[MINOR][DOC] Fix typo

## What changes were proposed in this pull request?

This PR fixes typo regarding `auxiliary verb + verb[s]`. This is a follow-on of #21956.

## How was this patch tested?

N/A

Closes #22040 from kiszk/spellcheck1.

Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>

core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java

@@ -662,7 +662,7 @@ public final class BytesToBytesMap extends MemoryConsumer {
      * It is only valid to call this method immediately after calling `lookup()` using the same key.
      * </p>
      * <p>
-     * The key and value must be word-aligned (that is, their sizes must multiples of 8).
+     * The key and value must be word-aligned (that is, their sizes must be a multiple of 8).
      * </p>
      * <p>
      * After calling this method, calls to `get[Key|Value]Address()` and `get[Key|Value]Length`

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

@@ -51,7 +51,7 @@ final class UnsafeSorterSpillMerger {
     if (spillReader.hasNext()) {
       // We only add the spillReader to the priorityQueue if it is not empty. We do this to
       // make sure the hasNext method of UnsafeSorterIterator returned by getSortedIterator
-      // does not return wrong result because hasNext will returns true
+      // does not return wrong result because hasNext will return true
       // at least priorityQueue.size() times. If we allow n spillReaders in the
       // priorityQueue, we will have n extra empty records in the result of UnsafeSorterIterator.
       spillReader.loadNext();

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

@@ -107,7 +107,7 @@ class SparkHadoopUtil extends Logging {
   }
 
   /**
-   * Return an appropriate (subclass) of Configuration. Creating config can initializes some Hadoop
+   * Return an appropriate (subclass) of Configuration. Creating config can initialize some Hadoop
    * subsystems.
    */
   def newConfiguration(conf: SparkConf): Configuration = {

core/src/test/java/test/org/apache/spark/JavaSparkContextSuite.java

@@ -30,7 +30,7 @@ import org.apache.spark.api.java.*;
 import org.apache.spark.*;
 
 /**
- * Java apps can uses both Java-friendly JavaSparkContext and Scala SparkContext.
+ * Java apps can use both Java-friendly JavaSparkContext and Scala SparkContext.
  */
 public class JavaSparkContextSuite implements Serializable {
 

external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaDataConsumer.scala

@@ -155,7 +155,7 @@ private[kafka010] case class InternalKafkaConsumer(
     var toFetchOffset = offset
     var consumerRecord: ConsumerRecord[Array[Byte], Array[Byte]] = null
     // We want to break out of the while loop on a successful fetch to avoid using "return"
-    // which may causes a NonLocalReturnControl exception when this method is used as a function.
+    // which may cause a NonLocalReturnControl exception when this method is used as a function.
     var isFetchComplete = false
 
     while (toFetchOffset != UNKNOWN_OFFSET && !isFetchComplete) {

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

@@ -1484,7 +1484,7 @@ sealed trait LogisticRegressionSummary extends Serializable {
 
   /**
    * Convenient method for casting to binary logistic regression summary.
-   * This method will throws an Exception if the summary is not a binary summary.
+   * This method will throw an Exception if the summary is not a binary summary.
    */
   @Since("2.3.0")
   def asBinary: BinaryLogisticRegressionSummary = this match {

python/pyspark/sql/types.py

@@ -206,7 +206,7 @@ class DecimalType(FractionalType):
     and scale (the number of digits on the right of dot). For example, (5, 2) can
     support the value from [-999.99 to 999.99].
 
-    The precision can be up to 38, the scale must less or equal to precision.
+    The precision can be up to 38, the scale must be less or equal to precision.
 
     When create a DecimalType, the default precision and scale is (10, 0). When infer
     schema from decimal.Decimal objects, it will be DecimalType(38, 18).

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecision.scala

@@ -286,7 +286,7 @@ object DecimalPrecision extends TypeCoercionRule {
         // Consider the following example: multiplying a column which is DECIMAL(38, 18) by 2.
         // If we use the default precision and scale for the integer type, 2 is considered a
         // DECIMAL(10, 0). According to the rules, the result would be DECIMAL(38 + 10 + 1, 18),
-        // which is out of range and therefore it will becomes DECIMAL(38, 7), leading to
+        // which is out of range and therefore it will become DECIMAL(38, 7), leading to
         // potentially loosing 11 digits of the fractional part. Using only the precision needed
         // by the Literal, instead, the result would be DECIMAL(38 + 1 + 1, 18), which would
         // become DECIMAL(38, 16), safely having a much lower precision loss.

