PythonCodeDfTransformer

Configuration of a custom Spark-DataFrame transformation between one input and one output (1:1) as Python/PySpark code. Note that this transformer needs a Python and PySpark environment installed. PySpark session is initialize and available under variables sc, session, sqlContext. Other variables available are - inputDf: Input DataFrame - options: Transformation options as Map[String,String] - dataObjectId: Id of input dataObject as String Output DataFrame must be set with setOutputDf(df).

name: name of the transformer
description: Optional description of the transformer
code: Optional python code to user for python transformation. The python code can use variables inputDf, dataObjectId and options. The transformed DataFrame has to be set with setOutputDf.
file: Optional file with python code to use for python transformation. The python code can use variables inputDf, dataObjectId and options. The transformed DataFrame has to be set with setOutputDf.
options: Options to pass to the transformation
runtimeOptions: optional tuples of [key, spark sql expression] to be added as additional options when executing transformation. The spark sql expressions are evaluated against an instance of DefaultExpressionData.

Linear Supertypes

Serializable, Serializable, Product, Equals, OptionsDfTransformer, ParsableDfTransformer, ParsableFromConfig[ParsableDfTransformer], DfTransformer, PartitionValueTransformer, AnyRef, Any

Instance Constructors

new PythonCodeDfTransformer(name: String = "pythonTransform", description: Option[String] = None, code: Option[String] = None, file: Option[String] = None, options: Map[String, String] = Map(), runtimeOptions: Map[String, String] = Map())

name
name of the transformer
description
Optional description of the transformer
code
Optional python code to user for python transformation. The python code can use variables inputDf, dataObjectId and options. The transformed DataFrame has to be set with setOutputDf.
file
Optional file with python code to use for python transformation. The python code can use variables inputDf, dataObjectId and options. The transformed DataFrame has to be set with setOutputDf.
options
Options to pass to the transformation
runtimeOptions
optional tuples of [key, spark sql expression] to be added as additional options when executing transformation. The spark sql expressions are evaluated against an instance of DefaultExpressionData.

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val code: Option[String]

Optional python code to user for python transformation.
Optional python code to user for python transformation. The python code can use variables inputDf, dataObjectId and options. The transformed DataFrame has to be set with setOutputDf.
val description: Option[String]

Optional description of the transformer
Optional description of the transformer

Definition Classes
PythonCodeDfTransformer → DfTransformer
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def factory: FromConfigFactory[ParsableDfTransformer]

Returns the factory that can parse this type (that is, type CO).
Returns the factory that can parse this type (that is, type CO).
Typically, implementations of this method should return the companion object of the implementing class. The companion object in turn should implement FromConfigFactory.
returns
the factory (object) for this class.

Definition Classes
PythonCodeDfTransformer → ParsableFromConfig
val file: Option[String]

Optional file with python code to use for python transformation.
Optional file with python code to use for python transformation. The python code can use variables inputDf, dataObjectId and options. The transformed DataFrame has to be set with setOutputDf.
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val name: String

name of the transformer
name of the transformer

Definition Classes
PythonCodeDfTransformer → DfTransformer
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val options: Map[String, String]

Options to pass to the transformation
Options to pass to the transformation

Definition Classes
PythonCodeDfTransformer → OptionsDfTransformer
def prepare(actionId: ActionId)(implicit session: SparkSession, context: ActionPipelineContext): Unit

Optional function to implement validations in prepare phase.
Optional function to implement validations in prepare phase.

Definition Classes
DfTransformer
val runtimeOptions: Map[String, String]

optional tuples of [key, spark sql expression] to be added as additional options when executing transformation.
optional tuples of [key, spark sql expression] to be added as additional options when executing transformation. The spark sql expressions are evaluated against an instance of DefaultExpressionData.

