On Thu, Oct 10, 2013 at 1:27 PM, Pradeep Gollakota <pradeep...@gmail.com>wrote:
> I don't think it necessarily means that the job is a bad candidate for MR. > It's a different type of a workload. Hortonworks has a great article on the > different types of workloads you might see and how that affects your > provisioning choices at > http://docs.hortonworks.com/HDPDocuments/HDP1/HDP-1.3.2/bk_cluster-planning-guide/content/ch_hardware-recommendations.html > One statement that stood out to me in the link above is "For these reasons, Hortonworks recommends that you either use the Balanced workload configuration or invest in a pilot Hadoop cluster and plan to evolve as you analyze the workload patterns in your environment." Now, this is not a critique/concern of HW but rather of hadoop. Well, what if my workloads can be both CPU and IO intensive? Do I take the approach of throw-enough-excess-hardware-just-in-case? > > I have not looked at the Grep code so I'm not sure why it's behaving the > way it is. Still curious that streaming has a higher IO throughput and > lower CPU usage. It may have to do with the fact that /bin/grep is a native > implementation and Grep (Hadoop) is probably using Java Pattern/Matcher api. > The Grep code is from the bundled examples in CDH. I made one line modification for it to read Sequence files. The streaming job probably does not have lower CPU utilization but I see that it does even out the CPU utilization among all the available processors. I guess the native grep binary threads better than the java MR job? Which brings me to ask - If you have the mapper/reducer functionality built into a platform specific binary, then won't it always be more efficient than a java MR job? And, in such cases, am I better off with streaming than Java MR? Thanks for your responses. > > > On Thu, Oct 10, 2013 at 12:29 PM, Xuri Nagarin <secs...@gmail.com> wrote: > >> Thanks Pradeep. Does it mean this job is a bad candidate for MR? >> >> Interestingly, running the cmdline '/bin/grep' under a streaming job >> provides (1) Much better disk throughput and, (2) CPU load is almost evenly >> spread across all cores/threads (no CPU gets pegged to 100%). >> >> >> >> >> On Thu, Oct 10, 2013 at 11:15 AM, Pradeep Gollakota <pradeep...@gmail.com >> > wrote: >> >>> Actually... I believe that is expected behavior. Since your CPU is >>> pegged at 100% you're not going to be IO bound. Typically jobs tend to be >>> CPU bound or IO bound. If you're CPU bound you expect to see low IO >>> throughput. If you're IO bound, you expect to see low CPU usage. >>> >>> >>> On Thu, Oct 10, 2013 at 11:05 AM, Xuri Nagarin <secs...@gmail.com>wrote: >>> >>>> Hi, >>>> >>>> I have a simple Grep job (from bundled examples) that I am running on a >>>> 11-node cluster. Each node is 2x8-core Intel Xeons (shows 32 CPUs with HT >>>> on), 64GB RAM and 8 x 1TB disks. I have mappers set to 20 per node. >>>> >>>> When I run the Grep job, I notice that CPU gets pegged to 100% on >>>> multiple cores but disk throughput remains a dismal 1-2 Mbytes/sec on a >>>> single disk on each node. So I guess, the cluster is poorly performing in >>>> terms of disk IO. Running Terasort, I see each disk puts out 25-35 >>>> Mbytes/sec with a total cluster throughput of above 1.5 Gbytes/sec. >>>> >>>> How do I go about re-configuring or re-writing the job to utilize >>>> maximum disk IO? >>>> >>>> TIA, >>>> >>>> Xuri >>>> >>>> >>>> >>> >> >
