Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/5096#discussion_r26808779
--- Diff: core/src/main/scala/org/apache/spark/api/r/RRDD.scala ---
@@ -0,0 +1,515 @@
+/*
+ * 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.api.r
+
+import java.io._
+import java.net.ServerSocket
+import java.util.{Map => JMap}
+
+import scala.collection.JavaConversions._
+import scala.io.Source
+import scala.reflect.ClassTag
+import scala.util.Try
+
+import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext}
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark._
+
+private abstract class BaseRRDD[T: ClassTag, U: ClassTag](
+ parent: RDD[T],
+ numPartitions: Int,
+ func: Array[Byte],
+ deserializer: String,
+ serializer: String,
+ packageNames: Array[Byte],
+ rLibDir: String,
+ broadcastVars: Array[Broadcast[Object]])
+ extends RDD[U](parent) with Logging {
+ override def getPartitions = parent.partitions
+
+ override def compute(split: Partition, context: TaskContext):
Iterator[U] = {
+
+ // The parent may be also an RRDD, so we should launch it first.
+ val parentIterator = firstParent[T].iterator(split, context)
+
+ // we expect two connections
+ val serverSocket = new ServerSocket(0, 2)
+ val listenPort = serverSocket.getLocalPort()
+
+ // The stdout/stderr is shared by multiple tasks, because we use one
daemon
+ // to launch child process as worker.
+ val errThread = RRDD.createRWorker(rLibDir, listenPort)
+
+ // We use two sockets to separate input and output, then it's easy to
manage
+ // the lifecycle of them to avoid deadlock.
+ // TODO: optimize it to use one socket
+
+ // the socket used to send out the input of task
+ serverSocket.setSoTimeout(10000)
+ val inSocket = serverSocket.accept()
+ startStdinThread(inSocket.getOutputStream(), parentIterator,
split.index)
+
+ // the socket used to receive the output of task
+ val outSocket = serverSocket.accept()
+ val inputStream = new BufferedInputStream(outSocket.getInputStream)
+ val dataStream = openDataStream(inputStream)
+ serverSocket.close()
+
+ try {
+
+ return new Iterator[U] {
+ def next(): U = {
+ val obj = _nextObj
+ if (hasNext) {
+ _nextObj = read()
+ }
+ obj
+ }
+
+ var _nextObj = read()
+
+ def hasNext(): Boolean = {
+ val hasMore = (_nextObj != null)
+ if (!hasMore) {
+ dataStream.close()
+ }
+ hasMore
+ }
+ }
+ } catch {
+ case e: Exception =>
+ throw new SparkException("R computation failed with\n " +
errThread.getLines())
+ }
+ }
+
+ /**
+ * Start a thread to write RDD data to the R process.
+ */
+ private def startStdinThread[T](
+ output: OutputStream,
+ iter: Iterator[T],
+ splitIndex: Int) = {
+
+ val env = SparkEnv.get
+ val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt
+ val stream = new BufferedOutputStream(output, bufferSize)
+
+ new Thread("writer for R") {
+ override def run() {
+ try {
+ SparkEnv.set(env)
+ val dataOut = new DataOutputStream(stream)
+ dataOut.writeInt(splitIndex)
+
+ SerDe.writeString(dataOut, deserializer)
+ SerDe.writeString(dataOut, serializer)
+
+ dataOut.writeInt(packageNames.length)
+ dataOut.write(packageNames)
+
+ dataOut.writeInt(func.length)
+ dataOut.write(func)
+
+ dataOut.writeInt(broadcastVars.length)
+ broadcastVars.foreach { broadcast =>
+ // TODO(shivaram): Read a Long in R to avoid this cast
+ dataOut.writeInt(broadcast.id.toInt)
+ // TODO: Pass a byte array from R to avoid this cast ?
