[MINOR][DOCS][WIP] Fix Typos

## What changes were proposed in this pull request?
Fix Typos.

## How was this patch tested?
NA

Closes #23145 from kjmrknsn/docUpdate.

Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>

Author: kjmrknsn

docs/index.md

@@ -66,8 +66,8 @@ Example applications are also provided in Python. For example,
 
     ./bin/spark-submit examples/src/main/python/pi.py 10
 
-Spark also provides an experimental [R API](sparkr.html) since 1.4 (only DataFrames APIs included).
-To run Spark interactively in a R interpreter, use `bin/sparkR`:
+Spark also provides an [R API](sparkr.html) since 1.4 (only DataFrames APIs included).
+To run Spark interactively in an R interpreter, use `bin/sparkR`:
 
     ./bin/sparkR --master local[2]
 

docs/rdd-programming-guide.md

@@ -332,7 +332,7 @@ One important parameter for parallel collections is the number of *partitions* t
 
 Spark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc. Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop [InputFormat](http://hadoop.apache.org/docs/stable/api/org/apache/hadoop/mapred/InputFormat.html).
 
-Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3a://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
+Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes a URI for the file (either a local path on the machine, or a `hdfs://`, `s3a://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
 
 {% highlight scala %}
 scala> val distFile = sc.textFile("data.txt")
@@ -365,7 +365,7 @@ Apart from text files, Spark's Scala API also supports several other data format
 
 Spark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc. Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop [InputFormat](http://hadoop.apache.org/docs/stable/api/org/apache/hadoop/mapred/InputFormat.html).
 
-Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3a://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
+Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes a URI for the file (either a local path on the machine, or a `hdfs://`, `s3a://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
 
 {% highlight java %}
 JavaRDD<String> distFile = sc.textFile("data.txt");
@@ -397,7 +397,7 @@ Apart from text files, Spark's Java API also supports several other data formats
 
 PySpark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc. Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop [InputFormat](http://hadoop.apache.org/docs/stable/api/org/apache/hadoop/mapred/InputFormat.html).
 
-Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3a://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
+Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes a URI for the file (either a local path on the machine, or a `hdfs://`, `s3a://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
 
 {% highlight python %}
 >>> distFile = sc.textFile("data.txt")
@@ -1122,7 +1122,7 @@ costly operation.
 
 #### Background
 
-To understand what happens during the shuffle we can consider the example of the
+To understand what happens during the shuffle, we can consider the example of the
 [`reduceByKey`](#ReduceByLink) operation. The `reduceByKey` operation generates a new RDD where all
 values for a single key are combined into a tuple - the key and the result of executing a reduce
 function against all values associated with that key. The challenge is that not all values for a

docs/running-on-mesos.md

@@ -687,7 +687,7 @@ See the [configuration page](configuration.html) for information on Spark config
   <td><code>0</code></td>
   <td>
     Set the maximum number GPU resources to acquire for this job. Note that executors will still launch when no GPU resources are found
-    since this configuration is just a upper limit and not a guaranteed amount.
+    since this configuration is just an upper limit and not a guaranteed amount.
   </td>
   </tr>
 <tr>

docs/sql-data-sources-avro.md

@@ -66,9 +66,9 @@ write.df(select(df, "name", "favorite_color"), "namesAndFavColors.avro", "avro")
 ## to_avro() and from_avro()
 The Avro package provides function `to_avro` to encode a column as binary in Avro 
 format, and `from_avro()` to decode Avro binary data into a column. Both functions transform one column to 
-another column, and the input/output SQL data type can be complex type or primitive type.
+another column, and the input/output SQL data type can be a complex type or a primitive type.
 
