Re: Regarding increasing Parallelism of the keyby and window function while migrating

Hello there Sambhav! > I tried using setParallelism() here but I think it doesn't comply with flink schema and is using default parallelism instead So are you disabling chaining to set a

Re: Regarding increasing Parallelism of the keyby and window function while migrating

Hi Pedro, I tried using disabling changing but it is not working Providing code snippets for better understanding ``` Datastream> discrepancy= db2input.keyBy((Key selector) .coGroup(kafkaInput) .where(key) .equalTo(key)

Re: Regarding increasing Parallelism of the keyby and window function while migrating

Hi all, Following up on request Thanks On Mon, 26 May 2025, 15:18 Sambhav Gupta, wrote: > Hey, > > There is no error with the parallelism .I want to increase it for this > function as it is creating a bottleneck for the disk space which I am not > able to do. > > I tried using setParallelism()

Re: Regarding increasing Parallelism of the keyby and window function while migrating

What is the error on the parallelism you're facing? Att, Pedro Mázala Be awesome On Mon, 26 May 2025 at 10:13, Sambhav Gupta wrote: > Hi Team, > > We are migrating our codebase of flink to V2.1 version. Here were using > dataset jobs which we need to migrate to data stream now and while doin

Re: Regarding increasing Parallelism of the keyby and window function while migrating

Hey, There is no error with the parallelism .I want to increase it for this function as it is creating a bottleneck for the disk space which I am not able to do. I tried using setParallelism() here but I think it doesn't comply with flink schema and is using default parallelism instead Can you p

Regarding increasing Parallelism of the keyby and window function while migrating

Hi Team, We are migrating our codebase of flink to V2.1 version. Here were using dataset jobs which we need to migrate to data stream now and while doing this we faced an error of parallelism of keyby and window function in our full outerjoin function which is creating bottleneck for us in case of