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From RJ Nowling <rnowl...@gmail.com>
Subject Re: Surprising Spark SQL benchmark
Date Sat, 01 Nov 2014 13:57:03 GMT
Two thoughts here:

1. The real flaw with the sort benchmark was that Hadoop wasn't run on the
same hardware. Given the advances in networking (availabIlity of
10GB Ethernet) and disks (SSDs) since the Hadoop benchmarks it was compared
to, it's an apples to oranges comparison. Without that, it doesn't tell me
whether the improvement is due to Spark or just hardware.

To me, that's the biggest flaw -- not the reproducibility of it. As you
say, most people won't have the financial means to access those resources
to reproduce it.

And that's the same sort of flaw every other marketing benchmark has --
apples to oranges comparisons.

2. The BDD benchmark is hard to run outside of EC2 and I and other users
were not able to access all of the data via S3.  I could reproduce some of
the data using HiBench but not the web corpus sub sample. As a result, for
all the hard work put into documenting it, it's still hard to reproduce :(

On Friday, October 31, 2014, Nicholas Chammas <nicholas.chammas@gmail.com>
wrote:

> I believe that benchmark has a pending certification on it. See
> http://sortbenchmark.org under "Process".
>
> It's true they did not share enough details on the blog for readers to
> reproduce the benchmark, but they will have to share enough with the
> committee behind the benchmark in order to be certified. Given that this is
> a benchmark not many people will be able to reproduce due to size and
> complexity, I don't see it as a big negative that the details are not laid
> out as long as there is independent certification from a third party.
>
> From what I've seen so far, the best big data benchmark anywhere is this:
> https://amplab.cs.berkeley.edu/benchmark/
>
> Is has all the details you'd expect, including hosted datasets, to allow
> anyone to reproduce the full benchmark, covering a number of systems. I
> look forward to the next update to that benchmark (a lot has changed since
> Feb). And from what I can tell, it's produced by the same people behind
> Spark (Patrick being among them).
>
> So I disagree that the Spark community "hasn't been any better" in this
> regard.
>
> Nick
>
>
> 2014년 10월 31일 금요일, Steve Nunez<snunez@hortonworks.com <javascript:;>>님이
> 작성한 메시지:
>
> > To be fair, we (Spark community) haven’t been any better, for example
> this
> > benchmark:
> >
> >         https://databricks.com/blog/2014/10/10/spark-petabyte-sort.html
> >
> >
> > For which no details or code have been released to allow others to
> > reproduce it. I would encourage anyone doing a Spark benchmark in future
> > to avoid the stigma of vendor reported benchmarks and publish enough
> > information and code to let others repeat the exercise easily.
> >
> >         - Steve
> >
> >
> >
> > On 10/31/14, 11:30, "Nicholas Chammas" <nicholas.chammas@gmail.com
> <javascript:;>
> > <javascript:;>> wrote:
> >
> > >Thanks for the response, Patrick.
> > >
> > >I guess the key takeaways are 1) the tuning/config details are
> everything
> > >(they're not laid out here), 2) the benchmark should be reproducible
> (it's
> > >not), and 3) reach out to the relevant devs before publishing (didn't
> > >happen).
> > >
> > >Probably key takeaways for any kind of benchmark, really...
> > >
> > >Nick
> > >
> > >
> > >2014년 10월 31일 금요일, Patrick Wendell<pwendell@gmail.com <javascript:;>
> <javascript:;>>님이
> > 작성한 메시지:
> > >
> > >> Hey Nick,
> > >>
> > >> Unfortunately Citus Data didn't contact any of the Spark or Spark SQL
> > >> developers when running this. It is really easy to make one system
> > >> look better than others when you are running a benchmark yourself
> > >> because tuning and sizing can lead to a 10X performance improvement.
> > >> This benchmark doesn't share the mechanism in a reproducible way.
> > >>
> > >> There are a bunch of things that aren't clear here:
> > >>
> > >> 1. Spark SQL has optimized parquet features, were these turned on?
> > >> 2. It doesn't mention computing statistics in Spark SQL, but it does
> > >> this for Impala and Parquet. Statistics allow Spark SQL to broadcast
> > >> small tables which can make a 10X difference in TPC-H.
> > >> 3. For data larger than memory, Spark SQL often performs better if you
> > >> don't call "cache", did they try this?
> > >>
> > >> Basically, a self-reported marketing benchmark like this that
> > >> *shocker* concludes this vendor's solution is the best, is not
> > >> particularly useful.
> > >>
> > >> If Citus data wants to run a credible benchmark, I'd invite them to
> > >> directly involve Spark SQL developers in the future. Until then, I
> > >> wouldn't give much credence to this or any other similar vendor
> > >> benchmark.
> > >>
> > >> - Patrick
> > >>
> > >> On Fri, Oct 31, 2014 at 10:38 AM, Nicholas Chammas
> > >> <nicholas.chammas@gmail.com <javascript:;> <javascript:;>
> <javascript:;>> wrote:
> > >> > I know we don't want to be jumping at every benchmark someone posts
> > >>out
> > >> > there, but this one surprised me:
> > >> >
> > >> >
> http://www.citusdata.com/blog/86-making-postgresql-scale-hadoop-style
> > >> >
> > >> > This benchmark has Spark SQL failing to complete several queries in
> > >>the
> > >> > TPC-H benchmark. I don't understand much about the details of
> > >>performing
> > >> > benchmarks, but this was surprising.
> > >> >
> > >> > Are these results expected?
> > >> >
> > >> > Related HN discussion here:
> > >>https://news.ycombinator.com/item?id=8539678
> > >> >
> > >> > Nick
> > >>
> >
> >
> >
> > --
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>


-- 
em rnowling@gmail.com
c 954.496.2314

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