rwpenney commented on a change in pull request #30745: URL: https://github.com/apache/spark/pull/30745#discussion_r544491474
########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Product.scala ########## @@ -0,0 +1,94 @@ +/* + * 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.expressions.aggregate + +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.util.TypeUtils +import org.apache.spark.sql.types.{ AbstractDataType, DataType, DoubleType, NumericType } + + +@ExpressionDescription( + usage = "_FUNC_(expr) - Returns the product calculated from values of a group.", + examples = """ + Examples: + > SELECT _FUNC_(col) FROM VALUES (2), (3), (5) AS tab(col); + 30 + > SELECT _FUNC_(col) FROM VALUES (NULL), (5), (7) AS tab(col); + 35 + > SELECT _FUNC_(col) FROM VALUES (NULL), (NULL) AS tab(col); + NULL + """, + group = "agg_funcs", + since = "3.2.0") +case class Product(child: Expression, scale: Double = 1.0) + extends DeclarativeAggregate with ImplicitCastInputTypes { + + override def children: Seq[Expression] = child :: Nil + + override def nullable: Boolean = true + + override def dataType: DataType = resultType + + override def inputTypes: Seq[AbstractDataType] = Seq(NumericType) Review comment: I agree that overflow is an occupational hazard with any non-trivial numerical computation, and that's no worse than the `exp(sum(log(...)))` work-around. The use-case I'm personally most interested in is combining probabilities that are in the range [0, 1], where the risk of overflow or underflow is predictably small. The choice of the `Double` type is intended to provide a wide dynamic range that should work for many use-cases, and the optional "scale" parameter allows some extra protection against overflow when one has some a-priori information about the typical size of the numbers being multiplied together. (Theoretically it should be of similar size to one over the geometric mean of the numbers being multiplied.) I'm doubtful that the code-complexity of supporting `Float` or other native Spark datatypes is justifiable. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
