1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
|
#
# 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.
#'
#' @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}
#' @export
#' @examples
#'\dontrun{
#' sc <- sparkR.init()
#' lines <- textFile(sc, "myfile.txt")
#'}
textFile <- function(sc, path, minPartitions = NULL) {
# Allow the user to have a more flexible definiton 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
#' @export
#' @examples
#'\dontrun{
#' sc <- sparkR.init()
#' rdd <- objectFile(sc, "myfile")
#'}
objectFile <- function(sc, path, minPartitions = NULL) {
# Allow the user to have a more flexible definiton 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")
}
#' 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.
#'
#' @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
#' @export
#' @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
if ((!is.list(coll) && !is.vector(coll)) || is.data.frame(coll)) {
if (is.data.frame(coll)) {
message(paste("context.R: A data frame is parallelized by columns."))
} else {
if (is.matrix(coll)) {
message(paste("context.R: A matrix is parallelized by elements."))
} else {
message(paste("context.R: parallelize() currently only supports lists and vectors.",
"Calling as.list() to coerce coll into a list."))
}
}
coll <- as.list(coll)
}
if (numSlices > length(coll))
numSlices <- length(coll)
sliceLen <- ceiling(length(coll) / numSlices)
slices <- split(coll, rep(1:(numSlices + 1), each = sliceLen)[1:length(coll)])
# 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)
jrdd <- callJStatic("org.apache.spark.api.r.RRDD",
"createRDDFromArray", sc, serializedSlices)
RDD(jrdd, "byte")
}
#' 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
#'
#' @export
#' @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
}
#' @title Broadcast a variable to all workers
#'
#' @description
#' 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
#' @export
#' @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 <- broadcast(sc, randomMat)
#'
#' # Use the broadcast variable inside the function
#' useBroadcast <- function(x) {
#' sum(value(randomMatBr) * x)
#' }
#' sumRDD <- lapply(rdd, useBroadcast)
#'}
broadcast <- 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)
}
#' @title Set the checkpoint directory
#'
#' Set the directory under which RDDs are going to be checkpointed. The
#' directory must be a HDFS path if running on a cluster.
#'
#' @param sc Spark Context to use
#' @param dirName Directory path
#' @export
#' @examples
#'\dontrun{
#' sc <- sparkR.init()
#' setCheckpointDir(sc, "~/checkpoint")
#' rdd <- parallelize(sc, 1:2, 2L)
#' checkpoint(rdd)
#'}
setCheckpointDir <- function(sc, dirName) {
invisible(callJMethod(sc, "setCheckpointDir", suppressWarnings(normalizePath(dirName))))
}
|
