spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Matei Zaharia <matei.zaha...@gmail.com>
Subject Re: Help with Initial Cluster Configuration / Tuning
Date Tue, 22 Oct 2013 01:18:11 GMT
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


Mime
View raw message