parthchandra commented on code in PR #44021: URL: https://github.com/apache/spark/pull/44021#discussion_r1450862604
########## connector/profiler/src/main/scala/org/apache/spark/executor/profiler/ExecutorJVMProfiler.scala: ########## @@ -0,0 +1,183 @@ +/* + * 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, IOException} +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 JVM code profiler + */ +private[spark] class ExecutorJVMProfiler(conf: SparkConf, executorId: String) extends Logging { + + private var running = false + private val enableProfiler = conf.get(EXECUTOR_PROFILING_ENABLED) + private val profilerOptions = conf.get(EXECUTOR_PROFILING_OPTIONS) + private val profilerDfsDir = conf.get(EXECUTOR_PROFILING_DFS_DIR) + private val profilerLocalDir = conf.get(EXECUTOR_PROFILING_LOCAL_DIR) + private val writeInterval = conf.get(EXECUTOR_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: Option[AsyncProfiler] = { + Option( + if (enableProfiler && AsyncProfilerLoader.isSupported) AsyncProfilerLoader.load() else null + ) + } + + def start(): Unit = { + if (!running) { + try { + profiler.foreach(p => { + p.execute(startcmd) + logInfo("Executor JVM profiling started.") + running = true + startWriting() + } + ) Review Comment: Done ########## connector/profiler/src/main/scala/org/apache/spark/executor/profiler/ExecutorJVMProfiler.scala: ########## @@ -0,0 +1,183 @@ +/* + * 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, IOException} +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 JVM code profiler + */ +private[spark] class ExecutorJVMProfiler(conf: SparkConf, executorId: String) extends Logging { + + private var running = false + private val enableProfiler = conf.get(EXECUTOR_PROFILING_ENABLED) + private val profilerOptions = conf.get(EXECUTOR_PROFILING_OPTIONS) + private val profilerDfsDir = conf.get(EXECUTOR_PROFILING_DFS_DIR) + private val profilerLocalDir = conf.get(EXECUTOR_PROFILING_LOCAL_DIR) + private val writeInterval = conf.get(EXECUTOR_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: Option[AsyncProfiler] = { + Option( + if (enableProfiler && AsyncProfilerLoader.isSupported) AsyncProfilerLoader.load() else null + ) + } + + def start(): Unit = { + if (!running) { + try { + profiler.foreach(p => { + p.execute(startcmd) + logInfo("Executor JVM profiling started.") + running = true + startWriting() + } + ) + } catch { + case e@(_: IllegalArgumentException | _: IllegalStateException | _: IOException) => Review Comment: Done ########## connector/profiler/src/main/scala/org/apache/spark/executor/profiler/ExecutorJVMProfiler.scala: ########## @@ -0,0 +1,183 @@ +/* + * 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, IOException} +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 JVM code profiler + */ +private[spark] class ExecutorJVMProfiler(conf: SparkConf, executorId: String) extends Logging { + + private var running = false + private val enableProfiler = conf.get(EXECUTOR_PROFILING_ENABLED) + private val profilerOptions = conf.get(EXECUTOR_PROFILING_OPTIONS) + private val profilerDfsDir = conf.get(EXECUTOR_PROFILING_DFS_DIR) + private val profilerLocalDir = conf.get(EXECUTOR_PROFILING_LOCAL_DIR) + private val writeInterval = conf.get(EXECUTOR_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: Option[AsyncProfiler] = { + Option( + if (enableProfiler && AsyncProfilerLoader.isSupported) AsyncProfilerLoader.load() else null + ) + } + + def start(): Unit = { + if (!running) { + try { + profiler.foreach(p => { + p.execute(startcmd) + logInfo("Executor JVM profiling started.") + running = true + startWriting() + } + ) + } catch { + case e@(_: IllegalArgumentException | _: IllegalStateException | _: IOException) => + logError("JVM profiling aborted. Exception occurred in profiler native code: ", e) + case e: Exception => logWarning("Executor JVM profiling aborted due to exception: ", e) + } + } + } + + /** Stops the profiling and saves output to dfs location. */ + def stop(): Unit = { + if (running) { + profiler.foreach(p => { + p.execute(stopcmd) + logInfo("JVM profiler stopped") + running = false + finishWriting() + }) + } + } + + private def startWriting(): Unit = { + if (profilerDfsDir.isDefined) { + val applicationId = try { + conf.getAppId + } catch { + case _: NoSuchElementException => "local-" + System.currentTimeMillis + } + val config = SparkHadoopUtil.get.newConfiguration(conf) + val appName = conf.get("spark.app.name").replace(" ", "-") + val profilerOutputDirname = profilerDfsDir.