I'm afraid you cannot do that. The inputs having the same key should be
processed by the same CEP operator. Otherwise the results will be
nondeterministic and also be wrong.
Regards,
Dian
> 在 2019年1月24日，下午2:56，dhanuka ranasinghe 写道：
>
> In this example key will be same. I am using 1 million m
In this example key will be same. I am using 1 million messages with same
key for performance testing. But still I want to process them parallel.
Can't I use Split function and get a SplitStream for that purpose?
On Thu, Jan 24, 2019 at 2:49 PM Dian Fu wrote:
> Hi Dhanuka,
>
> Does the KeySelect
Hi Dhanuka,
Does the KeySelector of Event::getTriggerID generate the same key for all the
inputs or only generate very few key values and these key values happen to be
hashed to the same downstream operator? You can print the results of
Event::getTriggerID to check if it's that case.
Regards,
Whether using KeyedStream depends on the logic of your job, i.e, whether you
are looking for patterns for some partitions, i.e, patterns for a particular
user. If so, you should partition the input data before the CEP operator.
Otherwise, the input data should not be partitioned.
Regards,
Dian
Hi Dhanuka,
In order to make the CEP operator to run parallel, the input stream should be
KeyedStream. You can refer [1] for detailed information.
Regards,
Dian
[1]:
https://ci.apache.org/projects/flink/flink-docs-master/dev/libs/cep.html#detecting-patterns
> 在 2019年1月24日，上午10:18，dhanuka rana
Hi Dian,
I tried that but then kafkaproducer only produce to single partition and
only single flink host working while rest not contribute for processing . I
will share the code and screenshot
Cheers
Dhanuka
On Thu, 24 Jan 2019, 12:31 Dian Fu Hi Dhanuka,
>
> In order to make the CEP operator to