+ val broadcastByteArr =
broadcast.value.asInstanceOf[Array[Byte]]
+ dataOut.writeInt(broadcastByteArr.length)
+ dataOut.write(broadcastByteArr)
+ }
+
+ dataOut.writeInt(numPartitions)
+
+ if (!iter.hasNext) {
+ dataOut.writeInt(0)
+ } else {
+ dataOut.writeInt(1)
+ }
+
+ val printOut = new PrintStream(stream)
+
+ def writeElem(elem: Any): Unit = {
+ if (deserializer == SerializationFormats.BYTE) {
+ val elemArr = elem.asInstanceOf[Array[Byte]]
+ dataOut.writeInt(elemArr.length)
+ dataOut.write(elemArr)
+ } else if (deserializer == SerializationFormats.ROW) {
+ dataOut.write(elem.asInstanceOf[Array[Byte]])
+ } else if (deserializer == SerializationFormats.STRING) {
+ printOut.println(elem)
+ }
+ }
+
+ for (elem <- iter) {
+ elem match {
+ case (key, value) =>
+ writeElem(key)
+ writeElem(value)
+ case _ =>
+ writeElem(elem)
+ }
+ }
+ stream.flush()
+ } catch {
+ // TODO: We should propogate this error to the task thread
+ case e: Exception =>
+ logError("R Writer thread got an exception", e)
+ } finally {
+ Try(output.close())
+ }
+ }
+ }.start()
+ }
+
+ protected def openDataStream(input: InputStream): Closeable
+
+ protected def read(): U
+}
+
+/**
+ * Form an RDD[(Int, Array[Byte])] from key-value pairs returned from R.
+ * This is used by SparkR's shuffle operations.
+ */
+private class PairwiseRRDD[T: ClassTag](
+ parent: RDD[T],
+ numPartitions: Int,
+ hashFunc: Array[Byte],
+ deserializer: String,
+ packageNames: Array[Byte],
+ rLibDir: String,
+ broadcastVars: Array[Object])
+ extends BaseRRDD[T, (Int, Array[Byte])](
+ parent, numPartitions, hashFunc, deserializer,
+ SerializationFormats.BYTE, packageNames, rLibDir,
+ broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) {
+
+ private var dataStream: DataInputStream = _
+
+ override protected def openDataStream(input: InputStream) = {
+ dataStream = new DataInputStream(input)
+ dataStream
+ }
+
+ override protected def read(): (Int, Array[Byte]) = {
+ try {
+ val length = dataStream.readInt()
+
+ length match {
+ case length if length == 2 =>
+ val hashedKey = dataStream.readInt()
+ val contentPairsLength = dataStream.readInt()
+ val contentPairs = new Array[Byte](contentPairsLength)
+ dataStream.readFully(contentPairs)
+ (hashedKey, contentPairs)
+ case _ => null // End of input
+ }
+ } catch {
+ case eof: EOFException => {
+ throw new SparkException("R worker exited unexpectedly (crashed)",
eof)
+ }
+ }
+ }
+
+ lazy val asJavaPairRDD : JavaPairRDD[Int, Array[Byte]] =
JavaPairRDD.fromRDD(this)
+}
+
+/**
+ * An RDD that stores serialized R objects as Array[Byte].
+ */
+private class RRDD[T: ClassTag](
+ parent: RDD[T],
+ func: Array[Byte],
+ deserializer: String,
+ serializer: String,
+ packageNames: Array[Byte],
+ rLibDir: String,
+ broadcastVars: Array[Object])
+ extends BaseRRDD[T, Array[Byte]](parent, -1, func, deserializer,
+ serializer, packageNames, rLibDir,
+ broadcastVars.map(x =>
x.asInstanceOf[Broadcast[Object]])) {
+
+ private var dataStream: DataInputStream = _
+
+ override protected def openDataStream(input: InputStream) = {
+ dataStream = new DataInputStream(input)
+ dataStream
+ }
+
+ override protected def read(): Array[Byte] = {
+ try {
+ val length = dataStream.readInt()
+
+ length match {
+ case length if length > 0 =>
+ val obj = new Array[Byte](length)
+ dataStream.readFully(obj, 0, length)
+ obj
+ case _ => null
+ }
+ } catch {
+ case eof: EOFException => {
+ throw new SparkException("R worker exited unexpectedly (crashed)",
eof)
+ }
+ }
+ }
+
+ lazy val asJavaRDD : JavaRDD[Array[Byte]] = JavaRDD.fromRDD(this)
+}
+
+/**
+ * An RDD that stores R objects as Array[String].
