Goroutine Profiling in the Debugging Hot Seat: Diagnosing Write Failures After Enabling Parallel Writes

Introduction

In the life of a distributed systems engineer, few moments are as tense as the one captured in this message. The user has just reported "Writes failing now, investigate" after a seemingly innocuous configuration change—enabling parallel writes with two sectors per node in a QA environment. The assistant, having already checked logs and found no obvious errors, now pivots to a deeper diagnostic technique: goroutine profiling via Go's built-in pprof tool. This message, brief as it is, represents a critical inflection point in a debugging session where surface-level investigation has failed and the engineer must descend into the runtime internals to find the root cause.

The message at index 2861 reads:

The goroutines look normal - mostly network I/O, bitswap, yamux, and quic. Let me search for goroutines that might be stuck on locks or waiting: [bash] curl -s "http://10.1.232.83:9010/debug/pprof/goroutine?debug=1" 2>&1 | grep -A5 "rbstor\|parallel\|writable\|writeLk" # 0x1792785 github.com/CIDgravity/filecoin-gateway/rbstor/cidlocation.StartWorkers.func1.1+0x125 /home/theuser/gw/rbstor/cidlocation/worker.go:120

>

16 @ 0x4818ce 0x45e0b7 0x2492b66 0x48a161 # 0x2492b65 github.com/ipfs/boxo/bitswap/network/httpnet.(*Network).httpWorker+0xc5 /home/theuser/.opt/go/pkg/mod/github.com/ipfs/boxo@v0.33.0/bitswap/network/httpnet/httpnet.go:633

>

11 @ 0x4818ce 0x45e0b7 0x2448b85 0x48a161 -- # 0x17842d9 github.com/CIDgravity/filecoin-gateway/rbstor.(*rbs).groupWorker+0xd9 ...

This is a deceptively simple message. On its surface, it reports that goroutines appear normal and that a targeted search for lock-related or storage-related goroutines didn't reveal anything obviously stuck. But beneath that surface lies a rich story about how experienced engineers debug production systems, the assumptions they make, and the tools they reach for when logs and metrics aren't enough.

The Context: A Configuration Change Gone Wrong

To understand why this message was written, we need to trace the chain of events that led to it. The user had just requested enabling parallel writes in the QA environment—a feature designed to allow multiple groups to receive writes concurrently instead of serializing all writes through a single group. The assistant dutifully updated the Ansible configuration, deployed the changes to both Kuri nodes (kuri1 at 10.1.232.83 and kuri2 at 10.1.232.84), and verified via RPC that Enabled: true was now reporting correctly.

But almost immediately, the user reported that writes were failing. This is the nightmare scenario for any engineer: a change that should be purely additive and non-disruptive has apparently broken something. The assistant's first response was to check the system logs (journalctl), which showed only benign messages—bitswap connection errors, CIDGravity API token warnings (expected), and routine HTTP requests. No panic, no error stack traces, no obvious write failures.

The user then offered a specific hypothesis: "Probably check goroutines seems like some timeout?" This is a remarkably insightful suggestion. In Go systems, when writes mysteriously stall or fail without error messages, the culprit is often a goroutine that's blocked on a lock, a channel, or a network operation that's silently timing out. The user's intuition pointed directly at the runtime concurrency model, bypassing the usual log-based investigation entirely.## The Tool: pprof and Goroutine Profiling

The assistant's first action in this message is to fetch a goroutine profile dump from the running Kuri node. The URL is telling: http://10.1.232.83:9010/debug/pprof/goroutine?debug=1. This endpoint is served by Go's net/http/pprof package, which exposes runtime profiling data over HTTP when enabled. The debug=1 parameter produces a human-readable text dump rather than a binary protobuf profile, showing every goroutine's stack trace grouped by similarity.

This is a powerful diagnostic technique. Unlike a CPU profile (which shows where time is being spent) or a heap profile (which shows memory allocation), a goroutine profile reveals the state of every concurrent execution thread in the Go runtime. It answers questions like: Are goroutines blocked on I/O? Are they waiting on mutexes? Are they stuck in infinite loops? Are they parked on channel operations?

