SPARK-1160: Deprecate toArray in RDD

https://spark-project.atlassian.net/browse/SPARK-1160

reported by @mateiz: "It's redundant with collect() and the name doesn't make sense in Java, where we return a List (we can't return an array due to the way Java generics work). It's also missing in Python."

In this patch, I deprecated the method and changed the source files using it by replacing toArray with collect() directly

Author: CodingCat <zhunansjtu@gmail.com>

Closes #105 from CodingCat/SPARK-1060 and squashes the following commits:

286f163 [CodingCat] deprecate in JavaRDDLike
ee17b4e [CodingCat] add message and since
2ff7319 [CodingCat] deprecate toArray in RDD

2 files changed, 5 insertions, 5 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala
index 8803c4c1a0..e4a26eeb07 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala
@@ -109,7 +109,7 @@ object SVD {
 
     // Construct jblas A^T A locally
     val ata = DoubleMatrix.zeros(n, n)
-    for (entry <- emits.toArray) {
+    for (entry <- emits.collect()) {
       ata.put(entry._1._1, entry._1._2, entry._2)
     }
 
@@ -178,7 +178,7 @@ object SVD {
     val s = decomposed.S.data
     val v = decomposed.V.data
     
-    println("Computed " + s.toArray.length + " singular values and vectors")
+    println("Computed " + s.collect().length + " singular values and vectors")
     u.saveAsTextFile(output_u)
     s.saveAsTextFile(output_s)
     v.saveAsTextFile(output_v)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala
index 32f3f141cd..a92386865a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala
@@ -50,7 +50,7 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
     val m = matrix.m
     val n = matrix.n
     val ret = DoubleMatrix.zeros(m, n)
-    matrix.data.toArray.map(x => ret.put(x.i, x.j, x.mval))
+    matrix.data.collect().map(x => ret.put(x.i, x.j, x.mval))
     ret
   }
 
@@ -106,7 +106,7 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
     val u = decomposed.U
     val s = decomposed.S
     val v = decomposed.V
-    val retrank = s.data.toArray.length
+    val retrank = s.data.collect().length
 
     assert(retrank == 1, "rank returned not one")
 
@@ -139,7 +139,7 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll {
     val u = decomposed.U
     val s = decomposed.S
     val v = decomposed.V
-    val retrank = s.data.toArray.length
+    val retrank = s.data.collect().length
 
     val densea = getDenseMatrix(a)
     val svd = Singular.sparseSVD(densea)