Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073714
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -65,11 +51,13 @@ val words = lines.as[String].flatMap(_.split(" "))
val wordCounts = words.groupBy("value").count()
{% endhighlight %}
-This `lines` DataFrame represents an unbounded table containing the
streaming text data. This table contains one column of strings named
âvalueâ, and each line in the streaming text data becomes a row in the
table. Note, that this is not currently receiving any data as we are just
setting up the transformation, and have not yet started it. Next, we have
converted the DataFrame to a Dataset of String using `.as(Encoders.STRING())`,
so that we can apply the `flatMap` operation to split each line into multiple
words. The resultant `words` Dataset contains all the words. Finally, we have
defined the `wordCounts` DataFrame by grouping by the unique values in the
Dataset and counting them. Note that this is a streaming DataFrame which
represents the running word counts of the stream.
--- End diff --
`.as(Encoders.STRING())`, java's, changed to `.as[String]`, scala's
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