The assistant's initial read of the full goroutine dump concluded that things "look normal"—the goroutines are mostly engaged in network I/O (bitswap, yamux, quic), which is expected for a distributed storage node that's constantly communicating with peers and clients. But the assistant knows that "normal" can be deceptive. A goroutine that's blocked on a mutex deep in the write path might not stand out in a sea of network workers. So the assistant takes the next logical step: a targeted search.

The Search: What the Grep Reveals About the Engineer's Mental Model

The command grep -A5 "rbstor\|parallel\|writable\|writeLk" is a window into the assistant's assumptions about where the problem might be. Let's break down each search term:

What the Output Actually Shows

The grep output is sparse. It finds only a few matches:

  1. A goroutine in rbstor/cidlocation/worker.go:120—this is the CID location worker, which handles resolving where content-addressed data is stored. It's not directly in the write path.
  2. Sixteen goroutines in bitswap/network/httpnet/httpnet.go:633—these are HTTP workers for the bitswap protocol, which is used for peer-to-peer data exchange. Again, not directly in the write path.
  3. Eleven goroutines in rbstor.(*rbs).groupWorker—this is more interesting. The groupWorker is likely involved in managing storage groups, which could be related to writes. But the stack trace is cut off by the -- separator, so we can't see what it's actually doing. The critical observation is that none of the matched goroutines appear to be stuck on locks or waiting indefinitely. They're all in expected states—waiting for network I/O, parked in worker pools, or doing routine background work. The parallel and writeLk terms returned no matches at all, which means no goroutines are currently executing in those code paths. This could mean either that the parallel write code isn't being exercised (because no writes are arriving) or that it's not the source of the blockage.

Assumptions and Potential Blind Spots

The assistant makes several assumptions in this message that are worth examining:

Assumption 1: The problem manifests as blocked goroutines. The user suggested a timeout, and the assistant is looking for goroutines that are stuck waiting. But a write failure could also be caused by a goroutine that's not being created at all, or one that's crashing silently. A goroutine profile won't show goroutines that don't exist.

Assumption 2: The relevant goroutines will be in the rbstor, parallel, writable, or writeLk code paths. This is a reasonable assumption given the recent change, but it could miss the real problem. What if the parallel write feature is correctly creating goroutines, but those goroutines are blocked on a database connection pool that's exhausted? The goroutines would show up as blocked on SQL queries, not in the parallel write code.

Assumption 3: The goroutine profile is a representative snapshot. A goroutine dump captures the state at a single instant. If the problem is intermittent—say, a deadlock that occurs only under specific conditions—a single snapshot might miss it entirely. The assistant doesn't take multiple samples or look at trends over time.

Assumption 4: The absence of obvious blockage means the problem isn't in the goroutines. This is the most dangerous assumption. The assistant concludes "goroutines look normal" and moves on. But the problem could still be in the goroutines—just not in a way that's visible from a single text dump. For example, a goroutine could be spinning in a tight loop (consuming CPU but not blocking), or it could be making progress but at an extremely slow rate due to a contention issue.

Input Knowledge Required

To fully understand this message, the reader needs:

  1. Go runtime internals: Knowledge of goroutines, stack traces, and the pprof debugging interface. Understanding that debug=1 produces a human-readable format and that the numbers before @ indicate the count of goroutines with that stack trace.
  2. The Filecoin Gateway architecture: Familiarity with the rbstor package, the parallel write feature, the bitswap protocol, and the CID location system. Knowing which code paths are involved in write operations.
  3. The debugging context: Awareness that parallel writes were just enabled, that the user reported write failures, and that log-based investigation yielded nothing. This message is the second phase of a multi-step diagnostic process.
  4. Network topology: Understanding that 10.1.232.83 is kuri1 (one of two Kuri storage nodes), that port 9010 is the RPC/debug port, and that the system uses a three-layer architecture (S3 proxy → Kuri nodes → YugabyteDB).