+ */
+private class StringRRDD[T: ClassTag](
+ parent: RDD[T],
+ func: Array[Byte],
+ deserializer: String,
+ packageNames: Array[Byte],
+ rLibDir: String,
+ broadcastVars: Array[Object])
+ extends BaseRRDD[T, String](parent, -1, func, deserializer,
SerializationFormats.STRING,
+ packageNames, rLibDir,
+ broadcastVars.map(x =>
x.asInstanceOf[Broadcast[Object]])) {
+
+ private var dataStream: BufferedReader = _
+
+ override protected def openDataStream(input: InputStream) = {
+ dataStream = new BufferedReader(new InputStreamReader(input))
+ dataStream
+ }
+
+ override protected def read(): String = {
+ try {
+ dataStream.readLine()
+ } catch {
+ case e: IOException => {
+ throw new SparkException("R worker exited unexpectedly (crashed)",
e)
+ }
+ }
+ }
+
+ lazy val asJavaRDD : JavaRDD[String] = JavaRDD.fromRDD(this)
+}
+
+private[r] class BufferedStreamThread(
+ in: InputStream,
+ name: String,
+ errBufferSize: Int) extends Thread(name) with Logging {
+ val lines = new Array[String](errBufferSize)
+ var lineIdx = 0
+ override def run() {
+ for (line <- Source.fromInputStream(in).getLines) {
+ synchronized {
+ lines(lineIdx) = line
+ lineIdx = (lineIdx + 1) % errBufferSize
+ }
+ logInfo(line)
+ }
+ }
+
+ def getLines(): String = synchronized {
+ (0 until errBufferSize).filter { x =>
+ lines((x + lineIdx) % errBufferSize) != null
+ }.map { x =>
+ lines((x + lineIdx) % errBufferSize)
+ }.mkString("\n")
+ }
+}
+
+private[r] object RRDD {
+ // Because forking processes from Java is expensive, we prefer to launch
+ // a single R daemon (daemon.R) and tell it to fork new workers for our
tasks.
+ // This daemon currently only works on UNIX-based systems now, so we
should
+ // also fall back to launching workers (worker.R) directly.
+ val inWindows = System.getProperty("os.name").startsWith("Windows")
+ private[this] var errThread: BufferedStreamThread = _
+ private[this] var daemonChannel: DataOutputStream = _
+
+ def createSparkContext(
+ master: String,
+ appName: String,
+ sparkHome: String,
+ jars: Array[String],
+ sparkEnvirMap: JMap[Object, Object],
+ sparkExecutorEnvMap: JMap[Object, Object]): JavaSparkContext = {
+
+ val sparkConf = new SparkConf().setAppName(appName)
+ .setSparkHome(sparkHome)
+ .setJars(jars)
+
+ // Override `master` if we have a user-specified value
+ if (master != "") {
+ sparkConf.setMaster(master)
+ } else {
+ // If conf has no master set it to "local" to maintain
+ // backwards compatibility
+ sparkConf.setIfMissing("spark.master", "local")
+ }
+
+ for ((name, value) <- sparkEnvirMap) {
+ sparkConf.set(name.asInstanceOf[String], value.asInstanceOf[String])
+ }
+ for ((name, value) <- sparkExecutorEnvMap) {
+ sparkConf.setExecutorEnv(name.asInstanceOf[String],
value.asInstanceOf[String])
+ }
+
+ new JavaSparkContext(sparkConf)
+ }
+
+ /**
+ * Start a thread to print the process's stderr to ours
+ */
+ private def startStdoutThread(proc: Process): BufferedStreamThread = {
+ val BUFFER_SIZE = 100
+ val thread = new BufferedStreamThread(proc.getInputStream, "stdout
reader for R", BUFFER_SIZE)
+ thread.setDaemon(true)
+ thread.start()
+ thread
+ }
+
+ private def createRProcess(rLibDir: String, port: Int, script: String) =
{
+ val rCommand = "Rscript"
+ val rOptions = "--vanilla"
+ val rExecScript = rLibDir + "/SparkR/worker/" + script
+ val pb = new ProcessBuilder(List(rCommand, rOptions, rExecScript))
+ // Unset the R_TESTS environment variable for workers.
