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. >> >> >> >> >> >> >> >> >>
