PySpark Memory & Container Sizing Calculator

Spark's off-heap usage — Python worker processes, JVM overhead, native buffers — grows past spark.executor.memoryOverhead, so YARN kills the container for exceeding its total physical memory limit. The fix is almost always a bigger memoryOverhead relative to executor.memory, not a bigger executor.memory alone. This calculator sizes overhead as a percentage of the executor's memory budget, and raises that percentage automatically for Python-heavy and ML workloads.

Every core running a Python UDF spawns its own Python worker process outside the JVM heap. Packing 5 cores onto one executor means 5 concurrent Python processes competing for the same memoryOverhead budget — the classic cause of container kills. Using 2–3 cores per executor for Python-heavy workloads keeps that off-heap pressure bounded, at the cost of slightly more executors.

It's the standard Spark-on-YARN sizing heuristic (originally from Cloudera's tuning guide): every node needs some capacity reserved for the operating system and its own daemons — the YARN NodeManager on EMR, the kubelet on Kubernetes — before Spark can claim the rest for executors.

Yes. The core executor/driver memory and core math is the same everywhere — only the reserved overhead, the Application Master slot (YARN-only) and the deployment snippet change. Kubernetes and standalone get a spark-submit command with the right --master flag; Databricks gets a note to paste the config into the cluster's Spark Config box instead, since it doesn't use spark-submit.

No — the Spark Config Optimizer is an AI tool that reads a free-text description of your cluster and job. This calculator is a fixed formula: the same inputs always produce the same output, so you can reproduce and explain every number it gives you.