[SPARK-12919][SPARKR] Implement dapply() on DataFrame in SparkR.

## What changes were proposed in this pull request?

dapply() applies an R function on each partition of a DataFrame and returns a new DataFrame.

The function signature is:

	dapply(df, function(localDF) {}, schema = NULL)

R function input: local data.frame from the partition on local node
R function output: local data.frame

Schema specifies the Row format of the resulting DataFrame. It must match the R function's output.
If schema is not specified, each partition of the result DataFrame will be serialized in R into a single byte array. Such resulting DataFrame can be processed by successive calls to dapply().

## How was this patch tested?
SparkR unit tests.

Author: Sun Rui <rui.sun@intel.com>
Author: Sun Rui <sunrui2016@gmail.com>

Closes #12493 from sun-rui/SPARK-12919.

1 files changed, 40 insertions, 0 deletions

diff --git a/R/pkg/inst/tests/testthat/test_sparkSQL.R b/R/pkg/inst/tests/testthat/test_sparkSQL.R
index 7058265ea3..5cf9dc405b 100644
--- a/R/pkg/inst/tests/testthat/test_sparkSQL.R
+++ b/R/pkg/inst/tests/testthat/test_sparkSQL.R
@@ -2043,6 +2043,46 @@ test_that("Histogram", {
   df <- as.DataFrame(sqlContext, data.frame(x = c(1, 2, 3, 4, 100)))
   expect_equal(histogram(df, "x")$counts, c(4, 0, 0, 0, 0, 0, 0, 0, 0, 1))
 })
+
+test_that("dapply() on a DataFrame", {
+  df <- createDataFrame (
+          sqlContext,
+          list(list(1L, 1, "1"), list(2L, 2, "2"), list(3L, 3, "3")),
+          c("a", "b", "c"))
+  ldf <- collect(df)
+  df1 <- dapply(df, function(x) { x }, schema(df))
+  result <- collect(df1)
+  expect_identical(ldf, result)
+
+
+  # Filter and add a column
+  schema <- structType(structField("a", "integer"), structField("b", "double"),
+                       structField("c", "string"), structField("d", "integer"))
+  df1 <- dapply(
+           df,
+           function(x) {
+             y <- x[x$a > 1, ]
+             y <- cbind(y, y$a + 1L)
+           },
+           schema)
+  result <- collect(df1)
+  expected <- ldf[ldf$a > 1, ]
+  expected$d <- expected$a + 1L
+  rownames(expected) <- NULL
+  expect_identical(expected, result)
+
+  # Remove the added column
+  df2 <- dapply(
+           df1,
+           function(x) {
+             x[, c("a", "b", "c")]
+           },
+           schema(df))
+  result <- collect(df2)
+  expected <- expected[, c("a", "b", "c")]
+  expect_identical(expected, result)
+})
+
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