Why it matters

Flink is often the right answer when Spark Streaming isn't fast enough. Understanding when to reach for it matters for streaming design.

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The architecture

DataStream API: streams as first-class. Map, filter, join, windowing operators.

State: managed keyed state (per-key values), operator state (per-operator).

Flink modelDataStreamfirst-class streamsState backendkeyed + operatorCheckpointsdurable state snapshotsEvent time + watermarks handle out-of-order events correctly
Flink components.
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How it works end to end

Event time: process by when event occurred, not when arrived. Watermarks handle out-of-order.

Checkpoints: periodic distributed state snapshots. Recovery to consistent state.

State backends: memory, RocksDB (disk-spilling for large state), FsStateBackend.

Deployment: standalone, YARN, Kubernetes.