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[SPARK-45505][PYTHON] Refactor analyzeInPython to make it reusable
### What changes were proposed in this pull request? Currently, the `analyzeInPython` method in UserDefinedPythonTableFunction object can starts a Python process in driver and run a Python function in the Python process. This PR aims to refactor this logic into a reusable runner class. ### Why are the changes needed? To make the code more reusable. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existing tests ### Was this patch authored or co-authored using generative AI tooling? No Closes apache#43340 from allisonwang-db/spark-45505-refactor-analyze-in-py. Authored-by: allisonwang-db <allison.wang@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
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sql/core/src/main/scala/org/apache/spark/sql/execution/python/PythonPlannerRunner.scala
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.execution.python | ||
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| import java.io.{BufferedInputStream, BufferedOutputStream, DataInputStream, DataOutputStream, EOFException, InputStream} | ||
| import java.nio.ByteBuffer | ||
| import java.nio.channels.SelectionKey | ||
| import java.util.HashMap | ||
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| import scala.jdk.CollectionConverters._ | ||
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| import net.razorvine.pickle.Pickler | ||
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| import org.apache.spark.{JobArtifactSet, SparkEnv, SparkException} | ||
| import org.apache.spark.api.python.{PythonFunction, PythonWorker, PythonWorkerUtils, SpecialLengths} | ||
| import org.apache.spark.internal.config.BUFFER_SIZE | ||
| import org.apache.spark.internal.config.Python._ | ||
| import org.apache.spark.sql.internal.SQLConf | ||
| import org.apache.spark.util.DirectByteBufferOutputStream | ||
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| /** | ||
| * A helper class to run Python functions in Spark driver. | ||
| */ | ||
| abstract class PythonPlannerRunner[T](func: PythonFunction) { | ||
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| protected val workerModule: String | ||
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| protected def writeToPython(dataOut: DataOutputStream, pickler: Pickler): Unit | ||
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| protected def receiveFromPython(dataIn: DataInputStream): T | ||
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| def runInPython(): T = { | ||
| val env = SparkEnv.get | ||
| val bufferSize: Int = env.conf.get(BUFFER_SIZE) | ||
| val authSocketTimeout = env.conf.get(PYTHON_AUTH_SOCKET_TIMEOUT) | ||
| val reuseWorker = env.conf.get(PYTHON_WORKER_REUSE) | ||
| val localdir = env.blockManager.diskBlockManager.localDirs.map(f => f.getPath()).mkString(",") | ||
| val simplifiedTraceback: Boolean = SQLConf.get.pysparkSimplifiedTraceback | ||
| val workerMemoryMb = SQLConf.get.pythonPlannerExecMemory | ||
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| val jobArtifactUUID = JobArtifactSet.getCurrentJobArtifactState.map(_.uuid) | ||
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| val envVars = new HashMap[String, String](func.envVars) | ||
| val pythonExec = func.pythonExec | ||
| val pythonVer = func.pythonVer | ||
| val pythonIncludes = func.pythonIncludes.asScala.toSet | ||
| val broadcastVars = func.broadcastVars.asScala.toSeq | ||
| val maybeAccumulator = Option(func.accumulator).map(_.copyAndReset()) | ||
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| envVars.put("SPARK_LOCAL_DIRS", localdir) | ||
| if (reuseWorker) { | ||
| envVars.put("SPARK_REUSE_WORKER", "1") | ||
| } | ||
| if (simplifiedTraceback) { | ||
| envVars.put("SPARK_SIMPLIFIED_TRACEBACK", "1") | ||
| } | ||
| workerMemoryMb.foreach { memoryMb => | ||
| envVars.put("PYSPARK_PLANNER_MEMORY_MB", memoryMb.toString) | ||
| } | ||
| envVars.put("SPARK_AUTH_SOCKET_TIMEOUT", authSocketTimeout.toString) | ||
| envVars.put("SPARK_BUFFER_SIZE", bufferSize.toString) | ||
