For 1), this is a recurring question in this mailing list, and the answer is: no, Spark does not support the coordination between multiple Spark applications. Spark relies on an external resource manager, such as Yarn and Kubernetes, to allocate resources to multiple Spark applications. For example, to achieve a fair allocation of resources on Yarn, one should configure Yarn Fair Scheduler.
Databricks seems to have their own solution to this problem (with the multi-cluster optimization option). For Apache Spark, there is an extension called Spark-MR3 which can manage resources among multiple Spark applications. If you are interested, see the blog article: https://www.datamonad.com/post/2021-08-18-spark-mr3/ >From the blog: *The main motivation for developing Spark on MR3 is to allow multiple Spark applications to share compute resources such as Yarn containers or Kubernetes Pods.* We have released Spark 3.0.3 on MR3, and Spark 3.2.1 on MR3 will be released sometime soon. If you are further interested, see the webpage of Spark on MR3: https://mr3docs.datamonad.com/docs/spark/ --- Sungwoo On Wed, Jul 13, 2022 at 4:55 AM Amin Borjian <borjianami...@outlook.com> wrote: > 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? >