-Using Avro record as columns are useful when reading from or writing to a streaming source like Kafka. Each 
+Using Avro record as columns is useful when reading from or writing to a streaming source like Kafka. Each
 Kafka key-value record will be augmented with some metadata, such as the ingestion timestamp into Kafka, the offset in Kafka, etc.
 * If the "value" field that contains your data is in Avro, you could use `from_avro()` to extract your data, enrich it, clean it, and then push it downstream to Kafka again or write it out to a file.
 * `to_avro()` can be used to turn structs into Avro records. This method is particularly useful when you would like to re-encode multiple columns into a single one when writing data out to Kafka.
@@ -151,7 +151,7 @@ Data source options of Avro can be set via:
   <tr>
     <td><code>avroSchema</code></td>
     <td>None</td>
-    <td>Optional Avro schema provided by an user in JSON format. The date type and naming of record fields
+    <td>Optional Avro schema provided by a user in JSON format. The date type and naming of record fields
     should match the input Avro data or Catalyst data, otherwise the read/write action will fail.</td>
     <td>read and write</td>
   </tr>

docs/sql-data-sources-hive-tables.md

@@ -74,7 +74,7 @@ creating table, you can create a table using storage handler at Hive side, and u
     <td><code>inputFormat, outputFormat</code></td>
     <td>
       These 2 options specify the name of a corresponding `InputFormat` and `OutputFormat` class as a string literal,
-      e.g. `org.apache.hadoop.hive.ql.io.orc.OrcInputFormat`. These 2 options must be appeared in pair, and you can not
+      e.g. `org.apache.hadoop.hive.ql.io.orc.OrcInputFormat`. These 2 options must be appeared in a pair, and you can not
       specify them if you already specified the `fileFormat` option.
     </td>
   </tr>

docs/sql-data-sources-jdbc.md

@@ -55,7 +55,7 @@ the following case-insensitive options:
       as a subquery in the <code>FROM</code> clause. Spark will also assign an alias to the subquery clause.
       As an example, spark will issue a query of the following form to the JDBC Source.<br><br>
       <code> SELECT &lt;columns&gt; FROM (&lt;user_specified_query&gt;) spark_gen_alias</code><br><br>
-      Below are couple of restrictions while using this option.<br>
+      Below are a couple of restrictions while using this option.<br>
       <ol>
          <li> It is not allowed to specify `dbtable` and `query` options at the same time. </li>
          <li> It is not allowed to specify `query` and `partitionColumn` options at the same time. When specifying

docs/sql-data-sources-load-save-functions.md

@@ -324,4 +324,4 @@ CLUSTERED BY(name) SORTED BY (favorite_numbers) INTO 42 BUCKETS;
 `partitionBy` creates a directory structure as described in the [Partition Discovery](sql-data-sources-parquet.html#partition-discovery) section.
 Thus, it has limited applicability to columns with high cardinality. In contrast
  `bucketBy` distributes
-data across a fixed number of buckets and can be used when a number of unique values is unbounded.
+data across a fixed number of buckets and can be used when the number of unique values is unbounded.

docs/sql-getting-started.md

@@ -99,7 +99,7 @@ Here we include some basic examples of structured data processing using Datasets
 <div data-lang="scala"  markdown="1">
 {% include_example untyped_ops scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}
 
-For a complete list of the types of operations that can be performed on a Dataset refer to the [API Documentation](api/scala/index.html#org.apache.spark.sql.Dataset).
+For a complete list of the types of operations that can be performed on a Dataset, refer to the [API Documentation](api/scala/index.html#org.apache.spark.sql.Dataset).
 
 In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the [DataFrame Function Reference](api/scala/index.html#org.apache.spark.sql.functions$).
 </div>

docs/sql-programming-guide.md

@@ -7,7 +7,7 @@ title: Spark SQL and DataFrames
 Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided
 by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally,
 Spark SQL uses this extra information to perform extra optimizations. There are several ways to
-interact with Spark SQL including SQL and the Dataset API. When computing a result
+interact with Spark SQL including SQL and the Dataset API. When computing a result,
 the same execution engine is used, independent of which API/language you are using to express the
 computation. This unification means that developers can easily switch back and forth between
 different APIs based on which provides the most natural way to express a given transformation.

docs/sql-pyspark-pandas-with-arrow.md

@@ -129,7 +129,7 @@ For detailed usage, please see [`pyspark.sql.functions.pandas_udf`](api/python/p
 
 Currently, all Spark SQL data types are supported by Arrow-based conversion except `MapType`,
 `ArrayType` of `TimestampType`, and nested `StructType`. `BinaryType` is supported only when
-installed PyArrow is equal to or higher then 0.10.0.
+installed PyArrow is equal to or higher than 0.10.0.
 
 ### Setting Arrow Batch Size