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implicit class SparkSessionMethods extends AnyRef

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Instance Constructors

  1. new SparkSessionMethods(spark: SparkSession)

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  6. def createDF[U, T <: Product](rowData: List[U], fields: List[T]): DataFrame

    Creates a DataFrame, similar to createDataFrame, but with better syntax spark-daria defined a createDF method that allows for the terse syntax of toDF and the control of createDataFrame.

    Creates a DataFrame, similar to createDataFrame, but with better syntax spark-daria defined a createDF method that allows for the terse syntax of toDF and the control of createDataFrame.

    spark.createDF( List( ("bob", 45), ("liz", 25), ("freeman", 32) ), List( ("name", StringType, true), ("age", IntegerType, false) ) )

    The createDF method can also be used with lists of Row and StructField objects.

    spark.createDF( List( Row("bob", 45), Row("liz", 25), Row("freeman", 32) ), List( StructField("name", StringType, true), StructField("age", IntegerType, false) ) )

  7. def createEmptyDF[T <: Product](fields: List[T]): DataFrame

    Creates an empty DataFrame given schema fields

    Creates an empty DataFrame given schema fields

    This is a handy fallback when you fail to read from a data source

    val schema = List(StructField("col1", IntegerType)) val df = Try { spark.read.parquet("non-existent-path") }.getOrElse(spark.createEmptyDf(schema))

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