Hi Georg,
I'm not aware of those examples being available publicly.
Best regards,
Martijn
On Mon, 9 May 2022 at 23:04, Georg Heiler wrote:
> Hi Martijn,
>
> many thanks for this clarification. Do you know of any example somewhere
> which would showcase such an approach?
>
> Best,
> Georg
>
>
Hi Martijn,
many thanks for this clarification. Do you know of any example somewhere
which would showcase such an approach?
Best,
Georg
Am Mo., 9. Mai 2022 um 14:45 Uhr schrieb Martijn Visser <
martijnvis...@apache.org>:
> Hi Georg,
>
> No they wouldn't. There is no capability out of the box th
Hi Georg,
No they wouldn't. There is no capability out of the box that lets you start
Flink in streaming mode, run everything that's available at that moment and
then stops when there's no data anymore. You would need to trigger the stop
yourself.
Best regards,
Martijn
On Fri, 6 May 2022 at 13:
Hi,
I would disagree:
In the case of spark, it is a streaming application that is offering full
streaming semantics (but with less cost and bigger latency) as it triggers
less often. In particular, windowing and stateful semantics as well as
late-arriving data are handled automatically using the r
Hi Georg,
Flink batch applications run until all their input is processed. When
that's the case, the application finishes. You can read more about this in
the documentation for DataStream [1] or Table API [2]. I think this matches
the same as Spark is explaining in the documentation.
Best regards
Hi,
spark
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#triggers
offers a variety of triggers.
In particular, it also has the "once" mode:
*One-time micro-batch* The query will execute *only one* micro-batch to
process all the available data and then stop on it
