Remove System.setProperty calls in DistributedSuite

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

@@ -23,14 +23,12 @@ import org.scalatest.Matchers
 import org.scalatest.time.{Millis, Span}
 
 import org.apache.spark.storage.{RDDBlockId, StorageLevel}
-import org.apache.spark.util.ResetSystemProperties
 
 class NotSerializableClass
 class NotSerializableExn(val notSer: NotSerializableClass) extends Throwable() {}
 
 
-class DistributedSuite extends FunSuite with Matchers with ResetSystemProperties
-  with LocalSparkContext {
+class DistributedSuite extends FunSuite with Matchers with LocalSparkContext {
 
   val clusterUrl = "local-cluster[2,1,512]"
 
@@ -79,8 +77,8 @@ class DistributedSuite extends FunSuite with Matchers with ResetSystemProperties
   }
 
   test("groupByKey where map output sizes exceed maxMbInFlight") {
-    System.setProperty("spark.reducer.maxMbInFlight", "1")
-    sc = new SparkContext(clusterUrl, "test")
+    val conf = new SparkConf().set("spark.reducer.maxMbInFlight", "1")
+    sc = new SparkContext(clusterUrl, "test", conf)
     // This data should be around 20 MB, so even with 4 mappers and 2 reducers, each map output
     // file should be about 2.5 MB
     val pairs = sc.parallelize(1 to 2000, 4).map(x => (x % 16, new Array[Byte](10000)))
@@ -214,8 +212,8 @@ class DistributedSuite extends FunSuite with Matchers with ResetSystemProperties
   }
 
   test("compute when only some partitions fit in memory") {
-    System.setProperty("spark.storage.memoryFraction", "0.01")
-    sc = new SparkContext(clusterUrl, "test")
+    val conf = new SparkConf().set("spark.storage.memoryFraction", "0.01")
+    sc = new SparkContext(clusterUrl, "test", conf)
     // data will be 4 million * 4 bytes = 16 MB in size, but our memoryFraction set the cache
     // to only 5 MB (0.01 of 512 MB), so not all of it will fit in memory; we use 20 partitions
     // to make sure that *some* of them do fit though