The Architecture Plan That Defined a Distributed S3 System

Introduction

In the course of building a horizontally scalable S3-compatible storage architecture for the Filecoin Gateway, there is a single message that stands as the pivotal moment where scattered exploration crystallized into a coherent design. Message 36 in the conversation captures the moment when an AI assistant, after conducting six separate deep-dive explorations into the existing codebase, synthesized everything into a comprehensive architecture document. This message—containing the assistant's reasoning and the command to write the file /home/theuser/gw/doc/architecture-horizontal-scale.md—represents the intellectual fulcrum of the entire development effort. It is the point at which raw investigation gave way to deliberate design, and where the blueprint for a distributed storage system was first committed to disk.

The Context: Six Explorations in Search of a Plan

To understand why this message was written, one must first understand the intense preparatory work that preceded it. The user had requested a horizontally scalable architecture for their Filecoin Gateway system, with specific requirements: stateless S3 frontend proxy nodes, independent Kuri storage nodes, a shared YCQL database for object placement tracking, multipart upload assembly across nodes, and read-after-write guarantees. These were not simple requests—they touched nearly every layer of the existing system.

The assistant responded by delegating four exploration tasks in parallel, each targeting a different aspect of the codebase. The first explored the S3 implementation, uncovering the layered architecture of HTTP API handlers, business logic, and storage backends. The second examined the CQL/YCQL schema, revealing the existing MultihashToGroup and CidGroups tables that would need to be extended. The third traced the multipart upload flow from initiation through part upload to final assembly. The fourth analyzed the blockstore interface, showing how data flows from S3 objects through the RIBS blockstore to storage groups.

Two additional explorations followed: one into YCQL capabilities and consistency levels, and another into the S3 server's routing and authentication structure. By the time the assistant reached message 36, it had accumulated an extraordinary depth of knowledge about the system—knowledge that needed to be distilled into a single coherent document.

The Message: Reasoning and Action

The subject message itself is deceptively brief. It opens with the assistant's reasoning:

Excellent! Now I have all the information I need to create a comprehensive architecture plan. Let me write a detailed architecture document that covers: 1. High-level architecture overview 2. S3 Frontend Proxy (Load Balancer) nodes 3. Kuri Backend Storage nodes 4. YCQL Schema changes for object placement awareness 5. Detailed multipart upload flow across nodes 6. Read path with directed requests 7. Read-after-write guarantee mechanism 8. Test suite plan Let me create this document now.

Then the action: [write] /home/theuser/gw/doc/architecture-horizontal-scale.md

The reasoning reveals a clear mental model. The assistant has organized the problem into eight distinct sections, each corresponding to a major architectural concern. The confidence in "I have all the information I need" is earned—the assistant has indeed conducted exhaustive exploration. But the reasoning also reveals an assumption: that the architecture document is the appropriate next step. This assumption would prove correct, as the document would become the foundation for all subsequent implementation work.

Input Knowledge: What Was Required to Understand This Message

To fully grasp the significance of this message, one needs substantial context. The reader must understand that the existing system had a monolithic architecture where a single Kuri node handled both S3 protocol and storage. The concept of separating these concerns into a stateless frontend layer and independent storage nodes was a fundamental architectural shift.

The reader also needs to know the existing YCQL schema—the MultihashToGroup and CidGroups tables—and understand why these were insufficient for object-level placement tracking. The existing schema tracked multihash-to-group mappings for content-addressed storage, but the new architecture needed to track which specific node owned which S3 object, a different indexing paradigm entirely.

Understanding the multipart upload flow is equally critical. The existing implementation assembled parts on a single node, but the new architecture needed to distribute parts across multiple nodes and assemble them from remote sources. This required a coordination mechanism that didn't exist in the codebase.

Finally, the reader must appreciate the constraint that Kuri nodes should see "minimal changes." This constraint shaped every decision in the architecture plan, pushing complexity into the new frontend proxy layer rather than modifying existing storage nodes.## Assumptions Embedded in the Architecture Plan

The message makes several assumptions that deserve scrutiny. The first is that round-robin write distribution is the correct strategy for load balancing. The assistant assumes that all Kuri nodes are equally capable and that objects can be assigned to any node without consideration of size, type, or access patterns. This is a reasonable assumption for a system focused on "performance over replication," but it does mean that hot objects could theoretically concentrate on a single node if the round-robin counter happens to align poorly with access patterns.

