Hi Arnaud!
Thank you for the warm words! Let's find a good way to get this to work...
As a bit of background:
In Flink, the API needs to now a bit about the types that go through the
functions, because Flink pre-generates and configures serializers, and
validates that things fit together.
It is also important that keys are exposed rather specifically, because
Flink internally tries to work on serialized data (that makes it in-memory
operations predictable and robust).
If you expose a key as a "String", or "long" or "double", then Flink knows
how to work on it in a binary fashion.
Also, if you expose a key as a POJO, then Flink interprets the key as a
combination of the fields, and can again work on the serialized data.
If you only expose "Comparable" (which is the bare minimum for a key), you
experience performance degradation (most notably for sorts), because every
key operation involves serialization and deserialization.
So the goal would be to expose the key properly. We can always hint to the
API what the key type is, precisely for the cases where the inference
cannot do it.
- To understand things a bit better: What is your "Key" type? Is it an
abstract class, an interface, a generic parameter?
Greetings,
Stephan
FYI: In Scala, this works actually quite a bit easier, since Scala does
preserve generic types. In Java, we built a lot of reflection tooling, but
there are cases where it is impossible to infer the types via reflection,
like yours.
On Thu, Apr 23, 2015 at 6:35 PM, Soumitra Kumar <kumar.soumi...@gmail.com>
wrote:
> Will you elaborate on your use case? It would help to find out where Flink
> shines. IMO, its a great project, but needs more differentiation from Spark.
>
> On Thu, Apr 23, 2015 at 7:25 AM, LINZ, Arnaud <al...@bouyguestelecom.fr>
> wrote:
>
>> Hello,
>>
>>
>>
>> After a quite successful benchmark yesterday (Flink being about twice
>> faster than Spark on my use cases), I’ve turned instantly from spark-fan to
>> flink-fan – great job, committers!
>>
>> So I’ve decided to port my existing Spark tools to Flink. Happily, most
>> of the difficulty was renaming classes, packages and variables with “spark”
>> in them to something more neutral J
>>
>>
>>
>> However there is one easy thing in Spark I’m still wondering how to do in
>> Flink : generic keys.
>>
>>
>>
>> I’m trying to make a framework on which my applications are built. That
>> framework thus manipulate “generic types” representing the data, inheriting
>> from an abstract class with a common contract, let’s call it “Bean”.
>>
>>
>>
>> Among other things Bean exposes an abstract method
>>
>> *public* Key getKey();
>>
>>
>>
>> Key being one of my core types used in several java algorithms.
>>
>>
>>
>> Let’s say I have the class :
>>
>> *public* *class* Framework<T *extends* Bean> *implements* Serializable {
>>
>>
>>
>> *public *DataSet<T> doCoolStuff(*final* DataSet<T> inputDataset) {
>>
>> // Group lines according to a key
>>
>> *final* UnsortedGrouping<YT> groupe = inputDataset.groupBy(*new*
>> KeySelector<T, Key>() {
>>
>> @Override
>>
>> *public* Key getKey(T record) {
>>
>> *return* record.getKey();
>>
>> }
>>
>> });
>>
>> (…)
>>
>> }
>>
>> }
>>
>>
>>
>> With Spark, a mapToPair works fine because all I have to do is
>> implements correctly hashCode() and equals() on my Key type.
>>
>> With Flink, Key is not recognized as a POJO object (well it is not) and
>> that does not work.
>>
>>
>>
>> I have tried to expose something like *public* Tuple getKeyAsTuple(); in Key
>> but Flink does not accept generic Tuples. I’ve tried to parameterize my
>> Tuple but Flink does not know how to infer
>>
>> the generic type value.
>>
>>
>>
>> So I’m wondering what is the best way to implement it.
>>
>> For now I have exposed something like *public* String getKeyAsString(); and
>> turned my generic treatment into :
>>
>> *final* UnsortedGrouping<YT> groupe = inputDataset.groupBy(*new*
>> KeySelector<T, String>() {
>>
>> @Override
>>
>> *public* String getKey(T record) {
>>
>> *return* record.getKey().getKeyAsString();
>>
>> }
>>
>> });
>>
>> But that “ASCII” representation is suboptimal.
>>
>>
>>
>> I thought of passing a key to tuple conversion lambda upon creation of
>> the Framework class but that would be boiler-plate code on the user’s end,
>> which I’m not fond of.
>>
>>
>>
>> So my questions are :
>>
>> - Is there a smarter way to do this ?
>>
>> - What kind of objects can be passed as a Key ? Is there an
>> Interface to respect ?
>>
>> - In the worst case, is byte[] ok as a Key ? (I can code the
>> serialization on the framework side…)
>>
>>
>>
>>
>>
>> Best regards,
>>
>> Arnaud
>>
>>
>>
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