The Zero Stats Problem: Debugging Cache Metrics in a Distributed S3 Architecture

A Single Diagnostic Step That Uncovered an Architectural Assumption

In the course of building a horizontally scalable, Filecoin-backed S3 storage system, a seemingly simple bug report arrived: the cache statistics displayed in the WebUI were stuck at zero. A user had performed a real data retrieval using rclone to copy a file from the S3 gateway, then checked the newly deployed cache metrics dashboard — only to find that every counter, every gauge, every hit and miss indicator read exactly zero. The cache, which had just been wired into the UI with color-coded hit rates and detailed L1/L2 breakdowns, appeared completely inert.

The assistant's response to this report, captured in message 2808, is a masterclass in systematic debugging. It is not a fix. It is not a conclusion. It is a single, deliberate diagnostic step — one that reveals the assistant's mental model of the system, tests a key hypothesis, and sets the stage for a deeper architectural discovery. This article examines that message in detail, unpacking the reasoning, assumptions, and knowledge that make it a pivotal moment in the debugging session.

The Message in Full

The assistant wrote:

The stats are all zeros. Let me check the Prometheus metrics directly to see if there's activity being tracked there: `` [bash] curl -s http://10.1.232.83:2112/metrics 2>&1 | grep -E "fgw_cache|fgw_deal_retrieval" | head -30 # HELP fgw_cache_l1_block_evictions_total Total cache evictions # TYPE fgw_cache_l1_block_evictions_total counter fgw_cache_l1_block_evictions_total 0 # HELP fgw_cache_l1_block_ghost_hits_b1_total Hits in B1 ghost list (indicates need for more recent items) # TYPE fgw_cache_l1_block_ghost_hits_b1_total counter fgw_cache_l1_block_ghost_hits_b1_total 0 # HELP fgw_cache_l1_block_ghost_hits_b2_total Hits in B2 ghost list (indicates need for more frequent items) # TYPE fgw_cache_l1_block_ghost_hits_b2_total counter fgw_cache_l1_block_ghost_hits_b2_total 0 ``

The message is brief — a single observation followed by a command execution and its output. But within those lines lies a carefully reasoned diagnostic strategy.

Why This Message Was Written: The Context of Investigation

To understand why the assistant wrote this, we must reconstruct the situation. Just moments earlier, the user had reported that the cache UI stats "don't do anything" after an S3 read operation. The user had executed rclone --s3-no-check-bucket --s3-force-path-style --s3-list-version=2 copy gw:mybucket/data32/4ouv5zhw/dm9po166dolbi7/ch-95cvz0y1/vkmt4rdb2x /tmp/aa — a real retrieval that should have exercised the system's read path. Yet the cache metrics showed nothing.

The assistant's first response (message 2807) was to query the RPC endpoint directly, bypassing the UI rendering layer. That call returned zeros across the board: l1Size: 0, l1Items: 0, hits: 0, misses: 0. The RetrStats endpoint also returned all zeros. This eliminated the possibility that the UI was simply misrendering the data — the raw data was genuinely zero.

Message 2808 represents the next logical step in the diagnostic chain. The assistant had confirmed that the RPC layer was returning zeros. Now the question became: are the metrics being collected at all? The Prometheus endpoint is the lowest-level observability interface in the system — it exposes raw counters directly from the instrumented code, before any RPC serialization or aggregation. If Prometheus showed non-zero values, the problem would be in the RPC wiring. If Prometheus also showed zeros, the problem was deeper: either the metrics were never being incremented, or the code path being exercised never touched the cache instrumentation.

The Reasoning and Decision-Making Process

The assistant's decision to check Prometheus metrics reveals a clear debugging philosophy: isolate the layer of failure by comparing data sources. The diagnostic stack, from highest to lowest abstraction, was:

WebUI — React component rendering cached data (already known to show zeros)
RPC endpoint — JSON-RPC method returning serialized structs (confirmed zeros in message 2807)
Prometheus metrics — Raw /metrics HTTP endpoint with counter values (target of message 2808)
In-code instrumentation — The actual metrics.NewCounter() calls and .Inc() operations By checking Prometheus, the assistant could determine whether the problem was in the metrics collection pipeline (RPC → UI) or in the metrics generation pipeline (code → Prometheus). If Prometheus showed non-zero values, the RPC endpoint was simply not querying the right data. If Prometheus showed zeros, the cache code path was never being executed during the S3 read. The assistant also made a deliberate choice about which Prometheus metrics to inspect. The grep pattern fgw_cache|fgw_deal_retrieval targeted two families of metrics: cache-specific counters (evictions, ghost list hits, sizes) and retrieval-specific counters (successes, failures, bytes). This dual check would reveal whether the cache was untouched or whether the entire retrieval subsystem was inactive.

Assumptions Embedded in the Investigation

Every diagnostic step rests on assumptions, and message 2808 is no exception. The most significant assumption is that the S3 read path should, under normal circumstances, interact with the cache system. The user's rclone command was reading data from an S3 bucket. The assistant's mental model, at this point, likely assumed that any data retrieval — whether from local storage or from the Filecoin network — would pass through the caching layer. This assumption is reasonable in a system where the cache is positioned as a general read-throughput accelerator.

