In a Spark job, how do you assign total executor memory? If you have 16GB per executor, what will be your total executor memory?

In Spark, the amount of memory allocated to each executor is controlled by the spark.executor.memory configuration property. The total memory consumed by all executors is simply the per‑executor memory multiplied by the number of executors that Spark launches. For example, if you set spark.executor.memory=16g and Spark launches 4 executors, the total executor memory is 4 × 16 = 64 GB. In addition to the executor memory, you must also consider the executor overhead memory (spark.executor.memoryOverhead) and the driver memory (spark.driver.memory). The overall memory footprint of the application is therefore: total executor memory + total overhead + driver memory. When planning resources, you should also leave some headroom for the operating system and any other processes running on the node. So, if you configure each executor to use 16 GB, the total executor memory for the job is 16 GB × (number of executors).