Could you please help us and provide the source which says about the general guidelines (80-85)?

Even if there is a general guideline, it is probably to keep the performance of Spark application high (And to *distinguish* it from Hadoop). But if you are not too concerned about the *performance* hit from memory to disk, then you could use virtual memory to your advantage. Infact I think the OS could do a pretty good job of data management by keeping only the necessary data in RAM and at the same time having no hard-limit (It would be great to have benchmarks if anyone has done any test before)

Also we should *tread* carefully when applying general guidelines to problems. They might not be *relevant* at all.

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On Mon, Apr 30, 2018 at 9:06 PM, Lalwani, Jayesh <> wrote:

Although there is such a thing as virtualization of memory done at the OS layer, JVM imposes it’s own limit that is controlled by the spark.executor.memory and spark.driver.memory configurations. The amount of memory allocated by JVM will be controlled by those parameters. General guidelines say that executor and driver memory should be kept at 80-85% of available RAM. So, if general guidelines are followed, *virtual memory* is moot.

From: Deepak Goel <>
Date: Saturday, April 28, 2018 at 12:58 PM
To: Stephen Boesch <>
Cc: klrmowse <>, "user @spark" <>
Subject: Re: [Spark 2.x Core] .collect() size limit



On Sat, 28 Apr 2018, 22:22 Stephen Boesch, <> wrote:

While it is certainly possible to use VM I have seen in a number of places warnings that collect() results must be able to be fit in memory. I'm not sure if that applies to *all" spark calculations: but in the very least each of the specific collect()'s that are performed would need to be verified. 


And maybe all collects do require sufficient memory - would you like to check the source code to see if there were disk backed collects actually happening for some cases?


2018-04-28 9:48 GMT-07:00 Deepak Goel <>:

There is something as *virtual memory*


On Sat, 28 Apr 2018, 21:19 Stephen Boesch, <> wrote:

Do you have a machine with  terabytes of RAM?  afaik collect() requires RAM - so that would be your limiting factor.


2018-04-28 8:41 GMT-07:00 klrmowse <>:

i am currently trying to find a workaround for the Spark application i am
working on so that it does not have to use .collect()

but, for now, it is going to have to use .collect()

what is the size limit (memory for the driver) of RDD file that .collect()
can work with?

i've been scouring google-search - S.O., blogs, etc, and everyone is
cautioning about .collect(), but does not specify how huge is huge... are we
talking about a few gigabytes? terabytes?? petabytes???

thank you

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