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 #
 # 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.
 #
 
 # context.R: SparkContext driven functions
 
 getMinPartitions <- function(sc, minPartitions) {
   if (is.null(minPartitions)) {
     defaultParallelism <- callJMethod(sc, "defaultParallelism")
     minPartitions <- min(defaultParallelism, 2)
   }
   as.integer(minPartitions)
 }
 
 #' Create an RDD from a text file.
 #'
 #' This function reads a text file from HDFS, a local file system (available on all
 #' nodes), or any Hadoop-supported file system URI, and creates an
 #' RDD of strings from it. The text files must be encoded as UTF-8.
 #'
 #' @param sc SparkContext to use
 #' @param path Path of file to read. A vector of multiple paths is allowed.
 #' @param minPartitions Minimum number of partitions to be created. If NULL, the default
 #'  value is chosen based on available parallelism.
 #' @return RDD where each item is of type \code{character}
 #' @noRd
 #' @examples
 #'\dontrun{
 #'  sc <- sparkR.init()
 #'  lines <- textFile(sc, "myfile.txt")
 #'}
 textFile <- function(sc, path, minPartitions = NULL) {
   # Allow the user to have a more flexible definition of the text file path
   path <- suppressWarnings(normalizePath(path))
   # Convert a string vector of paths to a string containing comma separated paths
   path <- paste(path, collapse = ",")
 
   jrdd <- callJMethod(sc, "textFile", path, getMinPartitions(sc, minPartitions))
   # jrdd is of type JavaRDD[String]
   RDD(jrdd, "string")
 }
 
 #' Load an RDD saved as a SequenceFile containing serialized objects.
 #'
 #' The file to be loaded should be one that was previously generated by calling
 #' saveAsObjectFile() of the RDD class.
 #'
 #' @param sc SparkContext to use
 #' @param path Path of file to read. A vector of multiple paths is allowed.
 #' @param minPartitions Minimum number of partitions to be created. If NULL, the default
 #'  value is chosen based on available parallelism.
 #' @return RDD containing serialized R objects.
 #' @seealso saveAsObjectFile
 #' @noRd
 #' @examples
 #'\dontrun{
 #'  sc <- sparkR.init()
 #'  rdd <- objectFile(sc, "myfile")
 #'}
 objectFile <- function(sc, path, minPartitions = NULL) {
   # Allow the user to have a more flexible definition of the text file path
   path <- suppressWarnings(normalizePath(path))
   # Convert a string vector of paths to a string containing comma separated paths
   path <- paste(path, collapse = ",")
 
   jrdd <- callJMethod(sc, "objectFile", path, getMinPartitions(sc, minPartitions))
   # Assume the RDD contains serialized R objects.
   RDD(jrdd, "byte")
 }
 
 makeSplits <- function(numSerializedSlices, length) {
   # Generate the slice ids to put each row
   # For instance, for numSerializedSlices of 22, length of 50
   #  [1]  0  0  2  2  4  4  6  6  6  9  9 11 11 13 13 15 15 15 18 18 20 20 22 22 22
   # [26] 25 25 27 27 29 29 31 31 31 34 34 36 36 38 38 40 40 40 43 43 45 45 47 47 47
   # Notice the slice group with 3 slices (ie. 6, 15, 22) are roughly evenly spaced.
   # We are trying to reimplement the calculation in the positions method in ParallelCollectionRDD
   if (numSerializedSlices > 0) {
     unlist(lapply(0: (numSerializedSlices - 1), function(x) {
       # nolint start
       start <- trunc((as.numeric(x) * length) / numSerializedSlices)
       end <- trunc(((as.numeric(x) + 1) * length) / numSerializedSlices)
       # nolint end
       rep(start, end - start)
     }))
   } else {
     1
   }
 }
 
