title: Parallelism of Streams in Spark Streaming Context
body: <p>I have multiple input sources (~200) coming in on Kafka topics - the data for each is similar, but each must be run separately because there are differences in schemas - and we need to perform aggregate health checks on the feeds (so we can't throw them all into 1 topic in a simple way, without creating more work downstream). I've created a spark app with a spark streaming context, and everything seems to be working, except that it is only running the streams sequentially. There are certain bottlenecks in each stream which make this very inefficient, and <strong>I would like all streams to run at the same time - is this possible</strong>? I haven't been able to find a simple way to do this. I've seen the concurrentJobs parameter, but that doesn't worked as desired. Any design suggestions are also welcome, if there is not an easy technical solution.</p>
  
  <p>Thanks</p>