Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/14183#discussion_r70700829
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -626,52 +626,48 @@ The result tables would look something like the
following.
-Since this windowing is similar to grouping, in code, you can use
`groupBy()` and `window()` operations to express windowed aggregations.
+Since this windowing is similar to grouping, in code, you can use
`groupBy()` and `window()` operations to express windowed aggregations. You can
see the full code for the below examples in
+[Scala]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/sql/streaming/StructuredNetworkWordCountWindowed.scala)/
+[Java]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCountWindowed.java)/
+[Python]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/sql/streaming/structured_network_wordcount_windowed.py).
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
-// Number of events in every 1 minute time windows
-df.groupBy(window(df.col("time"), "1 minute"))
- .count()
+import spark.implicits._
+val words = ... // streaming DataFrame of schema { timestamp: Timestamp,
word: String }
-// Average number of events for each device type in every 1 minute time
windows
-df.groupBy(
- df.col("type"),
- window(df.col("time"), "1 minute"))
- .avg("signal")
+// Group the data by window and word and compute the count of each group
+val windowedCounts = words.groupBy(
+ window($"timestamp", "10 minutes", "5 minutes"), $"word"
+).count().orderBy("window")
--- End diff --
orderBy("window") is not essential. it was only for pretty printing in the
example.
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