[SPARK-10662] [DOCS] Code snippets are not properly formatted in tables

* Backticks are processed properly in Spark Properties table
* Removed unnecessary spaces
* See http://people.apache.org/~pwendell/spark-nightly/spark-master-docs/latest/running-on-yarn.html

Author: Jacek Laskowski <jacek.laskowski@deepsense.io>

Closes #8795 from jaceklaskowski/docs-yarn-formatting.

1 files changed, 8 insertions, 8 deletions

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 7ae9244c27..a1cbc7de97 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1676,7 +1676,7 @@ results <- collect(sql(sqlContext, "FROM src SELECT key, value"))
 ### Interacting with Different Versions of Hive Metastore
 
 One of the most important pieces of Spark SQL's Hive support is interaction with Hive metastore,
-which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary 
+which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary
 build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below.
 Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL
 will compile against Hive 1.2.1 and use those classes for internal execution (serdes, UDFs, UDAFs, etc).
@@ -1706,8 +1706,8 @@ The following options can be used to configure the version of Hive that is used
         either <code>1.2.1</code> or not defined.
         <li><code>maven</code></li>
         Use Hive jars of specified version downloaded from Maven repositories.  This configuration
-        is not generally recommended for production deployments. 
-        <li>A classpath in the standard format for the JVM.  This classpath must include all of Hive 
+        is not generally recommended for production deployments.
+        <li>A classpath in the standard format for the JVM.  This classpath must include all of Hive
         and its dependencies, including the correct version of Hadoop.  These jars only need to be
         present on the driver, but if you are running in yarn cluster mode then you must ensure
         they are packaged with you application.</li>
@@ -1806,7 +1806,7 @@ the Data Sources API.  The following options are supported:
 <div data-lang="scala"  markdown="1">
 
 {% highlight scala %}
-val jdbcDF = sqlContext.read.format("jdbc").options( 
+val jdbcDF = sqlContext.read.format("jdbc").options(
   Map("url" -> "jdbc:postgresql:dbserver",
   "dbtable" -> "schema.tablename")).load()
 {% endhighlight %}
@@ -2023,11 +2023,11 @@ options.
 
  - Optimized execution using manually managed memory (Tungsten) is now enabled by default, along with
    code generation for expression evaluation.  These features can both be disabled by setting
-   `spark.sql.tungsten.enabled` to `false.
- - Parquet schema merging is no longer enabled by default.  It can be re-enabled by setting 
+   `spark.sql.tungsten.enabled` to `false`.
+ - Parquet schema merging is no longer enabled by default.  It can be re-enabled by setting
    `spark.sql.parquet.mergeSchema` to `true`.
- - Resolution of strings to columns in python now supports using dots (`.`) to qualify the column or 
-   access nested values.  For example `df['table.column.nestedField']`.  However, this means that if 
+ - Resolution of strings to columns in python now supports using dots (`.`) to qualify the column or
+   access nested values.  For example `df['table.column.nestedField']`.  However, this means that if
    your column name contains any dots you must now escape them using backticks (e.g., ``table.`column.with.dots`.nested``).   
  - In-memory columnar storage partition pruning is on by default. It can be disabled by setting
    `spark.sql.inMemoryColumnarStorage.partitionPruning` to `false`.