The Pivot to Disk: Tracing a Cache Statistics Bug Through SSH

The Message

[assistant] The groups response shows just keys [101, 1], not full group objects. Let me check the local data on disk: [bash] ssh 10.1.232.83 "ls -la /data/fgw/groups/ 2>/dev/null | head -20"

This message, from index 2814 of a lengthy coding session, appears at first glance to be a routine debugging step. The assistant has just received an RPC response that returned only numeric keys instead of structured group objects, and is pivoting to direct disk inspection via SSH. But beneath this surface-level action lies a rich moment of investigative reasoning, architectural discovery, and the confrontation of an incorrect mental model. Understanding why this message was written, what assumptions it challenged, and what knowledge it produced reveals a great deal about the nature of debugging distributed systems.

Context: The User's Complaint

The story begins with a user report that the cache statistics displayed in the WebUI were not updating after performing an S3 read operation. The user had run an rclone command to copy data from the S3 gateway to a local temporary directory and observed that the cache hit and miss counters remained stubbornly at zero. This was confusing: if data was being read through the system, surely some cache activity should be visible.

The assistant's initial investigation was thorough and systematic. First, it queried the CacheStats RPC endpoint directly, confirming that all counters were indeed zero: l1Size: 0, l1Items: 0, hits: 0, misses: 0. It then checked the RetrStats endpoint, which also showed zeros across the board. Finally, it scraped the Prometheus metrics endpoint, looking for any signs of cache activity, and found nothing — all cache-related counters were at zero.

This was the puzzle: a live system with data being read, but no cache statistics being recorded.

Tracing the Code Path

The assistant then did what any competent engineer would do: it traced the read path through the source code. It opened rbstor/rbs.go and examined the View method, which is the entry point for reading data by content hash. The code revealed a critical architectural detail: the read path has two distinct branches.

For local groups — data that resides on the local disk and has not been offloaded to the Filecoin network — the read path goes directly to Group.View(), which reads from the local filesystem. No cache is consulted. No cache counters are incremented. The cache is simply not in the picture.

For offloaded groups — data that has been moved to the Filecoin network with deals — the read path calls ext.FetchBlocks() via the retrieval provider, which does go through the L1 (in-memory ARC) and L2 (SSD) caches, and does update the hit/miss counters.

This was the "aha" moment: the cache statistics were working correctly; they just weren't applicable to the data the user was reading. The data was local, not offloaded.

The Confusing RPC Response

But the assistant needed to confirm this hypothesis. It queried the RIBS.Groups RPC endpoint to see what groups existed and what state they were in. The response was baffling:

{"id":1,"jsonrpc":"2.0","result":[101,1]}

Instead of structured objects with keys, states, and deal counts, the RPC returned just two integers: 101 and 1. This was not the expected format. The assistant's first attempt to parse it with a Python script had failed with a TypeError: 'int' object is not subscriptable because it was expecting a list of dictionaries, not a list of integers.

The Subject Message: A Pivot in Strategy

This brings us to the subject message. The assistant's statement — "The groups response shows just keys [101, 1], not full group objects" — is both an observation and a decision. The observation is that the RPC endpoint is returning a compressed or abbreviated format, perhaps just group keys rather than full metadata. The decision is to abandon further RPC-based investigation and pivot to direct disk inspection.

This pivot is significant. The assistant could have spent time debugging the RPC endpoint, figuring out why it returned keys instead of objects, and then fixing it. Instead, it chose the faster path: SSH into the QA node and look at the actual data on disk. This reflects a pragmatic, results-oriented approach common in production debugging. When a tool (the RPC) gives you confusing results, sometimes the fastest path to understanding is to bypass the tool entirely and look at the raw state.

Assumptions and Their Consequences

Several assumptions are visible in this message, both correct and incorrect.

Correct assumption: The assistant assumed that the data directory structure would reveal which groups exist and whether they contain local data. This turned out to be correct — the subsequent SSH commands revealed directories for groups 1 and 35 with 29GB and 14GB of data respectively.

