Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* 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.{InetAddress, ServerSocket}
import java.util.Arrays
import scala.io.Source
import scala.util.Try
import org.apache.spark._
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.internal.Logging
import org.apache.spark.util.Utils
/**
* A helper class to run R UDFs in Spark.
*/
private[spark] class RRunner[U](
func: Array[Byte],
deserializer: String,
serializer: String,
packageNames: Array[Byte],
broadcastVars: Array[Broadcast[Object]],
numPartitions: Int = -1,
isDataFrame: Boolean = false,
colNames: Array[String] = null,
mode: Int = RRunnerModes.RDD)
extends Logging {
private var bootTime: Double = _
private var dataStream: DataInputStream = _
val readData = numPartitions match {
case -1 =>
serializer match {
case SerializationFormats.STRING => readStringData _
case _ => readByteArrayData _
}
case _ => readShuffledData _
}
def compute(
inputIterator: Iterator[_],
partitionIndex: Int): Iterator[U] = {
// Timing start
bootTime = System.currentTimeMillis / 1000.0
// we expect two connections
val serverSocket = new ServerSocket(0, 2, InetAddress.getByName("localhost"))
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 = RRunner.createRWorker(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)
dataStream = try {
val inSocket = serverSocket.accept()
RRunner.authHelper.authClient(inSocket)
startStdinThread(inSocket.getOutputStream(), inputIterator, partitionIndex)
// the socket used to receive the output of task
val outSocket = serverSocket.accept()
RRunner.authHelper.authClient(outSocket)
val inputStream = new BufferedInputStream(outSocket.getInputStream)
new DataInputStream(inputStream)
} finally {
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(
output: OutputStream,
iter: Iterator[_],
partitionIndex: Int): Unit = {
val env = SparkEnv.get
val taskContext = TaskContext.get()
val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt
val stream = new BufferedOutputStream(output, bufferSize)
new Thread("writer for R") {
override def run(): Unit = {
try {
SparkEnv.set(env)
TaskContext.setTaskContext(taskContext)
val dataOut = new DataOutputStream(stream)
dataOut.writeInt(partitionIndex)
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)
dataOut.writeInt(mode)
if (isDataFrame) {
SerDe.writeObject(dataOut, colNames, jvmObjectTracker = null)
}
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) {
// write string(for StringRRDD)
// scalastyle:off println
printOut.println(elem)
// scalastyle:on println
}
}
for (elem <- iter) {
elem match {
case (key, innerIter: Iterator[_]) =>
for (innerElem <- innerIter) {
writeElem(innerElem)
}
// Writes key which can be used as a boundary in group-aggregate
dataOut.writeByte('r')
writeElem(key)
case (key, value) =>
writeElem(key)
writeElem(value)
case _ =>
writeElem(elem)
}
}
stream.flush()
} catch {
// TODO: We should propagate this error to the task thread
case e: Exception =>
logError("R Writer thread got an exception", e)
} finally {
Try(output.close())
}
}
}.start()
}
private def read(): U = {
try {
val length = dataStream.readInt()
length match {
case SpecialLengths.TIMING_DATA =>
// Timing data from R worker
val boot = dataStream.readDouble - bootTime
val init = dataStream.readDouble
val broadcast = dataStream.readDouble
val input = dataStream.readDouble
val compute = dataStream.readDouble
val output = dataStream.readDouble
logInfo(
("Times: boot = %.3f s, init = %.3f s, broadcast = %.3f s, " +
"read-input = %.3f s, compute = %.3f s, write-output = %.3f s, " +
"total = %.3f s").format(
boot,
init,
broadcast,
input,
compute,
output,
boot + init + broadcast + input + compute + output))
read()
case length if length >= 0 =>
readData(length).asInstanceOf[U]
}
} catch {
case eof: EOFException =>
throw new SparkException("R worker exited unexpectedly (cranshed)", eof)
}
}
private def readShuffledData(length: Int): (Int, Array[Byte]) = {
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
}
}
private def readByteArrayData(length: Int): Array[Byte] = {
length match {
case length if length > 0 =>
val obj = new Array[Byte](length)
dataStream.readFully(obj)
obj
case _ => null
}
}
private def readStringData(length: Int): String = {
length match {
case length if length > 0 =>
SerDe.readStringBytes(dataStream, length)
case _ => null
}
}
}
private object SpecialLengths {
val TIMING_DATA = -1
}
private[spark] object RRunnerModes {
val RDD = 0
val DATAFRAME_DAPPLY = 1
val DATAFRAME_GAPPLY = 2
}
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 RRunner {
// 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.
private[this] var errThread: BufferedStreamThread = _
private[this] var daemonChannel: DataOutputStream = _
private lazy val authHelper = {
val conf = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf())
new RAuthHelper(conf)
}
/**
* 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(port: Int, script: String): BufferedStreamThread = {
// "spark.sparkr.r.command" is deprecated and replaced by "spark.r.command",
// but kept here for backward compatibility.
val sparkConf = SparkEnv.get.conf
var rCommand = sparkConf.get("spark.sparkr.r.command", "Rscript")
rCommand = sparkConf.get("spark.r.command", rCommand)
val rConnectionTimeout = sparkConf.getInt(
"spark.r.backendConnectionTimeout", SparkRDefaults.DEFAULT_CONNECTION_TIMEOUT)
val rOptions = "--vanilla"
val rLibDir = RUtils.sparkRPackagePath(isDriver = false)
val rExecScript = rLibDir(0) + "/SparkR/worker/" + script
val pb = new ProcessBuilder(Arrays.asList(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.mkString(","))
pb.environment().put("SPARKR_WORKER_PORT", port.toString)
pb.environment().put("SPARKR_BACKEND_CONNECTION_TIMEOUT", rConnectionTimeout.toString)
pb.environment().put("SPARKR_SPARKFILES_ROOT_DIR", SparkFiles.getRootDirectory())
pb.environment().put("SPARKR_IS_RUNNING_ON_WORKER", "TRUE")
pb.environment().put("SPARKR_WORKER_SECRET", authHelper.secret)
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(port: Int): BufferedStreamThread = {
val useDaemon = SparkEnv.get.conf.getBoolean("spark.sparkr.use.daemon", true)
if (!Utils.isWindows && useDaemon) {
synchronized {
if (daemonChannel == null) {
// we expect one connections
val serverSocket = new ServerSocket(0, 1, InetAddress.getByName("localhost"))
val daemonPort = serverSocket.getLocalPort
errThread = createRProcess(daemonPort, "daemon.R")
// the socket used to send out the input of task
serverSocket.setSoTimeout(10000)
val sock = serverSocket.accept()
try {
authHelper.authClient(sock)
daemonChannel = new DataOutputStream(new BufferedOutputStream(sock.getOutputStream))
} finally {
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(port, "worker.R")
}
}
}