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| envVars.put("SPARK_JOB_ARTIFACT_UUID", jobArtifactUUID.getOrElse("default")) | ||
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| EvaluatePython.registerPicklers() | ||
| val pickler = new Pickler(/* useMemo = */ true, | ||
| /* valueCompare = */ false) | ||
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| val (worker: PythonWorker, _) = | ||
| env.createPythonWorker(pythonExec, workerModule, envVars.asScala.toMap) | ||
| var releasedOrClosed = false | ||
| val bufferStream = new DirectByteBufferOutputStream() | ||
| try { | ||
| val dataOut = new DataOutputStream(new BufferedOutputStream(bufferStream, bufferSize)) | ||
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| PythonWorkerUtils.writePythonVersion(pythonVer, dataOut) | ||
| PythonWorkerUtils.writeSparkFiles(jobArtifactUUID, pythonIncludes, dataOut) | ||
| PythonWorkerUtils.writeBroadcasts(broadcastVars, worker, env, dataOut) | ||
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| writeToPython(dataOut, pickler) | ||
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| dataOut.writeInt(SpecialLengths.END_OF_STREAM) | ||
| dataOut.flush() | ||
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| val dataIn = new DataInputStream(new BufferedInputStream( | ||
| new WorkerInputStream(worker, bufferStream.toByteBuffer), bufferSize)) | ||
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| val res = receiveFromPython(dataIn) | ||
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| PythonWorkerUtils.receiveAccumulatorUpdates(maybeAccumulator, dataIn) | ||
| Option(func.accumulator).foreach(_.merge(maybeAccumulator.get)) | ||
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| dataIn.readInt() match { | ||
| case SpecialLengths.END_OF_STREAM if reuseWorker => | ||
| env.releasePythonWorker(pythonExec, workerModule, envVars.asScala.toMap, worker) | ||
| case _ => | ||
| env.destroyPythonWorker(pythonExec, workerModule, envVars.asScala.toMap, worker) | ||
| } | ||
| releasedOrClosed = true | ||
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| res | ||
| } catch { | ||
| case eof: EOFException => | ||
| throw new SparkException("Python worker exited unexpectedly (crashed)", eof) | ||
| } finally { | ||
| try { | ||
| bufferStream.close() | ||
| } finally { | ||
| if (!releasedOrClosed) { | ||
| // An error happened. Force to close the worker. | ||
| env.destroyPythonWorker(pythonExec, workerModule, envVars.asScala.toMap, worker) | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| /** | ||
| * A wrapper of the non-blocking IO to write to/read from the worker. | ||
| * | ||
| * Since we use non-blocking IO to communicate with workers; see SPARK-44705, | ||
| * a wrapper is needed to do IO with the worker. | ||
| * This is a port and simplified version of `PythonRunner.ReaderInputStream`, | ||
| * and only supports to write all at once and then read all. | ||
| */ | ||
| private class WorkerInputStream(worker: PythonWorker, buffer: ByteBuffer) extends InputStream { | ||
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| private[this] val temp = new Array[Byte](1) | ||
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| override def read(): Int = { | ||
| val n = read(temp) | ||
| if (n <= 0) { | ||
| -1 | ||
| } else { | ||
| // Signed byte to unsigned integer | ||
| temp(0) & 0xff | ||
| } | ||
| } | ||
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| override def read(b: Array[Byte], off: Int, len: Int): Int = { | ||
| val buf = ByteBuffer.wrap(b, off, len) | ||
| var n = 0 | ||
| while (n == 0) { | ||
| worker.selector.select() | ||
| if (worker.selectionKey.isReadable) { | ||
| n = worker.channel.read(buf) | ||
| } | ||
| if (worker.selectionKey.isWritable) { | ||
| var acceptsInput = true | ||
| while (acceptsInput && buffer.hasRemaining) { | ||
| val n = worker.channel.write(buffer) | ||
| acceptsInput = n > 0 | ||
| } | ||
| if (!buffer.hasRemaining) { | ||
| // We no longer have any data to write to the socket. | ||
| worker.selectionKey.interestOps(SelectionKey.OP_READ) | ||
| } | ||
| } | ||
| } | ||
| n | ||
| } | ||
| } | ||
| } |
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