In a single machine system this may work ok. In a multi-machine system this is not as reliable as the time skew from one machine (source) to another (sink) can impact the measurements. This also does not account for back presure on the source. We are using an external process to in parallel read the source and output of the sink to measure the latency on a single system clock. It does account for those issues, but of course does not account for delivery delays in the messaging system (kafka in our case). But, does measure real world latency as seen by the rest of the system which is ultimately what matters to us.
Michael > On Apr 26, 2018, at 12:01 PM, Dhruv Kumar <gargdhru...@gmail.com> wrote: > > Hi > > I was trying to compute the end-to-end-latency for each record processed by > Flink. By end-to-end latency, I mean the difference between the time at which > the record entered the Flink system (came at source) and the time at which > the record is finally emitted into the sink. What is the best way to measure > this? I was thinking of doing the following: > 1. Add the current system timestamp to the record when the record arrives at > Flink. > 2. Add the current system timestamp to the record when the record is finally > being emitted into the sink. > 3. Take the difference between 2 and 1 offline when all the records have been > written into the sink. > > Does this sound ok? > > Also, if I use Processing time characteristic for this end-to-end-latency, > will it be fine? > > Thanks > -------------------------------------------------- > Dhruv Kumar > PhD Candidate > Department of Computer Science and Engineering > University of Minnesota > www.dhruvkumar.me <http://www.dhruvkumar.me/>