Incorrect assumption (implicit): The assistant initially assumed that the RPC response format was standard and parseable. The Python script it used to parse the groups response expected a list of dictionaries, but got a list of integers. This assumption was embedded in the earlier investigation (msg 2812) where the script used d['result'] expecting an array of objects.

Incorrect assumption (corrected): More broadly, the assistant (and likely the user) had assumed that the cache statistics would reflect all read activity. The investigation revealed that the cache only applies to offloaded data retrieval, not local reads. This is not a bug — it's an architectural design choice — but it was not immediately obvious from the UI or the RPC interface.

Input Knowledge Required

To understand this message, one needs knowledge of:

  1. The system architecture: The FGW (Filecoin Gateway) system has a two-tier storage model. Data is initially written to local groups on disk. When a group is "offloaded," the local data is removed and deals are made with Filecoin storage providers. Subsequent reads of offloaded data go through a retrieval path that uses L1 (memory ARC cache) and L2 (SSD cache) to improve performance.
  2. The RPC interface: The RIBS.Groups endpoint returns information about storage groups. The assistant expected structured objects but received abbreviated numeric keys.
  3. The deployment topology: The QA environment has nodes at 10.1.232.83 and 10.1.232.84, with data stored at /data/fgw/. The assistant has SSH access and can run arbitrary commands.
  4. Go programming patterns: The code path tracing through rbstor/rbs.go required understanding Go interfaces, method dispatch, and the distinction between local and external storage providers.
  5. Prometheus metrics: The assistant checked the /metrics endpoint on port 2112, which is a standard Prometheus exposition format, to look for cache activity counters.

Output Knowledge Created

This message and its follow-up produced several important pieces of knowledge:

  1. Confirmation that the cache is working correctly: The cache counters were zero because no offloaded data was being read, not because the cache was broken.
  2. Understanding of the read path architecture: The investigation clarified the distinction between local reads (no cache) and offloaded reads (cache-enabled). This is important documentation for anyone operating the system.
  3. A bug in the Groups RPC: The RPC endpoint returned only numeric keys instead of structured group objects. This is either a bug or an intentional abbreviated format, but it confused both the assistant and the automated parsing script.
  4. Quantification of local data: The disk inspection revealed 43GB of local data across two groups (1 and 35), confirming that the system was serving data from local storage.
  5. A missing feature: The user's expectation — that cache statistics should reflect all read activity — suggests a potential feature gap. If operators want to see read activity for local data, separate metrics would need to be added for local reads, distinct from the cache statistics.

The Thinking Process

The reasoning visible in this message is a classic example of investigative debugging:

  1. Observe the symptom: Cache stats are zero after a read.
  2. Verify the symptom: Query RPC endpoints directly to confirm.
  3. Broaden the search: Check Prometheus metrics for any cache activity.
  4. Trace the code path: Read the source to understand when the cache is invoked.
  5. Form a hypothesis: The cache only applies to offloaded data.
  6. Test the hypothesis: Query the Groups RPC to see if data is local or offloaded.
  7. Encounter a new obstacle: The RPC response is in an unexpected format.
  8. Pivot to direct evidence: SSH into the node and inspect the disk directly. The pivot in step 8 is the key decision captured in the subject message. Rather than getting bogged down in debugging the RPC format, the assistant chose the most direct path to evidence. This is a hallmark of effective debugging: always prefer the simplest, most direct observation over indirect inference through potentially buggy tools.

Conclusion

Message 2814 is a small but revealing moment in a larger debugging session. It captures the moment when an engineer, confronted with confusing tool output, decides to bypass the abstraction layer and look at the raw state directly. The SSH command is not just a technical action — it's a philosophical statement about the value of direct observation over indirect inference. In distributed systems, where layers of abstraction can obscure what's actually happening, the ability to pivot from RPC calls to disk inspection is a critical skill. This message embodies that skill in action.