anchor.diff:  
   .API differences
   From the {eh} user perspectives, the differences between Spark SQL 1.3 and its previous versions are fairly trivial. This https://spark.apache.org/docs/latest/sql-programming-guide.html#migration-guide[document] describes at length the differences which are briefly mentioned below:
  -+DataFrame+ vs +SchemaRDD+:: The core unit of SparkSQL in 1.3 is a +DataFrame+ while previously it was a +SchemaRDD+.
  -Unified API vs dedicated Java/Scala APIs:: In SparkSQL 1.3 there is only one API for both Java and Scala, previous versions had dedicated APIs in particular with regards to data types.
  +
  ++DataFrame+ vs +SchemaRDD+:: The core unit of Spark SQL in 1.3 is a +DataFrame+ while previously it was a +SchemaRDD+
  +Unified API vs dedicated Java/Scala APIs:: In Spark SQL 1.3 there is only one API for both Java and Scala, previous versions had dedicated APIs in particular with regards to data types.
   
   The documentation below will focus on Spark SQL 1.3 however accompanies each example with the suitable Spark SQL 1.1-1.2 code.
   
  
positive.code: [float]
  ==== Using pure SQL to read from {es}
  
  IMPORTANT: Available in Apache SparkSQL 1.2 (or higher)
  IMPORTANT: The index and its mapping, have to exist prior to creating the temporary table
  
  SparkSQL 1.2 http://spark.apache.org/releases/spark-release-1-2-0.html[introduced] a new https://github.com/apache/spark/pull/2475[API] for reading from external data sources, which is supported by {eh} 
  simplifying the SQL configured needed for interacting with {es}. Further more, behind the scenes it understands the operations executed by Spark and thus can optimize the data and queries made (such as filtering or pruning), 
  improving performance.
  
  
negative.code: 
  .Java
  
  In a similar fashion, for Java usage the dedicated package +org.elasticsearch.spark.sql.java.api+ provides similar functionality through the +JavaEsSpark SQL+ :
  
  [source,java]
  ----
  import org.apache.spark.sql.api.java.*;                      <1>
  import org.elasticsearch.spark.sql.java.api.JavaEsSpark SQL;  <2>
  ...
  
  DataFrame people = ...
  JavaEsSpark SQL.saveToEs("spark/people");                     <3>
  ----
  
  <1> Spark SQL Java imports
  