Structured Streaming concepts

Structured Streaming processes an unbounded input as data arrives and incrementally updates results. The same DataFrame-style operations can often be used for batch and streaming, but streaming queries have state, checkpoint, output-mode, and trigger considerations. A checkpoint stores progress and state needed for fault-tolerant recovery and should be durable and isolated for the query. Choose a trigger based on latency and cost requirements, and understand the sink's behavior on restart. Exactly-once guarantees depend on the source, processing, and sink semantics working together; do not assume every external side effect is automatically exactly once. Practice task: explain why two independent queries should not casually share checkpoint state. Applying the pattern: Define the streaming source, transformations, trigger, checkpoint, and sink together, then validate restart behavior with a controlled test. Checks and failure modes: Deleting checkpoints without a replay plan can duplicate output or lose expected state. Before production, test both a representative input and a boundary case. Confirm the expected row grain, permissions, recovery behavior, and observable output for the task. Record the configuration needed to reproduce the run and compare its result with the agreed data contract. Practice task: Why does a streaming query need durable checkpoint storage?