So are you suggesting that the long delays happened in %1 percentile happens in the slower partitions that are further away? Thanks.
On Wed, Sep 9, 2015 at 3:15 PM, Helleren, Erik <erik.helle...@cmegroup.com> wrote: > So, I did my own latency test on a cluster of 3 nodes, and there is a > significant difference around the 99%’ile and higher for partitions when > measuring the the ack time when configured for a single ack. The graph > that I wish I could attach or post clearly shows that around 1/3 of the > partitions significantly diverge from the other two. So, at least in my > case, one of my brokers is further than the others. > -Erik > > On 9/4/15, 1:06 PM, "Yuheng Du" <yuheng.du.h...@gmail.com> wrote: > > >No problem. Thanks for your advice. I think it would be fun to explore. I > >only know how to program in java though. Hope it will work. > > > >On Fri, Sep 4, 2015 at 2:03 PM, Helleren, Erik > ><erik.helle...@cmegroup.com> > >wrote: > > > >> I thing the suggestion is to have partitions/brokers >=1, so 32 should > >>be > >> enough. > >> > >> As for latency tests, there isn’t a lot of code to do a latency test. > >>If > >> you just want to measure ack time its around 100 lines. I will try to > >> push out some good latency testing code to github, but my company is > >> scared of open sourcing code… so it might be a while… > >> -Erik > >> > >> > >> On 9/4/15, 12:55 PM, "Yuheng Du" <yuheng.du.h...@gmail.com> wrote: > >> > >> >Thanks for your reply Erik. I am running some more tests according to > >>your > >> >suggestions now and I will share with my results here. Is it necessary > >>to > >> >use a fixed number of partitions (32 partitions maybe) for my test? > >> > > >> >I am testing 2, 4, 8, 16 and 32 brokers scenarios, all of them are > >>running > >> >on individual physical nodes. So I think using at least 32 partitions > >>will > >> >make more sense? I have seen latencies increase as the number of > >> >partitions > >> >goes up in my experiments. > >> > > >> >To get the latency of each event data recorded, are you suggesting > >>that I > >> >rewrite my own test program (in Java perhaps) or I can just modify the > >> >standard test program provided by kafka ( > >> >https://gist.github.com/jkreps/c7ddb4041ef62a900e6c )? I guess I need > >>to > >> >rebuild the source if I modify the standard java test program > >> >ProducerPerformance provided in kafka, right? Now this standard program > >> >only has average latencies and percentile latencies but no per event > >> >latencies. > >> > > >> >Thanks. > >> > > >> >On Fri, Sep 4, 2015 at 1:42 PM, Helleren, Erik > >> ><erik.helle...@cmegroup.com> > >> >wrote: > >> > > >> >> That is an excellent question! There are a bunch of ways to monitor > >> >> jitter and see when that is happening. Here are a few: > >> >> > >> >> - You could slice the histogram every few seconds, save it out with a > >> >> timestamp, and then look at how they compare. This would be mostly > >> >> manual, or you can graph line charts of the percentiles over time in > >> >>excel > >> >> where each percentile would be a series. If you are using HDR > >> >>Histogram, > >> >> you should look at how to use the Recorder class to do this coupled > >> >>with a > >> >> ScheduledExecutorService. > >> >> > >> >> - You can just save the starting timestamp of the event and the > >>latency > >> >>of > >> >> each event. If you put it into a CSV, you can just load it up into > >> >>excel > >> >> and graph as a XY chart. That way you can see every point during the > >> >> running of your program and you can see trends. You want to be > >>careful > >> >> about this one, especially of writing to a file in the callback that > >> >>kfaka > >> >> provides. > >> >> > >> >> Also, I have noticed that most of the very slow observations are at > >> >> startup. But don’t trust me, trust the data and share your findings. > >> >> Also, having a 99.9 percentile provides a pretty good standard for > >> >>typical > >> >> poor case performance. Average is borderline useless, 50%’ile is a > >> >>better > >> >> typical case because that’s the number that says “half of events > >>will be > >> >> this slow or faster”, or for values that are high like 99.9%’ile, > >>“0.1% > >> >>of > >> >> all events will be slower than this”. > >> >> -Erik > >> >> > >> >> On 9/4/15, 12:05 PM, "Yuheng Du" <yuheng.du.h...@gmail.com> wrote: > >> >> > >> >> >Thank you Erik! That's is helpful! > >> >> > > >> >> >But also I see jitters of the maximum latencies when running the > >> >> >experiment. > >> >> > > >> >> >The average end to acknowledgement latency from producer to broker > >>is > >> >> >around 5ms when using 92 producers and 4 brokers, and the 99.9 > >> >>percentile > >> >> >latency is 58ms, but the maximum latency goes up to 1359 ms. How to > >> >>locate > >> >> >the source of this jitter? > >> >> > > >> >> >Thanks. > >> >> > > >> >> >On Fri, Sep 4, 2015 at 10:54 AM, Helleren, Erik > >> >> ><erik.helle...