How does Scala support multi-layer event deduplication in distributed streaming architectures?

Scala streaming systems ensure deduplication using event IDs, state stores, and idempotent processing pipelines.

In large-scale Scala streaming architectures (Kafka + Akka Streams/Spark), duplicate events can occur due to retries, at-least-once delivery, or reprocessing after failure. Deduplication is implemented at multiple layers: ingestion (Kafka key constraints), stream processing (stateful stores tracking processed event IDs), and sink layer (idempotent writes using upserts). State stores (RocksDB in Flink/Spark stateful ops) maintain a sliding window of processed IDs. This ensures exactly-once effect even in at-least-once systems.