A second assumption concerns the YCQL consistency model. The plan calls for QUORUM consistency on all reads and writes to ensure read-after-write guarantees. This assumes that the YugabyteDB cluster can sustain QUORUM-level consistency without becoming a bottleneck. In a geographically distributed deployment, this assumption might not hold—QUORUM reads require contacting multiple replicas, which adds latency. The plan does not discuss eventual consistency fallbacks or read-your-writes session guarantees, which might be more practical for some deployment scenarios.

The third assumption is that the coordinator-based multipart assembly model is the right approach. The plan designates one Kuri node as the "coordinator" for each multipart upload, responsible for fetching parts from other nodes and assembling the final object. This creates a single point of coordination that could become a bottleneck for large multipart uploads. The assistant assumes that the coordinator's internal API for fetching remote parts is fast enough, but this depends on network bandwidth and latency between nodes—factors not addressed in the plan.

Perhaps the most significant assumption is that the architecture document itself would be sufficient to guide implementation. The assistant wrote the document and then immediately began implementing the changes described within it. This assumes that the document's specifications are complete and correct enough to serve as a programming specification. In practice, the implementation would reveal ambiguities and gaps—most notably, the assistant would later discover that it had fundamentally misunderstood the role of the S3 frontend proxy, initially configuring Kuri nodes as direct S3 endpoints instead of routing through the separate proxy layer. This architectural error, caught by the user in a later message, stemmed from an incomplete understanding of the roadmap that the architecture document was supposed to codify.

Output Knowledge: What This Message Created

The immediate output of this message was the file /home/theuser/gw/doc/architecture-horizontal-scale.md. But the true output was far larger: a shared mental model of the distributed system that would guide all subsequent development. The architecture document established the vocabulary (frontend proxy, backend pool, coordinator node, directed read), the data structures (the new YCQL tables for object placement and multipart tracking), and the flow diagrams that would be referenced throughout the implementation.

The document also created a phased implementation plan (Phases 1 through 5), which structured the work that followed. Phase 1 (YCQL schema and node identification) and Phase 2 (frontend proxy) would be implemented immediately. Phase 3 (multipart coordination) would come later. The existence of this phased plan allowed the assistant and user to track progress and identify when the implementation diverged from the architecture—as happened when the assistant mistakenly conflated Kuri nodes with S3 frontend proxies.

The document also created explicit design decisions with rationales, such as "No Replication" justified by "Performance focus, Filecoin provides redundancy, cost efficiency." These decisions served as a decision log that could be referenced when questions arose about why certain approaches were chosen.

The Thinking Process: Synthesis Under the Surface

The reasoning block in the message reveals a structured thinking process. The assistant lists eight topics in a numbered sequence, but the order is itself significant. It starts with the high-level overview (establishing the big picture), then drills into each component (frontend, backend, schema), then addresses the complex flows (multipart, reads), then tackles the hard problem (read-after-write), and finally grounds everything in a test plan. This is classic top-down decomposition: establish context, define components, specify interactions, address edge cases, validate.

The phrase "Excellent! Now I have all the information I need" is more than just enthusiasm—it reflects a cognitive threshold the assistant had crossed. The six exploration tasks had produced a fragmented understanding of the system. The S3 exploration showed how requests flowed. The CQL exploration showed the existing schema. The multipart exploration showed the assembly mechanism. But none of these alone constituted an architecture. The synthesis happened in this message, where the assistant connected the fragments into a coherent whole. The YCQL schema changes would enable the routing logic. The routing logic would enable the multipart coordination. The coordination would enable read-after-write guarantees. Each piece depended on the others, and the architecture document was the first place where all those dependencies were explicitly mapped.

Conclusion

Message 36 is a message about translation—translating deep technical investigation into a design document that would serve as the blueprint for a distributed storage system. It captures the moment when analysis becomes synthesis, when scattered knowledge becomes organized understanding. The architecture document it created would guide the implementation of a horizontally scalable S3-compatible storage layer, even as the implementation would later reveal gaps in the plan. In the end, the document was not the final word but the first word—a starting point that would be refined through the collaborative debugging and architectural correction that followed. For anyone studying how complex systems are designed, this message offers a rare window into the moment of creation, where an AI assistant, having gathered all the pieces, finally saw the whole picture and committed it to disk.