerownumber: 3


After you have applied the last header transform, your data should look like the following:

3Racer Y41.33DQ38.04
2Racer X37.2238.2237.61
4Racer Z39.2739.0438.85

Example - Using sourcerownumber to create unique row identifiers

The following example demonstrates how to unpack nested data. As part of this example, the SOURCEROWNUMBER function is used as part of a method to create unique row identifiers.


You have the following data on student test scores. Scores on individual scores are stored in the Scores array, and you need to be able to track each test on a uniquely identifiable row. This example has two goals:

  1. One row for each student test
  2. Unique identifier for each student-score combination


When the data is imported from CSV format, you must add a header transform and remove the quotes from the Scores column:


replace col:Scores with:'' on:`"` global:true

Validate test date: To begin, you might want to check to see if you have the proper number of test scores for each student. You can use the following transform to calculate the difference between the expected number of elements in the Scores array (4) and the actual number:

derive type:single value: (4 - ARRAYLEN(Scores)) as: 'numMissingTests'

When the transform is previewed, you can see in the sample dataset that all tests are included. You might or might not want to include this column in the final dataset, as you might identify missing tests when the recipe is run at scale.

Unique row identifier: The Scores array must be broken out into individual rows for each test. However, there is no unique identifier for the row to track individual tests. In theory, you could use the combination of LastName-FirstName-Scores values to do so, but if a student recorded the same score twice, your dataset has duplicate rows. In the following transform, you create a parallel array called Tests, which contains an index array for the number of values in the Scores column. Index values start at 0:

derive type:single value:RANGE(0,ARRAYLEN(Scores)) as:'Tests'

Also, we will want to create an identifier for the source row using the SOURCEROWNUMBER function:

derive type:single value:SOURCEROWNUMBER() as:'orderIndex'

One row for each student test: Your data should look like the following:


Now, you want to bring together the Tests and Scores arrays into a single nested array using the ARRAYZIP function:

derive type:single value:ARRAYZIP([Tests,Scores])

Your dataset has been changed:


With the flatten transform, you can unpack the nested array:

flatten col: column1

Each test-score combination is now broken out into a separate row. The nested Test-Score combinations must be broken out into separate columns using unnest:

unnest col:column1 keys:'[0]','[1]'

After you delete column1, which is no longer needed you should rename the two generated columns:

rename mapping:[column_0,'TestNum']

rename mapping:[column_1,'TestScore']

Unique row identifier: You can do one more step to create unique test identifiers, which identify the specific test for each student. The following uses the original row identifier OrderIndex as an identifier for the student and the TestNumber value to create the TestId column value:

derive type:single value: (orderIndex * 10) + TestNum as: 'TestId'

The above are integer values. To make your identifiers look prettier, you might add the following:

merge col:'TestId00','TestId'

You might want to generate some summary statistical information on this dataset. For example, you might be interested in calculating each student's average test score. This step requires figuring out how to properly group the test values. In this case, you cannot group by the LastName value, and when executed at scale, there might be collisions between first names when this recipe is run at scale. So, you might need to create a kind of primary key using the following:

merge col:'LastName','FirstName' with:'-' as:'studentId'

You can now use this as a grouping parameter for your calculation:

derive type:single value:AVERAGE(TestScore) group:studentId as:'avg_TestScore'


After you delete unnecessary columns and move your columns around, the dataset should look like the following: