Merge branch 'master' into rk/more-upstreamn

Author: Robert Kruszewski

LICENSE

@@ -237,6 +237,7 @@ The text of each license is also included at licenses/LICENSE-[project].txt.
 
      (BSD 3 Clause) netlib core (com.github.fommil.netlib:core:1.1.2 - https://github.com/fommil/netlib-java/core)
      (BSD 3 Clause) JPMML-Model (org.jpmml:pmml-model:1.2.7 - https://github.com/jpmml/jpmml-model)
+     (BSD 3 Clause) jmock (org.jmock:jmock-junit4:2.8.4 - http://jmock.org/)
      (BSD License) AntLR Parser Generator (antlr:antlr:2.7.7 - http://www.antlr.org/)
      (BSD License) ANTLR 4.5.2-1 (org.antlr:antlr4:4.5.2-1 - http://wwww.antlr.org/)
      (BSD licence) ANTLR ST4 4.0.4 (org.antlr:ST4:4.0.4 - http://www.stringtemplate.org)

R/pkg/NAMESPACE

@@ -201,10 +201,17 @@ exportMethods("%<=>%",
               "approxCountDistinct",
               "approxQuantile",
               "array_contains",
+              "array_join",
               "array_max",
               "array_min",
               "array_position",
+<<<<<<< HEAD
               "array_sort",
+=======
+              "array_repeat",
+              "array_sort",
+              "arrays_overlap",
+>>>>>>> master
               "asc",
               "ascii",
               "asin",
@@ -302,6 +309,7 @@ exportMethods("%<=>%",
               "lower",
               "lpad",
               "ltrim",
+              "map_entries",
               "map_keys",
               "map_values",
               "max",

R/pkg/R/DataFrame.R

@@ -2297,6 +2297,8 @@ setMethod("rename",
 
 setClassUnion("characterOrColumn", c("character", "Column"))
 
+setClassUnion("numericOrColumn", c("numeric", "Column"))
+
 #' Arrange Rows by Variables
 #'
 #' Sort a SparkDataFrame by the specified column(s).

R/pkg/R/client.R

@@ -63,7 +63,11 @@ generateSparkSubmitArgs <- function(args, sparkHome, jars, sparkSubmitOpts, pack
 checkJavaVersion <- function() {
   javaBin <- "java"
   javaHome <- Sys.getenv("JAVA_HOME")
+<<<<<<< HEAD
   javaReqs <- utils::packageDescription(utils::packageName(), fields=c("SystemRequirements"))
+=======
+  javaReqs <- utils::packageDescription(utils::packageName(), fields = c("SystemRequirements"))
+>>>>>>> master
   sparkJavaVersion <- as.numeric(tail(strsplit(javaReqs, "[(=)]")[[1]], n = 1L))
   if (javaHome != "") {
     javaBin <- file.path(javaHome, "bin", javaBin)
@@ -90,7 +94,12 @@ checkJavaVersion <- function() {
   # Extract 8 from it to compare to sparkJavaVersion
   javaVersionNum <- as.integer(strsplit(javaVersionStr, "[.]")[[1L]][2])
   if (javaVersionNum != sparkJavaVersion) {
+<<<<<<< HEAD
     stop(paste("Java version", sparkJavaVersion, "is required for this package; found version:", javaVersionStr))
+=======
+    stop(paste("Java version", sparkJavaVersion, "is required for this package; found version:",
+               javaVersionStr))
+>>>>>>> master
   }
 }
 

R/pkg/R/functions.R

@@ -189,6 +189,7 @@ NULL
 #'              the map or array of maps.
 #'          \item \code{from_json}: it is the column containing the JSON string.
 #'          }
+#' @param y Column to compute on.
 #' @param value A value to compute on.
 #'          \itemize{
 #'          \item \code{array_contains}: a value to be checked if contained in the column.
@@ -207,7 +208,11 @@ NULL
 #' tmp <- mutate(df, v1 = create_array(df$mpg, df$cyl, df$hp))
 #' head(select(tmp, array_contains(tmp$v1, 21), size(tmp$v1)))
 #' head(select(tmp, array_max(tmp$v1), array_min(tmp$v1)))
+<<<<<<< HEAD
 #' head(select(tmp, array_position(tmp$v1, 21), array_sort(tmp$v1)))
+=======
+#' head(select(tmp, array_position(tmp$v1, 21), array_repeat(df$mpg, 3), array_sort(tmp$v1)))
+>>>>>>> master
 #' head(select(tmp, flatten(tmp$v1), reverse(tmp$v1)))
 #' tmp2 <- mutate(tmp, v2 = explode(tmp$v1))
 #' head(tmp2)
@@ -216,12 +221,22 @@ NULL
 #' head(select(tmp, sort_array(tmp$v1)))
 #' head(select(tmp, sort_array(tmp$v1, asc = FALSE)))
 #' tmp3 <- mutate(df, v3 = create_map(df$model, df$cyl))
+<<<<<<< HEAD
 #' head(select(tmp3, map_keys(tmp3$v3)))
 #' head(select(tmp3, map_values(tmp3$v3)))
 #' head(select(tmp3, element_at(tmp3$v3, "Valiant")))
 #' tmp4 <- mutate(df, v4 = create_array(df$mpg, df$cyl), v5 = create_array(df$hp))
 #' head(select(tmp4, concat(tmp4$v4, tmp4$v5)))
 #' head(select(tmp, concat(df$mpg, df$cyl, df$hp)))}
+=======
+#' head(select(tmp3, map_entries(tmp3$v3), map_keys(tmp3$v3), map_values(tmp3$v3)))
+#' head(select(tmp3, element_at(tmp3$v3, "Valiant")))
+#' tmp4 <- mutate(df, v4 = create_array(df$mpg, df$cyl), v5 = create_array(df$cyl, df$hp))
+#' head(select(tmp4, concat(tmp4$v4, tmp4$v5), arrays_overlap(tmp4$v4, tmp4$v5)))
+#' head(select(tmp, concat(df$mpg, df$cyl, df$hp)))
+#' tmp5 <- mutate(df, v6 = create_array(df$model, df$model))
+#' head(select(tmp5, array_join(tmp5$v6, "#"), array_join(tmp5$v6, "#", "NULL")))}
+>>>>>>> master
 NULL
 
 #' Window functions for Column operations
@@ -3006,6 +3021,27 @@ setMethod("array_contains",
             column(jc)
           })
 
+#' @details
+#' \code{array_join}: Concatenates the elements of column using the delimiter.
+#' Null values are replaced with nullReplacement if set, otherwise they are ignored.
+#'
+#' @param delimiter a character string that is used to concatenate the elements of column.
+#' @param nullReplacement an optional character string that is used to replace the Null values.
+#' @rdname column_collection_functions
+#' @aliases array_join array_join,Column-method
+#' @note array_join since 2.4.0
+setMethod("array_join",
+         signature(x = "Column", delimiter = "character"),
+         function(x, delimiter, nullReplacement = NULL) {
+           jc <- if (is.null(nullReplacement)) {
+             callJStatic("org.apache.spark.sql.functions", "array_join", x@jc, delimiter)
+           } else {
+             callJStatic("org.apache.spark.sql.functions", "array_join", x@jc, delimiter,
+                         as.character(nullReplacement))
+           }
+           column(jc)
+         })
+
 #' @details
 #' \code{array_max}: Returns the maximum value of the array.
 #'
@@ -3049,6 +3085,29 @@ setMethod("array_position",
           })
 
 #' @details
+<<<<<<< HEAD
+=======
+#' \code{array_repeat}: Creates an array containing \code{x} repeated the number of times
+#' given by \code{count}.
+#'
+#' @param count a Column or constant determining the number of repetitions.
+#' @rdname column_collection_functions
+#' @aliases array_repeat array_repeat,Column,numericOrColumn-method
+#' @note array_repeat since 2.4.0
+setMethod("array_repeat",
+          signature(x = "Column", count = "numericOrColumn"),
+          function(x, count) {
+            if (class(count) == "Column") {
+              count <- count@jc
+            } else {
+              count <- as.integer(count)
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "array_repeat", x@jc, count)
+            column(jc)
+          })
+
+#' @details
+>>>>>>> master
 #' \code{array_sort}: Sorts the input array in ascending order. The elements of the input array
 #' must be orderable. NA elements will be placed at the end of the returned array.
 #'
@@ -3063,6 +3122,24 @@ setMethod("array_sort",
           })
 
 #' @details
+<<<<<<< HEAD
+=======
+#' \code{arrays_overlap}: Returns true if the input arrays have at least one non-null element in
+#' common. If not and both arrays are non-empty and any of them contains a null, it returns null.
+#' It returns false otherwise.
+#'
+#' @rdname column_collection_functions
+#' @aliases arrays_overlap arrays_overlap,Column-method
+#' @note arrays_overlap since 2.4.0
+setMethod("arrays_overlap",
+          signature(x = "Column", y = "Column"),
+          function(x, y) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "arrays_overlap", x@jc, y@jc)
+            column(jc)
+          })
+
+#' @details
+>>>>>>> master
 #' \code{flatten}: Creates a single array from an array of arrays.
 #' If a structure of nested arrays is deeper than two levels, only one level of nesting is removed.
 #'
@@ -3076,6 +3153,19 @@ setMethod("flatten",
             column(jc)
           })
 
