Deciding proper parallelisms of operators is not an easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling.
To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism of an operator according to the size of its consumed datasets.
Read on to see some of the benefits of using the adaptive batch scheduler, as well as some of the decision points it uses along the way.