Hey HN, Apache Flink is a beast for heavy-duty stream processing – no doubt about it. We've used it, admired its power, and it heavily influenced our thinking. But let's be honest: for a lot of common, everyday stream processing tasks, Flink (and similar frameworks) can feel like bringing a bazooka to a knife fight. The operational overhead and complexity for simpler, stateless jobs often outweigh the benefits. That's why we built ZephFlow. What is ZephFlow? ZephFlow is a stream processing framework built for lightweight operations and simplicity. It distills the core principles of high-performance, low-latency data processing but specifically targets stateless or simpler stateful workloads. This focus allows ZephFlow to be significantly more lightweight and easier to operate compared to more comprehensive platforms like Apache Flink. A single ZephFlow job can run as a lean, efficient process, making it highly versatile. Why We Think ZephFlow Hits the Mark Today (and in 2025): - Stop Overspending on Resources: Run your streaming tasks efficiently, whether on modest cloud instances or even edge devices, without paying for idle complexity. - Slash Operational Headaches: Deploying and managing ZephFlow is refreshingly simple. Spend your time on logic, not on wrestling with the infrastructure. - Ship Faster: If you need to get a streaming pipeline from idea to production quickly, ZephFlow's lean nature makes for rapid iteration. - Actually Versatile: It's proven great for us on real-time ETL, transforming logs and telemetry on the fly, prepping data for ML models, and even as a responsive backend for APIs. Our Philosophy: Simplicity Scales (Development, if Not Yet a Cluster). Many common real-time data tasks don't require distributed state management across a massive cluster from day one. ZephFlow lets you build and test your pipelines on a single instance with remarkable ease. While Flink is the go-to for truly complex distributed state, ZephFlow offers a nimble, focused alternative for a wide array of everyday streaming needs. We've been dogfooding ZephFlow for our own log and telemetry processing, and the sheer speed from coding to live has been a breath of fresh air. What's Next? We Need Your Brain. We're now sketching out how to scale ZephFlow deployments horizontally while obsessively preserving that core simplicity. For the kind of stateless or less-complex stateful streaming you do, what distributed features would genuinely make your life easier (and which ones are just noise)? We'd love your feedback in the comments. And if this sounds interesting, check out the project and maybe star it if you like where we're headed: https://github.com/fleaktech/zephflow-core Thanks for reading! |