Apache Spark 3.4.1 - Dynamic Resource Allocation scenarios with YARN in Cluster Mode
I'm a bit lost with I'm currently working with Apache Spark 3.4.1 in a YARN cluster mode setup, and I'm working with issues with dynamic resource allocation. Despite enabling it in the configuration, the executors are not being dynamically allocated as expected. I've set the following configurations in my Spark session: ```json { "spark.dynamicAllocation.enabled": true, "spark.dynamicAllocation.minExecutors": 2, "spark.dynamicAllocation.maxExecutors": 10, "spark.dynamicAllocation.initialExecutors": 5, "spark.shuffle.service.enabled": true } ``` I start my Spark job with a substantial workload, but it seems to run with only the initial executors allocated without scaling up, even under heavy load. I’ve also checked the YARN Resource Manager UI and observed that it shows the requested resources remain constant, and there are no indications of new executors being allocated as the task progresses. I suspected that perhaps there’s a misconfiguration or a communication scenario between Spark and YARN. To troubleshoot, I tried setting `spark.dynamicAllocation.executorIdleTimeout` to a lower value, hoping it would force the allocation to kick in, but that didn't help. The logs show the following warning messages repeatedly: ``` WARN org.apache.spark.scheduler.cluster.YarnClusterScheduler: Exception while trying to request executors from the cluster: 0 available executors ``` I would appreciate any insights into what might be going wrong or any additional configurations I should check to ensure that dynamic allocation works as intended. I'm also interested in best practices for monitoring the dynamic allocation process in Spark, if that's relevant. Any help would be greatly appreciated! I'm working with Python in a Docker container on Debian. Could this be a known issue? This is my first time working with Python 3.9. What am I doing wrong?