Jazco's post argues that Bluesky's "lossy" timelines, where some posts aren't delivered to all followers, are actually beneficial. Instead of striving for perfect delivery like traditional social media, Bluesky embraces the imperfection. This lossiness, according to Jazco, creates a more relaxed posting environment, reduces the pressure for virality, and encourages genuine interaction. It fosters a feeling of casual conversation rather than a performance, making the platform feel more human and less like a broadcast. This approach prioritizes the experience of connection over complete information dissemination.
Jazmyn Coleman, in their blog post titled "When imperfect systems are good: Bluesky's lossy timelines," explores the concept of embracing imperfection in system design, specifically within the context of social media platforms like Bluesky. They argue against the prevailing assumption that perfectly replicating data across all nodes in a distributed system, like the ActivityPub protocol Bluesky utilizes, is inherently superior. Coleman posits that this pursuit of perfect replication can introduce significant complexities and performance bottlenecks, ultimately hindering the user experience.
Instead, Coleman advocates for what they term "lossy" timelines, where a degree of inconsistency in data propagation is accepted. This means that a user's feed might not display every single post from every account they follow in perfect chronological order across all their devices or instances. This imperfection, they argue, is a trade-off worth making for the benefits it brings, particularly in terms of scalability and responsiveness. A system designed to tolerate some data loss can be more resilient to network interruptions, server failures, and other disruptions that are inevitable in a distributed environment. It can also be more performant, as it doesn't need to expend resources ensuring perfect synchronization across all nodes, allowing for faster loading times and a smoother user experience.
Coleman uses Bluesky's implementation of the ActivityPub protocol as a case study for this approach. While Bluesky aims for eventual consistency, where data eventually propagates across the network, it doesn't guarantee perfect replication or ordering. This design choice allows Bluesky to prioritize speed and efficiency, even if it means some posts might be delayed or even missed in certain scenarios. This, Coleman suggests, aligns better with the inherently messy and unpredictable nature of social media interactions, where a small degree of inconsistency has minimal impact on the overall user experience.
The core of Coleman's argument revolves around the idea that striving for perfect replication in a distributed system like a social network is often a misplaced priority. The complexity and overhead required for such perfection can negatively impact the very qualities – speed, responsiveness, and resilience – that are crucial for a positive user experience. By embracing a degree of imperfection and designing systems that can tolerate occasional data loss, platforms like Bluesky can prioritize these key performance indicators, ultimately creating a more robust and enjoyable user experience despite the occasional inconsistencies. The "lossy" approach, they argue, isn't a bug but a feature, a conscious design choice that prioritizes practicality and performance over the often-illusory goal of perfect replication in a complex, distributed environment.
Summary of Comments ( 271 )
https://news.ycombinator.com/item?id=43105028
HN users discussed the tradeoffs of Bluesky's sometimes-lossy timeline, with many agreeing that occasional missed posts are acceptable for a more performant, decentralized system. Some compared it favorably to email, which also isn't perfectly reliable but remains useful. Others pointed out that perceived reliability in centralized systems is often an illusion, as data loss can still occur. Several commenters suggested technical improvements or alternative approaches like local-first software or better synchronization mechanisms, while others focused on the philosophical implications of accepting imperfection in technology. A few highlighted the importance of clear communication about potential data loss to manage user expectations. There's also a thread discussing the differences between "lossy" and "eventually consistent," with users arguing about the appropriate terminology for Bluesky's behavior.
The Hacker News post "When imperfect systems are good: Bluesky's lossy timelines" discussing the linked blog post about imperfect systems has generated a moderate amount of discussion, with a number of commenters exploring the various facets of the topic.
Several commenters focused on the trade-offs between consistency and performance in distributed systems, agreeing with the author's point that sometimes accepting some loss of data or consistency can lead to significant gains in performance and scalability. One commenter specifically highlighted the example of DNS, arguing that its eventual consistency model is crucial for its resilience and global reach. They argued that requiring strong consistency for DNS would cripple its performance and make it far less practical.
Another commenter drew parallels to the CAP theorem, which states that a distributed data store can only provide two out of three guarantees: Consistency, Availability, and Partition tolerance. They pointed out that Bluesky's choice to prioritize availability and partition tolerance by accepting some data loss aligns with this theorem and is a valid design decision, particularly in a social media context.
There's a discussion around the practical implications of "lossy" systems. One commenter questioned how Bluesky handles disagreements about what constitutes "truth" in a federated system where different servers might have different versions of the timeline. This raises concerns about potential conflicts and the need for mechanisms to resolve discrepancies.
The concept of "eventual consistency" is also a recurring theme, with commenters discussing its applicability in various scenarios. One commenter noted that eventual consistency is a common characteristic of many successful distributed systems and that the trade-off in consistency is often acceptable in exchange for improved performance and scalability.
Some commenters pushed back on the premise of the article, arguing that the imperfections described are not inherent limitations but rather design choices. They suggested that alternative architectures and technologies could potentially achieve similar levels of performance and scalability without sacrificing data integrity. One such commenter suggested CRDTs (Conflict-free Replicated Data Types) as a potential solution for achieving strong consistency in a distributed environment.
Finally, a few commenters provided anecdotal examples of systems they had worked on where embracing imperfection led to positive outcomes. These examples reinforced the author's central argument that striving for perfect consistency can sometimes be counterproductive.
Overall, the comments section offers a diverse range of perspectives on the topic of imperfect systems, exploring both the theoretical underpinnings and practical implications of designing systems that prioritize performance and scalability over strict consistency. While there's general agreement on the validity of this approach in certain contexts, there's also healthy skepticism and discussion of potential drawbacks and alternative solutions.