Seems like all of these setups involve a small number of CPU's??? Does storm typically require more RAM than CPU.. ie which is usually the bottleneck?
On Wed, Apr 30, 2014 at 8:54 PM, Michael Rose <mich...@fullcontact.com> wrote: > 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) > Senior Platform Engineer, FullContact > mich...@fullcontact.com > > > > On Wed, Apr 30, 2014 at 9:48 PM, Cody A. Ray <cody.a....@gmail.com> wrote: >> >> We use m1.larges in EC2 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 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 <static.void....@gmail.com> >> wrote: >>> >>> What kind of specs are we looking at for >>> >>> 1) Nimbus >>> 2) Workers >>> >>> Any recommendations? >> >> >> >> >> -- >> Cody A. Ray, LEED AP >> cody.a....@gmail.com >> 215.501.7891 > >
