haiyangsun-db commented on code in PR #55768: URL: https://github.com/apache/spark/pull/55768#discussion_r3224715548
########## sql/core/src/main/scala/org/apache/spark/sql/execution/externalUDF/ExternalUDFExec.scala: ########## @@ -0,0 +1,83 @@ +/* + * 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.externalUDF + +import org.apache.spark.{SparkEnv, TaskContext} +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.execution.UnaryExecNode +import org.apache.spark.sql.execution.metric.SQLMetric +import org.apache.spark.udf.worker.UDFWorkerSpecification +import org.apache.spark.udf.worker.core.{WorkerSecurityScope, WorkerSession} + +/** + * :: Experimental :: + * Base trait for physical plan nodes that execute UDFs in an external + * worker process via the language-agnostic UDF worker framework. + * + * Dispatchers are obtained via [[SparkEnv#getExternalUDFDispatcher]], + * which uses the [[UDFDispatcherManager]] registered on the + * environment. This avoids serializing the manager as part of the + * physical plan. + */ +@Experimental +trait ExternalUDFExec extends UnaryExecNode { + + /** + * Specification describing how to create and communicate with the UDF worker. + * There is exactly one specification per [[ExternalUDFExec]] node. + */ + def workerSpec: UDFWorkerSpecification + + // --------------------------------------------------------------------------- + // Metrics + // --------------------------------------------------------------------------- + + protected def externalUdfMetrics: Map[String, SQLMetric] = Map( + // TODO [SPARK-55278]: Emit the correct metrics here + ) + + override lazy val metrics: Map[String, SQLMetric] = externalUdfMetrics + + // --------------------------------------------------------------------------- + // Session lifecycle + // --------------------------------------------------------------------------- + + /** + * Creates a [[WorkerSession]] via [[SparkEnv#getExternalUDFDispatcher]] + * and registers cancellation on task failure. The provided function + * receives the session and must return the result iterator. Moreover, + * the function MUST close the session once all input data has been sent. + */ + protected def withUDFWorkerSession( + taskContext: TaskContext, + securityScope: Option[WorkerSecurityScope] = None)( + f: WorkerSession => Iterator[InternalRow] + ): Iterator[InternalRow] = { + val dispatcher = SparkEnv.get.getExternalUDFDispatcher( + workerSpec) + val session = dispatcher.createSession(securityScope) + + // Make sure to cancel the session, if the task fails + taskContext.addTaskFailureListener { (_, _) => Review Comment: we may need to add another completion listener as well to call session.close() The reason is that, spark doesn't have to consume the whole result iterator, e.g., in case of 'limit'. So if we rely on the iterator's last element being consumed, then we may miss the `close`. -- 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]
