![]() ![]() Other challenges include order and logic, as we have to determine how our application should process the input stream.įinally, stream processing is real-time. The first is performance, as the application must handle the load of incoming data streams. Stream processing architectures do have their challenges. A stream is an infinite sequence of messages that are generated and sent continuously. The events go to a message bus, where the streaming service picks them up.Īny workload generating a large flow of data (a_ _stream) that needs to be processed in real-time is well-suited for stream processing. Stream processing typically involves a more significant stream of data events that have already occurred. In turn, application target B picks up and processes the message from the message broker. Intelligence may also be an issue, depending on how developers recognize if the broker has already picked up or processed a message.įor example, application source A sends a message to a message broker. Its reliability also depends on how the application handles failed messages. Reliability may also be an issue if the message queue’s unavailability affects the application’s stability. For example, they have latency, so processing a message takes time. While queues are a fantastic way to send data across different application or service components, they also have some challenges. Think of a message queue as a sequential list of data blocks waiting to be processed. ![]() Any online transaction processing (OLTP) is a good candidate for message queues. Message queues transport messages between application components, across applications, or across services in traditional monolith applications, containers, or microservices. Let’s start by exploring the major differences between message queues and event streaming. Differences Between Message Queues and Streaming Finally, we’ll discuss how the open-source Apache Pulsar platform supports both message queues and streams, with a few subtle differences. We’ll touch on some use cases to highlight why sometimes one approach is better than the other. ![]() We’ll compare them here and examine the pros and cons of each solution, touching on message brokers, publisher-subscriber (pub/sub) architecture, and event-driven scenarios. While message queues and streaming apply to similar use cases and use similar technologies, on a technical level they’re entirely different. But weather apps, smart cars, health status apps with smartwatch technology, or anything Internet of things (IoT) typically rely on a message queue or streaming engine as well. Online food ordering apps, e-commerce sites, media streaming services, and online gaming are straightforward examples. Almost any application that requires real-time or near-real-time data processing benefits from having a message queue or streaming data processing component in its architecture. ![]()
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