Unnest Transform

Identifies the column to which to apply the transform. You can specify only one column.

Usage Notes:

Comma-separated list of keys to use to extract data from the specified source column.

The comma-separated list of keys determines the columns to generate from the source data. If you specify three values for keys, the three new columns contain the corresponding values from the source column.

This parameter has different syntax to use for single-level and multi-level nested data. There are also variations in syntax between Object and Array data type.

Usage Notes:

{ "Key1" :
  { "Key1A" :
    { "Key1A1" : "Value1" }
  }
}
{ "Key2" :
  { "Key2A" :
    { "Key2A1" : "Value2" }
  }
}
{ "Key3" :
  { "Key3A" :
    { "Key3A1" : "Value3" }
  }
}

{ "Key1A1" : "Value1" }

["red","orange","yellow","green","blue","indigo","violet"]

[ [ "Item1", ["Item1A", ["Item1A1","Value1"] ] ], [ "Item2", ["Item2A",  ["Item2A1","Value2"] ] ], [ "Item3", ["Item3A",["Item3A1","Value3"] ] ] ] 

["Item2A1","Value2"]

Indicates whether any values added from source to output columns should be removed from the source.

Usage Notes:

When set to true, the names of new columns are prepended with the name of the source column. Example:

mySourceColumn

mySourceColumn_column1

Nested key references are appended to the column name:

mySourceColumn

keys: '[Key1][Key2]'

mySourceColumn_Key1_Key2

Usage Notes:

You have the following dataset. The Sizes column contains Object data on available sizes.

Source:

Transformation:

If it is not inferred already, set the type of the Sizes column to Object:

Sizes

Object

Unnest the data into separate columns. The following prepends Sizes_ to the newly generated column name.

Sizes

'Small','Medium','Large','Extra-Large'

test

You might find it useful to addpluck:trueto the above transform. When added, values that are un-nested are removed from the source, leaving only the values that weren't processed:

Sizes

'Small','Medium','Large','Extra-Large'

true

true

If all values have been processed, the Sizes column now contains a set of maps missing data. You can use the following to determine if the length of the remaining data is longer than two characters. This transform is a good one to just preview:

Single row formula

(len(Sizes) > 2)

'len_Sizes'

You can delete the source column:

Sizes

Delete selected columns

Results:

When you are finished, the dataset should look like the following:

The following example demonstrates differences between the unnest and the flatten transform, including how you use unnest to flatten array data based on specified keys.

This example illustrates you to use the flatten and unnest transforms.

Source:

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:

Transformation:

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

Use row(s) as column names

Use a single row to name columns

1

colScores

'\"'

''

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:

Single row formula

(4 - arraylen(Scores))

'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:

Single row formula

range(0,arraylen(Scores))

'Tests'

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

Single row formula

sourcerownumber()

'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:

Single row formula

arrayzip([Tests,Scores])

Your dataset has been changed:

Use the following to unpack the nested array:

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 the following:

column1

'[0]','[1]'

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

Manual rename

column_0

'TestNum'

Manual rename

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:

Single row formula

(orderIndex * 10) + TestNum

'TestId'

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

'TestId00','TestId'

Extending: 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:

'LastName','FirstName'

'-'

'studentId'

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

Single row formula

average(TestScore)

studentId

'avg_TestScore'

Results:

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

This example shows how you can unpack data nested in an Object into separate columns.

Source:

You have the following information on used cars. The VIN column contains vehicle identifiers, and the Properties column contains key-value pairs describing characteristics of each vehicle. You want to unpack this data into separate columns.

Transformation:

Add the following transformation, which identifies all of the key values in the column as beginning with alphabetical characters.

Properties

`{alpha}+`

`=`

','

Now that the Object of values has been created, you can use the unnest transform to unpack this mapped data. In the following, each key is specified, which results in separate columns headed by the named key:

extractkv_Properties

year

make

model

color

mileage

cost

Results:

When you delete the unnecessary Properties columns, the dataset now looks like the following: