Suppose you have a Spark job with four executors and you want to perform a broadcast join. How do you decide the size threshold for the small table to be broadcasted?

Spark automatically decides whether to broadcast a table based on the configuration spark.sql.autoBroadcastJoinThreshold, which defaults to 10 MB. To determine the appropriate threshold for a small table when you have four executors, you should consider the memory available per executor and the total memory budget. For example, if each executor has 8 GB of heap, you might set the threshold to 50 MB to avoid spilling. You can tune this by running a dry‑run with spark.sql.autoBroadcastJoinThreshold set to a higher value and monitoring the executor memory usage. Additionally, you can use the broadcast hint in the query (e.g., broadcast(table)) to force a broadcast regardless of size. Remember to also set spark.sql.broadcastTimeout to a higher value if the broadcast takes longer. In practice, a good rule of thumb is to keep the broadcast size below 10–20 % of the executor memory, so for 8 GB executors you might set the threshold to 800–1 GB. This balances the cost of shuffling versus the cost of broadcasting.