The Goroutine Check: A Single Diagnostic Step in Distributed Systems Debugging

The Message

[assistant] [bash] curl -s http://10.1.232.83:2112/metrics 2>&1 | grep -E "goroutine|go_goroutines" # HELP go_goroutines Number of goroutines that currently exist. # TYPE go_goroutines gauge go_goroutines 893

This message, brief as it is, represents a single, deliberate step in a live debugging session of a distributed storage system. It is not a solution, a configuration change, or a code commit. It is a probe—a question asked of a running system to gather evidence. And like any good diagnostic question, it was shaped by a specific hypothesis, a particular context, and a set of assumptions about where the problem might be hiding. To understand this message fully, we must reconstruct the debugging narrative that produced it, the reasoning that led to this particular command being issued at this particular moment, and the knowledge—both gained and assumed—that surrounds it.

The Debugging Context: Parallel Writes and Silent Failures

Moments before this message was sent, the assistant had enabled parallel write support in the QA environment. This was a significant operational change: the RIBS_ENABLE_PARALLEL_WRITES=true and RIBS_MAX_PARALLEL_GROUPS=2 environment variables had been added to both Kuri nodes (kuri1 at 10.1.232.83 and kuri2 at 10.1.232.84), the services had been restarted, and the parallel write feature was confirmed active via the RIBS.ParallelWriteStats RPC endpoint. The user then reported a problem: "Writes failing now, investigate."

The assistant's initial investigation followed a logical path. First, it examined the systemd journal logs for error messages, finding only benign noise: Bitswap connection resets (normal for a peer-to-peer network) and a deal-check loop failure due to a missing CIDGravity API token (a known, expected condition). No write failures appeared in the logs. Next, the assistant attempted a direct S3 write using rclone rcat, but this failed with a URL parsing error—a client-side issue with the rclone endpoint format, not a system failure. At this point, the assistant reported back: "I don't see any write failures."

The user then offered a diagnostic hypothesis: "Probably check goroutines seems like some timeout?" This suggestion is revealing. It tells us that the user suspected the problem might not be an explicit error message or a crash, but rather a performance degradation—something that manifests as slowness or timeout rather than a hard failure. In Go systems, goroutine leaks or excessive goroutine creation can cause exactly this kind of symptom: the system remains "alive" and responds to health checks, but requests slow down or time out because goroutines are piling up waiting for locks, channels, or I/O.

Why This Specific Command?

The assistant's response to the user's hypothesis was to run a curl against the Prometheus metrics endpoint at port 2112, filtering for goroutine-related metrics. This choice is itself a decision worth examining.

The assistant could have used several approaches to check goroutine state. The Go runtime provides the net/http/pprof package, which exposes a /debug/pprof/goroutine endpoint with detailed stack traces for every goroutine. This is the gold standard for diagnosing goroutine issues—it shows not just the count, but what each goroutine is doing. Alternatively, the assistant could have used the pprof tool directly to download and analyze a profile. Or it could have used a runtime trace.

Instead, the assistant chose the Prometheus metrics endpoint. This decision reflects a particular diagnostic philosophy: start with the simplest, least-invasive measurement before escalating to more detailed tools. The metrics endpoint requires no special permissions, no tooling beyond curl and grep, and imposes minimal overhead on the running system. It answers a single, narrow question: "How many goroutines exist right now?" This number, 893, is a baseline. It tells the assistant whether the goroutine count is in a normal range or whether it has exploded to thousands or tens of thousands—a clear sign of a leak.

The choice of port 2112 is also significant. This is the standard Prometheus metrics port for the Kuri service, separate from the RPC port (9010) and the S3 port (8079). The assistant knew this port was available because it had been used earlier in the conversation to check various metrics. This is an example of operational knowledge—knowing which port exposes which information, and being able to reach for the right tool without hesitation.

What the Output Reveals

The output is straightforward: go_goroutines 893. The Prometheus exposition format includes the HELP and TYPE metadata lines, but the actual measurement is a single gauge value. Eight hundred and ninety-three goroutines.

Is this a lot? In a Go application that handles network I/O, peer-to-peer connections (Bitswap, libp2p), HTTP serving, database connections, and background workers, 893 goroutines is actually quite modest. A typical Go service might have dozens to hundreds of goroutines. A service with heavy concurrency, multiple connection pools, and background task workers could easily have several hundred. The number 893, without further context, does not scream "leak." It suggests a system with moderate concurrency but not pathological goroutine creation.

The assistant implicitly recognized this. After receiving this output, the assistant did not declare victory or move on. Instead, it immediately checked the other node (kuri2 at 10.1.232.84), finding 858 goroutines—a similar number. Then it attempted to get the detailed pprof goroutine dump, initially hitting a 404 on the metrics port before finding the correct endpoint on the RPC port (9010). This progression—from simple metric to detailed profile—shows a diagnostic escalation pattern: start broad, then drill down.

