Unnest Transform

The  unnest  transform must include keys that you specify as part of the transform step. To unnest a column of array data that contains no keys, use the flatten transform. See Flatten Transform.

This transform might be automatically applied as one of the first steps of your recipe. See Initial Parsing Steps.

Basic Usage

unnest col: myObj keys:'sourceA','sourceB' pluck:true markLineage:true

Output:

• Extracts from the myObj column the corresponding values for the keys sourceA and sourceB into two new columns. 
• Since markLineage is true, these new column names are prepended with the source name: sourceA_column1 and sourceB_column2. 
• Any non-missing values from the source columns are added to the corresponding new columns and are removed from the source column, since pluck is true.

Syntax and Parameters

unnest col:column_ref keys:'key1','key2' [pluck:true|false] [markLineage:true|false]

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

col

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

Usage Notes:

keys

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

• Key values must be quoted. (e.g 'key1','key2'). Any quoted value is considered the path to a single key.
• Key values are case-sensitive.
• Each key must be listed. A range of keys cannot be specified.

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:

Keys for Object data - single-level

For a single, top-level key in an Object field, you can specify the key as a simple quoted string:

unnest col:myCol keys: 'myObjKey'

The above looks for the key myObjKey among the top-level keys in the Object and returns the corresponding value for the new column. You can also bracket this key in square brackets:

unnest col:myCol keys: '[myObjKey]'

To specify multiple first-level keys, use the following:

unnest col:myCol keys:'myObjKey','my2ndObjKey'

The above generates two new columns ( myObjKey and my2ndObjKey) containing the corresponding values for the keys.

Keys for Object data - multi-level

You can also reference keys that are below the first level in the Object. 

Example data:

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

To acquire the data for the Key1A key, use the following:

unnest col: myCol keys: 'Key1[Key1A]'

In the new column, the displayed value is the following:

{ "Key1A1" : "Value1" }

To unnest a third-layer value, use a transform similar to the following:

unnest col: myCol keys: 'Key2[Key2A][Key2A1]'

In the new column, this transform generates a value of Value2.

Keys for Array data - single level

You can reference array elements using zero-based indexes or key names.

Example array data:

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

unnest col: myCol keys:'[1]'

The above transform retrieves the value orange from the array.  

unnest col: myCol keys:'[1]','[3]'

Returned values: orange and green.

Keys for Array data - multi-level

The following example nested Array data matches the structure of the Object data in the previous example:

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

To unnest the value for Items2A:

unnest col:myCol keys:'[1][0]'

The value inserted into the new column is the following:

["Item2A1","Value2"]

To unnest from the third level:

unnest col:myCol keys:'[2][0][0]'

The inserted value is Item3A.

pluck

Usage Notes:

markLineage

Usage Notes:

Examples

Example - Unnest an Object

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

Source:

Transform:

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

settype col: Sizes type: 'Object'

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

unnest col:Sizes keys:'Small','Medium','Large','Extra-Large' markLineage:true

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

unnest col:Sizes keys:'Small','Medium','Large','Extra-Large' markLineage:true pluck: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:

derive type:single value:(LEN(Sizes) > 2) as:'len_Sizes'

If you sort the values in the generated column, you can review the true values to see if you need to modify your preceding unnest transform.You can drop the source column:

drop col:Sizes

Results:

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

Example - Unnest an array

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.

• For more information, see Flatten Transform.

Example - extracting key values from car data and then unnesting into separate columns