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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.spark.sql.catalyst.plans.logical.statsEstimation
import scala.math.BigDecimal.RoundingMode
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap}
import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, LogicalPlan, Statistics}
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.{DecimalType, _}
object EstimationUtils {
/** Check if each plan has rowCount in its statistics. */
def rowCountsExist(conf: SQLConf, plans: LogicalPlan*): Boolean =
plans.forall(_.stats(conf).rowCount.isDefined)
/** Check if each attribute has column stat in the corresponding statistics. */
def columnStatsExist(statsAndAttr: (Statistics, Attribute)*): Boolean = {
statsAndAttr.forall { case (stats, attr) =>
stats.attributeStats.contains(attr)
}
}
def nullColumnStat(dataType: DataType, rowCount: BigInt): ColumnStat = {
ColumnStat(distinctCount = 0, min = None, max = None, nullCount = rowCount,
avgLen = dataType.defaultSize, maxLen = dataType.defaultSize)
}
def ceil(bigDecimal: BigDecimal): BigInt = bigDecimal.setScale(0, RoundingMode.CEILING).toBigInt()
/** Get column stats for output attributes. */
def getOutputMap(inputMap: AttributeMap[ColumnStat], output: Seq[Attribute])
: AttributeMap[ColumnStat] = {
AttributeMap(output.flatMap(a => inputMap.get(a).map(a -> _)))
}
def getOutputSize(
attributes: Seq[Attribute],
outputRowCount: BigInt,
attrStats: AttributeMap[ColumnStat] = AttributeMap(Nil)): BigInt = {
// We assign a generic overhead for a Row object, the actual overhead is different for different
// Row format.
val sizePerRow = 8 + attributes.map { attr =>
if (attrStats.contains(attr)) {
attr.dataType match {
case StringType =>
// UTF8String: base + offset + numBytes
attrStats(attr).avgLen + 8 + 4
case _ =>
attrStats(attr).avgLen
}
} else {
attr.dataType.defaultSize
}
}.sum
// Output size can't be zero, or sizeInBytes of BinaryNode will also be zero
// (simple computation of statistics returns product of children).
if (outputRowCount > 0) outputRowCount * sizePerRow else 1
}
/**
* For simplicity we use Decimal to unify operations for data types whose min/max values can be
* represented as numbers, e.g. Boolean can be represented as 0 (false) or 1 (true).
* The two methods below are the contract of conversion.
*/
def toDecimal(value: Any, dataType: DataType): Decimal = {
dataType match {
case _: NumericType | DateType | TimestampType => Decimal(value.toString)
case BooleanType => if (value.asInstanceOf[Boolean]) Decimal(1) else Decimal(0)
}
}
def fromDecimal(dec: Decimal, dataType: DataType): Any = {
dataType match {
case BooleanType => dec.toLong == 1
case DateType => dec.toInt
case TimestampType => dec.toLong
case ByteType => dec.toByte
case ShortType => dec.toShort
case IntegerType => dec.toInt
case LongType => dec.toLong
case FloatType => dec.toFloat
case DoubleType => dec.toDouble
case _: DecimalType => dec
}
}
}
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