Yes, there is some magic in the KryoSerializer and other serialisers that
detect whether the flink-avro dependency is there and then use special
TypeSerializers from there.
(Specifically, this is AvroUtils which has a default implementation that
doesn't do much and a special implementation call
So I just added the dependency but didn't change the getProducedType method
and it worked fine. Would you expect that to be the case?
On Fri, Jan 5, 2018 at 5:43 PM Aljoscha Krettek wrote:
> Yes, that should do the trick.
>
>
> On 5. Jan 2018, at 18:37, Kyle Hamlin wrote:
>
> I can add that dep
Yes, that should do the trick.
> On 5. Jan 2018, at 18:37, Kyle Hamlin wrote:
>
> I can add that dependency. So I would replace
>
> override def getProducedType: TypeInformation[T] = {
> TypeExtractor.getForClass(implicitly[ClassTag[T]].runtimeClass.asInstanceOf[Class[T]])
> }
>
> with somethi
I can add that dependency. So I would replace
override def getProducedType: TypeInformation[T] = {
TypeExtractor.getForClass(implicitly[ClassTag[T]].runtimeClass.asInstanceOf[Class[T]])
}
with something like:
override def getProducedType: TypeInformation[T] = {
new AvroTypeInfo(classOf[T])
}
Hi,
I think you might be able to use AvroTypeInfo which you can use by including
the flink-avro dependencies. Is that an option for you?
Best,
Aljoscha
> On 3. Jan 2018, at 21:34, Kyle Hamlin wrote:
>
> Hi,
>
> It appears that Kryo can't properly extract/deserialize Avro array types. I
> ha
Hi,
It appears that Kryo can't properly extract/deserialize Avro array types. I
have a very simple Avro schema that has an array type and when I remove the
array field the error is not thrown. Is there any way around this without
using a specific type?
*Avro Schema:*
{
"type": "record",
"
