SparkSessionMethods

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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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))

8. final def eq(arg0: AnyRef): Boolean

   

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9. def equals(arg0: Any): Boolean

   

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10. def finalize(): Unit

    

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11. final def getClass(): Class[_]

    

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12. def hashCode(): Int

    

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13. final def isInstanceOf[T0]: Boolean

    

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14. final def ne(arg0: AnyRef): Boolean

    

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15. final def notify(): Unit

    

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16. final def notifyAll(): Unit

    

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17. final def synchronized[T0](arg0: ⇒ T0): T0

    

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18. def toString(): String

    

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19. final def wait(): Unit

    

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20. final def wait(arg0: Long, arg1: Int): Unit

    

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21. final def wait(arg0: Long): Unit

    

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