+#' @details
+#' \code{map_entries}: Returns an unordered array of all entries in the given map.
+#'
+#' @rdname column_collection_functions
+#' @aliases map_entries map_entries,Column-method
+#' @note map_entries since 2.4.0
+setMethod("map_entries",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "map_entries", x@jc)
+            column(jc)
+         })
+
 #' @details
 #' \code{map_keys}: Returns an unordered array containing the keys of the map.
 #'
@@ -3149,8 +3239,13 @@ setMethod("size",
 #' (or starting from the end if start is negative) with the specified length.
 #'
 #' @rdname column_collection_functions
+<<<<<<< HEAD
 #' @param start an index indicating the first element occuring in the result.
 #' @param length a number of consecutive elements choosen to the result.
+=======
+#' @param start an index indicating the first element occurring in the result.
+#' @param length a number of consecutive elements chosen to the result.
+>>>>>>> master
 #' @aliases slice slice,Column-method
 #' @note slice since 2.4.0
 setMethod("slice",

R/pkg/R/generics.R

@@ -624,7 +624,7 @@ setGeneric("summarize", function(x, ...) { standardGeneric("summarize") })
 #' @rdname summary
 setGeneric("summary", function(object, ...) { standardGeneric("summary") })
 
-setGeneric("toJSON", function(x) { standardGeneric("toJSON") })
+setGeneric("toJSON", function(x, ...) { standardGeneric("toJSON") })
 
 setGeneric("toRDD", function(x) { standardGeneric("toRDD") })
 
@@ -757,6 +757,10 @@ setGeneric("approxCountDistinct", function(x, ...) { standardGeneric("approxCoun
 #' @name NULL
 setGeneric("array_contains", function(x, value) { standardGeneric("array_contains") })
 
+#' @rdname column_collection_functions
+#' @name NULL
+setGeneric("array_join", function(x, delimiter, ...) { standardGeneric("array_join") })
+
 #' @rdname column_collection_functions
 #' @name NULL
 setGeneric("array_max", function(x) { standardGeneric("array_max") })
@@ -769,10 +773,23 @@ setGeneric("array_min", function(x) { standardGeneric("array_min") })
 #' @name NULL
 setGeneric("array_position", function(x, value) { standardGeneric("array_position") })
 
+#' @rdname column_collection_functions
+#' @name NULL
+<<<<<<< HEAD
+setGeneric("array_sort", function(x) { standardGeneric("array_sort") })
+
+=======
+setGeneric("array_repeat", function(x, count) { standardGeneric("array_repeat") })
+
 #' @rdname column_collection_functions
 #' @name NULL
 setGeneric("array_sort", function(x) { standardGeneric("array_sort") })
 
+#' @rdname column_collection_functions
+#' @name NULL
+setGeneric("arrays_overlap", function(x, y) { standardGeneric("arrays_overlap") })
+
+>>>>>>> master
 #' @rdname column_string_functions
 #' @name NULL
 setGeneric("ascii", function(x) { standardGeneric("ascii") })
@@ -1034,6 +1051,10 @@ setGeneric("lpad", function(x, len, pad) { standardGeneric("lpad") })
 #' @name NULL
 setGeneric("ltrim", function(x, trimString) { standardGeneric("ltrim") })
 
+#' @rdname column_collection_functions
+#' @name NULL
+setGeneric("map_entries", function(x) { standardGeneric("map_entries") })
+
 #' @rdname column_collection_functions
 #' @name NULL
 setGeneric("map_keys", function(x) { standardGeneric("map_keys") })

R/pkg/R/sparkR.R

@@ -194,7 +194,7 @@ sparkR.sparkContext <- function(
 
     # Don't use readString() so that we can provide a useful
     # error message if the R and Java versions are mismatched.
-    authSecretLen = readInt(f)
+    authSecretLen <- readInt(f)
     if (length(authSecretLen) == 0 || authSecretLen == 0) {
       stop("Unexpected EOF in JVM connection data. Mismatched versions?")
     }

R/pkg/tests/fulltests/test_sparkSQL.R

@@ -1503,6 +1503,39 @@ test_that("column functions", {
   result <- collect(select(df2, reverse(df2[[1]])))[[1]]
   expect_equal(result, "cba")
 
