To have a readable blog, all the links are at the bottom except the link to the Fluvio project.
Benchmarking Fluvio: Community Insights and the Future of Streaming
Yesterday, I shared a blog on benchmarking results comparing Fluvio, our next-generation streaming engine, with Apache Kafka.
The response from the Rust community was encouraging, with over 30,000 impressions, 80+ upvotes, and 40+ comments in just 24 hours. The feedback was invaluable, and I want to share the 5 things I learned from all the developer feedback.
What is Fluvio?
Fluvio is a distributed streaming engine built in Rust over the past six years. While it follows Apache Kafka's conceptual patterns, it introduces programmable design patterns through Rust and WebAssembly-based stream processing called Stateful DataFlow (SDF). This makes Fluvio a complete platform for event streaming.
Key Community Insights
1. Developers care a lot about the benchmark environment.
The community emphasized the importance of comprehensive testing environments:
- Need for bare metal servers to eliminate virtualization artifacts
- Production-grade setups with proper replication (factor of 3)
- Large-scale validation with terabyte-scale live data
The ideal benchmarks will be using real-world data from Blockchain, High-Frequency Trading, or Ad-Tech on bare metal servers and compare multiple systems like Kafka, RedPanda, Pulsar.
2. Intelligent developers know about the trade-offs of using different hardware.
Developers highlighted several hardware-specific considerations:
- ARM Graviton chips' latency variations in virtualized environments
- Importance of testing across different CPU architectures including x86
- Thermal throttling differences between consumer laptops and server-grade hardware
3. Seasoned developers want production-ready configuration for each solution being configured
Runtime mechanics need to reflect real-world scenarios:
- Specific JVM and Garbage Collector configurations for Kafka benchmarking
- Resource utilization patterns under various loads
- Multi-node deployment testing at scale
4. While benchmarks are great benchmarking in mature categories require mention of table-stakes features
Key functionality developers look for:
- Consumer groups for ordered delivery per partition
- Stream and batch processing capabilities
- Robust delivery guarantees
5. Benchmarks also immediately makes developers think of reliability and debugging experience
Critical operational features:
- Dead letter queue implementations
- Retry strategies for network issues
- Delivery proof mechanisms beyond best-effort
The New Streaming Paradigm
Event streaming is a basic pattern in a world filled with agents.
Wise developers focus on:
- Practical performance over theoretical maxima
- Transparent benchmarking methodology
- Intuitive deployment and management
Our Vision for Next Generation Data Intensive Applications
We believe the next wave of intelligent applications will come from builders who:
- Challenge traditional infrastructure assumptions
- Require millisecond latencies at scale
- Prioritize resource efficiency
We don't just need faster systems - we need smarter ones that don't drain budgets or sanity.
The future belongs to systems that balance raw performance with operational wisdom. The question isn't just about speed—it's about enabling rapid innovation delivering an intuitive developer ergonomics while maintaining efficiency and reliability.
Get Involved
Explore Fluvio:
- Star our GitHub repository
- Share with your friends
- Try our Rust-core development
- Experiment with our WASM based programmability
- Check out InfinyOn Cloud
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