ExecutionUtil

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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. final def eq(arg0: AnyRef): Boolean

   

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7. def equals(arg0: Any): Boolean

   

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8. def executionsFromDates[T](duration: Duration)(fn: (DateRange) ⇒ Execution[T])(implicit dr: DateRange): Seq[Execution[T]]

   

   Generate a list of executions from a date range

   Generate a list of executions from a date range

   duration

   Duration to split daterange

   fn

   Function to run a execution given a date range

   returns

   Sequence of Executions per Day

9. def finalize(): Unit

   

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

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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

    

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14. final def notify(): Unit

    

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15. final def notifyAll(): Unit

    

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16. def runDateRangeWithParallelism[T](duration: Duration, parallelism: Int = 1)(fn: (DateRange) ⇒ Execution[T])(implicit dr: DateRange): Execution[Seq[T]]

    

    Split a DateRange and allow for max parallel running of executions

    Split a DateRange and allow for max parallel running of executions

    duration

    Duration to split daterange

    parallelism

    How many jobs to run in parallel

    fn

    Function to run a execution given a date range

    returns

    Execution of Sequences

17. def runDateRangeWithParallelismSum[T](duration: Duration, parallelism: Int = 1)(fn: (DateRange) ⇒ Execution[T])(implicit dr: DateRange, semigroup: Semigroup[T]): Execution[T]

    

    Same as runDateRangeWithParallelism, but sums the sequence of values after running.

    Same as runDateRangeWithParallelism, but sums the sequence of values after running. This is useful when you want to do a calculation in parallel over many durations and join the results together.

    For example, a common use case is when T is a TypedPipe[U] and you want to independently compute the pipes on each day and union them into a single TypedPipe at the end.

    Another possible use case would be if the executions were created by summing intermediate monoids (e.g. T was a Map[String,HLL] since algebird supports monoids for maps and hll) and you wanted to do a final aggregation of the Monoids computed for each duration.

18. def runDatesWithParallelism[T](duration: Duration, parallelism: Int = 1)(fn: (DateRange) ⇒ Execution[T])(implicit dr: DateRange): Execution[Seq[(DateRange, T)]]

    

    Split a DateRange and allow for max parallel running of executions

    Split a DateRange and allow for max parallel running of executions

    duration

    Duration to split daterange

    parallelism

    How many jobs to run in parallel

    fn

    Function to run a execution given a date range

    returns

    Seq of Dates split by Duration with corresponding execution result

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

    

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20. def toString(): String

    

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21. final def wait(): Unit

    

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

    

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

    

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