+<<<<<<< HEAD
+=======
+  # Test array_repeat()
+  df <- createDataFrame(list(list("a", 3L), list("b", 2L)))
+  result <- collect(select(df, array_repeat(df[[1]], df[[2]])))[[1]]
+  expect_equal(result, list(list("a", "a", "a"), list("b", "b")))
+
+  result <- collect(select(df, array_repeat(df[[1]], 2L)))[[1]]
+  expect_equal(result, list(list("a", "a"), list("b", "b")))
+
+  # Test arrays_overlap()
+  df <- createDataFrame(list(list(list(1L, 2L), list(3L, 1L)),
+                             list(list(1L, 2L), list(3L, 4L)),
+                             list(list(1L, NA), list(3L, 4L))))
+  result <- collect(select(df, arrays_overlap(df[[1]], df[[2]])))[[1]]
+  expect_equal(result, c(TRUE, FALSE, NA))
+
+  # Test array_join()
+  df <- createDataFrame(list(list(list("Hello", "World!"))))
+  result <- collect(select(df, array_join(df[[1]], "#")))[[1]]
+  expect_equal(result, "Hello#World!")
+  df2 <- createDataFrame(list(list(list("Hello", NA, "World!"))))
+  result <- collect(select(df2, array_join(df2[[1]], "#", "Beautiful")))[[1]]
+  expect_equal(result, "Hello#Beautiful#World!")
+  result <- collect(select(df2, array_join(df2[[1]], "#")))[[1]]
+  expect_equal(result, "Hello#World!")
+  df3 <- createDataFrame(list(list(list("Hello", NULL, "World!"))))
+  result <- collect(select(df3, array_join(df3[[1]], "#", "Beautiful")))[[1]]
+  expect_equal(result, "Hello#Beautiful#World!")
+  result <- collect(select(df3, array_join(df3[[1]], "#")))[[1]]
+  expect_equal(result, "Hello#World!")
+
+>>>>>>> master
   # Test array_sort() and sort_array()
   df <- createDataFrame(list(list(list(2L, 1L, 3L, NA)), list(list(NA, 6L, 5L, NA, 4L))))
 
@@ -1518,21 +1551,36 @@ test_that("column functions", {
   df <- createDataFrame(list(list(list(1L, 2L, 3L)), list(list(4L, 5L))))
   result <- collect(select(df, slice(df[[1]], 2L, 2L)))[[1]]
   expect_equal(result, list(list(2L, 3L), list(5L)))
+<<<<<<< HEAD
 
   # Test concat()
   df <- createDataFrame(list(list(list(1L, 2L, 3L), list(4L, 5L, 6L)),
                         list(list(7L, 8L, 9L), list(10L, 11L, 12L))))
   result <- collect(select(df, concat(df[[1]], df[[2]])))[[1]]
   expect_equal(result, list(list(1L, 2L, 3L, 4L, 5L, 6L), list(7L, 8L, 9L, 10L, 11L, 12L)))
 
+=======
+
+  # Test concat()
+  df <- createDataFrame(list(list(list(1L, 2L, 3L), list(4L, 5L, 6L)),
+                        list(list(7L, 8L, 9L), list(10L, 11L, 12L))))
+  result <- collect(select(df, concat(df[[1]], df[[2]])))[[1]]
+  expect_equal(result, list(list(1L, 2L, 3L, 4L, 5L, 6L), list(7L, 8L, 9L, 10L, 11L, 12L)))
+
+>>>>>>> master
   # Test flatten()
   df <- createDataFrame(list(list(list(list(1L, 2L), list(3L, 4L))),
                         list(list(list(5L, 6L), list(7L, 8L)))))
   result <- collect(select(df, flatten(df[[1]])))[[1]]
   expect_equal(result, list(list(1L, 2L, 3L, 4L), list(5L, 6L, 7L, 8L)))
 
-  # Test map_keys(), map_values() and element_at()
+  # Test map_entries(), map_keys(), map_values() and element_at()
   df <- createDataFrame(list(list(map = as.environment(list(x = 1, y = 2)))))
+  result <- collect(select(df, map_entries(df$map)))[[1]]
+  expected_entries <-  list(listToStruct(list(key = "x", value = 1)),
+                            listToStruct(list(key = "y", value = 2)))
+  expect_equal(result, list(expected_entries))
+
   result <- collect(select(df, map_keys(df$map)))[[1]]
   expect_equal(result, list(list("x", "y")))
 

README.md

@@ -99,6 +99,8 @@ can be run using:
 Please see the guidance on how to
 [run tests for a module, or individual tests](http://spark.apache.org/developer-tools.html#individual-tests).
 
+There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
+
 ## A Note About Hadoop Versions
 
 Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported