Hi Mark, you may want to check the spark interpreter settings. In the most recent version of zeppelin you can set it to shared, isolated or scoped.
Shared: single interpreter and spark context (and the queuing you see) Isolated: every notebook has its own interpreter and spark context Scoped: every notebook has its own interpreter but they share a spark context https://zeppelin.apache.org/docs/latest/interpreter/spark.html Isolated is the most stable for what you want to do and shared the more resource efficient for the machine you run zeppelin on. The comment of Mohit might be important if you have spark.dynamicAllocation.enabled set to true and no limits on the number and resources of executors. Andreas On Thu, 6 Oct 2016 at 16:28 Mark Libucha <mlibu...@gmail.com> wrote: > Mich, thanks for the suggestion. I tried your settings, but they did not > solve the problem. > > I'm running in yarn-client mode, not local or standalone, so the resources > in the Spark cluster (which is very large) should not be an issue. Right? > > The problem seems to be that Zeppelin is not submitting the 2nd job to the > Spark cluster. >
