835a79d78e
The basic idea is that: The archive of the SparkR package itself, that is sparkr.zip, is created during build process and is contained in the Spark binary distribution. No change to it after the distribution is installed as the directory it resides ($SPARK_HOME/R/lib) may not be writable. When there is R source code contained in jars or Spark packages specified with "--jars" or "--packages" command line option, a temporary directory is created by calling Utils.createTempDir() where the R packages built from the R source code will be installed. The temporary directory is writable, and won't interfere with each other when there are multiple SparkR sessions, and will be deleted when this SparkR session ends. The R binary packages installed in the temporary directory then are packed into an archive named rpkg.zip. sparkr.zip and rpkg.zip are distributed to the cluster in YARN modes. The distribution of rpkg.zip in Standalone modes is not supported in this PR, and will be address in another PR. Various R files are updated to accept multiple lib paths (one is for SparkR package, the other is for other R packages) so that these package can be accessed in R. Author: Sun Rui <rui.sun@intel.com> Closes #9390 from sun-rui/SPARK-10500.
179 lines
5.7 KiB
R
179 lines
5.7 KiB
R
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Worker class
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# Get current system time
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currentTimeSecs <- function() {
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as.numeric(Sys.time())
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}
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# Get elapsed time
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elapsedSecs <- function() {
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proc.time()[3]
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}
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# Constants
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specialLengths <- list(END_OF_STERAM = 0L, TIMING_DATA = -1L)
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# Timing R process boot
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bootTime <- currentTimeSecs()
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bootElap <- elapsedSecs()
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rLibDir <- Sys.getenv("SPARKR_RLIBDIR")
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dirs <- strsplit(rLibDir, ",")[[1]]
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# Set libPaths to include SparkR package as loadNamespace needs this
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# TODO: Figure out if we can avoid this by not loading any objects that require
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# SparkR namespace
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.libPaths(c(dirs, .libPaths()))
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suppressPackageStartupMessages(library(SparkR))
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port <- as.integer(Sys.getenv("SPARKR_WORKER_PORT"))
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inputCon <- socketConnection(port = port, blocking = TRUE, open = "rb")
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outputCon <- socketConnection(port = port, blocking = TRUE, open = "wb")
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# read the index of the current partition inside the RDD
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partition <- SparkR:::readInt(inputCon)
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deserializer <- SparkR:::readString(inputCon)
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serializer <- SparkR:::readString(inputCon)
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# Include packages as required
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packageNames <- unserialize(SparkR:::readRaw(inputCon))
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for (pkg in packageNames) {
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suppressPackageStartupMessages(library(as.character(pkg), character.only=TRUE))
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}
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# read function dependencies
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funcLen <- SparkR:::readInt(inputCon)
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computeFunc <- unserialize(SparkR:::readRawLen(inputCon, funcLen))
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env <- environment(computeFunc)
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parent.env(env) <- .GlobalEnv # Attach under global environment.
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# Timing init envs for computing
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initElap <- elapsedSecs()
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# Read and set broadcast variables
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numBroadcastVars <- SparkR:::readInt(inputCon)
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if (numBroadcastVars > 0) {
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for (bcast in seq(1:numBroadcastVars)) {
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bcastId <- SparkR:::readInt(inputCon)
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value <- unserialize(SparkR:::readRaw(inputCon))
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SparkR:::setBroadcastValue(bcastId, value)
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}
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}
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# Timing broadcast
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broadcastElap <- elapsedSecs()
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# If -1: read as normal RDD; if >= 0, treat as pairwise RDD and treat the int
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# as number of partitions to create.
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numPartitions <- SparkR:::readInt(inputCon)
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isEmpty <- SparkR:::readInt(inputCon)
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if (isEmpty != 0) {
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if (numPartitions == -1) {
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if (deserializer == "byte") {
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# Now read as many characters as described in funcLen
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data <- SparkR:::readDeserialize(inputCon)
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} else if (deserializer == "string") {
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data <- as.list(readLines(inputCon))
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} else if (deserializer == "row") {
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data <- SparkR:::readMultipleObjects(inputCon)
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}
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# Timing reading input data for execution
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inputElap <- elapsedSecs()
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output <- computeFunc(partition, data)
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# Timing computing
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computeElap <- elapsedSecs()
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if (serializer == "byte") {
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SparkR:::writeRawSerialize(outputCon, output)
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} else if (serializer == "row") {
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SparkR:::writeRowSerialize(outputCon, output)
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} else {
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# write lines one-by-one with flag
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lapply(output, function(line) SparkR:::writeString(outputCon, line))
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}
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# Timing output
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outputElap <- elapsedSecs()
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} else {
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if (deserializer == "byte") {
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# Now read as many characters as described in funcLen
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data <- SparkR:::readDeserialize(inputCon)
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} else if (deserializer == "string") {
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data <- readLines(inputCon)
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} else if (deserializer == "row") {
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data <- SparkR:::readMultipleObjects(inputCon)
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}
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# Timing reading input data for execution
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inputElap <- elapsedSecs()
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res <- new.env()
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# Step 1: hash the data to an environment
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hashTupleToEnvir <- function(tuple) {
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# NOTE: execFunction is the hash function here
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hashVal <- computeFunc(tuple[[1]])
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bucket <- as.character(hashVal %% numPartitions)
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acc <- res[[bucket]]
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# Create a new accumulator
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if (is.null(acc)) {
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acc <- SparkR:::initAccumulator()
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}
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SparkR:::addItemToAccumulator(acc, tuple)
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res[[bucket]] <- acc
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}
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invisible(lapply(data, hashTupleToEnvir))
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# Timing computing
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computeElap <- elapsedSecs()
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# Step 2: write out all of the environment as key-value pairs.
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for (name in ls(res)) {
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SparkR:::writeInt(outputCon, 2L)
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SparkR:::writeInt(outputCon, as.integer(name))
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# Truncate the accumulator list to the number of elements we have
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length(res[[name]]$data) <- res[[name]]$counter
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SparkR:::writeRawSerialize(outputCon, res[[name]]$data)
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}
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# Timing output
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outputElap <- elapsedSecs()
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}
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} else {
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inputElap <- broadcastElap
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computeElap <- broadcastElap
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outputElap <- broadcastElap
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}
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# Report timing
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SparkR:::writeInt(outputCon, specialLengths$TIMING_DATA)
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SparkR:::writeDouble(outputCon, bootTime)
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SparkR:::writeDouble(outputCon, initElap - bootElap) # init
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SparkR:::writeDouble(outputCon, broadcastElap - initElap) # broadcast
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SparkR:::writeDouble(outputCon, inputElap - broadcastElap) # input
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SparkR:::writeDouble(outputCon, computeElap - inputElap) # compute
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SparkR:::writeDouble(outputCon, outputElap - computeElap) # output
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# End of output
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SparkR:::writeInt(outputCon, specialLengths$END_OF_STERAM)
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close(outputCon)
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close(inputCon)
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