Update upstream

common/network-common/src/main/java/org/apache/spark/network/util/MapConfigProvider.java

@@ -42,6 +42,12 @@ public String get(String name) {
     return value;
   }
 
+  @Override
+  public String get(String name, String defaultValue) {
+    String value = config.get(name);
+    return value == null ? defaultValue : value;
+  }
+
   @Override
   public Iterable<Map.Entry<String, String>> getAll() {
     return config.entrySet();

common/network-yarn/src/main/java/org/apache/spark/network/yarn/util/HadoopConfigProvider.java

@@ -41,6 +41,12 @@ public String get(String name) {
     return value;
   }
 
+  @Override
+  public String get(String name, String defaultValue) {
+    String value = conf.get(name);
+    return value == null ? defaultValue : value;
+  }
+
   @Override
   public Iterable<Map.Entry<String, String>> getAll() {
     return conf;

core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala

@@ -349,7 +349,7 @@ class SparkHadoopUtil extends Logging {
       }
     } catch {
       case e: IOException =>
-        logDebug("Failed to decode $token: $e", e)
+        logDebug(s"Failed to decode $token: $e", e)
     }
     buffer.toString
   }

core/src/main/scala/org/apache/spark/network/netty/SparkTransportConf.scala

@@ -60,7 +60,7 @@ object SparkTransportConf {
 
     new TransportConf(module, new ConfigProvider {
       override def get(name: String): String = conf.get(name)
-
+      override def get(name: String, defaultValue: String): String = conf.get(name, defaultValue)
       override def getAll(): java.lang.Iterable[java.util.Map.Entry[String, String]] = {
         conf.getAll.toMap.asJava.entrySet()
       }

core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala

@@ -116,7 +116,7 @@ private object ParallelCollectionRDD {
    */
   def slice[T: ClassTag](seq: Seq[T], numSlices: Int): Seq[Seq[T]] = {
     if (numSlices < 1) {
-      throw new IllegalArgumentException("Positive number of slices required")
+      throw new IllegalArgumentException("Positive number of partitions required")
     }
     // Sequences need to be sliced at the same set of index positions for operations
     // like RDD.zip() to behave as expected

examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java

@@ -26,7 +26,7 @@
 
 /**
  * Computes an approximation to pi
- * Usage: JavaSparkPi [slices]
+ * Usage: JavaSparkPi [partitions]
  */
 public final class JavaSparkPi {
 

examples/src/main/java/org/apache/spark/examples/JavaTC.java

@@ -32,7 +32,7 @@
 
 /**
  * Transitive closure on a graph, implemented in Java.
- * Usage: JavaTC [slices]
+ * Usage: JavaTC [partitions]
  */
 public final class JavaTC {
 

examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala

@@ -21,7 +21,7 @@ package org.apache.spark.examples
 import org.apache.spark.sql.SparkSession
 
 /**
- * Usage: BroadcastTest [slices] [numElem] [blockSize]
+ * Usage: BroadcastTest [partitions] [numElem] [blockSize]
  */
 object BroadcastTest {
   def main(args: Array[String]) {

examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala

@@ -23,7 +23,7 @@ import org.apache.spark.sql.SparkSession
 
 
 /**
- * Usage: MultiBroadcastTest [slices] [numElem]
+ * Usage: MultiBroadcastTest [partitions] [numElem]
  */
 object MultiBroadcastTest {
   def main(args: Array[String]) {

examples/src/main/scala/org/apache/spark/examples/SparkALS.scala

@@ -100,7 +100,7 @@ object SparkALS {
         ITERATIONS = iters.getOrElse("5").toInt
         slices = slices_.getOrElse("2").toInt
       case _ =>
-        System.err.println("Usage: SparkALS [M] [U] [F] [iters] [slices]")
+        System.err.println("Usage: SparkALS [M] [U] [F] [iters] [partitions]")
         System.exit(1)
     }
 

examples/src/main/scala/org/apache/spark/examples/SparkLR.scala

@@ -28,7 +28,7 @@ import org.apache.spark.sql.SparkSession
 
 /**
  * Logistic regression based classification.
- * Usage: SparkLR [slices]
+ * Usage: SparkLR [partitions]
  *
  * This is an example implementation for learning how to use Spark. For more conventional use,
  * please refer to org.apache.spark.ml.classification.LogisticRegression.

