[SPARK-14355][BUILD] Fix typos in Exception/Testcase/Comments and static analysis results

common/network-common/src/main/java/org/apache/spark/network/client/TransportClientFactory.java

@@ -94,7 +94,7 @@ public TransportClientFactory(
     this.context = Preconditions.checkNotNull(context);
     this.conf = context.getConf();
     this.clientBootstraps = Lists.newArrayList(Preconditions.checkNotNull(clientBootstraps));
-    this.connectionPool = new ConcurrentHashMap<SocketAddress, ClientPool>();
+    this.connectionPool = new ConcurrentHashMap<>();
     this.numConnectionsPerPeer = conf.numConnectionsPerPeer();
     this.rand = new Random();
 

common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java

@@ -64,9 +64,9 @@ public class TransportResponseHandler extends MessageHandler<ResponseMessage> {
 
   public TransportResponseHandler(Channel channel) {
     this.channel = channel;
-    this.outstandingFetches = new ConcurrentHashMap<StreamChunkId, ChunkReceivedCallback>();
-    this.outstandingRpcs = new ConcurrentHashMap<Long, RpcResponseCallback>();
-    this.streamCallbacks = new ConcurrentLinkedQueue<StreamCallback>();
+    this.outstandingFetches = new ConcurrentHashMap<>();
+    this.outstandingRpcs = new ConcurrentHashMap<>();
+    this.streamCallbacks = new ConcurrentLinkedQueue<>();
     this.timeOfLastRequestNs = new AtomicLong(0);
   }
 

common/network-common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java

@@ -63,7 +63,7 @@ public OneForOneStreamManager() {
     // For debugging purposes, start with a random stream id to help identifying different streams.
     // This does not need to be globally unique, only unique to this class.
     nextStreamId = new AtomicLong((long) new Random().nextInt(Integer.MAX_VALUE) * 1000);
-    streams = new ConcurrentHashMap<Long, StreamState>();
+    streams = new ConcurrentHashMap<>();
   }
 
   @Override

common/network-shuffle/src/main/java/org/apache/spark/network/sasl/ShuffleSecretManager.java

@@ -37,7 +37,7 @@ public class ShuffleSecretManager implements SecretKeyHolder {
   private static final String SPARK_SASL_USER = "sparkSaslUser";
 
   public ShuffleSecretManager() {
-    shuffleSecretMap = new ConcurrentHashMap<String, String>();
+    shuffleSecretMap = new ConcurrentHashMap<>();
   }
 
   /**

core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java

@@ -45,7 +45,7 @@ public int compare(UnsafeSorterIterator left, UnsafeSorterIterator right) {
         }
       }
     };
-    priorityQueue = new PriorityQueue<UnsafeSorterIterator>(numSpills, comparator);
+    priorityQueue = new PriorityQueue<>(numSpills, comparator);
   }
 
   /**

core/src/main/scala/org/apache/spark/api/r/RRunner.scala

@@ -182,7 +182,7 @@ private[spark] class RRunner[U](
           }
           stream.flush()
         } catch {
-          // TODO: We should propogate this error to the task thread
+          // TODO: We should propagate this error to the task thread
           case e: Exception =>
             logError("R Writer thread got an exception", e)
         } finally {

core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala

@@ -326,7 +326,7 @@ class GapSamplingReplacement(
   /**
    * Skip elements with replication factor zero (i.e. elements that won't be sampled).
    * Samples 'k' from geometric distribution  P(k) = (1-q)(q)^k, where q = e^(-f), that is
-   * q is the probabililty of Poisson(0; f)
+   * q is the probability of Poisson(0; f)
    */
   private def advance(): Unit = {
     val u = math.max(rng.nextDouble(), epsilon)

core/src/test/java/org/apache/spark/shuffle/sort/UnsafeShuffleWriterSuite.java

@@ -170,11 +170,11 @@ public Tuple2<TempShuffleBlockId, File> answer(
   private UnsafeShuffleWriter<Object, Object> createWriter(
       boolean transferToEnabled) throws IOException {
     conf.set("spark.file.transferTo", String.valueOf(transferToEnabled));
-    return new UnsafeShuffleWriter<Object, Object>(
+    return new UnsafeShuffleWriter<>(
       blockManager,
       shuffleBlockResolver,
       taskMemoryManager,
-      new SerializedShuffleHandle<Object, Object>(0, 1, shuffleDep),
+      new SerializedShuffleHandle<>(0, 1, shuffleDep),
       0, // map id
       taskContext,
       conf