+ // This is set by R CMD check as startup.Rs
+ // (http://svn.r-project.org/R/trunk/src/library/tools/R/testing.R)
+ // and confuses worker script which tries to load a non-existent file
+ pb.environment().put("R_TESTS", "")
+ pb.environment().put("SPARKR_RLIBDIR", rLibDir)
+ pb.environment().put("SPARKR_WORKER_PORT", port.toString)
+ pb.redirectErrorStream(true) // redirect stderr into stdout
+ val proc = pb.start()
+ val errThread = startStdoutThread(proc)
+ errThread
+ }
+
+ /**
+ * ProcessBuilder used to launch worker R processes.
+ */
+ def createRWorker(rLibDir: String, port: Int): BufferedStreamThread = {
+ val useDaemon =
SparkEnv.get.conf.getBoolean("spark.sparkr.use.daemon", true)
+ if (!inWindows && useDaemon) {
+ synchronized {
+ if (daemonChannel == null) {
+ // we expect one connections
+ val serverSocket = new ServerSocket(0, 1)
+ val daemonPort = serverSocket.getLocalPort
+ errThread = createRProcess(rLibDir, daemonPort, "daemon.R")
+ // the socket used to send out the input of task
+ serverSocket.setSoTimeout(10000)
+ val sock = serverSocket.accept()
+ daemonChannel = new DataOutputStream(new
BufferedOutputStream(sock.getOutputStream))
+ serverSocket.close()
+ }
+ try {
+ daemonChannel.writeInt(port)
+ daemonChannel.flush()
+ } catch {
+ case e: IOException =>
+ // daemon process died
+ daemonChannel.close()
+ daemonChannel = null
+ errThread = null
+ // fail the current task, retry by scheduler
+ throw e
+ }
+ errThread
+ }
+ } else {
+ createRProcess(rLibDir, port, "worker.R")
+ }
+ }
+
+ /**
+ * Create an RRDD given a sequence of byte arrays. Used to create RRDD
when `parallelize` is
+ * called from R.
+ */
+ def createRDDFromArray(jsc: JavaSparkContext, arr: Array[Array[Byte]]):
JavaRDD[Array[Byte]] = {
+ JavaRDD.fromRDD(jsc.sc.parallelize(arr, arr.length))
+ }
+
+ def isRunningInYarnContainer(conf: SparkConf): Boolean = {
+ // These environment variables are set by YARN.
+ // For Hadoop 0.23.X, we check for YARN_LOCAL_DIRS (we use this below
in getYarnLocalDirs())
+ // For Hadoop 2.X, we check for CONTAINER_ID.
+ System.getenv("CONTAINER_ID") != null ||
System.getenv("YARN_LOCAL_DIRS") != null
+ }
+
+ /**
+ * Get the path of a temporary directory. Spark's local directories can
be configured through
+ * multiple settings, which are used with the following precedence:
+ *
+ * - If called from inside of a YARN container, this will return a
directory chosen by YARN.
+ * - If the SPARK_LOCAL_DIRS environment variable is set, this will
return a directory from it.
+ * - Otherwise, if the spark.local.dir is set, this will return a
directory from it.
+ * - Otherwise, this will return java.io.tmpdir.
+ *
+ * Some of these configuration options might be lists of multiple paths,
but this method will
+ * always return a single directory.
+ */
+ def getLocalDir(conf: SparkConf): String = {
+ getOrCreateLocalRootDirs(conf)(0)
+ }
+
+ /**
+ * Gets or creates the directories listed in spark.local.dir or
SPARK_LOCAL_DIRS,
+ * and returns only the directories that exist / could be created.
+ *
+ * If no directories could be created, this will return an empty list.
+ */
+ def getOrCreateLocalRootDirs(conf: SparkConf): Array[String] = {
--- End diff --
Same here, and for `getYarnLocalDirs`. Looks like this was just copied
from Utils, so I'd replace it with calls to Utils instead.
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