Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/830#discussion_r12870184
--- Diff: docs/streaming-programming-guide.md ---
@@ -105,23 +104,22 @@ generating multiple new records from each record in
the source DStream. In this
each line will be split into multiple words and the stream of words is
represented as the
`words` DStream. Next, we want to count these words.
+The `words` DStream is further mapped (one-to-one transformation) to a
DStream of `(word,
+1)` pairs, which is then reduced to get the frequency of words in each
batch of data.
+Finally, `wordCounts.print()` will print the first ten counts generated
every second.
+
{% highlight scala %}
-import org.apache.spark.streaming.StreamingContext._
// Count each word in each batch
-val pairs = words.map(word => (word, 1))
-val wordCounts = pairs.reduceByKey(_ + _)
+val pairs: DStream[(String, Int)] = words.map((_, 1))
+val wordCounts: DStream[(String, Int)] = pairs.reduceByKey(_ + _)
-// Print a few of the counts to the console
+// Print the first ten elements of each RDD generated in this DStream to
the console
--- End diff --
Done
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