dongjoon-hyun commented on code in PR #44021: URL: https://github.com/apache/spark/pull/44021#discussion_r1424409883
########## connector/profiler/src/main/scala/org/apache/spark/executor/profiler/ExecutorJVMProfiler.scala: ########## @@ -0,0 +1,171 @@ +/* + * 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.executor.profiler + +import java.io.{BufferedInputStream, FileInputStream, InputStream} +import java.net.URI +import java.util.concurrent.{ScheduledExecutorService, TimeUnit} + +import one.profiler.{AsyncProfiler, AsyncProfilerLoader} +import org.apache.hadoop.fs.{FileSystem, FSDataOutputStream, Path} + +import org.apache.spark.SparkConf +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.internal.Logging +import org.apache.spark.util.ThreadUtils + + +/** + * A class that enables the async code profiler + */ +private[spark] class ExecutorJVMProfiler(conf: SparkConf, executorId: String) extends Logging { + + private var running = false + private val enableProfiler = conf.get(EXECUTOR_CODE_PROFILING_ENABLED) + private val profilerOptions = conf.get(EXECUTOR_CODE_PROFILING_OPTIONS) + private val profilerOutputDir = conf.get(EXECUTOR_CODE_PROFILING_OUTPUT_DIR) + private val profilerLocalDir = conf.get(EXECUTOR_CODE_PROFILING_LOCAL_DIR) + private val writeInterval = conf.get(EXECUTOR_CODE_PROFILING_WRITE_INTERVAL) + + private val startcmd = s"start,$profilerOptions,file=$profilerLocalDir/profile.jfr" + private val stopcmd = s"stop,$profilerOptions,file=$profilerLocalDir/profile.jfr" + private val dumpcmd = s"dump,$profilerOptions,file=$profilerLocalDir/profile.jfr" + private val resumecmd = s"resume,$profilerOptions,file=$profilerLocalDir/profile.jfr" + + private val UPLOAD_SIZE = 8 * 1024 * 1024 // 8 MB + private var outputStream: FSDataOutputStream = _ + private var inputStream: InputStream = _ + private val dataBuffer = new Array[Byte](UPLOAD_SIZE) + private var threadpool: ScheduledExecutorService = _ + private var writing: Boolean = false + + val profiler: AsyncProfiler = if (enableProfiler) { + if (AsyncProfilerLoader.isSupported) { + AsyncProfilerLoader.load() + } else { + logWarning("Executor code profiling is enabled but is not supported for this platform") + null + } + } else { + null + } + + def start(): Unit = { + if (profiler != null && !running) { + logInfo("Executor code profiling starting.") + try { + profiler.execute(startcmd) + } catch { + case e: Exception => + logWarning("Executor code profiling aborted due to exception: ", e) + return + } + logInfo("Executor code profiling started.") + running = true + } + startWriting() + } + + /** Stops the profiling and saves output to hdfs location. */ + def stop(): Unit = { + if (profiler != null && running) { + profiler.execute(stopcmd) + logInfo("Code profiler stopped") + running = false + finishWriting() + } + } + + private def startWriting(): Unit = { + if (profilerOutputDir.isDefined) { + val applicationId = conf.getAppId + val config = SparkHadoopUtil.get.newConfiguration(conf) + val appName = conf.get("spark.app.name"); + val profilerOutputDirname = profilerOutputDir.get + val profileOutputFile = + s"$profilerOutputDirname/$applicationId/profile-$appName-exec-$executorId.jfr" + val fs = FileSystem.get(new URI(profileOutputFile), config); + val filenamePath = new Path(profileOutputFile) + outputStream = fs.create(filenamePath) + try { + if (fs.exists(filenamePath)) { + fs.delete(filenamePath, true) + } + logInfo(s"Copying executor profiling file to $profileOutputFile") + inputStream = new BufferedInputStream(new FileInputStream(s"$profilerLocalDir/profile.jfr")) + threadpool = ThreadUtils.newDaemonSingleThreadScheduledExecutor("profilerOutputThread") + threadpool.scheduleWithFixedDelay(new Runnable() { + override def run(): Unit = writeChunk() + }, writeInterval, writeInterval, + TimeUnit.SECONDS) + writing = true + } catch { + case e: Exception => + logError("Failed to start code profiler", e) + if (threadpool != null) { + threadpool.shutdownNow() + } + if (inputStream != null) { + inputStream.close() + } + if (outputStream != null) { + outputStream.close() + } + } + } + } + + private def writeChunk(): Unit = { + if (!writing) { + return + } + try { + // stop (pause) the profiler, dump the results and then resume. This is not ideal as we miss + // the events while the file is being dumped, but that is the only way to make sure that + // the chunk of data we are copying to dfs is in a consistent state. + profiler.execute(stopcmd) + profiler.execute(dumpcmd) + var remaining = inputStream.available() + profiler.execute(resumecmd) + while (remaining > 0) { + val read = inputStream.read(dataBuffer, 0, math.min(remaining, UPLOAD_SIZE)) + outputStream.write(dataBuffer, 0, read) + remaining -= read + } + } catch { + case e: Exception => logError("Exception occurred while writing profiler output", e) + } + } + + private def finishWriting(): Unit = { + if (profilerOutputDir.isDefined && writing) { + try { + // shutdown background writer + threadpool.shutdown() + threadpool.awaitTermination(30, TimeUnit.SECONDS) + // flush remaining data + writeChunk() + inputStream.close() + outputStream.close() + } catch { + case e: Exception => + logError("Exception in completing profiler output", e) Review Comment: Could you add a descriptive message about what happens if we log and ignore this exception? -- This is an automated message from the Apache Git Service. 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