org.checkita.dqf.core.metrics.df.regular.ApproxCardinalityDFMetrics
ApproximateSequenceCompletenessDFMetricCalculator
case class ApproximateSequenceCompletenessDFMetricCalculator(metricId: String, columns: Seq[String], accuracyError: Double, increment: Long) extends DFMetricCalculator with Product with Serializable
Calculates approximate 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
- accuracyError
Error of calculation
- increment
Sequence increment
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- ApproximateSequenceCompletenessDFMetricCalculator
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- DFMetricCalculator
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- val accuracyError: Double
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val
columns: Seq[String]
- Definition Classes
- ApproximateSequenceCompletenessDFMetricCalculator → DFMetricCalculator
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val
emptyValue: Column
Value which is returned when metric result is null.
Value which is returned when metric result is null.
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- ApproximateSequenceCompletenessDFMetricCalculator → DFMetricCalculator
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final
def
eq(arg0: AnyRef): Boolean
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def
errorConditionExpr(implicit colTypes: Map[String, DataType]): Column
If casting value to LongType yields null, then it is a signal that value is not a natural number.
If casting value to LongType yields null, then it is a signal that value is not a natural number. Thus, HyperLogLog computation can't be incremented for this row. This is a metric increment failure.
- colTypes
Map of column names to their datatype.
- returns
Spark row-level expression yielding boolean result.
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- protected
- Definition Classes
- ApproximateSequenceCompletenessDFMetricCalculator → DFMetricCalculator
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def
errorExpr(rowData: Column)(implicit colTypes: Map[String, DataType]): Column
Error collection expression: collects row data in case of metric error.
Error collection expression: collects row data in case of metric error.
- rowData
Array of row data from columns related to this metric calculator (source keyFields + metric columns + window start time column for streaming applications)
- colTypes
Map of column names to their datatype.
- returns
Spark expression that will yield row data in case of metric error.
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- DFMetricCalculator
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def
errorMessage: String
Error message that will be returned when column value cannot be cast to Long type.
Error message that will be returned when column value cannot be cast to Long type.
- returns
Metric increment failure message.
- Definition Classes
- ApproximateSequenceCompletenessDFMetricCalculator → DFMetricCalculator
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def
errors(implicit errorDumpSize: Int, keyFields: Seq[String], colTypes: Map[String, DataType]): Column
Final metric errors aggregation expression.
Final metric errors aggregation expression. Collects all metric errors into an array column. The size of array is limited by maximum allowed error dump size parameter.
- errorDumpSize
Maximum allowed number of errors to be collected per single metric.
- keyFields
Sequence of source/stream key fields.
- colTypes
Map of column names to their datatype.
- returns
Spark expression that will yield array of metric errors.
- Definition Classes
- DFMetricCalculator
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val
errorsCol: String
Name of the column that will store metric errors
Name of the column that will store metric errors
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- DFMetricCalculator
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finalize(): Unit
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- val increment: Long
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final
def
isInstanceOf[T0]: Boolean
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val
metricId: String
Unlike RDD calculators, DF calculators are not groped by its type.
Unlike RDD calculators, DF calculators are not groped by its type. For each metric defined in DQ job, there will be created its own instance of DF calculator. Thus, DF metric calculators can be linked to metric definitions by metricId.
- Definition Classes
- ApproximateSequenceCompletenessDFMetricCalculator → DFMetricCalculator
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val
metricName: MetricName
- Definition Classes
- ApproximateSequenceCompletenessDFMetricCalculator → DFMetricCalculator
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final
def
ne(arg0: AnyRef): Boolean
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notify(): Unit
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notifyAll(): Unit
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def
result(implicit colTypes: Map[String, DataType]): Column
Final metric aggregation expression that MUST yield double value.
Final metric aggregation expression that MUST yield double value.
- colTypes
Map of column names to their datatype.
- returns
Spark expression that will yield double metric calculator result
- Definition Classes
- DFMetricCalculator
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val
resultAggregateFunction: (Column) ⇒ Column
Use approx_count_distinct function which calculates data cardinality using HyperLogLog++ algorithm.
Use approx_count_distinct function which calculates data cardinality using HyperLogLog++ algorithm.
- Attributes
- protected
- Definition Classes
- ApproximateSequenceCompletenessDFMetricCalculator → DFMetricCalculator
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val
resultCol: String
Name of the column that will store metric result
Name of the column that will store metric result
- Definition Classes
- DFMetricCalculator
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def
resultExpr(implicit colTypes: Map[String, DataType]): Column
Retrieves long value from requested column of row.
Retrieves long value from requested column of row.
- colTypes
Map of column names to their datatype.
- returns
Spark row-level expression yielding numeric result.
- Attributes
- protected
- Definition Classes
- ApproximateSequenceCompletenessDFMetricCalculator → DFMetricCalculator
- Note
Spark expression MUST process single row but not aggregate multiple rows.
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def
rowDataExpr(keyFields: Seq[String]): Column
Row data collection expression: collects values of selected columns to array for row where metric error occurred.
Row data collection expression: collects values of selected columns to array for row where metric error occurred.
- keyFields
Sequence of source/stream key fields.
- returns
Spark expression that will yield array of row data for column related to this metric calculator.
- Attributes
- protected
- Definition Classes
- DFMetricCalculator
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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def
wait(arg0: Long): Unit
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