Performs a pivoting on the given Spark DataFrame/RDD using a selected number of columns for grouping and one column for pivoting. Each combination of values in the grouping columns will result into an output row. Each combination of pivot values and aggregations becomes a new output column.
The aggregations to perform can be specified (a) by selecting the columns directly in the "Manual Aggregation" tab, and (b) by a column name search pattern or regular expression in the "Pattern Based Aggregation" tab, and (c) by column type in the "Type Based Aggregation" tab. Each input column is only considered once, i.e. columns that are added directly on the "Manual Aggregation" tab are ignored even if their name matches a search pattern on the "Pattern Based Aggregation" tab or their type matches a type on the "Type Based Aggregation" tab. The same holds for columns that are added based on a search pattern. They are ignored even if they match a criterion that has been defined in the "Type Based Aggregation" tab.
A detailed description of the available aggregation methods can be found on the 'Description' tab in the node dialog. Further information can be also found on the Spark documentation and the Spark API documentation .
This node requires at least Apache Spark 2.0.