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-by .
On Thu, Mar 31, 2022 at 7:18 AM Yaroslav Tkachenko <yaros...@goldsky.io> wrote: > 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 conflicting transactions and deadlocks. I can force Flink > to partition the data by the PK ID before the INSERT by resorting to > DataStream API and leveraging the keyBy method, then transforming > DataStream back to the Table again... > > Is there a simpler way to do this? I understand that, for example, a GROUP > BY statement will probably perform similar data shuffling, but what if I > have a simple SELECT followed by INSERT? > > Thank you! > -- Marios
