spark-instrumented-optimizer/R/pkg/inst/tests/testthat/test_parallelize_collect.R
Felix Cheung c34b546d67 [SPARK-16519][SPARKR] Handle SparkR RDD generics that create warnings in R CMD check
## What changes were proposed in this pull request?

Rename RDD functions for now to avoid CRAN check warnings.
Some RDD functions are sharing generics with DataFrame functions (hence the problem) so after the renames we need to add new generics, for now.

## How was this patch tested?

unit tests

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #14626 from felixcheung/rrddfunctions.
2016-08-16 11:19:18 -07:00

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4.5 KiB
R

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context("parallelize() and collect()")
# Mock data
numVector <- c(-10:97)
numList <- list(sqrt(1), sqrt(2), sqrt(3), 4 ** 10)
strVector <- c("Dexter Morgan: I suppose I should be upset, even feel",
"violated, but I'm not. No, in fact, I think this is a friendly",
"message, like \"Hey, wanna play?\" and yes, I want to play. ",
"I really, really do.")
strList <- list("Dexter Morgan: Blood. Sometimes it sets my teeth on edge, ",
"other times it helps me control the chaos.",
"Dexter Morgan: Harry and Dorris Morgan did a wonderful job ",
"raising me. But they're both dead now. I didn't kill them. Honest.")
numPairs <- list(list(1, 1), list(1, 2), list(2, 2), list(2, 3))
strPairs <- list(list(strList, strList), list(strList, strList))
# JavaSparkContext handle
sparkSession <- sparkR.session(enableHiveSupport = FALSE)
jsc <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "getJavaSparkContext", sparkSession)
# Tests
test_that("parallelize() on simple vectors and lists returns an RDD", {
numVectorRDD <- parallelize(jsc, numVector, 1)
numVectorRDD2 <- parallelize(jsc, numVector, 10)
numListRDD <- parallelize(jsc, numList, 1)
numListRDD2 <- parallelize(jsc, numList, 4)
strVectorRDD <- parallelize(jsc, strVector, 2)
strVectorRDD2 <- parallelize(jsc, strVector, 3)
strListRDD <- parallelize(jsc, strList, 4)
strListRDD2 <- parallelize(jsc, strList, 1)
rdds <- c(numVectorRDD,
numVectorRDD2,
numListRDD,
numListRDD2,
strVectorRDD,
strVectorRDD2,
strListRDD,
strListRDD2)
for (rdd in rdds) {
expect_is(rdd, "RDD")
expect_true(.hasSlot(rdd, "jrdd")
&& inherits(rdd@jrdd, "jobj")
&& isInstanceOf(rdd@jrdd, "org.apache.spark.api.java.JavaRDD"))
}
})
test_that("collect(), following a parallelize(), gives back the original collections", {
numVectorRDD <- parallelize(jsc, numVector, 10)
expect_equal(collectRDD(numVectorRDD), as.list(numVector))
numListRDD <- parallelize(jsc, numList, 1)
numListRDD2 <- parallelize(jsc, numList, 4)
expect_equal(collectRDD(numListRDD), as.list(numList))
expect_equal(collectRDD(numListRDD2), as.list(numList))
strVectorRDD <- parallelize(jsc, strVector, 2)
strVectorRDD2 <- parallelize(jsc, strVector, 3)
expect_equal(collectRDD(strVectorRDD), as.list(strVector))
expect_equal(collectRDD(strVectorRDD2), as.list(strVector))
strListRDD <- parallelize(jsc, strList, 4)
strListRDD2 <- parallelize(jsc, strList, 1)
expect_equal(collectRDD(strListRDD), as.list(strList))
expect_equal(collectRDD(strListRDD2), as.list(strList))
})
test_that("regression: collect() following a parallelize() does not drop elements", {
# 10 %/% 6 = 1, ceiling(10 / 6) = 2
collLen <- 10
numPart <- 6
expected <- runif(collLen)
actual <- collectRDD(parallelize(jsc, expected, numPart))
expect_equal(actual, as.list(expected))
})
test_that("parallelize() and collect() work for lists of pairs (pairwise data)", {
# use the pairwise logical to indicate pairwise data
numPairsRDDD1 <- parallelize(jsc, numPairs, 1)
numPairsRDDD2 <- parallelize(jsc, numPairs, 2)
numPairsRDDD3 <- parallelize(jsc, numPairs, 3)
expect_equal(collectRDD(numPairsRDDD1), numPairs)
expect_equal(collectRDD(numPairsRDDD2), numPairs)
expect_equal(collectRDD(numPairsRDDD3), numPairs)
# can also leave out the parameter name, if the params are supplied in order
strPairsRDDD1 <- parallelize(jsc, strPairs, 1)
strPairsRDDD2 <- parallelize(jsc, strPairs, 2)
expect_equal(collectRDD(strPairsRDDD1), strPairs)
expect_equal(collectRDD(strPairsRDDD2), strPairs)
})
sparkR.session.stop()