object GroupingDFMetrics
WARNING: All grouping dataframe metric calculators will group dataset by the input metric columns. Thus, calculation of grouping calculators will definitely involve data shuffling! For large data sets shuffling may significantly slower the application and would require more resources to be completed.
Use grouping calculators with caution!
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case class
DistinctValuesDFMetricCalculator(metricId: String, columns: Seq[String]) extends GroupingDFMetricCalculator with Product with Serializable
Calculates count of distinct values in processed elements
Calculates count of distinct values in processed elements
- metricId
Id of the metric.
- columns
Sequence of columns which are used for metric calculation
- Note
If exact result is not mandatory, then it's better to use HyperLogLog-based metric calculator called "APPROXIMATE_DISTINCT_VALUES".
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case class
DuplicateValuesDFMetricCalculator(metricId: String, columns: Seq[String]) extends GroupingDFMetricCalculator with Product with Serializable
Calculates number of duplicate values for given column or tuple of columns.
Calculates number of duplicate values for given column or tuple of columns.
- metricId
Id of the metric.
- columns
Sequence of columns which are used for metric calculation
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case class
SequenceCompletenessDFMetricCalculator(metricId: String, columns: Seq[String], increment: Long) extends GroupingDFMetricCalculator with Product with Serializable
Calculates completeness of incremental integer (long) sequence, i.e.
Calculates completeness of incremental integer (long) sequence, i.e. checks if sequence does not have missing elements.
Works for single column only!
- metricId
Id of the metric.
- columns
Sequence of columns which are used for metric calculation
- Note
If exact result is not mandatory, then it's better to use HyperLogLog-based metric calculator called "APPROXIMATE_SEQUENCE_COMPLETENESS".
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