GreedyCache

Greedy caching strategy given a memory budget.

If no maxMem is provided, the memory budget defaults to 75% of the remaining memory on the cluster (given whatever RDDs are already cached)

maxMem: The memory budget (bytes)
partitionScales: The scales to sample at (average number of desired data points per partition)
numProfileTrials: The number of profiling samples to take per scale

Linear Supertypes

Serializable, Serializable, Product, Equals, CachingStrategy, AnyRef, Any

final def !=(arg0: AnyRef): Boolean

Definition Classes
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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: AnyRef): Boolean

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
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final def isInstanceOf[T0]: Boolean

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val maxMem: Option[Long]

The memory budget (bytes)
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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val numProfileTrials: Int

The number of profiling samples to take per scale
val partitionScales: Seq[Long]

The scales to sample at (average number of desired data points per partition)
final def synchronized[T0](arg0: ⇒ T0): T0

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final def wait(): Unit

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

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final def wait(arg0: Long): Unit

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