mllib-local/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala

@@ -215,7 +215,7 @@ object TestingUtils {
       if (r.fun(x, r.y, r.eps)) {
         throw new TestFailedException(
           s"Did not expect \n$x\n and \n${r.y}\n to be within " +
-            "${r.eps}${r.method} for all elements.", 0)
+            s"${r.eps}${r.method} for all elements.", 0)
       }
       true
     }

mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala

@@ -259,7 +259,7 @@ object PowerIterationClustering extends Logging {
         val j = ctx.dstId
         val s = ctx.attr
         if (s < 0.0) {
-          throw new SparkException("Similarity must be nonnegative but found s($i, $j) = $s.")
+          throw new SparkException(s"Similarity must be nonnegative but found s($i, $j) = $s.")
         }
         if (s > 0.0) {
           ctx.sendToSrc(s)
@@ -283,7 +283,7 @@ object PowerIterationClustering extends Logging {
     : Graph[Double, Double] = {
     val edges = similarities.flatMap { case (i, j, s) =>
       if (s < 0.0) {
-        throw new SparkException("Similarity must be nonnegative but found s($i, $j) = $s.")
+        throw new SparkException(s"Similarity must be nonnegative but found s($i, $j) = $s.")
       }
       if (i != j) {
         Seq(Edge(i, j, s), Edge(j, i, s))

mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala

@@ -248,7 +248,7 @@ object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
       // Build node data into a tree.
       val trees = constructTrees(nodes)
       assert(trees.length == 1,
-        "Decision tree should contain exactly one tree but got ${trees.size} trees.")
+        s"Decision tree should contain exactly one tree but got ${trees.size} trees.")
       val model = new DecisionTreeModel(trees(0), Algo.fromString(algo))
       assert(model.numNodes == numNodes, s"Unable to load DecisionTreeModel data from: $dataPath." +
         s" Expected $numNodes nodes but found ${model.numNodes}")

mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala

@@ -119,7 +119,7 @@ object MLUtils extends Logging {
     while (i < indicesLength) {
       val current = indices(i)
       require(current > previous, s"indices should be one-based and in ascending order;"
-        + " found current=$current, previous=$previous; line=\"$line\"")
+        + s""" found current=$current, previous=$previous; line="$line"""")
       previous = current
       i += 1
     }

mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala

@@ -207,7 +207,7 @@ object TestingUtils {
       if (r.fun(x, r.y, r.eps)) {
         throw new TestFailedException(
           s"Did not expect \n$x\n and \n${r.y}\n to be within " +
-            "${r.eps}${r.method} for all elements.", 0)
+            s"${r.eps}${r.method} for all elements.", 0)
       }
       true
     }

sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala

@@ -285,7 +285,7 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
       val queryOutput = selfJoin.queryExecution.analyzed.output
 
       assertResult(4, "Field count mismatches")(queryOutput.size)
-      assertResult(2, "Duplicated expression ID in query plan:\n $selfJoin") {
+      assertResult(2, s"Duplicated expression ID in query plan:\n $selfJoin") {
         queryOutput.filter(_.name == "_1").map(_.exprId).size
       }
 
@@ -294,7 +294,7 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
   }
 
   test("nested data - struct with array field") {
-    val data = (1 to 10).map(i => Tuple1((i, Seq("val_$i"))))
+    val data = (1 to 10).map(i => Tuple1((i, Seq(s"val_$i"))))
     withOrcTable(data, "t") {
       checkAnswer(sql("SELECT `_1`.`_2`[0] FROM t"), data.map {
         case Tuple1((_, Seq(string))) => Row(string)
@@ -303,7 +303,7 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
   }
 
   test("nested data - array of struct") {
-    val data = (1 to 10).map(i => Tuple1(Seq(i -> "val_$i")))
+    val data = (1 to 10).map(i => Tuple1(Seq(i -> s"val_$i")))
     withOrcTable(data, "t") {
       checkAnswer(sql("SELECT `_1`[0].`_2` FROM t"), data.map {
         case Tuple1(Seq((_, string))) => Row(string)