Ask HN: Why do message queue-based architectures seem less popular now?
📰 Hacker News · alexhutcheson
Learn why message queue-based architectures have declined in popularity and how alternative technologies like Redis, Kafka, and improved databases have filled the gap
Action Steps
- Evaluate your system's requirements to determine if a message queue is necessary
- Consider using Redis for caching and messaging needs
- Explore Kafka for high-scale applications
- Assess your database's capabilities for handling transient application state
- Design your system with scalability and operational costs in mind
Who Needs to Know This
Software engineers and architects can benefit from understanding the shift in technology trends and how to design scalable systems
Key Insight
💡 Alternative technologies have reduced the need for separate message brokers, making them less popular
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🤔 Why have message queues fallen out of favor? 📦 Redis, Kafka, and improved databases might be the answer 🚀
Key Takeaways
Learn why message queue-based architectures have declined in popularity and how alternative technologies like Redis, Kafka, and improved databases have filled the gap
Full Article
In the late 2000s and early 2010s, I remember seeing lots of hype around building distributed systems using message queues (e.g. Amazon SQS, RabbitMQ, ZeroMQ, etc.) A lot of companies had blog posts highlighting their use of message queues for asynchronous communication between nodes, and IIRC the official AWS design recommendations at the time pushed SQS pretty heavily. Now, I almost never see engineering blog posts or HN posts highlighting use of message queues. I see occasional content related to Kafka, but nothing like the hype that message queues used to have. What changed? Possible theories I'm aware of: * Redis tackled most of the use-case, plus caching, so it no longer made sense to pay the operational cost of running a separate message broker. Kafka picked up the really high-scale applications. * Databases (broadly defined) got a lot better at handling high scale, so system designers moved more of the "transient" application state into the main data stores. * We
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