This article shares practical experiences and insights on using different types of state in Apache Flink for real‑time stream processing, covering managed versus raw state, code examples in Scala and Java, handling late data, dimension table joins, distinct semantics, and best‑practice recommendations.
This article demonstrates how to build a real‑time education analytics system by streaming answer data through Kafka into Flink or Spark, performing per‑question, per‑grade, and per‑subject aggregations, and optionally accelerating development with UFlink SQL.
This article provides an in‑depth technical overview of Apache Storm, covering its core architecture, key components such as Nimbus, Supervisor, Worker, Executor, and Task, the role of ZooKeeper, high‑availability setup, API interfaces, code examples, grouping strategies, metrics, back‑pressure handling, and essential configuration parameters for building low‑latency stream processing topologies.
The study benchmarks Apache Flink against Apache Storm on a shared cluster, showing Flink delivering three‑to‑five times higher throughput and roughly half the latency across simple, sleep‑induced, and windowed workloads, with modest throughput loss for exactly‑once semantics, leading to a recommendation of Flink for high‑performance, stateful real‑time stream processing.