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(false) + }, writeInterval, writeInterval, + TimeUnit.SECONDS) Review Comment: Done. Hopefully this is better now. ########## connector/profiler/src/main/scala/org/apache/spark/executor/profiler/ExecutorJVMProfiler.scala: ########## @@ -0,0 +1,183 @@ +/* + * 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, IOException} +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 JVM code profiler + */ +private[spark] class ExecutorJVMProfiler(conf: SparkConf, executorId: String) extends Logging { + + private var running = false + private val enableProfiler = conf.get(EXECUTOR_PROFILING_ENABLED) + private val profilerOptions = conf.get(EXECUTOR_PROFILING_OPTIONS) + private val profilerDfsDir = conf.get(EXECUTOR_PROFILING_DFS_DIR) + private val profilerLocalDir = conf.get(EXECUTOR_PROFILING_LOCAL_DIR) + private val writeInterval = conf.get(EXECUTOR_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: Option[AsyncProfiler] = { + Option( + if (enableProfiler && AsyncProfilerLoader.isSupported) AsyncProfilerLoader.load() else null + ) + } + + def start(): Unit = { + if (!running) { + try { + profiler.foreach(p => { + p.execute(startcmd) + logInfo("Executor JVM profiling started.") + running = true + startWriting() + } + ) + } catch { + case e@(_: IllegalArgumentException | _: IllegalStateException | _: IOException) => + logError("JVM profiling aborted. Exception occurred in profiler native code: ", e) + case e: Exception => logWarning("Executor JVM profiling aborted due to exception: ", e) + } + } + } + + /** Stops the profiling and saves output to dfs location. */ + def stop(): Unit = { + if (running) { + profiler.foreach(p => { + p.execute(stopcmd) + logInfo("JVM profiler stopped") + running = false + finishWriting() + }) + } + } + + private def startWriting(): Unit = { + if (profilerDfsDir.isDefined) { + val applicationId = try { + conf.getAppId + } catch { + case _: NoSuchElementException => "local-" + System.currentTimeMillis + } + val config = SparkHadoopUtil.get.newConfiguration(conf) + val appName = conf.get("spark.app.name").replace(" ", "-") + val profilerOutputDirname = profilerDfsDir.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(false) + }, writeInterval, writeInterval, + TimeUnit.SECONDS) + writing = true + } catch { + case e: Exception => + logError("Failed to start JVM profiler", e) + if (threadpool != null) { + threadpool.shutdownNow() + } + if (inputStream != null) { + inputStream.close() + } + if (outputStream != null) { + outputStream.close() + } + } + } + } + + private def writeChunk(lastChunk: Boolean): 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.get.execute(stopcmd) + profiler.get.execute(dumpcmd) + var remaining = inputStream.available() + if (!lastChunk) { + profiler.get.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: IOException => logError("Exception occurred while writing some profiler output: ", e) + case e@(_: IllegalArgumentException | _: IllegalStateException) => + logError("Some profiler output not written." + + " Exception occurred in profiler native code: ", e) + case e: Exception => logError("Some profiler output not written. Unexpected exception: ", e) Review Comment: If we get a failure, we can still keep writing because only the portion (chunk) of the data being written may be lost. The output file would still be valid. `finishWriting` is eventually called to shutdown the thread and the streams cleanly. We could potentially stop profiling if we get any of these exceptions, but I feel that it is perhaps too drastic. ########## connector/profiler/src/main/scala/org/apache/spark/executor/profiler/ExecutorJVMProfiler.scala: ########## @@ -0,0 +1,183 @@ +/* + * 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, IOException} +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 JVM code profiler + */ +private[spark] class ExecutorJVMProfiler(conf: SparkConf, executorId: String) extends Logging { + + private var running = false + private val enableProfiler = conf.get(EXECUTOR_PROFILING_ENABLED) + private val profilerOptions = conf.get(EXECUTOR_PROFILING_OPTIONS) + private val profilerDfsDir = conf.get(EXECUTOR_PROFILING_DFS_DIR) + private val profilerLocalDir = conf.get(EXECUTOR_PROFILING_LOCAL_DIR) + private val writeInterval = conf.get(EXECUTOR_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: Option[AsyncProfiler] = { + Option( + if (enableProfiler && AsyncProfilerLoader.isSupported) AsyncProfilerLoader.load() else null + ) + } + + def start(): Unit = { + if (!running) { + try { + profiler.foreach(p => { + p.execute(startcmd) + logInfo("Executor JVM profiling started.") + running = true + startWriting() + } + ) + } catch { + case e@(_: IllegalArgumentException | _: IllegalStateException | _: IOException) => + logError("JVM profiling aborted. Exception occurred in profiler native code: ", e) + case e: Exception => logWarning("Executor JVM profiling aborted due to exception: ", e) + } + } + } + + /** Stops the profiling and saves output to dfs location. */ + def stop(): Unit = { + if (running) { + profiler.foreach(p => { + p.execute(stopcmd) + logInfo("JVM profiler stopped") + running = false + finishWriting() + }) + } + } + + private def startWriting(): Unit = { + if (profilerDfsDir.isDefined) { + val applicationId = try { + conf.getAppId + } catch { + case _: NoSuchElementException => "local-" + System.currentTimeMillis + } + val config = SparkHadoopUtil.get.newConfiguration(conf) + val appName = conf.get("spark.app.name").replace(" ", "-") + val profilerOutputDirname = profilerDfsDir.