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 regular streaming features.
Would these features be available in a Flink Batch job as well? Best, Georg Am Fr., 6. Mai 2022 um 13:26 Uhr schrieb Martijn Visser < martijnvis...@apache.org>: > 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, > > Martijn > > [1] > https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/execution_mode/ > [2] > https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/common/ > > On Mon, 2 May 2022 at 16:46, Georg Heiler <georg.kf.hei...@gmail.com> > wrote: > >> 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 its own. This is useful in >> scenarios you want to periodically spin up a cluster, process everything >> that is available since the last period, and then shutdown the cluster. In >> some case, this may lead to significant cost savings. >> >> Does flink have a similar possibility? >> >> Best, >> Georg >> >
