> If you actually take the time to read the article, there's a section discussing the Java/Scala angle.
They claim this isn't about the length of code, yet they select the most verbose way to use Spark and proudly display how long it is.
I mean sure, lack of event-time based processing is known limitation of Spark (and a pretty annoying one - though it is supposed to be worked on) but there are ways to write about it without code made to look bad on purpose.
EDIT: come to think of it, this whole article is "spark streaming can't do event time" written in thousands of words with contrived examples attached.
I think both length of code is a side effect that results from the primary argument, and "cant do event time" is one of the symptoms. Neither is a primary argument in the blog post.
The primary argument is demonstrated through color coding different logical bits, which end up being clearly portable and elegantly distinct in dataflow.
This is demonstrated in two ways:
1. The "juicy value add" code that does the aggregation is labeled yellow, and doesn't change across all the samples with Dataflow. With Spark, it needs to be rewritten for every use case. Similarly, for all colors.
2. In Dataflow all the colors are separate. This makes expressing your logic easier. In Spark, the colors mix in dramatic ways with every demonstrated use case.
As Tyler said, all this is described in the blog post itself, but I don't blame you for missing it, since it's a really long post :)
They claim this isn't about the length of code, yet they select the most verbose way to use Spark and proudly display how long it is.
I mean sure, lack of event-time based processing is known limitation of Spark (and a pretty annoying one - though it is supposed to be worked on) but there are ways to write about it without code made to look bad on purpose.
EDIT: come to think of it, this whole article is "spark streaming can't do event time" written in thousands of words with contrived examples attached.