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class QuantileSummaries extends Serializable

Helper class to compute approximate quantile summary. This implementation is based on the algorithm proposed in the paper: "Space-efficient Online Computation of Quantile Summaries" by Greenwald, Michael and Khanna, Sanjeev. (https://doi.org/10.1145/375663.375670)

In order to optimize for speed, it maintains an internal buffer of the last seen samples, and only inserts them after crossing a certain size threshold. This guarantees a near-constant runtime complexity compared to the original algorithm.

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Instance Constructors

  1. new QuantileSummaries(compressThreshold: Int, relativeError: Double, sampled: Array[Stats] = Array.empty, count: Long = 0L, compressed: Boolean = false)

    compressThreshold

    the compression threshold. After the internal buffer of statistics crosses this size, it attempts to compress the statistics together.

    relativeError

    the target relative error. It is uniform across the complete range of values.

    sampled

    a buffer of quantile statistics. See the G-K article for more details.

    count

    the count of all the elements *inserted in the sampled buffer* (excluding the head buffer)

    compressed

    whether the statistics have been compressed

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 def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. def compress(): QuantileSummaries

    Returns a new summary that compresses the summary statistics and the head buffer.

    Returns a new summary that compresses the summary statistics and the head buffer.

    This implements the COMPRESS function of the GK algorithm. It does not modify the object.

    returns

    a new summary object with compressed statistics

  7. val compressThreshold: Int
  8. var compressed: Boolean
  9. val count: Long
  10. final def eq(arg0: AnyRef): Boolean
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  11. def equals(arg0: AnyRef): Boolean
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  12. def finalize(): Unit
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  13. final def getClass(): Class[_ <: AnyRef]
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    @native()
  14. def hashCode(): Int
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    @native()
  15. def insert(x: Double): QuantileSummaries

    Returns a summary with the given observation inserted into the summary.

    Returns a summary with the given observation inserted into the summary. This method may either modify in place the current summary (and return the same summary, modified in place), or it may create a new summary from scratch it necessary.

    x

    the new observation to insert into the summary

  16. final def isInstanceOf[T0]: Boolean
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  17. def merge(other: QuantileSummaries): QuantileSummaries

    Merges two (compressed) summaries together.

    Merges two (compressed) summaries together.

    Returns a new summary.

  18. final def ne(arg0: AnyRef): Boolean
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  19. final def notify(): Unit
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  20. final def notifyAll(): Unit
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  21. def query(percentile: Double): Option[Double]

    Runs a query for a given percentile.

    Runs a query for a given percentile. The result follows the approximation guarantees detailed above. The query can only be run on a compressed summary: you need to call compress() before using it.

    percentile

    the target percentile

    returns

    the corresponding approximate quantile

  22. def query(percentiles: Seq[Double]): Option[Seq[Double]]

    Runs a query for a given sequence of percentiles.

    Runs a query for a given sequence of percentiles. The result follows the approximation guarantees detailed above. The query can only be run on a compressed summary: you need to call compress() before using it.

    percentiles

    the target percentiles

    returns

    the corresponding approximate quantiles, in the same order as the input

  23. val relativeError: Double
  24. val sampled: Array[Stats]
  25. final def synchronized[T0](arg0: => T0): T0
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  26. def toString(): String
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  27. final def wait(): Unit
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  28. final def wait(arg0: Long, arg1: Int): Unit
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  29. final def wait(arg0: Long): Unit
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