When Zero Is the Correct Answer: Debugging Cache Metrics in a Distributed S3 Architecture
Introduction
In software engineering, few moments are as disorienting as deploying a new feature, testing it with real traffic, and watching the dashboard show nothing but zeros. The cache metrics dashboard—freshly built, committed, and deployed to a QA cluster—stared back at its creators with an unnerving emptiness. Hits: 0. Misses: 0. L1 size: 0. L2: disabled. Every field a zero, every gauge flatlined.
The subject message, message 2807 in a long-running development conversation for the Filecoin Gateway (FGW) project, captures the exact moment an engineer begins investigating why a brand-new observability feature appears broken. It is a deceptively simple message: two curl commands, two JSON responses, both showing zeros. But beneath that surface lies a rich story about architectural assumptions, the difference between local and remote data paths, and the critical importance of understanding what a cache is actually caching.
The Message
The assistant writes:
Let me investigate the cache stats issue. First, let me check how the cache metrics are being tracked:
>
``` [bash] curl -s -X POST -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","method":"RIBS.CacheStats","params":[],"id":1}' http://10.1.232.83:9010/rpc/v0 2>&1 {"id":1,"jsonrpc":"2.0","result":{"l1Enabled":true,"l1Size":0,"l1Capacity":2147483648,"l1Items":0,"l1T1Size":0,"l1T2Size":0,"l1B1Len":0,"l1B2Len":0,"l1P":0,"l2Enabled":false,"l2Size":0,"l2MaxSize":0,"l2Items":0,"l2ProbationSize":0,"l2ProtectedSize":0,"l2FreeSpace":0,"hits":0,"misses":0}}
>
[bash] curl -s -X POST -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","method":"RIBS.RetrStats","params":[],"id":1}' http://10.1.232.83:9010/rpc/v0 2>&1 {"id":1,"jsonrpc":"2.0","result":{"Success":0,"Bytes":0,"Fail":0,"CacheHit":0,"CacheMiss":0,"Active":0,"HTTPTries":0,"HTTPSuccess":0,"HTTPBytes":0}} ```
Two RPC calls, two responses, all zeros. The assistant is doing what any competent engineer would do: verifying the symptom before diving into root cause analysis. The user had reported that after performing an rclone copy operation to fetch data from the S3 gateway, the cache UI stats on node :83 didn't update, and no misses were registered either. The assistant's first step is to confirm the observation independently by querying the live API endpoints.
Why This Message Was Written
The message exists because the user reported a discrepancy between expected behavior and observed behavior. The user had just deployed a new feature—L1/L2 cache metrics in the WebUI—and immediately tested it with a real S3 read operation. When the dashboard didn't reflect any activity, they reported it. The assistant's response is the opening move of a diagnostic investigation.
The context is crucial. Just a few messages earlier (message 2779), the assistant had proudly announced the completion of the cache metrics feature: "All done! Here's a summary of what was implemented." The feature added a CacheStats struct to the interface layer, implemented metric collection in the retrieval provider, exposed it via RPC, and built a React component with color-coded hit rates. The user's next message was "Update qa deployment" (message 2780), which the assistant did, committing the changes and deploying to both QA nodes. Then the user ran a real workload and found the metrics didn't budge.
The assistant's message is thus a response to a bug report. But it's a particular kind of bug report—one where the user has done the right thing: they tested the new feature immediately with a realistic operation and reported the failure concretely, including the exact rclone command used. This gives the assistant a reproducible starting point.## The Investigative Method: Confirming the Symptom
The message reveals a disciplined diagnostic approach. Before forming any hypothesis about why the stats are zero, the assistant first confirms that they are indeed zero. This may seem obvious, but it's a step that distinguishes careful debugging from guesswork. The assistant queries two separate RPC endpoints: RIBS.CacheStats for the newly built cache metrics and RIBS.RetrStats for the broader retrieval statistics. Both return zeros across every field.
The choice to query both endpoints is telling. The assistant is cross-referencing. If RetrStats showed non-zero values while CacheStats showed zeros, that would point to a problem in the cache metrics pipeline specifically. But since both are zero, the issue is likely upstream: the data isn't flowing through the cache path at all.
The assistant also chooses to query the live QA node (10.1.232.83:9010) rather than a local development instance. This is significant because it means the assistant trusts the deployment pipeline. The code was committed, built, copied to the remote server, and the service was restarted—all of which was verified in messages 2784-2791. By querying the live node, the assistant is testing the system in its actual operating environment, not a synthetic one.
The Knowledge Required to Understand This Message
To fully grasp what's happening in this message, a reader needs several layers of context:
Architectural knowledge: The FGW system uses a tiered storage model. Data is written to local groups (on-disk storage managed by the RBS blockstore). When a group is "offloaded," its data is removed from local storage and stored on the Filecoin network via deals with storage providers. The L1/L2 cache exists specifically to accelerate retrieval of offloaded data—it sits between the local read path and the remote Filecoin retrieval path.
The S3 proxy layer: The user's rclone command reads from an S3 endpoint (gw:mybucket/...). This S3 proxy translates S3 API calls into internal reads against the RBS blockstore. Whether those reads hit the cache depends on whether the requested data is local or offloaded.
