[MINOR][GRAPHX][3.0] Correct typos in the sub-modules: graphx, extern…

…al, and examples

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

This PR intends to fix typos in the sub-modules: graphx, external, and examples.
Split per holdenk apache#30323 (comment)

NOTE: The misspellings have been reported at jsoref@706a726#commitcomment-44064356

Backport of apache#30326

### Why are the changes needed?

Misspelled words make it harder to read / understand content.

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

No

### How was this patch tested?

No testing was performed

Closes apache#30342 from jsoref/branch-3.0-30326.

Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>

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

@@ -67,7 +67,7 @@ public static void main(String[] args) throws Exception {
     JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000));
 
     // Create an input stream with the custom receiver on target ip:port and count the
-    // words in input stream of \n delimited text (eg. generated by 'nc')
+    // words in input stream of \n delimited text (e.g. generated by 'nc')
     JavaReceiverInputDStream<String> lines = ssc.receiverStream(
       new JavaCustomReceiver(args[0], Integer.parseInt(args[1])));
     JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator());

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

@@ -57,7 +57,7 @@ public static void main(String[] args) throws Exception {
     JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1));
 
     // Create a JavaReceiverInputDStream on target ip:port and count the
-    // words in input stream of \n delimited text (eg. generated by 'nc')
+    // words in input stream of \n delimited text (e.g. generated by 'nc')
     // Note that no duplication in storage level only for running locally.
     // Replication necessary in distributed scenario for fault tolerance.
     JavaReceiverInputDStream<String> lines = ssc.socketTextStream(

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

@@ -126,7 +126,7 @@ private static JavaStreamingContext createContext(String ip,
     ssc.checkpoint(checkpointDirectory);
 
     // Create a socket stream on target ip:port and count the
-    // words in input stream of \n delimited text (eg. generated by 'nc')
+    // words in input stream of \n delimited text (e.g. generated by 'nc')
     JavaReceiverInputDStream<String> lines = ssc.socketTextStream(ip, port);
     JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator());
     JavaPairDStream<String, Integer> wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1))

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

@@ -59,7 +59,7 @@ public static void main(String[] args) throws Exception {
     JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1));
 
     // Create a JavaReceiverInputDStream on target ip:port and count the
-    // words in input stream of \n delimited text (eg. generated by 'nc')
+    // words in input stream of \n delimited text (e.g. generated by 'nc')
     // Note that no duplication in storage level only for running locally.
     // Replication necessary in distributed scenario for fault tolerance.
     JavaReceiverInputDStream<String> lines = ssc.socketTextStream(

examples/src/main/python/ml/train_validation_split.py

@@ -17,7 +17,7 @@
 
 """
 This example demonstrates applying TrainValidationSplit to split data
-and preform model selection.
+and perform model selection.
 Run with:
 
   bin/spark-submit examples/src/main/python/ml/train_validation_split.py

examples/src/main/python/sql/arrow.py

@@ -68,7 +68,7 @@ def func(s1: pd.Series, s2: pd.Series, s3: pd.DataFrame) -> pd.DataFrame:
         s3['col2'] = s1 + s2.str.len()
         return s3
 
-    # Create a Spark DataFrame that has three columns including a sturct column.
+    # Create a Spark DataFrame that has three columns including a struct column.
     df = spark.createDataFrame(
         [[1, "a string", ("a nested string",)]],
         "long_col long, string_col string, struct_col struct<col1:string>")
@@ -308,7 +308,7 @@ def asof_join(l, r):
     ser_to_frame_pandas_udf_example(spark)
     print("Running pandas_udf example: Series to Series")
     ser_to_ser_pandas_udf_example(spark)
-    print("Running pandas_udf example: Iterator of Series to Iterator of Seires")
+    print("Running pandas_udf example: Iterator of Series to Iterator of Series")
     iter_ser_to_iter_ser_pandas_udf_example(spark)
     print("Running pandas_udf example: Iterator of Multiple Series to Iterator of Series")
     iter_sers_to_iter_ser_pandas_udf_example(spark)

examples/src/main/python/streaming/recoverable_network_wordcount.py

@@ -68,7 +68,7 @@ def createContext(host, port, outputPath):
     ssc = StreamingContext(sc, 1)
 
     # Create a socket stream on target ip:port and count the
-    # words in input stream of \n delimited text (eg. generated by 'nc')
+    # words in input stream of \n delimited text (e.g. generated by 'nc')
     lines = ssc.socketTextStream(host, port)
     words = lines.flatMap(lambda line: line.split(" "))
     wordCounts = words.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x + y)

examples/src/main/python/streaming/sql_network_wordcount.py

@@ -54,7 +54,7 @@ def getSparkSessionInstance(sparkConf):
     ssc = StreamingContext(sc, 1)
 