 #' Create an RDD from a homogeneous list or vector.
 #'
 #' This function creates an RDD from a local homogeneous list in R. The elements
 #' in the list are split into \code{numSlices} slices and distributed to nodes
 #' in the cluster.
 #'
 #' If size of serialized slices is larger than spark.r.maxAllocationLimit or (200MiB), the function
 #' will write it to disk and send the file name to JVM. Also to make sure each slice is not
 #' larger than that limit, number of slices may be increased.
 #'
 #' In 2.2.0 we are changing how the numSlices are used/computed to handle
 #' 1 < (length(coll) / numSlices) << length(coll) better, and to get the exact number of slices.
 #' This change affects both createDataFrame and spark.lapply.
 #' In the specific one case that it is used to convert R native object into SparkDataFrame, it has
 #' always been kept at the default of 1. In the case the object is large, we are explicitly setting
 #' the parallism to numSlices (which is still 1).
 #'
 #' Specifically, we are changing to split positions to match the calculation in positions() of
 #' ParallelCollectionRDD in Spark.
 #'
 #' @param sc SparkContext to use
 #' @param coll collection to parallelize
 #' @param numSlices number of partitions to create in the RDD
 #' @return an RDD created from this collection
 #' @noRd
 #' @examples
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, 1:10, 2)
 #' # The RDD should contain 10 elements
 #' length(rdd)
 #'}
 parallelize <- function(sc, coll, numSlices = 1) {
   # TODO: bound/safeguard numSlices
   # TODO: unit tests for if the split works for all primitives
   # TODO: support matrix, data frame, etc
 
   # Note, for data.frame, createDataFrame turns it into a list before it calls here.
   # nolint start
   # suppress lintr warning: Place a space before left parenthesis, except in a function call.
   if ((!is.list(coll) && !is.vector(coll)) || is.data.frame(coll)) {
   # nolint end
     if (is.data.frame(coll)) {
       message("context.R: A data frame is parallelized by columns.")
     } else {
       if (is.matrix(coll)) {
         message("context.R: A matrix is parallelized by elements.")
       } else {
         message("context.R: parallelize() currently only supports lists and vectors. ",
                 "Calling as.list() to coerce coll into a list.")
       }
     }
     coll <- as.list(coll)
   }
 
   sizeLimit <- getMaxAllocationLimit(sc)
   objectSize <- object.size(coll)
   len <- length(coll)
 
   # For large objects we make sure the size of each slice is also smaller than sizeLimit
   numSerializedSlices <- min(len, max(numSlices, ceiling(objectSize / sizeLimit)))
 
   slices <- split(coll, makeSplits(numSerializedSlices, len))
 
   # Serialize each slice: obtain a list of raws, or a list of lists (slices) of
   # 2-tuples of raws
   serializedSlices <- lapply(slices, serialize, connection = NULL)
 
   # The RPC backend cannot handle arguments larger than 2GB (INT_MAX)
   # If serialized data is safely less than that threshold we send it over the PRC channel.
   # Otherwise, we write it to a file and send the file name
   if (objectSize < sizeLimit) {
     jrdd <- callJStatic("org.apache.spark.api.r.RRDD", "createRDDFromArray", sc, serializedSlices)
   } else {
     if (callJStatic("org.apache.spark.api.r.RUtils", "isEncryptionEnabled", sc)) {
       connectionTimeout <- as.numeric(Sys.getenv("SPARKR_BACKEND_CONNECTION_TIMEOUT", "6000"))
       # the length of slices here is the parallelism to use in the jvm's sc.parallelize()
       parallelism <- as.integer(numSlices)
       jserver <- newJObject("org.apache.spark.api.r.RParallelizeServer", sc, parallelism)
       authSecret <- callJMethod(jserver, "secret")
       port <- callJMethod(jserver, "port")
       conn <- socketConnection(
         port = port, blocking = TRUE, open = "wb", timeout = connectionTimeout)
       doServerAuth(conn, authSecret)
       writeToConnection(serializedSlices, conn)
       jrdd <- callJMethod(jserver, "getResult")
     } else {
       fileName <- writeToTempFile(serializedSlices)
       jrdd <- tryCatch(callJStatic(
           "org.apache.spark.api.r.RRDD", "createRDDFromFile", sc, fileName, as.integer(numSlices)),
         finally = {
           file.remove(fileName)
       })
     }
   }
 
   RDD(jrdd, "byte")
 }
 
 getMaxAllocationLimit <- function(sc) {
   conf <- callJMethod(sc, "getConf")
   as.numeric(
     callJMethod(conf,
       "get",
       "spark.r.maxAllocationLimit",
       toString(.Machine$integer.max / 10) # Default to a safe value: 200MB
   ))
 }
 
 writeToConnection <- function(serializedSlices, conn) {
   tryCatch({
     for (slice in serializedSlices) {
       writeBin(as.integer(length(slice)), conn, endian = "big")
       writeBin(slice, conn, endian = "big")
     }
   }, finally = {
     close(conn)
   })
 }
 
 writeToTempFile <- function(serializedSlices) {
   fileName <- tempfile()
   conn <- file(fileName, "wb")
   writeToConnection(serializedSlices, conn)
   fileName
 }
 
