I read this: Running Spark on Kubernetes. I want to know more details about the interaction between Kubernetes Controller/Scheduler and Spark runtime when launching a Spark job on K8s. Specially, assuming we launch an Spark app by :

bin/spark-submit \
--master k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port> \
--deploy-mode cluster \
--name spark-pi \
--class org.apache.spark.examples.SparkPi \
--conf spark.executor.instances=5 \
--..............

My question is: the K8s may not be able to allocate 5 executors (or called containers/pods) immediately due to unavailability of cluster resources at the moment the Spark app is launched. Which way does Spark app take? (1) Spark starts running tasks as soon as possible when there is at least one executor is allocated. (2) Spark won't launch any tasks until all of the 5 executors have been allocated.

If you know Hadoop YARN, it would be great if you could also answer the question in the scenario of running Spark app on Hadoop YARN(DynamicAllocation Disabled) and point out the difference.