SPARK-22830 Scala Coding style has been improved in Spark Examples

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

@@ -42,7 +42,7 @@ object BroadcastTest {
     val arr1 = (0 until num).toArray
 
     for (i <- 0 until 3) {
-      println("Iteration " + i)
+      println(s"Iteration $i")
       println("===========")
       val startTime = System.nanoTime
       val barr1 = sc.broadcast(arr1)

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

@@ -49,12 +49,10 @@ object DFSReadWriteTest {
   }
 
   private def printUsage(): Unit = {
-    val usage: String = "DFS Read-Write Test\n" +
-    "\n" +
-    "Usage: localFile dfsDir\n" +
-    "\n" +
-    "localFile - (string) local file to use in test\n" +
-    "dfsDir - (string) DFS directory for read/write tests\n"
+    val usage = """DFS Read-Write Test
+    |Usage: localFile dfsDir
+    |localFile - (string) local file to use in test
+    |dfsDir - (string) DFS directory for read/write tests""".stripMargin
 
     println(usage)
   }
@@ -69,13 +67,13 @@ object DFSReadWriteTest {
 
     localFilePath = new File(args(i))
     if (!localFilePath.exists) {
-      System.err.println("Given path (" + args(i) + ") does not exist.\n")
+      System.err.println(s"Given path (${args(i)}) does not exist")
       printUsage()
       System.exit(1)
     }
 
     if (!localFilePath.isFile) {
-      System.err.println("Given path (" + args(i) + ") is not a file.\n")
+      System.err.println(s"Given path (${args(i)}) is not a file")
       printUsage()
       System.exit(1)
     }
@@ -108,7 +106,7 @@ object DFSReadWriteTest {
       .getOrCreate()
 
     println("Writing local file to DFS")
-    val dfsFilename = dfsDirPath + "/dfs_read_write_test"
+    val dfsFilename = s"${dfsDirPath}/dfs_read_write_test"
     val fileRDD = spark.sparkContext.parallelize(fileContents)
     fileRDD.saveAsTextFile(dfsFilename)
 
@@ -127,11 +125,11 @@ object DFSReadWriteTest {
     spark.stop()
 
     if (localWordCount == dfsWordCount) {
-      println(s"Success! Local Word Count ($localWordCount) " +
-        s"and DFS Word Count ($dfsWordCount) agree.")
+      println(s"Success! Local Word Count $localWordCount and " +
+        s"DFS Word Count $dfsWordCount agree.")
     } else {
-      println(s"Failure! Local Word Count ($localWordCount) " +
-        s"and DFS Word Count ($dfsWordCount) disagree.")
+      println(s"Failure! Local Word Count $localWordCount " +
+        s"and DFS Word Count $dfsWordCount disagree.")
     }
 
   }

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

@@ -39,7 +39,7 @@ object HdfsTest {
       val start = System.currentTimeMillis()
       for (x <- mapped) { x + 2 }
       val end = System.currentTimeMillis()
-      println("Iteration " + iter + " took " + (end-start) + " ms")
+      println(s"Iteration ${iter} took ${(end-start)} ms")
     }
     spark.stop()
   }

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

@@ -129,8 +129,7 @@ object LocalALS {
       println(s"Iteration $iter:")
       ms = (0 until M).map(i => updateMovie(i, ms(i), us, R)).toArray
       us = (0 until U).map(j => updateUser(j, us(j), ms, R)).toArray
-      println("RMSE = " + rmse(R, ms, us))
-      println()
+      println(s"RMSE = ${rmse(R, ms, us)}")
     }
   }
 

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

@@ -58,10 +58,10 @@ object LocalFileLR {
 
     // Initialize w to a random value
     val w = DenseVector.fill(D) {2 * rand.nextDouble - 1}
-    println("Initial w: " + w)
+    println(s"Initial w: $w")
 
     for (i <- 1 to ITERATIONS) {
-      println("On iteration " + i)
+      println(s"On iteration $i")
       val gradient = DenseVector.zeros[Double](D)
       for (p <- points) {
         val scale = (1 / (1 + math.exp(-p.y * (w.dot(p.x)))) - 1) * p.y
@@ -71,7 +71,7 @@ object LocalFileLR {
     }
 
     fileSrc.close()
-    println("Final w: " + w)
+    println(s"Final w: $w")
   }
 }
 // scalastyle:on println

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

@@ -88,7 +88,7 @@ object LocalKMeans {
       kPoints.put(i, iter.next())
     }
 
-    println("Initial centers: " + kPoints)
+    println(s"Initial centers: $kPoints")
 
     while(tempDist > convergeDist) {
       val closest = data.map (p => (closestPoint(p, kPoints), (p, 1)))
@@ -114,7 +114,7 @@ object LocalKMeans {
       }
     }
 
-    println("Final centers: " + kPoints)
+    println(s"Final centers: $kPoints")
   }
 }
 // scalastyle:on println

