Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/4956#discussion_r26262224
--- Diff: docs/streaming-programming-guide.md ---
@@ -1327,6 +1420,178 @@ Note that the connections in the pool should be
lazily created on demand and tim
***
+## DataFrame and SQL Operations
+You can easily use [DataFrames and SQL](sql-programming-guide.html)
operations on streaming data. You have to create a SQLContext using the
SparkContext that the StreamingContext is using. Furthermore this has to done
such that it can be restarted on driver failures. This is done by creating a
lazily instantiated singleton instance of SQLContext. This is shown in the
following example. It modifies the earlier [word count
example](#a-quick-example) to generate word counts using DataFrames and SQL.
Each RDD is converted to a DataFrame, registered as a temporary table and then
queried it using SQL.
--- End diff --
"and then queried it using" -> drop the 'it'
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