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

@@ -82,10 +82,10 @@ public static Tuple3<String, String, String> extractKey(String line) {
       String user = m.group(3);
       String query = m.group(5);
       if (!user.equalsIgnoreCase("-")) {
-        return new Tuple3<String, String, String>(ip, user, query);
+        return new Tuple3<>(ip, user, query);
       }
     }
-    return new Tuple3<String, String, String>(null, null, null);
+    return new Tuple3<>(null, null, null);
   }
 
   public static Stats extractStats(String line) {

examples/src/main/java/org/apache/spark/examples/mllib/JavaMultiLabelClassificationMetricsExample.java

@@ -34,13 +34,13 @@ public static void main(String[] args) {
     JavaSparkContext sc = new JavaSparkContext(conf);
     // $example on$
     List<Tuple2<double[], double[]>> data = Arrays.asList(
-      new Tuple2<double[], double[]>(new double[]{0.0, 1.0}, new double[]{0.0, 2.0}),
-      new Tuple2<double[], double[]>(new double[]{0.0, 2.0}, new double[]{0.0, 1.0}),
-      new Tuple2<double[], double[]>(new double[]{}, new double[]{0.0}),
-      new Tuple2<double[], double[]>(new double[]{2.0}, new double[]{2.0}),
-      new Tuple2<double[], double[]>(new double[]{2.0, 0.0}, new double[]{2.0, 0.0}),
-      new Tuple2<double[], double[]>(new double[]{0.0, 1.0, 2.0}, new double[]{0.0, 1.0}),
-      new Tuple2<double[], double[]>(new double[]{1.0}, new double[]{1.0, 2.0})
+      new Tuple2<>(new double[]{0.0, 1.0}, new double[]{0.0, 2.0}),
+      new Tuple2<>(new double[]{0.0, 2.0}, new double[]{0.0, 1.0}),
+      new Tuple2<>(new double[]{}, new double[]{0.0}),
+      new Tuple2<>(new double[]{2.0}, new double[]{2.0}),
+      new Tuple2<>(new double[]{2.0, 0.0}, new double[]{2.0, 0.0}),
+      new Tuple2<>(new double[]{0.0, 1.0, 2.0}, new double[]{0.0, 1.0}),
+      new Tuple2<>(new double[]{1.0}, new double[]{1.0, 2.0})
     );
     JavaRDD<Tuple2<double[], double[]>> scoreAndLabels = sc.parallelize(data);
 

examples/src/main/java/org/apache/spark/examples/mllib/JavaPowerIterationClusteringExample.java

@@ -40,11 +40,11 @@ public static void main(String[] args) {
     @SuppressWarnings("unchecked")
     // $example on$
     JavaRDD<Tuple3<Long, Long, Double>> similarities = sc.parallelize(Lists.newArrayList(
-      new Tuple3<Long, Long, Double>(0L, 1L, 0.9),
-      new Tuple3<Long, Long, Double>(1L, 2L, 0.9),
-      new Tuple3<Long, Long, Double>(2L, 3L, 0.9),
-      new Tuple3<Long, Long, Double>(3L, 4L, 0.1),
-      new Tuple3<Long, Long, Double>(4L, 5L, 0.9)));
+      new Tuple3<>(0L, 1L, 0.9),
+      new Tuple3<>(1L, 2L, 0.9),
+      new Tuple3<>(2L, 3L, 0.9),
+      new Tuple3<>(3L, 4L, 0.1),
+      new Tuple3<>(4L, 5L, 0.9)));
 
     PowerIterationClustering pic = new PowerIterationClustering()
       .setK(2)

examples/src/main/java/org/apache/spark/examples/mllib/JavaStratifiedSamplingExample.java

@@ -36,7 +36,7 @@ public static void main(String[] args) {
     JavaSparkContext jsc = new JavaSparkContext(conf);
 
