BulkColumnExpressions

ProphecySparkBasicsScala0.1.9+Databricks UC Single Cluster14.3+Databricks UC Shared14.3+Livy3.0.1+

The BulkColumnExpressions gem primarily lets you cast or change the data type of multiple columns at once. It provides additional functionality, including:

• Adding a prefix or suffix to selected columns.
• Applying a custom expression to selected columns.

Parameters

Parameter	Description
Data Type of the columns to do operations on	The data type of columns to select.
Selected Columns	The columns on which to apply transformations.
Change output column name	An option to add a prefix or suffix to the selected column names.
Change output column type	The data type that the columns will be transformed into.
Output Expression	A Spark SQL expression that can be applied to the selected columns. This field is required. If you only want to select the column, use column_value as the expression.

Example

Assume you have some columns in a table that represent zero-based indices and are stored as long data types. You want them to represent one-based indices and be stored as integers to optimize memory use.

Using the BulkColumnExpressions gem, you can:

• Filter your columns by long data types.
• Select the columns you wish to transform.
• Cast the output column(s) to be integers.
• Include column_value + 1 in the expression field to shift the indices.

Example code

tip

To see the compiled code of your project, switch to the Code view in the project header.

Python

def bulk_column_casting(spark: SparkSession, in0: DataFrame) -> DataFrame:
    return in0.select(
        *(
          [expr("`customer_id`").alias("str_customer_id").cast(StringType())]
          + [col("`" + colName + "`") for colName in sorted(set(in0.columns) - {"customer_id"})]
          + []
        )
    )

Scala

object bulk_column_expressions {
  def apply(context: Context, in: DataFrame): DataFrame = {
    var allExpressions = List(
      expr(
        "column_value"
          .replace("column_value", "`status`")
          .replace("column_name",  "'status'")
      ).as("status").cast(StringType)
    ) ++ (in.columns.toSet -- List("status").toSet).map(columnName =>
      col("`" + columnName + "`")
    )
    in.select(allExpressions: _*)
  }
}