Assumptions Embedded in This Message

Every diagnostic step carries assumptions. This message assumes that:

The metrics endpoint is reachable and responsive. If the service were truly hung or overloaded, the metrics endpoint might not respond, or might respond slowly. The fact that it returned immediately with a value tells us the service is at least partially functional.
The goroutine count is a useful signal for the suspected problem. The user hypothesized a timeout-related issue. The assistant implicitly accepted that checking goroutines could reveal something relevant. This assumption turned out to be partially correct—the goroutine count was normal, which helped rule out one class of problem, but it didn't directly identify the issue.
The Prometheus metric name is go_goroutines. This is a standard metric exposed by the Go client library for Prometheus, but it's not guaranteed. The assistant relied on prior knowledge that this metric would be present and correctly named.
The grep pattern "goroutine|go_goroutines" would capture the relevant line. This is a reasonable heuristic, but it could miss edge cases. The metric could be named differently if the application registered it under a custom name. In this case, it worked.
A single snapshot of the goroutine count is meaningful. Goroutine counts can fluctuate rapidly in a concurrent system. A single measurement at a single point in time might miss a transient spike. The assistant implicitly treated 893 as a stable baseline, which was a reasonable assumption given that the system was under continuous load.

What the Assistant Did Not Know

At the moment this message was sent, the assistant did not yet know that the writes were actually succeeding. The parallel write stats (checked shortly after in message 2862) showed TotalWrites: 2481, WriteErrors: 0, and BytesWritten: ~2GB. The user's report of "writes failing" turned out to be either a miscommunication, a transient issue that had already resolved, or a problem on the client side rather than the server side.

This is a crucial point: the assistant was operating under the assumption that writes were genuinely failing, based on the user's report. The diagnostic steps—checking logs, testing a write directly, checking goroutines—were all conducted under this assumption. It was only after exhausting the server-side diagnostic avenues that the assistant asked the user to clarify what specific error they were seeing. This is a classic debugging pitfall: accepting a problem report at face value and searching for a cause that may not exist on the side you're investigating.

Input and Output Knowledge

The input knowledge required to understand this message includes:

Go runtime behavior: Understanding that goroutines are lightweight threads managed by the Go scheduler, and that an unusually high count can indicate a leak or contention.
Prometheus metrics conventions: Knowing that the Go runtime exposes go_goroutines as a standard metric, and that port 2112 is the conventional Prometheus metrics port.
The system architecture: Knowing that the Kuri service runs on 10.1.232.83 and exposes a metrics endpoint, and that this is the service whose writes are being investigated.
The debugging context: Understanding that parallel writes were just enabled, that the user reported failures, and that the user specifically suggested checking goroutines. The output knowledge created by this message is:
A data point: The goroutine count at a specific moment (893), which serves as a baseline for comparison.
A ruling-out: The goroutine count is not pathologically high, suggesting that a goroutine leak is not the primary cause of any observed slowness.
A diagnostic direction: The normal goroutine count steers the investigation toward other potential causes (which the assistant then pursued by examining detailed pprof dumps and eventually the parallel write stats).

The Thinking Process

The thinking process visible in this message is one of methodical, hypothesis-driven debugging. The user proposed a theory ("seems like some timeout?") and suggested a diagnostic avenue ("check goroutines"). The assistant accepted this suggestion and executed it with minimal ceremony. But the assistant did not stop there—it immediately followed up with additional checks on the second node and then escalated to more detailed profiling.

This reveals a debugging style that is both responsive and systematic. The assistant does not argue with the user's hypothesis or insist on its own theory. Instead, it treats the hypothesis as a lead to be investigated, gathers the evidence, and lets the evidence guide the next step. When the goroutine count turned out to be normal, the assistant did not declare the investigation complete. It moved on to the next diagnostic step: the detailed pprof dump.

The brevity of the message is itself a signal. In a live debugging session, speed matters. The assistant did not write a paragraph explaining why it was checking goroutines or what the number meant. It simply ran the command and presented the output, trusting that the user would understand the significance. This is the language of collaborative debugging: short commands, raw output, shared context.

Conclusion

This message—a single curl command and its output—is a microcosm of distributed systems debugging. It shows how a hypothesis (timeout → goroutine leak) leads to a specific diagnostic action (check metrics), how assumptions shape the interpretation of results (893 is normal), and how evidence guides the next step (escalate to pprof). It also reveals the limitations of server-side debugging when the problem may be on the client side, and the importance of asking clarifying questions when the evidence doesn't match the report.

In the end, the goroutine check was a dead end—the writes were working fine all along. But that doesn't make it a wasted step. In debugging, ruling out a hypothesis is just as valuable as confirming one. The assistant learned that the goroutine count was normal, that the pprof endpoint was on a different port, and that the parallel write stats showed zero errors. Each of these data points narrowed the search space. And when the assistant finally asked "What specific error are you seeing?", it was able to do so with confidence that the server side was healthy—a conclusion built on a series of small, deliberate diagnostic steps, of which this goroutine check was one.