Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/10060#discussion_r46624363
--- Diff: docs/sql-programming-guide.md ---
@@ -9,18 +9,51 @@ title: Spark SQL and DataFrames
# Overview
-Spark SQL is a Spark module for structured data processing. It provides a
programming abstraction called DataFrames and can also act as distributed SQL
query engine.
+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 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, the DataFrames API and the Datasets
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 the
+various APIs based on which provides the most natural way to express a
given transformation.
-Spark SQL can also be used to read data from an existing Hive
installation. For more on how to configure this feature, please refer to the
[Hive Tables](#hive-tables) section.
+All of the examples on this page use sample data included in the Spark
distribution and can be run in
+the `spark-shell`, `pyspark` shell, or `sparkR` shell.
-# DataFrames
+## SQL
-A DataFrame is a distributed collection of data organized into named
columns. It is conceptually equivalent to a table in a relational database or a
data frame in R/Python, but with richer optimizations under the hood.
DataFrames can be constructed from a wide array of sources such as: structured
data files, tables in Hive, external databases, or existing RDDs.
+One use of Spark SQL is to execute SQL queries written using either a
basic SQL syntax or HiveQL.
+Spark SQL can also be used to read data from an existing Hive
installation. For more on how to
+configure this feature, please refer to the [Hive Tables](#hive-tables)
section. When running
+SQL from within another programming language the results will be returned
as a [DataFrame](#DataFrames).
+You can also interact with the SQL interface using the
[command-line](#running-the-spark-sql-cli)
+or over [JDBC/ODBC](#running-the-thrift-jdbcodbc-server).
-The DataFrame API is available in
[Scala](api/scala/index.html#org.apache.spark.sql.DataFrame),
[Java](api/java/index.html?org/apache/spark/sql/DataFrame.html),
[Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame), and
[R](api/R/index.html).
+## DataFrames
-All of the examples on this page use sample data included in the Spark
distribution and can be run in the `spark-shell`, `pyspark` shell, or `sparkR`
shell.
+A DataFrame is a distributed collection of data organized into named
columns. It is conceptually
+equivalent to a table in a relational database or a data frame in
R/Python, but with richer
+optimizations under the hood. DataFrames can be constructed from a wide
array of [sources](#data-sources) such
+as: structured data files, tables in Hive, external databases, or existing
RDDs.
+The DataFrame API is available in
[Scala](api/scala/index.html#org.apache.spark.sql.DataFrame),
+[Java](api/java/index.html?org/apache/spark/sql/DataFrame.html),
+[Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame), and
[R](api/R/index.html).
+
+## Datasets
+
+A Dataset is a new experimental interface added in Spark 1.6 that tries to
provide the benefits of
+RDDs (strong typing, ability to use powerful lambda functions) with the
benifits of Spark SQL's
--- End diff --
benifits -> benefits
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