The blog post explores a hypothetical redesign of Kafka, leveraging modern technologies and learnings from the original's strengths and weaknesses. It suggests improvements like replacing ZooKeeper with a built-in consensus mechanism, utilizing a more modern storage engine like RocksDB for improved performance and tiered storage options, and adopting a pull-based consumer model inspired by systems like Pulsar for lower latency and more efficient resource utilization. The post emphasizes the potential benefits of a gRPC-based protocol for improved interoperability and extensibility, along with a redesigned API that addresses some of Kafka's complexities. Ultimately, the author envisions a "Kafka 2.0" that maintains core Kafka principles while offering improved performance, scalability, and developer experience.
The blog post "What If We Could Rebuild Kafka from Scratch?" by Gwen Shapira explores the hypothetical scenario of redesigning Apache Kafka, a popular distributed streaming platform, if given the opportunity to start anew with the benefit of hindsight and current technological advancements. Shapira emphasizes that this is a thought experiment, not a proposal for a Kafka replacement, focusing on how evolving needs and technological landscapes might influence a reimagining of Kafka's core architecture and functionality.
The post begins by acknowledging Kafka's strengths, particularly its robust performance, mature ecosystem, and wide adoption. However, it argues that certain aspects of Kafka, rooted in its initial design choices, now present complexities and limitations. These include the tight coupling between storage and compute, the intricacies of its partition-based architecture for scaling, and the inherent challenges of achieving exactly-once semantics across diverse use cases.
Shapira delves into several key areas where a redesigned Kafka could potentially diverge from the current implementation. One major area of focus is decoupling storage and compute. This would involve separating the responsibility for data persistence from the processing logic, potentially allowing for more flexible scaling and utilization of different storage backends tailored to specific workloads. The post suggests exploring cloud-native storage solutions, such as object stores, and leveraging technologies like tiered storage to optimize cost-effectiveness.
Furthermore, the blog post examines alternative approaches to partitioning, a fundamental mechanism in Kafka for distributing data and achieving parallelism. While acknowledging the benefits of partitioning, it highlights the operational complexities involved in managing and rebalancing partitions as data volumes and processing requirements change. The post speculates about exploring alternative data organization strategies that could offer simplified scaling and management, potentially drawing inspiration from newer database architectures.
Another aspect explored is the simplification of exactly-once semantics. Achieving exactly-once processing in distributed systems is notoriously difficult. Kafka offers robust guarantees, but their implementation can be complex for developers to grasp and utilize effectively. The blog post suggests exploring alternative approaches, potentially leveraging newer transaction processing technologies, to streamline the process and reduce the burden on application developers.
Additionally, the post touches on the potential for integrating more advanced stream processing capabilities directly into the core Kafka architecture. This could involve blurring the lines between Kafka and stream processing frameworks like Kafka Streams or Flink, offering a more unified and streamlined experience for users.
In conclusion, the blog post emphasizes that the hypothetical redesign of Kafka is a complex undertaking with significant trade-offs. While acknowledging the potential benefits of incorporating newer technologies and addressing existing limitations, it stresses the importance of carefully considering the impact on backward compatibility, ecosystem integration, and overall operational complexity. The goal is not to advocate for abandoning Kafka, but rather to stimulate discussion and exploration of how its core principles could be reimagined in light of evolving technological advancements and user needs.
Summary of Comments ( 74 )
https://news.ycombinator.com/item?id=43790420
HN commenters largely agree that Kafka's complexity and operational burden are significant drawbacks. Several suggest that a ground-up rewrite wouldn't fix the core issues stemming from its distributed nature and the inherent difficulty of exactly-once semantics. Some advocate for simpler alternatives like SQS for less demanding use cases, while others point to newer projects like Redpanda and Kestra as potential improvements. Performance is also a recurring theme, with some commenters arguing that Kafka's performance is ultimately good enough and that a rewrite wouldn't drastically change things. Finally, there's skepticism about the blog post itself, with some suggesting it's merely a lead generation tool for the author's company.
The Hacker News post "What If We Could Rebuild Kafka from Scratch?" generated a moderate amount of discussion, with several commenters offering perspectives on the original blog post's proposition.
A key theme in the comments revolves around questioning the practicality and necessity of rebuilding Kafka. Several commenters point out Kafka's maturity and robust ecosystem, suggesting that rebuilding it would be a monumental undertaking with questionable benefits. They argue that the effort involved in replicating Kafka's existing features and reliability would be immense, and that the potential gains outlined in the blog post might not justify such a significant investment. Some also highlight the risk of introducing new bugs and regressions in a rewritten version.
Another thread of discussion focuses on the potential benefits of exploring alternative approaches to distributed log systems. While acknowledging the dominance and effectiveness of Kafka, some commenters express interest in the idea of leveraging newer technologies and design principles to potentially address some of Kafka's perceived shortcomings. They discuss the potential for improved performance, simplified operation, and enhanced developer experience through a ground-up redesign. Specific technologies mentioned include cloud-native architectures, serverless computing, and alternative consensus protocols like Raft.
Some commenters delve into specific technical aspects of Kafka's architecture, debating the merits and drawbacks of certain design choices. Topics discussed include the trade-offs between performance and durability, the complexities of partition management, and the challenges of achieving exactly-once semantics.
Finally, a few comments touch upon the author's experience and perspective. Some commend the author for raising thought-provoking questions and sparking discussion about the future of distributed log systems. Others express skepticism about the feasibility of the proposed "Kafka killer," citing the difficulty of competing with an established and widely adopted technology like Kafka.
In summary, the comments generally acknowledge the value of exploring alternative approaches to distributed logging but express considerable skepticism about the practicality and necessity of a complete Kafka rewrite. The discussion highlights the significant challenges involved in replicating Kafka's existing functionality and ecosystem while emphasizing the potential benefits of exploring newer technologies and design principles.