Stateful Computation¶
Stateful stream processing remembers information across records while making that memory partitionable, durable, recoverable, and eventually removable.
flowchart LR
J[Junior: why operators remember] --> M[Middle: keyed managed state]
M --> S[Senior: TTL, skew, and rescaling] --> P[Professional: state backends]
flowchart LR
E[Events keyed by account] --> O[Stateful operator]
O <--> S[(Keyed state)]
O --> R[Updated result]
C[Checkpoint] -.snapshots.-> S
Choose a level¶
| Level | Guide | You are done when |
|---|---|---|
| Junior | Why state exists | You can identify state needed for aggregation and deduplication. |
| Middle | Managed keyed state | You can implement keyed state and explain checkpoints. |
| Senior | State lifecycle | You can handle TTL, skew, schema changes, and rescaling. |
| Professional | Backend internals | You can compare Flink and Kafka Streams state architecture. |
Practice rule¶
For every state entry, define its key, owner, update rule, recovery source, maximum lifetime, schema version, and migration path.