Would increasing the number of executors help resolve an out-of-memory issue in Spark?

Increasing the number of executors can help if the total cluster memory is the bottleneck, because each executor brings its own heap. However, simply adding more executors does not solve an OOM caused by a single executor running out of memory. If the OOM is due to a large shuffle or a cached RDD that exceeds the executor heap, you need to increase spark.executor.memory or adjust the shuffle partitioning. Moreover, adding executors increases overhead and can reduce the amount of memory per executor if the cluster has fixed resources. Therefore, you should first analyze the executor memory usage; if each executor is already near its limit, adding more executors will not help. If the cluster has spare memory, scaling out can distribute the load and reduce per‑executor memory pressure.