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

@@ -61,10 +61,10 @@ object LocalLR {
     val data = generateData
     // Initialize w to a random value
     val w = DenseVector.fill(D) {2 * rand.nextDouble - 1}
-    println("Initial w: " + w)
+    println(s"Initial w: $w")
 
     for (i <- 1 to ITERATIONS) {
-      println("On iteration " + i)
+      println(s"On iteration $i")
       val gradient = DenseVector.zeros[Double](D)
       for (p <- data) {
         val scale = (1 / (1 + math.exp(-p.y * (w.dot(p.x)))) - 1) * p.y
@@ -73,7 +73,7 @@ object LocalLR {
       w -= gradient
     }
 
-    println("Final w: " + w)
+    println(s"Final w: $w")
   }
 }
 // scalastyle:on println

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

@@ -28,7 +28,7 @@ object LocalPi {
       val y = random * 2 - 1
       if (x*x + y*y <= 1) count += 1
     }
-    println("Pi is roughly " + 4 * count / 100000.0)
+    println(s"Pi is roughly ${4 * count / 100000.0}")
   }
 }
 // scalastyle:on println

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

@@ -59,7 +59,7 @@ object SimpleSkewedGroupByTest {
     // Enforce that everything has been calculated and in cache
     pairs1.count
 
-    println("RESULT: " + pairs1.groupByKey(numReducers).count)
+    println(s"RESULT: ${pairs1.groupByKey(numReducers).count}")
     // Print how many keys each reducer got (for debugging)
     // println("RESULT: " + pairs1.groupByKey(numReducers)
     //                           .map{case (k,v) => (k, v.size)}

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

@@ -80,7 +80,7 @@ object SparkALS {
 
   def showWarning() {
     System.err.println(
-      """WARN: This is a naive implementation of ALS and is given as an example!
+      s"""WARN: This is a naive implementation of ALS and is given as an example!
         |Please use org.apache.spark.ml.recommendation.ALS
         |for more conventional use.
       """.stripMargin)
@@ -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] [partitions]")
+        System.err.println(s"Usage: SparkALS [M] [U] [F] [iters] [partitions]")
         System.exit(1)
     }
 
@@ -135,10 +135,8 @@ object SparkALS {
                 .map(i => update(i, usb.value(i), msb.value, Rc.value.transpose()))
                 .collect()
       usb = sc.broadcast(us) // Re-broadcast us because it was updated
-      println("RMSE = " + rmse(R, ms, us))
-      println()
+      println(s"RMSE = ${rmse(R, ms, us)}")
     }
-
     spark.stop()
   }
 

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

@@ -79,17 +79,17 @@ object SparkHdfsLR {
 
     // Initialize w to a random value
     val w = DenseVector.fill(D) {2 * rand.nextDouble - 1}
-    println("Initial w: " + w)
+    println(s"Initial w: $w")
 
     for (i <- 1 to ITERATIONS) {
-      println("On iteration " + i)
+      println(s"On iteration $i")
       val gradient = points.map { p =>
         p.x * (1 / (1 + exp(-p.y * (w.dot(p.x)))) - 1) * p.y
       }.reduce(_ + _)
       w -= gradient
     }
 
-    println("Final w: " + w)
+    println(s"Final w: $w")
     spark.stop()
   }
 }

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

@@ -95,7 +95,7 @@ object SparkKMeans {
       for (newP <- newPoints) {
         kPoints(newP._1) = newP._2
       }
-      println("Finished iteration (delta = " + tempDist + ")")
+      println(s"Finished iteration (delta = $tempDist)")
     }
 
     println("Final centers:")

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

@@ -73,17 +73,17 @@ object SparkLR {
 
     // Initialize w to a random value
     val w = DenseVector.fill(D) {2 * rand.nextDouble - 1}
-    println("Initial w: " + w)
+    println(s"Initial w: $w")
 
     for (i <- 1 to ITERATIONS) {
-      println("On iteration " + i)
+      println(s"On iteration $i")
       val gradient = points.map { p =>
         p.x * (1 / (1 + exp(-p.y * (w.dot(p.x)))) - 1) * p.y
       }.reduce(_ + _)
       w -= gradient
     }
 
-    println("Final w: " + w)
+    println(s"Final w: $w")
 
     spark.stop()
   }

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

@@ -77,7 +77,7 @@ object SparkPageRank {
     }
 
     val output = ranks.collect()
-    output.foreach(tup => println(tup._1 + " has rank: " + tup._2 + "."))
+    output.foreach(tup => println(s"${tup._1} has rank:  ${tup._2} ."))
 
     spark.stop()
   }

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

@@ -36,7 +36,7 @@ object SparkPi {
       val y = random * 2 - 1
       if (x*x + y*y <= 1) 1 else 0
     }.reduce(_ + _)
-    println("Pi is roughly " + 4.0 * count / (n - 1))
+    println(s"Pi is roughly ${4.0 * count / (n - 1)}")
     spark.stop()
   }
 }

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

@@ -68,7 +68,7 @@ object SparkTC {
       nextCount = tc.count()
     } while (nextCount != oldCount)
 
