This article explains how to use Flink's DataStream.union() to combine multiple streams of the same type, demonstrates Maven project setup and code examples for simple unions and for unions with custom event-time sorting, and shows the resulting ordered output.
This article explains how to compute the top‑N hot products or brands within a time window using Apache Flink, covering data modeling, event‑time handling, sliding windows, custom aggregation functions, and result sorting with complete Java code examples.
This article explains the three time notions supported by Apache Flink—ProcessTime, EventTime, and IngestionTime—detailing their semantics, how Watermarks enable event‑time processing, and provides Scala code samples for configuring time characteristics, assigning timestamps, and generating Watermarks in a streaming job.
This article explains Apache Flink's ProcessFunction and CoProcessFunction, detailing their use of events, state, and timers, compares event‑time and processing‑time semantics, and provides a complete Java example illustrating timer registration, onTimer handling, and debugging observations.
This article explains how Apache Flink uses event‑time windows, watermarks, and allowed lateness to handle out‑of‑order and late data, and provides complete Scala code examples that demonstrate timestamp assignment, watermark generation, window triggering, and side‑output of late records.
This article explains the three time concepts in Flink—Process Time, Event Time, and Ingestion Time—illustrates their impact on windowed computations with examples, introduces watermarks and allowed lateness for handling out‑of‑order data, and provides complete Scala code for both processing‑time and event‑time streaming applications.
This article explains how Apache Flink uses Watermarks to manage event‑time windows, describes the three time semantics, details periodic and punctuated Watermark generation methods with their Java interfaces, and shows practical DDL examples for handling late and out‑of‑order data in stream processing.
This article introduces the design and implementation of a real‑time user behavior engineering platform at Qunar using Apache Flink, covering Flink's core characteristics, distributed runtime, DataStream programming model, fault‑tolerance, back‑pressure handling, event‑time processing, windowing, watermarks, and practical code examples for filtering, splitting, joining, and state management.