 #' Include this specified package on all workers
 #'
 #' This function can be used to include a package on all workers before the
 #' user's code is executed. This is useful in scenarios where other R package
 #' functions are used in a function passed to functions like \code{lapply}.
 #' NOTE: The package is assumed to be installed on every node in the Spark
 #' cluster.
 #'
 #' @param sc SparkContext to use
 #' @param pkg Package name
 #' @noRd
 #' @examples
 #'\dontrun{
 #'  library(Matrix)
 #'
 #'  sc <- sparkR.init()
 #'  # Include the matrix library we will be using
 #'  includePackage(sc, Matrix)
 #'
 #'  generateSparse <- function(x) {
 #'    sparseMatrix(i=c(1, 2, 3), j=c(1, 2, 3), x=c(1, 2, 3))
 #'  }
 #'
 #'  rdd <- lapplyPartition(parallelize(sc, 1:2, 2L), generateSparse)
 #'  collect(rdd)
 #'}
 includePackage <- function(sc, pkg) {
   pkg <- as.character(substitute(pkg))
   if (exists(".packages", .sparkREnv)) {
     packages <- .sparkREnv$.packages
   } else {
     packages <- list()
   }
   packages <- c(packages, pkg)
   .sparkREnv$.packages <- packages
 }
 
 #' Broadcast a variable to all workers
 #'
 #' Broadcast a read-only variable to the cluster, returning a \code{Broadcast}
 #' object for reading it in distributed functions.
 #'
 #' @param sc Spark Context to use
 #' @param object Object to be broadcast
 #' @noRd
 #' @examples
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, 1:2, 2L)
 #'
 #' # Large Matrix object that we want to broadcast
 #' randomMat <- matrix(nrow=100, ncol=10, data=rnorm(1000))
 #' randomMatBr <- broadcastRDD(sc, randomMat)
 #'
 #' # Use the broadcast variable inside the function
 #' useBroadcast <- function(x) {
 #'   sum(value(randomMatBr) * x)
 #' }
 #' sumRDD <- lapply(rdd, useBroadcast)
 #'}
 broadcastRDD <- function(sc, object) {
   objName <- as.character(substitute(object))
   serializedObj <- serialize(object, connection = NULL)
 
   jBroadcast <- callJMethod(sc, "broadcast", serializedObj)
   id <- as.character(callJMethod(jBroadcast, "id"))
 
   Broadcast(id, object, jBroadcast, objName)
 }
 
 #' Set the checkpoint directory
 #'
 #' Set the directory under which RDDs are going to be checkpointed. The
 #' directory must be an HDFS path if running on a cluster.
 #'
 #' @param sc Spark Context to use
 #' @param dirName Directory path
 #' @noRd
 #' @examples
 #'\dontrun{
 #' sc <- sparkR.init()
 #' setCheckpointDir(sc, "~/checkpoint")
 #' rdd <- parallelize(sc, 1:2, 2L)
 #' checkpoint(rdd)
 #'}
 setCheckpointDirSC <- function(sc, dirName) {
   invisible(callJMethod(sc, "setCheckpointDir", suppressWarnings(normalizePath(dirName))))
 }
 
 #' Add a file or directory to be downloaded with this Spark job on every node.
 #'
 #' The path passed can be either a local file, a file in HDFS (or other Hadoop-supported
 #' filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs,
 #' use spark.getSparkFiles(fileName) to find its download location.
 #'
 #' A directory can be given if the recursive option is set to true.
 #' Currently directories are only supported for Hadoop-supported filesystems.
 #' Refer Hadoop-supported filesystems at
 #' \url{https://cwiki.apache.org/confluence/display/HADOOP2/HCFS}.
 #'
 #' Note: A path can be added only once. Subsequent additions of the same path are ignored.
 #'
 #' @rdname spark.addFile
 #' @param path The path of the file to be added
 #' @param recursive Whether to add files recursively from the path. Default is FALSE.
 #' @examples
 #'\dontrun{
 #' spark.addFile("~/myfile")
 #'}
 #' @note spark.addFile since 2.1.0
 spark.addFile <- function(path, recursive = FALSE) {
   sc <- getSparkContext()
   invisible(callJMethod(sc, "addFile", suppressWarnings(normalizePath(path)), recursive))
 }
 
