Thank you for the details! Would you mind speaking to what tools proved most useful as far as identifying bottlenecks or bugs? Thanks again. On Oct 13, 2014 5:36 PM, "Matei Zaharia" <matei.zaha...@gmail.com> wrote:
> The biggest scaling issue was supporting a large number of reduce tasks > efficiently, which the JIRAs in that post handle. In particular, our > current default shuffle (the hash-based one) has each map task open a > separate file output stream for each reduce task, which wastes a lot of > memory (since each stream has its own buffer). > > A second thing that helped efficiency tremendously was Reynold's new > network module (https://issues.apache.org/jira/browse/SPARK-2468). Doing > I/O on 32 cores, 10 Gbps Ethernet and 8+ disks efficiently is not easy, as > can be seen when you try to scale up other software. > > Finally, with 30,000 tasks even sending info about every map's output size > to each reducer was a problem, so Reynold has a patch that avoids that if > the number of tasks is large. > > Matei > > On Oct 10, 2014, at 10:09 PM, Ilya Ganelin <ilgan...@gmail.com> wrote: > > > Hi Matei - I read your post with great interest. Could you possibly > comment in more depth on some of the issues you guys saw when scaling up > spark and how you resolved them? I am interested specifically in > spark-related problems. I'm working on scaling up spark to very large > datasets and have been running into a variety of issues. Thanks in advance! > > > > On Oct 10, 2014 10:54 AM, "Matei Zaharia" <matei.zaha...@gmail.com> > wrote: > > Hi folks, > > > > I interrupt your regularly scheduled user / dev list to bring you some > pretty cool news for the project, which is that we've been able to use > Spark to break MapReduce's 100 TB and 1 PB sort records, sorting data 3x > faster on 10x fewer nodes. There's a detailed writeup at > http://databricks.com/blog/2014/10/10/spark-breaks-previous-large-scale-sort-record.html. > Summary: while Hadoop MapReduce held last year's 100 TB world record by > sorting 100 TB in 72 minutes on 2100 nodes, we sorted it in 23 minutes on > 206 nodes; and we also scaled up to sort 1 PB in 234 minutes. > > > > I want to thank Reynold Xin for leading this effort over the past few > weeks, along with Parviz Deyhim, Xiangrui Meng, Aaron Davidson and Ali > Ghodsi. In addition, we'd really like to thank Amazon's EC2 team for > providing the machines to make this possible. Finally, this result would of > course not be possible without the many many other contributions, testing > and feature requests from throughout the community. > > > > For an engine to scale from these multi-hour petabyte batch jobs down to > 100-millisecond streaming and interactive queries is quite uncommon, and > it's thanks to all of you folks that we are able to make this happen. > > > > Matei > > --------------------------------------------------------------------- > > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > > For additional commands, e-mail: user-h...@spark.apache.org > > > >