-    println("TC has " + tc.count() + " edges.")
+    println(s"TC has ${tc.count()} edges.")
     spark.stop()
   }
 }

examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala

@@ -27,19 +27,22 @@ import org.apache.spark.graphx.lib._
 import org.apache.spark.internal.Logging
 import org.apache.spark.storage.StorageLevel
 
+
 /**
  * Driver program for running graph algorithms.
  */
 object Analytics extends Logging {
 
   def main(args: Array[String]): Unit = {
     if (args.length < 2) {
-      System.err.println(
-        "Usage: Analytics <taskType> <file> --numEPart=<num_edge_partitions> [other options]")
-      System.err.println("Supported 'taskType' as follows:")
-      System.err.println("  pagerank    Compute PageRank")
-      System.err.println("  cc          Compute the connected components of vertices")
-      System.err.println("  triangles   Count the number of triangles")
+      val usage = """
+      |Usage: Analytics <taskType> <file> --numEPart=<num_edge_partitions> [other options]
+      |Supported 'taskType' as follows:
+      |pagerank    Compute PageRank
+      |cc          Compute the connected components of vertices
+      |triangles   Count the number of triangles""".stripMargin
+
+      System.err.println(usage)
       System.exit(1)
     }
 
@@ -48,7 +51,7 @@ object Analytics extends Logging {
     val optionsList = args.drop(2).map { arg =>
       arg.dropWhile(_ == '-').split('=') match {
         case Array(opt, v) => (opt -> v)
-        case _ => throw new IllegalArgumentException("Invalid argument: " + arg)
+        case _ => throw new IllegalArgumentException(s"Invalid argument: $arg")
       }
     }
     val options = mutable.Map(optionsList: _*)
@@ -74,68 +77,68 @@ object Analytics extends Logging {
         val numIterOpt = options.remove("numIter").map(_.toInt)
 
         options.foreach {
-          case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
+          case (opt, _) => throw new IllegalArgumentException(s"Invalid option: $opt")
         }
 
         println("======================================")
         println("|             PageRank               |")
         println("======================================")
 
-        val sc = new SparkContext(conf.setAppName("PageRank(" + fname + ")"))
+        val sc = new SparkContext(conf.setAppName(s"PageRank($fname)"))
 
         val unpartitionedGraph = GraphLoader.edgeListFile(sc, fname,
           numEdgePartitions = numEPart,
           edgeStorageLevel = edgeStorageLevel,
           vertexStorageLevel = vertexStorageLevel).cache()
         val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_))
 
-        println("GRAPHX: Number of vertices " + graph.vertices.count)
-        println("GRAPHX: Number of edges " + graph.edges.count)
+        println(s"GRAPHX: Number of vertices ${graph.vertices.count}")
+        println(s"GRAPHX: Number of edges ${graph.edges.count}")
 
         val pr = (numIterOpt match {
           case Some(numIter) => PageRank.run(graph, numIter)
           case None => PageRank.runUntilConvergence(graph, tol)
         }).vertices.cache()
 
-        println("GRAPHX: Total rank: " + pr.map(_._2).reduce(_ + _))
+        println(s"GRAPHX: Total rank: ${pr.map(_._2).reduce(_ + _)}")
 
         if (!outFname.isEmpty) {
-          logWarning("Saving pageranks of pages to " + outFname)
+          logWarning(s"Saving pageranks of pages to $outFname")
           pr.map { case (id, r) => id + "\t" + r }.saveAsTextFile(outFname)
         }
 
         sc.stop()
 
       case "cc" =>
         options.foreach {
-          case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
+          case (opt, _) => throw new IllegalArgumentException(s"Invalid option: $opt")
         }
 
         println("======================================")
         println("|      Connected Components          |")
         println("======================================")
 
-        val sc = new SparkContext(conf.setAppName("ConnectedComponents(" + fname + ")"))
+        val sc = new SparkContext(conf.setAppName(s"ConnectedComponents($fname)"))
         val unpartitionedGraph = GraphLoader.edgeListFile(sc, fname,
           numEdgePartitions = numEPart,
           edgeStorageLevel = edgeStorageLevel,
           vertexStorageLevel = vertexStorageLevel).cache()
         val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_))
 
         val cc = ConnectedComponents.run(graph)
-        println("Components: " + cc.vertices.map { case (vid, data) => data }.distinct())
+        println(s"Components: ${cc.vertices.map { case (vid, data) => data }.distinct()}")
         sc.stop()
 
       case "triangles" =>
         options.foreach {
-          case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
+          case (opt, _) => throw new IllegalArgumentException(s"Invalid option: $opt")
         }
 
         println("======================================")
         println("|      Triangle Count                |")
         println("======================================")
 
-        val sc = new SparkContext(conf.setAppName("TriangleCount(" + fname + ")"))
+        val sc = new SparkContext(conf.setAppName(s"TriangleCount($fname)"))
         val graph = GraphLoader.edgeListFile(sc, fname,
           canonicalOrientation = true,
           numEdgePartitions = numEPart,