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From Shay Seng <s...@1618labs.com>
Subject Re: Help with Initial Cluster Configuration / Tuning
Date Tue, 22 Oct 2013 14:22:58 GMT
Hi Matei,

I've seen several memory tuning queries on this mailing list, and also
heard the same kinds of queries at the spark meetup. In fact the last
bullet point in Josh Carver(?) slides, the guy from Bizo, was "memory
tuning is still a mystery".

I certainly had lots of issues in when I first started. From memory issues
to gc issues, things seem to run fine until you try something with 500GB of
data etc.

I was wondering if you could write up a little white paper or some guide
lines on how to set memory values, and what to look at when something goes
wrong? Eg. I would never gave guessed that countByValue happens on a single
machine etc.
On Oct 21, 2013 6:18 PM, "Matei Zaharia" <matei.zaharia@gmail.com> wrote:

> Hi there,
>
> The problem is that countByValue happens in only a single reduce task --
> this is probably something we should fix but it's basically not designed
> for lots of values. Instead, do the count in parallel as follows:
>
> val counts = mapped.map(str => (str, 1)).reduceByKey((a, b) => a + b)
>
> If this still has trouble, you can also increase the level of parallelism
> of reduceByKey by passing it a second parameter for the number of tasks
> (e.g. 100).
>
> BTW one other small thing with your code, flatMap should actually work
> fine if your function returns an Iterator to Traversable, so there's no
> need to call toList and return a Seq in ngrams; you can just return an
> Iterator[String].
>
> Matei
>
> On Oct 21, 2013, at 1:05 PM, Timothy Perrigo <tperrigo@gmail.com> wrote:
>
> > Hi everyone,
> > I am very new to Spark, so as a learning exercise I've set up a small
> cluster consisting of 4 EC2 m1.large instances (1 master, 3 slaves), which
> I'm hoping to use to calculate ngram frequencies from text files of various
> sizes (I'm not doing anything with them; I just thought this would be
> slightly more interesting than the usual 'word count' example).  Currently,
> I'm trying to work with a 1GB text file, but running into memory issues.
>  I'm wondering what parameters I should be setting (in spark-env.sh) in
> order to properly utilize the cluster.  Right now, I'd be happy just to
> have the process complete successfully with the 1 gig file, so I'd really
> appreciate any suggestions you all might have.
> >
> > Here's a summary of the code I'm running through the spark shell on the
> master:
> >
> > def ngrams(s: String, n: Int = 3): Seq[String] = {
> >   (s.split("\\s+").sliding(n)).filter(_.length == n).map(_.mkString("
> ")).map(_.trim).toList
> > }
> >
> > val text = sc.textFile("s3n://my-bucket/my-1gb-text-file")
> >
> > val mapped = text.filter(_.trim.length > 0).flatMap(ngrams(_, 3))
> >
> > So far so good; the problems come during the reduce phase.  With small
> files, I was able to issue the following to calculate the most frequently
> occurring trigram:
> >
> > val topNgram = (mapped countByValue) reduce((a:(String, Long),
> b:(String, Long)) => if (a._2 > b._2) a else b)
> >
> > With the 1 gig file, though, I've been running into OutOfMemory errors,
> so I decided to split the reduction to several steps, starting with simply
> issuing countByValue of my "mapped" RDD, but I have yet to get it to
> complete successfully.
> >
> > SPARK_MEM is currently set to 6154m.  I also bumped up the
> spark.akka.framesize setting to 500 (though at this point, I was grasping
> at straws; I'm not sure what a "proper" value would be).  What properties
> should I be setting for a job of this size on a cluster of 3 m1.large
> slaves? (The cluster was initially configured using the spark-ec2 scripts).
>  Also, programmatically, what should I be doing differently?  (For example,
> should I be setting the minimum number of splits when reading the text
> file?  If so, what would be a good default?).
> >
> > I apologize for what I'm sure are very naive questions.  I think Spark
> is a fantastic project and have enjoyed working with it, but I'm still very
> much a newbie and would appreciate any help you all can provide (as well as
> any 'rules-of-thumb' or best practices I should be following).
> >
> > Thanks,
> > Tim Perrigo
>
>

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