I have some problems that I am looking for if there is no solution for them (due to the current implementation) or if there is a way and I was not aware of it.
1) Currently, we can enable and configure dynamic resource allocation based on below documentation. https://spark.apache.org/docs/latest/job-scheduling.html#dynamic-resource-allocation Based on documentation, it is possible to use an initial value of executors at first, and if some tasks are idle, use more executors. Also, if some executors were idle and we didn't have more tasks, executors will be killed (to be used by others). My question is for when we have 2 SparkContext (Separate Applications). In such cases, I expect the dynamic method to work as fairly as possible and distribute resources equally. But what I observe is that if SparkContext 1 uses all of the executors due to having running tasks, it will not release them until it has no more tasks to run and executors become idle. While Spark could avoid executing the new tasks of the SparkContext 1 (because it is not logical to kill the running tasks) and instead make executors free for SparkContext 2, it didn't do so. I do not found any configuration for it. Have I understood correctly? And is there no way to achieve a fair dynamic allocation between contexts? 2) In dynamic or even static resource allocation, Spark must run a series of executors from among the resources in the cluster (workers). The data that exists on the cluster has as little skew and is distributed throughout the cluster. For this reason, it is better for executors to be distributed as much as possible at the cluster in order to benefit from the data locality. But what I observe is that Spark sometimes executes 2 or more executors on a same worker even if there are some idle workers. Is this intentional and there are other reasons for improvement, or is it a better way and not currently supported by Spark?