Output Knowledge Created

This message produces several valuable pieces of knowledge:

  1. A negative result: The goroutine profile does not show any obviously stuck or blocked goroutines in the write path. This rules out certain classes of problems (deadlocks, mutex contention, channel blocking) and narrows the search space.
  2. A refined hypothesis: Since the goroutines look normal, the problem might be elsewhere—perhaps in the S3 proxy layer, in the database, or in the application-level logic that's not captured by goroutine state.
  3. A diagnostic pattern: The message demonstrates a specific technique for debugging Go services—using pprof goroutine dumps with targeted grep to quickly assess whether goroutines are blocked in relevant code paths. This is a reusable pattern for any Go developer debugging production issues.
  4. Confidence in the runtime: The fact that goroutine counts are reasonable (~850-900) and that no goroutines are stuck on locks provides reassurance that the system isn't in a catastrophic state. The problem is likely subtle rather than a complete deadlock.## The Thinking Process: What the Assistant Is Really Doing One of the most revealing aspects of this message is what it shows about the assistant's thinking process. The message begins with a summary judgment: "The goroutines look normal." This tells us the assistant already fetched and reviewed the full goroutine dump before writing this message. The initial scan was a gestalt assessment—looking at the overall shape of the goroutine profile to see if anything jumps out as pathological. Then comes the pivot: "Let me search for goroutines that might be stuck on locks or waiting." This is the assistant acknowledging that the gestalt assessment isn't enough. The problem might be hiding in a specific code path that didn't stand out in the initial scan. The assistant is essentially saying, "I've done the broad overview, now let me do the targeted investigation." The choice of search terms reveals a hierarchy of suspicion. The assistant is most suspicious of the parallel write code (the feature that was just enabled), followed by the general storage layer (rbstor), followed by specific synchronization primitives (writeLk). This is a Bayesian update in action: the assistant started with a prior belief that the problem is related to the recent change, and is now testing that hypothesis against the runtime evidence. The fact that the grep returns nothing for parallel and writeLk is itself a significant finding. It means either: - The parallel write code isn't currently executing (no writes are in flight), or - The parallel write code is executing but not in a blocked state, or - The parallel write code paths don't contain goroutines that persist (they're short-lived and already completed) Each of these possibilities has different implications for the diagnosis. If no writes are in flight, the problem might be upstream—the S3 proxy might not be forwarding requests to this node. If the parallel write code is executing but not blocked, the problem might be a logic error rather than a concurrency issue. If the goroutines are short-lived, the problem might be that they're crashing before they can be observed.

The Broader Debugging Narrative

This message sits at a specific point in a larger debugging arc. Looking at the surrounding context, we can see that the assistant has been iterating rapidly: enabling parallel writes, checking logs, testing S3 writes directly, and now profiling goroutines. The next steps after this message would logically include:

  1. Taking a mutex profile (/debug/pprof/mutex?debug=1) to check for lock contention that doesn't manifest as blocked goroutines.
  2. Taking a block profile (/debug/pprof/block?debug=1) to see if goroutines are waiting on synchronization primitives.
  3. Checking the S3 proxy layer to see if requests are even reaching the Kuri nodes.
  4. Testing writes with a simpler tool (like curl with a PUT request) to isolate whether the problem is in the client or the server.
  5. Disabling parallel writes to see if the problem goes away, confirming the root cause. The message also reveals something about the relationship between the user and the assistant. The user suggested checking goroutines, and the assistant executed on that suggestion immediately. This is a high-trust, high-velocity collaboration where the user provides strategic direction ("check goroutines") and the assistant handles the tactical execution (fetching profiles, interpreting output, refining the search).

Conclusion

This message, for all its brevity, is a masterclass in systematic debugging. It demonstrates how experienced engineers use runtime introspection tools to investigate production issues when logs and metrics fail to provide answers. The assistant's approach—starting with a broad assessment, then narrowing to targeted searches based on domain knowledge and recent changes—is a pattern that applies far beyond this specific case.

The message also serves as a cautionary tale about assumptions in debugging. The assistant assumes that blocked goroutines will be the visible symptom, that the relevant code paths are known in advance, and that a single snapshot is sufficient. These assumptions are reasonable but not guaranteed, and a thorough investigation would need to validate them by exploring alternative hypotheses.

In the end, this message captures a moment of uncertainty in the debugging process. The goroutines look normal, but writes are still failing. The search continues. But thanks to this diagnostic step, the team has ruled out one class of problems and can focus their attention elsewhere. That's the essence of good debugging: not finding the answer immediately, but systematically narrowing the search space until the answer becomes unavoidable.