amaliujia commented on code in PR #36527: URL: https://github.com/apache/spark/pull/36527#discussion_r872703441
########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteNonAggregateFirst.scala: ########## @@ -0,0 +1,72 @@ +/* + * 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.optimizer + +import org.apache.spark.sql.catalyst.expressions.{Alias, Literal} +import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, First} +import org.apache.spark.sql.catalyst.plans.logical._ +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.catalyst.trees.TreePattern.FIRST +import org.apache.spark.sql.types.IntegerType + +/** + * Rewrite aggregate plan with a single [[First]] function when grouping is absent. In such a case + * the query is equivalent to simple projection with limit 1. + * + * Input Pseudo-Query: + * {{{ + * SELECT FIRST(col) FROM table + * }}} + * + * Rewritten Query: + * {{{ + * SELECT col FROM table LIMIT 1 Review Comment: Thanks for the insight about potential gain on the performance improvement! I see the tradeoff better now. I don't have strong opinion which one is better. Some systems are more sensible for query compilation latency. For the common workload that Spark SQL deals with, I believe it does not care about compilation latency much but more favor of faster execution on large data set. -- 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]
