[MINOR][Docs] Fix typos

docs/configuration.md

@@ -498,7 +498,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>
     Reuse Python worker or not. If yes, it will use a fixed number of Python workers,
     does not need to fork() a Python process for every task. It will be very useful
-    if there is large broadcast, then the broadcast will not be needed to transferred
+    if there is a large broadcast, then the broadcast will not need to be transferred
     from JVM to Python worker for every task.
   </td>
 </tr>

docs/graphx-programming-guide.md

@@ -522,7 +522,7 @@ val joinedGraph = graph.joinVertices(uniqueCosts,
 
 A key step in many graph analytics tasks is aggregating information about the neighborhood of each
 vertex.
-For example, we might want to know the number of followers each user has or the average age of the
+For example, we might want to know the number of followers each user has or the average age of
 the followers of each user.  Many iterative graph algorithms (e.g., PageRank, Shortest Path, and
 connected components) repeatedly aggregate properties of neighboring vertices (e.g., current
 PageRank Value, shortest path to the source, and smallest reachable vertex id).
@@ -700,7 +700,7 @@ a new value for the vertex property, and then send messages to neighboring verti
 super step.  Unlike Pregel, messages are computed in parallel as a
 function of the edge triplet and the message computation has access to both the source and
 destination vertex attributes.  Vertices that do not receive a message are skipped within a super
-step.  The Pregel operators terminates iteration and returns the final graph when there are no
+step.  The Pregel operator terminates iteration and returns the final graph when there are no
 messages remaining.
 
 > Note, unlike more standard Pregel implementations, vertices in GraphX can only send messages to

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/ml-datasource.md

@@ -5,7 +5,7 @@ displayTitle: Data sources
 ---
 
 In this section, we introduce how to use data source in ML to load data.
-Beside some general data sources such as Parquet, CSV, JSON and JDBC, we also provide some specific data sources for ML.
+Besides some general data sources such as Parquet, CSV, JSON and JDBC, we also provide some specific data sources for ML.
 
 **Table of Contents**
 

docs/ml-features.md

@@ -359,7 +359,7 @@ Assume that we have the following DataFrame with columns `id` and `raw`:
 ~~~~
  id | raw
 ----|----------
- 0  | [I, saw, the, red, baloon]
+ 0  | [I, saw, the, red, balloon]
  1  | [Mary, had, a, little, lamb]
 ~~~~
 
@@ -369,7 +369,7 @@ column, we should get the following:
 ~~~~
  id | raw                         | filtered
 ----|-----------------------------|--------------------
- 0  | [I, saw, the, red, baloon]  |  [saw, red, baloon]
+ 0  | [I, saw, the, red, balloon]  |  [saw, red, balloon]
  1  | [Mary, had, a, little, lamb]|[Mary, little, lamb]
 ~~~~
 
@@ -1308,15 +1308,15 @@ need to know vector size, can use that column as an input.
 To use `VectorSizeHint` a user must set the `inputCol` and `size` parameters. Applying this
 transformer to a dataframe produces a new dataframe with updated metadata for `inputCol` specifying
 the vector size. Downstream operations on the resulting dataframe can get this size using the
-meatadata.
+metadata.
 
 `VectorSizeHint` can also take an optional `handleInvalid` parameter which controls its
 behaviour when the vector column contains nulls or vectors of the wrong size. By default
 `handleInvalid` is set to "error", indicating an exception should be thrown. This parameter can
 also be set to "skip", indicating that rows containing invalid values should be filtered out from
 the resulting dataframe, or "optimistic", indicating that the column should not be checked for
 invalid values and all rows should be kept. Note that the use of "optimistic" can cause the
-resulting dataframe to be in an inconsistent state, me:aning the metadata for the column
+resulting dataframe to be in an inconsistent state, meaning the metadata for the column
 `VectorSizeHint` was applied to does not match the contents of that column. Users should take care
 to avoid this kind of inconsistent state.
 

docs/ml-pipeline.md

@@ -62,7 +62,7 @@ In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [
 
 A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`.  See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.
 
-Columns in a `DataFrame` are named.  The code examples below use names such as "text," "features," and "label."
+Columns in a `DataFrame` are named. The code examples below use names such as "text", "features", and "label".
 
 ## Pipeline components
 

docs/mllib-linear-methods.md

@@ -272,7 +272,7 @@ In `spark.mllib`, the first class $0$ is chosen as the "pivot" class.
 See Section 4.4 of
 [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for
 references.
-Here is an
+Here is a
 [detailed mathematical derivation](http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297).
 
 For multiclass classification problems, the algorithm will output a multinomial logistic regression
@@ -350,7 +350,7 @@ known as the [mean squared error](http://en.wikipedia.org/wiki/Mean_squared_erro
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
-The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint.
+The following example demonstrates how to load training data, parse it as an RDD of LabeledPoint.
 The example then uses LinearRegressionWithSGD to build a simple linear model to predict label
 values. We compute the mean squared error at the end to evaluate
 [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit).

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/security.md

@@ -337,7 +337,7 @@ Configuration for SSL is organized hierarchically. The user can configure the de
 which will be used for all the supported communication protocols unless they are overwritten by
 protocol-specific settings. This way the user can easily provide the common settings for all the
 protocols without disabling the ability to configure each one individually. The following table
-describes the the SSL configuration namespaces:
+describes the SSL configuration namespaces:
 
 <table class="table">
   <tr>

docs/sparkr.md

@@ -296,7 +296,7 @@ head(agg(rollup(df, "cyl", "disp", "gear"), avg(df$mpg)))
 
 ### Operating on Columns
 
-SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.
+SparkR also provides a number of functions that can be directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.
 
 <div data-lang="r"  markdown="1">
 {% highlight r %}

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
 

docs/sql-reference.md

@@ -38,15 +38,15 @@ Spark SQL and DataFrames support the following data types:
   elements with the type of `elementType`. `containsNull` is used to indicate if
   elements in a `ArrayType` value can have `null` values.
   - `MapType(keyType, valueType, valueContainsNull)`:
-  Represents values comprising a set of key-value pairs. The data type of keys are
-  described by `keyType` and the data type of values are described by `valueType`.
+  Represents values comprising a set of key-value pairs. The data type of keys is
+  described by `keyType` and the data type of values is described by `valueType`.
   For a `MapType` value, keys are not allowed to have `null` values. `valueContainsNull`
   is used to indicate if values of a `MapType` value can have `null` values.
   - `StructType(fields)`: Represents values with the structure described by
   a sequence of `StructField`s (`fields`).
     * `StructField(name, dataType, nullable)`: Represents a field in a `StructType`.
     The name of a field is indicated by `name`. The data type of a field is indicated
-    by `dataType`. `nullable` is used to indicate if values of this fields can have
+    by `dataType`. `nullable` is used to indicate if values of these fields can have
     `null` values.
 
 <div class="codetabs">

docs/streaming-programming-guide.md

@@ -733,7 +733,7 @@ for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.strea
 <span class="badge" style="background-color: grey">Python API</span> As of Spark {{site.SPARK_VERSION_SHORT}},
 out of these sources, Kafka and Kinesis are available in the Python API.
 
-This category of sources require interfacing with external non-Spark libraries, some of them with
+This category of sources requires interfacing with external non-Spark libraries, some of them with
 complex dependencies (e.g., Kafka). Hence, to minimize issues related to version conflicts
 of dependencies, the functionality to create DStreams from these sources has been moved to separate
 libraries that can be [linked](#linking) to explicitly when necessary.