     // $example on$
-    List<Tuple2<Integer, Character>> list = new ArrayList<Tuple2<Integer, Character>>(
+    List<Tuple2<Integer, Character>> list = new ArrayList<>(
       Arrays.<Tuple2<Integer, Character>>asList(
         new Tuple2(1, 'a'),
         new Tuple2(1, 'b'),

examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java

@@ -19,7 +19,6 @@
 
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.function.Function;
-import org.apache.spark.examples.streaming.StreamingExamples;
 import org.apache.spark.streaming.*;
 import org.apache.spark.streaming.api.java.*;
 import org.apache.spark.streaming.flume.FlumeUtils;
@@ -58,7 +57,8 @@ public static void main(String[] args) {
     Duration batchInterval = new Duration(2000);
     SparkConf sparkConf = new SparkConf().setAppName("JavaFlumeEventCount");
     JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, batchInterval);
-    JavaReceiverInputDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, host, port);
+    JavaReceiverInputDStream<SparkFlumeEvent> flumeStream =
+      FlumeUtils.createStream(ssc, host, port);
 
     flumeStream.count();
 

external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java

@@ -27,10 +27,11 @@ public class JavaFlumeStreamSuite extends LocalJavaStreamingContext {
   @Test
   public void testFlumeStream() {
     // tests the API, does not actually test data receiving
-    JavaReceiverInputDStream<SparkFlumeEvent> test1 = FlumeUtils.createStream(ssc, "localhost", 12345);
-    JavaReceiverInputDStream<SparkFlumeEvent> test2 = FlumeUtils.createStream(ssc, "localhost", 12345,
-      StorageLevel.MEMORY_AND_DISK_SER_2());
-    JavaReceiverInputDStream<SparkFlumeEvent> test3 = FlumeUtils.createStream(ssc, "localhost", 12345,
-      StorageLevel.MEMORY_AND_DISK_SER_2(), false);
+    JavaReceiverInputDStream<SparkFlumeEvent> test1 = FlumeUtils.createStream(ssc, "localhost",
+      12345);
+    JavaReceiverInputDStream<SparkFlumeEvent> test2 = FlumeUtils.createStream(ssc, "localhost",
+      12345, StorageLevel.MEMORY_AND_DISK_SER_2());
+    JavaReceiverInputDStream<SparkFlumeEvent> test3 = FlumeUtils.createStream(ssc, "localhost",
+      12345, StorageLevel.MEMORY_AND_DISK_SER_2(), false);
   }
 }

launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java

@@ -34,7 +34,7 @@ class CommandBuilderUtils {
   /** The set of known JVM vendors. */
   enum JavaVendor {
     Oracle, IBM, OpenJDK, Unknown
-  };
+  }
 
   /** Returns whether the given string is null or empty. */
   static boolean isEmpty(String s) {

launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java

@@ -477,6 +477,6 @@ protected void handleExtraArgs(List<String> extra) {
       // No op.
     }
 
-  };
+  }
 
 }

launcher/src/test/java/org/apache/spark/launcher/LauncherServerSuite.java

@@ -175,7 +175,7 @@ private static class TestClient extends LauncherConnection {
 
     TestClient(Socket s) throws IOException {
       super(s);
-      this.inbound = new LinkedBlockingQueue<Message>();
+      this.inbound = new LinkedBlockingQueue<>();
       this.clientThread = new Thread(this);
       clientThread.setName("TestClient");
       clientThread.setDaemon(true);

launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java

@@ -160,7 +160,7 @@ public void testExamplesRunner() throws Exception {
       "SparkPi",
       "42");
 
-    Map<String, String> env = new HashMap<String, String>();
+    Map<String, String> env = new HashMap<>();
     List<String> cmd = buildCommand(sparkSubmitArgs, env);
     assertEquals("foo", findArgValue(cmd, parser.MASTER));
     assertEquals("bar", findArgValue(cmd, parser.DEPLOY_MODE));

mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala

@@ -205,7 +205,7 @@ final class DecisionTreeClassificationModel private[ml] (
   @Since("2.0.0")
   lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(this, numFeatures)
 
-  /** Convert to spark.mllib DecisionTreeModel (losing some infomation) */
+  /** Convert to spark.mllib DecisionTreeModel (losing some information) */
   override private[spark] def toOld: OldDecisionTreeModel = {
     new OldDecisionTreeModel(rootNode.toOld(1), OldAlgo.Classification)
   }

mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala

@@ -62,7 +62,7 @@ private[shared] object SharedParamsCodeGen {
         "every 10 iterations", isValid = "(interval: Int) => interval == -1 || interval >= 1"),
       ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")),
       ParamDesc[String]("handleInvalid", "how to handle invalid entries. Options are skip (which " +
-        "will filter out rows with bad values), or error (which will throw an errror). More " +
+        "will filter out rows with bad values), or error (which will throw an error). More " +
         "options may be added later",
         isValid = "ParamValidators.inArray(Array(\"skip\", \"error\"))"),
       ParamDesc[Boolean]("standardization", "whether to standardize the training features" +

mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala

@@ -270,10 +270,10 @@ private[ml] trait HasFitIntercept extends Params {
 private[ml] trait HasHandleInvalid extends Params {
 