@cmegroup.com> > >> >> >wrote: > >> >> > > >> >> >> WellŠ not to be contrarian, but latency depends much more on the > >> >>latency > >> >> >> between the producer and the broker that is the leader for the > >> >>partition > >> >> >> you are publishing to. At least when your brokers are not > >>saturated > >> >> >>with > >> >> >> messages, and acks are set to 1. If acks are set to ALL, latency > >>on > >> >>an > >> >> >> non-saturated kafka cluster will be: Round Trip Latency from > >> >>producer to > >> >> >> leader for partition + Max( slowest Round Trip Latency to a > >>replicas > >> >>of > >> >> >> that partition). If a cluster is saturated with messages, we > >>have to > >> >> >> assume that all partitions receive an equal distribution of > >>messages > >> >>to > >> >> >> avoid linear algebra and queueing theory models. I don¹t like > >>linear > >> >> >> algebra :P > >> >> >> > >> >> >> Since you are probably putting all your latencies into a single > >> >> >>histogram > >> >> >> per producer, or worse, just an average, this pattern would have > >>been > >> >> >> obscured. Obligatory lecture about measuring latency by Gil Tene > >> >> >> (https://www.youtube.com/watch?v=9MKY4KypBzg). To verify this > >> >> >>hypothesis, > >> >> >> you should re-write the benchmark to plot the latencies for each > >> >>write > >> >> >>to > >> >> >> a partition for each producer into a histogram. (HRD histogram is > >> >>pretty > >> >> >> good for that). This would give you producers*partitions > >>histograms, > >> >> >> which might be unwieldy for that many producers. But wait, there > >>is > >> >> >>hope! > >> >> >> > >> >> >> To verify that this hypothesis holds, you just have to see that > >>there > >> >> >>is a > >> >> >> significant difference between different partitions on a SINGLE > >> >> >>producing > >> >> >> client. So, pick one producing client at random and use the data > >>from > >> >> >> that. The easy way to do that is just plot all the partition > >>latency > >> >> >> histograms on top of each other in the same plot, that way you > >>have a > >> >> >> pretty plot to show people. If you don¹t want to setup plotting, > >>you > >> >> >>can > >> >> >> just compare the medians (50¹th percentile) of the partitions¹ > >> >> >>histograms. > >> >> >> If there is a lot of variance, your latency anomaly is explained > >>by > >> >> >> brokers 4-7 being slower than nodes 0-3! If there isn¹t a lot of > >> >> >>variance > >> >> >> at 50%, look at higher percentiles. And if higher percentiles for > >> >>all > >> >> >>the > >> >> >> partitions look the same, this hypothesis is disproved. > >> >> >> > >> >> >> If you want to make a general statement about latency of writing > >>to > >> >> >>kafka, > >> >> >> you can merge all the histograms into a single histogram and plot > >> >>that. > >> >> >> > >> >> >> To Yuheng¹s credit, more brokers always results in more > >>throughput. > >> >>But > >> >> >> throughput and latency are two different creatures. Its worth > >>noting > >> >> >>that > >> >> >> kafka is designed to be high throughput first and low latency > >>second. > >> >> >>And > >> >> >> it does a really good job at both. > >> >> >> > >> >> >> Disclaimer: I might not like linear algebra, but I do like > >> >>statistics. > >> >> >> Let me know if there are topics that need more explanation above > >>that > >> >> >> aren¹t covered by Gil¹s lecture. > >> >> >> -Erik > >> >> >> > >> >> >> On 9/4/15, 9:03 AM, "Yuheng Du" <yuheng.du.h...@gmail.com> wrote: > >> >> >> > >> >> >> >When I using 32 partitions, the 4 brokers latency becomes larger > >> >>than > >> >> >>the > >> >> >> >8 > >> >> >> >brokers latency. > >> >> >> > > >> >> >> >So is it always true that using more brokers can give less > >>latency > >> >>when > >> >> >> >the > >> >> >> >number of partitions is at least the size of the brokers? > >> >> >> > > >> >> >> >Thanks. > >> >> >> > > >> >> >> >On Thu, Sep 3, 2015 at 10:45 PM, Yuheng Du > >> >><yuheng.du.h...@gmail.com> > >> >> >> >wrote: > >> >> >> > > >> >> >> >> I am running a producer latency test. When using 92 producers > >>in > >> >>92 > >> >> >> >> physical node publishing to 4 brokers, the latency is slightly > >> >>lower > >> >> >> >>than > >> >> >> >> using 8 brokers, I am using 8 partitions for the topic. > >> >> >> >> > >> >> >> >> I have rerun the test and it gives me the same result, the 4 > >> >>brokers > >> >> >> >> scenario still has lower latency than the 8 brokers scenarios. > >> >> >> >> > >> >> >> >> It is weird because I tested 1broker, 2 brokers, 4 brokers, 8 > >> >> >>brokers, > >> >> >> >>16 > >> >> >> >> brokers and 32 brokers. For the rest of the case the latency > >> >> >>decreases > >> >> >> >>as > >> >> >> >> the number of brokers increase. > >> >> >> >> > >> >> >> >> 4 brokers/8 brokers is the only pair that doesn't satisfy this > >> >>rule. > >> >> >> >>What > >> >> >> >> could be the cause? > >> >> >> >> > >> >> >> >> I am using a 200 bytes message, the test let each producer > >> >>publishes > >> >> >> >>500k > >> >> >> >> messages to a given topic. Every test run when I change the > >> >>number of > >> >> >> >> brokers, I use a new topic. > >> >> >> >> > >> >> >> >> Thanks for any advices. > >> >> >> >> > >> >> >> > >> >> >> > >> >> > >> >> > >> > >> > >
