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From "Ganelin, Ilya" <Ilya.Gane...@capitalone.com>
Subject Re: Long-running job cleanup
Date Wed, 31 Dec 2014 00:30:16 GMT
Hi Patrick, to follow up on the below discussion, I am including a short code snippet that
produces the problem on 1.1. This is kind of stupid code since it’s a greatly simplified
version of what I’m actually doing but it has a number of the key components in place. I’m
also including some example log output. Thank you.


def showMemoryUsage(sc : SparkContext) = {

  val usersPerStep = 25000
  val count = 1000000
  val numSteps = count/usersPerStep

  val users = sc.parallelize(1 to count)
  val zippedUsers = users.zipWithIndex().cache()
  val userFeatures : RDD[(Int, Int)] = sc.parallelize(1 to count).map(s=>(s,2)).cache()
  val productFeatures : RDD[(Int, Int)] = sc.parallelize(1 to 50000000)
    .map(s => (s, 4)).cache()

  for (i <- 1 to numSteps) {
    val usersFiltered = zippedUsers.filter(s => {
      ((i - 1) * usersPerStep <= s._2) && (s._2 < i * usersPerStep)
    }).map(_._1).collect()

    usersFiltered.foreach(user => {
      val mult = productFeatures.map(s => s._2 * userFeatures.lookup(user).head)
      mult.takeOrdered(20)

      // Normally this would then be written to disk
      // For the sake of the example this is all we're doing
    })
  }
}

Example broadcast variable added:

14/12/30 19:25:19 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on innovationclientnode01.cof.ds.capitalone.com:54640
(size: 794.0 B, free: 441.9 MB)


And then if I parse the entire log looking for “free : XXX.X MB” I see the available memory
slowly ticking away:

Free 441.8 MB

Free 441.8 MB

Free 441.8 MB

Free 441.8 MB

Free 441.8 MB

Free 441.8 MB

Free 441.8 MB

…

Free 441.7 MB

Free 441.7 MB

Free 441.7 MB

Free 441.7 MB

And so on.


Clearly the above code is not persisting the intermediate RDD (mult), yet memory is never
being properly freed up.

From: Ilya Ganelin <ilganeli@gmail.com<mailto:ilganeli@gmail.com>>
Date: Sunday, December 28, 2014 at 4:02 PM
To: Patrick Wendell <pwendell@gmail.com<mailto:pwendell@gmail.com>>, "Ganelin,
Ilya" <ilya.ganelin@capitalone.com<mailto:ilya.ganelin@capitalone.com>>
Cc: "user@spark.apache.org<mailto:user@spark.apache.org>" <user@spark.apache.org<mailto:user@spark.apache.org>>
Subject: Re: Long-running job cleanup

Hi Patrick - is that cleanup present in 1.1?

The overhead I am talking about is with regards to what I believe is shuffle related metadata.
If I watch the execution log I see small broadcast variables created for every stage of execution,
a few KB at a time, and a certain number of MB remaining of available memory on the driver.
As I run, this available memory goes down, and these variables are never erased. The only
RDDs that persist are those that are explicitly cached. The RDDs that are generated iteratively
are not retained or referenced, so I would expect things to get cleaned up but they do not.
The items consuming memory are not RDDs but what appears to be shuffle metadata.

I have a script that parses the logs to show memory consumption over time and the script shows
memory very steadily being consumed over many hours without clearing one small bit at a time.

The specific computation I am doing is the generation of dot products between two RDDs of
vectors. I need to generate this product for every combination of products between the two
RDDs but both RDDs are too big to fit in memory. Consequently, I iteratively generate this
product across one entry from the first RDD and all entries from the second and retain the
pared-down result within an accumulator (by retaining the top N results it is possible to
actually store the Cartesian which is otherwise too large to fit on disk). After a certain
number of iterations these intermediate results are then written to disk. Each of these steps
is tractable in itself but due to the accumulation of memory, the overall job becomes intractable.

I would appreciate any suggestions as to how to clean up these intermediate broadcast variables.
Thank you.


On Sun, Dec 28, 2014 at 1:56 PM Patrick Wendell <pwendell@gmail.com<mailto:pwendell@gmail.com>>
wrote:
What do you mean when you say "the overhead of spark shuffles start to
accumulate"? Could you elaborate more?

In newer versions of Spark shuffle data is cleaned up automatically
when an RDD goes out of scope. It is safe to remove shuffle data at
this point because the RDD can no longer be referenced. If you are
seeing a large build up of shuffle data, it's possible you are
retaining references to older RDDs inadvertently. Could you explain
what your job actually doing?

- Patrick

On Mon, Dec 22, 2014 at 2:36 PM, Ganelin, Ilya
<Ilya.Ganelin@capitalone.com<mailto:Ilya.Ganelin@capitalone.com>> wrote:
> 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?
>
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