The previously submitted code doesn’t actually show the problem I was trying to
show effectively since the issue becomes clear between subsequent steps. Within
a single step it appears things are cleared up properly. Memory usage becomes
evident pretty quickly.
def showMemoryUsage(sc: SparkContext) = {
val usersPerStep = 2500
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)).partitionBy(new HashPartitioner(200)).cache()
val productFeatures: RDD[(Int, Int)] = sc.parallelize(1 to 1000000).map(s =>
(s, 4)).repartition(1).cache()
for (i <- 1 to numSteps) {
val usersFiltered = zippedUsers.filter(s => {
((i - 1) * usersPerStep <= s._2) && (s._2 < i * usersPerStep)
}).map(_._1).collect()
val results = usersFiltered.map(user => {
val userScore = userFeatures.lookup(user).head
val recPerUser = Array(1,2,userScore)
recPerUser
})
val mapedResults: Array[Int] = results.flatMap(scores => scores).toArray
log("State: Computed " + mapedResults.length + " predictions for stage " +
i)
sc.parallelize(mapedResults)
// Write to disk (left out since problem is evident even without it)
}
}
Example broadcast variable added:
14/12/30 19:25:19 INFO BlockManagerInfo: Added broadcast_0piece0 in memory on
CLIENT_NODE:54640 (size: 794.0 B, free: 441.9 MB)
And then if I parse the entire log looking for “free : XXX.X MB” within a
single step memory is cleared properly:
Free 441.1 MB
Free 439.8 MB
Free 439.8 MB
Free 441.1 MB
Free 441.1 MB
Free 439.8 MB
But between steps, the amount of available memory decreases (e.g. That range
that things oscillate between shrinks) and over the course of many hours this
eventually reduces to zero.
Free 440.7 MB
Free 438.7 MB
Free 438.7 MB
Free 440.7 MB
Free 435.4 MB
Free 425.0 MB
Free 425.0 MB
Free 435.4 MB
Free 425.0 MB
Free 425.0 MB
Free 435.4 MB
Free 426.7 MB
Free 402.5 MB
Free 402.5 MB
Free 426.7 MB
Free 426.7 MB
Free 402.5 MB
Free 402.5 MB
Free 426.7 MB
From: <Ganelin>, "Ganelin, Ilya"
<ilya.gane...@capitalone.com<mailto:ilya.gane...@capitalone.com>>
Date: Tuesday, December 30, 2014 at 7:30 PM
To: Ilya Ganelin <ilgan...@gmail.com<mailto:ilgan...@gmail.com>>, Patrick
Wendell <pwend...@gmail.com<mailto:pwend...@gmail.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, 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 <ilgan...@gmail.com<mailto:ilgan...@gmail.com>>
Date: Sunday, December 28, 2014 at 4:02 PM
To: Patrick Wendell <pwend...@gmail.com<mailto:pwend...@gmail.com>>, "Ganelin,
Ilya" <ilya.gane...@capitalone.com<mailto:ilya.gane...@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
<pwend...@gmail.com<mailto:pwend...@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.gane...@capitalone.com<mailto:ilya.gane...@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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the reader of this message is not the intended recipient, you are hereby
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