Major Description
Spark Streaming lacks the ability to subscribe to newer topics or unsubscribe to current ones once the streaming context has been started. Restarting the streaming context increases the latency of update handling.
Consider a streaming application subscribed to n topics. Let's say 1 of the topics is no longer needed in streaming analytics and hence should be dropped. We could do this by stopping the streaming context, removing that topic from the topic list and restarting the streaming context. Since with some DStreams such as DirectKafkaStream, the per-partition offsets are maintained by Spark, we should be able to resume uninterrupted (I think?) from where we left off with a minor delay. However, in instances where expensive state initialization (from an external datastore) may be needed for datasets published to all topics, before streaming updates can be applied to it, it is more convenient to only subscribe or unsubcribe to the incremental changes to the topic list. Without such a feature, updates go unprocessed for longer than they need to be, thus affecting QoS.