EnricoMi commented on code in PR #36150: URL: https://github.com/apache/spark/pull/36150#discussion_r858607774
########## sql/core/src/main/scala/org/apache/spark/sql/Melt.scala: ########## @@ -0,0 +1,84 @@ +/* + * 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 + +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression, Literal} +import org.apache.spark.sql.catalyst.expressions.objects.AssertNotNull +import org.apache.spark.sql.catalyst.plans.logical.Expand +import org.apache.spark.sql.functions.col +import org.apache.spark.sql.types.StringType + +object Melt { + def of[_](ds: Dataset[_], + ids: Seq[String], + values: Seq[String], + dropNulls: Boolean = true, + variableColumnName: String = "variable", + valueColumnName: String = "value"): DataFrame = { + if (ids.isEmpty) { + throw new IllegalArgumentException(f"At least one id column required") + } + if (values.isEmpty) { + throw new IllegalArgumentException(f"At least one value column required") + } + + val valueTypes = ds.schema.fields + .filter(attr => values.contains(attr.name)) + .map(_.dataType).toSet + if (valueTypes.size > 1) { + throw new IllegalArgumentException(f"All values must be of same types, " + + f"found: ${valueTypes.mkString(", ")}") + } + val valueType = valueTypes.head + + val idAttrs = ds.logicalPlan.output.filter(attr => ids.contains(attr.name)) + val idNames = idAttrs.map(_.name) + if (idNames.contains(variableColumnName)) { + throw new IllegalArgumentException(f"Column name for variable column ($variableColumnName) " + + f"must not exist among id columns: ${idNames.mkString(", ")}") + } + if (idNames.contains(valueColumnName)) { + throw new IllegalArgumentException(f"Column name for value column ($valueColumnName) " + + f"must not exist among id columns: ${idNames.mkString(", ")}") + } + + val variableAttr = AttributeReference(variableColumnName, StringType, nullable = false)() + val valueAttr = AttributeReference(valueColumnName, valueType, nullable = true)() + val output = idAttrs ++ Seq(variableAttr, valueAttr) + + val valueAttrs = ds.queryExecution.logical.output.filter(attr => values.contains(attr.name)) + val exprs: Seq[Seq[Expression]] = valueAttrs.map { + attr => + idAttrs ++ Seq( + Literal(attr.name), + attr + ) + } + + val plan = Expand(exprs, output, ds.queryExecution.logical) + val df = Dataset.ofRows(ds.sparkSession, plan) + + if (dropNulls) { + val valueColumn = col(valueColumnName) + df.where(valueColumn.isNotNull) + .withColumn(valueColumnName, new Column(AssertNotNull(valueColumn.expr))) Review Comment: The `AssertNotNull` exprssion turns the value column in the schema to `nullable = true`, so the schema reflects that there are no null values. The `where` filter does not do that. -- 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. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
