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From Maximilian Michels <...@apache.org>
Subject Re: Dataset filter improvement
Date Tue, 23 Feb 2016 14:17:07 GMT
Hi Flavio,

I think the point is that Flink can use its serialization tools if you
register the class in advance. If you don't do that, it will use Kryo
as a fall-back which is slightly less efficient.

Equals and hash code have to be implemented correctly if you compare
Pojos. For standard types like String or Integer, this is done
automatically. For Pojos, Flink doesn't know whether it is implemented
correctly or not. Every object in Java has a default equals and
hashCode implementation.

Cheers,
Max

On Wed, Feb 17, 2016 at 10:22 AM, Flavio Pompermaier
<pompermaier@okkam.it> wrote:
> Hi Max,
> why do I need to register them? My job runs without problem also without
> that.
> The only problem with my POJOs was that I had to implement equals and hash
> correctly, Flink didn't enforce me to do it but then results were wrong :(
>
>
>
> On Wed, Feb 17, 2016 at 10:16 AM Maximilian Michels <mxm@apache.org> wrote:
>>
>> Hi Flavio,
>>
>> Stephan was referring to
>>
>> env.registerType(ExtendedClass1.class);
>> env.registerType(ExtendedClass2.class);
>>
>> Cheers,
>> Max
>>
>> On Wed, Feb 10, 2016 at 12:48 PM, Flavio Pompermaier
>> <pompermaier@okkam.it> wrote:
>> > What do you mean exactly..? Probably I'm missing something
>> > here..remember
>> > that I can specify the right subClass only after the last flatMap, after
>> > the
>> > first map neither me nor Flink can know the exact subclass of BaseClass
>> >
>> > On Wed, Feb 10, 2016 at 12:42 PM, Stephan Ewen <sewen@apache.org> wrote:
>> >>
>> >> Class hierarchies should definitely work, even if the base class has no
>> >> fields.
>> >>
>> >> They work more efficiently if you register the subclasses at the
>> >> execution
>> >> environment (Flink cannot infer them from the function signatures
>> >> because
>> >> the function signatures only contain the abstract base class).
>> >>
>> >> On Wed, Feb 10, 2016 at 12:23 PM, Flavio Pompermaier
>> >> <pompermaier@okkam.it> wrote:
>> >>>
>> >>> Because The classes are not related to each other. Do you think it's
a
>> >>> good idea to have something like this?
>> >>>
>> >>> abstract class BaseClass(){
>> >>>    String someField;
>> >>> }
>> >>>
>> >>> class ExtendedClass1 extends BaseClass (){
>> >>>    String someOtherField11;
>> >>>    String someOtherField12;
>> >>>    String someOtherField13;
>> >>>  ...
>> >>> }
>> >>>
>> >>> class ExtendedClass2 extends BaseClass (){
>> >>>    Integer someOtherField21;
>> >>>    Double someOtherField22;
>> >>>    Integer someOtherField23;
>> >>>  ...
>> >>> }
>> >>>
>> >>> and then declare my map as Map<Tuple2,BaseClass>. and then apply
a
>> >>> flatMap that can be used to generated the specific datasets?
>> >>> Doesn't this cause problem to Flink? Classes can be vrry different to
>> >>> each other..maybe this can cause problems with the plan
>> >>> generation..isn't
>> >>> it?
>> >>>
>> >>> Thanks Fabian and Stephan for the support!
>> >>>
>> >>>
>> >>> On Wed, Feb 10, 2016 at 11:47 AM, Stephan Ewen <sewen@apache.org>
>> >>> wrote:
>> >>>>
>> >>>> Why not use an abstract base class and N subclasses?
>> >>>>
>> >>>> On Wed, Feb 10, 2016 at 10:05 AM, Fabian Hueske <fhueske@gmail.com>
>> >>>> wrote:
>> >>>>>
>> >>>>> Unfortunately, there is no Either<1,...,n>.
>> >>>>> You could implement something like a Tuple3<Option<Type1>,
>> >>>>> Option<Type2>, Option<Type3>>. However, Flink
does not provide an
>> >>>>> Option
>> >>>>> type (comes with Java8). You would need to implement it yourself
>> >>>>> incl.
>> >>>>> TypeInfo and Serializer. You can get some inspiration from the
>> >>>>> Either type
>> >>>>> info /serializer, if you want to go this way.
>> >>>>>
>> >>>>> Using a byte array would also work but doesn't look much easier
than
