anchor.diff:  
   [[spark-sql-streaming]]
   [float]
  -=== Spark Structured Streaming support [Alpha]
  +=== Spark Structured Streaming support
   
   added[6.0]
   
  
positive.code: 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.
  
  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
  
negative.code: [[spark-sql-streaming-versions]]
  ==== Supported Spark Structured Streaming versions
  
  Spark Structured Streaming is considered _generally available_ as of Spark v2.2.0. As such, {eh} support
  for Structured Streaming (available in {eh} 6.0+) is only compatible with Spark versions 2.2.0 and onward. Similar
  to Spark SQL before it, Structured Streaming may be subject to significant changes between releases before its
  interfaces are considered _stable_.
  
  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
  