Kay, Is this effort related to the existing AMPLab Big Data benchmark that covers Spark, Redshift, Tez, and Impala?
Nick 2014년 10월 31일 금요일, Kay Ousterhout<k...@eecs.berkeley.edu>님이 작성한 메시지: > 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 > <javascript:_e(%7B%7D,'cvml','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 Nunez<snu...@hortonworks.com >> <javascript:_e(%7B%7D,'cvml','snu...@hortonworks.com');>>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.cham...@gmail.com >> <javascript:_e(%7B%7D,'cvml','nicholas.cham...@gmail.com');> >> > <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<pwend...@gmail.com >> <javascript:_e(%7B%7D,'cvml','pwend...@gmail.com');> <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.cham...@gmail.com >> <javascript:_e(%7B%7D,'cvml','nicholas.cham...@gmail.com');> >> <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 >> > >> >> > >> > >> > >> > -- >> > CONFIDENTIALITY NOTICE >> > NOTICE: This message is intended for the use of the individual or >> entity to >> > which it is addressed and may contain information that is confidential, >> > privileged and exempt from disclosure under applicable law. If the >> reader >> > of this message is not the intended recipient, you are hereby notified >> that >> > any printing, copying, dissemination, distribution, disclosure or >> > forwarding of this communication is strictly prohibited. If you have >> > received this communication in error, please contact the sender >> immediately >> > and delete it from your system. Thank You. >> > >> > >
