When "Writes Are Failing" Actually Means "Writes Are Working": A Case Study in Data-Driven Debugging

Introduction

In the course of developing and operating a distributed storage system, few moments are as tense as when a user reports that writes are failing. The instinctive reaction is to dive into logs, check error rates, and begin the frantic hunt for a bug introduced by the latest change. But sometimes, the most valuable debugging skill is knowing when to trust the data over the report. This article examines a single message from a coding session for the Filecoin Gateway (FGW) project—a horizontally scalable S3-compatible storage system built on a Kuri/YugabyteDB architecture—in which an assistant, after being told "Writes failing now, investigate," methodically gathered evidence and concluded that writes were, in fact, working perfectly. The message is a masterclass in data-driven debugging, the value of observability infrastructure, and the importance of verifying assumptions before acting.

The Subject Message

The assistant's response reads as follows:

Writes ARE working! I see: - TotalWrites: 2481 - ParallelWrites: 2481 (all writes using parallel mode) - WriteErrors: 0 - BytesWritten: 2077036212 (~2GB)

>

The writes are succeeding. Let me also check the groups to see the state:

>

``bash curl -s -X POST -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","method":"RIBS.WritableGroups","params":[],"id":1}' http://10.1.232.83:9010/rpc/v0 2>&1 ``

>

``json {"id":1,"jsonrpc":"2.0","result":[{"GroupKey":101,"Blocks":50178,"Bytes":14947938390,"AvailableSpace":15985053610,"ActiveWriters":0,"HasAffinity":false},{"GroupKey":201,"Blocks":42098,"Bytes":2104403749,"AvailableSpace":28828588251,"ActiveWriters":0,"HasAffinity":false}]} ``

This message is deceptively simple. It contains two RPC calls, their raw JSON responses, and a four-line summary. But behind it lies a chain of reasoning, a set of assumptions tested and discarded, and a deliberate decision to trust instrumentation over anecdotal reports.

The Context: What Led to This Moment

To understand why this message matters, we need to reconstruct the events that preceded it. The user had just requested that parallel write support be enabled in the QA environment with two sectors per node. The assistant dutifully located the configuration parameters—RIBS_ENABLE_PARALLEL_WRITES and RIBS_MAX_PARALLEL_GROUPS—updated the Ansible inventory and the Kuri settings template, and deployed the changes to both nodes in the QA cluster.

However, the initial deployment hit a snag. The assistant wrote the new environment variables to /opt/fgw/config/settings.env, but the systemd service unit was configured to read its environment from /data/fgw/config/settings.env. This is a classic operational pitfall: a configuration file exists in two locations, and the running process reads from the wrong one. The assistant discovered this mismatch by inspecting the systemd unit file, corrected the path, restarted the services, and verified that parallel writes were now reporting Enabled: true via the RPC endpoint.

Then the user reported: "Writes failing now, investigate."

This is the critical juncture. The user had just watched the assistant make a configuration change, fix a path error, and restart services. When they tested their workflow and observed failures, the natural conclusion was that the parallel write feature—the thing that had just been changed—was causing the problem. The assistant could have accepted this premise and started searching for bugs in the parallel write code path. Instead, the assistant chose to verify the claim first.

The Investigation: What the Assistant Actually Did

The assistant's investigation, visible in the messages immediately preceding the subject message, followed a systematic pattern:

  1. Checked system logs for error messages. The journalctl output showed only benign warnings—CIDGravity API token not configured (expected), Bitswap/libp2p connection closures (normal for peer-to-peer networks), and an RPC error for a non-critical endpoint. No write failures, no panics, no database errors.
  2. Examined goroutine profiles to look for blocked or stuck goroutines. The pprof output showed ~879 goroutines on kuri1, mostly engaged in network I/O, bitswap operations, yamux multiplexing, and quic connections. None were stuck on write locks or waiting indefinitely. The assistant specifically grepped for terms like "rbstor," "parallel," "writable," and "writeLk" to find goroutines related to the write path, finding none blocked.
  3. Checked the parallel write metrics via the RIBS.ParallelWriteStats RPC endpoint. This returned the full picture: 2,481 total writes, all using parallel mode, zero errors, ~2GB written, with an average write time of 5 milliseconds.
  4. Verified the writable groups via RIBS.WritableGroups to confirm that the storage groups were healthy, had available space, and were accepting writes. Each of these steps relied on observability infrastructure that had been built earlier in the project—the RPC endpoints for parallel write statistics, the pprof debug endpoints, the structured logging system. Without these tools, the assistant would have had to resort to guesswork or, worse, accept the user's premise and begin making unnecessary changes to the codebase.

The Reasoning: Why the Assistant Trusted the Data

The assistant's reasoning, though not explicitly stated in the message, can be reconstructed from the sequence of investigations. The key insight is that the assistant did not treat the user's report as a fact. Instead, it treated it as a hypothesis to be tested.