     # Create a socket stream on target ip:port and count the
-    # words in input stream of \n delimited text (eg. generated by 'nc')
+    # words in input stream of \n delimited text (e.g. generated by 'nc')
     lines = ssc.socketTextStream(host, int(port))
     words = lines.flatMap(lambda line: line.split(" "))
 

examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala

@@ -50,7 +50,7 @@ object CustomReceiver {
     val ssc = new StreamingContext(sparkConf, Seconds(1))
 
     // Create an input stream with the custom receiver on target ip:port and count the
-    // words in input stream of \n delimited text (eg. generated by 'nc')
+    // words in input stream of \n delimited text (e.g. generated by 'nc')
     val lines = ssc.receiverStream(new CustomReceiver(args(0), args(1).toInt))
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)

examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala

@@ -47,7 +47,7 @@ object NetworkWordCount {
     val ssc = new StreamingContext(sparkConf, Seconds(1))
 
     // Create a socket stream on target ip:port and count the
-    // words in input stream of \n delimited text (eg. generated by 'nc')
+    // words in input stream of \n delimited text (e.g. generated by 'nc')
     // Note that no duplication in storage level only for running locally.
     // Replication necessary in distributed scenario for fault tolerance.
     val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER)

examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala

@@ -112,7 +112,7 @@ object RecoverableNetworkWordCount {
     ssc.checkpoint(checkpointDirectory)
 
     // Create a socket stream on target ip:port and count the
-    // words in input stream of \n delimited text (eg. generated by 'nc')
+    // words in input stream of \n delimited text (e.g. generated by 'nc')
     val lines = ssc.socketTextStream(ip, port)
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map((_, 1)).reduceByKey(_ + _)

examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala

@@ -51,7 +51,7 @@ object SqlNetworkWordCount {
     val ssc = new StreamingContext(sparkConf, Seconds(2))
 
     // Create a socket stream on target ip:port and count the
-    // words in input stream of \n delimited text (eg. generated by 'nc')
+    // words in input stream of \n delimited text (e.g. generated by 'nc')
     // Note that no duplication in storage level only for running locally.
     // Replication necessary in distributed scenario for fault tolerance.
     val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER)

examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala

@@ -52,7 +52,7 @@ object StatefulNetworkWordCount {
     val initialRDD = ssc.sparkContext.parallelize(List(("hello", 1), ("world", 1)))
 
     // Create a ReceiverInputDStream on target ip:port and count the
-    // words in input stream of \n delimited test (eg. generated by 'nc')
+    // words in input stream of \n delimited test (e.g. generated by 'nc')
     val lines = ssc.socketTextStream(args(0), args(1).toInt)
     val words = lines.flatMap(_.split(" "))
     val wordDstream = words.map(x => (x, 1))

external/docker-integration-tests/src/test/scala/org/apache/spark/sql/jdbc/DockerJDBCIntegrationSuite.scala

@@ -44,7 +44,7 @@ abstract class DatabaseOnDocker {
   val env: Map[String, String]
 
   /**
-   * Wheather or not to use ipc mode for shared memory when starting docker image
+   * Whether or not to use ipc mode for shared memory when starting docker image
    */
   val usesIpc: Boolean
 

external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSourceSuite.scala

@@ -33,7 +33,7 @@ class KafkaContinuousSourceSuite extends KafkaSourceSuiteBase with KafkaContinuo
     withTable(table) {
       val topic = newTopic()
       testUtils.createTopic(topic)
-      testUtils.withTranscationalProducer { producer =>
+      testUtils.withTransactionalProducer { producer =>
         val df = spark
           .readStream
           .format("kafka")
@@ -99,7 +99,7 @@ class KafkaContinuousSourceSuite extends KafkaSourceSuiteBase with KafkaContinuo
     withTable(table) {
       val topic = newTopic()
       testUtils.createTopic(topic)
-      testUtils.withTranscationalProducer { producer =>
+      testUtils.withTransactionalProducer { producer =>
         val df = spark
           .readStream
           .format("kafka")

external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaMicroBatchSourceSuite.scala

@@ -567,7 +567,7 @@ abstract class KafkaMicroBatchSourceSuiteBase extends KafkaSourceSuiteBase {
     val rows = spark.table("kafkaWatermark").collect()
     assert(rows.length === 1, s"Unexpected results: ${rows.toList}")
     val row = rows(0)
-    // We cannot check the exact window start time as it depands on the time that messages were
+    // We cannot check the exact window start time as it depends on the time that messages were
     // inserted by the producer. So here we just use a low bound to make sure the internal
     // conversion works.
     assert(
@@ -833,7 +833,7 @@ abstract class KafkaMicroBatchSourceSuiteBase extends KafkaSourceSuiteBase {
 
     val topicPartition = new TopicPartition(topic, 0)
     // The message values are the same as their offsets to make the test easy to follow
-    testUtils.withTranscationalProducer { producer =>
+    testUtils.withTransactionalProducer { producer =>
       testStream(mapped)(
         StartStream(Trigger.ProcessingTime(100), clock),
         waitUntilBatchProcessed,
@@ -956,7 +956,7 @@ abstract class KafkaMicroBatchSourceSuiteBase extends KafkaSourceSuiteBase {
 
     val topicPartition = new TopicPartition(topic, 0)
     // The message values are the same as their offsets to make the test easy to follow
-    testUtils.withTranscationalProducer { producer =>
+    testUtils.withTransactionalProducer { producer =>
       testStream(mapped)(
         StartStream(Trigger.ProcessingTime(100), clock),
         waitUntilBatchProcessed,
@@ -1047,7 +1047,7 @@ abstract class KafkaMicroBatchSourceSuiteBase extends KafkaSourceSuiteBase {
       .load()
       .select($"value".as[String])
 
-    testUtils.withTranscationalProducer { producer =>
+    testUtils.withTransactionalProducer { producer =>
       producer.beginTransaction()
       (0 to 3).foreach { i =>
         producer.send(new ProducerRecord[String, String](topic, i.toString)).get()
@@ -1063,7 +1063,7 @@ abstract class KafkaMicroBatchSourceSuiteBase extends KafkaSourceSuiteBase {
         // this case, if we forget to reset `FetchedData._nextOffsetInFetchedData` or
         // `FetchedData._offsetAfterPoll` (See SPARK-25495), the next batch will see incorrect
         // values and return wrong results hence fail the test.
-        testUtils.withTranscationalProducer { producer =>
+        testUtils.withTransactionalProducer { producer =>
           producer.beginTransaction()
           (4 to 7).foreach { i =>
             producer.send(new ProducerRecord[String, String](topic, i.toString)).get()
@@ -1776,7 +1776,7 @@ abstract class KafkaSourceSuiteBase extends KafkaSourceTest {
     withTable(table) {
       val topic = newTopic()
       testUtils.createTopic(topic)
-      testUtils.withTranscationalProducer { producer =>
+      testUtils.withTransactionalProducer { producer =>
         val df = spark
           .readStream
           .format("kafka")

external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaRelationSuite.scala

@@ -503,7 +503,7 @@ abstract class KafkaRelationSuiteBase extends QueryTest with SharedSparkSession
   test("read Kafka transactional messages: read_committed") {
     val topic = newTopic()
     testUtils.createTopic(topic)
-    testUtils.withTranscationalProducer { producer =>
+    testUtils.withTransactionalProducer { producer =>
       val df = spark
         .read
         .format("kafka")
@@ -552,7 +552,7 @@ abstract class KafkaRelationSuiteBase extends QueryTest with SharedSparkSession
   test("read Kafka transactional messages: read_uncommitted") {
     val topic = newTopic()
     testUtils.createTopic(topic)
-    testUtils.withTranscationalProducer { producer =>
+    testUtils.withTransactionalProducer { producer =>
       val df = spark
         .read
         .format("kafka")

external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaTestUtils.scala

@@ -565,7 +565,7 @@ class KafkaTestUtils(
   }
 
   /** Call `f` with a `KafkaProducer` that has initialized transactions. */
-  def withTranscationalProducer(f: KafkaProducer[String, String] => Unit): Unit = {
+  def withTransactionalProducer(f: KafkaProducer[String, String] => Unit): Unit = {
     val props = producerConfiguration
     props.put("transactional.id", UUID.randomUUID().toString)
     val producer = new KafkaProducer[String, String](props)
@@ -614,7 +614,7 @@ class KafkaTestUtils(
     // ensure that logs from all replicas are deleted if delete topic is marked successful
     assert(servers.forall(server => topicAndPartitions.forall(tp =>
       server.getLogManager().getLog(tp).isEmpty)),
-      s"topic $topic still exists in log mananger")
+      s"topic $topic still exists in log manager")
     // ensure that topic is removed from all cleaner offsets
     assert(servers.forall(server => topicAndPartitions.forall { tp =>
       val checkpoints = server.getLogManager().liveLogDirs.map { logDir =>

external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/KafkaRDDSuite.scala

