I have a pretty complex nested structure with several levels. So in order to create it I use SQLContext.createDataFrame method and provide specific Rows with specific StrucTypes, both of which I build myself.

To build a Row I iterate over my values and literally build a Row.
        List<Object> row = new LinkedList<>();
        for (Attribute attributeNode : attributeNodes()) {
            final String name = attributeNode.getName();
            if (name.equals(“attr-simple-1")) {
            } else if (name.equals("attr-nested-1")) {
                List<Object> rowAttributes = new LinkedList<>();
                for (Attribute node : attributeNode.getAttributes()) {
                    String nodeName = node.getName();
                    if (obj.getSimpleAttributeNames().contains(nodeName)) {
                        rowAttributes.add( value );
                    } else if ( nested ) {
                        rowAttributes.add( // recursion );
                    } else rowAttributes.add(null);
                row.add(new GenericRow(rowAttributes.toArray(new Object[rowAttributes.size()])));
            } else {
        return new GenericRow(row.toArray(new Object[row.size()]));

To build StructType I create an array of StructFields
        List<StructField> structFields = ...
        if (attribute.isSingleValue()) {
            structFields.add(DataTypes.createStructField(attribute.getName(), dataType(attribute), true));
        } else {
            structFields.add(DataTypes.createStructField(attribute.getName(), DataTypes.createArrayType(dataType(attribute)), true));

and then

dataType() is a method to get corresponding o.a.spark.sql.types.DataType;

If you have to create Row with another structure you just can map original Row into the one with the new structure and build corresponding StructType. Although if you find a simpler way, I’d really like to know about that.

On 07 Aug 2015, at 12:43, Rishabh Bhardwaj <rbnext29@gmail.com> wrote:

I am doing it by creating a new data frame out of the fields to be nested and then join with the original DF.
Looking for some optimized solution here.

On Fri, Aug 7, 2015 at 2:06 PM, Rishabh Bhardwaj <rbnext29@gmail.com> wrote:
Hi all,

I want to have some nesting structure from the existing columns of the dataframe.
For that,,I am trying to transform a DF in the following way,but couldn't do it.

scala> df.printSchema
 |-- a: string (nullable = true)
 |-- b: string (nullable = true)
 |-- c: string (nullable = true)
 |-- d: string (nullable = true)
 |-- e: string (nullable = true)
 |-- f: string (nullable = true)


scala> newDF.printSchema
 |-- a: string (nullable = true)
 |-- b: string (nullable = true)
 |-- c: string (nullable = true)
 |-- newCol: struct (nullable = true)
 |    |-- d: string (nullable = true)
 |    |-- e: string (nullable = true)

help me.


Eugene Morozov