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org.apache.spark.sql.connect.planner

StreamingForeachBatchHelper

object StreamingForeachBatchHelper extends Logging

A helper class for handling ForeachBatch related functionality in Spark Connect servers

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Type Members

  1. class CleanerCache extends AnyRef

    This manages cache from queries to cleaner for runners used for streaming queries.

    This manages cache from queries to cleaner for runners used for streaming queries. This is used in SessionHolder.

  2. type ForeachBatchFnType = (DataFrame, Long) => Unit
  3. case class RunnerCleaner(runner: StreamingPythonRunner) extends AutoCloseable with Product with Serializable

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  6. final def eq(arg0: AnyRef): Boolean
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  8. def finalize(): Unit
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  9. final def getClass(): Class[_ <: AnyRef]
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    @native()
  10. def hashCode(): Int
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    @native()
  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  13. final def isInstanceOf[T0]: Boolean
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  14. def isTraceEnabled(): Boolean
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  15. def log: Logger
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  16. def logDebug(msg: => String, throwable: Throwable): Unit
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  17. def logDebug(msg: => String): Unit
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  18. def logError(msg: => String, throwable: Throwable): Unit
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  19. def logError(msg: => String): Unit
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  20. def logInfo(msg: => String, throwable: Throwable): Unit
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  21. def logInfo(msg: => String): Unit
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  22. def logName: String
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  23. def logTrace(msg: => String, throwable: Throwable): Unit
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  24. def logTrace(msg: => String): Unit
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  25. def logWarning(msg: => String, throwable: Throwable): Unit
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  26. def logWarning(msg: => String): Unit
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  27. final def ne(arg0: AnyRef): Boolean
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  28. final def notify(): Unit
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    @native()
  29. final def notifyAll(): Unit
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    @native()
  30. def pythonForeachBatchWrapper(pythonFn: SimplePythonFunction, sessionHolder: SessionHolder): (ForeachBatchFnType, RunnerCleaner)

    Starts up Python worker and initializes it with Python function.

    Starts up Python worker and initializes it with Python function. Returns a foreachBatch function that sets up the session and Dataframe cache and and interacts with the Python worker to execute user's function.

  31. def scalaForeachBatchWrapper(fn: ForeachBatchFnType, sessionHolder: SessionHolder): ForeachBatchFnType

    Handles setting up Scala remote session and other Spark Connect environment and then runs the provided foreachBatch function fn.

    Handles setting up Scala remote session and other Spark Connect environment and then runs the provided foreachBatch function fn.

    HACK ALERT: This version does not actually set up Spark Connect session. Directly passes the DataFrame, so the user code actually runs with legacy DataFrame and session..

  32. final def synchronized[T0](arg0: => T0): T0
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  33. def toString(): String
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  34. final def wait(): Unit
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    @throws(classOf[java.lang.InterruptedException])
  35. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  36. final def wait(arg0: Long): Unit
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    @throws(classOf[java.lang.InterruptedException]) @native()

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