Hi Lei,
In addition to the valuable suggested options above, maybe you can try to
optimize your partitioning function (since you know your data).
Maybe sample [subset of] your data if possible and/or check the key
distribution, before re-defining your partitioning function.
Regards,
Jeyhun
On Mo
Congrats!
Thanks to release managers and everyone involved.
Regards,
Jeyhun
On Mon, Mar 18, 2024 at 9:25 AM Lincoln Lee wrote:
> The Apache Flink community is very happy to announce the release of Apache
> Flink 1.19.0, which is the fisrt release for the Apache Flink 1.19 series.
>
> Apache Fli
Hi Arjun,
Thanks for your query. Flink is fault tolerant and supports exactly-once
semantics. In your case, the aggregated values can be recovered in case of
a failure or application restart.
You just need to enable checkpointing and configure an appropriate state
backend.
Regards,
Jeyhun
>
> O
Hi all,
We are currently working on this issue to make efficient mixing between
datastream window and dataset.
However, the simplest solution would be, to output each window in a
sequential file to HDFS and do computation on that datasource as dataset.
On Fri, Mar 4, 2016 at 4:05 PM sskhiri w
6 at 10:30 AM, Sane Lee wrote:
>>
>>> I have also, similar scenario. Any suggestion would be appreciated.
>>>
>>> On Thu, Feb 4, 2016 at 10:29 AM Jeyhun Karimov
>>> wrote:
>>>
>>>> Hi Matthias,
>>>>
>>>> This need no
Hi Matthias,
This need not to be necessarily in api functions. I just want to get a
roadmap to add this functionality. Should I save each window's data into
disk and create a new dataset environment in parallel? Or change trigger
functionality maybe?
I have large windows. As I asked in previous q
