The 43-Gigabyte Revelation: How a Simple du Command Unraveled a Cache Mystery

The Message

[assistant] [bash] ssh 10.1.232.83 "du -sh /data/fgw/grp/*"
29G	/data/fgw/grp/1
14G	/data/fgw/grp/35

At first glance, this appears to be an utterly mundane exchange: an engineer runs a disk usage command on a remote server and gets back two lines of output. Forty-three gigabytes of data spread across two directories. Nothing remarkable. Yet in the context of the debugging session from which it emerged, this single command represents the decisive moment in a diagnostic chain — the point where hypothesis crystallized into certainty, where speculation gave way to measurement, and where a seemingly broken feature was revealed to be working exactly as designed.

The Context: A Cache That Wouldn't Budge

The story begins with a user complaint. The Filecoin Gateway (FGW) project had recently gained a sophisticated new monitoring dashboard, complete with real-time cache statistics showing L1 (in-memory ARC) and L2 (SSD-based) cache performance, hit rates color-coded green above 80%, and detailed breakdowns of cache internals. The user had deployed this to a QA environment and promptly tested it by fetching a substantial dataset using rclone — a cloud-storage synchronization tool configured to talk to the FGW's S3-compatible interface. The command copied a multi-gigabyte directory of data from a bucket into a local temporary directory.

The expected outcome was straightforward: the cache statistics should have sprung to life, showing hits, misses, bytes transferred, and the various internal cache structures adjusting in real time. Instead, the dashboard remained stubbornly inert. Every metric read zero. The L1 cache showed 0 items, 0 hits, 0 misses. The L2 cache showed "Disabled." The retrieval statistics were equally barren. From the user's perspective, either the cache was broken, or the monitoring was lying.

The Investigation: Tracing the Read Path

The assistant's response to this report was methodical and systematic, following the contours of the system architecture from the outside in. The first step was purely observational: query the live RPC endpoints to confirm the symptom. The CacheStats endpoint returned zeros across the board. The RetrStats endpoint returned zeros. The Prometheus metrics endpoint, scraped directly from the running node, showed nothing but zero-valued counters. The symptom was real and reproducible.

But zero-valued metrics can mean two very different things: either the instrumentation is broken, or the code path being exercised never passes through the instrumented component. The assistant chose to investigate the second possibility first, tracing the S3 read path through the source code to understand exactly which components were involved when a client fetched data.

This is where the investigation reveals its first critical insight. The codebase contains two distinct read paths. For data stored in "local" groups — groups whose data resides on the local disk — reads flow through Group.View(), which reads directly from the filesystem. For data in "offloaded" groups — groups whose data has been moved to the Filecoin network via storage deals — reads flow through the retrieval provider's FetchBlocks() method, which consults the L1/L2 cache hierarchy. The cache statistics only track the second path.

The assistant had built the cache monitoring under an implicit assumption: that the QA environment would be exercising the retrieval path, either because data had been offloaded to Filecoin or because the test workload would trigger cache lookups. The user's rclone test, however, was reading data that had been written locally and never offloaded. The cache was not broken; it was simply never consulted.

The Pivot: From Code to Disk

Having identified the architectural explanation for the zero metrics, the assistant needed to confirm it empirically. The RPC endpoint for listing groups returned a terse [101, 1] — group keys without metadata. A series of ssh commands probed the filesystem layout: the data directory structure, the group storage locations, and finally, the decisive measurement.

The subject message — ssh 10.1.232.83 "du -sh /data/fgw/grp/*" — is the culmination of this probe. It reveals two groups on disk: group 1 holding 29 gigabytes and group 35 holding 14 gigabytes. Forty-three gigabytes of local data, all of it readable directly from disk without any cache involvement. This single data point confirmed the hypothesis beyond doubt. The cache statistics were accurate; they were reporting zero because zero cache operations had occurred. Every byte the user had fetched had been served from local storage, bypassing the cache entirely.

The Reasoning: What This Message Reveals About the Debugging Process

The subject message is notable for what it reveals about the assistant's debugging methodology. The assistant did not jump to conclusions about broken instrumentation or corrupted metrics. Instead, it worked backward from the symptom through the system architecture, forming a hypothesis about the read path, and then seeking concrete evidence to confirm or refute that hypothesis.

The choice of du -sh is itself instructive. The assistant could have checked many things: the database records for group states, the deal status for offloaded groups, or the configuration flags for cache enablement. But the simplest, most direct evidence would be the actual data on disk. If the groups contained substantial local data, reads would hit local storage. If the groups were empty or contained only metadata, reads would fall through to the retrieval path. The du command answered the question with maximum certainty and minimum overhead.

There is also an implicit assumption visible in the assistant's reasoning: that the QA environment was configured in a way that would exercise the retrieval path. This assumption was reasonable — the system had been deployed with cache-enabled builds, and the monitoring had been added specifically to observe retrieval behavior. But the assumption was wrong, and the debugging process corrected it.

The Output Knowledge: What Changed

The subject message produced concrete, actionable knowledge. It established that the QA environment held 43 gigabytes of local data across two groups, that neither group had been offloaded to Filecoin, and that the cache statistics were therefore reporting correctly. This knowledge immediately reframed the user's bug report: the issue was not a broken cache or faulty instrumentation, but a mismatch between the user's expectations and the system's architecture.

The assistant's subsequent response (message 2819) articulates this explicitly: "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."

This explanation transforms the user's understanding of the system. The cache is not a general-purpose read cache for all S3 operations; it is a specialized retrieval cache for data that has been moved to the Filecoin network. The monitoring dashboard is not broken; it is accurately reflecting that no retrieval operations have occurred.

The Deeper Lesson: Architecture Shapes Observability

The subject message, for all its brevity, illustrates a profound principle of distributed systems engineering: observability is inseparable from architecture. A monitoring dashboard that tracks cache performance is only meaningful if the workload actually exercises the cache. Zero-valued metrics are not necessarily evidence of a bug; they may be evidence that the system is operating in a different mode than the observer expects.

The assistant's debugging process — from symptom to code analysis to filesystem probe — models the kind of systematic reasoning that production debugging demands. The du -sh command at the heart of this message is a reminder that sometimes the most powerful diagnostic tool is not a sophisticated profiler or a distributed tracing system, but a simple question asked of the filesystem: how much data is actually here?

In the end, the cache wasn't broken. The monitoring wasn't lying. The system was working exactly as designed. The only thing missing was the shared understanding between the operator and the architecture — and that understanding was restored by 43 gigabytes of data sitting quietly on disk, waiting to be measured.