The paper "File Systems Unfit as Distributed Storage Back Ends" argues that relying on traditional file systems for distributed storage systems leads to significant performance and scalability bottlenecks. It identifies fundamental limitations in file systems' metadata management, consistency models, and single points of failure, particularly in large-scale deployments. The authors propose that purpose-built storage systems designed with distributed principles from the ground up, rather than layered on top of existing file systems, are necessary for achieving optimal performance and reliability in modern cloud environments. They highlight how issues like metadata scalability, consistency guarantees, and failure handling are better addressed by specialized distributed storage architectures.
Richard Sutton and Andrew Barto have been awarded the 2024 ACM A.M. Turing Award for their foundational contributions to reinforcement learning (RL). Their collaborative work, spanning decades and culminating in the influential textbook Reinforcement Learning: An Introduction, established key algorithms, conceptual frameworks, and theoretical understandings that propelled RL from a niche topic to a central area of artificial intelligence. Their research laid the groundwork for numerous breakthroughs in fields like robotics, game playing, and resource management, enabling the development of intelligent systems capable of learning through trial and error.
Hacker News commenters overwhelmingly praised Sutton and Barto's contributions to reinforcement learning, calling their book the "bible" of the field and highlighting its impact on generations of researchers. Several shared personal anecdotes about using their book, both in academia and industry. Some discussed the practical applications of reinforcement learning, ranging from robotics and game playing to personalized recommendations and resource management. A few commenters delved into specific technical aspects, mentioning temporal-difference learning and policy gradients. There was also discussion about the broader significance of the Turing Award and its recognition of fundamental research.
This paper chronicles the adoption and adaptation of APL in the Soviet Union up to 1991. Initially hampered by hardware limitations and the lack of official support, APL gained a foothold through enthusiastic individuals who saw its potential for scientific computing and education. The development of Soviet APL interpreters, notably on ES EVM mainframes and personal computers like the Iskra-226, fostered a growing user community. Despite challenges like Cyrillic character adaptation and limited access to Western resources, Soviet APL users formed active groups, organized conferences, and developed specialized applications in various fields, demonstrating a distinct and resilient APL subculture. The arrival of perestroika further facilitated collaboration and exchange with the international APL community.
HN commenters discuss the fascinating history of APL's adoption and adaptation within the Soviet Union, highlighting the ingenuity required to implement it on limited hardware. Several share personal anecdotes about using APL on Soviet computers, recalling its unique characteristics and the challenges of working with its specialized keyboard. Some commenters delve into the technical details of Soviet hardware limitations and the creative solutions employed to overcome them, including modifying character sets and developing custom input methods. The discussion also touches on the broader context of computing in the USSR, with mentions of other languages and the impact of restricted access to Western technology. A few commenters express interest in learning more about the specific dialects of APL developed in the Soviet Union and the influence of these adaptations on later versions of the language.
Summary of Comments ( 7 )
https://news.ycombinator.com/item?id=43526621
HN commenters generally agree with the paper's premise that traditional file systems are poorly suited for distributed storage backends. Several highlighted the impedance mismatch between POSIX semantics and distributed systems, citing issues with consistency, metadata management, and performance bottlenecks. Some questioned the novelty of the paper's findings, arguing these limitations are well-known. Others discussed alternative approaches like object storage and databases, emphasizing the importance of choosing the right tool for the job. A few commenters offered anecdotal experiences supporting the paper's claims, while others debated the practicality of replacing existing file system-based infrastructure. One compelling comment suggested that the paper's true contribution lies in quantifying the performance overhead, rather than merely identifying the issues. Another interesting discussion revolved around whether "cloud-native" storage solutions truly address these problems or merely abstract them away.
The Hacker News post titled "File Systems Unfit as Distributed Storage Back Ends (2019)" with the ID 43526621 has several comments discussing the linked ACM article. The discussion generally agrees with the premise of the paper, highlighting the inherent limitations of traditional file systems when used as the foundation for distributed storage systems.
Several commenters point out that using file systems in this way often leads to performance bottlenecks. One commenter specifically mentions the challenges of managing metadata at scale, noting that operations like listing directories or checking file existence become significantly slower as the number of files grows. They suggest that specialized distributed storage systems are designed to handle these metadata operations more efficiently.
Another commenter expands on this idea by describing the inherent trade-offs file systems make. They explain that file systems prioritize data consistency and durability, which are crucial for single-machine use cases. However, these guarantees come at the cost of performance and scalability in distributed environments, where eventual consistency and other relaxed guarantees are often more suitable.
One compelling comment argues that the issue isn't with file systems themselves, but rather with the mismatch between their design goals and the requirements of distributed storage. They propose that file systems are optimized for local storage on a single machine, where factors like latency and bandwidth are relatively predictable. In contrast, distributed systems must contend with network partitions, varying node performance, and other complexities that make traditional file system semantics difficult to maintain efficiently.
Another interesting perspective is offered by a commenter who suggests that the paper's title is slightly misleading. They argue that file systems can be used effectively in distributed storage, but only with careful consideration and significant modifications. They mention specific examples like GlusterFS and Ceph, which are distributed file systems designed to address the limitations of traditional file systems in distributed environments.
A couple of comments mention alternative approaches to building distributed storage, including key-value stores and object storage. These systems, they argue, are better suited to the demands of large-scale data management because they offer simpler interfaces and more flexible consistency models.
Finally, one commenter highlights the importance of understanding the trade-offs involved in choosing a storage back end. They emphasize that there is no one-size-fits-all solution and that the best choice depends on the specific requirements of the application. They advise considering factors like data volume, access patterns, and consistency requirements when making a decision.