Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/16512#discussion_r95514615
--- Diff: R/pkg/inst/tests/testthat/test_sparkSQL.R ---
@@ -196,6 +196,12 @@ test_that("create DataFrame from RDD", {
expect_equal(dtypes(df), list(c("name", "string"), c("age", "int"),
c("height", "float")))
expect_equal(as.list(collect(where(df, df$name == "John"))),
list(name = "John", age = 19L, height = 176.5))
+ expect_equal(getNumPartitions(toRDD(df)), 1)
--- End diff --
Hmm, good point, the behavior is a bit strange and I haven't thought of a
concise way to document this.
https://github.com/apache/spark/blob/master/R/pkg/R/context.R#L131
Basically, it's the largest of numSlices or ceiling of data size divided by
`spark.r.maxAllocationLimit` - *but* limited by length of the data (but this
length is wrong if the data is a data.frame - since that length becomes the
number of columns).
Is this an unintentional behavior (ie. limited always by the number of
columns even when the data size is larger then the
`spark.r.maxAllocationLimit`)? I can't tell...
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