 #' Get the root directory that contains files added through spark.addFile.
 #'
 #' @rdname spark.getSparkFilesRootDirectory
 #' @return the root directory that contains files added through spark.addFile
 #' @examples
 #'\dontrun{
 #' spark.getSparkFilesRootDirectory()
 #'}
 #' @note spark.getSparkFilesRootDirectory since 2.1.0
 spark.getSparkFilesRootDirectory <- function() { # nolint
   if (Sys.getenv("SPARKR_IS_RUNNING_ON_WORKER") == "") {
     # Running on driver.
     callJStatic("org.apache.spark.SparkFiles", "getRootDirectory")
   } else {
     # Running on worker.
     Sys.getenv("SPARKR_SPARKFILES_ROOT_DIR")
   }
 }
 
 #' Get the absolute path of a file added through spark.addFile.
 #'
 #' @rdname spark.getSparkFiles
 #' @param fileName The name of the file added through spark.addFile
 #' @return the absolute path of a file added through spark.addFile.
 #' @examples
 #'\dontrun{
 #' spark.getSparkFiles("myfile")
 #'}
 #' @note spark.getSparkFiles since 2.1.0
 spark.getSparkFiles <- function(fileName) {
   if (Sys.getenv("SPARKR_IS_RUNNING_ON_WORKER") == "") {
     # Running on driver.
     callJStatic("org.apache.spark.SparkFiles", "get", as.character(fileName))
   } else {
     # Running on worker.
     file.path(spark.getSparkFilesRootDirectory(), as.character(fileName))
   }
 }
 
 #' Run a function over a list of elements, distributing the computations with Spark
 #'
 #' Run a function over a list of elements, distributing the computations with Spark. Applies a
 #' function in a manner that is similar to doParallel or lapply to elements of a list.
 #' The computations are distributed using Spark. It is conceptually the same as the following code:
 #'   lapply(list, func)
 #'
 #' Known limitations:
 #' \itemize{
 #'    \item variable scoping and capture: compared to R's rich support for variable resolutions,
 #'    the distributed nature of SparkR limits how variables are resolved at runtime. All the
 #'    variables that are available through lexical scoping are embedded in the closure of the
 #'    function and available as read-only variables within the function. The environment variables
 #'    should be stored into temporary variables outside the function, and not directly accessed
 #'    within the function.
 #'
 #'   \item loading external packages: In order to use a package, you need to load it inside the
 #'   closure. For example, if you rely on the MASS module, here is how you would use it:
 #'   \preformatted{
 #'     train <- function(hyperparam) {
 #'       library(MASS)
 #'       lm.ridge("y ~ x+z", data, lambda=hyperparam)
 #'       model
 #'     }
 #'   }
 #' }
 #'
 #' @rdname spark.lapply
 #' @param list the list of elements
 #' @param func a function that takes one argument.
 #' @return a list of results (the exact type being determined by the function)
 #' @examples
 #'\dontrun{
 #' sparkR.session()
 #' doubled <- spark.lapply(1:10, function(x) {2 * x})
 #'}
 #' @note spark.lapply since 2.0.0
 spark.lapply <- function(list, func) {
   sc <- getSparkContext()
   rdd <- parallelize(sc, list, length(list))
   results <- map(rdd, func)
   local <- collectRDD(results)
   local
 }
 
 #' Set new log level
 #'
 #' Set new log level: "ALL", "DEBUG", "ERROR", "FATAL", "INFO", "OFF", "TRACE", "WARN"
 #'
 #' @rdname setLogLevel
 #' @param level New log level
 #' @examples
 #'\dontrun{
 #' setLogLevel("ERROR")
 #'}
 #' @note setLogLevel since 2.0.0
 setLogLevel <- function(level) {
   sc <- getSparkContext()
   invisible(callJMethod(sc, "setLogLevel", level))
 }
 
 #' Set checkpoint directory
 #'
 #' Set the directory under which SparkDataFrame are going to be checkpointed. The directory must be
 #' an HDFS path if running on a cluster.
 #'
 #' @rdname setCheckpointDir
 #' @param directory Directory path to checkpoint to
 #' @seealso \link{checkpoint}
 #' @examples
 #'\dontrun{
 #' setCheckpointDir("/checkpoint")
 #'}
 #' @note setCheckpointDir since 2.2.0
 setCheckpointDir <- function(directory) {
   sc <- getSparkContext()
   invisible(callJMethod(sc, "setCheckpointDir", suppressWarnings(normalizePath(directory))))
 }

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