The recent feature history: The cache metrics were just built and deployed. The CacheStats struct was added to iface/iface_ribs.go, implemented in rbdeal/retr_provider.go, and exposed through integrations/web/rpc.go. The UI component was added to Status.js in the React app. This was all fresh code that had never been tested with real traffic.
The QA environment: The two QA nodes (10.1.232.83 and 10.1.232.84) run Kuri, the storage node binary. They have local data directories at /data/fgw/. The assistant knows from earlier work that these nodes have been used for testing and contain real data.
The Assumptions at Play
The message operates under several implicit assumptions, some of which turn out to be incorrect:
Assumption 1: The cache should be active for S3 reads. The user's report implies an expectation that performing an S3 read should cause cache stats to update. This is a reasonable expectation—what good are cache metrics if they don't reflect any actual reads? But as the investigation will reveal, this assumption conflates two different read paths.
Assumption 2: The metrics pipeline is wired correctly. The assistant assumes that if the cache were being used, the metrics would be captured and exposed. The RPC endpoint returns zeros, but that could mean either (a) the cache isn't being used, or (b) the metrics aren't being collected properly. The assistant doesn't jump to conclusion (b) without evidence.
Assumption 3: The deployment is healthy. The assistant assumes the newly deployed binary is running correctly and the RPC endpoint is functional. The fact that the endpoint returns a valid JSON response (rather than an error or timeout) supports this assumption.
Assumption 4: The test workload is representative. The user's rclone command copies data from an S3 path. The assistant implicitly assumes this should exercise the retrieval path. In reality, the data being read might be local, bypassing the cache entirely.## The Thinking Process Visible in the Reasoning
While the subject message itself is brief—just two curl commands and their output—the reasoning behind it is revealed through the surrounding context. The assistant's thinking follows a classic debugging trajectory:
Step 1: Reproduce the observation. Before forming any hypothesis, confirm the reported symptom independently. The assistant queries the live endpoint and sees the same zeros the user reported.
Step 2: Gather baseline data. The assistant doesn't just check the cache-specific endpoint; it also checks the broader retrieval stats endpoint. This is a form of differential diagnosis—if one endpoint shows activity and the other doesn't, the problem is localized. If both show nothing, the problem is more fundamental.
Step 3: Prepare to trace the data path. The next messages (2808 onward) show the assistant moving to deeper investigation: checking Prometheus metrics directly, tracing the S3 read path through the codebase, and examining the local data on disk. The subject message is the launching point for this deeper dive.
The thinking is methodical and hypothesis-driven. The assistant doesn't immediately assume a bug in the new code. Instead, it considers the possibility that the system is behaving correctly and the user's expectation is mismatched with the architecture. This is a mature debugging stance—not every unexpected behavior is a bug.
The Output Knowledge Created
This message creates several pieces of output knowledge:
A confirmed observation: The cache stats are definitively zero on the live QA node. This is no longer a user report; it's a verified fact backed by direct API queries.
A baseline for comparison: The RetrStats endpoint also returns zeros, providing a second data point. If later investigation shows that RetrStats starts incrementing while CacheStats stays at zero, that would indicate a metrics wiring issue. If both stay at zero, the problem is upstream.
A documented starting point for the investigation: The message serves as a timestamped record of the system state at the beginning of the diagnostic process. Any future changes to the metrics can be compared against this baseline.
A test of the deployment pipeline: By successfully querying the RPC endpoints and getting valid responses, the message implicitly confirms that the new code was deployed correctly and the RPC layer is functional. The issue is not a deployment failure or a crash—it's a logical/architectural issue.
The Mistake That Wasn't Made
One of the most interesting aspects of this message is what the assistant doesn't do. It doesn't immediately blame the new code. It doesn't assume a bug in the CacheStats implementation. It doesn't start editing files or adding debug prints. Instead, it gathers data.
This restraint is notable because the cache metrics code was literally just written. The natural instinct might be to assume the new code has a bug—perhaps the hit/miss counters aren't being incremented, or the RPC endpoint isn't wired correctly. But the assistant resists this instinct and instead considers the broader system context.
The subsequent investigation (messages 2808-2820) reveals that the assistant's restraint was well-founded. The cache isn't broken; it's simply not applicable to the workload being tested. The data being read via S3 is stored in local groups (groups 1 and 35, totaling 43 GB), not in offloaded Filecoin deals. The L1/L2 cache is specifically designed for retrieving data from the Filecoin network, not for accelerating local reads. The zeros are the correct answer.
Architectural Lessons
This episode illuminates a subtle but important architectural distinction. In many systems, a cache sits transparently between the application and the data source—every read goes through the cache, and the cache either serves the data (hit) or fetches it from the backing store (miss). But in the FGW architecture, the cache is not transparent. It only exists on the retrieval path, which is activated when data has been offloaded from local storage to the Filecoin network.
This design makes sense for the system's purpose: Filecoin is a decentralized storage network optimized for long-term, cold storage. Data that's actively being read should stay local. Data that's been archived to Filecoin needs a cache to make retrieval practical. But it creates a confusing user experience when someone expects cache metrics to reflect all read activity.
The lesson for the assistant—and for the reader—is that observability features must be designed with a clear understanding of what they're measuring. A "cache hit rate" of 0% could mean the cache is broken, or it could mean the cache isn't in the data path for the current workload. The dashboard needs to communicate this distinction, perhaps by showing "Cache: N/A (local reads)" when no offloaded data is being accessed.