org.apache.spark.sql.catalyst.util.TypeUtils.scala Maven / Gradle / Ivy
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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,
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* See the License for the specific language governing permissions and
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*/
package org.apache.spark.sql.catalyst.util
import org.apache.spark.sql.catalyst.analysis.{TypeCheckResult, TypeCoercion}
import org.apache.spark.sql.catalyst.expressions.RowOrdering
import org.apache.spark.sql.types._
/**
* Functions to help with checking for valid data types and value comparison of various types.
*/
object TypeUtils {
def checkForNumericExpr(dt: DataType, caller: String): TypeCheckResult = {
if (dt.isInstanceOf[NumericType] || dt == NullType) {
TypeCheckResult.TypeCheckSuccess
} else {
TypeCheckResult.TypeCheckFailure(s"$caller requires numeric types, not ${dt.catalogString}")
}
}
def checkForOrderingExpr(dt: DataType, caller: String): TypeCheckResult = {
if (RowOrdering.isOrderable(dt)) {
TypeCheckResult.TypeCheckSuccess
} else {
TypeCheckResult.TypeCheckFailure(
s"$caller does not support ordering on type ${dt.catalogString}")
}
}
def checkForSameTypeInputExpr(types: Seq[DataType], caller: String): TypeCheckResult = {
if (TypeCoercion.haveSameType(types)) {
TypeCheckResult.TypeCheckSuccess
} else {
return TypeCheckResult.TypeCheckFailure(
s"input to $caller should all be the same type, but it's " +
types.map(_.catalogString).mkString("[", ", ", "]"))
}
}
def getNumeric(t: DataType): Numeric[Any] =
t.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]]
def getInterpretedOrdering(t: DataType): Ordering[Any] = {
t match {
case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]]
case a: ArrayType => a.interpretedOrdering.asInstanceOf[Ordering[Any]]
case s: StructType => s.interpretedOrdering.asInstanceOf[Ordering[Any]]
case udt: UserDefinedType[_] => getInterpretedOrdering(udt.sqlType)
}
}
def compareBinary(x: Array[Byte], y: Array[Byte]): Int = {
for (i <- 0 until x.length; if i < y.length) {
val v1 = x(i) & 0xff
val v2 = y(i) & 0xff
val res = v1 - v2
if (res != 0) return res
}
x.length - y.length
}
/**
* Returns true if the equals method of the elements of the data type is implemented properly.
* This also means that they can be safely used in collections relying on the equals method,
* as sets or maps.
*/
def typeWithProperEquals(dataType: DataType): Boolean = dataType match {
case BinaryType => false
case _: AtomicType => true
case _ => false
}
}