ulysses-you commented on code in PR #7570: URL: https://github.com/apache/kyuubi/pull/7570#discussion_r3595039556
########## extensions/spark/kyuubi-extension-spark-4-2/src/main/scala/org/apache/spark/sql/execution/CustomResourceProfileExec.scala: ########## @@ -0,0 +1,117 @@ +/* + * 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.execution + +import org.apache.spark.network.util.{ByteUnit, JavaUtils} +import org.apache.spark.rdd.RDD +import org.apache.spark.resource.{ExecutorResourceRequests, ResourceProfileBuilder} +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Attribute, SortOrder} +import org.apache.spark.sql.catalyst.plans.physical.Partitioning +import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics} +import org.apache.spark.sql.vectorized.ColumnarBatch +import org.apache.spark.util.Utils + +import org.apache.kyuubi.sql.KyuubiSQLConf._ + +/** + * This node wraps the final executed plan and inject custom resource profile to the RDD. + * It assumes that, the produced RDD would create the `ResultStage` in `DAGScheduler`, + * so it makes resource isolation between previous and final stage. + * + * Note that, Spark does not support config `minExecutors` for each resource profile. + * Which means, it would retain `minExecutors` for each resource profile. + * So, suggest set `spark.dynamicAllocation.minExecutors` to 0 if enable this feature. + */ +case class CustomResourceProfileExec(child: SparkPlan) extends UnaryExecNode { + override def output: Seq[Attribute] = child.output + override def outputPartitioning: Partitioning = child.outputPartitioning + override def outputOrdering: Seq[SortOrder] = child.outputOrdering + override def supportsColumnar: Boolean = child.supportsColumnar + override def supportsRowBased: Boolean = child.supportsRowBased + override protected def doCanonicalize(): SparkPlan = child.canonicalized + + private val executorCores = conf.getConf(FINAL_WRITE_STAGE_EXECUTOR_CORES).getOrElse( + sparkContext.getConf.getInt("spark.executor.cores", 1)) + private val executorMemory = conf.getConf(FINAL_WRITE_STAGE_EXECUTOR_MEMORY).getOrElse( + sparkContext.getConf.get("spark.executor.memory", "2G")) + private val executorMemoryOverhead = + conf.getConf(FINAL_WRITE_STAGE_EXECUTOR_MEMORY_OVERHEAD) + .getOrElse(sparkContext.getConf.get("spark.executor.memoryOverhead", "1G")) + private val executorOffHeapMemory = + if (sparkContext.getConf.getBoolean("spark.memory.offHeap.enabled", false)) { + conf.getConf(FINAL_WRITE_STAGE_EXECUTOR_OFF_HEAP_MEMORY) + } else { + None + } + + override lazy val metrics: Map[String, SQLMetric] = { + val base = Map( + "executorCores" -> SQLMetrics.createMetric(sparkContext, "executor cores"), + "executorMemory" -> SQLMetrics.createMetric(sparkContext, "executor memory (MiB)"), + "executorMemoryOverhead" -> SQLMetrics.createMetric( + sparkContext, + "executor memory overhead (MiB)")) + val addition = executorOffHeapMemory.map(_ => + "executorOffHeapMemory" -> + SQLMetrics.createMetric(sparkContext, "executor off heap memory (MiB)")).toMap + base ++ addition + } + + private def wrapResourceProfile[T](rdd: RDD[T]): RDD[T] = { + if (Utils.isTesting) { + // do nothing for local testing + return rdd + } + + metrics("executorCores") += executorCores + metrics("executorMemory") += JavaUtils.byteStringAs(executorMemory, ByteUnit.MiB) + metrics("executorMemoryOverhead") += JavaUtils.byteStringAs( + executorMemoryOverhead, + ByteUnit.MiB) + executorOffHeapMemory.foreach(m => + metrics("executorOffHeapMemory") += JavaUtils.byteStringAs(m, ByteUnit.MiB)) + + val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY) + SQLMetrics.postDriverMetricUpdates(sparkContext, executionId, metrics.values.toSeq) + + val resourceProfileBuilder = new ResourceProfileBuilder() + val executorResourceRequests = new ExecutorResourceRequests() + executorResourceRequests.cores(executorCores) + executorResourceRequests.memory(executorMemory) + executorResourceRequests.memoryOverhead(executorMemoryOverhead) + executorOffHeapMemory.foreach(executorResourceRequests.offHeapMemory) + resourceProfileBuilder.require(executorResourceRequests) + rdd.withResources(resourceProfileBuilder.build()) + rdd Review Comment: `withResources` will change the internal state, so its returned rdd same with the rdd. -- 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]
