Stream Processing¶
Stream processing turns unbounded event logs into continuously updated results by routing records through an operator graph while managing time, state, failures, and flow control explicitly.
flowchart LR
G[Stream graph] --> B[Backpressure]
G --> D[Delivery guarantees]
G --> W[Window computation]
W --> S[Stateful computation]
S --> J[Stream joins]
flowchart LR
K[Kafka / CDC source] --> O1[Parse and keyBy]
O1 --> O2[Window or join]
O2 --> ST[(Managed state)]
O2 --> SN[Warehouse / lakehouse sink]
CP[Checkpoint coordinator] -.barriers.-> O1
CP -.snapshots.-> ST
Topics¶
| Topic | Core question |
|---|---|
| Stream Graph | How does a logical pipeline become a parallel operator DAG? |
| Backpressure | How does overload propagate through a running stream job? |
| Delivery Guarantees | How do source offsets, state, and sinks recover consistently? |
| Window Computation | How are unbounded events grouped using event time and watermarks? |
| Stateful Computation | How is keyed state stored, checkpointed, and rescaled? |
| Join Operations | How do streams join when records arrive late and out of order? |
Learning path¶
Start with the graph and backpressure topics to understand execution. Then study delivery guarantees before windows, state, and joins, because all stateful operators rely on coordinated recovery semantics.
Practice rule¶
For every streaming design, write down the event-time policy, partition key, state retention, recovery boundary, sink commit protocol, and overload behavior. If one is implicit, correctness is implicit too.