In AWS, we're fans of c1.xlarges, m3.xlarges, and c3.2xlarges, but have seen Storm successfully run on cheaper hardware.
Our Nimbus server is usually bored on a m1.large. Michael Rose (@Xorlev <https://twitter.com/xorlev>) Senior Platform Engineer, FullContact <http://www.fullcontact.com/> [email protected] On Wed, Apr 30, 2014 at 9:48 PM, Cody A. Ray <[email protected]> wrote: > We use m1.larges in EC2 <http://aws.amazon.com/ec2/instance-types/> for > both nimbus and supervisor machines (though the m1 family have been > deprecated in favor of m3). Our use case is to do some pre-aggregation > before persisting the data in a store. (The main bottleneck in this setup > is the downstream datastore, but memory is the primary constraint on the > worker machines due to the in-memory cache which wraps the trident state.) > > For what its worth, Infochimps > suggests<https://github.com/infochimps-labs/big_data_for_chimps/blob/master/25-storm%2Btrident-tuning.asciidoc>c1.xlarge > or m3.xlarge machines. > > Using the Amazon cloud machines as a reference, we like to use either the > c1.xlarge machines (7GB ram, 8 cores, $424/month, giving the highest > CPU-performance-per-dollar) or the m3.xlargemachines (15 GB ram, 4 cores, > $365/month, the best balance of CPU-per-dollar and RAM-per-dollar). You > shouldn’t use fewer than four worker machines in production, so if your > needs are modest feel free to downsize the hardware accordingly. > > Not sure what others would recommend. > > -Cody > > > On Wed, Apr 30, 2014 at 5:57 PM, Software Dev > <[email protected]>wrote: > >> What kind of specs are we looking at for >> >> 1) Nimbus >> 2) Workers >> >> Any recommendations? >> > > > > -- > Cody A. Ray, LEED AP > [email protected] > 215.501.7891 >
