I want to load a large amount of data into Flinks state backend (RocksDB) and process events using this data (for example in a CoProcessFunction). The data is much larger than the memory of a TaskManager.
The data blocks for a key can be very large, which has a negative impact on latency if the data needs to be loaded from the state backend each time. Therefore I would like to keep the data for frequent keys locally in the CoProcessFunction.
However, the total data in the state backend is larger than the memory of a TaskManager so it is not possible to keep the corresponding data block from the state backend locally for each key.
To solve this problem I would need to know the current memory usage of a SubTask to decide if a data block for a key can be kept locally or if something needs to be deleted. So here is my question: Since keys are not clearly assigned to a subtask is there a way to access memory related subtask information or custom metrics related to subtasks in a KeyedStream ? Or is there another way to solve this problem ? (External access via Async I/O is not an option).
The RocksDB block cache is already doing roughly what you describe. Rather than implementing your own caching layer, you should be able to get good results by tuning RocksDB, including giving it plenty of memory to work with.
Using RocksDB State Backend in Apache Flink: When and How is a good starting point, and includes pointers to where you can learn more about the native RocksDB metrics, memory management, etc. I also recommend reading The Impact of Disks on RocksDB State Backend in Flink: A Case Study.
As for your question about accessing subtask metrics from within your Flink job -- I don't know of any way to do that locally. You could, I suppose, implement a Flink source connector that fetches them and streams them into the job as another data source.