[SPARK-29401][CORE][ML][SQL][GRAPHX][TESTS] Replace calls to .paralle…

…lize Arrays of tuples, ambiguous in Scala 2.13, with Seqs of tuples

### What changes were proposed in this pull request?

Invocations like `sc.parallelize(Array((1,2)))` cause a compile error in 2.13, like:
```
[ERROR] [Error] /Users/seanowen/Documents/spark_2.13/core/src/test/scala/org/apache/spark/ShuffleSuite.scala:47: overloaded method value apply with alternatives:
  (x: Unit,xs: Unit*)Array[Unit] <and>
  (x: Double,xs: Double*)Array[Double] <and>
  (x: Float,xs: Float*)Array[Float] <and>
  (x: Long,xs: Long*)Array[Long] <and>
  (x: Int,xs: Int*)Array[Int] <and>
  (x: Char,xs: Char*)Array[Char] <and>
  (x: Short,xs: Short*)Array[Short] <and>
  (x: Byte,xs: Byte*)Array[Byte] <and>
  (x: Boolean,xs: Boolean*)Array[Boolean]
 cannot be applied to ((Int, Int), (Int, Int), (Int, Int), (Int, Int))
```
Using a `Seq` instead appears to resolve it, and is effectively equivalent.

### Why are the changes needed?

To better cross-build for 2.13.

### Does this PR introduce any user-facing change?

None.

### How was this patch tested?

Existing tests.

Closes apache#26062 from srowen/SPARK-29401.

Authored-by: Sean Owen <[email protected]>
Signed-off-by: Dongjoon Hyun <[email protected]>

core/src/test/scala/org/apache/spark/DistributedSuite.scala

@@ -77,7 +77,7 @@ class DistributedSuite extends SparkFunSuite with Matchers with LocalSparkContex
 
   test("simple groupByKey") {
     sc = new SparkContext(clusterUrl, "test")
-    val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)), 5)
+    val pairs = sc.parallelize(Seq((1, 1), (1, 2), (1, 3), (2, 1)), 5)
     val groups = pairs.groupByKey(5).collect()
     assert(groups.size === 2)
     val valuesFor1 = groups.find(_._1 == 1).get._2

core/src/test/scala/org/apache/spark/FileSuite.scala

@@ -372,15 +372,15 @@ class FileSuite extends SparkFunSuite with LocalSparkContext {
 
   test ("prevent user from overwriting the empty directory (old Hadoop API)") {
     sc = new SparkContext("local", "test")
-    val randomRDD = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1)
+    val randomRDD = sc.parallelize(Seq((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1)
     intercept[FileAlreadyExistsException] {
       randomRDD.saveAsTextFile(tempDir.getPath)
     }
   }
 
   test ("prevent user from overwriting the non-empty directory (old Hadoop API)") {
     sc = new SparkContext("local", "test")
-    val randomRDD = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1)
+    val randomRDD = sc.parallelize(Seq((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1)
     randomRDD.saveAsTextFile(tempDir.getPath + "/output")
     assert(new File(tempDir.getPath + "/output/part-00000").exists())
     intercept[FileAlreadyExistsException] {
@@ -392,7 +392,7 @@ class FileSuite extends SparkFunSuite with LocalSparkContext {
     val conf = new SparkConf()
     conf.setAppName("test").setMaster("local").set("spark.hadoop.validateOutputSpecs", "false")
     sc = new SparkContext(conf)
-    val randomRDD = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1)
+    val randomRDD = sc.parallelize(Seq((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1)
     randomRDD.saveAsTextFile(tempDir.getPath + "/output")
     assert(new File(tempDir.getPath + "/output/part-00000").exists())
     randomRDD.saveAsTextFile(tempDir.getPath + "/output")

core/src/test/scala/org/apache/spark/PartitioningSuite.scala

@@ -262,11 +262,11 @@ class PartitioningSuite extends SparkFunSuite with SharedSparkContext with Priva
 
   test("defaultPartitioner") {
     val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 150)
-    val rdd2 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4)))
+    val rdd2 = sc.parallelize(Seq((1, 2), (2, 3), (2, 4), (3, 4)))
       .partitionBy(new HashPartitioner(10))
-    val rdd3 = sc.parallelize(Array((1, 6), (7, 8), (3, 10), (5, 12), (13, 14)))
+    val rdd3 = sc.parallelize(Seq((1, 6), (7, 8), (3, 10), (5, 12), (13, 14)))
       .partitionBy(new HashPartitioner(100))
-    val rdd4 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4)))
+    val rdd4 = sc.parallelize(Seq((1, 2), (2, 3), (2, 4), (3, 4)))
       .partitionBy(new HashPartitioner(9))
     val rdd5 = sc.parallelize((1 to 10).map(x => (x, x)), 11)
 
@@ -289,14 +289,14 @@ class PartitioningSuite extends SparkFunSuite with SharedSparkContext with Priva
       sc.conf.set("spark.default.parallelism", "4")
 
       val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 150)
-      val rdd2 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4)))
+      val rdd2 = sc.parallelize(Seq((1, 2), (2, 3), (2, 4), (3, 4)))
         .partitionBy(new HashPartitioner(10))
-      val rdd3 = sc.parallelize(Array((1, 6), (7, 8), (3, 10), (5, 12), (13, 14)))
+      val rdd3 = sc.parallelize(Seq((1, 6), (7, 8), (3, 10), (5, 12), (13, 14)))
         .partitionBy(new HashPartitioner(100))
-      val rdd4 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4)))
+      val rdd4 = sc.parallelize(Seq((1, 2), (2, 3), (2, 4), (3, 4)))
         .partitionBy(new HashPartitioner(9))
       val rdd5 = sc.parallelize((1 to 10).map(x => (x, x)), 11)
-      val rdd6 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4)))
+      val rdd6 = sc.parallelize(Seq((1, 2), (2, 3), (2, 4), (3, 4)))
         .partitionBy(new HashPartitioner(3))
 
       val partitioner1 = Partitioner.defaultPartitioner(rdd1, rdd2)

core/src/test/scala/org/apache/spark/ShuffleSuite.scala

@@ -44,7 +44,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
   test("groupByKey without compression") {
     val myConf = conf.clone().set(config.SHUFFLE_COMPRESS, false)
     sc = new SparkContext("local", "test", myConf)
-    val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)), 4)
+    val pairs = sc.parallelize(Seq((1, 1), (1, 2), (1, 3), (2, 1)), 4)
     val groups = pairs.groupByKey(4).collect()
     assert(groups.size === 2)
     val valuesFor1 = groups.find(_._1 == 1).get._2