Hi,
We're running a large Flink batch job and sometimes it throws serialization
errors in the middle of the job. It is always the same operator but the
error can be different. Then the following attempts work. Or sometimes
attempts get exhausted, then retrying the job.
The job is basically
Hi,
We are using Flink 1.4.0 at zookeeper high availability mode and with
externalized checkpoints. Today after we have restarted a zookeeper node,
several Flink clusters have lost connection to the zookeeper. This
triggered a leader election at effected clusters. After the leader
election, the
Hi,
I am using Flink 1.3.2. When I try to use KafkaProducer with timestamps it
fails to set name, uid or parallelism. It uses default values.
———
FlinkKafkaProducer010.FlinkKafkaProducer010Configuration producer =
FlinkKafkaProducer010
.writeToKafkaWithTimestamps(stream, topicName,
introduce some latency, e.g.
>> measuring throughput in taskA
>> and sending it to a side output with a taksID, then broadcasting the side
>> output to a downstream operator
>> which is sth like a coprocess function (taskB) and receives the original
>> stream and the s
more what is your use case?
> This way we may figure out another approach to achieve your goal.
> In fact, I am not sure if you earn anything by broadcasting the watermark,
> other than
> re-implementing (to some extent) Flink’s windowing mechanism.
>
> Thanks,
> Kos
Hi,
WindowedStream has sideOutputLateData and allowedLateness methods to handle
late data. A similar functionality at CoGroupedStreams would have been
nice. As it is, it silently ignores late data and it is error-prone.
- Is there a reason it does not exist?
- Any suggested workaround?
Hi,
Is it possible to synchronize two kafka sources? So they can consume from
different Kafka topics in close enough event times.
My use case is, I have two Kafka topics: A(very large) and B(large). There
is a mapping of one to one or zero between A and B. Topology is simply join
A and B in a