A second assumption is that Prometheus metrics are a reliable ground truth. The assistant implicitly trusts that if the counters are zero at the Prometheus level, they are truly zero — that no instrumentation bug is silently swallowing increments. This is a standard assumption in observability engineering, but it is worth noting because it shapes the diagnostic path. If Prometheus had shown non-zero values, the investigation would have turned toward the RPC layer.

A third, more subtle assumption is that the node at 10.1.232.83:2112 is representative. The user's report specifically mentioned node :83, and the assistant focused on that node. If the issue were node-specific (e.g., a configuration difference between nodes), the investigation would need to broaden. But starting with the reported node is the correct prioritization.

What the Message Reveals About System Architecture

Even in this early diagnostic step, the message reveals important architectural details. The Prometheus metric names are richly descriptive:

fgw_cache_l1_block_evictions_total — The L1 cache uses ARC (Adaptive Replacement Cache) eviction, and evictions are tracked as a counter.
fgw_cache_l1_block_ghost_hits_b1_total and fgw_cache_l1_block_ghost_hits_b2_total — The ARC algorithm maintains two "ghost" lists (B1 for recently evicted recent items, B2 for recently evicted frequent items). Hits in these ghost lists trigger adaptive tuning of the cache balance parameter p. The fact that these metrics exist tells us the L1 cache implements a full ARC algorithm with adaptive resizing, not a simple LRU.
The # HELP text for B1 ghost hits reads "indicates need for more recent items" and for B2 "indicates need for more frequent items" — this is a direct description of the ARC adaptation signal. These metric names and help strings are output knowledge created by the message: anyone reading this output learns that the cache uses ARC, that ghost-list hits are tracked, and that the system has a sophisticated adaptive caching strategy.

The Input Knowledge Required

To fully understand message 2808, a reader needs several pieces of context:

The cache architecture: The system has an L1 memory cache (ARC-based) and an L2 SSD cache (based on the SIEVE algorithm or similar). The L1 cache stores individual blocks with T1 (recent) and T2 (frequent) segments, plus B1/B2 ghost lists for adaptation.
The Prometheus instrumentation: The system exposes a standard /metrics endpoint on port 2112, following the Prometheus exposition format. Metrics use the fgw_ prefix and are organized by subsystem (cache, deal_retrieval, etc.).
The S3 read path: Data can be served from local groups (on-disk storage) or from the Filecoin network via the retrieval provider. The cache is positioned in the retrieval path, not the local read path.
The previous diagnostic step: The RPC endpoint had already been queried and returned zeros, establishing that the problem was not in the UI rendering.

Output Knowledge Created

Message 2808 produces several pieces of output knowledge:

Prometheus metrics are also zero: This rules out a simple RPC wiring bug and points to a deeper issue in the code path.
The cache counters exist and are initialized: The metrics appear in the Prometheus output with zero values, confirming that the instrumentation is registered and exposed. The problem is not a missing metric registration.
The system has rich cache instrumentation: The ARC ghost list metrics, eviction counters, and size gauges are all present, indicating a well-instrumented caching subsystem.
The retrieval path may not be exercised: The fgw_deal_retrieval metrics (included in the grep pattern) are also likely zero, suggesting that the S3 read is not triggering the retrieval provider at all.

The Thinking Process Visible in the Message

The assistant's thinking, while not explicitly stated in a reasoning block, is visible in the structure of the message. The phrase "The stats are all zeros" is a concise summary of the previous step's finding. "Let me check the Prometheus metrics directly" reveals the hypothesis: perhaps the RPC endpoint is not correctly aggregating or exposing the metrics, but the underlying counters are being incremented. The choice of head -30 indicates the assistant expects a manageable amount of output — enough to confirm the pattern without overwhelming the terminal.

The grep pattern fgw_cache|fgw_deal_retrieval is itself a thinking artifact. The assistant could have grepped for all fgw_ metrics, but chose to focus on cache and retrieval metrics specifically. This shows an understanding that the cache is part of the retrieval subsystem, and that both should show activity if data is being fetched through the expected path.

The command also reveals a practical debugging habit: using curl directly against the metrics endpoint rather than relying on a metrics dashboard or tool. This is a "close to the metal" approach that avoids any intermediary interpretation layers.

What Happened Next

The investigation continued beyond message 2808. The assistant traced the S3 read path through the codebase, discovering that data reads have two distinct paths: local groups read directly from disk via Group.View() with no cache involvement, while offloaded groups (data stored on Filecoin) read through the retrieval provider's FetchBlocks() method, which does use the cache. The data being read was in local groups 1 and 35, totaling 43 GB of on-disk storage. The cache was never involved because the data had never been offloaded to Filecoin.

This was not a bug — it was an architectural misunderstanding. The cache metrics were working correctly; they simply reflected that no cache activity had occurred. The user's rclone command was reading local data, which bypasses the cache entirely. The assistant's systematic debugging, starting with the Prometheus check in message 2808, led to this correct understanding.

Conclusion

Message 2808 is a small but revealing moment in a larger debugging session. It demonstrates the value of layered diagnostics, the importance of questioning assumptions about code paths, and the power of a single well-chosen command to narrow the search space. The assistant's decision to check Prometheus metrics directly — bypassing both the UI and the RPC layer — was a textbook debugging move that efficiently tested a key hypothesis. While the message itself contains no fix, no code change, and no conclusion, it is the hinge point on which the entire investigation turned. Without this step, the assistant might have spent hours debugging cache instrumentation code that was working perfectly, never realizing that the data path simply never touched the cache.