Well done guys. MapReduce sort at that time was a good feat and Spark now has raised the bar with the ability to sort a PB. Like some of the folks in the list, a summary of what worked (and didn't) as well as the monitoring practices would be good. Cheers <k/> P.S: What are you folks planning next ?
On Fri, Oct 10, 2014 at 7:54 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 > >
