Happy to help,
Let us know if it helped in your use case.
On Tue, Apr 5, 2022 at 1:34 AM Yaroslav Tkachenko
wrote:
> Hi Marios,
>
> Thank you, this looks very promising!
>
> On Mon, Apr 4, 2022 at 2:42 AM Marios Trivyzas wrote:
>
>> Hi again,
>>
>> Maybe you can use the
>> https://nightlies.ap
Hi Marios,
Thank you, this looks very promising!
On Mon, Apr 4, 2022 at 2:42 AM Marios Trivyzas wrote:
> Hi again,
>
> Maybe you can use the
> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/config/#table-exec-sink-keyed-shuffle
> *table.exec.sink.keyed-shuffle* and set it t
Hi again,
Maybe you can use the
https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/config/#table-exec-sink-keyed-shuffle
*table.exec.sink.keyed-shuffle* and set it to *FORCE, *which will use the
primary key column(s) to partition and distribute the data.
On Fri, Apr 1, 2022 at 6:
Hi!
I don't think there is a way to achieve that without resorting to
DataStream API.
I don't know if using the PARTITIONED BY clause in the create statement of
the table can help to "balance" the data, see
https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/create/#partitioned
Hey everyone,
I'm trying to use Flink SQL to construct a set of transformations for my
application. Let's say the topology just has three steps:
- SQL Source
- SQL SELECT statement
- SQL Sink (via INSERT)
The sink I'm using (JDBC) would really benefit from data partitioning (by
PK ID) to avoid c
