[MINOR] Fix typos and misspellings

## What changes were proposed in this pull request?

Fix typos and misspellings, per apache/spark-website#158 (comment)

## How was this patch tested?

Existing tests.

Closes apache#22950 from srowen/Typos.

Authored-by: Sean Owen <[email protected]>
Signed-off-by: Sean Owen <[email protected]>
(cherry picked from commit c0d1bf0)
Signed-off-by: Sean Owen <[email protected]>

Author: srowen

core/src/main/java/org/apache/spark/ExecutorPlugin.java

@@ -20,18 +20,18 @@
 import org.apache.spark.annotation.DeveloperApi;
 
 /**
- * A plugin which can be automaticaly instantiated within each Spark executor.  Users can specify
+ * A plugin which can be automatically instantiated within each Spark executor.  Users can specify
  * plugins which should be created with the "spark.executor.plugins" configuration.  An instance
  * of each plugin will be created for every executor, including those created by dynamic allocation,
  * before the executor starts running any tasks.
  *
  * The specific api exposed to the end users still considered to be very unstable.  We will
- * hopefully be able to keep compatability by providing default implementations for any methods
+ * hopefully be able to keep compatibility by providing default implementations for any methods
  * added, but make no guarantees this will always be possible across all Spark releases.
  *
  * Spark does nothing to verify the plugin is doing legitimate things, or to manage the resources
  * it uses.  A plugin acquires the same privileges as the user running the task.  A bad plugin
- * could also intefere with task execution and make the executor fail in unexpected ways.
+ * could also interfere with task execution and make the executor fail in unexpected ways.
  */
 @DeveloperApi
 public interface ExecutorPlugin {

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

@@ -63,10 +63,10 @@ private SparkConf initializeSparkConf(String pluginNames) {
 
   @Test
   public void testPluginClassDoesNotExist() {
-    SparkConf conf = initializeSparkConf("nonexistant.plugin");
+    SparkConf conf = initializeSparkConf("nonexistent.plugin");
     try {
       sc = new JavaSparkContext(conf);
-      fail("No exception thrown for nonexistant plugin");
+      fail("No exception thrown for nonexistent plugin");
     } catch (Exception e) {
       // We cannot catch ClassNotFoundException directly because Java doesn't think it'll be thrown
       assertTrue(e.toString().startsWith("java.lang.ClassNotFoundException"));

docs/sql-migration-guide-upgrade.md

@@ -107,7 +107,7 @@ displayTitle: Spark SQL Upgrading Guide
 
   - Since Spark 2.4, Metadata files (e.g. Parquet summary files) and temporary files are not counted as data files when calculating table size during Statistics computation.
 
-  - Since Spark 2.4, empty strings are saved as quoted empty strings `""`. In version 2.3 and earlier, empty strings are equal to `null` values and do not reflect to any characters in saved CSV files. For example, the row of `"a", null, "", 1` was writted as `a,,,1`. Since Spark 2.4, the same row is saved as `a,,"",1`. To restore the previous behavior, set the CSV option `emptyValue` to empty (not quoted) string.  
+  - Since Spark 2.4, empty strings are saved as quoted empty strings `""`. In version 2.3 and earlier, empty strings are equal to `null` values and do not reflect to any characters in saved CSV files. For example, the row of `"a", null, "", 1` was written as `a,,,1`. Since Spark 2.4, the same row is saved as `a,,"",1`. To restore the previous behavior, set the CSV option `emptyValue` to empty (not quoted) string.  
 
   - Since Spark 2.4, The LOAD DATA command supports wildcard `?` and `*`, which match any one character, and zero or more characters, respectively. Example: `LOAD DATA INPATH '/tmp/folder*/'` or `LOAD DATA INPATH '/tmp/part-?'`. Special Characters like `space` also now work in paths. Example: `LOAD DATA INPATH '/tmp/folder name/'`.
 

mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala

@@ -62,7 +62,7 @@ private[r] object AFTSurvivalRegressionWrapper extends MLReadable[AFTSurvivalReg
   private val FORMULA_REGEXP = """Surv\(([^,]+), ([^,]+)\) ~ (.+)""".r
 
   private def formulaRewrite(formula: String): (String, String) = {
-    var rewritedFormula: String = null
+    var rewrittenFormula: String = null
     var censorCol: String = null
     try {
       val FORMULA_REGEXP(label, censor, features) = formula
@@ -71,14 +71,14 @@ private[r] object AFTSurvivalRegressionWrapper extends MLReadable[AFTSurvivalReg
         throw new UnsupportedOperationException(
           "Terms of survreg formula can not support dot operator.")
       }
-      rewritedFormula = label.trim + "~" + features.trim
+      rewrittenFormula = label.trim + "~" + features.trim
       censorCol = censor.trim
     } catch {
       case e: MatchError =>
         throw new SparkException(s"Could not parse formula: $formula")
     }
 
-    (rewritedFormula, censorCol)
+    (rewrittenFormula, censorCol)
   }
 
 

mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala

@@ -96,7 +96,7 @@ object Summarizer extends Logging {
    *  - numNonzeros: a vector with the number of non-zeros for each coefficients
    *  - max: the maximum for each coefficient.
    *  - min: the minimum for each coefficient.
-   *  - normL2: the Euclidian norm for each coefficient.
+   *  - normL2: the Euclidean norm for each coefficient.
    *  - normL1: the L1 norm of each coefficient (sum of the absolute values).
    * @param metrics metrics that can be provided.
    * @return a builder.
@@ -536,7 +536,7 @@ private[ml] object SummaryBuilderImpl extends Logging {
     }
 
     /**
-     * L2 (Euclidian) norm of each dimension.
+     * L2 (Euclidean) norm of each dimension.
      */
     def normL2: Vector = {
       require(requestedMetrics.contains(NormL2))

mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala

@@ -273,7 +273,7 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
-   * L2 (Euclidian) norm of each dimension.
+   * L2 (Euclidean) norm of each dimension.
    *
    */
   @Since("1.2.0")

python/pyspark/ml/stat.py

@@ -336,7 +336,7 @@ def metrics(*metrics):
          - numNonzeros: a vector with the number of non-zeros for each coefficients
          - max: the maximum for each coefficient.
          - min: the minimum for each coefficient.
-         - normL2: the Euclidian norm for each coefficient.
+         - normL2: the Euclidean norm for each coefficient.
          - normL1: the L1 norm of each coefficient (sum of the absolute values).
 
         :param metrics:

sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala

@@ -20,7 +20,6 @@ package org.apache.spark.sql.hive
 import java.io.File
 
 import org.apache.spark.sql.{AnalysisException, Dataset, QueryTest, SaveMode}
-import org.apache.spark.sql.catalyst.analysis.NoSuchTableException
 import org.apache.spark.sql.catalyst.parser.ParseException
 import org.apache.spark.sql.execution.columnar.InMemoryTableScanExec
 import org.apache.spark.sql.execution.datasources.{CatalogFileIndex, HadoopFsRelation, LogicalRelation}
@@ -97,24 +96,24 @@ class CachedTableSuite extends QueryTest with SQLTestUtils with TestHiveSingleto
     }
   }
 
-  test("DROP nonexistant table") {
-    sql("DROP TABLE IF EXISTS nonexistantTable")
+  test("DROP nonexistent table") {
+    sql("DROP TABLE IF EXISTS nonexistentTable")
   }
 
-  test("uncache of nonexistant tables") {
-    val expectedErrorMsg = "Table or view not found: nonexistantTable"
+  test("uncache of nonexistent tables") {
+    val expectedErrorMsg = "Table or view not found: nonexistentTable"
     // make sure table doesn't exist
-    var e = intercept[AnalysisException](spark.table("nonexistantTable")).getMessage
+    var e = intercept[AnalysisException](spark.table("nonexistentTable")).getMessage
     assert(e.contains(expectedErrorMsg))
     e = intercept[AnalysisException] {
-      spark.catalog.uncacheTable("nonexistantTable")
+      spark.catalog.uncacheTable("nonexistentTable")
     }.getMessage
     assert(e.contains(expectedErrorMsg))
     e = intercept[AnalysisException] {
-      sql("UNCACHE TABLE nonexistantTable")
+      sql("UNCACHE TABLE nonexistentTable")
     }.getMessage
     assert(e.contains(expectedErrorMsg))
-    sql("UNCACHE TABLE IF EXISTS nonexistantTable")
+    sql("UNCACHE TABLE IF EXISTS nonexistentTable")
   }
 
   test("no error on uncache of non-cached table") {