>> >>>>> the Option approach to me.
>> >>>>>
>> >>>>> 2016-02-10 9:47 GMT+01:00 Flavio Pompermaier <pompermaier@okkam.it>:
>> >>>>>>
>> >>>>>> Yes, the intermediate dataset I create then join again between
>> >>>>>> themselves. What I'd need is a Either<1,...,n>. Is
that possible to
>> >>>>>> add?
>> >>>>>> Otherwise I was thinking to generate a Tuple2<String,byte[]>
and in
>> >>>>>> the subsequent filter+map/flatMap deserialize only those
elements I
>> >>>>>> want to
>> >>>>>> group togheter (e.g. t.f0=="someEventType") in order to
generate
>> >>>>>> the typed
>> >>>>>> dataset based.
>> >>>>>> Which one  do you think is the best solution?
>> >>>>>>
>> >>>>>> On Wed, Feb 10, 2016 at 9:40 AM, Fabian Hueske <fhueske@gmail.com>
>> >>>>>> wrote:
>> >>>>>>>
>> >>>>>>> Hi Flavio,
>> >>>>>>>
>> >>>>>>> I did not completely understand which objects should
go where, but
>> >>>>>>> here are some general guidelines:
>> >>>>>>>
>> >>>>>>> - early filtering is mostly a good idea (unless evaluating
the
>> >>>>>>> filter
>> >>>>>>> expression is very expensive)
>> >>>>>>> - you can use a flatMap function to combine a map and
a filter
>> >>>>>>> - applying multiple functions on the same data set does
not
>> >>>>>>> necessarily materialize the data set (in memory or on
disk). In
>> >>>>>>> most cases
>> >>>>>>> it prevents chaining, hence there is serialization overhead.
In
>> >>>>>>> some cases
>> >>>>>>> where the forked data streams are joined again, the
data set must
>> >>>>>>> be
>> >>>>>>> materialized in order to avoid deadlocks.
>> >>>>>>> - it is not possible to write a map that generates two
different
>> >>>>>>> types, but you could implement a mapper that returns
an
>> >>>>>>> Either<First,
>> >>>>>>> Second> type.
>> >>>>>>>
>> >>>>>>> Hope this helps,
>> >>>>>>> Fabian
>> >>>>>>>
>> >>>>>>> 2016-02-10 8:43 GMT+01:00 Flavio Pompermaier
>> >>>>>>> <pompermaier@okkam.it>:
>> >>>>>>>>
>> >>>>>>>> Any help on this?
>> >>>>>>>>
>> >>>>>>>> On 9 Feb 2016 18:03, "Flavio Pompermaier" <pompermaier@okkam.it>
>> >>>>>>>> wrote:
>> >>>>>>>>>
>> >>>>>>>>> Hi to all,
>> >>>>>>>>>
>> >>>>>>>>> in my program I have a Dataset that generated
different types of
>> >>>>>>>>> object wrt the incoming element.
>> >>>>>>>>> Thus it's like a Map<Tuple2,Object>.
>> >>>>>>>>> In order to type the different generated datasets
I do
>> >>>>>>>>> something:
>> >>>>>>>>>
>> >>>>>>>>> Dataset<Tuple2> start =...
>> >>>>>>>>>
>> >>>>>>>>> Dataset<MyObj1> ds1 = start.filter().map(..);
>> >>>>>>>>> Dataset<MyObj1> ds2 = start.filter().map(..);
>> >>>>>>>>> Dataset<MyObj3> ds3 = start.filter().map(..);
>> >>>>>>>>> Dataset<MyObj3> ds4 = start.filter().map(..);
>> >>>>>>>>>
>> >>>>>>>>> However this is very inefficient (I think because
Flink needs to
>> >>>>>>>>> materialize the entire source dataset for every
slot).
>> >>>>>>>>>
>> >>>>>>>>> It's much more efficient to group the generation
of objects of
>> >>>>>>>>> the
>> >>>>>>>>> same type. E.g.:
>> >>>>>>>>>
>> >>>>>>>>> Dataset<Tuple2> start =..
>> >>>>>>>>>
>> >>>>>>>>> Dataset<MyObj1> tmp1 = start.map(..);
>> >>>>>>>>> Dataset<MyObj3> tmp2 = start.map(..);
>> >>>>>>>>> Dataset<MyObj1> ds1 = tmp1.filter();
>> >>>>>>>>> Dataset<MyObj1> ds2 = tmp1.filter();
>> >>>>>>>>> Dataset<MyObj3> ds3 = tmp2.filter();
>> >>>>>>>>> Dataset<MyObj3> ds4 = tmp2.filter();
>> >>>>>>>>>
>> >>>>>>>>> Increasing the number of slots per task manager
make things
>> >>>>>>>>> worse
>> >>>>>>>>> and worse :)
>> >>>>>>>>> Is there a way to improve this situation? Is
it possible to
>> >>>>>>>>> write a
>> >>>>>>>>> "map" generating different type of object and
then filter them
>> >>>>>>>>> by generated
>> >>>>>>>>> class type?
>> >>>>>>>>>
>> >>>>>>>>> Best,
>> >>>>>>>>> Flavio
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>
>> >>>>>>
>> >>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> >

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