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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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 org.apache.wayang.api.dataquanta.builder
import org.apache.wayang.api.JavaPlanBuilder
import org.apache.wayang.api.dataquanta.{DataQuanta, DataQuantaBuilder}
import org.apache.wayang.api.util.TypeTrap
import org.apache.wayang.basic.operators.MapOperator
import org.apache.wayang.core.function.FunctionDescriptor.{SerializableFunction, SerializablePredicate}
import org.apache.wayang.core.optimizer.ProbabilisticDoubleInterval
import org.apache.wayang.core.optimizer.costs.{LoadProfile, LoadProfileEstimator}
import org.apache.wayang.core.types.DataSetType
import org.apache.wayang.core.util.ReflectionUtils
/**
* [[DataQuantaBuilder]] implementation for [[org.apache.wayang.basic.operators.MapOperator]]s.
*
* @param inputDataQuanta [[DataQuantaBuilder]] for the input [[DataQuanta]]
* @param udf UDF for the [[MapOperator]]
*/
class FilterDataQuantaBuilder[T](inputDataQuanta: DataQuantaBuilder[_, T], udf: SerializablePredicate[T])
(implicit javaPlanBuilder: JavaPlanBuilder)
extends BasicDataQuantaBuilder[FilterDataQuantaBuilder[T], T] {
// Reuse the input TypeTrap to enforce type equality between input and output.
override def getOutputTypeTrap: TypeTrap = inputDataQuanta.outputTypeTrap
/** [[LoadProfileEstimator]] to estimate the [[LoadProfile]] of the [[udf]]. */
private var udfLoadProfileEstimator: LoadProfileEstimator = _
/** Selectivity of the filter predicate. */
private var selectivity: ProbabilisticDoubleInterval = _
/** SQL UDF implementing the filter predicate. */
private var sqlUdf: String = _
// Try to infer the type classes from the udf.
locally {
val parameters = ReflectionUtils.getTypeParameters(udf.getClass, classOf[SerializableFunction[_, _]])
parameters.get("Input") match {
case cls: Class[T] => this.outputTypeTrap.dataSetType = DataSetType.createDefault(cls)
case _ => logger.warn("Could not infer types from {}.", udf)
}
}
/**
* Set a [[LoadProfileEstimator]] for the load of the UDF.
*
* @param udfLoadProfileEstimator the [[LoadProfileEstimator]]
* @return this instance
*/
def withUdfLoad(udfLoadProfileEstimator: LoadProfileEstimator) = {
this.udfLoadProfileEstimator = udfLoadProfileEstimator
this
}
/**
* Add a SQL implementation of the UDF.
*
* @param sqlUdf a SQL condition that can be plugged into a `WHERE` clause
* @return this instance
*/
def withSqlUdf(sqlUdf: String) = {
this.sqlUdf = sqlUdf
this
}
/**
* Specify the selectivity of the UDF.
*
* @param lowerEstimate the lower bound of the expected selectivity
* @param upperEstimate the upper bound of the expected selectivity
* @param confidence the probability of the actual selectivity being within these bounds
* @return this instance
*/
def withSelectivity(lowerEstimate: Double, upperEstimate: Double, confidence: Double) = {
this.selectivity = new ProbabilisticDoubleInterval(lowerEstimate, upperEstimate, confidence)
this
}
override protected def build = inputDataQuanta.dataQuanta().filterJava(
udf, this.sqlUdf, this.selectivity, this.udfLoadProfileEstimator
)
}