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 <tperr...@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
