If you have 4 executors each with 16GB of memory, how would you allocate memory for a broadcast join?

Broadcast joins in Spark require the smaller dataset to be sent to all executors as a broadcast variable. The memory needed for the broadcast variable is allocated on the driver, not on the executors. With 4 executors each having 16 GB, you first decide how many executors you will actually launch (e.g., 4). You then set spark.executor.memory to a value that leaves enough headroom for overhead and shuffle. A typical configuration might be spark.executor.memory=12 GB and spark.executor.memoryOverhead=2 GB, giving 14 GB per executor and leaving 2 GB for OS and other processes. The driver should have enough memory to hold the broadcast variable; you can set spark.driver.memory=4 GB or higher depending on the broadcast size. You also set spark.sql.autoBroadcastJoinThreshold to a value that matches the size of the smaller table (e.g., 10 MB). If the broadcast variable is larger than this threshold, Spark will fall back to a shuffle join. By carefully allocating executor memory and driver memory, you ensure that the broadcast join does not cause out‑of‑memory errors while still benefiting from the low‑latency join.