case class PipelineTransform(id: String, name: Option[String], description: Option[String], parentIds: List[String], transformType: String, dataTransform: List[String], options: Map[String, String]) extends WithOptions with Product with Serializable
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Instance Constructors
- new PipelineTransform(id: String, name: Option[String], description: Option[String], parentIds: List[String], transformType: String, dataTransform: List[String], options: Map[String, String])
Value Members
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final
def
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final
def
##(): Int
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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- protected[lang]
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- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
- val dataTransform: List[String]
- def dataTransformString: String
- val description: Option[String]
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(obj: Any): Boolean
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- def forceCache: Boolean
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final
def
getClass(): Class[_]
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- def getFunction: String
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def
getOptions: Options
- Definition Classes
- PipelineTransform → WithOptions
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def
hashCode(): Int
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- val id: String
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final
def
isInstanceOf[T0]: Boolean
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- val name: Option[String]
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final
def
ne(arg0: AnyRef): Boolean
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- def neverCache: Boolean
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def
noOutput: Boolean
Determines if the transform have no output.
Determines if the transform have no output. This is when skipOutput is true or this transform is a Scala transform with the last line being spark.emptyDataFrame. Note that transforms can be constructed without any output, where this method return true
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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- val options: Map[String, String]
- val parentIds: List[String]
- def skipModules: Boolean
- def skipOutput: Boolean
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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- val transformType: String
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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final
def
wait(): Unit
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