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.
Nick Janetakis's blog post explores the maximum number of Alpine Linux packages installable at once. He systematically tested installation limits, encountering various errors related to package database size, memory usage, and filesystem capacity. Ultimately, he managed to install around 7,800 packages simultaneously before hitting unavoidable resource constraints, demonstrating that while Alpine's package manager can technically handle a vast number of packages, practical limitations arise from system resources. His experiment highlights the balance between package manager capabilities and the realistic constraints of a system's available memory and storage.
Hacker News users generally agree with the article's premise that Alpine Linux's package manager allows for installing a remarkably high number of packages simultaneously, far exceeding other distributions. Some commenters point out that this isn't necessarily a practical metric, arguing it's more of a fun experiment than a reflection of real-world usage. A few suggest the high number is likely due to Alpine's smaller package size and its minimalist approach. Others discuss the potential implications for dependency management and the possibility of conflicts arising from installing so many packages. One commenter questions the significance of the experiment, suggesting a focus on package quality and usability is more important than sheer quantity.
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.