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(false) + }, writeInterval, writeInterval, + TimeUnit.SECONDS) + writing = true + } catch { + case e: Exception => + logError("Failed to start JVM profiler", e) + if (threadpool != null) { + threadpool.shutdownNow() + } + if (inputStream != null) { + inputStream.close() + } + if (outputStream != null) { + outputStream.close() + } + } + } + } + + private def writeChunk(lastChunk: Boolean): 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.get.execute(stopcmd) + profiler.get.execute(dumpcmd) + var remaining = inputStream.available() + if (!lastChunk) { + profiler.get.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: IOException => logError("Exception occurred while writing some profiler output: ", e) + case e@(_: IllegalArgumentException | _: IllegalStateException) => Review Comment: Done ########## connector/profiler/src/main/scala/org/apache/spark/executor/profiler/ExecutorJVMProfiler.scala: ########## @@ -0,0 +1,183 @@ +/* + * 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, IOException} +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 JVM code profiler + */ +private[spark] class ExecutorJVMProfiler(conf: SparkConf, executorId: String) extends Logging { + + private var running = false + private val enableProfiler = conf.get(EXECUTOR_PROFILING_ENABLED) + private val profilerOptions = conf.get(EXECUTOR_PROFILING_OPTIONS) + private val profilerDfsDir = conf.get(EXECUTOR_PROFILING_DFS_DIR) + private val profilerLocalDir = conf.get(EXECUTOR_PROFILING_LOCAL_DIR) + private val writeInterval = conf.get(EXECUTOR_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: Option[AsyncProfiler] = { + Option( + if (enableProfiler && AsyncProfilerLoader.isSupported) AsyncProfilerLoader.load() else null + ) + } + + def start(): Unit = { + if (!running) { + try { + profiler.foreach(p => { + p.execute(startcmd) + logInfo("Executor JVM profiling started.") + running = true + startWriting() + } + ) + } catch { + case e@(_: IllegalArgumentException | _: IllegalStateException | _: IOException) => + logError("JVM profiling aborted. Exception occurred in profiler native code: ", e) + case e: Exception => logWarning("Executor JVM profiling aborted due to exception: ", e) + } + } + } + + /** Stops the profiling and saves output to dfs location. */ + def stop(): Unit = { + if (running) { + profiler.foreach(p => { + p.execute(stopcmd) + logInfo("JVM profiler stopped") + running = false + finishWriting() + }) + } + } + + private def startWriting(): Unit = { + if (profilerDfsDir.isDefined) { + val applicationId = try { + conf.getAppId + } catch { + case _: NoSuchElementException => "local-" + System.currentTimeMillis + } + val config = SparkHadoopUtil.get.newConfiguration(conf) + val appName = conf.get("spark.app.name").replace(" ", "-") + val profilerOutputDirname = profilerDfsDir.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(false) + }, writeInterval, writeInterval, + TimeUnit.SECONDS) + writing = true + } catch { + case e: Exception => + logError("Failed to start JVM profiler", e) + if (threadpool != null) { + threadpool.shutdownNow() + } + if (inputStream != null) { + inputStream.close() + } + if (outputStream != null) { + outputStream.close() + } + } + } + } + + private def writeChunk(lastChunk: Boolean): 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.get.execute(stopcmd) + profiler.get.execute(dumpcmd) + var remaining = inputStream.available() + if (!lastChunk) { + profiler.get.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: IOException => logError("Exception occurred while writing some profiler output: ", e) + case e@(_: IllegalArgumentException | _: IllegalStateException) => + logError("Some profiler output not written." + + " Exception occurred in profiler native code: ", e) + case e: Exception => logError("Some profiler output not written. Unexpected exception: ", e) + } + } + + private def finishWriting(): Unit = { + if (profilerDfsDir.isDefined && writing) { + try { + // shutdown background writer + threadpool.shutdown() + threadpool.awaitTermination(30, TimeUnit.SECONDS) + // flush remaining data + writeChunk(true) + inputStream.close() + outputStream.close() + } catch { + case _: InterruptedException => Thread.currentThread().interrupt() + case e: IOException => Review Comment: The exceptions may come from `threadpool.shutdown`, `awaitTermination` which may throw `InterruptedException` or from `{input,output}Stream.close` which may get an `IOException` -- 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. 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