Hi all, I have a long running job iterating over a huge dataset. Parts of this operation are cached. Since the job runs for so long, eventually the overhead of spark shuffles starts to accumulate culminating in the driver starting to swap.
I am aware of the spark.cleanup.tll parameter that allows me to configure when cleanup happens but the issue with doing this is that it isn’t done safely, e.g. I can be in the middle of processing a stage when this cleanup happens and my cached RDDs get cleared. This ultimately causes a KeyNotFoundException when I try to reference the now cleared cached RDD. This behavior doesn’t make much sense to me, I would expect the cached RDD to either get regenerated or at the very least for there to be an option to execute this cleanup without deleting those RDDs. Is there a programmatically safe way of doing this cleanup that doesn’t break everything? If I instead tear down the spark context and bring up a new context for every iteration (assuming that each iteration is sufficiently long-lived), would memory get released appropriately? ________________________________________________________ The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.
