If you would like an overview of Spark Stream and fault tolerance, these
slides are great (Slides 24+ focus on fault tolerance; Slide 52 is on
resilience to traffic spikes):
http://www.lightbend.com/blog/four-things-to-know-about-reliable-spark-streaming-typesafe-databricks
This recent Spark
One of the challenges we need to prepare for with streaming apps is bursty
data. Typically we need to estimate our worst case data load and make sure
we have enough capacity
It not obvious what best practices are with spark streaming.
* we have implemented check pointing as described in the
