Hi Robert and Jay, Thanks for your answers. The petstore jobs could indeed be used as a roseta code for Flink and Spark.
Regarding the memory requirements, those are very good news to me, just 2GB of RAM is certainly a modest amount of memory, you can use even some Single Board Computers for that. Is there any reference load test programs and benchmarks that can be used to compare different deployments of Flink? Maybe the petstore implementation mentioned by Jay could be used for that, and also to compare the performance of Flink to other systems like Spark or Hadoop MapReduce, which I understand is the current goal. Greetings, Juan 2015-09-02 14:56 GMT+02:00 jay vyas <jayunit100.apa...@gmail.com>: > We're also working on a bigpetstore implementation of flink which will > help onboard spark/mapreduce folks. > > I have prototypical code here that runs a simple job in memory, > contributions welcome, > > right now there is a serialization error > https://github.com/bigpetstore/bigpetstore-flink . > > On Wed, Sep 2, 2015 at 8:50 AM, Robert Metzger <rmetz...@apache.org> > wrote: > >> Hi Juan, >> >> I think the recommendations in the Spark guide are quite good, and are >> similar to what I would recommend for Flink as well. >> Depending on the workloads you are interested to run, you can certainly >> use Flink with less than 8 GB per machine. I think you can start Flink >> TaskManagers with 500 MB of heap space and they'll still be able to process >> some GB of data. >> >> Everything above 2 GB is probably good enough for some initial >> experimentation (again depending on your workloads, network, disk speed >> etc.) >> >> >> >> >> On Wed, Sep 2, 2015 at 2:30 PM, Kostas Tzoumas <ktzou...@apache.org> >> wrote: >> >>> Hi Juan, >>> >>> Flink is quite nimble with hardware requirements; people have run it in >>> old-ish laptops and also the largest instances available in cloud >>> providers. I will let others chime in with more details. >>> >>> I am not aware of something along the lines of a cheatsheet that you >>> mention. If you actually try to do this, I would love to see it, and it >>> might be useful to others as well. Both use similar abstractions at the API >>> level (i.e., parallel collections), so if you stay true to the functional >>> paradigm and not try to "abuse" the system by exploiting knowledge of its >>> internals things should be straightforward. These apply to the batch APIs; >>> the streaming API in Flink follows a true streaming paradigm, where you get >>> an unbounded stream of records and operators on these streams. >>> >>> Funny that you ask about a video for the DataStream slides. There is a >>> Flink training happening as we speak, and a video is being recorded right >>> now :-) Hopefully it will be made available soon. >>> >>> Best, >>> Kostas >>> >>> >>> On Wed, Sep 2, 2015 at 1:13 PM, Juan Rodríguez Hortalá < >>> juan.rodriguez.hort...@gmail.com> wrote: >>> >>>> Answering to myself, I have found some nice training material at >>>> http://dataartisans.github.io/flink-training. There are even videos at >>>> youtube for some of the slides >>>> >>>> - http://dataartisans.github.io/flink-training/overview/intro.html >>>> https://www.youtube.com/watch?v=XgC6c4Wiqvs >>>> >>>> - >>>> http://dataartisans.github.io/flink-training/dataSetBasics/intro.html >>>> https://www.youtube.com/watch?v=0EARqW15dDk >>>> >>>> The third lecture >>>> http://dataartisans.github.io/flink-training/dataSetAdvanced/intro.html >>>> more or less corresponds to https://www.youtube.com/watch?v=1yWKZ26NQeU >>>> but not exactly, and there are more lessons at >>>> http://dataartisans.github.io/flink-training, for stream processing >>>> and the table API for which I haven't found a video. Does anyone have >>>> pointers to the missing videos? >>>> >>>> Greetings, >>>> >>>> Juan >>>> >>>> 2015-09-02 12:50 GMT+02:00 Juan Rodríguez Hortalá < >>>> juan.rodriguez.hort...@gmail.com>: >>>> >>>>> Hi list, >>>>> >>>>> I'm new to Flink, and I find this project very interesting. I have >>>>> experience with Apache Spark, and for I've seen so far I find that Flink >>>>> provides an API at a similar abstraction level but based on single record >>>>> processing instead of batch processing. I've read in Quora that Flink >>>>> extends stream processing to batch processing, while Spark extends batch >>>>> processing to streaming. Therefore I find Flink specially attractive for >>>>> low latency stream processing. Anyway, I would appreciate if someone could >>>>> give some indication about where I could find a list of hardware >>>>> requirements for the slave nodes in a Flink cluster. Something along the >>>>> lines of >>>>> https://spark.apache.org/docs/latest/hardware-provisioning.html. >>>>> Spark is known for having quite high minimal memory requirements (8GB RAM >>>>> and 8 cores minimum), and I was wondering if it is also the case for >>>>> Flink. >>>>> Lower memory requirements would be very interesting for building small >>>>> Flink clusters for educational purposes, or for small projects. >>>>> >>>>> Apart from that, I wonder if there is some blog post by the comunity >>>>> about transitioning from Spark to Flink. I think it could be interesting, >>>>> as there are some similarities in the APIs, but also deep differences in >>>>> the underlying approaches. I was thinking in something like Breeze's >>>>> cheatsheet comparing its matrix operatations with those available in >>>>> Matlab >>>>> and Numpy >>>>> https://github.com/scalanlp/breeze/wiki/Linear-Algebra-Cheat-Sheet, >>>>> or like http://rosettacode.org/wiki/Factorial. Just an idea anyway. >>>>> Also, any pointer to some online course, book or training for Flink >>>>> besides >>>>> the official programming guides would be much appreciated >>>>> >>>>> Thanks in advance for help >>>>> >>>>> Greetings, >>>>> >>>>> Juan >>>>> >>>>> >>>> >>> >> > > > -- > jay vyas >
