Hi Key,
Thank you so much for your update!!
Look forward to the shared code from AMPLab. As a member of the Spark
community, I really hope that I could help to run TPC-DS on SparkSQL. At the
moment, I am trying TPC-H 22 queries on SparkSQL 1.1.0 +Hive 0.12, and Hive
0.13.1 respectively (waiting Spark 1.2).
Arthur
On 1 Nov, 2014, at 3:51 am, Kay Ousterhout wrote:
> There's been an effort in the AMPLab at Berkeley to set up a shared
> codebase that makes it easy to run TPC-DS on SparkSQL, since it's something
> we do frequently in the lab to evaluate new research. Based on this
> thread, it sounds like making this more widely-available is something that
> would be useful to folks for reproducing the results published by
> Databricks / Hortonworks / Cloudera / etc.; we'll share the code on the
> list as soon as we're done.
>
> -Kay
>
> On Fri, Oct 31, 2014 at 12:45 PM, Nicholas Chammas <
> nicholas.cham...@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 Nunez님이 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" >> > 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>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
> > 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 surpris