Definition Classes
PythonCodeDfTransformer → OptionsDfTransformer
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def transform(actionId: ActionId, partitionValues: Seq[PartitionValues], df: DataFrame, dataObjectId: DataObjectId)(implicit session: SparkSession, context: ActionPipelineContext): DataFrame

Function to be implemented to define the transformation between an input and output DataFrame (1:1)
Function to be implemented to define the transformation between an input and output DataFrame (1:1)

Definition Classes
OptionsDfTransformer → DfTransformer
def transformPartitionValues(actionId: ActionId, partitionValues: Seq[PartitionValues])(implicit session: SparkSession, context: ActionPipelineContext): Option[Map[PartitionValues, PartitionValues]]

Optional function to define the transformation of input to output partition values.
Optional function to define the transformation of input to output partition values. For example this enables to implement aggregations where multiple input partitions are combined into one output partition. Note that the default value is input = output partition values, which should be correct for most use cases.
actionId
id of the action which executes this transformation. This is mainly used to prefix error messages.
partitionValues
partition values to transform
returns
Map of input to output partition values. This allows to map partition values forward and backward, which is needed in execution modes. Return None if mapping is 1:1.

Definition Classes
OptionsDfTransformer → PartitionValueTransformer
def transformPartitionValuesWithOptions(actionId: ActionId, partitionValues: Seq[PartitionValues], options: Map[String, String])(implicit session: SparkSession): Option[Map[PartitionValues, PartitionValues]]

Optional function to define the transformation of input to output partition values.
Optional function to define the transformation of input to output partition values. For example this enables to implement aggregations where multiple input partitions are combined into one output partition. Note that the default value is input = output partition values, which should be correct for most use cases.
options
Options specified in the configuration for this transformation, including evaluated runtimeOptions

Definition Classes
OptionsDfTransformer
def transformWithOptions(actionId: ActionId, partitionValues: Seq[PartitionValues], df: DataFrame, dataObjectId: DataObjectId, options: Map[String, String])(implicit session: SparkSession): DataFrame

Function to be implemented to define the transformation between an input and output DataFrame (1:1)
Function to be implemented to define the transformation between an input and output DataFrame (1:1)
options
Options specified in the configuration for this transformation, including evaluated runtimeOptions

Definition Classes
PythonCodeDfTransformer → OptionsDfTransformer
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object PythonCodeDfTransformer | package sparktransformer

Instance Constructors

new PythonCodeDfTransformer(name: String = "pythonTransform", description: Option[String] = None, code: Option[String] = None, file: Option[String] = None, options: Map[String, String] = Map(), runtimeOptions: Map[String, String] = Map())

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val code: Option[String]

val description: Option[String]

final def eq(arg0: AnyRef): Boolean

def factory: FromConfigFactory[ParsableDfTransformer]

val file: Option[String]

def finalize(): Unit

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

val name: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val options: Map[String, String]

def prepare(actionId: ActionId)(implicit session: SparkSession, context: ActionPipelineContext): Unit

val runtimeOptions: Map[String, String]

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

def transform(actionId: ActionId, partitionValues: Seq[PartitionValues], df: DataFrame, dataObjectId: DataObjectId)(implicit session: SparkSession, context: ActionPipelineContext): DataFrame

def transformPartitionValues(actionId: ActionId, partitionValues: Seq[PartitionValues])(implicit session: SparkSession, context: ActionPipelineContext): Option[Map[PartitionValues, PartitionValues]]

def transformPartitionValuesWithOptions(actionId: ActionId, partitionValues: Seq[PartitionValues], options: Map[String, String])(implicit session: SparkSession): Option[Map[PartitionValues, PartitionValues]]

def transformWithOptions(actionId: ActionId, partitionValues: Seq[PartitionValues], df: DataFrame, dataObjectId: DataObjectId, options: Map[String, String])(implicit session: SparkSession): DataFrame

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from OptionsDfTransformer

Inherited from ParsableDfTransformer

Inherited from ParsableFromConfig[ParsableDfTransformer]

Inherited from DfTransformer

Inherited from PartitionValueTransformer

Inherited from AnyRef

Inherited from Any

Ungrouped