From 51a6706b1339bb761602e33276a469f71be2cd90 Mon Sep 17 00:00:00 2001
From: James Thomas <jamesjoethomas@gmail.com>
Date: Wed, 13 Jul 2016 13:26:23 -0700
Subject: [PATCH] [SPARK-16114][SQL] updated structured streaming guide
## What changes were proposed in this pull request?
Updated structured streaming programming guide with new windowed example.
## How was this patch tested?
Docs
Author: James Thomas <jamesjoethomas@gmail.com>
Closes #14183 from jjthomas/ss_docs_update.
---
.../structured-streaming-programming-guide.md | 49 +++++++++----------
1 file changed, 23 insertions(+), 26 deletions(-)
diff --git a/docs/structured-streaming-programming-guide.md b/docs/structured-streaming-programming-guide.md
index 79493968db..3ef39e4885 100644
--- a/docs/structured-streaming-programming-guide.md
+++ b/docs/structured-streaming-programming-guide.md
@@ -626,52 +626,49 @@ 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()
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
-import static org.apache.spark.sql.functions.window;
-
-// Number of events in every 1 minute time windows
-df.groupBy(window(df.col("time"), "1 minute"))
- .count();
-
-// 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");
+Dataset<Row> words = ... // streaming DataFrame of schema { timestamp: Timestamp, word: String }
+// Group the data by window and word and compute the count of each group
+Dataset<Row> windowedCounts = words.groupBy(
+ functions.window(words.col("timestamp"), "10 minutes", "5 minutes"),
+ words.col("word")
+).count();
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
-from pyspark.sql.functions import window
-
-# Number of events in every 1 minute time windows
-df.groupBy(window("time", "1 minute")).count()
+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("type", window("time", "1 minute")).avg("signal")
+# Group the data by window and word and compute the count of each group
+windowedCounts = words.groupBy(
+ window(words.timestamp, '10 minutes', '5 minutes'),
+ words.word
+).count()
{% endhighlight %}
</div>
--
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