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From Stephan Ewen <se...@apache.org>
Subject Re: Bigpetstore - Flink integration
Date Wed, 02 Sep 2015 13:31:47 GMT
If a lot of the data is generated locally, this may face the same issue as
Greg did with oversized payloads (dropped by Akka).

On Wed, Sep 2, 2015 at 3:21 PM, Robert Metzger <rmetzger@apache.org> wrote:

> I'm starting a new discussion thread for the bigpetstore-flink integration
> ...
> I took a closer look into the code you've posted.
> It seems to me that you are generating a lot of data locally on the
> client, before you actually submit a job to Flink. (Both "customers" and
> "stores" are generated locally)
> Is that only some "seed" data?
> I would actually try to generate as much data as possible in the cluster,
> making the generator very scalable.
> I don't think that you need to register a Kryo serializer for the Product
> and Transaction type.
> I was able to run the code without the serializer registration.
> ---------- Forwarded message ----------
> From: jay vyas <jayunit100.apache@gmail.com>
> Date: Wed, Sep 2, 2015 at 2:56 PM
> Subject: Re: Hardware requirements and learning resources
> To: user@flink.apache.org
> 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 <rmetzger@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 <ktzoumas@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.hortala@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.hortala@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
>>>>> low latency stream processing. Anyway, I would appreciate if someone
>>>>> give some indication about where I could find a list of hardware
>>>>> requirements for the slave nodes in a Flink cluster. Something along
>>>>> lines of
>>>>> https://spark.apache.org/docs/latest/hardware-provisioning.html.
>>>>> Spark is known for having quite high minimal memory requirements (8GB
>>>>> and 8 cores minimum), and I was wondering if it is also the case for
>>>>> 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
>>>>> the underlying approaches. I was thinking in something like Breeze's
>>>>> cheatsheet comparing its matrix operatations with those available in
>>>>> 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

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