The Architecture of Observability: Tracing a Cache Stats Mystery in a Distributed S3 System

Introduction

In the midst of an intense development session on a horizontally scalable S3 storage system built on the Filecoin network, a seemingly simple observation from the user triggered a deep investigative journey. The user noticed that after performing an S3 read operation using rclone, the cache statistics displayed in the WebUI remained stubbornly at zero—no hits, no misses, no activity of any kind. This observation, captured in message index 2820 of the coding session, became the catalyst for a critical architectural clarification that reveals fundamental design decisions about how caching works in distributed storage systems.

The assistant's response is a masterclass in diagnostic reasoning, system architecture communication, and user expectation management. It is not merely an answer to a question—it is a carefully constructed bridge between observed behavior and underlying system design, delivered at a moment when the user's intuition about how the system should work collides with how it actually works.

The Subject Message

[assistant] There are some metrics, but they're minimal. The issue is that local S3 reads from the blockstore don't have their own dedicated metrics being tracked and displayed in the UI.

Let me clarify the architecture: - L1/L2 Cache = For retrieval from Filecoin network (offloaded data) - Local groups = Data stored locally, read directly from disk (no cache involved)

Would you like me to: 1. Add local read metrics to track reads from local groups? 2. Update the UI to show both local reads and cache stats separately?

Or did you expect the cache to be used for local reads as well? That would be a significant architectural change.

The Reasoning and Motivation: Why This Message Was Written

This message exists because the assistant had just completed a multi-step investigation into why cache statistics showed no activity after a user-initiated S3 read operation. The investigation, visible in the preceding messages (2806–2819), involved a systematic process of elimination. The assistant first confirmed the symptom by querying the RPC endpoint and finding all cache stats at zero. It then checked Prometheus metrics to see if any activity was being tracked at all. When those also showed zero, the assistant traced the code path from the S3 read entry point through the blockstore to understand exactly which code paths were being exercised.

The critical discovery came when the assistant traced the View method in rbstor/rbs.go and found a branching path: for data stored in local groups, the system reads directly from disk via Group.View(), completely bypassing any caching layer. The L1/L2 cache is only invoked when data has been offloaded to the Filecoin network and needs to be retrieved from storage providers via ext.FetchBlocks().

This discovery created a communication imperative. The user had observed behavior that appeared broken—cache stats that never changed—but the behavior was actually correct according to the system's architecture. The assistant needed to:

Explain what was actually happening (the cache is designed for a specific purpose)
Acknowledge the user's valid concern (there are no local read metrics either)
Offer a path forward (three clear options with different trade-offs) The message is thus a triage report, an architectural clarification, and a decision-point all rolled into one. It represents the moment where raw investigation transforms into actionable intelligence for the product owner.## How Decisions Were Made: The Diagnostic Method The assistant's decision-making process in this message is a textbook example of systematic debugging in distributed systems. The investigation unfolded in several deliberate phases: Phase 1: Symptom Confirmation. The assistant first verified the user's report by querying the RIBS.CacheStats RPC endpoint directly. This confirmed that all values—hits, misses, L1 size, L2 size—were indeed zero. This step is crucial because it eliminates the possibility of a UI rendering bug or a stale display. Phase 2: Broader Metrics Check. The assistant then checked Prometheus metrics (exposed on port 2112) to see if the system was tracking any cache or retrieval activity at a lower level. This revealed that all cache-related counters were also zero, confirming the absence was systemic, not just a UI issue. Phase 3: Code Path Tracing. The assistant traced the S3 read path through the codebase, starting from the View method in rbstor/rbs.go and following the branching logic. This is where the critical architectural insight emerged: the code has two distinct paths depending on whether a group is "local" or "offloaded." Phase 4: Disk Verification. To confirm which path was being taken, the assistant checked the actual data on disk via SSH, discovering 43GB of local data in groups 1 and 35. This empirical evidence confirmed that the reads were hitting the local path, not the retrieval/cache path. This methodical approach—from symptom to metrics to code to disk—demonstrates a deep understanding of the system's layers and a commitment to evidence-based diagnosis. The assistant did not guess or speculate; it followed the data trail until the root cause was incontrovertible.

Assumptions Made by the User and Agent

The user's message carried an implicit assumption: that the cache statistics displayed in the WebUI should reflect all read activity in the system, including reads from local storage. This is a natural assumption for anyone familiar with caching systems—typically, a cache sits between the application and persistent storage and records hits and misses for all read operations. The user's expectation was that the cache metrics would be a general-purpose observability tool for understanding read performance.

