anchor.diff:  val options = Map("pushdown"<1> -> "true", "es.nodes"<2> -> "someNode", "es.port" -> "9200")
   
   // Spark 1.4 style
  -val spark14DF = sql.read().format("org.elasticsearch.spark.sql")
  -                          .options<3>(options).load("spark/index")
  +val spark14DF = sql.read.format("org.elasticsearch.spark.sql")
  +                        .options<3>(options).load("spark/index")
   ----
   
   <1> `pushdown` option - specific to Spark data sources
  
positive.code: 
  Further more, the pushdown filters can work on +analyzed+ terms (the default) or can be configured to be _strict_ and provide +exact+ matches (work only on ++not-analyzed++ fields). Unless one manually specifies the mapping, it is highly recommended to leave the defaults as they are.  This and other topics are discussed at length in the {es} https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-term-query.html[Reference Documentation].
  
  [[spark-data-sources-12]]
  [float]
  ==== Data Sources in Spark SQL 1.2
  
negative.code: |===
  | Name | Default value| Description
  
  | `path`             | {es} index/type    | _required_
  | `pushdown`         | `true`             | Whether to translate (__push-down__) Spark SQL into {es} Query DSL
  | `strict`           | `false`            | Whether to use _exact_ (not analyzed) matching or not (analyzed)
  
  3+h| Usable in Spark 1.6 or higher
  
  | `double.filtering` | `true`             | Whether to tell Spark apply its own filtering on the fitlers pushed down 
  
  |===
  
  