Stream processing and event processing are two related but distinct approaches to handling data in real-time systems. Stream processing focuses on continuously processing sequences of data as they are generated, allowing for high throughput and low latency. It typically involves manipulating and analyzing large streams of data, which can be constant and unbounded. For example, in a financial trading application, stream processing can track stock prices in real-time, triggering alerts or executing trades based on set conditions as new price data comes in.
On the other hand, event processing centers around the discrete events that occur within a system. Each event is a significant occurrence that carries context and can trigger specific actions. Event processing usually involves identifying patterns or conditions over these events. For instance, in an online shopping platform, event processing can be used to monitor user behavior, such as placing items in a cart or completing a purchase, to identify trends or to recommend products. Each of these actions can be treated as a separate event that may influence subsequent decisions or trigger responses.
While both methodologies deal with data handling in real-time, stream processing is more about continuous flow and real-time analysis of large volumes of data, whereas event processing is concerned with the interpretation and reaction to specific, discrete events. A suitable implementation may involve using tools like Apache Kafka for stream processing and complex event processing (CEP) engines for event processing, allowing developers to choose the right paradigm based on the application’s specific needs and using patterns effectively to manage system behavior.