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From Dhruv Kumar <gargdhru...@gmail.com>
Subject Re: Measure End-to-End latency/delay for each record
Date Thu, 26 Apr 2018 23:27:59 GMT
Ok that answers my questions.

What are you keeping the source and sink as? Is it Kafka for both?

--------------------------------------------------
Dhruv Kumar
PhD Candidate
Department of Computer Science and Engineering
University of Minnesota
www.dhruvkumar.me

> On Apr 26, 2018, at 16:37, TechnoMage <mlatta@technomage.com> wrote:
> 
> Yes NTP can still have skew.  It may be measured in fractions of a second, but with Flink
that can be significant if you care about sub-second latency accuracy.  Since I have a 20
stage stream with 0.002 second latency it can matter.
> 
> Back pressure is the limiting of input due to the inability of down-stream tasks to accept
input.  For example if you have a map that reads from a database to enhance an element, that
may limit earlier steps performance as they can not push elements to it faster than it can
read from the database.  This can flow all the way back to the source and slow records coming
into the system.
> 
> Michael
> 
>> On Apr 26, 2018, at 12:38 PM, Dhruv Kumar <gargdhruv36@gmail.com <mailto:gargdhruv36@gmail.com>>
wrote:
>> 
>> What do you mean by the time skew from one machine(source) to another(sink)? Do you
mean the system time clocks of the source and sink may not be in sync. If I regularly use
NTP to keep the system clocks in sync, will time skew still happen?
>> 
>> Could you also elaborate on what do you mean by back pressure on source and how will
it impact the latency calculations?
>> 
>> Sorry if these are trivial questions. I am a bit new to the real world streaming
systems.
>> 
>> --------------------------------------------------
>> Dhruv Kumar
>> PhD Candidate
>> Department of Computer Science and Engineering
>> University of Minnesota
>> www.dhruvkumar.me <http://www.dhruvkumar.me/>
>> 
>>> On Apr 26, 2018, at 13:26, TechnoMage <mlatta@technomage.com <mailto:mlatta@technomage.com>>
wrote:
>>> 
>>> 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 <gargdhruv36@gmail.com <mailto:gargdhruv36@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/>
>>> 
>> 
> 


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