DStreamSlideWindowTransformer

abstract def inputStream: DStream[IN]

abstract def name: String

abstract def onData(name: String, columns: Array[String], data: Array[Row]): Unit

abstract def slideSec: Int

abstract def sql: String

abstract def windowSec: Int

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def filter(p: (Unit) ⇒ Boolean): Transformer[Unit]

Shouldn't be required in for-comprehension Known issue: https://issues.

def finalize(): Unit

def flatMap[NEXT](f: (Unit) ⇒ Transformer[NEXT]): Transformer[NEXT]

monadic operation that can chain another transformer (will be called implicitly in for-comprehension )

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

lazy val logger: Logger

def map[NEXT](f: (Unit) ⇒ NEXT): Transformer[NEXT]

monadic operation that can yield the last transformer (will be called implicitly at the end of for-comprehension )

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

def transform(): Unit

Override to perform transformation and return the desired type

def validateInput(): Unit

Override this to do input validation

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def zip[ANOTHER_OUT](that: Transformer[ANOTHER_OUT]): Transformer[(Unit, ANOTHER_OUT)]

can be used to chain transformers that can run in parallel