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ai.chronon.spark.stats

StatsGenerator

Related Doc: package stats

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object StatsGenerator

StatsGenerator takes care of computation of metadata for dataframes as well as measuring differences between two dataframes. This applies to drifts as well between two dataframes.

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Type Members

  1. case class MetricTransform(name: String, expression: Column, operation: Operation, argMap: Map[String, String] = null) extends Product with Serializable

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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. def anyTransforms(column: Column): Seq[MetricTransform]

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    Stats applied to any column

  5. final def asInstanceOf[T0]: T0

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  6. def buildAggregator(metrics: Seq[MetricTransform], inputDf: DataFrame): RowAggregator

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    Build RowAggregator to use for computing stats on a dataframe based on metrics

  7. def buildMetrics(fields: Array[(String, DataType)]): Seq[MetricTransform]

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    For the schema of the data define metrics to be aggregated

  8. def clone(): AnyRef

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

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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  12. val finalizedPercentiles: Seq[Double]

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

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  14. def hashCode(): Int

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  15. val ignoreColumns: Seq[String]

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  16. final def isInstanceOf[T0]: Boolean

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

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. val nullPrefix: String

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  21. val nullRatePrefix: String

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  22. def numericTransforms(column: Column): Seq[MetricTransform]

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    Stats applied to numeric columns

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

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  24. def toString(): String

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  25. val totalColumn: String

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  26. final def wait(): Unit

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

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

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