   /**
-   * Param for how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.
+   * Param for how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an error). More options may be added later.
    * @group param
    */
-  final val handleInvalid: Param[String] = new Param[String](this, "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later", ParamValidators.inArray(Array("skip", "error")))
+  final val handleInvalid: Param[String] = new Param[String](this, "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an error). More options may be added later", ParamValidators.inArray(Array("skip", "error")))
 
   /** @group getParam */
   final def getHandleInvalid: String = $(handleInvalid)

mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala

@@ -205,7 +205,7 @@ final class DecisionTreeRegressionModel private[ml] (
   @Since("2.0.0")
   lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(this, numFeatures)
 
-  /** Convert to spark.mllib DecisionTreeModel (losing some infomation) */
+  /** Convert to spark.mllib DecisionTreeModel (losing some information) */
   override private[spark] def toOld: OldDecisionTreeModel = {
     new OldDecisionTreeModel(rootNode.toOld(1), OldAlgo.Regression)
   }

mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala

@@ -71,7 +71,7 @@ private[spark] trait DecisionTreeModel {
    */
   private[ml] def maxSplitFeatureIndex(): Int = rootNode.maxSplitFeatureIndex()
 
-  /** Convert to spark.mllib DecisionTreeModel (losing some infomation) */
+  /** Convert to spark.mllib DecisionTreeModel (losing some information) */
   private[spark] def toOld: OldDecisionTreeModel
 }
 

mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala

@@ -418,7 +418,7 @@ class LogisticRegressionWithLBFGS
 
   private def run(input: RDD[LabeledPoint], initialWeights: Vector, userSuppliedWeights: Boolean):
       LogisticRegressionModel = {
-    // ml's Logisitic regression only supports binary classifcation currently.
+    // ml's Logistic regression only supports binary classification currently.
     if (numOfLinearPredictor == 1) {
       def runWithMlLogisitcRegression(elasticNetParam: Double) = {
         // Prepare the ml LogisticRegression based on our settings

mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java

@@ -89,7 +89,7 @@ private void init() {
     myDoubleParam_ = new DoubleParam(this, "myDoubleParam", "this is a double param",
       ParamValidators.inRange(0.0, 1.0));
     List<String> validStrings = Arrays.asList("a", "b");
-    myStringParam_ = new Param<String>(this, "myStringParam", "this is a string param",
+    myStringParam_ = new Param<>(this, "myStringParam", "this is a string param",
       ParamValidators.inArray(validStrings));
     myDoubleArrayParam_ =
       new DoubleArrayParam(this, "myDoubleArrayParam", "this is a double param");

mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java

@@ -66,8 +66,8 @@ public void javaAPI() {
     JavaDStream<LabeledPoint> training =
       attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2);
     List<Tuple2<Integer, Vector>> testBatch = Arrays.asList(
-      new Tuple2<Integer, Vector>(10, Vectors.dense(1.0)),
-      new Tuple2<Integer, Vector>(11, Vectors.dense(0.0)));
+      new Tuple2<>(10, Vectors.dense(1.0)),
+      new Tuple2<>(11, Vectors.dense(0.0)));
     JavaPairDStream<Integer, Vector> test = JavaPairDStream.fromJavaDStream(
       attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2));
     StreamingLogisticRegressionWithSGD slr = new StreamingLogisticRegressionWithSGD()

mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java

@@ -66,8 +66,8 @@ public void javaAPI() {
     JavaDStream<Vector> training =
       attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2);
     List<Tuple2<Integer, Vector>> testBatch = Arrays.asList(
-      new Tuple2<Integer, Vector>(10, Vectors.dense(1.0)),
-      new Tuple2<Integer, Vector>(11, Vectors.dense(0.0)));
+      new Tuple2<>(10, Vectors.dense(1.0)),
+      new Tuple2<>(11, Vectors.dense(0.0)));
     JavaPairDStream<Integer, Vector> test = JavaPairDStream.fromJavaDStream(
       attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2));
     StreamingKMeans skmeans = new StreamingKMeans()

mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java

@@ -37,8 +37,8 @@ public void denseArrayConstruction() {
   public void sparseArrayConstruction() {
     @SuppressWarnings("unchecked")
     Vector v = Vectors.sparse(3, Arrays.asList(
-        new Tuple2<Integer, Double>(0, 2.0),
-        new Tuple2<Integer, Double>(2, 3.0)));
+        new Tuple2<>(0, 2.0),
+        new Tuple2<>(2, 3.0)));
     assertArrayEquals(new double[]{2.0, 0.0, 3.0}, v.toArray(), 0.0);
   }
 }

mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java

@@ -65,8 +65,8 @@ public void javaAPI() {
     JavaDStream<LabeledPoint> training =
       attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2);
     List<Tuple2<Integer, Vector>> testBatch = Arrays.asList(
-      new Tuple2<Integer, Vector>(10, Vectors.dense(1.0)),
-      new Tuple2<Integer, Vector>(11, Vectors.dense(0.0)));
+      new Tuple2<>(10, Vectors.dense(1.0)),
+      new Tuple2<>(11, Vectors.dense(0.0)));
     JavaPairDStream<Integer, Vector> test = JavaPairDStream.fromJavaDStream(
       attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2));
     StreamingLinearRegressionWithSGD slr = new StreamingLinearRegressionWithSGD()

mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala

@@ -759,7 +759,7 @@ class LinearRegressionSuite
             .sliding(2)
             .forall(x => x(0) >= x(1)))
       } else {
-        // To clalify that the normal solver is used here.
+        // To clarify that the normal solver is used here.
         assert(model.summary.objectiveHistory.length == 1)
         assert(model.summary.objectiveHistory(0) == 0.0)
         val devianceResidualsR = Array(-0.47082, 0.34635)

sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java

@@ -151,7 +151,7 @@ public UnsafeRow next() {
             Platform.throwException(e);
           }
           throw new RuntimeException("Exception should have been re-thrown in next()");
-        };
+        }
       };
     } catch (IOException e) {
       cleanupResources();

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystConf.scala

@@ -26,7 +26,7 @@ private[spark] trait CatalystConf {
   def groupByOrdinal: Boolean
 
   /**
-   * Returns the [[Resolver]] for the current configuration, which can be used to determin if two
+   * Returns the [[Resolver]] for the current configuration, which can be used to determine if two
    * identifiers are equal.
    */
   def resolver: Resolver = {

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala

@@ -153,8 +153,8 @@ abstract class Expression extends TreeNode[Expression] {
    * evaluate to the same result.
    */
   lazy val canonicalized: Expression = {
-    val canonicalizedChildred = children.map(_.canonicalized)
-    Canonicalize.execute(withNewChildren(canonicalizedChildred))
+    val canonicalizedChildren = children.map(_.canonicalized)
+    Canonicalize.execute(withNewChildren(canonicalizedChildren))
   }
 
   /**

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala

@@ -509,7 +509,7 @@ class CodegenContext {
 
   /**
    * Checks and sets up the state and codegen for subexpression elimination. This finds the
-   * common subexpresses, generates the functions that evaluate those expressions and populates
+   * common subexpressions, generates the functions that evaluate those expressions and populates
    * the mapping of common subexpressions to the generated functions.
    */
   private def subexpressionElimination(expressions: Seq[Expression]) = {

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionalExpressions.scala

@@ -222,7 +222,7 @@ object CaseWhen {
   }
 
   /**
-   * A factory method to faciliate the creation of this expression when used in parsers.
+   * A factory method to facilitate the creation of this expression when used in parsers.
    * @param branches Expressions at even position are the branch conditions, and expressions at odd
    *                 position are branch values.
    */

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala

@@ -965,7 +965,7 @@ class AstBuilder extends SqlBaseBaseVisitor[AnyRef] with Logging {
 
   /**
    * Create a binary arithmetic expression. The following arithmetic operators are supported:
-   * - Mulitplication: '*'
+   * - Multiplication: '*'
    * - Division: '/'
    * - Hive Long Division: 'DIV'
    * - Modulo: '%'
@@ -1270,7 +1270,7 @@ class AstBuilder extends SqlBaseBaseVisitor[AnyRef] with Logging {
   }
 
   /**
-   * Create a double literal for a number denoted in scientifc notation.
+   * Create a double literal for a number denoted in scientific notation.
    */
   override def visitScientificDecimalLiteral(
       ctx: ScientificDecimalLiteralContext): Literal = withOrigin(ctx) {