ReadConf

Value Members

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 val DefaultFetchSize: Int(1000)

   

5. final val DefaultSplitCount: Int(10)

   

6. final val DefaultTsTimestampBinding: UseTimestamp.type

   

7. final val DefaultUseStreamingValues4FBRead: Boolean

   

8. def apply(conf: SparkConf, options: Map[String, String]): ReadConf

   

   Creates ReadConf based on an externally provided map of properties to override those of SparkCon

   Creates ReadConf based on an externally provided map of properties to override those of SparkCon

   conf

   SparkConf of Spark context to be taken as defaults

   options

   externally provided map of properties

9. def apply(conf: SparkConf): ReadConf

   

   Creates ReadConf based on properties provided to Spark Conf

   Creates ReadConf based on properties provided to Spark Conf

   conf

   SparkConf of Spark context with Riak-related properties

10. final def asInstanceOf[T0]: T0

    

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11. def clone(): AnyRef

    

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12. final def eq(arg0: AnyRef): Boolean

    

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13. def equals(arg0: Any): Boolean

    

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14. final val fetchSizePropName: String("spark.riak.input.fetch-size")

    

15. def finalize(): Unit

    

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

    

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17. def hashCode(): Int

    

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18. final def isInstanceOf[T0]: Boolean

    

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

    

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20. final def notify(): Unit

    

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21. final def notifyAll(): Unit

    

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22. final val smartSplitMultiplier: Int(3)

    

    The docs recommend to have your number of partitions set to 3 or 4 times the number of CPUs in your cluster so that the work gets distributed more evenly among the CPUs.

    The docs recommend to have your number of partitions set to 3 or 4 times the number of CPUs in your cluster so that the work gets distributed more evenly among the CPUs. Meaning, if you only have 1 partition per core in the cluster you will have to wait for the one longest running task to complete but if you had broken that down further the workload would be more evenly balanced with fast and slow running tasks evening out.

    Since there is no enough information about available Spark resources such as real number of cores, 3x multiplier will be used.

23. final val splitCountPropName: String("spark.riak.input.split.count")

    

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

    

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25. def toString(): String

    

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26. final val tsBindingsTimestamp: String("spark.riakts.bindings.timestamp")

    

27. final val tsQuantumPropName: String("spark.riak.partitioning.ts-quantum")

    

28. final val tsRangeFieldPropName: String("spark.riak.partitioning.ts-range-field-name")

    

29. final val useStreamingValuesPropName: String("spark.riak.fullbucket.use-streaming-values")

    

30. final def wait(): Unit

    

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

    

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

    

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