If I have a use case where I need to join two streams or aggregate some kind of metrics from a single stream, and I use keyed streams to partition the events, how does Flink handle the operations for hot partitions where the data might not fit into memory and needs to be split across partitions?
Flink doesn't do anything automatic regarding hot partitions.
If you have a consistently hot partition, you can manually split it and pre-aggregate the splits.
If your concern is about avoiding out-of-memory errors due to unexpected load spikes for one partition, you can use a state backend that spills to disk.
If you want more dynamic data routing / partitioning, look at the Stateful Functions API or the Dynamic Data Routing section of this blog post.
If you want auto-scaling, see Autoscaling Apache Flink with Ververica Platform Autopilot.