@@ -42,7 +42,7 @@ class KafkaRDDSuite extends SparkFunSuite with BeforeAndAfterAll {
   private val sparkConf = new SparkConf().setMaster("local[4]")
     .setAppName(this.getClass.getSimpleName)
     // Set a timeout of 10 seconds that's going to be used to fetch topics/partitions from kafka.
-    // Othewise the poll timeout defaults to 2 minutes and causes test cases to run longer.
+    // Otherwise the poll timeout defaults to 2 minutes and causes test cases to run longer.
     .set("spark.streaming.kafka.consumer.poll.ms", "10000")
 
   private var sc: SparkContext = _

external/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java

@@ -49,7 +49,7 @@
  *
  * Usage: JavaKinesisWordCountASL [app-name] [stream-name] [endpoint-url] [region-name]
  *   [app-name] is the name of the consumer app, used to track the read data in DynamoDB
- *   [stream-name] name of the Kinesis stream (ie. mySparkStream)
+ *   [stream-name] name of the Kinesis stream (i.e. mySparkStream)
  *   [endpoint-url] endpoint of the Kinesis service
  *     (e.g. https://kinesis.us-east-1.amazonaws.com)
  *

external/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py

@@ -23,7 +23,7 @@
 
   Usage: kinesis_wordcount_asl.py <app-name> <stream-name> <endpoint-url> <region-name>
     <app-name> is the name of the consumer app, used to track the read data in DynamoDB
-    <stream-name> name of the Kinesis stream (ie. mySparkStream)
+    <stream-name> name of the Kinesis stream (i.e. mySparkStream)
     <endpoint-url> endpoint of the Kinesis service
       (e.g. https://kinesis.us-east-1.amazonaws.com)
     <region-name> region name of the Kinesis endpoint (e.g. us-east-1)

external/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala

@@ -43,7 +43,7 @@ import org.apache.spark.streaming.kinesis.KinesisInputDStream
  *
  * Usage: KinesisWordCountASL <app-name> <stream-name> <endpoint-url> <region-name>
  *   <app-name> is the name of the consumer app, used to track the read data in DynamoDB
- *   <stream-name> name of the Kinesis stream (ie. mySparkStream)
+ *   <stream-name> name of the Kinesis stream (i.e. mySparkStream)
  *   <endpoint-url> endpoint of the Kinesis service
  *     (e.g. https://kinesis.us-east-1.amazonaws.com)
  *
@@ -167,9 +167,9 @@ object KinesisWordCountASL extends Logging {
  * Usage: KinesisWordProducerASL <stream-name> <endpoint-url> \
  *   <records-per-sec> <words-per-record>
  *
- *   <stream-name> is the name of the Kinesis stream (ie. mySparkStream)
+ *   <stream-name> is the name of the Kinesis stream (i.e. mySparkStream)
  *   <endpoint-url> is the endpoint of the Kinesis service
- *     (ie. https://kinesis.us-east-1.amazonaws.com)
+ *     (i.e. https://kinesis.us-east-1.amazonaws.com)
  *   <records-per-sec> is the rate of records per second to put onto the stream
  *   <words-per-record> is the number of words per record
  *

external/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtilsPythonHelper.scala

@@ -46,7 +46,7 @@ private class KinesisUtilsPythonHelper {
     // scalastyle:on
     if (!(stsAssumeRoleArn != null && stsSessionName != null && stsExternalId != null)
         && !(stsAssumeRoleArn == null && stsSessionName == null && stsExternalId == null)) {
-      throw new IllegalArgumentException("stsAssumeRoleArn, stsSessionName, and stsExtenalId " +
+      throw new IllegalArgumentException("stsAssumeRoleArn, stsSessionName, and stsExternalId " +
         "must all be defined or all be null")
     }
     if (awsAccessKeyId == null && awsSecretKey != null) {

graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala

@@ -274,8 +274,8 @@ class PageRankSuite extends SparkFunSuite with LocalSparkContext {
     withSpark { sc =>
       // Check that implementation can handle large vertexIds, SPARK-25149
       val vertexIdOffset = Int.MaxValue.toLong + 1
-      val sourceOffest = 4
-      val source = vertexIdOffset + sourceOffest
+      val sourceOffset = 4
+      val source = vertexIdOffset + sourceOffset
       val numIter = 10
       val vertices = vertexIdOffset until vertexIdOffset + numIter
       val chain1 = vertices.zip(vertices.tail)
@@ -285,7 +285,7 @@ class PageRankSuite extends SparkFunSuite with LocalSparkContext {
       val tol = 0.0001
       val errorTol = 1.0e-1
 
-      val a = resetProb / (1 - Math.pow(1 - resetProb, numIter - sourceOffest))
+      val a = resetProb / (1 - Math.pow(1 - resetProb, numIter - sourceOffset))
       // We expect the rank to decay as (1 - resetProb) ^ distance
       val expectedRanks = sc.parallelize(vertices).map { vid =>
         val rank = if (vid < source) {