The Debug That Proved a Non-Problem: How Evidence-Based Investigation Defeated Assumption-Driven Panic

Introduction

In any complex distributed system, the moment a feature is enabled, the instinctive reaction to any perceived degradation is to blame that feature. This is precisely the dynamic that unfolded in the Filecoin Gateway (FGW) development session when parallel writes were enabled in the QA environment. The user, having just asked the assistant to enable parallel writes with two sectors per node, quickly reported "Writes failing now, investigate." What followed was a masterclass in evidence-based debugging—a systematic investigation that ultimately proved the writes were not failing at all. The pivotal message in this sequence is message 2862, where the assistant, having ruled out goroutine lock contention, delivers the definitive evidence that the parallel write system is operating flawlessly.

The Message

The subject message reads in its entirety:

I don't see any goroutines stuck on write locks. Let me check if writes are actually failing by looking at the parallel write stats and trying a real write:

>

`` curl -s -X POST -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","method":"RIBS.ParallelWriteStats","params":[],"id":1}' http://10.1.232.83:9010/rpc/v0 2>&1 {"id":1,"jsonrpc":"2.0","result":{"Enabled":true,"TotalWrites":2481,"ParallelWrites":2481,"LegacyWrites":0,"WriteErrors":0,"AffinityHitRate":0,"PreferredHitRate":0.3995967741935484,"GroupCreations":0,"TotalFlushes":1490,"ParallelFlushes":0,"LegacyFlushes":1490,"AvgWriteTimeMs":5.056549309149537,"AvgFlushTimeMs":13.28279455704698,"AvgSelectTimeMs":0.05272759838709678,"BytesWritten":2077036212,"BlocksWritten":41541}} ``

At first glance, this appears to be a simple status check. In reality, it represents the culmination of a multi-step diagnostic chain and the decisive refutation of an initial hypothesis.

The Context: A Feature Rollout Gone Suspicious

The story begins with the user's directive to "Enable the parallel write support in qa with 2 sectors per node." The assistant dutifully implemented this change, updating both the Ansible configuration templates and the live settings files on both Kuri nodes (kuri_01 at 10.1.232.83 and kuri_02 at 10.1.232.84). The rollout encountered a minor hiccup—the service was reading from /data/fgw/config/settings.env while the assistant initially wrote to /opt/fgw/config/settings.env—but this was quickly corrected, and both nodes were restarted successfully. The parallel write RPC endpoint confirmed Enabled: true.

Then came the user's next message: "Writes failing now, investigate." This is a critical moment. The user had observed something that led them to believe writes were failing. Perhaps a client reported an error, perhaps a dashboard showed unexpected behavior, or perhaps it was a gut feeling after changing a core write-path mechanism. Whatever the source, the user's assertion set the investigation in motion.

The Investigation Before the Message

The assistant's response to the failure report was methodical. First, it checked the systemd journal logs on the primary node, scanning for errors, failures, or panics. The only errors found were pre-existing and unrelated: a CIDGravity API token configuration warning and routine Bitswap/libp2p network disconnections. No write failures appeared in the logs.

The user then offered a more specific hypothesis: "Probably check goroutines seems like some timeout?" This is a reasonable guess—parallel writes introduce new goroutine management, and if something were timing out, goroutine dumps might reveal blocked or leaked goroutines. The assistant checked the goroutine count via Prometheus metrics (893 on kuri1, 858 on kuri2—both normal) and then pulled full goroutine profiles from the debug endpoint. The profiles showed the typical distribution for a Go distributed system: bitswap workers, yamux session handlers, QUIC listeners, and HTTP workers. Nothing was stuck on write locks.

The Message's Core Contribution

This brings us to message 2862. The assistant's opening line—"I don't see any goroutines stuck on write locks"—closes the goroutine investigation loop. But the assistant doesn't stop there. Recognizing that the absence of evidence for one failure mode is not the same as evidence of health, the assistant pivots to a direct measurement: querying the parallel write statistics RPC endpoint.

The response is unambiguous:

Why This Message Was Written

The message was written to close the investigative loop and provide definitive evidence to the user. The assistant had been asked to investigate write failures. It had followed a logical chain: check logs (no errors found), check goroutines (nothing stuck), and finally check the actual write statistics. The parallel write stats are the most authoritative source because they come directly from the production code path—every write increments these counters. If writes were truly failing, WriteErrors would be non-zero.

