anchor.diff:  scalable, fault-tolerant, end-to-end exactly-once stream processing without the user having to reason about streaming.
   ____
   
  -Released as an alpha feature in Spark 2.0, Spark Structured Streaming provides a unified streaming and batch interface
  -built into the Spark SQL integration. As of {eh} 6.0, we provide native functionality to index streaming data into {es}.
  -
  -NOTE: Spark Structured Streaming (available in Spark SQL 2.0+) is still in alpha as of 2.1.1. As such, {eh} support for
  -Structured Streaming (available in {eh} 6.0+) is considered an alpha feature. It is not advised to use this for
  -production code.
  +Released as an experimental feature in Spark 2.0, Spark Structured Streaming provides a unified streaming and batch
  +interface built into the Spark SQL integration. As of {eh} 6.0, we provide native functionality to index streaming data
  +into {es}.
   
   IMPORTANT: Like Spark SQL, Structured Streaming works with _structured_ data. All entries are expected to have the
   _same_ structure (same number of fields, of the same type and name). Using unstructured data (documents with different
  
positive.code: [[spark-sql-streaming-versions]]
  ==== Supported Spark Structured Streaming versions
  
  Spark Structured Streaming was introduced in Spark 2.0 and is still in alpha as of Spark 2.1.1. Similar to Spark SQL
  before it, Structured Streaming may be subject to significant changes between releases. While {eh} supports Spark
  versions 1.3-1.6 as compatibility releases, Spark Structured Streaming is only supported in the
  +elasticsearch-spark-20-<version>.jar+ artifact.
  
  Spark Structured Streaming support is available under the +org.elasticsearch.spark.sql+ and
  +org.elasticsearch.spark.sql.streaming+ packages. It shares a unified interface with Spark SQL in the form of the
  
negative.code: 
  [[spark-sql-streaming]]
  [float]
  === Spark Structured Streaming support
  
  added[6.0]
  
  