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

@@ -44,7 +44,7 @@ object CodegenObjectFactoryMode extends Enumeration {
 
 /**
  * A codegen object generator which creates objects with codegen path first. Once any compile
- * error happens, it can fallbacks to interpreted implementation. In tests, we can use a SQL config
+ * error happens, it can fallback to interpreted implementation. In tests, we can use a SQL config
  * `SQLConf.CODEGEN_FACTORY_MODE` to control fallback behavior.
  */
 abstract class CodeGeneratorWithInterpretedFallback[IN, OUT] {

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

@@ -26,7 +26,7 @@ import org.apache.spark.sql.types.AbstractDataType
  * This trait is typically used by operator expressions (e.g. [[Add]], [[Subtract]]) to define
  * expected input types without any implicit casting.
  *
- * Most function expressions (e.g. [[Substring]] should extends [[ImplicitCastInputTypes]]) instead.
+ * Most function expressions (e.g. [[Substring]] should extend [[ImplicitCastInputTypes]]) instead.
  */
 trait ExpectsInputTypes extends Expression {
 

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/UnsupportedOperationsSuite.scala

@@ -766,7 +766,7 @@ class UnsupportedOperationsSuite extends SparkFunSuite {
    *
    * To test this correctly, the given logical plan is wrapped in a fake operator that makes the
    * whole plan look like a streaming plan. Otherwise, a batch plan may throw not supported
-   * exception simply for not being a streaming plan, even though that plan could exists as batch
+   * exception simply for not being a streaming plan, even though that plan could exist as batch
    * subplan inside some streaming plan.
    */
   def assertSupportedInStreamingPlan(
@@ -793,7 +793,7 @@ class UnsupportedOperationsSuite extends SparkFunSuite {
    *
    * To test this correctly, the given logical plan is wrapped in a fake operator that makes the
    * whole plan look like a streaming plan. Otherwise, a batch plan may throw not supported
-   * exception simply for not being a streaming plan, even though that plan could exists as batch
+   * exception simply for not being a streaming plan, even though that plan could exist as batch
    * subplan inside some streaming plan.
    */
   def assertNotSupportedInStreamingPlan(

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/encoders/EncoderResolutionSuite.scala

@@ -144,7 +144,7 @@ class EncoderResolutionSuite extends PlanTest {
     // It should pass analysis
     val bound = encoder.resolveAndBind(attrs)
 
-    // If no null values appear, it should works fine
+    // If no null values appear, it should work fine
     bound.fromRow(InternalRow(new GenericArrayData(Array(1, 2))))
 
     // If there is null value, it should throw runtime exception

sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala

@@ -110,7 +110,7 @@ object SQLMetrics {
    * spill size, etc.
    */
   def createSizeMetric(sc: SparkContext, name: String): SQLMetric = {
-    // The final result of this metric in physical operator UI may looks like:
+    // The final result of this metric in physical operator UI may look like:
     // data size total (min, med, max):
     // 100GB (100MB, 1GB, 10GB)
     val acc = new SQLMetric(SIZE_METRIC, -1)

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSource.scala

@@ -50,7 +50,7 @@ class FileStreamSource(
   @transient private val fs = new Path(path).getFileSystem(hadoopConf)
 
   private val qualifiedBasePath: Path = {
-    fs.makeQualified(new Path(path))  // can contains glob patterns
+    fs.makeQualified(new Path(path))  // can contain glob patterns
   }
 
   private val optionsWithPartitionBasePath = sourceOptions.optionMapWithoutPath ++ {

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/ProgressReporter.scala

@@ -312,7 +312,7 @@ trait ProgressReporter extends Logging {
       // DataSourceV2ScanExec records the number of rows it has read using SQLMetrics. However,
       // just collecting all DataSourceV2ScanExec nodes and getting the metric is not correct as
       // a DataSourceV2ScanExec instance may be referred to in the execution plan from two (or
-      // even multiple times) points and considering it twice will leads to double counting. We
+      // even multiple times) points and considering it twice will lead to double counting. We
       // can't dedup them using their hashcode either because two different instances of
       // DataSourceV2ScanExec can have the same hashcode but account for separate sets of
       // records read, and deduping them to consider only one of them would be undercounting the

sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala

@@ -76,7 +76,7 @@ private[sql] trait SQLTestUtils extends SparkFunSuite with SQLTestUtilsBase with
 
   /**
    * Disable stdout and stderr when running the test. To not output the logs to the console,
-   * ConsoleAppender's `follow` should be set to `true` so that it will honors reassignments of
+   * ConsoleAppender's `follow` should be set to `true` so that it will honor reassignments of
    * System.out or System.err. Otherwise, ConsoleAppender will still output to the console even if
    * we change System.out and System.err.
    */

sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateHiveTableAsSelectCommand.scala

@@ -30,7 +30,7 @@ import org.apache.spark.sql.execution.command.DataWritingCommand
 /**
  * Create table and insert the query result into it.
  *
- * @param tableDesc the Table Describe, which may contains serde, storage handler etc.
+ * @param tableDesc the Table Describe, which may contain serde, storage handler etc.
  * @param query the query whose result will be insert into the new relation
  * @param mode SaveMode
  */

sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala

@@ -84,7 +84,7 @@ class HiveQuerySuite extends HiveComparisonTest with SQLTestUtils with BeforeAnd
   }
 
   // Testing the Broadcast based join for cartesian join (cross join)
-  // We assume that the Broadcast Join Threshold will works since the src is a small table
+  // We assume that the Broadcast Join Threshold will work since the src is a small table
   private val spark_10484_1 = """
                                 | SELECT a.key, b.key
                                 | FROM src a LEFT JOIN src b WHERE a.key > b.key + 300