Garbage Collection Spark runs on the Java Virtual Machine (JVM). Because Spark can store large amounts of data in memory, it has a major reliance on Java’s memory management and garbage collection (GC). Therefore, garbage collection (GC) can be a major issue that can affect many Spark applications. Common symptoms of excessive GC in Spark are: Slowness of application Executor heartbeat timeout GC overhead limit exceeded error Spark’s memory-centric approach and data-intensive applications make it a more common issue than other Java applications. Thankfully, it’s easy to diagnose if your Spark application is suffering from a GC problem. The Spark UI marks executors in red if they have spent too much time doing GC. Spark executors are spending a significant amount of CPU cycles performing garbage collection. This can be determined by looking at the “Executors” tab in the Spark application UI. Spark will mark an executor in red if the executor has spent more than 10% of the time in gar...