The Cache Stats That Wouldn't Budge: Tracing Throughput Boundaries in a Distributed Storage System

Introduction

In the life of a distributed systems engineer, few moments are as simultaneously satisfying and humbling as watching a carefully instrumented dashboard display a perfect row of zeros after a successful data transfer. The cache metrics were live, the WebUI was refreshing every two seconds, and the user had just pulled data from the cluster using rclone. Yet the hit counter sat at zero, the miss counter sat at zero, and the L1 cache size remained stubbornly empty. This was not a bug in the instrumentation—it was a revelation about the architecture itself.

The message under analysis, assistant message 2819, is the culmination of a diagnostic investigation triggered by the user's report that newly deployed cache statistics were not reflecting any activity after an S3 read operation. What makes this message remarkable is not the code change it contains—there is none—but the conceptual clarification it delivers. It is a message about understanding the boundaries of a system, about the difference between a feature being broken and a feature being correctly silent because the conditions for its activation have not been met.

The Context: A Feature Just Deployed

In the messages immediately preceding this one, the assistant had implemented and deployed a comprehensive cache statistics dashboard. The CacheStats RPC endpoint exposed detailed metrics from both the L1 ARC (Adaptive Replacement Cache) in memory and the L2 SSD-based cache, including hit rates, item counts, ghost list lengths, and probation/protected segment sizes. The WebUI displayed these with color-coded hit rates—green above 80%, yellow above 50%, red below—and separate sections for each cache tier. The feature had been committed, built, deployed to both QA nodes, and verified as responding correctly via curl.

The user then tested the system by running an rclone copy command to fetch data from the S3 gateway:

rclone --s3-no-check-bucket --s3-force-path-style --s3-list-version=2 copy gw:mybucket/data32/... /tmp/aa

After this operation, the cache stats still showed all zeros. The user reported this as a problem: "Cache - ui stats on at least :83 don't do anything after a fetch... no misses registered either."

The Investigation: Following the Read Path

The assistant's response begins with a statement of fact discovered through investigation: "There's 43GB of local data in groups 1 and 35." This single observation is the key that unlocks the entire mystery. To understand how the assistant arrived here, we must trace the diagnostic steps visible in the preceding messages.

The assistant first queried the CacheStats and RetrStats RPC endpoints directly, confirming both returned zeros. It then checked the Prometheus metrics endpoint on port 2112, filtering for cache and retrieval-related metrics. All counters were zero. At this point, a less experienced engineer might have concluded the instrumentation was broken and begun adding debug logging or tracing. Instead, the assistant did something more fundamental: it traced the S3 read path through the codebase.

The critical code path is in rbstor/rbs.go. When a read request arrives, the system first checks whether the requested data resides in a "local" group or an "offloaded" group. If the group is local, data is served directly from the on-disk group storage via Group.View(). If the group has been offloaded—meaning the data has been moved to Filecoin storage providers and the local copy removed—the system calls ext.FetchBlocks() through the retrieval provider, which is the path that involves the L1 and L2 caches.

The assistant found that the data being read was in groups 1 and 35, which were local groups with 43GB of data on disk. The read path never touched the cache system.

The Message Itself: A Conceptual Clarification

The full text of the subject message reads:

There's 43GB of local data in groups 1 and 35. So when you do an S3 read, the data is being served from local storage, NOT from the cache/retrieval system.

>

The cache only applies when: 1. The group has been "offloaded" (data removed locally, deals made on Filecoin) 2. A read request requires fetching from storage providers

>

Since the data is local, no cache is being used. This is actually correct behavior - the cache is specifically for retrieval from the Filecoin network.

>

However, I understand you might want to see read activity stats even for local data. Let me check if there are local read metrics:

This message accomplishes several things simultaneously. First, it definitively answers the user's question: the cache stats are not broken, they are simply not applicable to the current workload. Second, it educates the user about the architecture—the distinction between local and offloaded storage is fundamental to the system's design. Third, it acknowledges the user's unspoken desire: even if the cache stats are technically correct, the user wants visibility into read activity of any kind. The assistant immediately pivots to checking for local read metrics, demonstrating an understanding that the user's real need is observability, not just cache instrumentation.

Assumptions and Knowledge Boundaries

This message reveals several assumptions held by both the user and the assistant. The user assumed that the cache metrics would reflect all read activity in the system. This is a natural assumption—in many storage systems, caching is a transparent layer that sits between the client and the backing store, and cache metrics reflect all I/O. The Filecoin Gateway's architecture is different: the cache is specifically a retrieval cache for data that has been moved to the Filecoin network, not a general-purpose read cache for locally stored data.

The assistant's own assumptions are visible in the implementation. When building the CacheStats feature, the assistant did not add local read metrics or question whether the cache stats would be meaningful for the current deployment state. The implementation was architecturally correct—the cache stats accurately reflected the state of the retrieval cache—but the assistant did not anticipate that the user would interpret zero cache activity as a bug rather than as a signal that no offloaded data was being accessed.

The input knowledge required to understand this message includes: the architecture of the two-tier storage system (local groups vs offloaded groups), the role of the retrieval provider and its cache, the S3 read path through rbstor/rbs.go, and the fact that the QA deployment contained recently written data that had not yet been offloaded to Filecoin. The output knowledge created by this message is a clearer mental model of the system's data flow and the boundaries of the caching layer.

The Thinking Process: Systematic Debugging

The assistant's reasoning process, visible in the sequence of commands and code inspections leading up to this message, is a model of systematic debugging. The pattern is:

  1. Verify the instrumentation: Check the RPC endpoint directly to confirm the stats are truly zero, not just displaying incorrectly in the UI.
  2. Check alternative metrics sources: Query Prometheus metrics to see if the data is being tracked elsewhere.
  3. Trace the code path: Read the source code to understand exactly when the cache is and isn't involved.
  4. Examine the actual state: Check disk usage to confirm the data is local.
  5. Synthesize: Combine the code analysis with the observed state to produce an explanation. This approach avoids the common pitfall of assuming the instrumentation is broken. Instead, it treats the zeros as valid data that needs interpretation.

Broader Implications

This message has implications beyond the immediate debugging session. It highlights a tension in distributed systems observability: metrics are always measured relative to a specific layer of the stack, and what looks like a gap in coverage may actually be a correct measurement of a subsystem that is not currently active. The challenge for system designers is to make these boundaries visible to operators, so that a zero counter can be interpreted as "this path is not being exercised" rather than "this instrumentation is broken."

The assistant's response also demonstrates an important engineering communication skill: when a user reports a problem that is actually correct behavior, the engineer must explain why it is correct while simultaneously addressing the underlying need that motivated the report. The assistant does this by immediately offering to investigate local read metrics, showing that the user's desire for visibility is understood and will be addressed even though the specific cache stats issue is a non-issue.

Conclusion

Message 2819 is a turning point in the conversation. It resolves the immediate confusion about the cache stats, but more importantly, it deepens both the user's and the assistant's understanding of the system's architecture. The cache is not a general-purpose read cache—it is a retrieval cache for offloaded data. The zeros on the dashboard were not a bug; they were a signal that the system was operating in its local-only mode, serving data directly from disk without needing to fetch from the Filecoin network.

This message is a reminder that in complex distributed systems, the most valuable diagnostic tool is not a better dashboard or more instrumentation—it is a clear mental model of how data flows through the system. With that model, even a dashboard full of zeros can tell a meaningful story.