I'll give you an example in Spark. We had a huge job that was failing and after checking the logs, it was when results were being spooled to disk. More log diving showed a lot of GC on every node. At that point we could have gone down the route of tuning something in the JVM, but more digging found the real culprit. IOstats when the jobs ran showed while reading data, writes were completely blocked and write latency was in the 100s of ms. The spark executor trying to dump data was blocked and the first symptom that things were falling apart was... GC. We changed the scheduler on the nodes and magically everything worked great. The VM in JVM is virtual machine. Same rules for resources apply and if you run out of resources, don't expect the mythical ops faeries to save you.