Hey all,
Is it somehow possible to hook into all states in a current Job and
clear them all at once? Currently the way I do it is just to stop the
job and then restarting it.
Was wonderding if there is a way where I can do it without restarting
the job. I know about adding TTL to states but
It depends on the user case, in Shilpa's use case it is about users so
the user ids are probably know beforehand.
https://dpaste.org/cRe3G <= This is an example with out an window but
essentially Shilpa you would be reregistering the timers every time they
fire.
You would also have to ingest
Hi,
I think you are looking for this answer from David:
https://stackoverflow.com/questions/69799181/flink-streaming-do-the-events-get-distributed-to-each-task-slots-separately-acc
I think then you could technically create your partitioner - though
little bit cubersome - by mapping your exist
just want to do
tumbling window over the "processing time" I.e: count whatever I have
in the last 5 minutes.
On Mon, 31 Jan 2022 at 17:09, Dario Heinisch
wrote:
Hi John
This is because you are using event time
(TumblingEventTimeWinodws) but you do not have a event tim
Hi John
This is because you are using event time (TumblingEventTimeWinodws) but
you do not have a event time watermark strategy.
It is also why I opened:
https://issues.apache.org/jira/browse/FLINK-24623 because I feel like
Flink should be throwing an exception in that case
on startup.
Take
Hey there,
Hope everyone is well!
I have a question:
```
StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
DataStream dataStream = env.addSource(new CustomSource());
OutputTag outputTag = new OutputTag("late"
A shot in the dark but could it be this:
https://mux.com/blog/5-years-of-flink-at-mux/ ?
> The JVM will cache DNS entries forever by default. This is
undesirable in Kubernetes deployments where there’s an expectation that
DNS entries can and do change frequently as pod deployments move between
Union creates a new stream containing all elements of the unioned
streams:
https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/datastream/operators/overview/#union
On 05.11.21 14:25, Marco Villalobos wrote:
Can two different streams flow to the same operator (an operator with
t
Hey there,
So I have a stream of data, let the stream be a_1, a_2, a_3, a_4, a_5.
Now I would like to have a sliding window which slides by 1 second and
takes the data of 1 second. But here comes the caveat:
- I only want to keep the data in the window that does not have matching
elements.
t approach, then adding a
random key to create a keyed stream should work in all cases, right?
Jan
On 7/18/21 3:52 PM, Dario Heinisch wrote:
Hey Kiran,
Yeah was thinking of another solution, so I have one posgresql sink &
one kafka sink.
So I can just process the data in real time a
8.07.21 15:29, Kiran Japannavar wrote:
Hi Dario,
Did you explore other options? If your use case (apart from delaying
sink writes) can be solved via spark streaming. Then maybe spark
streaming with a micro-batch of 15 mins would help.
On Sat, Jul 17, 2021 at 10:17 PM Dario Heinisch
Hey there,
Hope all is well!
I would like to delay the time by 15minutes before my data arrives at my
sinks:
stream()
.map()
[]
.
.print()
I tried implementing my own ProcessFunction where TimeStamper is a
custom Interface:
public abstract class Timestamper {
public abstract long
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