Could it be that your data is skewed? This could lead to different loads on different task managers.
With the latest Flink version, the web interface should show you how many bytes each operator has written and received. There you could see if one operator receives more elements than the others. Cheers, Till On Wed, Jan 27, 2016 at 1:35 PM, Pieter Hameete <phame...@gmail.com> wrote: > Hi guys, > > Currently I am running a job in the GCloud in a configuration with 4 task > managers that each have 4 CPUs (for a total parallelism of 16). > > However, I noticed my job is running much slower than expected and after > some more investigation I found that one of the workers is doing a majority > of the work (its CPU load was at 100% while the others were almost idle). > > My job execution plan can be found here: http://i.imgur.com/fHKhVFf.png > > The input is split into multiple files so loading the data is properly > distributed over the workers. > > I am wondering if you can provide me with some tips on how to figure out > what is going wrong here: > > - Could this imbalance in workload be the result of an imbalance in > the hash paritioning? > - Is there a convenient way to see how many elements each worker gets > to process? Would it work to write the output of the CoGroup to disk > because each worker writes to its own output file and investigate the > differences? > - Is there something strange about the execution plan that could cause > this? > > Thanks and kind regards, > > Pieter >