The hypothesis was: "Parallel writes are causing write failures." To test this, the assistant needed to answer three questions:

  1. Are writes actually failing? The parallel write stats said no—2,481 writes, zero errors. The goroutine profiles showed no blocked writers. The logs showed no error messages related to writes.
  2. If writes are succeeding, why does the user think they're failing? This is the more interesting question. The assistant didn't pursue it in this message, but the implication is that the user might have been testing writes through a different path (perhaps through the S3 frontend proxy rather than directly to the Kuri node), or they might have been observing a transient issue that had since resolved itself, or they might have been confused by a different symptom.
  3. Is the parallel write feature actually active and functioning? The stats confirmed Enabled: true and all 2,481 writes went through the parallel path. The writable groups showed healthy capacity. The feature was working as designed. The assistant's conclusion—"Writes ARE working!"—is stated with emphasis, almost as if surprised to find good news. This reflects the tension of the moment: the assistant had been bracing for a bug hunt, only to discover that the system was operating normally.

Assumptions Made and Corrected

Several assumptions were tested during this investigation:

Assumption 1: The user's report is accurate. This was the first assumption to be discarded. The assistant implicitly recognized that user reports, especially in the midst of active development and deployment, can be based on incomplete information, transient conditions, or testing against the wrong endpoint.

Assumption 2: The parallel write change caused the failure. This was the natural causal inference: change A was made, then problem B appeared, therefore A caused B. The assistant resisted this fallacy by checking whether B actually existed.

Assumption 3: The configuration was correctly applied. This assumption had already been tested and corrected earlier when the assistant discovered the path mismatch between /opt/fgw/config/ and /data/fgw/config/. Having fixed that, the assistant could trust that the parallel write feature was actually enabled.

Assumption 4: Goroutine count indicates a problem. The user specifically suggested checking goroutines, implying a suspicion of goroutine leaks or deadlocks. The assistant checked and found normal counts (~850-900) with no blocked write goroutines.

Input Knowledge Required

To fully understand this message, a reader needs knowledge of:

Output Knowledge Created

This message creates several valuable outputs:

  1. A verified system state: The assistant has confirmed that the parallel write feature is operational, writes are succeeding, and both storage groups are healthy. This is actionable knowledge that can be communicated back to the user.
  2. A debugging methodology: The message implicitly documents a process for investigating write-related issues: check metrics first, then logs, then goroutine profiles, then group state. This sequence can be reused for future investigations.
  3. A baseline for comparison: The specific numbers—2,481 writes, 2GB written, 5ms average write time, 13ms average flush time—establish a performance baseline for the parallel write feature in the QA environment. Future regressions can be measured against these values.
  4. A rejection of the failure hypothesis: The most important output is the negative result: writes are not failing. This prevents wasted effort chasing a non-existent bug and redirects attention to whatever the actual issue might be.

The Thinking Process: What the Message Reveals

The assistant's thinking process, visible in the sequence of investigations leading up to this message, reveals a disciplined approach to debugging:

Step 1: Gather data before forming hypotheses. The assistant did not start by asking "what could be wrong with parallel writes?" It started by collecting data: logs, goroutine counts, pprof stacks, and RPC metrics. Only after assembling this evidence did it draw a conclusion.

Step 2: Use the right tool for each question. Logs answer "what events occurred?" Goroutine profiles answer "what is the system doing right now?" RPC metrics answer "what has the system accumulated over its lifetime?" Each tool addresses a different aspect of system behavior.

Step 3: Trust measurements over reports. When the user says "writes failing" and the metrics say "2,481 writes, zero errors," the metrics win. This is not about distrusting the user—it's about recognizing that human perception is fallible, especially under the stress of a production incident.

Step 4: Communicate findings clearly and confidently. The message begins with "Writes ARE working!"—an unambiguous, emphatic statement. It then presents the evidence in a structured format: bullet points for the key metrics, a code block for the raw RPC response. This clarity helps the user quickly understand the situation and adjust their own investigation.

Broader Implications

This message, while small, illustrates a fundamental principle of operating distributed systems: observability is not optional. The assistant was able to resolve this investigation in minutes because the system had been built with proper metrics endpoints, structured logging, and debugging interfaces. Without these, the assistant would have been forced to SSH into the nodes, read log files manually, or—worst of all—make code changes to add debug logging. The investment in observability infrastructure paid off immediately.

The message also highlights the importance of resisting premature causal attribution. When a change is made and a problem appears, the natural human instinct is to blame the change. But in complex systems, correlation is not causation. The assistant's willingness to question the user's premise—to check whether the problem actually existed before trying to fix it—saved time and prevented unnecessary code changes.

Finally, the message demonstrates the value of building debugging tools into the system from the start. The RIBS.ParallelWriteStats and RIBS.WritableGroups RPC endpoints were not added in response to this incident; they were part of the parallel write feature's original implementation. The pprof endpoints were standard Go library features. The structured logging was a project-wide convention. All of these existed before the problem was reported, which is exactly when they should exist.

Conclusion

The message "Writes ARE working!" is, on its surface, a simple status update. But examined closely, it reveals a sophisticated debugging process grounded in data, disciplined reasoning, and trust in instrumentation. The assistant received a report of failure, investigated systematically, found no evidence of failure, and communicated that finding with clarity and confidence. In doing so, it demonstrated the most important skill for any operator of distributed systems: knowing when to believe the data, even when it contradicts the report.