[GitHub] spark pull request #14183: [SPARK-16114] [SQL] updated structured streaming ...

[tdas <gi...@git.apache.org>]

Github user tdas commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14183#discussion_r70687380
  
    --- Diff: docs/structured-streaming-programming-guide.md ---
    @@ -626,52 +626,95 @@ The result tables would look something like the following.
     
     ![Window Operations](img/structured-streaming-window.png)
     
    -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._
     
    -// 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")
    +// Create DataFrame representing the stream of input lines from connection to host:port
    +val lines = spark.readStream
    +  .format("socket")
    +  .option("host", "localhost")
    +  .option("port", 9999)
    +  .option("includeTimestamp", true)
    +  .load().as[(String, Timestamp)]
    +
    +// Split the lines into words, retaining timestamps
    +val words = lines.flatMap(line =>
    +  line._1.split(" ").map(word => (word, line._2))
    +).toDF("word", "timestamp")
    +
    +// Group the data by window and word and compute the count of each group
    +val windowedCounts = words.groupBy(
    --- End diff --
    
    I took a look at the built doc again and imagined what it would look like. This would look very verbose. I think since the nearest example in the doc (Basic Operations - Selection, Projection, Aggregation) uses device data and already has all the boilerplate code to define DeviceData class, etc., lets not change the code snippet to the exact one in the example. 
    
    
    Can you revert all the code snippet changes, and just do one change for the Scala snippet. 
    - Change df.col("..") to use $"..."
    - Add import spark.implicits._
    
    



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