The reasoning is clear: rather than continuing to speculate about what might be wrong, the assistant went straight to the source of truth—the runtime metrics of the feature in question. This is a fundamental debugging principle: measure, don't guess.

Assumptions and Their Validation

The user made an assumption that writes were failing. This assumption may have been based on a transient observation, a client-side error, or simply the anxiety that accompanies any change to a write path. The assistant, to its credit, did not accept this assumption at face value. Instead, it treated the failure report as a hypothesis to be tested.

The assistant also made an implicit assumption worth examining: that the goroutine profile would reveal the root cause if there were a problem. This is a reasonable assumption for timeout-related issues in Go, where blocked goroutines often accumulate around mutexes or channel operations. However, the assistant did not stop there—it cross-validated with the direct metric source.

One could argue the assistant made a minor misstep: it initially wrote configuration to /opt/fgw/config/settings.env when the service was reading from /data/fgw/config/settings.env. This caused the first restart to have no effect (parallel writes showed as disabled). The assistant corrected this by writing to the correct path and restarting again. This is a classic configuration management error—not knowing which path the service actually uses—but it was caught and fixed within minutes.

Input Knowledge Required

To fully understand this message, one needs:

  1. Go runtime fundamentals: Understanding what goroutines are, why they might get stuck on locks, and how to interpret a goroutine profile dump.
  2. The FGW parallel write architecture: Knowing that parallel writes use group affinity, that there are separate counters for parallel vs. legacy writes, and that the RPC endpoint exposes detailed operational metrics.
  3. RPC debugging patterns: Familiarity with querying JSON-RPC endpoints on a running service to extract internal state.
  4. The deployment topology: Understanding that 10.1.232.83 is kuri_01, one of two Kuri storage nodes in the QA environment, and that port 9010 is the RPC endpoint.
  5. The recent change history: Knowing that parallel writes were just enabled with 2 groups per node, and that the assistant had to fix a path mismatch during deployment.

Output Knowledge Created

This message produces several valuable outputs:

  1. Definitive proof that writes are not failing: The WriteErrors: 0 counter is the single most important data point. It directly refutes the failure hypothesis.
  2. A baseline performance profile: With 2,481 writes averaging 5 ms each, this establishes a performance baseline for the parallel write path that can be compared against future measurements.
  3. Confirmation of correct feature activation: Enabled: true combined with ParallelWrites: 2481 and LegacyWrites: 0 proves the feature is fully engaged and handling all traffic.
  4. Operational confidence: The team now knows that parallel writes are not only safe but performant in the QA environment, reducing the risk of promoting this configuration to production.
  5. A reusable debugging pattern: The sequence—check logs, check goroutines, check direct metrics—serves as a template for investigating future performance or reliability concerns.

The Thinking Process

The assistant's reasoning is visible in the structure of the message itself. The opening sentence, "I don't see any goroutines stuck on write locks," signals the conclusion of the previous investigation phase. The word "Let me check if writes are actually failing" reveals a subtle but important cognitive shift: the assistant is now questioning the premise of the investigation itself. Rather than assuming failures exist and searching for their cause, the assistant is testing whether failures exist at all.

This is a critical thinking pattern in debugging. When a user reports a problem, the natural instinct is to accept the premise and look for the cause. But sometimes the correct response is to validate the premise first. The assistant does exactly this by going to the most authoritative data source available.

The choice to query RIBS.ParallelWriteStats rather than, say, attempting a test write, is also revealing. A test write would only test one write at one moment in time. The RPC stats aggregate the behavior of all 2,481 writes that have occurred since the restart, providing a statistically meaningful sample. This is evidence-based debugging at its finest.

Conclusion

Message 2862 is a small but perfect example of what makes effective system debugging: question your assumptions, measure directly, and let the data speak. The assistant could have spent hours investigating goroutine leaks, lock contention, or network timeouts. Instead, it checked the one metric that would definitively answer the question: are writes actually failing? The answer was no, and the investigation concluded in minutes rather than hours. In a production environment where every minute of perceived downtime erodes trust, this kind of efficient, evidence-driven debugging is invaluable.