Hey everyone, I'm evaluating whether to use the SMACK stack as part of our data pipeline, as demonstrated in this reference app: https://github.com/killrweather/killrweather
I don't want to introduce unnecessary complexity in our stack unless it gives enough bang for the buck. From that reference app, I'm trying to convince myself of the value that an Akka Cluster brings to that design. I can see Akka-Streams being valuable as an ingestion layer to Kafka. However, what is the benefit of wrapping the Spark Streaming Kafka within an actor? There doesn't really seem to be much internal state within those actors (except I guess a wrapper around the spark streaming context), and thus couldn't Akka just be removed in favor of a simpler non-clustered design? Or is the streaming context not thread safe and thus an actor provides safeguards around that? Some of our current use cases are streaming analytics, as well as batch analytics on months of historical data (similar to what the aggregation code in that reference app is doing). Any insight is appreciated! -- >>>>>>>>>> Read the docs: http://akka.io/docs/ >>>>>>>>>> Check the FAQ: >>>>>>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html >>>>>>>>>> Search the archives: https://groups.google.com/group/akka-user --- You received this message because you are subscribed to the Google Groups "Akka User List" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/akka-user. For more options, visit https://groups.google.com/d/optout.