The assistant, in its investigation, had to confront a different assumption embedded in the system's architecture: that the L1/L2 cache is a specialized component for Filecoin retrieval only, not a general-purpose read cache. This architectural choice makes sense in context—the system is designed to store data locally and only offload to Filecoin when local storage is full or data is cold. The cache is there to make retrieval from the Filecoin network performant, not to accelerate local reads.

The tension between these two assumptions is the core drama of this message. The assistant had to recognize that the user's expectation was reasonable while also defending the architectural integrity of the system. The message handles this tension gracefully by acknowledging the gap ("local S3 reads from the blockstore don't have their own dedicated metrics") and offering to fill it, while also clearly explaining why the cache isn't the right mechanism for local reads.

Mistakes and Incorrect Assumptions

One could argue that the system has a gap in observability rather than a bug. The mistake is not in the cache implementation but in the absence of local read metrics. The assistant implicitly acknowledges this by offering to "add local read metrics" as option one. The system tracks retrieval metrics (cache hits/misses, HTTP tries, bytes transferred) but has no equivalent counters for local disk reads. This means operators cannot distinguish between "the system is idle" and "the system is serving data entirely from local storage."

The assistant's own investigation revealed no coding errors or logic bugs. The cache was working exactly as designed. The only "mistake" was a blind spot in the observability surface—a gap between what the system measures internally and what it exposes to operators through the WebUI.

Input Knowledge Required to Understand This Message

To fully grasp the significance of this message, a reader needs several pieces of contextual knowledge:

The system architecture: The Filecoin Gateway (FGW) system has two tiers of storage—local groups on disk and offloaded data stored on the Filecoin network. The L1/L2 cache sits in the retrieval path for offloaded data only.
The cache design: L1 is an ARC (Adaptive Replacement Cache) in memory, and L2 is an SSD-based cache. These are designed to accelerate block-level retrieval from storage providers when data has been removed from local storage.
The S3 read path: When an S3 read request arrives, the system first checks if the data is in a local group. If yes, it reads directly from disk. If the group has been offloaded, it fetches blocks from the Filecoin network through the retrieval provider, which uses the cache.
The observability stack: The system exposes metrics via Prometheus (port 2112) and RPC endpoints (port 9010), which feed into a React-based WebUI dashboard.
The development context: The assistant had just added L1/L2 cache metrics to the WebUI in a previous commit (43160e1), making this the first time cache stats were visible to the user. The user was naturally testing the new feature by performing a real S3 read operation.

Output Knowledge Created by This Message

This message creates several important outputs:

A clear architectural document: The assistant's two-line summary ("L1/L2 Cache = For retrieval from Filecoin network" vs. "Local groups = Data stored locally, read directly from disk") is a concise architectural reference that can guide future development and debugging.
A decision framework: By offering three specific options, the message transforms an open-ended problem into a structured choice. The user can decide to add local read metrics, update the UI, or reconsider the caching architecture.
A validated investigation trail: The message implicitly documents the diagnostic process, creating a reusable pattern for future performance investigations.
An expectation reset: The message gently corrects the user's assumption about what the cache stats represent, preventing future confusion and aligning mental models.

The Thinking Process Visible in the Reasoning

The assistant's reasoning is visible in the careful progression from investigation to explanation to offer. The message opens with a concession ("There are some metrics, but they're minimal") that acknowledges the user's observation without being defensive. It then delivers the key architectural insight in a clear, structured format using bold headers and bullet points—a format designed for quick comprehension.

The offer at the end is particularly thoughtful. Rather than simply explaining away the behavior and closing the issue, the assistant presents three actionable paths forward. This transforms the interaction from a bug report into a design conversation. The third option—"did you expect the cache to be used for local reads as well?"—is especially important because it invites the user to challenge the architectural assumption if they believe it's wrong. The assistant explicitly flags that this would be "a significant architectural change," setting appropriate expectations about the scope of work.

The message also demonstrates emotional intelligence. The assistant does not say "you're wrong" or "the system is working correctly." Instead, it says "Let me clarify the architecture" and presents the facts neutrally. This preserves the collaborative relationship while correcting a misunderstanding.

Conclusion

Message 2820 is a small but perfect example of what makes effective technical communication in software development. It is the product of rigorous investigation, presented with clarity and humility, and structured to drive decision-making rather than debate. The assistant navigated a potentially awkward moment—a user reporting behavior that looked like a bug but was actually correct—by focusing on explanation, acknowledgment, and forward-looking options.

The deeper lesson is about the relationship between architecture and observability. The cache stats were not lying; they were telling the truth about a system that the user did not yet fully understand. The assistant's job was not to fix the stats but to fix the understanding. In doing so, it created the foundation for better observability, better decision-making, and a stronger shared mental model of the system.