Best way to avoid updateStateByKey from running without data

UpdateStateByKey will run the update function on every interval, even if the incoming batch is empty. Is there a way to prevent that? If the incoming DStream contains no RDDs (or RDDs of count 0) then I don't want my update function to run. Note that this is different from running the update

Does spark guarantee that the same task will process the same key over time?

For example in the simplest word count example, I want to update the count in memory and always have the same word getting updated by the same task - not use any distributed memstore. I know that updateStateByKey should guarantee that, but how do you approach this problem outside of spark

Spark Streaming broadcast to all keys

UpdateStateByKey is useful but what if I want to perform an operation to all existing keys (not only the ones in this RDD). Word count for example - is there a way to decrease *all* words seen so far by 1? I was thinking of keeping a static class per node with the count information and issuing a