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/*
* Copyright 2018 ABSA Group Limited
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package za.co.absa.enceladus.conformance.interpreter.rules
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.sql.{Column, Dataset, Row, SparkSession}
import za.co.absa.spark.hats.Extensions._
import za.co.absa.enceladus.conformance.interpreter.{ExplosionState, InterpreterContextArgs, RuleValidators}
import za.co.absa.enceladus.dao.MenasDAO
import za.co.absa.enceladus.model.conformanceRule.{ConformanceRule, NegationConformanceRule}
import za.co.absa.enceladus.utils.schema.SchemaUtils
import za.co.absa.enceladus.utils.types.GlobalDefaults
import za.co.absa.enceladus.utils.udf.UDFNames
import za.co.absa.enceladus.utils.validation.SchemaPathValidator
import za.co.absa.spark.hats.transformations.NestedArrayTransformations
case class NegationRuleInterpreter(rule: NegationConformanceRule) extends RuleInterpreter {
override def conformanceRule: Option[ConformanceRule] = Some(rule)
override def conform(df: Dataset[Row])
(implicit spark: SparkSession, explosionState: ExplosionState, dao: MenasDAO,
progArgs: InterpreterContextArgs): Dataset[Row] = {
NegationRuleInterpreter.validateInputField(progArgs.datasetName, df.schema, rule.inputColumn)
val field = SchemaUtils.getField(rule.inputColumn, df.schema).get
val negationErrUdfCall = callUDF(UDFNames.confNegErr, lit(rule.outputColumn), col(rule.inputColumn))
val errCol = "errCol"
field.dataType match {
case _: DecimalType =>
// Negating decimal cannot fail
df.nestedMapColumn(rule.inputColumn, rule.outputColumn, c => negate(c))
case _: BooleanType =>
// Negating Boolean cannot fail
df.nestedMapColumn(rule.inputColumn, rule.outputColumn, c => not(c))
case _: DoubleType | _: FloatType =>
// Negating floating point numbers cannot fail, but we need to account
// for signed zeros (see the note for getNegator()).
df.nestedMapColumn(rule.inputColumn, rule.outputColumn, c => getNegator(c, field))
case dt =>
// The generic negation with checking for error conditions
NestedArrayTransformations.nestedWithColumnAndErrorMap(df, rule.inputColumn, rule.outputColumn, errCol,
c => getNegator(c, field), c => getError(c, negationErrUdfCall, dt))
}
}
private def getNegator(inputColumn: Column, field: StructField): Column = {
// Just a couple JVM things:
// 1. Beware of silent integer overflow, -Int.MinValue == Int.MaxValue + 1 == Int.MinValue
// Proof:
// a) Int.MaxValue = 2^31 - 1
// b) Int.MinValue = -2^31
// c) -Int.MinValue = 2^31 = Int.MaxValue + 1 = Int.MinValue
// 2. Beware negative floating-point zeroes, i.e. 0.0 * -1 == -0.0
// Equality (0.0 == -0.0) holds true, but Spark SQL considers 0.0 and -0.0 distinct and fails joins
// The above is true not only for JVM, but for the most of the CPU/hardware implementations of numeric data types
def defaultValue(dt: DataType, nullable: Boolean): Any = {
GlobalDefaults.getDataTypeDefaultValueWithNull(dt, field.nullable).get.orNull
}
val neg = negate(inputColumn)
field.dataType match {
case _: DoubleType | _: FloatType => when(inputColumn === 0.0, 0.0).otherwise(neg)
case dt: ByteType => when(inputColumn === Byte.MinValue, defaultValue(dt, field.nullable)).otherwise(neg)
case dt: ShortType => when(inputColumn === Short.MinValue, defaultValue(dt, field.nullable)).otherwise(neg)
case dt: IntegerType => when(inputColumn === Int.MinValue,defaultValue(dt, field.nullable)).otherwise(neg)
case dt: LongType => when(inputColumn === Long.MinValue, defaultValue(dt, field.nullable)).otherwise(neg)
case _ => neg
}
}
private def getError(inputColumn: Column, errorColumnUDF: Column, fieldType: DataType): Column = {
fieldType match {
// scalastyle:off null
case _: ByteType => when(inputColumn === Byte.MinValue, errorColumnUDF).otherwise(null)
case _: ShortType => when(inputColumn === Short.MinValue, errorColumnUDF).otherwise(null)
case _: IntegerType => when(inputColumn === Int.MinValue, errorColumnUDF).otherwise(null)
case _: LongType => when(inputColumn === Long.MinValue, errorColumnUDF).otherwise(null)
case a => throw new IllegalArgumentException("NegationRuleInterpreter.getError() should be called only for " +
s"data types that can produce errors. It is called for $a data type.")
// scalastyle:on null
}
}
}
object NegationRuleInterpreter {
@throws[ValidationException]
def validateInputField(datasetName: String, schema: StructType, fieldPath: String): Unit = {
val issues = SchemaPathValidator.validateSchemaPathAlgebraic(schema, fieldPath)
RuleValidators.checkAndThrowValidationErrors(datasetName: String, "Negation rule input field is incorrect.", issues)
}
}
