Apache Flink状态后端存储与磁盘溢写机制的疑问

Great question! Let's break down how each of Flink's state backends handles state storage and disk spilling in plain terms:

• RocksDBStateBackend: This is the only backend that natively supports storing state in memory and spilling to disk when needed. It uses an embedded RocksDB instance (a persistent key-value store) to manage state—RocksDB automatically moves less frequently accessed data from memory to disk once the allocated memory limit is hit. This is what lets your application hold state far larger than the JVM heap size of your TaskManagers.

• MemoryStateBackend: All active state lives directly in the TaskManager's JVM heap during runtime. It does not spill to disk under normal operation; if your state grows beyond the available heap space, your job will throw an OutOfMemoryError. You can configure it to take asynchronous snapshots to the JobManager's memory or a filesystem, but this is only for fault tolerance—not handling runtime state overflow.

• FsStateBackend: Just like the MemoryStateBackend, runtime state stays in the TaskManager's JVM heap. It also does not spill to disk while your job is running. The main difference is that its state snapshots are saved to a distributed filesystem (like HDFS or S3) instead of the JobManager's memory. Exceeding the heap limit here will also trigger an OutOfMemoryError.

To connect this to the official documentation quote you mentioned:

"Depending on your state backend, Flink can also manage the state for the application, meaning Flink deals with the memory management (possibly spilling to disk if necessary) to allow applications to hold very large state."

This description specifically refers to the RocksDBStateBackend. The other two backends don't handle runtime disk spilling—they rely entirely on the JVM heap for active state storage.

内容的提问来源于stack exchange，提问作者YuFeng Shen