Copilot commented on code in PR #12617: URL: https://github.com/apache/gluten/pull/12617#discussion_r3643170673
########## gluten-core/src/test/scala/org/apache/spark/util/SparkResourceUtilSuite.scala: ########## @@ -0,0 +1,56 @@ +/* + * 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.util + +import org.apache.spark.SparkConf + +import org.scalatest.funsuite.AnyFunSuite + +class SparkResourceUtilSuite extends AnyFunSuite { + + test("getTaskSlots floors at one when task cpus exceed executor cores") { + // spark.task.cpus > executor cores is an invalid config that Spark rejects later, but Gluten + // reads task slots during plugin init and divides by the result. Returning 0 here makes that + // division throw ArithmeticException before Spark's validateTaskCpusLargeEnough can report the + // real misconfiguration. + val conf = new SparkConf(false) + .set("spark.master", "local[1]") + .set("spark.task.cpus", "2") + assert(SparkResourceUtil.getTaskSlots(conf) == 1) + } + + test("getTaskSlots does not divide by zero when task cpus is zero") { + // spark.task.cpus is read via raw conf.getInt, which bypasses Spark's checkValue(_ > 0), so a + // zero value must not reach the division. + val conf = new SparkConf(false) + .set("spark.master", "local[8]") + .set("spark.task.cpus", "0") + assert(SparkResourceUtil.getTaskSlots(conf) == 1) + } + + test("getTaskSlots divides executor cores by task cpus") { + val conf = new SparkConf(false) + .set("spark.master", "local[8]") + .set("spark.task.cpus", "2") + assert(SparkResourceUtil.getTaskSlots(conf) == 4) + } + + test("getTaskSlots returns one core per slot by default") { + val conf = new SparkConf(false).set("spark.master", "local[1]") + assert(SparkResourceUtil.getTaskSlots(conf) == 1) + } Review Comment: The test name says the default is "one core per slot", but the current assertion only checks `local[1] == 1`, which doesn’t actually validate the default behavior when multiple cores are available. Consider using a multi-core local master (e.g. `local[8]`) and asserting the slot count equals the core count when `spark.task.cpus` is not set (default 1). -- 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]
