The blog post explores the performance limitations of Kafka when dealing with small messages and high throughput. The author systematically benchmarks Kafka's performance under various configurations, focusing on the impact of message size, batching, compression, and acknowledgment settings. They discover that while Kafka excels with larger messages, its performance degrades significantly with smaller payloads, especially when acknowledgements are required. This degradation stems from the overhead associated with network round trips and metadata management, which outweighs the benefits of Kafka's design in such scenarios. Ultimately, the post concludes that while Kafka remains a powerful tool, it's not ideally suited for all use cases, particularly those involving small messages and strict latency requirements.
The blog post "Kafka at the Low End: How Bad Can It Get?" by Kris Nóva explores the performance characteristics of Apache Kafka, a popular distributed streaming platform, when operating under resource-constrained conditions. Specifically, the author investigates how Kafka performs when deployed on a single, low-powered Raspberry Pi 4 Model B, equipped with a mere 4GB of RAM and a relatively slow SD card. This unconventional setup is intentionally chosen to push Kafka to its limits and understand its behavior in a worst-case scenario, far removed from the robust, multi-node deployments typically seen in production environments.
Nóva meticulously documents their experimental setup, including the specific hardware and software versions used, providing a transparent and reproducible methodology. They articulate the rationale behind choosing the Raspberry Pi, highlighting the desire to understand the absolute minimum resource requirements for operating Kafka and to potentially uncover performance bottlenecks that might not be apparent in more powerful environments. This approach allows for a granular examination of Kafka's internal workings and resource utilization patterns.
The experiment focuses on measuring Kafka's throughput, latency, and resource consumption (CPU, memory, disk I/O) under varying workloads. Nóva employs a simple producer-consumer setup, systematically increasing the message size and throughput to stress the system. The results reveal that, even on such a resource-limited device, Kafka can surprisingly handle a modest workload with reasonable latency, albeit with significantly lower throughput compared to production-grade deployments. The author meticulously presents the collected data through graphs and tables, illustrating the relationship between message size, throughput, and latency.
The investigation further dives into the impact of the storage medium, comparing the performance of the SD card with a USB-attached SSD. As expected, the SSD drastically improves performance, particularly in terms of write latency, demonstrating the significant influence of storage speed on Kafka's overall performance. This underscores the importance of choosing appropriate storage hardware for Kafka deployments, especially in scenarios where write performance is critical.
Nóva also discusses the practical implications of running Kafka on such a low-powered device, acknowledging the limitations and trade-offs involved. While not advocating for production deployments on Raspberry Pis, the author suggests that this kind of low-end experimentation can be valuable for educational purposes, allowing for hands-on exploration of Kafka's internals and performance characteristics without requiring substantial infrastructure investment. The blog post concludes with reflections on the surprising resilience of Kafka even under extreme resource constraints and emphasizes the value of understanding the system's behavior across a wide spectrum of hardware configurations.
Summary of Comments ( 97 )
https://news.ycombinator.com/item?id=43095070
HN users generally agree with the author's premise that Kafka's complexity makes it a poor choice for simple tasks. Several commenters shared anecdotes of simpler, more efficient solutions they'd used in similar situations, including Redis, SQLite, and even just plain files. Some argued that the overhead of managing Kafka outweighs its benefits unless you have a genuine need for its distributed, fault-tolerant nature. Others pointed out that the article focuses on a very specific, low-throughput use case and that Kafka shines in different scenarios. A few users mentioned kdb+ as a viable alternative for high-performance, low-latency needs. The discussion also touched on the challenges of introducing and maintaining Kafka, including the need for dedicated expertise.
The Hacker News thread linked discusses the blog post "Kafka at the low end: how bad can it get?" which explores the performance of Kafka with limited resources. The comments are generally focused on the practicality of using Kafka in resource-constrained environments, alternative solutions, and the validity of the author's testing methodology.
Several commenters question the author's setup and methodology, arguing that the chosen hardware and configuration aren't representative of real-world use cases, even for low-end deployments. They point out that using a Raspberry Pi 4 with limited RAM and an SD card for storage is an exceptionally constrained environment that would likely hinder the performance of any database, not just Kafka. Some suggest that using an SSD or more RAM would significantly improve performance, even on a low-power device. Furthermore, some commenters question the author's focus on single-partition performance, arguing that Kafka is designed for multi-partition scaling and that testing a single partition doesn't accurately reflect real-world usage.
Alternative solutions are also a recurring theme in the comments. Several commenters suggest using SQLite, Redis, or even a simple file-based approach for logging and queuing in resource-constrained environments. They argue that these solutions are simpler to manage and require fewer resources than Kafka, making them better suited for low-end applications. Some also suggest exploring message queues specifically designed for embedded systems or IoT devices, highlighting the overhead associated with Kafka's distributed nature.
Some commenters acknowledge the author's point about the resource intensity of Kafka. They agree that Kafka is not the ideal solution for every situation, particularly when resources are extremely limited. They appreciate the author's exploration of Kafka's performance limitations and the insights provided into its internal workings.
A few commenters delve into more technical aspects, discussing the impact of Kafka's configuration parameters on performance, the overhead of the Java Virtual Machine (JVM), and the trade-offs between durability and performance. One commenter specifically mentions the importance of tuning parameters like the number of file descriptors and the page cache size for optimal performance.
Finally, some commenters express skepticism about the author's conclusion that Kafka is unsuitable for low-end deployments. They argue that Kafka's robustness, scalability, and fault tolerance can be valuable even in resource-constrained environments, and that careful configuration and hardware selection can mitigate performance issues.